{"seq_id": "31760171912", "text": "from sqlite3 import connect\n\n\ndef add_words(cur, new):\n    cur.execute('select * from words where words.word = ?', (new['keywords'],))\n    res = cur.fetchone()\n    print(res)\n    if res:\n        if res[2] < new['count']:\n            # обновление строки таблицы\n            cur.execute('update words set count = ?, up = ?, down = ? where words.id = ?',\n                        (new['count'], new['up'], new['down'], res[0]))\n            print('Edit')\n        else:\n            print('Not edit')\n    else:\n        # добавление строки в таблице\n        cur.execute('insert into words values (null, ?, ?, ?, ?)',\n                    (new['keywords'], new['count'], new['up'], new['down']))\n        print('Done')\n    return cur\n\n\ndef add_skills(cur, new):\n    for item in new['requirements']:\n        res = cur.execute('select * from skills where skills.name = ?', (item['name'],))\n        if not res.fetchone():\n            print(item['name'])\n            cur.execute('insert into skills values (null, ?)', (item['name'],))\n    return cur\n\n\ndef add_ws(cur, new):\n    cur.execute('select id, count from words where words.word = ?', (new['keywords'],))\n    word_id, word_count = cur.fetchone()\n    for item in new['requirements']:\n        cur.execute('select id from skills where skills.name = ?', (item['name'],))\n        skill_id = cur.fetchone()[0]\n        print(word_id, skill_id)\n        cur.execute('select * from wordskills as ws where ws.id_word = ? and ws.id_skill = ?',\n                    (word_id, skill_id))\n        res = cur.fetchone()\n        if not res:\n            cur.execute('insert into wordskills values (null, ?, ?, ?, ?)',\n                        (word_id, skill_id, item['count'], item['percent']))\n            print('ws done')\n        elif word_count < new['count']:\n            cur.execute('update wordskills as ws set count = ?, percent = ? where ws.id_word = ? and ws.id_skill = ?',\n                        (item['count'], item['percent'], word_id, skill_id))\n            print('ws edit')\n        print('ws not edit')\n    return cur\n\n\ndef add_row(new):\n    con = connect('base.db')\n    cur = con.cursor()\n    cur = add_words(cur, new)\n    cur = add_skills(cur, new)\n    cur = add_ws(cur, new)\n    con.commit()\n    con.close()\n", "repo_name": "nemu-haibane/python17", "sub_path": "crud.py", "file_name": "crud.py", "file_ext": "py", "file_size_in_byte": 2295, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sqlite3.connect", "line_number": 56, "usage_type": "call"}]}
{"seq_id": "42989466623", "text": "from geopy.geocoders import Nominatim\nfrom geopy.distance import geodesic\nfrom pprint import pprint\n\ngeolocator = Nominatim(user_agent=\"snakes-distance-geopy\")\nschool = geolocator.geocode(\"raffles institution singapore\")\n\nprint('Snakey school is located at {}'.format(school.address))\nprint('Snakey school\\'s coordinates are ({}, {}) '.format(\n    school.latitude, school.longitude))\n\nhome = geolocator.geocode(\"58 college green singapore\")\nprint('Home is located at {}'.format(home.address))\n\nhome_coordinates = home.latitude, home.longitude\nschool_coordinates = school.latitude, school.longitude\ndistance = geodesic(home_coordinates, school_coordinates).km\nprint('The distance from home to school is {:.2f} km'.format(distance))", "repo_name": "siowyisheng/30-things-in-python", "sub_path": "22-geo-calculate-distance/snakes_distance.py", "file_name": "snakes_distance.py", "file_ext": "py", "file_size_in_byte": 730, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "41", "api": [{"api_name": "geopy.geocoders.Nominatim", "line_number": 5, "usage_type": "call"}, {"api_name": "geopy.distance.geodesic", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "36825740582", "text": "\"\"\"Add Ingredients table\n\nRevision ID: 28b34376bb94\nRevises: d3685ce0e0db\nCreate Date: 2021-10-25 21:24:17.530543\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '28b34376bb94'\ndown_revision = 'd3685ce0e0db'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    op.create_table(\n        \"ingredients\",\n        sa.Column(\"id\", sa.Integer, primary_key=True),\n        sa.Column(\"name\", sa.String(40), nullable=False),\n        sa.Column(\"unit\", sa.String(25)),\n        sa.Column(\"quantity\", sa.Integer(), nullable=True),\n        sa.Column(\"recipe_id\", sa.Integer(), sa.ForeignKey(\"recipe.id\"))\n    )\n\n\ndef downgrade():\n    op.drop_table(\"ingredients\")\n", "repo_name": "alucardthefish/RecipesApi", "sub_path": "alembic/versions/28b34376bb94_add_ingredients_table.py", "file_name": "28b34376bb94_add_ingredients_table.py", "file_ext": "py", "file_size_in_byte": 712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "alembic.op.create_table", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}]}
{"seq_id": "21223952918", "text": "#Algoritham to create a program which performs three essential operations\n#Open the file and process each line.\n#Either add each word to the dictionary with a frequency of 1 or update the word’s count by 1.\n#Nicely print the output, in this case from high to low frequency.\n#DSC510-T303 Introduction to Programming (2205-1)\n#Created by Rajkumar Kuppuswami\n#Created on 05/01/2020\n#Program to perform processing the line by adding each word to the dictionary.\n#Once values are added need to format as list to sort the values by desc.\n\nimport collections\n\ndef main():\n    dictionary = dict()\n#1st method to open the file\n# with open('Gettysburg.txt', 'r') as file_read:\n# Read the text file\n    file_read = open('Gettysburg.txt', 'r')\n# Process word one by one\n    for line in file_read:\n        process_line(line, dictionary)\n    print(\"Length of Dictionary is : {}\".format(len(dictionary.keys())))#Total Lenght of the dictionary\n\n    print(pretty_print(dictionary))#Output of each word with count\n\n\ndef add_word(word, word_count):#Adding the words to the dictionary with count of words\n    if word in word_count: #To validate the words to avoid duplicate\n            word_count[word] = word_count[word]+1 #counting the number of words\n    else:\n            word_count[word] =1\n\ndef process_line (line, dictionary):\n    line = line.lower() ##convert to lower case\n    line = line.strip()# remove /n line and unwanted space\n    words = line.split(\" \")#Split the words from the text by line\n    for word in words:\n        add_word(word, dictionary)\n\n\ndef pretty_print(dictionary):\n    table = collections.defaultdict(list)\n    for a , b in dictionary.items():\n        table[b].append(a)\n    sort_table = sorted(table.items(), reverse=True) # Sort list of tuple by count in descending order.\n    for item in sort_table:\n        count = item[0]\n        for word in item[1]:\n            print(word + \" \" * (30 - len(word)) + str(count))\n\nmain()\n\n\n", "repo_name": "dlingerfelt/DSC510Spring2020", "sub_path": "Kuppuswami_DSC510/Week8.1.py", "file_name": "Week8.1.py", "file_ext": "py", "file_size_in_byte": 1942, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.defaultdict", "line_number": 42, "usage_type": "call"}]}
{"seq_id": "3150281386", "text": "from datetime import datetime\nfrom typing import Union, Any\nfrom flask_migrate import stamp, upgrade\nfrom sqlalchemy.exc import OperationalError, InvalidRequestError\nfrom sqlalchemy.orm import Query\nfrom mcserver.app import db\nfrom mcserver.app.models import ResourceType\nfrom mcserver.config import Config\nfrom mcserver.models_auto import Corpus, Exercise, UpdateInfo, LearningResult\n\n\nclass DatabaseService:\n    @staticmethod\n    def commit():\n        \"\"\"Commits the last action to the database and, if it fails, rolls back the current session.\"\"\"\n        try:\n            db.session.commit()\n        except (OperationalError, InvalidRequestError):\n            db.session.rollback()\n            raise\n\n    @staticmethod\n    def has_table(table: str) -> bool:\n        \"\"\"Checks if a table is present in the database or not.\"\"\"\n        return db.engine.dialect.has_table(db.engine, table)\n\n    @staticmethod\n    def init_db_alembic() -> None:\n        \"\"\"In Docker, the alembic version is not initially written to the database, so we need to set it manually.\"\"\"\n        if not DatabaseService.has_table(Config.DATABASE_TABLE_ALEMBIC):\n            stamp(directory=Config.MIGRATIONS_DIRECTORY)\n        upgrade(directory=Config.MIGRATIONS_DIRECTORY)\n\n    @staticmethod\n    def init_db_update_info() -> None:\n        \"\"\"Initializes update entries for all resources that have not yet been created.\"\"\"\n        if DatabaseService.has_table(Config.DATABASE_TABLE_UPDATEINFO):\n            for rt in ResourceType:\n                ui_cts: UpdateInfo = DatabaseService.query(\n                    UpdateInfo, filter_by=dict(resource_type=rt.name), first=True)\n                if ui_cts is None:\n                    ui_cts = UpdateInfo.from_dict(resource_type=rt.name, last_modified_time=1,\n                                                  created_time=datetime.utcnow().timestamp())\n                    db.session.add(ui_cts)\n                    DatabaseService.commit()\n\n    @staticmethod\n    def query(table: Union[Corpus, Exercise, LearningResult, UpdateInfo], filter_by: dict = None,\n              first: bool = False) -> Any:\n        \"\"\"Executes a query on the database and rolls back the session if errors occur.\"\"\"\n        try:\n            ret_val: Query = db.session.query(table)\n            if filter_by:\n                ret_val = ret_val.filter_by(**filter_by)\n            ret_val = ret_val.first() if first else ret_val.all()\n            DatabaseService.commit()\n            return ret_val\n        except:\n            db.session.rollback()\n            return None\n", "repo_name": "korpling/machina-callida", "sub_path": "mc_backend/mcserver/app/services/databaseService.py", "file_name": "databaseService.py", "file_ext": "py", "file_size_in_byte": 2562, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "mcserver.app.db.session.commit", "line_number": 17, "usage_type": "call"}, {"api_name": "mcserver.app.db.session", "line_number": 17, "usage_type": "attribute"}, {"api_name": "mcserver.app.db", "line_number": 17, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.OperationalError", "line_number": 18, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.InvalidRequestError", "line_number": 18, "usage_type": "name"}, {"api_name": "mcserver.app.db.session.rollback", "line_number": 19, "usage_type": "call"}, {"api_name": "mcserver.app.db.session", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mcserver.app.db", "line_number": 19, "usage_type": "name"}, {"api_name": "mcserver.app.db.engine.dialect.has_table", "line_number": 25, "usage_type": "call"}, {"api_name": "mcserver.app.db.engine", "line_number": 25, "usage_type": "attribute"}, {"api_name": "mcserver.app.db", "line_number": 25, "usage_type": "name"}, {"api_name": "mcserver.config.Config.DATABASE_TABLE_ALEMBIC", "line_number": 30, "usage_type": "attribute"}, {"api_name": "mcserver.config.Config", "line_number": 30, "usage_type": "name"}, {"api_name": "flask_migrate.stamp", "line_number": 31, "usage_type": "call"}, {"api_name": "mcserver.config.Config.MIGRATIONS_DIRECTORY", "line_number": 31, "usage_type": "attribute"}, {"api_name": "mcserver.config.Config", "line_number": 31, "usage_type": "name"}, {"api_name": "flask_migrate.upgrade", "line_number": 32, "usage_type": "call"}, {"api_name": "mcserver.config.Config.MIGRATIONS_DIRECTORY", "line_number": 32, "usage_type": "attribute"}, {"api_name": "mcserver.config.Config", "line_number": 32, "usage_type": "name"}, {"api_name": "mcserver.config.Config.DATABASE_TABLE_UPDATEINFO", "line_number": 37, "usage_type": "attribute"}, {"api_name": "mcserver.config.Config", "line_number": 37, "usage_type": "name"}, {"api_name": "mcserver.app.models.ResourceType", "line_number": 38, "usage_type": "name"}, {"api_name": "mcserver.models_auto.UpdateInfo", "line_number": 39, "usage_type": "name"}, {"api_name": "mcserver.models_auto.UpdateInfo", "line_number": 40, "usage_type": "argument"}, {"api_name": "mcserver.models_auto.UpdateInfo.from_dict", "line_number": 42, "usage_type": "call"}, {"api_name": "mcserver.models_auto.UpdateInfo", "line_number": 42, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "mcserver.app.db.session.add", "line_number": 44, "usage_type": "call"}, {"api_name": "mcserver.app.db.session", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mcserver.app.db", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 48, "usage_type": "name"}, {"api_name": "mcserver.models_auto.Corpus", "line_number": 48, "usage_type": "name"}, {"api_name": "mcserver.models_auto.Exercise", "line_number": 48, "usage_type": "name"}, {"api_name": "mcserver.models_auto.LearningResult", "line_number": 48, "usage_type": "name"}, {"api_name": "mcserver.models_auto.UpdateInfo", "line_number": 48, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Query", "line_number": 52, "usage_type": "name"}, {"api_name": "mcserver.app.db.session.query", "line_number": 52, "usage_type": "call"}, {"api_name": "mcserver.app.db.session", "line_number": 52, "usage_type": "attribute"}, {"api_name": "mcserver.app.db", "line_number": 52, "usage_type": "name"}, {"api_name": "mcserver.app.db.session.rollback", "line_number": 59, "usage_type": "call"}, {"api_name": "mcserver.app.db.session", "line_number": 59, "usage_type": "attribute"}, {"api_name": "mcserver.app.db", "line_number": 59, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 49, "usage_type": "name"}]}
{"seq_id": "23389263038", "text": "import os\nimport json\nimport pandas as pd\nfrom helper import json_extract\n\nuber_eats = []\ndirectory = \"../data_output/ubereats/stores/\"\nfor filename in os.listdir(directory):\n    if filename.endswith(\".json\"):\n        f = os.path.join(directory, filename)\n        fd = open(f, 'rb')\n        file_json = json.load(fd)\n        try:\n            uuid = json_extract(file_json, 'uuid')[0]\n            slug = json_extract(file_json, 'slug')[0]\n            postcode = json_extract(file_json, 'postalCode')[0]\n            postcode = str(postcode)\n            latitude = json_extract(file_json, 'latitude')[0]\n            longitude = json_extract(file_json, 'longitude')[0]\n            data = [uuid, slug, postcode, latitude, longitude]\n            uber_eats.append(data)\n        except:\n            print(filename)\n\ndf_uber_eats = pd.DataFrame(uber_eats, columns=['uuid', 'slug', 'zipcode', 'latitude', 'longitude'])\ndf_uber_eats.to_csv('../data_output/ubereats_stores.csv', index=False)\n\n\n", "repo_name": "chengjun-curb/sandbox", "sub_path": "location_attributes/data_prep/etl_ubereats.py", "file_name": "etl_ubereats.py", "file_ext": "py", "file_size_in_byte": 982, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.listdir", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 12, "usage_type": "call"}, {"api_name": "helper.json_extract", "line_number": 14, "usage_type": "call"}, {"api_name": "helper.json_extract", "line_number": 15, "usage_type": "call"}, {"api_name": "helper.json_extract", "line_number": 16, "usage_type": "call"}, {"api_name": "helper.json_extract", "line_number": 18, "usage_type": "call"}, {"api_name": "helper.json_extract", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "11332465149", "text": "from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\n\n\ndef convert_text_to_speech(text):\n    \"\"\"\n    This function converts a given text to speech using the 'facebook/tts_transformer-fr-cv7_css10' model from Fairseq.\n    The model is specialized in converting French text to speech.\n    \n    Args:\n    text (str): The text to be converted to speech.\n    \n    Returns:\n    wav (numpy array): The generated speech in the form of a wave file.\n    rate (int): The sample rate of the generated speech.\n    \"\"\"\n    models, cfg, task = load_model_ensemble_and_task_from_hf_hub(\n        'facebook/tts_transformer-fr-cv7_css10',\n        arg_overrides={'vocoder': 'hifigan', 'fp16': False}\n    )\n    model = models[0]\n    TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\n    generator = task.build_generator(model, cfg)\n    sample = TTSHubInterface.get_model_input(task, text)\n    wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\n    return wav, rate", "repo_name": "vixuowis/Research-2309", "sub_path": "Exp-2/output/hf-eval-data-v1/f00421_convert_text_to_speech.py", "file_name": "f00421_convert_text_to_speech.py", "file_ext": "py", "file_size_in_byte": 1075, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "fairseq.checkpoint_utils.load_model_ensemble_and_task_from_hf_hub", "line_number": 17, "usage_type": "call"}, {"api_name": "fairseq.models.text_to_speech.hub_interface.TTSHubInterface.update_cfg_with_data_cfg", "line_number": 22, "usage_type": "call"}, {"api_name": "fairseq.models.text_to_speech.hub_interface.TTSHubInterface", "line_number": 22, "usage_type": "name"}, {"api_name": "fairseq.models.text_to_speech.hub_interface.TTSHubInterface.get_model_input", "line_number": 24, "usage_type": "call"}, {"api_name": "fairseq.models.text_to_speech.hub_interface.TTSHubInterface", "line_number": 24, "usage_type": "name"}, {"api_name": "fairseq.models.text_to_speech.hub_interface.TTSHubInterface.get_prediction", "line_number": 25, "usage_type": "call"}, {"api_name": "fairseq.models.text_to_speech.hub_interface.TTSHubInterface", "line_number": 25, "usage_type": "name"}]}
{"seq_id": "26216402025", "text": "import sys\nimport unittest\nfrom appium import webdriver\nfrom appium.options.common.base import AppiumOptions\nfrom appium.webdriver.common.appiumby import AppiumBy\nfrom appium.options.common.app_option import AppOption\nfrom selenium.webdriver.common.keys import Keys\n\n\nclass ATSPIOptions(AppiumOptions, AppOption):\n    pass\n\n\nclass TimelineTest(unittest.TestCase):\n\n    def setUp(self):\n        options = ATSPIOptions()\n        options.app = tokodon_offline_path\n        self.driver = webdriver.Remote(\n            command_executor='http://127.0.0.1:4723',\n            options=options)\n\n    def tearDown(self):\n        self.driver.get_screenshot_as_file(\"failed_test_shot_{}.png\".format(self.id()))\n        self.driver.quit()\n\n    def test_status_type(self):\n        self.assertTrue(self.driver.find_element(by='description', value=\"Normal Status\"))\n        self.assertTrue(self.driver.find_element(by='description', value=\"Spoiler Status\"))\n\n    def test_favourite_interactions(self):\n        favouriteButton=self.driver.find_element(by='description',value=\"Favourite\")\n        favouriteButton.click()\n        self.assertTrue(self.driver.find_element(by='description', value=\"Favourited\"))\n\n    def test_bookmark_interactions(self):\n        bookmarkButton=self.driver.find_element(by='description',value=\"Bookmark\")\n        bookmarkButton.click()\n        self.assertTrue(self.driver.find_element(by='description', value=\"Bookmarked\"))\n\n    def test_boost_interactions(self):\n        boostButton=self.driver.find_element(by='description',value=\"Boost\")\n        boostButton.click()\n        self.assertTrue(self.driver.find_element(by='description', value=\"Boosted\"))\n\n    def test_status_media(self):\n        searchElement = self.driver.find_element(by=AppiumBy.NAME, value=\"Home\")\n        searchElement.send_keys(Keys.DOWN)\n        searchElement.send_keys(Keys.DOWN)\n        searchElement.send_keys(Keys.DOWN)\n        self.assertTrue(self.driver.find_element(by='description', value=\"Status with image attachment\"))\n        self.assertTrue(self.driver.find_element(by='description', value=\"Status with Video attachment\"))\n        self.assertTrue(self.driver.find_element(by='description', value=\"Status with GifV attachment\"))\n\n\nif __name__ == '__main__':\n    tokodon_offline_path = sys.argv[1]\n    sys.argv.pop()\n    unittest.main()", "repo_name": "KDE/tokodon", "sub_path": "src/autotests/appiumtests/TimelineTest.py", "file_name": "TimelineTest.py", "file_ext": "py", "file_size_in_byte": 2332, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 37, "dataset": "github-code", "pt": "41", "api": [{"api_name": "appium.options.common.base.AppiumOptions", "line_number": 10, "usage_type": "name"}, {"api_name": "appium.options.common.app_option.AppOption", "line_number": 10, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 14, "usage_type": "attribute"}, {"api_name": "appium.webdriver.Remote", "line_number": 19, "usage_type": "call"}, {"api_name": "appium.webdriver", "line_number": 19, "usage_type": "name"}, {"api_name": "appium.webdriver.common.appiumby.AppiumBy.NAME", "line_number": 47, "usage_type": "attribute"}, {"api_name": "appium.webdriver.common.appiumby.AppiumBy", "line_number": 47, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.DOWN", "line_number": 48, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 48, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.DOWN", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 49, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.DOWN", "line_number": 50, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 50, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.argv.pop", "line_number": 58, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 58, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 59, "usage_type": "call"}]}
{"seq_id": "3008671809", "text": "\"\"\"\nFixtures for fanout\npurpose : one node to many nodes\nThe dataset has\n- 3 levels : L1, L2, L3\n- 2 resources: RI, RO\n- R in/out are connected\n\"\"\"\nimport pytest\nfrom os import path\n\nimport pandas as pd\n\n# module import\nfrom dependencynet.schema import SchemaBuilder\nfrom dependencynet.model import ModelBuilder\n\nfrom dependencynet.network.graphbuilder import LevelNode, InputNode, OutputNode\n\n\n@pytest.fixture\ndef schema_fanout():\n    schema = SchemaBuilder().level('L1', 'L1') \\\n                            .level('L2', 'L2') \\\n                            .level('L3', 'L3') \\\n                            .resource('RI', 'RI', role='INPUT', connect_id_name='R') \\\n                            .resource('RO', 'RO', role='OUTPUT', connect_id_name='R') \\\n                            .connect('RO', 'RI') \\\n                            .render()\n    return schema\n\n\n@pytest.fixture(scope=\"session\")\ndef compact_columns_fanout():\n    columns = ['L1', 'L2', 'L3', 'RO', 'RI']\n    return columns\n\n\n@pytest.fixture\ndef source_data_fanout(schema_fanout, compact_columns_fanout):\n    filename = path.join('tests', 'resources', 'data', 'compact', 'fanout.csv')\n    data = pd.read_csv(filename, delimiter=';')\n\n    df = pd.DataFrame(data, columns=compact_columns_fanout)\n    return df\n\n\n@pytest.fixture\ndef model_fanout(source_data_fanout, schema_fanout):\n    model = ModelBuilder().from_compact(source_data_fanout) \\\n                          .with_schema(schema_fanout) \\\n                          .render()\n    return model\n\n\n@pytest.fixture\ndef class_mapping_fanout():\n    return {'L1': L1Node, 'L2': L2Node, 'L3': L3Node,\n            'RO': RONode, 'RI': RINode}\n\n\n# networkx classes\n\nclass L1Node(LevelNode):\n    def __init__(self, properties):\n        super().__init__(properties, 'L1')\n\n\nclass L2Node(LevelNode):\n    def __init__(self, properties):\n        super().__init__(properties, 'L2')\n\n\nclass L3Node(LevelNode):\n    def __init__(self, properties):\n        super().__init__(properties, 'L3')\n\n\nclass RINode(InputNode):\n    def __init__(self, properties):\n        super().__init__(properties, 'RI', 'R')\n\n\nclass RONode(OutputNode):\n    def __init__(self, properties):\n        super().__init__(properties, 'RO', 'R')\n", "repo_name": "cfalguiere/dependencynet", "sub_path": "tests/scenario/fanout/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 2217, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "dependencynet.schema.SchemaBuilder", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 39, "usage_type": "attribute"}, {"api_name": "dependencynet.model.ModelBuilder", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 56, "usage_type": "attribute"}, {"api_name": "dependencynet.network.graphbuilder.LevelNode", "line_number": 64, "usage_type": "name"}, {"api_name": "dependencynet.network.graphbuilder.LevelNode", "line_number": 69, "usage_type": "name"}, {"api_name": "dependencynet.network.graphbuilder.LevelNode", "line_number": 74, "usage_type": "name"}, {"api_name": "dependencynet.network.graphbuilder.InputNode", "line_number": 79, "usage_type": "name"}, {"api_name": "dependencynet.network.graphbuilder.OutputNode", "line_number": 84, "usage_type": "name"}]}
{"seq_id": "6178054017", "text": "import io\nimport os.path\n\nimport stardicter.czechenglish\nfrom stardicter.test_base import BaseTest\n\n\nclass CzechEnglishTest(BaseTest):\n    writer_class = stardicter.czechenglish.CzechEnglishWriter\n\n\nclass CzechEnglishFileTest(CzechEnglishTest):\n    def get_writer(self):\n        '''\n        Gets prepared writer class.\n        '''\n        return self.writer_class(\n            file=io.open(os.path.join(\n                os.path.dirname(__file__),\n                'test_data.txt'\n            ), 'rb')\n        )\n", "repo_name": "nijel/stardicter", "sub_path": "stardicter/test_czechenglish.py", "file_name": "test_czechenglish.py", "file_ext": "py", "file_size_in_byte": 510, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 29, "dataset": "github-code", "pt": "41", "api": [{"api_name": "stardicter.test_base.BaseTest", "line_number": 8, "usage_type": "name"}, {"api_name": "stardicter.czechenglish.czechenglish", "line_number": 9, "usage_type": "attribute"}, {"api_name": "stardicter.czechenglish", "line_number": 9, "usage_type": "name"}, {"api_name": "io.open", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 19, "usage_type": "name"}]}
{"seq_id": "2529970079", "text": "# fmtlib Conan package\n# Dmitriy Vetutnev, ODANT 2020\n\n\nfrom conans import ConanFile, CMake, tools\nimport os\n\n\nclass GoogletestConan(ConanFile):\n    name = \"fmt\"\n    version = \"10.1.1+0\"\n    license = \"https://raw.githubusercontent.com/fmtlib/fmt/master/LICENSE.rst\"\n    description = \"{fmt} is an open-source formatting library for C++. It can be used as a safe and fast alternative to (s)printf and iostreams.\"\n    url = \"https://github.com/odant/conan-fmt\"\n    settings = {\n        \"os\": [\"Windows\", \"Linux\"],\n        \"compiler\": [\"Visual Studio\", \"gcc\"],\n        \"build_type\": [\"Debug\", \"Release\"],\n        \"arch\": [\"x86\", \"x86_64\", \"mips\", \"armv7\"]\n    }\n    options = {\n        \"with_unit_tests\": [True, False],\n        \"ninja\": [True, False]\n    }\n    default_options = {\n        \"with_unit_tests\": False,\n        \"ninja\": True\n    }\n    generators = \"cmake\"\n    exports_sources = \"src/*\", \"CMakeLists.txt\"\n    no_copy_source = True\n    build_policy = \"missing\"\n\n    def build_requirements(self):\n        if self.options.ninja:\n            self.build_requires(\"ninja/[>=1.9.0]\")\n\n    def build(self):\n        cmake = CMake(self, msbuild_verbosity='normal')\n        cmake.verbose = True\n        cmake.definitions[\"FMT_INSTALL\"] = \"ON\"\n        cmake.definitions[\"FMT_DOC\"] = \"OFF\"\n        if self.options.with_unit_tests:\n            cmake.definitions[\"FMT_TEST\"] = \"ON\"\n        if self.settings.get_safe(\"compiler.runtime\") in (\"MT\", \"MTd\"):\n            cmake.definitions[\"MSVC_BUILD_STATIC\"] = \"ON\"\n        cmake.configure()\n        cmake.build()\n        if self.options.with_unit_tests:\n            if cmake.is_multi_configuration:\n                self.run(\"ctest --output-on-failure --build-config %s\" % build_type)\n            else:\n                self.run(\"ctest --output-on-failure\")\n        cmake.install()\n        tools.rmdir(os.path.join(self.package_folder, \"lib/pkgconfig\"))\n\n    def package(self):\n        self.copy(\"*fmt.pdb\", dst=\"bin\", keep_path=False)\n\n    def package_id(self):\n        self.info.options.with_unit_tests = \"any\"\n        self.info.options.ninja = \"any\"\n\n    def package_info(self):\n        self.cpp_info.libs = tools.collect_libs(self)\n\n", "repo_name": "odant/conan-fmt", "sub_path": "conanfile.py", "file_name": "conanfile.py", "file_ext": "py", "file_size_in_byte": 2176, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "conans.ConanFile", "line_number": 9, "usage_type": "name"}, {"api_name": "conans.CMake", "line_number": 39, "usage_type": "call"}, {"api_name": "conans.tools.rmdir", "line_number": 55, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 55, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "conans.tools.collect_libs", "line_number": 65, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 65, "usage_type": "name"}]}
{"seq_id": "36321491926", "text": "from pydeck import Deck, Layer, ViewState\n\nfeatures = {\n    \"type\": \"FeatureCollection\",\n    \"features\": [\n        {\n            \"type\": \"Feature\",\n            \"properties\": {},\n            \"geometry\": {\n                \"type\": \"Polygon\",\n                \"coordinates\": [\n                    [\n                        [-122.42923736572264, 37.80544394934271],\n                        [0, 37.80544394934271],\n                        [-122.42923736572264, 0],\n                        [-122.42923736572264, 37.80544394934271],\n                    ]\n                ],\n            },\n        }\n    ],\n}\n\n\ndef create_geojson_layer_with_gmaps_test_object():\n    return Deck(\n        description=\"Test of GeoJsonLayer, with Google Maps basemap\",\n        map_style=\"satellite\",\n        map_provider=\"google_maps\",\n        initial_view_state=ViewState(longitude=-122.45, latitude=37.8, zoom=0),\n        layers=[\n            Layer(\n                \"GeoJsonLayer\",\n                id=\"geojson-layer\",\n                data=features,\n                stroked=True,\n                filled=True,\n                line_width_min_pixels=2,\n                opacity=0.4,\n                get_line_color=[255, 100, 100],\n                get_fill_color=[200, 160, 0, 180],\n            )\n        ],\n        views=None,\n    )\n\n\nif __name__ == \"__main__\":\n    create_geojson_layer_with_gmaps_test_object().to_html(\"test.html\", offline=True)\n", "repo_name": "visgl/deck.gl", "sub_path": "bindings/pydeck/tests/bindings/pydeck_examples/geojson_layer_with_gmaps.py", "file_name": "geojson_layer_with_gmaps.py", "file_ext": "py", "file_size_in_byte": 1414, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11339, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pydeck.Deck", "line_number": 26, "usage_type": "call"}, {"api_name": "pydeck.ViewState", "line_number": 30, "usage_type": "call"}, {"api_name": "pydeck.Layer", "line_number": 32, "usage_type": "call"}]}
{"seq_id": "4324703105", "text": "import argparse\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.model_selection import cross_val_predict, StratifiedKFold\nfrom sklearn.metrics import roc_auc_score, average_precision_score\n\nfrom constants import *\nfrom datasets import load_and_preprocess\nfrom utils.model_utils import save_model\nfrom utils.data_utils import get_roadmap_col_order\nimport models\n\nimport torch\nimport torch.nn.functional as F\nimport optuna\nfrom skorch import NeuralNetClassifier\nfrom skorch.callbacks import EpochScoring, LRScheduler\n\nMODEL_CHOICES = ['glm', 'standard', 'neighbors', 'e116_neigh']\n\n\ndef main(args):\n    X, y = load_and_preprocess(args.project, args.model, split='train')\n\n    if args.model == 'e116_neigh':\n        def objective(trial):\n            auc = EpochScoring(scoring='roc_auc', lower_is_better=False)\n            apr = EpochScoring(scoring='average_precision', lower_is_better=False)\n            lrs = LRScheduler(policy='StepLR', step_size=10, gamma=0.5)\n            \n            bs = trial.suggest_categorical('batch_size', [128])\n            l2 = trial.suggest_uniform('l2', 5e-5, 1e-2)\n            lr = trial.suggest_uniform('lr', 1e-4, 5e-3)\n            epochs = trial.suggest_categorical('epochs', [30])\n            n_filt = trial.suggest_categorical('n_filt', [8, 16, 32])\n            width = trial.suggest_categorical('width', [3, 5, 7])\n            lin_units = trial.suggest_categorical('lin_units', [100, 200, 400])\n\n            net = NeuralNetClassifier(\n                models.MpraCNN,\n\n                optimizer=torch.optim.Adam,\n                optimizer__weight_decay=l2,\n                lr=lr,\n                batch_size=bs,\n                max_epochs=epochs,\n\n                module__n_filt=n_filt,\n                module__width=width,\n                module__lin_units=lin_units,\n\n                callbacks=[auc, apr],\n                iterator_train__shuffle=True,\n                train_split=None,\n                verbose=0\n            )\n        \n            kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=1000)\n            np.random.seed(1000)\n            torch.manual_seed(1000)\n            cv_scores = cross_val_predict(net, X, y, cv=kf,\n                                        method='predict_proba', n_jobs=-1)\n            return roc_auc_score(y, cv_scores[:, 1])\n    \n    elif args.model == 'neighbors':\n        def objective(trial):\n            auc = EpochScoring(scoring='roc_auc', lower_is_better=False)\n            apr = EpochScoring(scoring='average_precision', lower_is_better=False)\n            lrs = LRScheduler(policy='StepLR', step_size=10, gamma=0.5)\n            \n            bs = trial.suggest_categorical('batch_size', [256])\n            l2 = trial.suggest_uniform('l2', 5e-5, 5e-4)\n            lr = trial.suggest_uniform('lr', 5e-5, 5e-4)\n            epochs = trial.suggest_categorical('epochs', [30, 40])\n            n_filt = trial.suggest_categorical('n_filt', [8, 16, 32])\n            width = trial.suggest_categorical('width', [5])\n            n_lin1 = trial.suggest_categorical('n_lin1', [400, 600])\n            n_lin2 = trial.suggest_categorical('n_lin2', [400])\n\n            net = NeuralNetClassifier(\n                models.MpraFullCNN,\n\n                optimizer=torch.optim.Adam,\n                optimizer__weight_decay=l2,\n                lr=lr,\n                batch_size=bs,\n                max_epochs=epochs,\n\n                module__n_filt=n_filt,\n                module__width=width,\n                module__n_lin1=n_lin1,\n                module__n_lin2=n_lin2,\n                module__nonlin=F.leaky_relu,\n\n                callbacks=[auc, apr],\n                iterator_train__shuffle=True,\n                train_split=None,\n                verbose=0\n            )\n        \n            kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=1000)\n            np.random.seed(1000)\n            torch.manual_seed(1000)\n            cv_scores = cross_val_predict(net, X, y, cv=kf,\n                                        method='predict_proba', n_jobs=-1)\n            return roc_auc_score(y, cv_scores[:, 1])\n    print('Starting trials')\n    study = optuna.create_study(direction='maximize')\n    study.optimize(objective, n_trials=args.iter)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--project', '-p', choices=PROJ_CHOICES, default='mpra_e116')\n    parser.add_argument('--model', '-m', default='standard', choices=MODEL_CHOICES,\n                        help='Which data/model to train on')\n    parser.add_argument('--iter', '-i', type=int,\n                        help='Number of search iterations')\n    args = parser.parse_args()\n\n    main(args)\n", "repo_name": "fl16180/MpraScreen", "sub_path": "search_hparam.py", "file_name": "search_hparam.py", "file_ext": "py", "file_size_in_byte": 4679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "datasets.load_and_preprocess", "line_number": 24, "usage_type": "call"}, {"api_name": "skorch.callbacks.EpochScoring", "line_number": 28, "usage_type": "call"}, {"api_name": "skorch.callbacks.EpochScoring", "line_number": 29, "usage_type": "call"}, {"api_name": "skorch.callbacks.LRScheduler", "line_number": 30, "usage_type": "call"}, {"api_name": "skorch.NeuralNetClassifier", "line_number": 40, "usage_type": "call"}, {"api_name": "models.MpraCNN", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_predict", "line_number": 62, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 64, "usage_type": "call"}, {"api_name": "skorch.callbacks.EpochScoring", "line_number": 68, "usage_type": "call"}, {"api_name": "skorch.callbacks.EpochScoring", "line_number": 69, "usage_type": "call"}, {"api_name": "skorch.callbacks.LRScheduler", "line_number": 70, "usage_type": "call"}, {"api_name": "skorch.NeuralNetClassifier", "line_number": 81, "usage_type": "call"}, {"api_name": "models.MpraFullCNN", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.leaky_relu", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.nn.functional", "line_number": 94, "usage_type": "name"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 104, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_predict", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 107, "usage_type": "call"}, {"api_name": "optuna.create_study", "line_number": 109, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 114, "usage_type": "call"}]}
{"seq_id": "31612205772", "text": "from django.urls import path\nfrom . import views\nfrom re import template\nfrom django.urls import path \nfrom . import views\nfrom django.contrib.auth import views as auth_view\n\nurlpatterns = [\n    path('', views.home, name='home1'),\n    path('home/', views.homepage, name='home'),\n    path('table/', views.students, name='data'),\n    path('addrecord/', views.addrecord, name='add'),\n    path('activity/', views.activity, name='activity'),\n    path('update/<str:pk>/', views.updaterecord, name='update'),\n    path('delete/<str:pk>/', views.deleterecord, name='delete'),\n        path('', views.home, name='home'),\n    path('register/', views.register, name='register'),\n    path('profile/', views.profile, name='profile'),\n    path('login/', views.login, name='login'),\n    path('logout/', views.logout, name='logout'),\n\n\n]", "repo_name": "Mariam22-hub/student-affairs-website", "sub_path": "updateTable/Table/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "71790060284", "text": "import trino\nconn = trino.dbapi.connect(\n    host='localhost',\n    port=9091,\n    user='eliar'\n)\n\n# Execute a cross-database query\ncur = conn.cursor()\ncur.execute(\"\"\"\n        SELECT * FROM postgresql1.dwh.machinedim\n        UNION ALL\n        SELECT * FROM postgresql2.dwh.machinedim\n    \"\"\")\nrows = cur.fetchall()\nfor row in rows:\n    print(row)", "repo_name": "Efejann0/Trino", "sub_path": "trino-python-code/trino-engine.py", "file_name": "trino-engine.py", "file_ext": "py", "file_size_in_byte": 345, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "trino.dbapi.connect", "line_number": 2, "usage_type": "call"}, {"api_name": "trino.dbapi", "line_number": 2, "usage_type": "attribute"}]}
{"seq_id": "75243311804", "text": "import parmed\nfrom io import StringIO\n\nparams_sam = parmed.amber.AmberParameterSet.from_leaprc(StringIO(u'SAM = loadMol2 parameters/SAM.mol2\\nloadAmberParams parameters/frcmod.SAM'))\nparams_sam = parmed.openmm.OpenMMParameterSet.from_parameterset(params_sam)\nparams_sam.write('parameters/SAM.xml')\n\nparams_sah = parmed.amber.AmberParameterSet.from_leaprc(StringIO(u'SAH = loadMol2 parameters/SAH.mol2\\nloadAmberParams parameters/frcmod.SAH'))\nparams_sah = parmed.openmm.OpenMMParameterSet.from_parameterset(params_sah)\nparams_sah.write('parameters/SAH.xml')\n\n# convert GAFF here for reproducibility etc. rather than taking it converted from my OpenMM conversion which has not been merged yet - gaff.dat was copied into files/ from AmberTools16\n# gotta set write_unused to True\nparams_gaff = parmed.amber.AmberParameterSet.from_leaprc(StringIO(u'loadAmberParams gaff.dat'))\nparams_gaff = parmed.openmm.OpenMMParameterSet.from_parameterset(params_gaff)\nparams_gaff.write('parameters/gaff.xml', write_unused=True)\n", "repo_name": "choderalab/pimento", "sub_path": "SETD8/Catalytic_Cycle_p11708_scripted/after_antechamber.py", "file_name": "after_antechamber.py", "file_ext": "py", "file_size_in_byte": 1011, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "parmed.amber.AmberParameterSet.from_leaprc", "line_number": 4, "usage_type": "call"}, {"api_name": "parmed.amber", "line_number": 4, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 4, "usage_type": "call"}, {"api_name": "parmed.openmm.OpenMMParameterSet.from_parameterset", "line_number": 5, "usage_type": "call"}, {"api_name": "parmed.openmm", "line_number": 5, "usage_type": "attribute"}, {"api_name": "parmed.amber.AmberParameterSet.from_leaprc", "line_number": 8, "usage_type": "call"}, {"api_name": "parmed.amber", "line_number": 8, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 8, "usage_type": "call"}, {"api_name": "parmed.openmm.OpenMMParameterSet.from_parameterset", "line_number": 9, "usage_type": "call"}, {"api_name": "parmed.openmm", "line_number": 9, "usage_type": "attribute"}, {"api_name": "parmed.amber.AmberParameterSet.from_leaprc", "line_number": 14, "usage_type": "call"}, {"api_name": "parmed.amber", "line_number": 14, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 14, "usage_type": "call"}, {"api_name": "parmed.openmm.OpenMMParameterSet.from_parameterset", "line_number": 15, "usage_type": "call"}, {"api_name": "parmed.openmm", "line_number": 15, "usage_type": "attribute"}]}
{"seq_id": "1551033529", "text": "import regex as re  # regex string finding/replacing\nimport urllib.parse  # convert link characters like %\n\n\n# -- [3] Convert Obsidian type img links to proper md image links\n# Further conversion will be done in the block below\ndef obs_img_to_md_img(pb, page):\n    for matched_link in re.findall(\"(?<=\\!\\[\\[)(.*?)(?=\\]\\])\", page):\n        link = \"\"\n        if \"|\" in matched_link:\n            parts = matched_link.split(\"|\")\n            link = parts.pop(0)\n            alias = \"|\".join(parts)\n            new_link = f\"![{alias}](\" + urllib.parse.quote(link) + \")\"\n        else:\n            new_link = \"![](\" + urllib.parse.quote(matched_link) + \")\"\n\n        # Obsidian page inclusions use the same tag...\n        # Skip if we don't match image suffixes. Inclusions are handled at the end.\n        link = matched_link.split(\"|\")[0]\n        if len(link.split(\".\")) == 1 or link.split(\".\")[-1].lower() not in pb.gc(\"included_file_suffixes\", cached=True):\n            new_link = f'<inclusion href=\"{link}\" />'\n\n        safe_link = re.escape(\"![[\" + matched_link + \"]]\")\n        page = re.sub(safe_link, new_link, page)\n\n    return page\n", "repo_name": "tinsirius/obsidian-html", "sub_path": "obsidianhtml/note2md/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "41", "api": [{"api_name": "regex.findall", "line_number": 8, "usage_type": "call"}, {"api_name": "urllib.parse.parse.quote", "line_number": 14, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 14, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 14, "usage_type": "name"}, {"api_name": "urllib.parse.parse.quote", "line_number": 16, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 16, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 16, "usage_type": "name"}, {"api_name": "regex.escape", "line_number": 24, "usage_type": "call"}, {"api_name": "regex.sub", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "36778872629", "text": "import matplotlib.pyplot as plt\n\nimport networkx as nx\nimport matplotlib.pyplot\n\ndef bug_fix_permutate(testedges, graph, indivudal_edge_thickness):\n    #print testedges\n    for i in range(0,len(graph)):\n        if testedges[i]!=graph[i]:\n            try:\n                indbadges=testedges.index(graph[i])\n            except Exception as ex:\n                indbadges=testedges.index((graph[i][1],graph[i][0]))\n\n            testedges[i],testedges[indbadges]=testedges[indbadges],testedges[i]\n            indivudal_edge_thickness[i],indivudal_edge_thickness[indbadges]=indivudal_edge_thickness[indbadges],indivudal_edge_thickness[i]\n\n    #print indivudal_edge_thickness\n    return indivudal_edge_thickness\n\n\ndef draw_graph(graph,individual_edge_thickness, labels=None, graph_layout='shell',\n               node_size=1600, node_color='blue', node_alpha=0.3,\n               node_text_size=12,\n               edge_color='blue', edge_alpha=0.3, edge_tickness=1,\n               edge_text_pos=0.3,\n               text_font='sans-serif'):\n\n    G=nx.Graph()\n    G.add_edges_from(graph)\n\n    #Fixing autistic bug\n    testedges=G.edges()\n    individual_edge_thickness=bug_fix_permutate(testedges,graph,individual_edge_thickness)\n\n    if graph_layout == 'spring':\n        graph_pos=nx.spring_layout(G)\n    elif graph_layout == 'spectral':\n        graph_pos=nx.spectral_layout(G)\n    elif graph_layout == 'random':\n        graph_pos=nx.random_layout(G)\n    else:\n        graph_pos=nx.shell_layout(G)\n\n    nx.draw_networkx_nodes(G,graph_pos,node_size=node_size,\n                           alpha=node_alpha, node_color=node_color)\n    nx.draw_networkx_edges(G,graph_pos,width=individual_edge_thickness,\n                           alpha=edge_alpha,edge_color=edge_color)\n    nx.draw_networkx_labels(G, graph_pos,font_size=node_text_size,\n                            font_family=text_font)\n\n    if labels is None:\n        labels = [\"\" for element in range(len(graph))]\n\n    edge_labels = dict(zip(graph, labels))\n    nx.draw_networkx_edge_labels(G, graph_pos, edge_labels=edge_labels,\n                                 label_pos=edge_text_pos)\n\n    plt.show()", "repo_name": "lucaderi/sgr", "sub_path": "2011-2020/2017/Maraz/progetto/grapher.py", "file_name": "grapher.py", "file_ext": "py", "file_size_in_byte": 2142, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "41", "api": [{"api_name": "networkx.Graph", "line_number": 29, "usage_type": "call"}, {"api_name": "networkx.spring_layout", "line_number": 37, "usage_type": "call"}, {"api_name": "networkx.spectral_layout", "line_number": 39, "usage_type": "call"}, {"api_name": "networkx.random_layout", "line_number": 41, "usage_type": "call"}, {"api_name": "networkx.shell_layout", "line_number": 43, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_nodes", "line_number": 45, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_edges", "line_number": 47, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_labels", "line_number": 49, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_edge_labels", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}]}
{"seq_id": "31389944524", "text": "import discord\nimport aiohttp\nimport io\nfrom akito import Embed\nfrom discord.ext import commands\n\n\nclass CommentCmd(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    # gae\n    @commands.command(\n        name = \"comment\",\n        aliases=[\"ytcomment\"],\n        description = \"Fake YouTube Comment\",\n        usage = \"[Member] [Comment]\",\n        help = \"You must **tag** `Member` and then write the `Comment`\"\n    )\n    @commands.cooldown(1, 2, commands.BucketType.user)\n    async def Comment(self, ctx, user: discord.Member = None, *, comment):\n        if not user:\n            user = ctx.author\n\n        url = f\"https://some-random-api.ml/canvas/youtube-comment?avatar={user.display_avatar}&username={user.display_name}&comment={comment}\"\n\n        async with aiohttp.ClientSession() as session:\n            response = await session.get(url)\n            imageData = io.BytesIO(await response.read())\n            file = discord.File(imageData, filename=\"comment.png\")\n\n            embed = discord.Embed(colour=ctx.author.color)\n            embed.set_image(url=\"attachment://comment.png\")\n            embed.set_footer(\n                text=f\"Requested By {ctx.author}\", icon_url=ctx.author.display_avatar\n            )\n\n            await ctx.respond(embed=embed, file=file)\n\n    @Comment.error\n    async def comment_error(self, ctx, error):\n        if isinstance(error, commands.MissingRequiredArgument):\n            embed = await Embed.missingrequiredargument(self, ctx)\n            await ctx.respond(embed = embed,  delete_after= 60)\n\n        elif isinstance(error, commands.MemberNotFound):\n            embed = await Embed.membernotfound(self, ctx)\n            await ctx.respond(embed = embed,  delete_after= 60)\n\n        else:\n            pass\n\n\ndef setup(bot):\n    bot.add_cog(CommentCmd(bot))\n", "repo_name": "eitozx/AkitoBot", "sub_path": "extension/image/comment.py", "file_name": "comment.py", "file_ext": "py", "file_size_in_byte": 1818, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 34, "dataset": "github-code", "pt": "41", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 8, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 8, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 21, "usage_type": "attribute"}, {"api_name": "aiohttp.ClientSession", "line_number": 27, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 29, "usage_type": "call"}, {"api_name": "discord.File", "line_number": 30, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 32, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 13, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 13, "usage_type": "name"}, {"api_name": "discord.ext.commands.cooldown", "line_number": 20, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 20, "usage_type": "name"}, {"api_name": "discord.ext.commands.BucketType", "line_number": 20, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.MissingRequiredArgument", "line_number": 42, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 42, "usage_type": "name"}, {"api_name": "akito.Embed.missingrequiredargument", "line_number": 43, "usage_type": "call"}, {"api_name": "akito.Embed", "line_number": 43, "usage_type": "name"}, {"api_name": "discord.ext.commands.MemberNotFound", "line_number": 46, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 46, "usage_type": "name"}, {"api_name": "akito.Embed.membernotfound", "line_number": 47, "usage_type": "call"}, {"api_name": "akito.Embed", "line_number": 47, "usage_type": "name"}]}
{"seq_id": "40035044910", "text": "from bson import json_util\nfrom flask import make_response\nfrom flask.ext.restful import Api\nfrom flask.ext.cuddlyrest.views import ListMongoResource, SingleMongoResource\n\n\nclass CuddlyRest(Api):\n    \n    def __init__(self, **kwargs):\n        Api.__init__(self, **kwargs)\n        \n    def init_app(self, app):\n        self.app = app\n        self.representation('application/json')(self.json_encode)\n\n    def json_encode(self, data, code, headers=None):\n        resp = make_response(json_util.dumps(data, indent=4), code)\n        if headers:\n            resp.headers.extend(headers)\n        return resp\n\n    def register(self, collection, name):\n        collection_resource = SingleMongoResource(collection)\n        collection_list = ListMongoResource(collection)\n        self.add_resource(collection_resource, '/%s/<string:doc_id>'\n                          % name,\n                          endpoint=name + '_single',\n                          document=collection)\n        self.add_resource(collection_list, '/%s' % name,\n                          endpoint=name + '_multiple',\n                          document=collection)\n\n    def run(self, *args, **kwargs):\n        self.app.run(*args, **kwargs)\n\n    def add_resource(self, resource, *urls, **kwargs):\n        \"\"\"Adds a resource to the api.\n\n        :param resource: the class name of your resource\n        :type resource: :class:`Resource`\n        :param urls: one or more url routes to match for the resource, standard\n                     flask routing rules apply.  Any url variables will be\n                     passed to the resource method as args.\n        :type urls: str\n\n        :param endpoint: endpoint name (defaults to\n            :meth:`Resource.__name__.lower`\n            Can be used to reference this route in :class:`fields.Url` fields\n        :type endpoint: str\n\n        Additional keyword arguments not specified above will be passed as-is\n        to :meth:`flask.Flask.add_url_rule`.\n\n        Examples::\n\n            api.add_resource(HelloWorld, '/', '/hello')\n            api.add_resource(Foo, '/foo', endpoint=\"foo\")\n            api.add_resource(FooSpecial, '/special/foo', endpoint=\"foo\")\n\n        \"\"\"\n        endpoint = kwargs.pop('endpoint', None) or resource.__name__.lower()\n        self.endpoints.add(endpoint)\n\n        if endpoint in self.app.view_functions.keys():\n            previous_view_class = (self.app.view_functions[endpoint]\n                                   .__dict__['view_class'])\n\n            # if you override the endpoint with a different class, avoid the\n            # collision by raising an exception\n            if previous_view_class != resource:\n                raise ValueError(\n                    'This endpoint (%s) is already set to the class %s.'\n                    % (endpoint, previous_view_class.__name__))\n\n        resource.endpoint = endpoint\n        resource_func = self.output(resource.as_view(endpoint, **kwargs))\n\n        for decorator in self.decorators:\n            resource_func = decorator(resource_func)\n\n        for url in urls:\n            self.app.add_url_rule(self.prefix + url, view_func=resource_func)\n", "repo_name": "wuurrd/Flask-CuddlyRest", "sub_path": "flask_cuddlyrest/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3138, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "41", "api": [{"api_name": "flask.ext.restful.Api", "line_number": 7, "usage_type": "name"}, {"api_name": "flask.ext.restful.Api.__init__", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.ext.restful.Api", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 17, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 17, "usage_type": "call"}, {"api_name": "bson.json_util", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.ext.cuddlyrest.views.SingleMongoResource", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.ext.cuddlyrest.views.ListMongoResource", "line_number": 24, "usage_type": "call"}]}
{"seq_id": "39751505548", "text": "import websocket, json, requests, time, threading\r\nimport pandas as pd\r\n\r\n# ----------------------------------------------------- MAIN -----------------------------------------------------\r\n\r\ndef main():\r\n    # INITIALIZE DATA\r\n    initialize()\r\n\r\n    # WEBSOCKET THREAD\r\n    thread1 = threading.Thread(target=websocket_dataflow)\r\n\r\n    # POSITION THREAD\r\n    thread2 = threading.Thread(target=position_payload)\r\n\r\n    # STARTS THREADS\r\n    thread1.start()\r\n    thread2.start()\r\n\r\n# ----------------------------------------------------- GLOBAL -----------------------------------------------------\r\n\r\n# DATA\r\nstock = []\r\nstock_filtered = []\r\nstock_ordered = {}\r\nstock_data = {}\r\norders = {}\r\n\r\n# KEYS\r\nAPI_KEY = 'PKNWDZT640Q786J4Q3ZP'\r\nAPI_SECRET_KEY = 'CnHr41rc62hIzxh27bqrSBLnq1kZa3yENBg1BKp4'\r\nSUB = \"sip\"\r\n\r\n# TRADING URLS\r\nBASE_TRADE_URL = \"https://paper-api.alpaca.markets\"\r\nACCOUNT_URL = \"{}/v2/account\".format(BASE_TRADE_URL)\r\nORDERS_URL = \"{}/v2/orders\".format(BASE_TRADE_URL)\r\nPOSITIONS_URL = \"{}/v2/positions\".format(BASE_TRADE_URL)\r\nHEADERS = {'APCA-API-KEY-ID': API_KEY, 'APCA-API-SECRET-KEY': API_SECRET_KEY}\r\n\r\n# DATA URLS\r\nBASE_DATA_URL = \"https://data.alpaca.markets/v2/stocks/snapshots?symbols=\"\r\nCLOCK_URL = \"https://api.alpaca.markets/v2/clock\"\r\n\r\n# ----------------------------------------------------- INITIALIZE -----------------------------------------------------\r\n\r\ndef initialize():\r\n    # STOCK LIST SETUP (S&P 500 LIST)\r\n    sp_500_ticker = pd.read_html(\"https://en.wikipedia.org/wiki/List_of_S%26P_500_companies\")\r\n    sp_500_ticker = sp_500_ticker[0]\r\n    stock = sp_500_ticker['Symbol'].values.tolist()\r\n    symbol_string = \"\"\r\n    for symbol in stock:\r\n        symbol_string = symbol_string + symbol + \",\"\r\n    symbol_string = symbol_string[0:len(symbol_string)-1]\r\n    DATA_URL = BASE_DATA_URL + symbol_string\r\n\r\n    # DATA REQUEST\r\n    request = requests.get(DATA_URL, headers=HEADERS)\r\n    data = request.json()\r\n\r\n    # BOUNDS + DICT SETUP\r\n    for key in data:\r\n        if (data[key] != None): \r\n            stock_data[key] = {}\r\n            stock_data[key][\"current_price\"] = \"\"\r\n            delta = (data[key][\"prevDailyBar\"][\"h\"] - data[key][\"prevDailyBar\"][\"l\"]) / 2\r\n            stock_data[key][\"current_bar\"] = [data[key][\"dailyBar\"][\"l\"], data[key][\"dailyBar\"][\"h\"]]\r\n            stock_data[key][\"bounds\"] = [data[key][\"prevDailyBar\"][\"l\"] - delta, data[key][\"prevDailyBar\"][\"h\"] + delta]\r\n            if (stock_data[key][\"current_bar\"][0] < stock_data[key][\"bounds\"][0] or stock_data[key][\"current_bar\"][1] > stock_data[key][\"bounds\"][1]):\r\n                del stock_data[key]\r\n            else:\r\n                stock_filtered.append(key)\r\n\r\n# ----------------------------------------------------- WEBSOCKET -----------------------------------------------------\r\n\r\ndef websocket_dataflow():\r\n\r\n    # SOCKET LINK\r\n    socket = \"wss://stream.data.alpaca.markets/v2/\" + SUB\r\n\r\n    # SUBSCRIBE\r\n    def on_open(ws):\r\n        print(\"\\nConnection sucess...\", \"\\n\")\r\n        # AUTH\r\n        message = {\"action\": \"auth\", \"key\": API_KEY, \"secret\": API_SECRET_KEY}\r\n        ws.send(json.dumps(message))\r\n        # MESSAGE TO SEND\r\n        message = {\"action\":\"subscribe\", \"bars\":stock_filtered}\r\n        ws.send(json.dumps(message))\r\n\r\n    # PARSE DATA\r\n    def on_message(ws, message):\r\n        recieved = json.loads(message)\r\n        for x in recieved:\r\n            if (x[\"T\"] == \"b\"):\r\n                stock_data[x[\"S\"]][\"current_price\"] = x[\"c\"]\r\n                #print(x[\"S\"], \" updated: \", stock_data[x[\"S\"]], \"\\n\")\r\n                \r\n    # ERROR\r\n    def on_error(ws, message):\r\n        print(\"Error: \" + message, \"\\n\")\r\n\r\n    def on_close():\r\n        print(\"Connection terminated...\", \"\\n\")\r\n\r\n    # CONNECT TO SERVER\r\n    ws = websocket.WebSocketApp(socket, on_open=on_open, on_message=on_message, on_error=on_error, on_close=on_close)\r\n    ws.run_forever()\r\n\r\n# ----------------------------------------------------- POSITION PAYLOAD -----------------------------------------------------\r\n\r\ndef position_payload():\r\n    open_trades = 0\r\n    while True:\r\n        # RUNS EVERY 60 SEC\r\n        time.sleep(60)\r\n\r\n        # BUYS STOCK (SWAPPED THE IF STATEMENT </>)\r\n        for stock in stock_data:\r\n            if (stock_data[stock][\"current_price\"] != \"\" and not(stock in stock_ordered) and open_trades < 10):\r\n                if (stock_data[stock][\"current_price\"] < stock_data[stock][\"bounds\"][0] and open_trades < 10):\r\n                    short_order(stock)\r\n                    open_trades += 1\r\n                    print(open_trades)\r\n                elif (stock_data[stock][\"current_price\"] > stock_data[stock][\"bounds\"][1] and open_trades < 10):\r\n                    buy_order(stock)\r\n                    open_trades += 1\r\n                    print(open_trades)\r\n\r\n        # UPDATES ORDERS\r\n        update_orders()\r\n\r\n        # SELLS STOCK\r\n        for order in list(orders):\r\n            if ((orders[order][\"status\"] == \"new\" or orders[order][\"status\"] == \"filled\" or orders[order][\"status\"] == \"accepted\")):\r\n                print(\"order: \", order)\r\n                if (time_to_sell(orders[order][\"filled_at\"])):\r\n                    print(\"completed time_to_sell\")\r\n                    sell_position(orders[order][\"symbol\"])\r\n                    del orders[order]\r\n                    open_trades -= 1\r\n                    print(open_trades)\r\n                    \r\n# COMPARE TIME\r\n\r\ndef time_to_sell(order_time):\r\n    print(\"time_to_sell\")\r\n    request = requests.get(CLOCK_URL, headers=HEADERS)\r\n    data = request.json()\r\n    current_time = data['timestamp']\r\n    order_time = order_time[11 : 16]\r\n    current_time = current_time[11 : 16]\r\n    print(current_time)\r\n    print(order_time)\r\n    difference = (int((int(current_time[0:2]) - int(order_time[0:2])) * 60) + int((int(current_time[3:5]) - int(order_time[3:5])))) + 240\r\n    print(difference)\r\n    return difference >= 30\r\n\r\n# POSITIONS\r\n\r\ndef sell_position(symbol):\r\n    url = POSITIONS_URL + \"/\" + symbol \r\n    r = requests.delete(url, headers=HEADERS)\r\n    print(\"Position Sold: \", json.loads(r.content), \"\\n\")\r\n\r\ndef sell_all_position():\r\n    url = POSITIONS_URL + \"?cancel_orders=true\"\r\n    r = requests.delete(url, headers=HEADERS)\r\n    print(\"All Position Sold: \", json.loads(r.content), \"\\n\")\r\n\r\n# ORDERS\r\ndef buy_order(symbol):\r\n    data = {\r\n        \"symbol\": symbol,\r\n        \"notional\": \"10000.00\",\r\n        \"side\": \"buy\",\r\n        \"type\": \"market\",\r\n        \"time_in_force\": \"day\"\r\n    }\r\n    order(data)\r\n\r\n\r\ndef short_order(symbol):\r\n    qty = int(10000/stock_data[symbol][\"current_price\"])\r\n    data = {\r\n        \"symbol\": symbol,\r\n        \"qty\": qty,\r\n        \"side\": \"sell\",\r\n        \"type\": \"market\",\r\n        \"time_in_force\": \"day\"\r\n    }\r\n    order(data)\r\n\r\ndef order(data):\r\n    stock_ordered[data[\"symbol\"]] = True\r\n    r = requests.post(ORDERS_URL, json=data, headers=HEADERS)\r\n    print(\"Order Bought: \", json.loads(r.content), \"\\n\")\r\n\r\ndef cancel_orders():\r\n    r = requests.delete(ORDERS_URL, headers=HEADERS)\r\n    print(\"Order Canceled: \", json.loads(r.content), \"\\n\")\r\n\r\ndef update_orders():\r\n    r = requests.get(ORDERS_URL, headers=HEADERS)\r\n    data = json.loads(r.content)\r\n    for order in data:\r\n        orders[order['client_order_id']] = {\"symbol\":order['symbol'], \"status\": order['status'], \"filled_at\":order['created_at']}\r\n    print(\"Updated Orders: \", orders, \"\\n\")\r\n\r\n# ----------------------------------------------------- RUN -----------------------------------------------------\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n\r\n# ---------------------------------------------------------------------------------------------------------------", "repo_name": "JSidle/StockBot", "sub_path": "StockBot.py", "file_name": "StockBot.py", "file_ext": "py", "file_size_in_byte": 7694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "threading.Thread", "line_number": 11, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 59, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 87, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 90, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 94, "usage_type": "call"}, {"api_name": "websocket.WebSocketApp", "line_number": 108, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 117, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 149, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 164, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 165, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 169, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 170, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 197, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 198, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 201, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 202, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 205, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 206, "usage_type": "call"}]}
{"seq_id": "34364317779", "text": "import torch\nimport numpy as np\nimport pandas as pd\nimport os\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import shuffle\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import f1_score, confusion_matrix, accuracy_score\nfrom torch.utils.data import TensorDataset, DataLoader\nfrom torch.utils.data.sampler import Sampler\n\nfrom folktables import ACSDataSource, ACSIncome, ACSEmployment\nfrom fairbatch_local import FairBatch\n\ndef load_celeba_partition(img_list, celeba_feat_dir, ydict, groupdict):\n    x, y, group = [], [], []\n    for img in img_list:\n        feat_path = os.path.join(celeba_feat_dir, img[:-3] + 'npy')\n        if not os.path.exists(feat_path):\n            continue\n\n        feat = np.load(feat_path)\n        x.append(feat)\n        y.append(ydict[img])\n        group.append(groupdict[img])\n\n    return np.array(x), np.array(y), np.array(group)\n\ndef load_celeba_dataset():\n    celeba_dir = '/mnt/LargeDisk/Data/celeba'\n    celeba_label_file = os.path.join(celeba_dir, 'list_attr_celeba.csv')\n    celeba_partition_file = os.path.join(celeba_dir, 'list_eval_partition.csv')\n    celeba_feat_dir = os.path.join(celeba_dir, 'feat_align_celeba')\n\n    dflabel = pd.read_csv(celeba_label_file)\n    ydict = {img_id: smiling_label==1 for img_id, smiling_label in zip(dflabel['image_id'], dflabel['Smiling'])}\n    groupdict = {img_id: 1-max(male_label, 0) for img_id, male_label in zip(dflabel['image_id'], dflabel['Male'])}\n\n    dfpart = pd.read_csv(celeba_partition_file)\n    img_list = dfpart['image_id']\n    partition = dfpart['partition']\n    train_img = img_list[partition==0]\n    valid_img = img_list[partition==1]\n    test_img = img_list[partition==2]\n\n    x_train, y_train, group_train = load_celeba_partition(train_img, celeba_feat_dir, ydict, groupdict)\n    x_valid, y_valid, group_valid = load_celeba_partition(valid_img, celeba_feat_dir, ydict, groupdict)\n    x_test, y_test, group_test = load_celeba_partition(test_img, celeba_feat_dir, ydict, groupdict)\n\n    return x_train, y_train, group_train, x_test, y_test, group_test, x_valid, y_valid, group_valid\n\ndef get_dataset(dataset='acsincome', protected_class='sex',\n                shuffle_seed=0, batch_size=128, train_shuffle=True,\n                fairbatch=False, model=None):\n\n    if 'acs' in dataset:\n        data_source = ACSDataSource(survey_year='2018', horizon='1-Year', survey='person')\n        acs_data = data_source.get_data(states=['CA'], download=True)\n\n        if dataset=='acsincome':\n            task_class = ACSIncome\n        elif dataset=='acsemployment':\n            task_class = ACSEmployment\n\n        if protected_class=='sex':\n            task_class._group = 'SEX'\n            features, label, group = task_class.df_to_numpy(acs_data)\n            group = group - 1\n        elif protected_class=='race':\n            task_class._group = 'RAC1P'\n            features, label, group = task_class.df_to_numpy(acs_data)\n            group[group>1] = 2 # White vs Others\n            group = group - 1\n\n        x_train, x_test, y_train, y_test, group_train, group_test = train_test_split(features, label, group, test_size=0.2, random_state=0) # Test Split 20%\n        x_train, x_valid, y_train, y_valid, group_train, group_valid = train_test_split(x_train, y_train, group_train, test_size=0.1/0.8, random_state=0) # Val Split 10%\n\n    elif dataset=='celeba':\n        x_train, y_train, group_train, x_test, y_test, group_test, x_valid, y_valid, group_valid = load_celeba_dataset()\n\n    ## Shuffle Training Data\n    x_train, y_train, group_train = shuffle(x_train, y_train, group_train, random_state=shuffle_seed)\n\n    datascaler = MinMaxScaler()\n    datascaler.fit(x_train)\n    x_train, x_valid, x_test = datascaler.transform(x_train), datascaler.transform(x_valid), datascaler.transform(x_test)\n\n    train_dataset = TensorDataset(torch.from_numpy(x_train), torch.from_numpy(y_train), torch.from_numpy(group_train))\n    valid_dataset = TensorDataset(torch.from_numpy(x_valid), torch.from_numpy(y_valid), torch.from_numpy(group_valid))\n    test_dataset = TensorDataset(torch.from_numpy(x_test), torch.from_numpy(y_test), torch.from_numpy(group_test))\n\n    if fairbatch:\n        tensorx_train, tensory_train, tensorgroup_train = torch.from_numpy(x_train), torch.from_numpy(y_train), torch.from_numpy(group_train)\n        sampler = FairBatch(model, tensorx_train.cuda().float(), tensory_train.cuda().long(), tensorgroup_train.cuda(), batch_size=128,\n                            alpha=0.005, target_fairness='eqodds', replacement=False, seed=0)\n        trainloader = DataLoader(train_dataset, sampler=sampler)\n    else:\n        trainloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=train_shuffle, drop_last=False)\n\n    validloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, drop_last=False)\n    testloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=False)\n\n    return trainloader, validloader, testloader\n", "repo_name": "privacytrustlab/Data-Order-Randomness-versus-Group-Fairness", "sub_path": "dataloader.py", "file_name": "dataloader.py", "file_ext": "py", "file_size_in_byte": 5000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "folktables.ACSDataSource", "line_number": 58, "usage_type": "call"}, {"api_name": "folktables.ACSIncome", "line_number": 62, "usage_type": "name"}, {"api_name": "folktables.ACSEmployment", "line_number": 64, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 77, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 83, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 94, "usage_type": "call"}, {"api_name": "fairbatch_local.FairBatch", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 102, "usage_type": "call"}]}
{"seq_id": "36462733258", "text": "import setuptools\nfrom PublicDataReader.config.info import __version__, __author__, __contact__, __github__\n\nwith open(\"requirements.txt\") as f:\n    tests_require = f.readlines()\ninstall_requires = [t.strip() for t in tests_require]\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"PublicDataReader\",\n    version=__version__,\n    license=\"MIT\",\n    author=__author__,\n    author_email=__contact__,\n    description=\"Open Source Public Data Reader\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=__github__,\n    packages=setuptools.find_packages(),\n    package_data={\"PublicDataReader\": [\"raw/*.json\"]},\n    include_package_data=True,\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    install_requires=install_requires,\n)\n", "repo_name": "WooilJeong/PublicDataReader", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 425, "dataset": "github-code", "pt": "46", "api": [{"api_name": "setuptools.setup", "line_number": 11, "usage_type": "call"}, {"api_name": "PublicDataReader.config.info.__version__", "line_number": 13, "usage_type": "name"}, {"api_name": "PublicDataReader.config.info.__author__", "line_number": 15, "usage_type": "name"}, {"api_name": "PublicDataReader.config.info.__contact__", "line_number": 16, "usage_type": "name"}, {"api_name": "PublicDataReader.config.info.__github__", "line_number": 20, "usage_type": "name"}, {"api_name": "setuptools.find_packages", "line_number": 21, "usage_type": "call"}]}
{"seq_id": "2096552073", "text": "import numpy as np\nimport pandas as pd\nimport json\nimport math\nimport random\nimport pickle\nfrom tqdm import tqdm\nimport time\nimport os\n\nclass DataLoader():\n    def __init__(self, args, mode, batch, shuffle):\n\n        self.args = args\n\n        self.data_path = self.args.data_path\n        self.anno_path = os.path.join(self.data_path, 'annotation')\n\n        self.mode = mode\n        self.shuffle = shuffle\n        self.batch = batch\n\n        self.feature = os.path.join(self.data_path, 'I3D_features')\n        self.data_segments = self.gen_dataset()\n        self.size = len(self.data_segments)\n        self.nbatch = int(self.size / self.batch)\n\n    def gen_dataset(self):\n\n        alldata = json.load(open(os.path.join(self.anno_path, 'thumos14.json')))['database']\n        database = {}\n        for video in alldata.keys():\n            if alldata[video]['subset'] == self.mode:\n                database[video] = alldata[video]\n\n        data_segments = [] \n        for key, video in database.items():\n            t_granularity = self.args.t_granularity/self.args.fps[key]\n            t_step = self.args.t_step/self.args.fps[key]\n            fealength = int((video['fealength_step4']+self.args.down_sample-1) / self.args.down_sample)\n            actions = np.zeros([fealength, self.args.class_num])\n            points = np.zeros([2, fealength, self.args.class_num])\n            biases = np.zeros([2, fealength, self.args.class_num])\n            annotation = video['annotations']\n            for anno in annotation:\n                # time unit: sec\n                s0 = float(anno['segment'][0])\n                e0 = float(anno['segment'][1])\n                l = e0 - s0\n                s1 = max(s0-l/10., 0.0)\n                s2 = (s0+l/10.)\n                e1 = (e0-l/10.)\n                e2 = min(float((fealength-1)*t_step+(t_granularity/2.)), e0+l/10.)\n\n                is0 = max(0, round((s0-t_granularity/2.)/t_step))\n                is1 = max(0, round((s1-t_granularity/2.)/t_step))\n                is2 = max(0, round((s2-t_granularity/2.)/t_step))\n                ie0 = min((fealength-1), round((e0-t_granularity/2.)/t_step))\n                ie1 = min((fealength-1), round((e1-t_granularity/2.)/t_step))\n                ie2 = min((fealength-1), round((e2-t_granularity/2.)/t_step))\n\n\n                actions[is0:ie0+1,anno['labelidx']] = 1\n                points[0,is1:is2+1,anno['labelidx']] = 1\n                points[1,ie1:ie2+1,anno['labelidx']] = 1\n\n                if len(biases[0,is1:is2+1,anno['labelidx']]) != len(range(is1,is2+1)) or len(biases[1,ie1:ie2+1,anno['labelidx']]) != len(range(ie1,ie2+1)):\n                    # print(key,anno['labelidx'],fealength, is1,is2+1,ie1,ie2+1)\n                    continue\n                else:\n                    biases[0,is1:is2+1,anno['labelidx']] = [s0 - (t*t_step+t_granularity/2.) for t in range(is1,is2+1)] \n                    biases[1,ie1:ie2+1,anno['labelidx']] = [e0 - (t*t_step+t_granularity/2.) for t in range(ie1,ie2+1)]\n\n            data_segments.append((key, fealength, actions, points, biases))\n\n        if self.shuffle:\n            random.shuffle(data_segments)\n\n        return data_segments\n\n\n    def gen_train_batch(self, index):\n\n        batchdata = self.data_segments[index*self.batch:(index+1)*self.batch]\n        aa, pp, bb, ff, mm = [], [], [], [], []\n\n        for data in batchdata:\n            a = np.zeros([1, self.args.out_window, self.args.class_num])\n            p = np.zeros([1, 2, self.args.out_window, self.args.class_num])\n            b = np.zeros([1, 2, self.args.out_window, self.args.class_num])\n            f = np.zeros([1, self.args.in_window, 2048])\n            m = np.zeros([1, self.args.out_window, 1])\n\n            key, fealength, actions, points, biases = data\n\n            features = np.load(os.path.join(self.feature, key+'.npy'))\n            length = features.shape[0]\n\n            if fealength <= self.args.out_window:\n                a[0,:fealength,:] = actions\n                p[0,:,:fealength,:] = points\n                b[0,:,:fealength,:] = biases\n                f[0,:length,:] = features\n                m[0,:fealength,:] = 1\n            else:\n                actions_sum = np.sum(actions, 1)\n                flag = 0\n                count = 0\n                while flag == 0:\n                    count += 1\n                    s = np.random.randint(0, fealength-self.args.out_window+1)\n                    e = s + self.args.out_window\n                    if (s == 0 or actions_sum[s] == 0) and (e == fealength or actions_sum[e-1] == 0):\n                        a[0,:fealength,:] = actions[s:e,:]\n                        p[0,:,:fealength,:] = points[:,s:e,:]\n                        b[0,:,:fealength,:] = biases[:,s:e,:]\n                        tmp_length = features[s*self.args.down_sample:e*self.args.down_sample,:].shape[0]\n                        f[0,:tmp_length,:] = features[s*self.args.down_sample:e*self.args.down_sample,:]\n                        m[0,:,:] = 1\n                        flag = 1\n                    if count > 1000:\n                        break\n                if flag == 0:\n                    # print('no good sample')\n                    a[0,:fealength,:] = actions[0:self.args.out_window,:]\n                    p[0,:,:fealength,:] = points[:,0:self.args.out_window,:]\n                    b[0,:,:fealength,:] = biases[:,0:self.args.out_window,:]\n                    f[0,:,:] = features[0:self.args.in_window,:]\n                    m[0,:,:] = 1\n            aa.append(a)\n            pp.append(p)\n            bb.append(b)\n            ff.append(f)\n            mm.append(m)\n        aa = np.concatenate(aa)\n        pp = np.concatenate(pp)\n        bb = np.concatenate(bb)\n        ff = np.concatenate(ff)\n        mm = np.concatenate(mm)\n\n        return np.max(aa,2,keepdims=2), np.max(pp,3,keepdims=3), np.max(bb,3,keepdims=3)+np.min(bb,3,keepdims=3), ff, mm\n\n\n    def gen_eval_batch(self, index):\n\n\n        key, fealength, actions, points, biases = self.data_segments[index]\n\n        features = np.load(os.path.join(self.feature, key+'.npy'))\n\n        aa = np.expand_dims(actions, 0)\n        pp = np.expand_dims(points, 0)\n        bb = np.expand_dims(biases, 0)\n        ff = np.expand_dims(features, 0)\n        mm = np.ones((1,fealength,1))\n\n        return key, np.max(aa,2,keepdims=2), np.max(pp,3,keepdims=3), np.max(bb,3,keepdims=3)+np.min(bb,3,keepdims=3), ff, mm\n\n\n\n\nclass pem_DataLoader():\n    def __init__(self, batch, shuffle, datafile, evaluation=False):\n\n        self.data = pickle.load(open(datafile, 'rb'))\n        self.keys = list(self.data.keys())\n        self.batch = batch\n        self.num = len(self.keys)\n        if shuffle:\n            random.shuffle(self.keys)\n\n        if evaluation:\n            pass\n        else:\n            ratio = 0.9\n            self.train_key = self.keys[:int(self.num*ratio)]\n            self.val_key = self.keys[int(self.num*ratio):]\n\n            self.train_data = []\n            with tqdm(total=len(self.train_key)) as count:\n                for key in self.train_key:\n                    self.train_data += self.data[key]\n                    count.update(1)\n            self.train_num = len(self.train_data)\n            self.train_nbatch = int(self.train_num / batch)\n\n            self.val_data = []\n            with tqdm(total=len(self.val_key)) as count:\n                for key in self.val_key:\n                    self.val_data += self.data[key]\n                    count.update(1)\n            self.val_num = len(self.val_data)\n            self.val_nbatch = int(self.val_num / batch)\n\n    def generate_batch(self, mode, step):\n\n        if mode == 'train':\n            pem_data = self.train_data\n        else:\n            pem_data = self.val_data\n        feature = []\n        iou = []\n        for item in pem_data[step*self.batch:(step+1)*self.batch]:\n            feature.append(item[0])\n            iou.append(item[1])\n        feature = np.vstack(feature)\n        iou = np.vstack(iou)\n\n        return feature, iou\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "PeisenZhao/Bottom-Up-TAL-with-MR", "sub_path": "dataloader.py", "file_name": "dataloader.py", "file_ext": "py", "file_size_in_byte": 8014, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 44, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 43, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 157, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 165, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 170, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 180, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 207, "usage_type": "call"}]}
{"seq_id": "29926869924", "text": "import datetime\nfrom flask import Flask, render_template, request, redirect, session\napp = Flask(__name__)\napp.secret_key = 'development_key'\n\n\n@app.route('/')\ndef index():\n    return render_template(\"index.html\")\n\n\n@app.route('/checkout/submit', methods=['POST'])\ndef checkout_submit():\n    print(request.form)\n    session['checkout_form'] = request.form\n    return redirect('/checkout')\n\n\n@app.route('/checkout')\ndef checkout():\n    count = 0\n    count += int(session['checkout_form']['strawberry'])\n    count += int(session['checkout_form']['raspberry'])\n    count += int(session['checkout_form']['apple'])\n    session['count'] = count\n    session['date'] = datetime.datetime.now()\n    return render_template(\"checkout.html\", session=session)\n\n\n@app.route('/fruits')\ndef fruits():\n    return render_template(\"fruits.html\")\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n", "repo_name": "vaught-dawson/Python_v21.1_Assignments", "sub_path": "flask/fundamentals/dojo_fruit_store/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 879, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 26, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}]}
{"seq_id": "73227125578", "text": "\"\"\"added some more fields to the requests\n\nRevision ID: 463c6236fa58\nRevises: 02cc87f01969\nCreate Date: 2022-05-22 22:59:36.360116\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '463c6236fa58'\ndown_revision = '02cc87f01969'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('pdpa_requests', sa.Column('detail', sa.Text(), nullable=True))\n    op.add_column('pdpa_requests', sa.Column('received_at', sa.DateTime(timezone=True), nullable=True))\n    op.add_column('pdpa_requests', sa.Column('received_by', sa.Integer(), nullable=True))\n    op.create_foreign_key(None, 'pdpa_requests', 'staff_account', ['received_by'], ['id'])\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_constraint(None, 'pdpa_requests', type_='foreignkey')\n    op.drop_column('pdpa_requests', 'received_by')\n    op.drop_column('pdpa_requests', 'received_at')\n    op.drop_column('pdpa_requests', 'detail')\n    # ### end Alembic commands ###\n", "repo_name": "MUMT-IT/mis2018", "sub_path": "migrations/versions/463c6236fa58_added_some_more_fields_to_the_requests.py", "file_name": "463c6236fa58_added_some_more_fields_to_the_requests.py", "file_ext": "py", "file_size_in_byte": 1141, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "46", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}]}
{"seq_id": "15257226468", "text": "import numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom keras import layers\nimport tensorflow_hub as hub\nfrom collections import deque\nimport random\nimport math\nfrom tensorflow.keras import backend as K\n\n\"\"\"\nwe add two lines:\n\ne= tf.keras.backend.max(y_true,axis = -1)\ny_pred*= K.stack([e]*8, axis=-1)\n    \nto make the positions which doesn't contain neither unit or city by zero in the prediction probabilities, in order to focus only on the main occupied positions.\n\"\"\"\n\ndef custom_mean_squared_error(y_true, y_pred):\n    y_units_true = y_true[:,:,:,:6]\n    y_cities_true = y_true[:,:,:,6:]\n\n    y_units_pred = y_pred[:,:,:,:6]\n    y_cities_pred = y_pred[:,:,:,6:]\n\n\n    is_unit = tf.keras.backend.max(y_units_true,axis = -1)\n    is_city = tf.keras.backend.max(y_cities_true,axis = -1)\n\n    y_units_pred*= K.stack([is_unit]*6, axis=-1)\n    y_cities_pred*= K.stack([is_city]*2, axis=-1)\n\n    loss1 = K.square(y_units_pred - y_units_true)#/K.sum(is_unit)\n    loss2 = K.square(y_cities_pred - y_cities_true)#/K.sum(is_city)\n    return K.concatenate([loss1,loss2])\n\ndef units_accuracy(y_true, y_pred):\n    y_units_true = y_true[:,:,:,:6]\n    y_cities_true = y_true[:,:,:,6:]\n\n    y_units_pred = y_pred[:,:,:,:6]\n    y_cities_pred = y_pred[:,:,:,6:]\n\n    is_unit = tf.keras.backend.max(y_units_true,axis = -1)\n    y_units_pred*= K.stack([is_unit]*6, axis=-1)\n    return K.cast(K.equal(y_units_true, K.round(y_units_pred)), \"float32\")/K.sum(is_unit)\n\ndef cities_accuracy(y_true, y_pred):\n    y_units_true = y_true[:,:,:,:6]\n    y_cities_true = y_true[:,:,:,6:]\n\n    y_units_pred = y_pred[:,:,:,:6]\n    y_cities_pred = y_pred[:,:,:,6:]\n\n    is_city = tf.keras.backend.max(y_cities_true,axis = -1)\n    y_cities_pred*= K.stack([is_city]*2, axis=-1)\n\n    return K.cast(K.equal(y_cities_true, K.round(y_cities_pred)), \"float32\")/K.sum(is_city)\n\n\ndef get_model(s):\n    inputs = keras.Input(shape=(s,s,17),name = 'The game map')\n    f = layers.Flatten()(inputs)\n    h,w= s,s\n    f = layers.Dense(w*h,activation = \"sigmoid\")(f)\n    f = layers.Reshape((h,w,-1))(f)\n    units = layers.Dense(6,activation = \"softmax\",name = \"Units_actions\")(f)\n    cities = layers.Dense(2,activation = \"sigmoid\",name = \"Cities_actions\")(f)\n    output = layers.Concatenate()([units,cities])\n    model = keras.Model(inputs = inputs, outputs = output)\n    model.compile(optimizer= \"adam\", loss= custom_mean_squared_error ,metrics = [\"accuracy\"])\n\n    return model\n\n\nmodel =get_model(12)\nmodel.summary()\n\ntf.keras.utils.plot_model(\n    model,\n    to_file=\"model.png\",\n    show_shapes=1,\n    show_dtype=1,\n    show_layer_names=True,\n    rankdir=\"TB\",\n    expand_nested=False,\n    dpi=96)", "repo_name": "Ruben1701/Kaggle_Ai_Challenge", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 2671, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "tensorflow.keras.backend.max", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.backend.max", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.backend.stack", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 31, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.stack", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 32, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.square", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 34, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.square", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 35, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.concatenate", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 36, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.max", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 45, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.backend.stack", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 46, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.cast", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 47, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.equal", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.round", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.sum", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.max", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.backend.stack", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 57, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.cast", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 59, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.equal", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.round", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.sum", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.keras.Input", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 63, "usage_type": "name"}, {"api_name": "keras.layers.Flatten", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 64, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 66, "usage_type": "name"}, {"api_name": "keras.layers.Reshape", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 67, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 68, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 69, "usage_type": "name"}, {"api_name": "keras.layers.Concatenate", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 70, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 71, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.plot_model", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 80, "usage_type": "attribute"}]}
{"seq_id": "5579289730", "text": "from qcc import Qcc\n\nimport consts\nimport utils\nimport json\n\ndef query_industry(industry:str):\n  \"\"\"\n  查询企业行业主函数\n  industry:行业\n  \"\"\"\n  qcc = Qcc()\n  detail_url = qcc.query_url(industry)\n  if not detail_url:\n    print('未成功获取到公司详情页链接,请检查公司名错误或其他错误')\n    return ''\n  else:\n    industry = qcc.query_industry(detail_url)\n    if industry != '':\n      return utils.get_industry(industry)\n    else:\n      return ''\n        \nif __name__ == '__main__':\n  query_industry('腾讯')\n", "repo_name": "Jeremylee1234/query_industry", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 544, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "qcc.Qcc", "line_number": 12, "usage_type": "call"}, {"api_name": "qcc.query_url", "line_number": 13, "usage_type": "call"}, {"api_name": "qcc.query_industry", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.get_industry", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "71339995964", "text": "import numpy as np\nimport os\nimport tempfile\nimport unittest\nimport cv2\nimport torch\n\nfrom detectron2.data import MetadataCatalog\nfrom detectron2.structures import BoxMode, Instances, RotatedBoxes\nfrom detectron2.utils.visualizer import ColorMode, Visualizer\n\n\nclass TestVisualizer(unittest.TestCase):\n    def _random_data(self):\n        H, W = 100, 100\n        N = 10\n        img = np.random.rand(H, W, 3) * 255\n        boxxy = np.random.rand(N, 2) * (H // 2)\n        boxes = np.concatenate((boxxy, boxxy + H // 2), axis=1)\n\n        def _rand_poly():\n            return np.random.rand(3, 2).flatten() * H\n\n        polygons = [[_rand_poly() for _ in range(np.random.randint(1, 5))] for _ in range(N)]\n\n        mask = np.zeros_like(img[:, :, 0], dtype=bool)\n        mask[:40, 10:20] = 1\n\n        labels = [str(i) for i in range(N)]\n        return img, boxes, labels, polygons, [mask] * N\n\n    @property\n    def metadata(self):\n        return MetadataCatalog.get(\"coco_2017_train\")\n\n    def test_draw_dataset_dict(self):\n        img = np.random.rand(512, 512, 3) * 255\n        dic = {\n            \"annotations\": [\n                {\n                    \"bbox\": [\n                        368.9946492271106,\n                        330.891438763377,\n                        13.148537455410235,\n                        13.644708680142685,\n                    ],\n                    \"bbox_mode\": BoxMode.XYWH_ABS,\n                    \"category_id\": 0,\n                    \"iscrowd\": 1,\n                    \"segmentation\": {\n                        \"counts\": \"_jh52m?2N2N2N2O100O10O001N1O2MceP2\",\n                        \"size\": [512, 512],\n                    },\n                }\n            ],\n            \"height\": 512,\n            \"image_id\": 1,\n            \"width\": 512,\n        }\n        v = Visualizer(img)\n        v.draw_dataset_dict(dic)\n\n        v = Visualizer(img, self.metadata)\n        v.draw_dataset_dict(dic)\n\n    def test_draw_rotated_dataset_dict(self):\n        img = np.random.rand(512, 512, 3) * 255\n        dic = {\n            \"annotations\": [\n                {\n                    \"bbox\": [\n                        368.9946492271106,\n                        330.891438763377,\n                        13.148537455410235,\n                        13.644708680142685,\n                        45.0,\n                    ],\n                    \"bbox_mode\": BoxMode.XYWHA_ABS,\n                    \"category_id\": 0,\n                    \"iscrowd\": 1,\n                }\n            ],\n            \"height\": 512,\n            \"image_id\": 1,\n            \"width\": 512,\n        }\n        v = Visualizer(img, self.metadata)\n        v.draw_dataset_dict(dic)\n\n    def test_overlay_instances(self):\n        img, boxes, labels, polygons, masks = self._random_data()\n\n        v = Visualizer(img, self.metadata)\n        output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image()\n        self.assertEqual(output.shape, img.shape)\n\n        # Test 2x scaling\n        v = Visualizer(img, self.metadata, scale=2.0)\n        output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image()\n        self.assertEqual(output.shape[0], img.shape[0] * 2)\n\n        # Test overlay masks\n        v = Visualizer(img, self.metadata)\n        output = v.overlay_instances(masks=masks, boxes=boxes, labels=labels).get_image()\n        self.assertEqual(output.shape, img.shape)\n\n    def test_overlay_instances_no_boxes(self):\n        img, boxes, labels, polygons, _ = self._random_data()\n        v = Visualizer(img, self.metadata)\n        v.overlay_instances(masks=polygons, boxes=None, labels=labels).get_image()\n\n    def test_draw_instance_predictions(self):\n        img, boxes, _, _, masks = self._random_data()\n        num_inst = len(boxes)\n        inst = Instances((img.shape[0], img.shape[1]))\n        inst.pred_classes = torch.randint(0, 80, size=(num_inst,))\n        inst.scores = torch.rand(num_inst)\n        inst.pred_boxes = torch.from_numpy(boxes)\n        inst.pred_masks = torch.from_numpy(np.asarray(masks))\n\n        v = Visualizer(img)\n        v.draw_instance_predictions(inst)\n\n        v = Visualizer(img, self.metadata)\n        v.draw_instance_predictions(inst)\n\n    def test_BWmode_nomask(self):\n        img, boxes, _, _, masks = self._random_data()\n        num_inst = len(boxes)\n        inst = Instances((img.shape[0], img.shape[1]))\n        inst.pred_classes = torch.randint(0, 80, size=(num_inst,))\n        inst.scores = torch.rand(num_inst)\n        inst.pred_boxes = torch.from_numpy(boxes)\n\n        v = Visualizer(img, self.metadata, instance_mode=ColorMode.IMAGE_BW)\n        v.draw_instance_predictions(inst)\n\n        # check that output is grayscale\n        inst = inst[:0]\n        v = Visualizer(img, self.metadata, instance_mode=ColorMode.IMAGE_BW)\n        output = v.draw_instance_predictions(inst).get_image()\n        self.assertTrue(np.allclose(output[:, :, 0], output[:, :, 1]))\n        self.assertTrue(np.allclose(output[:, :, 0], output[:, :, 2]))\n\n    def test_draw_empty_mask_predictions(self):\n        img, boxes, _, _, masks = self._random_data()\n        num_inst = len(boxes)\n        inst = Instances((img.shape[0], img.shape[1]))\n        inst.pred_classes = torch.randint(0, 80, size=(num_inst,))\n        inst.scores = torch.rand(num_inst)\n        inst.pred_boxes = torch.from_numpy(boxes)\n        inst.pred_masks = torch.from_numpy(np.zeros_like(np.asarray(masks)))\n\n        v = Visualizer(img, self.metadata)\n        v.draw_instance_predictions(inst)\n\n    def test_correct_output_shape(self):\n        img = np.random.rand(928, 928, 3) * 255\n        v = Visualizer(img, self.metadata)\n        out = v.output.get_image()\n        self.assertEqual(out.shape, img.shape)\n\n    def test_overlay_rotated_instances(self):\n        H, W = 100, 150\n        img = np.random.rand(H, W, 3) * 255\n        num_boxes = 50\n        boxes_5d = torch.zeros(num_boxes, 5)\n        boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-0.1 * W, 1.1 * W)\n        boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-0.1 * H, 1.1 * H)\n        boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H))\n        boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H))\n        boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800)\n        rotated_boxes = RotatedBoxes(boxes_5d)\n        labels = [str(i) for i in range(num_boxes)]\n\n        v = Visualizer(img, self.metadata)\n        output = v.overlay_instances(boxes=rotated_boxes, labels=labels).get_image()\n        self.assertEqual(output.shape, img.shape)\n\n    def test_draw_no_metadata(self):\n        img, boxes, _, _, masks = self._random_data()\n        num_inst = len(boxes)\n        inst = Instances((img.shape[0], img.shape[1]))\n        inst.pred_classes = torch.randint(0, 80, size=(num_inst,))\n        inst.scores = torch.rand(num_inst)\n        inst.pred_boxes = torch.from_numpy(boxes)\n        inst.pred_masks = torch.from_numpy(np.asarray(masks))\n\n        v = Visualizer(img, MetadataCatalog.get(\"asdfasdf\"))\n        v.draw_instance_predictions(inst)\n\n    def test_draw_binary_mask(self):\n        img, boxes, _, _, masks = self._random_data()\n        img[:, :, 0] = 0  # remove red color\n        mask = masks[0]\n        mask_with_hole = np.zeros_like(mask).astype(\"uint8\")\n        mask_with_hole = cv2.rectangle(mask_with_hole, (10, 10), (50, 50), 1, 5)\n\n        for m in [mask, mask_with_hole]:\n            for save in [True, False]:\n                v = Visualizer(img)\n                o = v.draw_binary_mask(m, color=\"red\", text=\"test\")\n                if save:\n                    with tempfile.TemporaryDirectory(prefix=\"detectron2_viz\") as d:\n                        path = os.path.join(d, \"output.png\")\n                        o.save(path)\n                        o = cv2.imread(path)[:, :, ::-1]\n                else:\n                    o = o.get_image().astype(\"float32\")\n                    # red color is drawn on the image\n                self.assertTrue(o[:, :, 0].sum() > 0)\n\n    def test_draw_soft_mask(self):\n        img = np.random.rand(100, 100, 3) * 255\n        img[:, :, 0] = 0  # remove red color\n        mask = np.zeros((100, 100), dtype=np.float32)\n        mask[30:50, 40:50] = 1.0\n        cv2.GaussianBlur(mask, (21, 21), 10)\n\n        v = Visualizer(img)\n        o = v.draw_soft_mask(mask, color=\"red\", text=\"test\")\n        o = o.get_image().astype(\"float32\")\n        # red color is drawn on the image\n        self.assertTrue(o[:, :, 0].sum() > 0)\n\n        # test draw empty mask\n        v = Visualizer(img)\n        o = v.draw_soft_mask(np.zeros((100, 100), dtype=np.float32), color=\"red\", text=\"test\")\n        o = o.get_image().astype(\"float32\")\n\n    def test_border_mask_with_holes(self):\n        H, W = 200, 200\n        img = np.zeros((H, W, 3))\n        img[:, :, 0] = 255.0\n        v = Visualizer(img, scale=3)\n\n        mask = np.zeros((H, W))\n        mask[:, 100:150] = 1\n        # create a hole, to trigger imshow\n        mask = cv2.rectangle(mask, (110, 110), (130, 130), 0, thickness=-1)\n        output = v.draw_binary_mask(mask, color=\"blue\")\n        output = output.get_image()[:, :, ::-1]\n\n        first_row = {tuple(x.tolist()) for x in output[0]}\n        last_row = {tuple(x.tolist()) for x in output[-1]}\n        # Check quantization / off-by-1 error: the first and last row must have two colors\n        self.assertEqual(len(last_row), 2)\n        self.assertEqual(len(first_row), 2)\n        self.assertIn((0, 0, 255), last_row)\n        self.assertIn((0, 0, 255), first_row)\n\n    def test_border_polygons(self):\n        H, W = 200, 200\n        img = np.zeros((H, W, 3))\n        img[:, :, 0] = 255.0\n        v = Visualizer(img, scale=3)\n        mask = np.zeros((H, W))\n        mask[:, 100:150] = 1\n\n        output = v.draw_binary_mask(mask, color=\"blue\")\n        output = output.get_image()[:, :, ::-1]\n\n        first_row = {tuple(x.tolist()) for x in output[0]}\n        last_row = {tuple(x.tolist()) for x in output[-1]}\n        # Check quantization / off-by-1 error:\n        # the first and last row must have >=2 colors, because the polygon\n        # touches both rows\n        self.assertGreaterEqual(len(last_row), 2)\n        self.assertGreaterEqual(len(first_row), 2)\n        self.assertIn((0, 0, 255), last_row)\n        self.assertIn((0, 0, 255), first_row)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n", "repo_name": "facebookresearch/detectron2", "sub_path": "tests/test_visualizer.py", "file_name": "test_visualizer.py", "file_ext": "py", "file_size_in_byte": 10378, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 27217, "dataset": "github-code", "pt": "41", "api": [{"api_name": "unittest.TestCase", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 26, "usage_type": "call"}, {"api_name": "detectron2.data.MetadataCatalog.get", "line_number": 34, "usage_type": "call"}, {"api_name": "detectron2.data.MetadataCatalog", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "detectron2.structures.BoxMode.XYWH_ABS", "line_number": 47, "usage_type": "attribute"}, {"api_name": "detectron2.structures.BoxMode", "line_number": 47, "usage_type": "name"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 60, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 67, "usage_type": "attribute"}, {"api_name": "detectron2.structures.BoxMode.XYWHA_ABS", "line_number": 78, "usage_type": "attribute"}, {"api_name": "detectron2.structures.BoxMode", "line_number": 78, "usage_type": "name"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 87, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 93, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 98, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 103, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 109, "usage_type": "call"}, {"api_name": "detectron2.structures.Instances", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 119, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 121, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 124, "usage_type": "call"}, {"api_name": "detectron2.structures.Instances", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 133, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 135, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.ColorMode.IMAGE_BW", "line_number": 135, "usage_type": "attribute"}, {"api_name": "detectron2.utils.visualizer.ColorMode", "line_number": 135, "usage_type": "name"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 140, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.ColorMode.IMAGE_BW", "line_number": 140, "usage_type": "attribute"}, {"api_name": "detectron2.utils.visualizer.ColorMode", "line_number": 140, "usage_type": "name"}, {"api_name": "numpy.allclose", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 143, "usage_type": "call"}, {"api_name": "detectron2.structures.Instances", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 152, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 158, "usage_type": "attribute"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 165, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 172, "usage_type": "call"}, {"api_name": "detectron2.structures.RotatedBoxes", "line_number": 173, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 176, "usage_type": "call"}, {"api_name": "detectron2.structures.Instances", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 187, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 189, "usage_type": "call"}, {"api_name": "detectron2.data.MetadataCatalog.get", "line_number": 189, "usage_type": "call"}, {"api_name": "detectron2.data.MetadataCatalog", "line_number": 189, "usage_type": "name"}, {"api_name": "numpy.zeros_like", "line_number": 196, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 197, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 201, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path", "line_number": 205, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 214, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 216, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 218, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 220, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 228, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 233, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 237, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 254, "usage_type": "call"}, {"api_name": "detectron2.utils.visualizer.Visualizer", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 257, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 275, "usage_type": "call"}]}
{"seq_id": "14364668211", "text": "from actions.service_recommender_action import RecommendServices\nfrom rasa_sdk.executor import CollectingDispatcher\nimport unittest\nfrom unittest.mock import MagicMock, patch\nimport json\nimport sys\nimport os\nsys.path.append('actions')\n\nSERVICE_RECOMMENDER_JSON_RESPONSE_SUCCESS = [\n    {\n        \"service\": {\n            \"id\": \"8c6e25e9-e186-49fd-852c-f6f168d1351f\",\n            \"type\": \"Service\",\n            \"subtype\": None,\n            \"organizations\": [\n                {\n                    \"id\": \"c5f6914f-302e-41cc-bed7-4d4215aac640\",\n                    \"name\": \"Kela\"\n                },\n                {\n                    \"id\": \"c5f6914f-302e-41cc-bed7-4d4215aac640\",\n                    \"name\": \"Kela\"\n                }\n            ],\n            \"name\": {\n                \"en\": \"Kela's benefits for the unemployed\",\n                \"fi\": \"Kelan tuet työttömille\",\n                \"sv\": \"FPA:s stöd för arbetslösa\"\n            },\n            \"descriptions\": {\n                \"en\": [\n                    {\n                        \"value\": \"* labour market subsidy\\n* basic unemployment allowance\\n* commuting and relocation allowance\\n* job alternation compensation\",\n                        \"type\": \"Description\"\n                    },\n                    {\n                        \"value\": \"Unemployed\",\n                        \"type\": \"Summary\"\n                    }\n                ],\n                \"fi\": [\n                    {\n                        \"value\": \"* työmarkkinatuki\\n* peruspäiväraha\\n* liikkuvuusavustus\\n* vuorottelukorvaus\",\n                        \"type\": \"Description\"\n                    },\n                    {\n                        \"value\": \"Työttömät\",\n                        \"type\": \"Summary\"\n                    }\n                ],\n                \"sv\": [\n                    {\n                        \"value\": \"Arbetslösa\",\n                        \"type\": \"Summary\"\n                    },\n                    {\n                        \"value\": \"* arbetsmarknadsstöd\\n* grunddagpenning \\n* rörlighetsunderstöd\\n* alterneringsersättning\",\n                        \"type\": \"Description\"\n                    }\n                ]\n            },\n            \"requirement\": {\n                \"en\": \"http://www.kela.fi/unemployment\",\n                \"fi\": \"http://www.kela.fi/tyottomat\",\n                \"sv\": \"www.fpa.fi/utanarbete\"\n            },\n            \"targetGroups\": {\n                \"en\": [\n                    {\n                        \"name\": \"Finnish startups\",\n                        \"code\": \"KR2.8\"\n                    },\n                    {\n                        \"name\": \"Businesses and non-government organizations\",\n                        \"code\": \"KR2\"\n                    },\n                    {\n                        \"name\": \"Businesses operating in the domestic (Finnish) market\",\n                        \"code\": \"KR2.3\"\n                    },\n                    {\n                        \"name\": \"Citizens\",\n                        \"code\": \"KR1\"\n                    }\n                ],\n                \"fi\": [\n                    {\n                        \"name\": \"Yrityksen perustajat kotimaassa\",\n                        \"code\": \"KR2.8\"\n                    },\n                    {\n                        \"name\": \"Yritykset ja yhteisöt\",\n                        \"code\": \"KR2\"\n                    },\n                    {\n                        \"name\": \"Kotimarkkinoilla toimivat yritykset\",\n                        \"code\": \"KR2.3\"\n                    },\n                    {\n                        \"name\": \"Kansalaiset\",\n                        \"code\": \"KR1\"\n                    }\n                ],\n                \"sv\": [\n                    {\n                        \"name\": \"Inhemska företagsgrundare\",\n                        \"code\": \"KR2.8\"\n                    },\n                    {\n                        \"name\": \"Företag och samfund\",\n                        \"code\": \"KR2\"\n                    },\n                    {\n                        \"name\": \"Företag på den inhemska marknaden\",\n                        \"code\": \"KR2.3\"\n                    },\n                    {\n                        \"name\": \"Medborgare\",\n                        \"code\": \"KR1\"\n                    }\n                ]\n            },\n            \"serviceClasses\": {\n                \"en\": [\n                    {\n                        \"name\": \"Support and benefits for the unemployed\",\n                        \"description\": \"This service subclass contains different types of financial support for unemployed jobseekers, support eligibility criteria and services related to applying for support.\",\n                        \"code\": \"P10.6\"\n                    },\n                    {\n                        \"name\": \"Working life rules and collective agreements\",\n                        \"description\": \"This service subclass contains issues related to employment contracts and terms of employment, pay, and equality and flexibility in working life, including telework and part-time work, from the service point of view.\",\n                        \"code\": \"P10.3\"\n                    }\n                ],\n                \"fi\": [\n                    {\n                        \"name\": \"Työttömän tuet ja etuudet\",\n                        \"description\": \"Tässä palvelualaluokassa käsitellään työttömälle työnhakijalle suunnattuja erilaisia taloudellisia tukia, niiden saamisen edellytyksiä ja tukien hakupalveluja.\",\n                        \"code\": \"P10.6\"\n                    },\n                    {\n                        \"name\": \"Työelämän säännöt ja työehtosopimukset\",\n                        \"description\": \"Tähän palvelualaluokkaan kuuluvat palvelujen näkökulmasta työsopimuksiin ja -ehtoihin, palkkaukseen, työelämän yhdenvertaisuuteen ja joustoihin kuten etä- ja osa-aikatyöhön liittyvät asiat.\",\n                        \"code\": \"P10.3\"\n                    }\n                ],\n                \"sv\": [\n                    {\n                        \"name\": \"Stöd och förmåner för arbetslösa\",\n                        \"description\": \"I denna serviceundergrupp behandlas olika ekonomiska stödformer för arbetslösa arbetssökande, förutsättningar för beviljande av dem och tjänster för ansökan om stöd.\",\n                        \"code\": \"P10.6\"\n                    },\n                    {\n                        \"name\": \"Arbetslivets regler och kollektivavtal\",\n                        \"description\": \"Denna serviceundergrupp omfattar ärenden relaterade till arbetsavtal och -villkor, löner, jämställdhet och flexibilitet i arbetslivet, såsom distans- och deltidsarbete.\",\n                        \"code\": \"P10.3\"\n                    }\n                ]\n            },\n            \"areas\": {\n                \"en\": [],\n                \"fi\": [],\n                \"sv\": []\n            },\n            \"lastUpdated\": \"2021-06-15T07:37:25.395000\"\n        },\n        \"channels\": [\n            {\n                \"id\": \"16d63b97-0b8f-4f72-95e7-7cc2f9ab9e15\",\n                \"type\": \"EChannel\",\n                \"areaType\": \"Nationwide\",\n                \"organizationId\": \"c5f6914f-302e-41cc-bed7-4d4215aac640\",\n                \"serviceIds\": [\n                    \"105837fa-97d2-4f9a-916e-09fe7ca19e52\",\n                    \"b5b82555-9852-4a77-89bd-7dcd332d4f11\",\n                    \"7d655de8-76fd-4f24-bb92-e8f49e153e88\",\n                    \"b84af2c2-824f-4b27-a599-fa28de4e437c\",\n                    \"f8dd3060-543c-47ba-abba-a14fc1feacb3\",\n                    \"ff059acf-de3f-468d-8b6a-9a492d301cda\",\n                    \"6529e1c2-b9ac-4f00-8e8b-6d13616ccf81\",\n                    \"8c6e25e9-e186-49fd-852c-f6f168d1351f\",\n                    \"3ef3f1ef-6754-4308-8ef4-deef416b081e\",\n                    \"b5c945e5-a4d6-47b9-9362-0fe0f20adc2e\",\n                    \"30fe7757-32ad-4ea7-a8e4-857b44f81160\",\n                    \"dae7ba63-46af-4130-995d-1e88cafaa70c\",\n                    \"e0556386-0ffa-40f2-98aa-770b42dd792a\",\n                    \"579991be-f40f-4913-8130-0e07592b50c4\",\n                    \"e52f663f-df44-426c-b401-147d4ebd19cc\",\n                    \"aeb60b1d-2872-4841-9704-652246948990\",\n                    \"76472df7-25ed-4c55-94dd-fa3dd98ee862\",\n                    \"58a4bf82-dc19-4ca5-a57d-a0ef39d0e89d\",\n                    \"506d84d5-0ecf-400a-8b74-f9bd990dab7b\",\n                    \"3456be0a-a126-43af-a364-f24e24786cb1\",\n                    \"5ecdee89-0459-4b27-8271-206f314b801b\",\n                    \"b0372b6c-5ab5-4dd1-92e5-bde71dd25488\",\n                    \"52693b89-c7da-4c61-80df-b6f871672064\",\n                    \"ad234c6c-e24c-4d0f-8698-81980502278d\",\n                    \"caba7a03-40b7-439e-871d-1d9081bd3299\",\n                    \"a0a34972-1af4-41d2-ac89-198ce1875e4f\",\n                    \"7fd28107-d7d6-4158-96d1-fad0bd8c7499\",\n                    \"19a31135-a9d6-4926-a20b-bfe7db1780d3\",\n                    \"05f6a1fd-925f-46f0-b0d2-8a92881710a6\",\n                    \"9b6eb134-2764-47bd-9b98-e04f3a50b88b\",\n                    \"b7e6eddc-0f49-4bfa-876c-53fa01ba7907\",\n                    \"e09e783a-6363-412e-bed2-4082768c914d\",\n                    \"0157fe90-43d2-40e4-895a-a9446829d1d8\"\n                ],\n                \"name\": {\n                    \"en\": \"Kela's online customer service\",\n                    \"fi\": \"Kelan Asiointipalvelu\",\n                    \"sv\": \"FPA:s e-tjänst\"\n                },\n                \"descriptions\": {\n                    \"en\": [\n                        {\n                            \"value\": \"Check your own data, apply for benefits, send supporting documents and report changes.\",\n                            \"type\": \"Summary\"\n                        },\n                        {\n                            \"value\": \"In Kela's online customer service you can check your own data, apply for benefits, send supporting documets and report changes. You can handle almost all your transactions with Kela on the Internet. \",\n                            \"type\": \"Description\"\n                        }\n                    ],\n                    \"fi\": [\n                        {\n                            \"value\": \"Tarkastele omia Kela-tietojasi, hae etuuksia, lähetä liitteitä ja ilmoita muutoksista. Voit hoitaa lähes kaikki Kela-asiasi verkossa.\",\n                            \"type\": \"Summary\"\n                        },\n                        {\n                            \"value\": \"Kelan verkkoasiointipalvelussa voit tarkastella omia Kela-tietojasi, hakea etuuksia, lähettää liitteitä ja ilmoittaa muutoksista. Voit hoitaa lähes kaikki Kela-asiasi verkossa.\",\n                            \"type\": \"Description\"\n                        }\n                    ],\n                    \"sv\": [\n                        {\n                            \"value\": \"Kontrollera dina uppgifter hos FPA, ansök om förmåner, skicka bilagor och meddela förändringar. Du kan sköta så gott som alla FPA-ärenden på nätet.\",\n                            \"type\": \"Summary\"\n                        },\n                        {\n                            \"value\": \"I FPA:s e-tjänst kan du kontrollera dina egna uppgifter hos FPA, ansöka om förmåner, skicka bilagor och meddela förändringar. Du kan sköta så gott som alla FPA-ärenden på nätet. \",\n                            \"type\": \"Description\"\n                        }\n                    ]\n                },\n                \"webPages\": {\n                    \"en\": [\n                        \"https://asiointi.kela.fi/go_app?lg=en\"\n                    ],\n                    \"fi\": [\n                        \"https://asiointi.kela.fi/go_app\"\n                    ],\n                    \"sv\": [\n                        \"https://asiointi.kela.fi/go_app?lg=sv\"\n                    ]\n                },\n                \"emails\": {\n                    \"en\": [],\n                    \"fi\": [\n                        \"tekninentuki@kela.fi\"\n                    ],\n                    \"sv\": []\n                },\n                \"phoneNumbers\": {\n                    \"en\": [],\n                    \"fi\": [],\n                    \"sv\": []\n                },\n                \"areas\": {\n                    \"en\": [],\n                    \"fi\": [],\n                    \"sv\": []\n                },\n                \"addresses\": {\n                    \"en\": [],\n                    \"fi\": [],\n                    \"sv\": []\n                },\n                \"lastUpdated\": \"2021-06-16T07:19:48.498000\"\n            },\n            {\n                \"id\": \"fbeff57b-fdb7-4acc-9344-9d97193bf910\",\n                \"type\": \"ServiceLocation\",\n                \"areaType\": \"Nationwide\",\n                \"organizationId\": \"c5f6914f-302e-41cc-bed7-4d4215aac640\",\n                \"serviceIds\": [\n                    \"e52f663f-df44-426c-b401-147d4ebd19cc\",\n                    \"579991be-f40f-4913-8130-0e07592b50c4\",\n                    \"30fe7757-32ad-4ea7-a8e4-857b44f81160\",\n                    \"3ef3f1ef-6754-4308-8ef4-deef416b081e\",\n                    \"aeb60b1d-2872-4841-9704-652246948990\",\n                    \"76472df7-25ed-4c55-94dd-fa3dd98ee862\",\n                    \"e0556386-0ffa-40f2-98aa-770b42dd792a\",\n                    \"6529e1c2-b9ac-4f00-8e8b-6d13616ccf81\",\n                    \"8c6e25e9-e186-49fd-852c-f6f168d1351f\",\n                    \"dae7ba63-46af-4130-995d-1e88cafaa70c\",\n                    \"b5c945e5-a4d6-47b9-9362-0fe0f20adc2e\",\n                    \"ff059acf-de3f-468d-8b6a-9a492d301cda\"\n                ],\n                \"name\": {\n                    \"en\": None,\n                    \"fi\": \"Haukiputaan palvelupiste\",\n                    \"sv\": \"Servicestället i Haukipudas\"\n                },\n                \"descriptions\": {\n                    \"en\": [],\n                    \"fi\": [\n                        {\n                            \"value\": \"Kelan palvelupiste\",\n                            \"type\": \"Summary\"\n                        },\n                        {\n                            \"value\": \"Kelan palvelupisteessä opastetaan ja neuvotaan kaikissa Kelan etuuksiin liittyvissä asioissa. Voit hakea etuuksia ja toimittaa liitteet myös asiointipalvelussamme osoitteessa www.kela.fi/asiointi. Lue myös mahdollisuudesta ajanvaraukseen: www.kela.fi/ajanvaraus.\",\n                            \"type\": \"Description\"\n                        }\n                    ],\n                    \"sv\": [\n                        {\n                            \"value\": \"Fpa:s serviceställe\",\n                            \"type\": \"Summary\"\n                        },\n                        {\n                            \"value\": \"På FPA:s serviceställe kan du få information och rådgivning om alla FPA-förmåner. Du kan också ansöka om förmåner och lämna in bilagor i vår e-tjänst på adressen www.fpa.fi/etjanst. Läs mer om hur du bokar tid på adressen www.fpa.fi/tidsbokning.\",\n                            \"type\": \"Description\"\n                        }\n                    ]\n                },\n                \"webPages\": {\n                    \"en\": [],\n                    \"fi\": [],\n                    \"sv\": []\n                },\n                \"emails\": {\n                    \"en\": [],\n                    \"fi\": [],\n                    \"sv\": []\n                },\n                \"phoneNumbers\": {\n                    \"en\": [],\n                    \"fi\": [],\n                    \"sv\": []\n                },\n                \"areas\": {\n                    \"en\": [],\n                    \"fi\": [],\n                    \"sv\": []\n                },\n                \"addresses\": {\n                    \"en\": [\n                        {\n                            \"type\": \"Location\",\n                            \"subtype\": \"Single\",\n                            \"streetNumber\": \"15\",\n                            \"postalCode\": \"90830\",\n                            \"latitude\": \"7229135.399\",\n                            \"longitude\": \"422665.198\",\n                            \"streetName\": \"Simppulantie\",\n                            \"postOffice\": \"HAUKIPUDAS\",\n                            \"municipalityCode\": \"564\",\n                            \"municipalityName\": \"Aura\"\n                        },\n                        {\n                            \"type\": \"Postal\",\n                            \"subtype\": \"PostOfficeBox\",\n                            \"streetNumber\": None,\n                            \"postalCode\": None,\n                            \"latitude\": None,\n                            \"longitude\": None,\n                            \"streetName\": None,\n                            \"postOffice\": None,\n                            \"municipalityCode\": None,\n                            \"municipalityName\": None\n                        }\n                    ],\n                    \"fi\": [\n                        {\n                            \"type\": \"Location\",\n                            \"subtype\": \"Single\",\n                            \"streetNumber\": \"15\",\n                            \"postalCode\": \"90830\",\n                            \"latitude\": \"7229135.399\",\n                            \"longitude\": \"422665.198\",\n                            \"streetName\": \"Simppulantie\",\n                            \"postOffice\": \"HAUKIPUDAS\",\n                            \"municipalityCode\": \"564\",\n                            \"municipalityName\": \"Aura\"\n                        },\n                        {\n                            \"type\": \"Postal\",\n                            \"subtype\": \"PostOfficeBox\",\n                            \"streetNumber\": None,\n                            \"postalCode\": None,\n                            \"latitude\": None,\n                            \"longitude\": None,\n                            \"streetName\": None,\n                            \"postOffice\": None,\n                            \"municipalityCode\": None,\n                            \"municipalityName\": None\n                        }\n                    ],\n                    \"sv\": [\n                        {\n                            \"type\": \"Location\",\n                            \"subtype\": \"Single\",\n                            \"streetNumber\": \"15\",\n                            \"postalCode\": \"90830\",\n                            \"latitude\": \"7229135.399\",\n                            \"longitude\": \"422665.198\",\n                            \"streetName\": \"Simppulantie\",\n                            \"postOffice\": \"HAUKIPUDAS\",\n                            \"municipalityCode\": \"564\",\n                            \"municipalityName\": \"Aura\"\n                        },\n                        {\n                            \"type\": \"Postal\",\n                            \"subtype\": \"PostOfficeBox\",\n                            \"streetNumber\": None,\n                            \"postalCode\": None,\n                            \"latitude\": None,\n                            \"longitude\": None,\n                            \"streetName\": None,\n                            \"postOffice\": None,\n                            \"municipalityCode\": None,\n                            \"municipalityName\": None\n                        }\n                    ]\n                },\n                \"lastUpdated\": \"2021-06-28T01:00:00.719000\"\n            }\n        ],\n        \"score\": 0.8303728304964242\n    },\n]\nSERVICE_RECOMMENDER_JSON_RESPONSE_ERROR = {\n    \"detail\": [\n        {\n            \"loc\": [\n                \"body\",\n                48\n            ],\n            \"msg\": \"Expecting value: line 4 column 1 (char 48)\",\n            \"type\": \"value_error.jsondecode\",\n            \"ctx\": {\n                    \"msg\": \"Expecting value\",\n                    \"doc\": \"{\\n  \\\"need_text\\\": \\\"string\\\",\\n  \\\"municipality_id\\\":\\n}\",\n                    \"pos\": 48,\n                    \"lineno\": 4,\n                    \"colno\": 1\n            }\n        }\n    ]\n}\n\n# Test class for Rasa Tracker store which contains chatbot user message data\n\n\nclass TestRasaTracker():\n    def __init__(self):\n        self.slots = {\n            'general_service_search_text': 'olispa kahvia',\n            'municipality': 'turku',\n            \"fallback_language\": \"fi\",\n            \"session_started_metadata\": {\n                \"language\": \"it\"\n            }\n        }\n        self.latest_message = {\n            \"intent\": {\n                \"id\": -4114183629044666000,\n                \"name\": \"public_transport\",\n                \"confidence\": 0.9920039772987366\n            },\n            \"entities\": [],\n            \"text\": \"joukkoliikenne\",\n            \"message_id\": \"a4d3a71843eb449689e0eb4dc34ca7e9\",\n            \"metadata\": {\n                \"language\": \"fi\"\n            },\n            \"intent_ranking\": [\n            ]\n        }\n        self.events = [\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670810.1175656,\n                \"name\": \"action_session_start\",\n                \"confidence\": 1\n            },\n            {\n                \"event\": \"session_started\",\n                \"timestamp\": 1628670810.117589\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670810.117605,\n                \"name\": \"action_listen\"\n            },\n            {\n                \"event\": \"user\",\n                \"timestamp\": 1628670810.1180103,\n                \"text\": \"/get_started\",\n                \"parse_data\": {\n                    \"intent\": None,\n                    \"entities\": [],\n                    \"message_id\": \"d2f0600da3bc4648998c9727469121ce\",\n                    \"metadata\": {},\n                    \"intent_ranking\": [\n                        {\n                            \"name\": \"get_started\",\n                            \"confidence\": 1\n                        }\n                    ]\n                },\n                \"input_channel\": \"webchat\",\n                \"message_id\": \"d2f0600da3bc4648998c9727469121ce\",\n                \"metadata\": {}\n            },\n            {\n                \"event\": \"user_featurization\",\n                \"timestamp\": 1628670810.1396117,\n                \"use_text_for_featurization\": False\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670810.1396365,\n                \"name\": \"utter_get_started\",\n                \"policy\": \"policy_2_AugmentedMemoizationPolicy\",\n                \"confidence\": 1\n            },\n            {\n                \"event\": \"bot\",\n                \"timestamp\": 1628670810.1396775,\n                \"metadata\": {\n                    \"template_name\": \"utter_get_started\"\n                },\n                \"text\": \"Moi! Autan sinua löytämään palveluita eri elämäntilanteisiisi liittyen Varsinais-Suomen alueelta.\\n\\nYmmärrän helpoiten melko lyhyitä viestejä tai voit myös klikkailla nappeja.\",\n                \"data\": {}\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670810.1663811,\n                \"name\": \"utter_get_started_choose_life_event\",\n                \"policy\": \"policy_2_AugmentedMemoizationPolicy\",\n                \"confidence\": 1\n            },\n            {\n                \"event\": \"bot\",\n                \"timestamp\": 1628670810.166434,\n                \"metadata\": {\n                    \"template_name\": \"utter_get_started_choose_life_event\"\n                },\n                \"text\": \"Kuvaile ensiksi, millaiseen elämäntilanteeseen tarvitsisit apua tai voit myös etsiä vapaasti palveluita 😊\",\n                \"data\": {\n                    \"buttons\": [\n                        {\n                            \"title\": \"Työttömäksi jääminen\",\n                            \"type\": \"postback\",\n                            \"payload\": \"/ke8_losing_job\"\n                        },\n                        {\n                            \"title\": \"Velkaantuminen\",\n                            \"type\": \"postback\",\n                            \"payload\": \"/ke9_debt\"\n                        },\n                        {\n                            \"title\": \"Omaisen kuolema\",\n                            \"type\": \"postback\",\n                            \"payload\": \"/ke14_death\"\n                        },\n                        {\n                            \"title\": \"Etsi vapaasti palveluita\",\n                            \"type\": \"postback\",\n                            \"payload\": \"/service_search\"\n                        }\n                    ]\n                }\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670810.1708252,\n                \"name\": \"action_listen\",\n                \"policy\": \"policy_2_AugmentedMemoizationPolicy\",\n                \"confidence\": 1\n            },\n            {\n                \"event\": \"user\",\n                \"timestamp\": 1628670821.483857,\n                \"text\": \"/service_search\",\n                \"parse_data\": {\n                    \"intent\": None,\n                    \"entities\": [],\n                    \"message_id\": \"7c25cbf27151428589d7fc51620e508a\",\n                    \"metadata\": {},\n                    \"intent_ranking\": [\n                        {\n                            \"name\": \"service_search\",\n                            \"confidence\": 1\n                        }\n                    ],\n                    \"text\": \"<Etsi vapaasti palveluita>\"\n                },\n                \"input_channel\": \"webchat\",\n                \"message_id\": \"7c25cbf27151428589d7fc51620e508a\",\n                \"metadata\": {}\n            },\n            {\n                \"event\": \"user_featurization\",\n                \"timestamp\": 1628670821.5257907,\n                \"use_text_for_featurization\": False\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670821.5258121,\n                \"name\": \"general_service_search_form\",\n                \"policy\": \"policy_2_AugmentedMemoizationPolicy\",\n                \"confidence\": 1\n            },\n            {\n                \"event\": \"active_loop\",\n                \"timestamp\": 1628670821.5258555,\n                \"name\": \"general_service_search_form\"\n            },\n            {\n                \"event\": \"bot\",\n                \"timestamp\": 1628670821.5258627,\n                \"metadata\": {\n                    \"linkTarget\": \"_blank\",\n                    \"userInput\": \"show\",\n                    \"forceOpen\": False,\n                    \"forceClose\": False,\n                    \"pageChangeCallbacks\": None,\n                    \"pageEventCallbacks\": None,\n                    \"template_name\": \"utter_ask_general_service_search_text\"\n                },\n                \"text\": \"Kuvaile ensiksi palvelutarvettasi\",\n                \"data\": {}\n            },\n            {\n                \"event\": \"slot\",\n                \"timestamp\": 1628670821.5258706,\n                \"name\": \"requested_slot\",\n                \"value\": \"general_service_search_text\"\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670821.5335426,\n                \"name\": \"action_listen\",\n                \"policy\": \"policy_1_RulePolicy\",\n                \"confidence\": 1\n            },\n            {\n                \"event\": \"user\",\n                \"timestamp\": 1628670831.59987,\n                \"text\": \"haluaisin mennä uimarannalle\",\n                \"parse_data\": {\n                    \"intent\": None,\n                    \"entities\": [],\n                    \"text\": \"haluaisin mennä uimarannalle\",\n                    \"message_id\": \"1a5dad116ee34938b11f68e60c1814ba\",\n                    \"metadata\": {},\n                    \"intent_ranking\": [\n                        {\n                            \"id\": 1679135316125928700,\n                            \"name\": \"chitchat.bye\",\n                            \"confidence\": 0.8972764015197754,\n                            \"canonical\": \"hei hei\"\n                        }\n                    ]\n                },\n                \"input_channel\": \"webchat\",\n                \"message_id\": \"1a5dad116ee34938b11f68e60c1814ba\",\n                \"metadata\": {}\n            },\n            {\n                \"event\": \"user_featurization\",\n                \"timestamp\": 1628670831.625026,\n                \"use_text_for_featurization\": False\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670831.625047,\n                \"name\": \"general_service_search_form\",\n                \"policy\": \"policy_1_RulePolicy\",\n                \"confidence\": 1\n            },\n            {\n                \"event\": \"slot\",\n                \"timestamp\": 1628670831.6250882,\n                \"name\": \"general_service_search_text\",\n                \"value\": \"haluaisin mennä uimarannalle\"\n            },\n            {\n                \"event\": \"bot\",\n                \"timestamp\": 1628670831.6250937,\n                \"metadata\": {\n                    \"template_name\": \"utter_ask_municipality\"\n                },\n                \"text\": \"Kerrotko vielä, mistä kunnasta haluat palveluita?\",\n                \"data\": {}\n            },\n            {\n                \"event\": \"slot\",\n                \"timestamp\": 1628670831.6250968,\n                \"name\": \"requested_slot\",\n                \"value\": \"municipality\"\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670831.6321259,\n                \"name\": \"action_listen\",\n                \"policy\": \"policy_1_RulePolicy\",\n                \"confidence\": 1\n            },\n            {\n                \"event\": \"user\",\n                \"timestamp\": 1628670839.1954765,\n                \"text\": \"turku\",\n                \"parse_data\": {\n                    \"intent\": None,\n                    \"entities\": [\n                        {\n                            \"entity\": \"municipality_entity\",\n                            \"start\": 0,\n                            \"end\": 5,\n                            \"confidence_entity\": 0.9993183612823486,\n                            \"value\": \"turku\",\n                            \"extractor\": \"DIETClassifier\"\n                        }\n                    ],\n                    \"text\": \"turku\",\n                    \"message_id\": \"e8c8472b6ec04644a241510e418b5332\",\n                    \"metadata\": {},\n                    \"intent_ranking\": [\n                        {\n                            \"id\": 8390830771550880000,\n                            \"name\": \"service_municipality_choice\",\n                            \"confidence\": 0.999856173992157,\n                            \"canonical\": \"haluan palveluita turusta\"\n                        }\n                    ]\n                },\n                \"input_channel\": \"webchat\",\n                \"message_id\": \"e8c8472b6ec04644a241510e418b5332\",\n                \"metadata\": {}\n            },\n            {\n                \"event\": \"user_featurization\",\n                \"timestamp\": 1628670839.203917,\n                \"use_text_for_featurization\": False\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670839.2039268,\n                \"name\": \"general_service_search_form\",\n                \"policy\": \"policy_1_RulePolicy\",\n                \"confidence\": 1\n            },\n            {\n                \"event\": \"slot\",\n                \"timestamp\": 1628670839.2039347,\n                \"name\": \"municipality\",\n                \"value\": \"turku\"\n            },\n            {\n                \"event\": \"active_loop\",\n                \"timestamp\": 1628670839.203938\n            },\n            {\n                \"event\": \"slot\",\n                \"timestamp\": 1628670839.203941,\n                \"name\": \"requested_slot\"\n            },\n            {\n                \"event\": \"action\",\n                \"timestamp\": 1628670839.9863353,\n                \"name\": \"action_recommend_services\",\n                \"policy\": \"policy_2_AugmentedMemoizationPolicy\",\n                \"confidence\": 1\n            }\n        ]\n\n    def get_slot(self, key):\n        return self.slots[key]\n\n\n# This method will be used by the mock to replace requests.post to service recommender API\ndef mocked_requests_post(*args, **kwargs):\n\n    class MockResponse:\n        def __init__(self, json_data, status_code):\n            self.json_data = json_data\n            self.status_code = status_code\n\n        def json(self):\n            return self.json_data\n\n        def text(self):\n            return json.dumps(self.json_data)\n\n    if args[0] == 'request_success/services/recommend':\n        return MockResponse(SERVICE_RECOMMENDER_JSON_RESPONSE_SUCCESS, 200)\n    elif args[0] == 'request_error/services/recommend':\n        return MockResponse(SERVICE_RECOMMENDER_JSON_RESPONSE_ERROR, 400)\n\n\nclass TestRasaActionsRecommendServices(unittest.TestCase):\n\n    def setUp(self):\n        self.tracker = TestRasaTracker()\n\n    @patch('requests.post', side_effect=mocked_requests_post)\n    def test_recommend_services_action_success(self, mock_post):\n        os.environ['RASA_ACTIONS_SERVICE_RECOMMENDER_ENDPOINT'] = 'request_success'\n        dispatcher = CollectingDispatcher()\n        action = RecommendServices()\n        action.run(dispatcher, self.tracker, None)\n\n        self.assertEqual(\n            dispatcher.messages[0]['attachment']['payload']['elements'][0][\n                'title'], SERVICE_RECOMMENDER_JSON_RESPONSE_SUCCESS[0]['service']['name']['fi']\n        )\n\n    @patch('requests.post', side_effect=mocked_requests_post)\n    def test_recommend_services_action_error(self, mock_post):\n        os.environ['RASA_ACTIONS_SERVICE_RECOMMENDER_ENDPOINT'] = 'request_error'\n        dispatcher = CollectingDispatcher()\n        action = RecommendServices()\n        action.run(dispatcher, self.tracker, None)\n        self.assertEqual(\n            dispatcher.messages[0]['response'], 'utter_recommendation_error')\n\n\nif __name__ == '__main__':\n    unittest.main()\n", "repo_name": "City-of-Turku/PaohRasaPlatform", "sub_path": "test/test_rasa_actions.py", "file_name": "test_rasa_actions.py", "file_ext": "py", "file_size_in_byte": 33857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 799, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 807, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 814, "usage_type": "attribute"}, {"api_name": "rasa_sdk.executor.CollectingDispatcher", "line_number": 815, "usage_type": "call"}, {"api_name": "actions.service_recommender_action.RecommendServices", "line_number": 816, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 812, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 826, "usage_type": "attribute"}, {"api_name": "rasa_sdk.executor.CollectingDispatcher", "line_number": 827, "usage_type": "call"}, {"api_name": "actions.service_recommender_action.RecommendServices", "line_number": 828, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 824, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 835, "usage_type": "call"}]}
{"seq_id": "43634588065", "text": "import sys\nfrom typing import List\n\nimport testing.test_data\nfrom roland import DataBlock, RolandData, GenericRoland, GenericRolandWithBackwardCompatibility\nfrom Roland_JV1080 import jv_1080\nfrom Roland_JV80 import jv_80\n\nthis_module = sys.modules[__name__]\n\n# XV-3080 and XV-5080. But the XV-5080 has these Patch Split Key messages as well!? We can ignore them?\n_xv3080_patch_data = [DataBlock((0x00, 0x00, 0x00, 0x00), 0x4f, \"Patch common\"),\n                      DataBlock((0x00, 0x00, 0x02, 0x00), (0x01, 0x11), \"Patch common MFX\"),\n                      DataBlock((0x00, 0x00, 0x04, 0x00), 0x34, \"Patch common Chorus\"),\n                      DataBlock((0x00, 0x00, 0x06, 0x00), 0x53, \"Patch common Reverb\"),\n                      DataBlock((0x00, 0x00, 0x10, 0x00), 0x29, \"Patch common Tone Mix Table\"),\n                      DataBlock((0x00, 0x00, 0x20, 0x00), (0x01, 0x09), \"Tone 1\"),\n                      DataBlock((0x00, 0x00, 0x22, 0x00), (0x01, 0x09), \"Tone 2\"),\n                      DataBlock((0x00, 0x00, 0x24, 0x00), (0x01, 0x09), \"Tone 3\"),\n                      DataBlock((0x00, 0x00, 0x26, 0x00), (0x01, 0x09), \"Tone 4\")]\n_xv3080_edit_buffer_addresses = RolandData(\"XV-3080 Temporary Patch\", 1, 4, 4,\n                                           (0x1f, 0x00, 0x00, 0x00),\n                                           _xv3080_patch_data)\n_xv3080_program_buffer_addresses = RolandData(\"XV-3080 User Patches\", 128, 4, 4,\n                                              (0x30, 0x00, 0x00, 0x00),\n                                              _xv3080_patch_data)\n# This can be used as an alternative way to detect the XV-3080\n# _xv3080_system_common = RolandData(\"XV-3080 System Common\", 1, 4, 4, (0x00, 0x00, 0x00, 0x00),\n#                                   [DataBlock((0x00, 0x00, 0x00, 0x00), 0x28, \"System common\")])\nxv_3080_main = GenericRoland(\"Roland XV-3080\",\n                             model_id=[0x00, 0x10],\n                             address_size=4,\n                             edit_buffer=_xv3080_edit_buffer_addresses,\n                             program_dump=_xv3080_program_buffer_addresses,\n                             category_index=0x0c,\n                             device_family=[0x10, 0x01])  # Interestingly, the XV-3080 seems the first model to support the generic device inquiry\nxv_3080 = GenericRolandWithBackwardCompatibility(xv_3080_main, [jv_80, jv_1080])\nxv_3080.install(this_module)\n\n\n#  and XV-5080 and XV-5050?\n\n\ndef setupHelp():\n    return \"Make sure the Receive Exclusive parameter (SYSTEM/COMMON) is ON, and the synth is set to Patch Mode\"\n\n\n# Test data picked up by test_adaptation.py\ndef make_test_data():\n    def programs(data: testing.TestData) -> List[testing.ProgramTestData]:\n        patch = []\n        names = [\"RedPowerBass\", \"Sinus QSB\", \"Super W Bass\"]\n        i = 0\n        # Extract the first 3 programs from the sysex dump loaded, and yield them with name and number to the test code\n        for message in data.all_messages:\n            if xv_3080.isPartOfSingleProgramDump(message):\n                patch.extend(message)\n                if xv_3080.isSingleProgramDump(patch):\n                    yield testing.ProgramTestData(message=patch, name=names[i], number=i)\n                    patch = []\n                    i += 1\n                    if i >= len(names):\n                        break\n\n    return testing.TestData(sysex=\"testData/jv1080_AGSOUND1.SYX\",\n                            program_generator=programs,\n                            program_dump_request=\"f0 41 10 00 10 11 30 00 00 00 00 00 00 4f 01 f7\",\n                            device_detect_call=\"f0 7e 00 06 01 f7\",\n                            device_detect_reply=(\"f0 7e 10 06 02 41 10 01 00 00 00 00 00 00 f7\", 0))\n", "repo_name": "christofmuc/KnobKraft-orm", "sub_path": "adaptations/Roland_XV3080.py", "file_name": "Roland_XV3080.py", "file_ext": "py", "file_size_in_byte": 3752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 152, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sys.modules", "line_number": 9, "usage_type": "attribute"}, {"api_name": "roland.DataBlock", "line_number": 12, "usage_type": "call"}, {"api_name": "roland.DataBlock", "line_number": 13, "usage_type": "call"}, {"api_name": "roland.DataBlock", "line_number": 14, "usage_type": "call"}, {"api_name": "roland.DataBlock", "line_number": 15, "usage_type": "call"}, {"api_name": "roland.DataBlock", "line_number": 16, "usage_type": "call"}, {"api_name": "roland.DataBlock", "line_number": 17, "usage_type": "call"}, {"api_name": "roland.DataBlock", "line_number": 18, "usage_type": "call"}, {"api_name": "roland.DataBlock", "line_number": 19, "usage_type": "call"}, {"api_name": "roland.DataBlock", "line_number": 20, "usage_type": "call"}, {"api_name": "roland.RolandData", "line_number": 21, "usage_type": "call"}, {"api_name": "roland.RolandData", "line_number": 24, "usage_type": "call"}, {"api_name": "roland.GenericRoland", "line_number": 30, "usage_type": "call"}, {"api_name": "roland.GenericRolandWithBackwardCompatibility", "line_number": 37, "usage_type": "call"}, {"api_name": "Roland_JV80.jv_80", "line_number": 37, "usage_type": "name"}, {"api_name": "Roland_JV1080.jv_1080", "line_number": 37, "usage_type": "name"}, {"api_name": "testing.test_data.TestData", "line_number": 50, "usage_type": "attribute"}, {"api_name": "testing.test_data", "line_number": 50, "usage_type": "name"}, {"api_name": "testing.test_data.ProgramTestData", "line_number": 59, "usage_type": "call"}, {"api_name": "testing.test_data", "line_number": 59, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 50, "usage_type": "name"}, {"api_name": "testing.test_data.ProgramTestData", "line_number": 50, "usage_type": "attribute"}, {"api_name": "testing.test_data.TestData", "line_number": 65, "usage_type": "call"}, {"api_name": "testing.test_data", "line_number": 65, "usage_type": "name"}]}
{"seq_id": "30958864135", "text": "import tensorflow as tf\nfrom keras.models import Model\n\nuser_NN = tf.keras.models.Sequential([\n    tf.keras.layers.Dense(256, activation='relu'),\n    tf.keras.layers.Dense(128, activation='relu'),\n    tf.keras.layers.Dense(32)\n])\n\nitem_NN = tf.keras.models.Sequential([\n    tf.keras.layers.Dense(256, activation='relu'),\n    tf.keras.layers.Dense(128, activation='relu'),\n    tf.keras.layers.Dense(32)\n])\n\nnum_user_features = 10\nnum_item_features = 10\n\n# create the user input and point to the base network\nuser_input = tf.keras.layers.Input(shape=(num_user_features))\nvu = user_NN(user_input)\nvu = tf.linalg.l2_normalize(vu, axis=1)\n\n# create the item input and point to the base network\nitem_input = tf.keras.layers.Input(shape=(num_item_features))\nvm = item_NN(item_input)\nvm = tf.linalg.l2_normalize(vm, axis=1)\n\n# measure the similarity between the user and item embeddings\noutput = tf.keras.layers.Dot(axes=1)([vu, vm])\n\n# specify the inputs and output of the model\nmodel = Model(inputs=[user_input, item_input], outputs=output)\n\n# specify the cost function and optimization strategy\ncost_fn = tf.keras.losses.MeanSquaredError()", "repo_name": "ali-izhar/machine-learning", "sub_path": "Theory/Deep_Learning/Recommender_Systems/Content_Based/content_recommender.py", "file_name": "content_recommender.py", "file_ext": "py", "file_size_in_byte": 1134, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "46", "api": [{"api_name": "tensorflow.keras.models.Sequential", "line_number": 4, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 4, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 5, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 5, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 6, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 7, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 7, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 10, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.linalg.l2_normalize", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.linalg", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.linalg.l2_normalize", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.linalg", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dot", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 30, "usage_type": "attribute"}, {"api_name": "keras.models.Model", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.MeanSquaredError", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 36, "usage_type": "attribute"}]}
{"seq_id": "11519581908", "text": "import json\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import List\n\nimport github_action_utils as gha_utils\nimport requests\nimport yaml\nfrom dataclass_wizard import YAMLWizard\n\n\n@dataclass\nclass Specification:\n    \"\"\"Represents an OpenAPI specification.\"\"\"\n    id: str\n    label: str\n    url: str\n\n\n@dataclass\nclass Organization:\n    \"\"\"Represents an organization that provides OpenAPI specifications.\"\"\"\n    id: str\n    label: str\n    specifications: List[Specification]\n\n\n@dataclass\nclass SpecificationsFile(YAMLWizard):\n    \"\"\"Represents a file containing a list of organizations with their OpenAPI specifications.\"\"\"\n    organizations: List[Organization]\n\n\ndef download_specification(url: str) -> str:\n    \"\"\"Downloads an OpenAPI specification from a given URL and returns its content.\"\"\"\n    try:\n        response = requests.get(url)\n        response.raise_for_status()\n        return response.text\n    except requests.exceptions.RequestException as e:\n        raise ValueError(f\"Failed to download specification from {url}: {e}\")\n\n\ndef parse_specification(spec: str) -> str:\n    \"\"\"Parses a given OpenAPI specification content and returns it in JSON format.\"\"\"\n    try:\n        parsed_spec = yaml.safe_load(spec)\n        return json.dumps(parsed_spec, indent=4)\n    except yaml.YAMLError as e:\n        raise ValueError(f\"Failed to parse specification: {e}\")\n\n\ndef save_specification(spec: str, filepath: Path) -> None:\n    \"\"\"Saves a given OpenAPI specification content to a file at the given path.\"\"\"\n    try:\n        filepath.parent.mkdir(parents=True, exist_ok=True)\n        filepath.write_text(spec)\n    except OSError as e:\n        raise ValueError(f\"Failed to save specification to {filepath}: {e}\")\n\n\ndef download_format_and_save_specs(organizations: List[Organization]) -> None:\n    \"\"\"\n    Downloads, formats and saves OpenAPI specifications for the given organizations.\n\n    For each organization and its specifications, the function downloads the content\n    of each specification, parses it and saves it in JSON format to a file with a name\n    that includes the organization and specification IDs. If any errors occur during this\n    process, a warning is logged.\n    \"\"\"\n    for organization in organizations:\n        for spec in organization.specifications:\n            try:\n                spec_content = download_specification(spec.url)\n                validated_spec = parse_specification(spec_content)\n\n                path = Path('../../') / 'openapi' / f'{organization.id}_{spec.id}.json'\n                save_specification(validated_spec, path)\n\n                gha_utils.debug(f\"Saved specification for {organization.label}: {spec.label} ({path})\")\n            except (ValueError, requests.exceptions.RequestException, OSError) as exp:\n                with gha_utils.group(\"Warnings while downloading, formatting and saving OpenApi specs\"):\n                    gha_utils.warning(str(exp), title=\"Warning\")\n\n\nif __name__ == \"__main__\":\n    # Load the OpenAPI specifications file and extract the list of organizations.\n    specifications_yaml_file = '../../specifications.json'\n    parsed_specification_file = SpecificationsFile.from_yaml_file(specifications_yaml_file)\n\n    # Download, format and save the OpenAPI specifications for the organizations.\n    download_format_and_save_specs(parsed_specification_file.organizations)\n", "repo_name": "ivpk/api-specifications", "sub_path": "scripts/openapi/openapi.py", "file_name": "openapi.py", "file_ext": "py", "file_size_in_byte": 3384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "dataclasses.dataclass", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 20, "usage_type": "name"}, {"api_name": "dataclass_wizard.YAMLWizard", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 28, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 40, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 47, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}, {"api_name": "yaml.YAMLError", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 62, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 77, "usage_type": "call"}, {"api_name": "github_action_utils.debug", "line_number": 80, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 81, "usage_type": "attribute"}, {"api_name": "github_action_utils.group", "line_number": 82, "usage_type": "call"}, {"api_name": "github_action_utils.warning", "line_number": 83, "usage_type": "call"}]}
{"seq_id": "41994451883", "text": "import torch\nimport numpy as np\n\ndef get_uvcoords(H, W):\n    i, j = np.meshgrid(np.linspace(0, W - 1, W), np.linspace(0, H - 1, H))\n    dx = 1 / W\n    dy = 1 / H\n    for x in range(W):\n        i[:, x] = dx / 2 + dx * x\n    for y in range(H):\n        j[y, :] = dy / 2 + dy * y\n\n    j = np.flipud(j)\n    uvs = np.stack((i, j), axis=2).reshape(H * W, 2)\n    return uvs\n\ndef img2flat(img):\n    \"\"\"\n    [H,W,C] to [H*W,C] from row top down\n    :param img:\n    :return:\n    \"\"\"\n    rows=torch.chunk(img,len(img))\n    flat=torch.cat(rows,dim=1)\n    return flat.squeeze()\n\ndef flat2img(flat,H):\n    \"\"\"\n    [H*W,C] to [H,W,C] follow row top down rule\n    :param flat:\n    :return:\n    \"\"\"\n    rows=torch.chunk(flat,H)\n    img=torch.stack(rows)\n    return img\n\n\n# img=torch.tensor(np.arange(75).reshape((5,5,3)))\n# print(img)\n# print(flat2img(img2flat(img),len(img)))\n#\n# ten=torch.Tensor(np.arange(25*3).reshape(25,3))\n# # print(ten)\n# # rows=torch.chunk(ten,5,dim=0)\n# # img=torch.stack(rows)\n# # print(img.shape)\n# # img.transpose_(0,2) # hwc-cwh\n# # img.transpose_(1,2) # cwh-chw\n# # print(img.shape)\n# print(ten.data)\n\nten1=torch.tensor(np.arange(15).reshape(5,3))\nten2=torch.tensor(np.arange(15).reshape(5,3)+1)\nten3=torch.tensor(np.arange(15).reshape(5,3)+2)\nlis=[ten1,ten2,ten3]\nten4=torch.cat(lis,dim=0)\nprint(ten4)\n", "repo_name": "mudimingquedeyinmoujia/python_learning", "sub_path": "torch_test/chunk.py", "file_name": "chunk.py", "file_ext": "py", "file_size_in_byte": 1316, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "numpy.meshgrid", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.chunk", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.chunk", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 56, "usage_type": "call"}]}
{"seq_id": "17038517718", "text": "#!/usr/bin/env python\n\nfrom datetime import datetime\nimport re\nimport sys\n\ndef mapper():\n\ttld = \"http://www.theassociates.co.uk\"\n\tpattern = '^([\\d.]+) ([\\w-]+) ([\\w-]+) \\[(.+)\\] \\\"(.+)\\\" (\\d{3}) (\\d+)$'\n\tfor line in sys.stdin:\n\t\tresult = re.match(pattern, line)\n\t\tif result is None:\n\t\t\tcontinue\n\t\ttime_str, request = result.group(4), result.group(5)\n\t\ttry:\n\t\t\tmethod, resource, protocol = request.split(\" \")\n\t\texcept ValueError:\n\t\t\tcontinue\n\n\t\t# Need to convert to ordinal because we want to sort by day\n\t\ttime_dt = datetime.strptime(time_str.split(\" \")[0], \"%d/%b/%Y:%X\")\n\t\ttime_ordinal = time_dt.toordinal()\n\n\t\tif resource.startswith(tld):\n\t\t\tresource = resource[len(tld):]\n\n\t\tif resource == \"/\":\n\t\t\tprint(\"{}\\t1\".format(time_ordinal))\n\nif __name__ == \"__main__\":\n\tmapper()\n", "repo_name": "yahwang/Learn-Big-Data-Essentials-Yandex", "sub_path": "data/mapreduce_test/mapper.py", "file_name": "mapper.py", "file_ext": "py", "file_size_in_byte": 776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sys.stdin", "line_number": 10, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "7913187534", "text": "import logging\nimport uuid\n\nfrom django.core import mail\nfrom django.test import TestCase\n\nfrom classroom.factories import ClassroomFactory, UserFactory\nfrom classroom.forms import EnrollmentForm, PostForm\n\n\nclass EnrollmentFormTests(TestCase):\n    def test_valid_enrollment_form_with_new_enrollment_sends_email(self):\n        student = UserFactory()\n        classroom = ClassroomFactory()\n\n        code = str(classroom.id)\n        form = EnrollmentForm({'code': code})\n\n        self.assertTrue(form.is_valid())\n\n        with self.assertLogs('classroom.forms', level='INFO') as cm:\n            form.send_mail(student=student, classroom=classroom)\n\n        self.assertEqual(len(mail.outbox), 1)\n        self.assertEqual(mail.outbox[0].subject, 'Site message')\n        self.assertGreaterEqual(len(cm.output), 1)\n\n    def test_invalid_enrollment_form_does_not_sends_email(self):\n        form = EnrollmentForm({'code': '123456'})\n        self.assertFalse(form.is_valid())\n\n\nclass PostFormTests(TestCase):\n    def test_valid_post_form_works(self):\n        form = PostForm({'title': 'A new beginning', 'content': 'We have to start over again'})\n        self.assertTrue(form.is_valid())\n\n    def test_invalid_post_form(self):\n        form = PostForm({'title': 'A new beginning'})\n        self.assertFalse(form.is_valid())\n", "repo_name": "gurupratap-matharu/cities", "sub_path": "classroom/tests/test_forms.py", "file_name": "test_forms.py", "file_ext": "py", "file_size_in_byte": 1315, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.test.TestCase", "line_number": 11, "usage_type": "name"}, {"api_name": "classroom.factories.UserFactory", "line_number": 13, "usage_type": "call"}, {"api_name": "classroom.factories", "line_number": 14, "usage_type": "name"}, {"api_name": "classroom.factories.ClassroomFactory", "line_number": 14, "usage_type": "call"}, {"api_name": "classroom.factories.id", "line_number": 16, "usage_type": "attribute"}, {"api_name": "classroom.factories", "line_number": 16, "usage_type": "name"}, {"api_name": "classroom.forms.EnrollmentForm", "line_number": 17, "usage_type": "call"}, {"api_name": "classroom.factories", "line_number": 22, "usage_type": "name"}, {"api_name": "django.core.mail.outbox", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.core.mail", "line_number": 24, "usage_type": "name"}, {"api_name": "django.core.mail.outbox", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.core.mail", "line_number": 25, "usage_type": "name"}, {"api_name": "classroom.forms.EnrollmentForm", "line_number": 29, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 33, "usage_type": "name"}, {"api_name": "classroom.forms.PostForm", "line_number": 35, "usage_type": "call"}, {"api_name": "classroom.forms.PostForm", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "14809632057", "text": "\"\"\"\nImplementations for several probabilistic error models\n\"\"\"\n\nfrom typing import Optional, Tuple\n\nimport numpy as np\n\n\ndef single_dist_mc(\n    emap: np.ndarray,\n    x_dist: np.ndarray,\n    w_dist: np.ndarray,\n    fan_in: float,\n    num_samples: int = int(1e5),\n) -> Tuple[float, float]:\n    \"\"\"\n    Generate error mean and standard deviation using Monte Carlo\n    approach as described in: https://arxiv.org/abs/1912.00700\n\n    Args:\n        emap: The multiplier's error map\n        x_dist: Operand distribution of activations\n        w_dist: Operand distribution of weights\n        fan_in: Incoming connections for layer\n        num_samples: Number of Monte Carlo simulation runs. Defaults to int(1e5).\n\n    Returns:\n        Mean and standard deviation for a single operation\n    \"\"\"\n    prob_x, prob_w = np.meshgrid(\n        x_dist.astype(np.float64), w_dist.astype(np.float64), indexing=\"ij\"\n    )\n    probabilities = (prob_x * prob_w).flatten()\n    emap = emap.flatten()\n    monte_carlo_runs = np.random.choice(\n        emap, size=(num_samples, fan_in), p=probabilities\n    )\n    monte_carlo_runs = np.sum(monte_carlo_runs, axis=1)\n    return (\n        np.mean(monte_carlo_runs) / fan_in,\n        np.std(monte_carlo_runs, dtype=np.float64) / fan_in,\n    )\n\n\ndef error_prediction(\n    emap: np.ndarray, x_dist: np.ndarray, w_dist: np.ndarray, fan_in: float\n) -> Tuple[float, float]:\n    \"\"\"\n    Generate error mean and standard deviation using the\n    global distribution of activations and weights\n\n    Args:\n        emap: The multiplier's error map\n        x_dist: Operand distribution of activations\n        w_dist: Operand distribution of weights\n        fan_in: Incoming connections for layer\n\n    Returns:\n        Mean and standard deviation for a single operation\n    \"\"\"\n    emap = emap.astype(np.float64)\n    prob_x, prob_w = np.meshgrid(\n        x_dist.astype(np.float64), w_dist.astype(np.float64), indexing=\"ij\"\n    )\n    mean = np.sum(emap * prob_x * prob_w)\n    std = np.sqrt(np.sum(((emap - mean) ** 2) * prob_x * prob_w)) / np.sqrt(fan_in)\n    return mean, std\n\n\ndef get_sample_population(tensor: np.ndarray, num_samples: int = 512) -> np.ndarray:\n    \"\"\"\n    Randomly select samples from a tensor that cover the receptive field of one neuron\n\n    Args:\n        tensor: Tensor to draw samples from\n        num_samples: Number of samples to draw. Defaults to 512.\n\n    Returns:\n        Sampled 2D Tensor of shape [num_samples, tensor.shape[-1]]\n    \"\"\"\n    flat_dim = np.prod(tensor.shape[:-1])\n    rand_idx = np.random.choice(flat_dim, num_samples)\n    return tensor.reshape(flat_dim, tensor.shape[-1])[rand_idx]\n\n\ndef population_prediction(\n    emap: np.ndarray, x_multidist: np.ndarray, w_dist: np.ndarray, fan_in: float\n) -> Tuple[float, float]:\n    \"\"\"\n    Generate prediction of mean and standard deviation using several\n    sampled local distributions\n\n    Args:\n        emap: The multiplier's error map\n        x_multidist: Array of several operand distributions for activations\n        w_dist: Operand distribution of weights\n        fan_in: Incoming connections for layer\n\n    Returns:\n        Mean and standard deviation for a single operation\n    \"\"\"\n    # Single distribution error computation for each operand distribution\n    means, stds = [], []\n    for x_dist in x_multidist:\n        mean, std = error_prediction(emap, x_dist, w_dist, fan_in)\n        means.append(mean)\n        stds.append(std)\n    npmeans = np.array(means)\n    npstds = np.array(stds)\n\n    # Aggregate error distributions (Eq. 15 & Eq. 16)\n    mean_aggregate = np.mean(npmeans)\n    std_aggregate = np.sqrt(\n        (\n            np.sum(npmeans**2 + npstds**2)\n            - (np.sum(npmeans) ** 2) / x_multidist.shape[0]\n        )\n        / x_multidist.shape[0]\n    )\n    return mean_aggregate, std_aggregate\n\n\ndef to_distribution(\n    tensor: Optional[np.ndarray], min_val: int, max_val: int\n) -> Tuple[np.ndarray, np.ndarray]:\n    \"\"\"Turn tensor of weights/activations into a frequency distribution (i.e. build a histogram)\n\n    Args:\n        tensor: Tensor to build histogram from\n        min_val: Lowest possible operand value in tensor\n        max_val: Highest possible operand value in tensor\n\n    Returns:\n        Tuple of Arrays where first array contains the full numerical range between\n        min_val and max_val inclusively and second array contains the relative frequency\n        of each operand\n\n    Raises:\n        ValueError: If run before features maps have been populated\n        by call to `utils.model.get_feature_maps`\n\n    \"\"\"\n    if tensor is None:\n        raise ValueError(\"Populate input tensor with intermediate features maps\")\n    num_range = np.arange(min_val, max_val + 1)\n    counts = np.zeros_like(num_range)\n    nums, freqs = np.unique(tensor.flatten().astype(np.int32), return_counts=True)\n    counts[nums + min_val] = freqs.astype(np.float64)\n    counts = counts / np.sum(freqs)\n    return num_range, counts\n\n\ndef error_calculation(\n    accurate: np.ndarray, approximate: np.ndarray, fan_in: float\n) -> Tuple[float, float]:\n    \"\"\"\n    Calculate mean and standard deviation of the observed error between\n    accurate computation and approximate computation\n\n    Args:\n        accurate: Accurate computation results\n        approximate: Approximate computation results\n        fan_in: Number of incoming neuron connections\n\n    Returns:\n        Mean and standard deviation for a single operation\n    \"\"\"\n    mean = np.mean(accurate - approximate) / fan_in\n    std = np.std((accurate - approximate) / fan_in, dtype=np.float64)\n    return mean, std\n", "repo_name": "etrommer/agn-approx", "sub_path": "src/agnapprox/utils/error_stats.py", "file_name": "error_stats.py", "file_ext": "py", "file_size_in_byte": 5585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "46", "api": [{"api_name": "numpy.ndarray", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.meshgrid", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 42, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.meshgrid", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 67, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 117, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 89, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 125, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 150, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 126, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 170, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 156, "usage_type": "name"}]}
{"seq_id": "25457012513", "text": "\"\"\"This is the main file for icarus\"\"\"\n\nimport os\nimport curses\nimport sys\nimport configparser  # https://docs.python.org/3/library/configparser.html\nfrom multiprocessing import Process\nimport aiosmtpd.smtp\n\n# Below are my functions.\nfrom app.smtp import startsmtp\nfrom app.editor import editor\nfrom app.udp import runudp\nfrom app.tcp import runtcp\nfrom app.ftp import ftpserver\nfrom app.abuseipdb import largfeed\nimport app.cfg\n\n\n# pylint: disable=R0801\nconfig = configparser.ConfigParser()\nconfig.read('icarus.config')\nsmtpport = config['ADDRESSES']['SMTPPort']\nabuseip = config['IPDBAPI']['AbuseIPDB']\nabuseapikey = config['IPDBAPI']['IPDBAPI']\nvtapikey = config['APIKEY']['apikey']\nvirustotal = config['APIKEY']['Virustotal']\nsyslogenable = config['SYSLOG']['Syslog']\nsyslogip = config['SYSLOG']['IP']\nsyslogport = config['SYSLOG']['PORT']\nlargfeedon = config['LARGFEED']['Largfeed']\nlargfeedserver = config['LARGFEED']['Server']\nlargfeedport = config['LARGFEED']['Port']\ntcpports = config['PORTS']['tcpports']\nudpports = config['PORTS']['udpports']\n\naiosmtpd.smtp.__ident__ = \"Microsoft ESMTP MAIL Service\"\n\n\n# pylint: disable=R0915, W0613\ndef main(window):\n    \"\"\"MAIN!\"\"\"\n    # Starting SMTP Service\n    process2 = Process(name='smtp', target=startsmtp, daemon=True)\n    process2.start()\n    # startsmtp()\n    # Starting FTP Service\n    process1 = Process(name='Ftp', target=ftpserver, daemon=True)\n    process1.start()\n    # Largfeed Queue processor\n    if largfeedon != \"no\":\n        process3 = Process(name='largfeed', target=largfeed, daemon=True)\n        process3.start()\n\n    # Dynamic low interaction port services.\n\n    for tcpport in tcpports.replace(\" \", \"\").split(','):\n        dyntcpprocess = Process(name='DynamicTCP ' + str(tcpport), target=runtcp, daemon=True, args=(int(tcpport),))\n        dyntcpprocess.start()\n\n    for udpport in udpports.replace(\" \", \"\").split(','):\n        dynudpprocess = Process(name='DynamicUDP ' + str(udpport), target=runudp, daemon=True, args=(int(udpport),))\n        dynudpprocess.start()\n\n    while True:\n        scurses = curses.initscr()\n        curses.curs_set(0)\n        curses.noecho()\n        curses.napms(500)\n        # Pretty standard configs. I have the curses refresh set to 3 seconds.\n        # https://docs.python.org/3.5/library/curses.html#module-curses\n        curses.start_color()\n        curses.init_pair(1, curses.COLOR_RED, curses.COLOR_BLACK)\n        curses.init_pair(2, curses.COLOR_CYAN, curses.COLOR_BLACK)\n        curses.init_pair(3, curses.COLOR_GREEN, curses.COLOR_BLACK)\n        sheight, swidth = scurses.getmaxyx()\n        cursewinder = curses.newwin(sheight, swidth, 0, 0)\n        cursewinder.nodelay(True)\n        # No delay fixes a problem of the screen not updating properly.\n\n        # the above 5 are just standard curses commands.\n        # First number is vertical, 51 is horizontal\n        cursewinder.addstr(0, 51, \"Icarus.config\")\n        cursewinder.addstr(1, 51, \"Virustotal:\")\n        cursewinder.addstr(2, 51, \"Enabled: \" + virustotal)\n        cursewinder.addstr(3, 51, \"APIKEY: \" + vtapikey)\n        cursewinder.addstr(5, 51, \"AbuseIPDB:\")\n        cursewinder.addstr(6, 51, \"Enabled: \" + abuseip)\n        cursewinder.addstr(7, 51, \"APIKEY: \" + abuseapikey)\n        cursewinder.addstr(9, 51, \"Syslog:\")\n        cursewinder.addstr(10, 51, \"Enabled: \" + syslogenable)\n        cursewinder.addstr(11, 51, \"Syslog Server: \" + syslogip + \":\" + syslogport)\n        cursewinder.addstr(13, 51, \"LARGfeed:\")\n        cursewinder.addstr(14, 51, \"Enabled: \" + largfeedon)\n        cursewinder.addstr(15, 51, \"LARGfeed Server: \" + largfeedserver + \":\" + largfeedport)\n        cursewinder.addstr(17, 51, \"Press P to change values.\", curses.color_pair(2))\n        cursewinder.addstr(18, 51, \"Press R to restart.\", curses.color_pair(3))\n        cursewinder.addstr(19, 51, \"Press Q to quit.\", curses.color_pair(1))\n\n        cursewinder.addstr(0, 0, \"ICARUS HONEYPOT\", curses.color_pair(1))\n\n        cursewinder.addstr(12, 0, \"Attacks: \" + str(app.cfg.numattacks['num']))\n        cursewinder.addstr(13, 0, \"Last 5 Attackers: \", curses.color_pair(3))\n        if app.cfg.attackers:\n            for num, address in enumerate(app.cfg.attackers, start=1):\n                cursewinder.addstr((num + 13), 0, str(address))\n\n        cursewinder.refresh()\n\n        key = cursewinder.getch()\n        if key == ord('q'):\n            break\n        if key == ord('r'):\n            process1.terminate()\n            process2.terminate()\n            os.execv(sys.executable, ['python3'] + sys.argv)\n            # Nice little thing that restarts a python script.\n        elif key == ord('p'):\n            editor()  # from editor.py, opens your system editor.\n            cursewinder.erase()\n            cursewinder.refresh()\n\n\nif __name__ == '__main__':\n    curses.wrapper(main)\n", "repo_name": "tbiens/icarus", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 4836, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 46, "dataset": "github-code", "pt": "46", "api": [{"api_name": "configparser.ConfigParser", "line_number": 21, "usage_type": "call"}, {"api_name": "aiosmtpd.smtp.smtp", "line_number": 37, "usage_type": "attribute"}, {"api_name": "aiosmtpd.smtp", "line_number": 37, "usage_type": "name"}, {"api_name": "multiprocessing.Process", "line_number": 44, "usage_type": "call"}, {"api_name": "app.smtp.startsmtp", "line_number": 44, "usage_type": "name"}, {"api_name": "multiprocessing.Process", "line_number": 48, "usage_type": "call"}, {"api_name": "app.ftp.ftpserver", "line_number": 48, "usage_type": "name"}, {"api_name": "multiprocessing.Process", "line_number": 52, "usage_type": "call"}, {"api_name": "app.abuseipdb.largfeed", "line_number": 52, "usage_type": "name"}, {"api_name": "multiprocessing.Process", "line_number": 58, "usage_type": "call"}, {"api_name": "app.tcp.runtcp", "line_number": 58, "usage_type": "name"}, {"api_name": "multiprocessing.Process", "line_number": 62, "usage_type": "call"}, {"api_name": "app.udp.runudp", "line_number": 62, "usage_type": "name"}, {"api_name": "curses.initscr", "line_number": 66, "usage_type": "call"}, {"api_name": "curses.curs_set", "line_number": 67, "usage_type": "call"}, {"api_name": "curses.noecho", "line_number": 68, "usage_type": "call"}, {"api_name": "curses.napms", "line_number": 69, "usage_type": "call"}, {"api_name": "curses.start_color", "line_number": 72, "usage_type": "call"}, {"api_name": "curses.init_pair", "line_number": 73, "usage_type": "call"}, {"api_name": "curses.COLOR_RED", "line_number": 73, "usage_type": "attribute"}, {"api_name": "curses.COLOR_BLACK", "line_number": 73, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 74, "usage_type": "call"}, {"api_name": "curses.COLOR_CYAN", "line_number": 74, "usage_type": "attribute"}, {"api_name": "curses.COLOR_BLACK", "line_number": 74, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 75, "usage_type": "call"}, {"api_name": "curses.COLOR_GREEN", "line_number": 75, "usage_type": "attribute"}, {"api_name": "curses.COLOR_BLACK", "line_number": 75, "usage_type": "attribute"}, {"api_name": "curses.newwin", "line_number": 77, "usage_type": "call"}, {"api_name": "curses.color_pair", "line_number": 96, "usage_type": "call"}, {"api_name": "curses.color_pair", "line_number": 97, "usage_type": "call"}, {"api_name": "curses.color_pair", "line_number": 98, "usage_type": "call"}, {"api_name": "curses.color_pair", "line_number": 100, "usage_type": "call"}, {"api_name": "app.smtp.cfg", "line_number": 102, "usage_type": "attribute"}, {"api_name": "app.smtp", "line_number": 102, "usage_type": "name"}, {"api_name": "curses.color_pair", "line_number": 103, "usage_type": "call"}, {"api_name": "app.smtp.cfg", "line_number": 104, "usage_type": "attribute"}, {"api_name": "app.smtp", "line_number": 104, "usage_type": "name"}, {"api_name": "app.smtp.cfg", "line_number": 105, "usage_type": "attribute"}, {"api_name": "app.smtp", "line_number": 105, "usage_type": "name"}, {"api_name": "os.execv", "line_number": 116, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 116, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 116, "usage_type": "attribute"}, {"api_name": "app.editor.editor", "line_number": 119, "usage_type": "call"}, {"api_name": "curses.wrapper", "line_number": 125, "usage_type": "call"}]}
{"seq_id": "43807990703", "text": "import socket\nimport threading\nimport hashlib\nimport time\nimport datetime\nimport random\nimport sys\n\n\n# Packet class definition\nclass packet():\n    checksum = 0\n    length = 0\n    seqNo = 0\n    msg = 0\n\n    def make(self, data):\n        self.msg = data\n        self.length = str(len(data))\n        self.checksum=hashlib.sha1(data.encode('utf-8')).hexdigest()\n        print (\"Length: %s\\nSequence number: %s\" %(self.length, self.seqNo))\n\ndef RUDPServer(serverAddress, serverPort):\n    # Start - Connection initiation\n    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    # Bind the socket to the port\n    server_address = (serverAddress, serverPort)\n    print  ('Starting up on %s port %s' % server_address)\n    sock.bind(server_address)\n\n    # Listening for requests indefinitely\n    while True:\n        print  ('Waiting to receive message')\n        data, address = sock.recvfrom(600)\n        # Delimiter\n        delimiter = \"|:|:|\"\n\n        # Seq number flag\n        seqFlag = 0\n\n        packet_count=0\n        time.sleep(0.5)\n        start_time=time.time()\n        print (\"Request started at: \" + str(datetime.datetime.utcnow()))\n        # Initialise packet class\n        pkt = packet()\n        threadSock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        startTime=time.time()\n\n        # Check if file is valid \n        try:\n            print (\"Opening file %s\" % data)\n            fileName = data.decode('utf-8')\n            fileRead = open(fileName, 'r')\n            data = fileRead.read()\n            fileRead.close()\n        except:\n            msg=\"FNF\"\n            pkt.make(msg)\n            finalPacket = str(pkt.checksum) + delimiter + str(pkt.seqNo) + delimiter + str(pkt.length) + delimiter + pkt.msg\n            threadSock.sendto(finalPacket, address)\n            print (\"Requested file could not be found, replied with FNF\")\n            return\n\n        \n        # Fragment and send file 500 byte by 500 byte\n        x = 0\n        expectedPacket = (int((len(data) / 500) + 1))\n        while (x < int((len(data) / 500) + 1)):\n            packet_count += 1\n            msg = data[x * 500:x * 500 + 500];\n            pkt.make(msg);\n            finalPacket = str(pkt.checksum) + delimiter + str(pkt.seqNo) + delimiter + str(pkt.length) + delimiter + pkt.msg\n\n            # Send packet\n            sent = threadSock.sendto(finalPacket.encode('utf-8'), address)\n            print  ('Sent %s bytes back to %s, awaiting acknowledgment..' % (sent, address))\n            threadSock.settimeout(2)\n            try:\n                ack, address = threadSock.recvfrom(100);\n                ack = ack.decode('utf-8')\n            except:\n                # else after timeout, resend \n                print (\"Time out reached, resending ...%s\" % x)\n                continue;\n            # Check if acknowledgement is sent\n            if ack.split(\",\")[0] == str(pkt.seqNo):\n                pkt.seqNo = int(not pkt.seqNo)\n                print (\"Acknowledged by: \" + ack + \"\\nAcknowledged at: \" + str(datetime.datetime.utcnow()) + \"\\nElapsed: \" + str(time.time() - start_time))\n                x += 1\n        endTime=time.time()\n        print(\"\\nDone in within : \" + str(endTime-startTime))\n        packetLoss = expectedPacket - packet_count\n        print(\"Packet Loss : \"+ str(packetLoss))\n\n        print  ('Received %s bytes from %s' % (len(data), address))\n        \nif __name__ == \"__main__\":\n    # Set address and port\n    serverAddress = sys.argv[1]\n    serverPort = int(sys.argv[2])\n    print(\"Server Address : \" + serverAddress + \"\\nServer Port : \"+ str(serverPort) +\"\\n\")\n    RUDPServer(serverAddress, serverPort)\n\n\n", "repo_name": "chenwenshu/multithreaded-downloader", "sub_path": "TypeOfReliableUDP/RUDP_server.py", "file_name": "RUDP_server.py", "file_ext": "py", "file_size_in_byte": 3637, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "46", "api": [{"api_name": "hashlib.sha1", "line_number": 20, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 25, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 25, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 25, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 47, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 47, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 47, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "time.time", "line_number": 91, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 100, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 101, "usage_type": "attribute"}]}
{"seq_id": "23871332913", "text": "from pydantic.types import Json\nimport pytest\nfrom fastapi.testclient import TestClient\nfrom src.main import app\n\npytest.token = \"\"\n\n@pytest.fixture(scope=\"module\")\ndef client():\n    with TestClient(app) as client:\n        yield client\n\n@pytest.mark.order(1)\ndef test_create_user(client):\n    user_mock = {\n        \"name\": \"usertest\",\n        \"email\": \"user.test@gmail.com\",\n        \"password\": \"confidential\",\n        \"cpf\": \"12345678900\",\n        \"pis\": \"12345678900\",\n        \"active\": True,\n        \"address\": {\n            \"country\": \"brasil\",\n            \"state\": \"santa catarina\",\n            \"city\": \"florianopolis\",\n            \"complement\": \"ilha da magia\",\n            \"street\": \"lauro linhares\",\n            \"number\": \"123\",\n            \"cep\": \"12345678\"\n        }\n    }\n    response = client.post(\"/users\", json=user_mock)\n        \n    assert response.status_code == 201, response.text\n\n@pytest.mark.order(2)\ndef test_login(client):\n    credentials = {\n        \"username\": \"user.test@gmail.com\",\n        \"password\": \"confidential\",\n    }\n    response = client.post(\"/login\", data=credentials, headers={\n        \"Content-Type\": \"application/x-www-form-urlencoded\"\n    })\n        \n    assert response.status_code == 200, response.text\n    pytest.token = \"Bearer \" + response.json()[\"access_token\"]\n\n@pytest.mark.order(3)\ndef test_get_users(client):\n    response = client.get(\"/users\")\n    assert response.status_code == 200, response.text\n\n@pytest.mark.order(4)\ndef test_get_user(client):\n    response = client.get(\"/users/me\")\n    assert response.status_code == 200, response.text\n\n@pytest.mark.order(5)\ndef test_put_user(client):\n    updated_user = {\n        \"name\": \"new usertest\",\n        \"address\": {\n            \"number\": \"1234\",\n        }\n    }\n    response = client.put(\"/users/me\", json=updated_user)\n    assert response.status_code == 200, response.text\n\n@pytest.mark.order(6)\ndef test_delete_user(client):\n    response = client.delete(\"/users/me\")\n    assert response.status_code == 200, response.text\n", "repo_name": "inafranco/user-crud", "sub_path": "back-end/src/tests/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2024, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pytest.token", "line_number": 6, "usage_type": "attribute"}, {"api_name": "fastapi.testclient.TestClient", "line_number": 10, "usage_type": "call"}, {"api_name": "src.main.app", "line_number": 10, "usage_type": "argument"}, {"api_name": "pytest.fixture", "line_number": 8, "usage_type": "call"}, {"api_name": "pytest.mark.order", "line_number": 13, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pytest.token", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pytest.mark.order", "line_number": 36, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pytest.mark.order", "line_number": 49, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pytest.mark.order", "line_number": 54, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pytest.mark.order", "line_number": 59, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pytest.mark.order", "line_number": 70, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 70, "usage_type": "attribute"}]}
{"seq_id": "2860350775", "text": "#coding=utf-8\nfrom base.models import BaseModel\nfrom django.db import models\n\nclass Questions_Model(BaseModel):\n    project=models.CharField(u'所属项目',max_length=200,blank=False)\n    question_desc=models.TextField(u'问题描述',blank=True)\n    question_reason=models.TextField(u'产生问题原因',blank=True)\n    question_module=models.CharField(u'问题所属模块',max_length=200,blank=False)\n    question_reporter=models.CharField(u'提出问题人',max_length=200,blank=False)\n    question_answer=models.CharField(u'问题跟进者',max_length=200,blank=False)\n    question_progress=models.CharField(u'问题进度',max_length=200,blank=False)\n    question_comments=models.CharField(u'问题备注',max_length=200,blank=False)\n    is_system_cause=models.CharField(u'是否系统问题',max_length=200,blank=False)\n    week=models.CharField(u'week',max_length=200,blank=False)\n\n    class Meta:\n        verbose_name = u'问题列表'\n        verbose_name_plural = verbose_name\n\n    def __unicode__(self):\n        return '{project} {question_desc} {question_reason} {question_module} {question_reporter} {question_answer} {question_progress} {question_comments} {is_system_cause} {week}'.format(\n            project=self.project,\n            question_desc=self.question_desc,\n            question_reason=self.question_reason,\n            question_module=self.question_module,\n            question_reporter=self.question_reporter,\n            question_answer=self.question_answer,\n            question_progress=self.question_progress,\n            question_comments=self.question_comments,\n            is_system_cause=self.is_system_cause,\n            week=self.week\n        )\n\nclass Project_Model(BaseModel):\n    project_name=models.CharField(u'项目名称',max_length=200,blank=False)\n\n\nclass Module_Model(BaseModel):\n    module_name=models.CharField(u'模块名称',max_length=200,blank=True)\n\nclass Progress_Model(BaseModel):\n    progress_name=models.CharField(u'进度名称',max_length=200,blank=True)\n\n", "repo_name": "longyue123/tech", "sub_path": "tech/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2020, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "base.models.BaseModel", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "base.models.BaseModel", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "base.models.BaseModel", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "base.models.BaseModel", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}]}
{"seq_id": "14989135656", "text": "from torchvision.models import resnet34\nimport torch.nn as nn\nimport torch\nimport streamlit as st\nfrom torchvision.transforms import transforms\nfrom torchvision.transforms import Compose\nfrom torchvision.transforms import ToTensor\nfrom torchvision.transforms import Resize\nimport numpy as np\n\nfrom torchvision.transforms import Compose, ToTensor, Resize\n\ndevice = torch.device(\"mps\")\ntransform = Compose([ToTensor(), Resize([150, 150])])\n\nf = transforms.Compose([\n        # transforms.CenterCrop(300),\n        transforms.RandomAffine(degrees=100, translate=(0.15, 0.15)),\n        transforms.RandomGrayscale(p=0.2),\n        transforms.Lambda(lambda x: x + torch.randn_like(x) * 0.03),\n        transforms.Resize(150)\n    ])\n\n@st.cache\ndef load_model(path):\n    print(\"Loading model...\")\n    model = resnet34()\n    last_layer = model.fc\n    model.fc = nn.Linear(last_layer.in_features, 2)\n    model.load_state_dict(torch.load(path, map_location=device))\n    model.to(device)\n    return model\n\n\ndef predict(model, image):\n    model.eval()\n\n    transform = Compose([ToTensor(), Resize([150, 150])])\n\n    image = transform(image)\n    image = image.reshape([1, 3, 150, 150])\n    output = model(image.to(device))\n    _, predicted = torch.max(output.data, 1)\n    return predicted\n\n\ndef compute_saliency_maps(X, y, model):\n    model.eval()\n    X = transform(X).reshape([1, 3, 150, 150])\n    X.requires_grad_()\n\n    saliency = None\n\n    loss_function = nn.CrossEntropyLoss()\n    output = model(X.to(device))\n    loss = loss_function(output, y)\n    loss.backward()\n\n    saliency, _ = torch.max(torch.abs(X._grad), axis=1)\n    return saliency.detach().numpy().reshape([150, 150])\n\ndef smoothing_loss(X):\n    loss = 0.0\n    loss += torch.sqrt(torch.mean(torch.pow(X[:, :, :, :-1]-X[:, :, :, 1:], 2)))\n    loss += torch.sqrt(torch.mean(torch.pow(X[:, :, :, 1:]-X[:, :, :, :-1], 2)))\n    loss += torch.sqrt(torch.mean(torch.pow(X[:, :, :-1, :]-X[:, :, 1:, :], 2)))\n    loss += torch.sqrt(torch.mean(torch.pow(X[:, :, 1:, :]-X[:, :, :-1, :], 2)))\n    return loss\n\n\ndef my_loss(output, y):\n    return torch.sum(-1 / 10 * output[:, y])  # + torch.sum(output[:, 1-y])\n\n\ndef generate_images(X, y, model, lr, n):\n    model.eval()\n\n    X.requires_grad_()\n    X_f = torch.stack([f(x) for x in X for y in range(n)])  # bs*n 150 150 3\n\n    # loss_function = nn.CrossEntropyLoss()\n    loss_function = my_loss\n    # outputs = torch.stack([model(x.to(device) for x in X_f)]) # bs*n 2\n    outputs = model(X_f.to(device))  # bs*n 2\n\n    y_f = torch.stack([y_i for y_i in y for _ in range(n)])  # bs*n\n\n    loss_main = loss_function(outputs, y_f) / n\n\n    smoothing_loss_ = smoothing_loss(X)\n    loss = loss_main + smoothing_loss_\n\n    # y.shape: bs\n\n    # bs*n 150 150 3\n\n    loss.backward()\n    # if randint(0, 20) == 20:\n    X.requires_grad_(False)\n    with torch.no_grad():\n        X_new = X - lr * X.grad\n    X.grad.zero_()\n\n    difference = torch.sum(torch.abs(X_new - X))\n    out_of_bound = torch.sum((X_new > 1) + (X_new < 0))\n\n    print(\n        loss_main.item(),\n        smoothing_loss_.item(),\n        difference,\n        out_of_bound,\n        # output.cpu().detach().numpy().tolist()\n    )\n\n    X_new[X_new < 0] = 0\n    X_new[X_new > 1] = 1\n\n    return X_new\n\n\ndef generate_image(x, y, model, lr, n):\n    X_new = generate_images(x.unsqueeze(0), y, model, lr, n)\n    return X_new[0]\n\ndef tweak_image(X, y, model):\n\n    im = transform(X)\n\n    n = 16\n    lr = 0.3\n\n    new_im = generate_image(im, y, model, lr, n)\n\n    my_bar = st.progress(0)\n\n    for i in range(80):\n        my_bar.progress(i/80)\n        new_im = generate_image(new_im, y, model, lr, n)\n\n    new_numpy_im = new_im.detach().numpy().transpose(1, 2, 0)\n\n    new_numpy_im[new_numpy_im < 0] = 0\n    new_numpy_im[new_numpy_im > 1] = 1\n\n    return new_numpy_im\n", "repo_name": "GSKW/ML", "sub_path": "practice/html/network.py", "file_name": "network.py", "file_ext": "py", "file_size_in_byte": 3799, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "torch.device", "line_number": 13, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.transforms.Resize", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms.Compose", "line_number": 16, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 16, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.RandomAffine", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 18, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.RandomGrayscale", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 19, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.Lambda", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.randn_like", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms.Resize", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 21, "usage_type": "name"}, {"api_name": "torchvision.models.resnet34", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 30, "usage_type": "call"}, {"api_name": "streamlit.cache", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms.Resize", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 105, "usage_type": "call"}, {"api_name": "streamlit.progress", "line_number": 134, "usage_type": "call"}]}
{"seq_id": "12317841946", "text": "import json\nfrom numpy import array\n\nif __name__ == \"__main__\" :\n\n    file=True\n    with open(\"stickers_vectors_generation.json\") as file:\n        generation = json.load(file)\n    with open(\"main_data.json\") as file:\n        data = json.load(file)\n\n    corners = generation['C']\n    edges = generation['E']\n    faces = data['faces']\n\n    print('{\\n    \"C\": {')\n    i = 0\n    for corner in corners:\n        i += 1\n        val = array([0, 0, 0])\n        for letter in corner:\n            if letter != ' ':\n                val += faces[letter]\n        print('        \"' + str(i) + '\": [' + str(val[0]) + ', ' + str(val[1]) + ', ' + str(val[2]) + ('],' if i < 24 else ']'))\n\n    print('    },\\n    \"E\": {')\n    i = 0\n    for edge in edges:\n        i += 1\n        val = array([0, 0, 0])\n        for letter in edge:\n            if letter != ' ':\n                val += faces[letter]\n        print('        \"' + str(i) + '\": [' + str(val[0]) + ', ' + str(val[1]) + ', ' + str(val[2]) + ('],' if i < 24 else ']'))\n    print('    }\\n}')\n", "repo_name": "juliengiraud/Rubik-s_Cube", "sub_path": "CircuitBuilder/stickersVectorsGeneration.py", "file_name": "stickersVectorsGeneration.py", "file_ext": "py", "file_size_in_byte": 1028, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "json.load", "line_number": 8, "usage_type": "call"}, {"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}]}
{"seq_id": "9907747205", "text": "from django.test import TestCase, Client\n\nfrom account.models import User\nfrom care_point.forms import IllnessForm\nfrom care_point.models import Illness, Manager, Point_of_care\n\n\nclass IllnessTest(TestCase):\n    def setUp(self):\n        self.client = Client()\n\n        Illness.objects.create(name='illness_1', description='desc_1').save()\n        Illness.objects.create(name='illness_2', description='desc_2').save()\n        Illness.objects.create(name='illness_3', description='desc_3').save()\n\n        self.user = User.objects.create(username='m1', first_name='name_m1', last_name='sure_name_m1', is_manager=True,\n                                        is_caregiver=False, password='123456Mp')\n        self.user.save()\n        point_of_care = Point_of_care.objects.create(city='Bstok')\n        point_of_care.save()\n        manager = Manager.objects.create(user=self.user, point_of_care=point_of_care)\n        manager.save()\n\n    def test_redirect_if_not_logged_in(self):\n        response = self.client.get('/care_point/illness/')\n\n        self.assertEqual(response.status_code, 302)\n        self.assertEqual(response.url, '/account/login/?next=/care_point/illness/')\n\n    def test_should_return_all_objects_from_DB(self):\n        self.client.force_login(user=self.user)\n        response = self.client.get('/care_point/illness/')\n\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(len(response.context['illness']), 3)\n\n    def test_should_return_details_of_one_object(self):\n        self.client.force_login(user=self.user)\n        response = self.client.get('/care_point/illness/1/')\n\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(response.context['illness'].name, 'illness_1')\n\n    def test_should_return_status_code_404_if_object_not_exist(self):\n        self.client.force_login(user=self.user)\n        response = self.client.get('/care_point/illness/5/')\n        illnesses = Illness.objects.all()\n\n        self.assertEqual(response.status_code, 404)\n        self.assertEqual(len(illnesses), 3)\n\n    def test_should_delete_one_object(self):\n        self.client.force_login(user=self.user)\n        response = self.client.get('/care_point/illness/1/delete/')\n        illnesses = Illness.objects.all()\n\n        self.assertEqual(response.status_code, 302)\n        self.assertEqual(len(illnesses), 2)\n\n    def test_should_return_illness_form(self):\n        self.client.force_login(user=self.user)\n        response = self.client.get('/care_point/illness/add/')\n\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(type(response.context['form']), IllnessForm)\n\n    def test_should_add_one_object(self):\n        self.client.force_login(user=self.user)\n        response = self.client.post('/care_point/illness/add/', {'name': 'illness_4', 'description': 'desc4'})\n        illnesses = Illness.objects.all()\n\n        self.assertEqual(response.status_code, 302)\n        self.assertEqual(len(illnesses), 4)\n\n    def test_should_return_illness_update_form(self):\n        self.client.force_login(user=self.user)\n        response = self.client.get('/care_point/illness/2/update/')\n\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(type(response.context['form']), IllnessForm)\n\n    def test_should_update_object(self):\n        self.client.force_login(user=self.user)\n        response = self.client.post('/care_point/illness/2/update/', {'name': 'illness_update', 'description': 'desc_update'})\n        updated_illness = Illness.objects.filter(pk=2).first()\n\n        self.assertEqual(response.status_code, 302)\n        self.assertEqual(response.url, \"/care_point/illness/\")\n        self.assertEqual(updated_illness.name, 'illness_update')\n        self.assertEqual(updated_illness.description, 'desc_update')\n\n    def test_should_not_update_object_when_parameter_is_wrong(self):\n        self.client.force_login(user=self.user)\n        response = self.client.post('/care_point/illness/2/update/', {'call': 'illness_update', 'description': 'desc_update'})\n        updated_illness = Illness.objects.filter(pk=2).first()\n\n        self.assertEqual(response.status_code, 302)\n        self.assertEqual(updated_illness.name, 'illness_2')\n        self.assertEqual(updated_illness.description, 'desc_2')\n", "repo_name": "mario-pe/CarePoint", "sub_path": "care_point/tests/test_illness.py", "file_name": "test_illness.py", "file_ext": "py", "file_size_in_byte": 4290, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.test.TestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 10, "usage_type": "call"}, {"api_name": "care_point.models.Illness.objects.create", "line_number": 12, "usage_type": "call"}, {"api_name": "care_point.models.Illness.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "care_point.models.Illness", "line_number": 12, "usage_type": "name"}, {"api_name": "care_point.models.Illness.objects.create", "line_number": 13, "usage_type": "call"}, {"api_name": "care_point.models.Illness.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "care_point.models.Illness", "line_number": 13, "usage_type": "name"}, {"api_name": "care_point.models.Illness.objects.create", "line_number": 14, "usage_type": "call"}, {"api_name": "care_point.models.Illness.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "care_point.models.Illness", "line_number": 14, "usage_type": "name"}, {"api_name": "account.models.User.objects.create", "line_number": 16, "usage_type": "call"}, {"api_name": "account.models.User.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "account.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "care_point.models.Point_of_care.objects.create", "line_number": 19, "usage_type": "call"}, {"api_name": "care_point.models.Point_of_care.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "care_point.models.Point_of_care", "line_number": 19, "usage_type": "name"}, {"api_name": "care_point.models.Manager.objects.create", "line_number": 21, "usage_type": "call"}, {"api_name": "care_point.models.Manager.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "care_point.models.Manager", "line_number": 21, "usage_type": "name"}, {"api_name": "care_point.models.Illness.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "care_point.models.Illness.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "care_point.models.Illness", "line_number": 47, "usage_type": "name"}, {"api_name": "care_point.models.Illness.objects.all", "line_number": 55, "usage_type": "call"}, {"api_name": "care_point.models.Illness.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "care_point.models.Illness", "line_number": 55, "usage_type": "name"}, {"api_name": "care_point.forms.IllnessForm", "line_number": 65, "usage_type": "argument"}, {"api_name": "care_point.models.Illness.objects.all", "line_number": 70, "usage_type": "call"}, {"api_name": "care_point.models.Illness.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "care_point.models.Illness", "line_number": 70, "usage_type": "name"}, {"api_name": "care_point.forms.IllnessForm", "line_number": 80, "usage_type": "argument"}, {"api_name": "care_point.models.Illness.objects.filter", "line_number": 85, "usage_type": "call"}, {"api_name": "care_point.models.Illness.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "care_point.models.Illness", "line_number": 85, "usage_type": "name"}, {"api_name": "care_point.models.Illness.objects.filter", "line_number": 95, "usage_type": "call"}, {"api_name": "care_point.models.Illness.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "care_point.models.Illness", "line_number": 95, "usage_type": "name"}]}
{"seq_id": "20448119537", "text": "import pytest\nfrom pytest import approx\n\nfrom rules_engine import engine\n\n\n@pytest.mark.parametrize(\n    \"avg_temp, balance_point, expected_result\",\n    [\n        (72, 60, 0),  # outside hotter than balance point\n        (60, 60, 0),  # outside equal to balance point\n        (57, 60, 3),  # outside cooler than balance point\n    ],\n)\ndef test_hdd(avg_temp, balance_point, expected_result):\n    assert engine.hdd(avg_temp, balance_point) == expected_result\n\n\n@pytest.mark.parametrize(\n    \"temps, expected_result\",\n    [\n        ([72, 60, 55, 61], 5),  # one day with HDDs\n        ([52, 60, 55], 13),  # two days with HDDs\n        ([72, 60, 65, 60, 80], 0),  # no days with HDDs\n    ],\n)\ndef test_period_hdd(temps, expected_result):\n    assert engine.period_hdd(temps, 60) == expected_result\n\n\ndef test_average_indoor_temp():\n    set_temp = 68\n    setback = 62\n    setback_hrs = 8\n\n    # when there is no setback, just put 0 for the setback parameters\n    assert engine.average_indoor_temp(set_temp, 0, 0) == set_temp\n    assert engine.average_indoor_temp(set_temp, setback, setback_hrs) == 66\n\n\ndef test_bp_ua_estimates():\n    home = engine.Home(\n        engine.FuelType.GAS, heat_sys_efficiency=0.88, initial_balance_point=58\n    )\n\n    daily_temps_lists = [\n        [28, 29, 30, 29],\n        [32, 35, 35, 38],\n        [41, 43, 42, 42],\n        [72, 71, 70, 69],\n    ]\n    usages = [50, 45, 30, 0.96]\n    inclusion_codes = [1, 1, 1, -1]\n    home.initialize_billing_periods(daily_temps_lists, usages, inclusion_codes)\n    home.calculate_avg_non_heating_usage()\n    home.calculate_balance_point_and_ua()\n\n    ua_1, ua_2, ua_3 = [bill.ua for bill in home.bills_winter]\n\n    assert home.balance_point == 60\n    assert ua_1 == approx(1450.5, abs=1)\n    assert ua_2 == approx(1615.3, abs=1)\n    assert ua_3 == approx(1479.6, abs=1)\n    assert home.avg_ua == approx(1515.1, abs=1)\n    assert home.stdev_pct == approx(0.0474, abs=0.01)\n\n\ndef test_bp_ua_with_outlier():\n    home = engine.Home(\n        engine.FuelType.GAS, heat_sys_efficiency=0.88, initial_balance_point=58\n    )\n    daily_temps_lists = [\n        [41.7, 41.6, 32, 25.4],\n        [28, 29, 30, 29],\n        [32, 35, 35, 38],\n        [41, 43, 42, 42],\n        [72, 71, 70, 69],\n    ]\n    usages = [60, 50, 45, 30, 0.96]\n    inclusion_codes = [1, 1, 1, 1, -1]\n    home.initialize_billing_periods(daily_temps_lists, usages, inclusion_codes)\n    home.calculate_avg_non_heating_usage()\n    home.calculate_balance_point_and_ua()\n    ua_1, ua_2, ua_3 = [bill.ua for bill in home.bills_winter]\n\n    assert home.balance_point == 60\n    assert ua_1 == approx(1450.5, abs=1)\n    assert ua_2 == approx(1615.3, abs=1)\n    assert ua_3 == approx(1479.6, abs=1)\n    assert home.avg_ua == approx(1515.1, abs=1)\n    assert home.stdev_pct == approx(0.0474, abs=0.01)\n", "repo_name": "codeforboston/home-energy-analysis-tool", "sub_path": "rules-engine/tests/test_rules_engine/test_engine.py", "file_name": "test_engine.py", "file_ext": "py", "file_size_in_byte": 2806, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "rules_engine.engine.hdd", "line_number": 16, "usage_type": "call"}, {"api_name": "rules_engine.engine", "line_number": 16, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 7, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rules_engine.engine.period_hdd", "line_number": 28, "usage_type": "call"}, {"api_name": "rules_engine.engine", "line_number": 28, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 19, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rules_engine.engine.average_indoor_temp", "line_number": 37, "usage_type": "call"}, {"api_name": "rules_engine.engine", "line_number": 37, "usage_type": "name"}, {"api_name": "rules_engine.engine.average_indoor_temp", "line_number": 38, "usage_type": "call"}, {"api_name": "rules_engine.engine", "line_number": 38, "usage_type": "name"}, {"api_name": "rules_engine.engine.Home", "line_number": 42, "usage_type": "call"}, {"api_name": "rules_engine.engine", "line_number": 42, "usage_type": "name"}, {"api_name": "rules_engine.engine.FuelType", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rules_engine.engine", "line_number": 43, "usage_type": "name"}, {"api_name": "pytest.approx", "line_number": 61, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 63, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 64, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 65, "usage_type": "call"}, {"api_name": "rules_engine.engine.Home", "line_number": 69, "usage_type": "call"}, {"api_name": "rules_engine.engine", "line_number": 69, "usage_type": "name"}, {"api_name": "rules_engine.engine.FuelType", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rules_engine.engine", "line_number": 70, "usage_type": "name"}, {"api_name": "pytest.approx", "line_number": 87, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 88, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 89, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 90, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 91, "usage_type": "call"}]}
{"seq_id": "11902651207", "text": "import requests\r\nimport openpyxl\r\n\r\n# Open a workbook and the active sheet\r\nwb = openpyxl.Workbook()\r\nws = wb.active\r\nws.title = \"Farmers Markets\"\r\n\r\ndef get_business_lists(city, last_row):\r\n    print(city)\r\n\r\n    # API Definition\r\n    # my_API_Key = \"insert Yelp API key\"\r\n    endPoint = \"https://api.yelp.com/v3/businesses/search\"\r\n    api_headers = {'Authorization': 'bearer {}'.format(my_API_Key)}\r\n\r\n    # Parameters\r\n    offset = 0\r\n    limit = 5\r\n    total = 1000\r\n\r\n    # Variable declarations\r\n    biz_name = []\r\n    biz_address = []\r\n    biz_phone = []\r\n    biz_reviews = []\r\n\r\n    while offset < total:\r\n        parameters = {\"term\": \"Farmers Markets\",\r\n                      \"limit\": limit,\r\n                     \"location\": city,\r\n                     \"sort_by\": \"rating\",\r\n                     \"offset\": offset}\r\n\r\n        #  Make the request and then convert the json to a dictionary\r\n        businesses_json_response = requests.get(url=endPoint, params=parameters, headers=api_headers)\r\n        businesses = businesses_json_response.json()\r\n\r\n        # Update Total\r\n        try:\r\n            total = businesses[\"total\"]\r\n            #  print(len(businesses[\"businesses\"]))\r\n        except:\r\n            print(\"Total is less than {}\".format(limit))\r\n            break\r\n\r\n        # Append business names, addresses, and phones to their respective lists\r\n        for business in businesses[\"businesses\"]:\r\n            biz_name.append(business[\"name\"])\r\n            biz_address.append(business[\"location\"][\"address1\"])\r\n            biz_phone.append(business['display_phone'])\r\n            biz_reviews.append(business['review_count'])\r\n\r\n        # Write data of excel function\r\n        last_row = print_to_excel(biz_name, biz_address, biz_phone, biz_reviews, city,last_row)\r\n\r\n        #  print(\"Total = {}; Limit = {}; Offset = {}\".format(total, limit, offset))\r\n\r\n        if total < limit:\r\n            limit = total\r\n            offset += limit\r\n        elif total >= limit:\r\n            offset += limit\r\n\r\n    return last_row\r\n\r\n\r\ndef print_to_excel(names, addresses, phones, reviews, city, last_row):\r\n    # Write data to excel\r\n    elem = 0\r\n    while elem < len(names):\r\n        ws.cell(row = last_row + elem + 1, column = 1).value = names[elem]\r\n        ws.cell(row = last_row + elem + 1, column = 2).value = addresses[elem]\r\n        ws.cell(row = last_row + elem + 1, column = 3).value = phones[elem]\r\n        ws.cell(row = last_row + elem + 1, column = 4).value = city\r\n        ws.cell(row = last_row + elem + 1, column = 5).value = \"ID\"\r\n        ws.cell(row = last_row + elem + 1, column = 6).value = reviews[elem]\r\n        elem += 1\r\n\r\n    last_row = ws.max_row\r\n\r\n    return last_row\r\n\r\n\r\ndef main():\r\n\r\n    MSA_Columbus = (\r\n        \"Columbus\", \"Dublin\", \"Newark\", \"Delaware\", \"Lancaster\", \"Pickerington\", \"London\", \"Marysville\", \"Circleville\",\r\n        \"Marion\", \"Zanesville\", \"Chillicothe\", \"New Lexington\", \"Cambridge\", \"Washington Court House\")\r\n    MSA_Dayton = (\"Centerville\", \"Dayton\", \"Kettering\", \"Beavercreek\", \"Huber Heights\", \"Fairborn\", \"Miamisburg\",\r\n                  \"West Carrollton\", \"Springfield\", \"Urbana\", \"Greenville\", \"Sidney\")\r\n    MSA_Indianapolis = (\"Indianapolis\", \"Carmel\", \"Fishers\", \"Noblesville\", \"Greenwood\", \"Anderson\", \"Lawrence\",\r\n                        \"Westfield\", \"Plainfield\", \"Zionsville\", \"Brownsburg\", \"Franklin\", \"Greenfrield\", \"Shelbyville\",\r\n                        \"Avon\", \"Lebanon\", \"Beech Grove\", \"Speedway\", \"Martinsville\",\r\n                        \"Greencastle\",\"Danville\", \"Moorseville\")\r\n\r\n    last_row = 0 # Keeps track of position in Excel\r\n    for city in MSA_Indianapolis:\r\n        last_row = get_business_lists(city, last_row)\r\n\r\n\r\n# BEGIN PROGRAM\r\nmain()\r\nwb.save(\"MSA_Indianapolis_Farmers_Markets.xlsx\")\r\n", "repo_name": "Mhauga/Extraction", "sub_path": "Extraction.py", "file_name": "Extraction.py", "file_ext": "py", "file_size_in_byte": 3790, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "openpyxl.Workbook", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "33299107648", "text": "# 백준 단어공부\nimport collections\n\nstring1 = input()\n\ndef strstudy(s):\n    s = s.upper()\n    string = []\n    max_dict = []\n    for i in s:\n        string += i\n    string_dict = collections.Counter(string)\n    for key, value in string_dict.items():\n        if value == max(string_dict.values()):\n            max_dict.append(key)\n    if len(max_dict) >= 2:\n        return \"?\"\n    else:\n        return max_dict[0]\na = strstudy(string1)\n\nprint(a)", "repo_name": "ohjooyeong/baekjoonalgo", "sub_path": "string/1157.py", "file_name": "1157.py", "file_ext": "py", "file_size_in_byte": 449, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "collections.Counter", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "25494823151", "text": "from bs4 import BeautifulSoup\nimport urllib2\nfrom random import choice,randint\nfrom pymongo import MongoClient\n\nurl = 'http://foodgawker.com/page/1/?s_exclude=drinks'\nrequest = urllib2.Request(url, headers={'User-Agent' : 'Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36'})\nsite = urllib2.urlopen(request)\nhtml = site.read()\n\nparsed_html = BeautifulSoup(html)\nmaxpage = int(parsed_html.body.find('div', attrs={'class' : 'post-section'}).attrs['data-maxpage'])\n\nfor x in xrange(1, maxpage):\n\n    url = 'http://foodgawker.com/page/' + str(x) + '/?s_exclude=drinks'\n    request = urllib2.Request(url, headers={'User-Agent' : 'Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36'})\n    site = urllib2.urlopen(request)\n    html = site.read()\n\n    parsed_html = BeautifulSoup(html)\n\n    dishes = parsed_html.body.find_all('div', attrs={'class' : 'flipwrapper'})\n\n    client = MongoClient('localhost', 27017)\n    db = client.recipeasy\n    collection = db.recipes\n    recipes = db.recipes\n\n    for dish in dishes:\n        img = dish.find('a', attrs={'class' : 'picture-link'})\n\n        recipe = { 'title' : dish.attrs['data-sharetitle'],\n                   'description' : dish.find('section', attrs={'class' : 'description'}).text,\n                   'link' : img.attrs['href'],\n                   'image' : img.find('img').attrs['src'] }\n\n        recipes.insert(recipe)\n\n    amtDone = float(x)/float(maxpage)\n    print(\"\\rProgress: [{0:100s}] {1:.1f}% ({2}/{3})\".format('#' * int(amtDone * 50), amtDone * 100, x, maxpage)),", "repo_name": "aberle/recipeasy", "sub_path": "foodscrape.py", "file_name": "foodscrape.py", "file_ext": "py", "file_size_in_byte": 1643, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "urllib2.Request", "line_number": 7, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 8, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 11, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 17, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 18, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 21, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "17196509488", "text": "from typing import List\nfrom collections import deque\n\nclass Solution:\n    def numIslands(self, grid: List[List[str]], method='bfs') -> int:\n        if method == 'union_joint':\n            return self.numIslandsUnionJoint(grid)\n        if method == 'dfs':\n            return self.numIslandsDfs(grid)\n        if method == 'bfs':\n            return self.numIslandsBfs(grid)\n\n    def numIslandsUnionJoint(self, grid):\n        def find(x):\n            nonlocal pa\n            while x != pa[x]:\n                x = pa[x]\n            return x\n\n        def union_joint(x, y, nr_groups):\n            nonlocal pa\n            nonlocal size\n\n            root_x, root_y = find(x), find(y)\n            if root_x == root_y:\n                return nr_groups\n            else:\n                if size[root_x] > size[root_y]:\n                    pa[root_y] = root_x\n                    size[root_x] += size[root_y]\n                else:\n                    pa[root_x] = root_y\n                    size[root_y] += root_x\n                return nr_groups - 1\n\n        m, n = len(grid), len(grid[0])\n        nr_groups = m * n\n        pa = [i for i in range(nr_groups)]\n        size = [1] * nr_groups\n        nr_land_cell = 0\n        for i in range(m):\n            for j in range(n):\n                if grid[i][j] == '1':\n                    nr_land_cell += 1\n                    for direction in [[1,0],[0,1]]:\n                        new_i = i + direction[0]\n                        new_j = j + direction[1]\n                        if new_i < m and new_j < n and grid[new_i][new_j] == '1':\n                            nr_groups = union_joint(i * n + j, new_i * n + new_j, nr_groups)\n        return nr_groups - (n*m - nr_land_cell)\n\n\n    def numIslandsDfs(self, grid):\n        def dfs(i, j, m, n, visited):\n            nonlocal grid\n            visited[i][j] = 1\n            for dir in [[-1, 0], [1, 0], [0, -1], [0, 1]]:\n                new_i = i + dir[0]\n                new_j = j + dir[1]\n                if -1 < new_i < m and -1 < new_j < n:\n                     if visited[new_i][new_j] == 0 and grid[new_i][new_j] == '1':\n                         dfs(new_i, new_j, m, n, visited)\n\n        m, n = len(grid), len(grid[0])\n        visited = [[0] * n for _ in range(m)]\n        nr_island = 0\n        for i in range(m):\n            for j in range(n):\n                if grid[i][j] == '0':\n                    visited[i][j] = 1\n                    continue\n                if visited[i][j] == 1:\n                    continue\n                dfs(i, j, m, n, visited)\n                nr_island += 1\n        return nr_island\n\n    def numIslandsBfs(self, grid):\n        def bfs(x, y, m, n, visited):\n            nonlocal grid\n            cell_queue = deque([(x, y)])\n            visited[x][y] = 1\n            while cell_queue:\n                pos = cell_queue.popleft()\n                i, j = pos\n                for dir in [[-1, 0], [1, 0], [0, -1], [0, 1]]:\n                    new_i = i + dir[0]\n                    new_j = j + dir[1]\n                    if -1 < new_i < m and -1 < new_j < n:\n                         if visited[new_i][new_j] == 0 and grid[new_i][new_j] == '1':\n                             visited[new_i][new_j] = 1\n                             cell_queue.append((new_i, new_j))\n\n        m, n = len(grid), len(grid[0])\n        visited = [[0] * n for _ in range(m)]\n        nr_island = 0\n        for i in range(m):\n            for j in range(n):\n                if grid[i][j] == '0':\n                    visited[i][j] = 1\n                    continue\n                if visited[i][j] == 1:\n                    continue\n                bfs(i, j, m, n, visited)\n                nr_island += 1\n        return nr_island\n\n\nsol = Solution()\ncases = [\n    {\n        \"input\": [\n                      [\"1\",\"1\",\"1\",\"1\",\"0\"],\n                      [\"1\",\"1\",\"0\",\"1\",\"0\"],\n                      [\"1\",\"1\",\"0\",\"0\",\"0\"],\n                      [\"0\",\"0\",\"0\",\"0\",\"0\"]\n                  ],\n        \"expect\": 1\n    },\n    {\n        \"input\": [\n                    [\"1\",\"1\",\"0\",\"0\",\"0\"],\n                    [\"1\",\"1\",\"0\",\"0\",\"0\"],\n                    [\"0\",\"0\",\"1\",\"0\",\"0\"],\n                    [\"0\",\"0\",\"0\",\"1\",\"1\"]\n                 ],\n        \"expect\": 3\n    },\n]\n\nfor case in cases:\n    result = sol.numIslands(case[\"input\"])\n    print(case[\"input\"], result)\n    assert result == case['expect']\n", "repo_name": "tilaboy/work_note", "sub_path": "algorithms/leetcode_weekly/disjoint_union/200_Number_of_Islands.py", "file_name": "200_Number_of_Islands.py", "file_ext": "py", "file_size_in_byte": 4370, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 81, "usage_type": "call"}]}
{"seq_id": "12876151330", "text": "import copy\nimport uuid\n\nimport pytest\nfrom httpx import AsyncClient\nfrom sqlalchemy import delete\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.orm import Session\n\nimport services\nfrom app.exceptions import CreatingError\nfrom app.models import stations\nfrom app.schemas import schemas_stations, schemas_washing\nfrom app.schemas import schemas_washing as washing\n# from app.utils.general import read_location\nfrom app.static.enums import RegionEnum, StationStatusEnum, RoleEnum, StationParamsEnum, \\\n\tStationsSortingEnum\nfrom tests.additional import auth, users as users_funcs\nfrom tests.additional.stations import get_station_by_id, generate_station, StationData, change_station_params, \\\n\trand_serial, delete_all_stations, generate_station_programs\nfrom tests.fills import stations as station_fills\n\n\n@pytest.mark.usefixtures(\"generate_users\", \"generate_default_station\")\nclass TestStations:\n\tinstaller: users_funcs.UserData\n\tmanager: users_funcs.UserData\n\tregion_manager: users_funcs.UserData\n\tsysadmin: users_funcs.UserData\n\tlaundry: users_funcs.UserData\n\tstation: StationData\n\n\tasync def test_create_station_with_default_params(self, ac: AsyncClient, session: AsyncSession,\n\t\t\t\t\t\t\t\t\t\t\t\t\t  monkeypatch):\n\t\t\"\"\"\n\t\tСоздание станции без указания опциональных параметров.\n\t\t\"\"\"\n\n\t\tstation_data = dict(station={\n\t\t\t\"name\": \"Qwerty\",\n\t\t\t\"wifi_name\": \"qwerty\",\n\t\t\t\"wifi_password\": \"qwerty\",\n\t\t\t# \"address\": \"Санкт-Петербург\",\n\t\t\t\"region\": RegionEnum.NORTHWEST.value,\n\t\t\t\"serial\": rand_serial()\n\t\t})\n\t\tresponse = await ac.post(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=station_data\n\t\t)\n\t\t# real_location = await read_location(\"Санкт-Петербург\")\n\t\tstation_response = schemas_stations.Station(\n\t\t\t**response.json()\n\t\t)  # Validation error может подняться, если что-то не так\n\t\tstation_in_db = await get_station_by_id(station_response.id, session)\n\t\tassert station_response.dict() == station_in_db.dict()\n\t\tassert len(station_in_db.station_washing_agents) == services.DEFAULT_STATION_WASHING_AGENTS_AMOUNT\n\t\tassert len(station_in_db.station_washing_machines) == services.DEFAULT_STATION_WASHING_MACHINES_AMOUNT\n\t\tassert station_in_db.station_programs\n\t\t# assert station_in_db.location[\"latitude\"] == real_location.latitude and \\\n\t\t# \t   station_in_db.location[\"longitude\"] == real_location.longitude\n\t\tassert station_in_db.is_active == services.DEFAULT_STATION_IS_ACTIVE\n\t\tassert station_in_db.is_protected == services.DEFAULT_STATION_IS_PROTECTED\n\t\tassert station_in_db.station_control.status == services.DEFAULT_STATION_STATUS\n\t\tassert not (station_in_db.is_active and not station_in_db.station_settings.teh_power), \\\n\t\t\t\"If station is active, teh must be powered on\"\n\t\tassert not (not station_in_db.is_active and (station_in_db.station_settings.station_power is True or\n\t\t\t\t\t\t\t\t\t\t\t\t\t station_in_db.station_control.status is not None\n\t\t\t\t\t\t\t\t\t\t\t\t\t or station_in_db.station_settings.teh_power is True)), \\\n\t\t\t\"If station isn't active, station power and TEH power must be False and station status must be null\"\n\t\tassert not (\n\t\t\tstation_in_db.station_settings.station_power is True and station_in_db.station_control.status is None), \\\n\t\t\t\"If station is powered on, station status must be not null\"\n\t\tassert not (station_in_db.station_control.status == StationStatusEnum.WORKING and not all(\n\t\t\tstation_in_db.station_control.washing_machine and any(\n\t\t\t\t(station_in_db.station_control.program_step, station_in_db.station_control.washing_agents)\n\t\t\t)\n\t\t)), \"If station status is working, washing machine must be defined, and one of params [program_step, washing_agents] \" \\\n\t\t\t\"must be not null\"\n\t\tassert station_in_db.name == station_data[\"station\"][\"name\"]\n\n\tasync def test_create_station_with_advanced_params(self, ac: AsyncClient, session: AsyncSession):\n\t\t\"\"\"\n\t\tСоздание станции с ручным вводом параметров.\n\t\t\"\"\"\n\t\tparams = station_fills.test_create_station_with_advanced_params\n\n\t\tresponse = await ac.post(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=params\n\t\t)\n\n\t\tstation_response = schemas_stations.Station(**response.json())\n\t\tstation_in_db = await get_station_by_id(station_response.id, session)\n\t\tparams = params[\"station\"]\n\t\tparams[\"region\"] = RegionEnum.NORTHWEST  # или менять на строковый регион в полученных объектах\n\n\t\tassert station_response.dict() == station_in_db.dict()\n\t\tassert len(station_in_db.station_washing_agents) == len(params[\"washing_agents\"])\n\t\tassert len(station_in_db.station_washing_machines) == len(params[\"washing_machines\"])\n\t\tassert len(station_in_db.station_programs) == len(params[\"programs\"])\n\t\tfor k, v in params.items():\n\t\t\tif k in station_in_db.dict():\n\t\t\t\tassert station_in_db.dict()[k] == v\n\t\tfor k, v in params[\"settings\"].items():\n\t\t\tassert getattr(station_in_db.station_settings, k) == v\n\n\t\tfor program in station_in_db.station_programs:\n\t\t\tdefined_program = next(pg for pg in params[\"programs\"] if pg[\"program_step\"] == program.program_step)\n\t\t\tassert program.program_step == defined_program[\"program_step\"]\n\t\t\tassert program.name == defined_program[\"name\"]\n\t\t\tfor washing_agent in program.washing_agents:\n\t\t\t\tfor ag in defined_program[\"washing_agents\"]:\n\t\t\t\t\tif isinstance(ag, int) and ag == washing_agent.agent_number:\n\t\t\t\t\t\tdefined_washing_agent = next(agent for agent in params[\"washing_agents\"] if\n\t\t\t\t\t\t\t\t\t\t\t\t\t agent[\"agent_number\"] == ag)\n\t\t\t\t\t\tdefined_washing_agent = washing.WashingAgentCreateMixedInfo(**defined_washing_agent)\n\t\t\t\t\telif isinstance(ag, dict) and ag[\"agent_number\"] == washing_agent.agent_number:\n\t\t\t\t\t\tdefined_washing_agent = washing.WashingAgentWithoutRollback(**ag)\n\t\t\t\tassert washing_agent.volume == defined_washing_agent.volume\n\n\t\tdefault_washing_agents_params = {\n\t\t\t\"rollback\": services.DEFAULT_WASHING_AGENTS_ROLLBACK,\n\t\t\t\"volume\": services.DEFAULT_WASHING_AGENTS_VOLUME\n\t\t}\n\n\t\tfor washing_agent in station_in_db.station_washing_agents:\n\t\t\tdefined_washing_agent = next(ag for ag in params[\"washing_agents\"]\n\t\t\t\t\t\t\t\t\t\t if ag[\"agent_number\"] == washing_agent.agent_number)\n\t\t\tfor param in default_washing_agents_params:\n\t\t\t\tdefault_param = default_washing_agents_params.get(param)\n\t\t\t\tif param in defined_washing_agent:\n\t\t\t\t\tassert getattr(washing_agent, param) == defined_washing_agent[param]\n\t\t\t\telse:\n\t\t\t\t\tassert getattr(washing_agent, param) == default_param\n\n\t\tdefault_washing_machines_params = {\n\t\t\t\"track_length\": services.DEFAULT_WASHING_MACHINES_TRACK_LENGTH,\n\t\t\t\"is_active\": services.DEFAULT_WASHING_MACHINES_IS_ACTIVE,\n\t\t\t\"volume\": services.DEFAULT_WASHING_MACHINES_VOLUME\n\t\t}\n\t\tfor washing_machine in station_in_db.station_washing_machines:\n\t\t\tdefined_washing_machine = next(machine for machine in params[\"washing_machines\"]\n\t\t\t\t\t\t\t\t\t\t   if machine[\"machine_number\"] == washing_machine.machine_number)\n\t\t\tfor param in default_washing_machines_params:\n\t\t\t\tdefault_param = default_washing_machines_params.get(param)\n\t\t\t\tif param in defined_washing_machine:\n\t\t\t\t\tassert getattr(washing_machine, param) == defined_washing_machine[param]\n\t\t\t\telse:\n\t\t\t\t\tassert getattr(washing_machine, param) == default_param\n\n\t\tparams[\"region\"] = \"Северо-западный\"  # меняю обратно для след тестов\n\n\tasync def test_create_station_with_default_programs(self, session: AsyncSession, ac: AsyncClient,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmonkeypatch, sync_session: Session):\n\t\tprograms = generate_station_programs(amount=4, as_schema=True)\n\n\t\tasync def get_default_programs(*args, **kwargs):\n\t\t\treturn programs\n\t\tmonkeypatch.setattr(stations.StationProgram, \"get_default_programs\", get_default_programs)\n\n\t\tstation = await generate_station(ac, sync_session, self.sysadmin, use_default_programs=True)\n\n\t\tassert any(station.station_programs)\n\t\tassert len(station.station_programs) == len(programs)\n\t\tfor pg in programs:\n\t\t\tstation_pg = next(p for p in station.station_programs if p.program_step == pg.program_step)\n\t\t\tassert pg.program_number == station_pg.program_number\n\t\t\tassert pg.name == station_pg.name\n\t\t\tstation_pg_washing_agent_number = [ag.agent_number for ag in station_pg.washing_agents]\n\t\t\tfor ag in pg.washing_agents:\n\t\t\t\tag = schemas_washing.WashingAgentWithoutRollback(**ag)  # тип почему-то нарушен\n\t\t\t\tassert ag.agent_number in station_pg_washing_agent_number\n\n\tasync def test_create_station_with_invalid_default_programs(self, session: AsyncSession, ac: AsyncClient,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  monkeypatch, sync_session: Session):\n\t\tasync def get_default_programs(*args, **kwargs):\n\t\t\traise CreatingError(\"Some getting default program exception\")\n\t\tmonkeypatch.setattr(stations.StationProgram, \"get_default_programs\", get_default_programs)\n\n\t\tstation_data = dict(station={\n\t\t\t\"name\": \"Qwerty\",\n\t\t\t\"wifi_name\": \"qwerty\",\n\t\t\t\"wifi_password\": \"qwerty\",\n\t\t\t# \"address\": \"Санкт-Петербург\",\n\t\t\t\"region\": RegionEnum.NORTHWEST.value,\n\t\t\t\"serial\": rand_serial()\n\t\t})\n\n\t\tr = await ac.post(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=station_data\n\t\t)\n\t\tassert r.status_code == 422\n\n\tasync def test_create_station_with_getting_default_programs_connection_error(self, session: AsyncSession,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ac: AsyncClient,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t monkeypatch):\n\t\tasync def get_default_programs(*args, **kwargs):\n\t\t\traise ConnectionError(\"Some getting default program exception\")\n\t\tmonkeypatch.setattr(stations.StationProgram, \"get_default_programs\", get_default_programs)\n\n\t\tstation_data = dict(station={\n\t\t\t\"name\": \"Qwerty\",\n\t\t\t\"wifi_name\": \"qwerty\",\n\t\t\t\"wifi_password\": \"qwerty\",\n\t\t\t# \"address\": \"Санкт-Петербург\",\n\t\t\t\"region\": RegionEnum.NORTHWEST.value,\n\t\t\t\"serial\": rand_serial()\n\t\t})\n\n\t\tr = await ac.post(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=station_data\n\t\t)\n\t\tassert r.status_code == 400\n\n\tasync def test_create_station_with_comment(self, ac: AsyncClient, session: AsyncSession):\n\t\t\"\"\"\n\t\tНовый параметр, поэтому добавлю отдельный тест\n\t\t\"\"\"\n\t\tstation_data = dict(station={\n\t\t\t\"name\": \"qwerty\",\n\t\t\t\"wifi_name\": \"qwerty\",\n\t\t\t\"wifi_password\": \"qwerty\",\n\t\t\t# \"address\": \"Санкт-Петербург\",\n\t\t\t\"region\": RegionEnum.NORTHWEST.value,\n\t\t\t\"serial\": rand_serial(),\n\t\t\t\"comment\": \"it is test!\"\n\t\t})\n\t\tresponse = await ac.post(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=station_data\n\t\t)\n\t\tassert response.status_code == 201\n\t\tr = schemas_stations.Station(**response.json())\n\t\tassert r.comment == station_data[\"station\"][\"comment\"]\n\n\t\t# ______________\n\n\t\t\"\"\"\n\t\tпустой коммент\n\t\t\"\"\"\n\t\tdel station_data[\"station\"][\"comment\"]\n\t\tstation_data[\"station\"][\"serial\"] = rand_serial()\n\t\tresponse = await ac.post(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=station_data\n\t\t)\n\t\tassert response.status_code == 201\n\t\tassert response.json()[\"comment\"] is None\n\n\tasync def test_create_station_not_released(self, session: AsyncSession, ac: AsyncClient):\n\t\t\"\"\"\n\t\tЕсли станция не выпущена - установится пустая дата создания\n\t\t\"\"\"\n\t\tstation_data = dict(station={\n\t\t\t\"name\": \"qwerty\",\n\t\t\t\"wifi_name\": \"qwerty\",\n\t\t\t\"wifi_password\": \"qwerty\",\n\t\t\t# \"address\": \"Санкт-Петербург\",\n\t\t\t\"region\": RegionEnum.NORTHWEST.value,\n\t\t\t\"serial\": rand_serial()\n\t\t})\n\t\turl = \"/v1/stations/?released=false\"\n\n\t\tr = await ac.post(\n\t\t\turl,\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=station_data\n\t\t)\n\n\t\tassert r.status_code == 201\n\t\tr = schemas_stations.Station(**r.json())\n\t\tassert r.created_at is None\n\n\tasync def test_release_station(self, session: AsyncSession, ac: AsyncClient):\n\t\t\"\"\"\n\t\tЕсли станция не выпущена, можно ее выпустить.\n\t\t\"\"\"\n\t\tstation_data = dict(station={\n\t\t\t\"name\": \"qwerty\",\n\t\t\t\"wifi_name\": \"qwerty\",\n\t\t\t\"wifi_password\": \"qwerty\",\n\t\t\t# \"address\": \"Санкт-Петербург\",\n\t\t\t\"region\": RegionEnum.NORTHWEST.value,\n\t\t\t\"serial\": rand_serial()\n\t\t})\n\t\turl = \"/v1/stations/?released=false\"\n\t\tr = await ac.post(\n\t\t\turl,\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=station_data\n\t\t)\n\n\t\tstation = schemas_stations.Station(**r.json())\n\n\t\trelease_r = await ac.patch(\n\t\t\tf\"/v1/stations/release/{station.id}\",\n\t\t\theaders=self.sysadmin.headers\n\t\t)\n\t\tassert release_r.status_code == 200\n\t\tstation = schemas_stations.StationGeneralParams(**release_r.json())\n\t\tassert station.created_at\n\n\tasync def test_not_released_station(self, session: AsyncSession, ac: AsyncClient):\n\t\tstation_data = dict(station={\n\t\t\t\"name\": \"qwerty\",\n\t\t\t\"wifi_name\": \"qwerty\",\n\t\t\t\"wifi_password\": \"qwerty\",\n\t\t\t# \"address\": \"Санкт-Петербург\",\n\t\t\t\"region\": RegionEnum.NORTHWEST.value,\n\t\t\t\"serial\": rand_serial()\n\t\t})\n\t\turl = \"/v1/stations/?released=false\"\n\t\tr = await ac.post(\n\t\t\turl,\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=station_data\n\t\t)\n\t\tstation = schemas_stations.Station(**r.json())\n\n\t\trequest_to_station_r = await ac.get(\n\t\t\tf\"/v1/manage/station/{station.id}/{StationParamsEnum.GENERAL}\",\n\t\t\theaders=self.sysadmin.headers\n\t\t)\n\t\tassert request_to_station_r.status_code == 403\n\n\t\t# ____\n\t\tstation_headers = {\"X-Station-Uuid\": str(station.id)}\n\n\t\trequest_from_station_r = await ac.get(\n\t\t\t\"/v1/stations/me\",\n\t\t\theaders=station_headers\n\t\t)\n\t\tassert request_from_station_r.status_code == 403\n\n\tasync def test_release_station_not_sysadmin_role(self, session: AsyncSession, ac: AsyncClient):\n\t\turl = f\"/v1/stations/release/{self.station.id}\"\n\t\tr = await ac.patch(\n\t\t\turl,\n\t\t\theaders=self.manager.headers\n\t\t)\n\t\tassert r.status_code == 403\n\n\tasync def test_release_station_not_existing_station_id(self, session: AsyncSession, ac: AsyncClient):\n\t\trand_uuid = uuid.uuid4()\n\t\turl = f\"/v1/stations/release/{rand_uuid}\"\n\t\tr = await ac.patch(\n\t\t\turl,\n\t\t\theaders=self.sysadmin.headers\n\t\t)\n\t\tassert r.status_code == 404\n\n\tasync def test_release_station_already_released(self, session: AsyncSession, ac: AsyncClient):\n\t\turl = f\"/v1/stations/release/{self.station.id}\"\n\t\tr = await ac.patch(\n\t\t\turl,\n\t\t\theaders=self.sysadmin.headers\n\t\t)\n\t\tassert r.status_code == 400\n\n\tasync def test_create_station_errors(self, ac: AsyncClient, session: AsyncSession):\n\t\t\"\"\"\n\t\t- Нельзя передать в программу несуществующее стиральное средство;\n\t\t- Нельзя передать невалидные параметры станции (норм проверяются в schemas_stations);\n\t\t- roles auto test;\n\t\t- users auth auto test.\n\t\t\"\"\"\n\t\tparams = copy.deepcopy(station_fills.test_create_station_with_advanced_params)\n\t\tif not isinstance(params[\"station\"][\"region\"], str):\n\t\t\tparams[\"station\"][\"region\"] = params[\"station\"][\n\t\t\t\t\"region\"].value  # не успевает поменяться обратно на строку (\n\t\t# _______________________________________________________________\n\t\tparams[\"station\"][\"programs\"].append(\n\t\t\t{\n\t\t\t\t\"program_step\": 13,\n\t\t\t\t\"washing_agents\": [\n\t\t\t\t\t{\n\t\t\t\t\t\t\"agent_number\": 5  # такого в списке нет\n\t\t\t\t\t}\n\t\t\t\t]\n\t\t\t}\n\t\t)\n\n\t\tnon_existing_washing_agent_r = await ac.post(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=params\n\t\t)\n\n\t\tassert non_existing_washing_agent_r.status_code == 422\n\n\t\tparams[\"station\"][\"programs\"].remove(params[\"station\"][\"programs\"][-1])\n\n\t\t# ________________________________________________________\n\n\t\tparams[\"station\"][\"settings\"][\"station_power\"] = True\n\t\tparams[\"station\"][\"is_active\"] = False\n\n\t\tinvalid_params_r = await ac.post(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.sysadmin.headers,\n\t\t\tjson=params\n\t\t)\n\n\t\tassert invalid_params_r.status_code == 422\n\n\t\t# ________________________________________________________\n\n\t\tawait auth.url_auth_roles_test(\n\t\t\t\"/v1/stations/\", \"post\",\n\t\t\tRoleEnum.SYSADMIN, self.sysadmin,\n\t\t\tsession, ac, json=station_fills.test_create_station_with_advanced_params\n\t\t)\n\t\tawait auth.url_auth_test(\n\t\t\t\"/v1/stations/\", \"post\", self.sysadmin, ac, session,\n\t\t\tjson=station_fills.test_create_station_with_advanced_params\n\t\t)\n\n\tasync def test_read_all_stations(self, ac: AsyncClient, session: AsyncSession,\n\t\t\t\t\t\t\t\t\t sync_session: Session):\n\t\t\"\"\"\n\t\tЧтение списка станций.\n\t\t\"\"\"\n\t\tawait delete_all_stations(session)  # не могу проследить, откуда появляется ошибка - где-то\n\t\t# из контроля станции удалил стиральную машину при рабочем состоянии\n\t\tstations_ = await StationData.generate_stations_list(ac, sync_session, self.sysadmin, session,\n\t\t\t\t\t\t\t\t\t\t\t\t amount=10)\n\t\t# ____\n\t\t# sys, manager role\n\t\tfor user in (self.sysadmin, self.manager):\n\t\t\tr = await ac.get(\n\t\t\t\t\"/v1/stations/\",\n\t\t\t\theaders=user.headers\n\t\t\t)\n\t\t\tassert r.status_code == 200\n\t\t\tr = [schemas_stations.StationInList(**s) for s in r.json()]\n\t\t\tassert len(r) >= len(stations_)\n\n\t\t# ____\n\t\t# region manager & installer role\n\t\tfor user in (self.installer, self.region_manager):\n\t\t\tr = await ac.get(\n\t\t\t\t\"/v1/stations/\",\n\t\t\t\theaders=user.headers\n\t\t\t)\n\t\t\tassert r.status_code == 200\n\t\t\tr = [schemas_stations.StationInList(**s) for s in r.json()]\n\t\t\tassert len(r)\n\t\t\tassert all(\n\t\t\t\t(st.general.region == user.region for st in r)\n\t\t\t)\n\n\tasync def test_read_all_stations_(self, session: AsyncSession, ac: AsyncClient,\n\t\t\t\t\t\t\t\t\t  sync_session: Session):\n\t\t\"\"\"\n\t\tпроверить, что точно возвращаются параметры\n\t\t\"\"\"\n\t\tawait self.station.generate_data_for_read_stations_list(\n\t\t\tsession, ac, sync_session, ctrl=True, owner=True, logs=True\n\t\t)\n\t\tr = await ac.get(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.sysadmin.headers\n\t\t)\n\t\tr = [schemas_stations.StationInList(**s) for s in r.json()]\n\t\tstation = next(s for s in r if str(s.general.id) == str(self.station.id))\n\t\tassert station.last_work_at\n\t\tassert station.last_maintenance_at\n\t\tassert station.owner\n\t\tassert station.control.status\n\n\tasync def test_read_all_stations_with_ordering(self, ac: AsyncClient, session: AsyncSession,\n\t\t\t\t\t\t\t\t\t\t\t\t   sync_session: Session):\n\t\theaders = self.sysadmin.headers\n\t\tawait StationData.generate_stations_list(ac, sync_session, self.sysadmin, session,\n\t\t\t\t\t\t\t\t\t\t\t\t amount=10)\n\t\tsorting_keys = {StationsSortingEnum.OWNER: lambda st_: st_.owner.last_name,\n\t\t\t\t\t\tStationsSortingEnum.STATUS: lambda st_: st_.control.status.value,\n\t\t\t\t\t\tStationsSortingEnum.MAINTENANCE: lambda st_: st_.last_maintenance_at,\n\t\t\t\t\t\tStationsSortingEnum.LAST_WORK: lambda st_: st_.last_work_at,\n\t\t\t\t\t\tStationsSortingEnum.NAME: lambda st_: st_.general.name,\n\t\t\t\t\t\tStationsSortingEnum.REGION: lambda st_: st_.general.region.value}\n\t\tfor order in list(StationsSortingEnum):\n\t\t\tfor desc in (True, False):\n\t\t\t\turl = f\"/v1/stations/?order_by={order.value}\"\n\t\t\t\tsorting_params = {\"key\": sorting_keys[order]}\n\t\t\t\tif desc:\n\t\t\t\t\turl += \"&desc=true\"\n\t\t\t\t\tsorting_params[\"reverse\"] = True\n\t\t\t\tr = await ac.get(url, headers=headers)\n\t\t\t\tassert r.status_code == 200\n\t\t\t\tr = [schemas_stations.StationInList(**s) for s in r.json()]\n\t\t\t\tnullable_objs = []\n\t\t\t\tfor s in r:\n\t\t\t\t\tnullable_fields = {StationsSortingEnum.OWNER: s.owner,\n\t\t\t\t\t\t\t\t\t  StationsSortingEnum.STATUS: s.control.status,\n\t\t\t\t\t\t\t\t\t  StationsSortingEnum.MAINTENANCE: s.last_maintenance_at,\n\t\t\t\t\t\t\t\t\t  StationsSortingEnum.LAST_WORK: s.last_work_at}\n\t\t\t\t\tif order in nullable_fields:\n\t\t\t\t\t\tnullable_field = nullable_fields[order]\n\t\t\t\t\t\tif nullable_field is None:\n\t\t\t\t\t\t\tnullable_objs.append(s)\n\t\t\t\tfor obj in nullable_objs:\n\t\t\t\t\tdel r[r.index(obj)]\n\t\t\t\tassert r == sorted(r, **sorting_params)\n\n\tasync def test_read_all_stations_by_not_permitted_user(self, ac: AsyncClient, session: AsyncSession):\n\t\tr = await ac.get(\n\t\t\t\"/v1/stations/\",\n\t\t\theaders=self.laundry.headers\n\t\t)\n\t\tassert r.status_code == 403\n\n\tasync def test_read_all_stations_not_authenticated(self, ac: AsyncClient,\n\t\t\t\t\t\t\t\t\t\t\t\t\t   session: AsyncSession):\n\t\tawait auth.url_auth_test(\n\t\t\t\"/v1/stations/\", \"get\", self.sysadmin,\n\t\t\tac, session\n\t\t)\n\n\tasync def test_read_stations_params(self, ac: AsyncClient, session: AsyncSession):\n\t\t\"\"\"\n\t\tЧастичное чтение данных станции станцией.\n\t\t\"\"\"\n\t\tgeneral_params_r = await ac.get(\n\t\t\t\"/v1/stations/me/\" + StationParamsEnum.GENERAL.value,\n\t\t\theaders=self.station.headers\n\t\t)\n\t\tassert general_params_r.status_code == 200\n\t\tresult = schemas_stations.StationGeneralParamsForStation(**general_params_r.json())\n\t\tfor k, v in self.station.__dict__.items():\n\t\t\tif k in result.dict():\n\t\t\t\tassert getattr(result, k) == v\n\n\t\t# _____________________________________________________\n\n\t\tsettings_r = await ac.get(\n\t\t\t\"/v1/stations/me/\" + StationParamsEnum.SETTINGS.value,\n\t\t\theaders=self.station.headers\n\t\t)\n\n\t\tassert settings_r.status_code == 200\n\t\tsettings_result = schemas_stations.StationSettings(**settings_r.json())\n\n\t\t# _____________________________________________________\n\n\t\tcontrol_r = await ac.get(\n\t\t\t\"/v1/stations/me/\" + StationParamsEnum.CONTROL.value,\n\t\t\theaders=self.station.headers\n\t\t)\n\t\tassert control_r.status_code == 200\n\t\tresult = schemas_stations.StationControl(**control_r.json())\n\n\t\tassert settings_result.station_power is True and result.status == StationStatusEnum.AWAITING or \\\n\t\t\t   settings_result.station_power is False and result.status is None\n\n\t\t# _____________________________________________________\n\n\t\twashing_agents_r = await ac.get(\n\t\t\t\"/v1/stations/me/\" + StationParamsEnum.WASHING_AGENTS.value,\n\t\t\theaders=self.station.headers\n\t\t)\n\t\tassert washing_agents_r.status_code == 200\n\t\twashing_agents_result = washing_agents_r.json()\n\n\t\tfor washing_agent in washing_agents_result:\n\t\t\twashing_agent = washing.WashingAgent(**washing_agent)  # Validation error\n\t\t\tassert services.MIN_WASHING_AGENTS_VOLUME <= washing_agent.volume <= services.MAX_WASHING_AGENTS_VOLUME\n\t\t\tassert washing_agent.rollback is services.DEFAULT_WASHING_AGENTS_ROLLBACK\n\t\t# _____________________________________________________\n\n\t\tprograms_r = await ac.get(\n\t\t\t\"/v1/stations/me/\" + StationParamsEnum.PROGRAMS.value,\n\t\t\theaders=self.station.headers\n\t\t)\n\t\tassert programs_r.status_code == 200\n\n\t\tresult = programs_r.json()\n\t\tfor program in result:\n\t\t\tprogram = schemas_stations.StationProgram(**program)\n\t\t\tassert program.program_number == program.program_step // 10\n\t\t\tfor washing_agent in program.washing_agents:\n\t\t\t\tassert washing_agent.agent_number in [ag[\"agent_number\"] for ag in washing_agents_result]\n\t\t\t\tassert services.MIN_STATION_WASHING_AGENTS_AMOUNT <= washing_agent.agent_number <= \\\n\t\t\t\t\t   services.MAX_STATION_WASHING_AGENTS_AMOUNT\n\n\t\t# ____________________________________________________\n\n\t\twashing_machines_r = await ac.get(\n\t\t\t\"/v1/stations/me/\" + StationParamsEnum.WASHING_MACHINES.value,\n\t\t\theaders=self.station.headers\n\t\t)\n\n\t\tassert washing_machines_r.status_code == 200\n\t\tresult = washing_machines_r.json()\n\n\t\tfor machine in result:\n\t\t\tmachine = washing.WashingMachine(**machine)\n\t\t\tassert services.MIN_WASHING_MACHINE_VOLUME <= machine.volume <= services.MAX_WASHING_MACHINE_VOLUME\n\t\t\tassert services.MIN_STATION_WASHING_MACHINES_AMOUNT <= machine.machine_number \\\n\t\t\t\t   <= services.MAX_STATION_WASHING_MACHINES_AMOUNT\n\t\t\tassert services.MIN_WASHING_MACHINE_TRACK_LENGTH <= machine.track_length <= \\\n\t\t\t\t   services.MAX_WASHING_MACHINE_TRACK_LENGTH\n\t\t\tassert machine.is_active == services.DEFAULT_WASHING_MACHINES_IS_ACTIVE\n\n\tasync def test_read_stations_params_errors(self, ac: AsyncClient, session: AsyncSession):\n\t\t\"\"\"\n\t\t- Отсутствие данных по станции;\n\t\t- stations auth auto test\n\t\t\"\"\"\n\t\tawait session.execute(\n\t\t\tdelete(stations.StationControl).where(stations.StationControl.station_id == self.station.id)\n\t\t)\n\t\tawait session.commit()\n\n\t\tnon_existing_data_r = await ac.get(\n\t\t\t\"/v1/stations/me/\" + StationParamsEnum.CONTROL.value,\n\t\t\theaders=self.station.headers\n\t\t)\n\n\t\tassert non_existing_data_r.status_code == 404\n\n\t\tstation = await generate_station(ac, user=self.sysadmin)\n\n\t\tawait auth.url_auth_stations_test(\n\t\t\t\"/v1/stations/me/\" + StationParamsEnum.GENERAL.value,\n\t\t\t\"get\", station, session, ac\n\t\t)\n\n\tasync def test_read_stations_me(self, ac: AsyncClient, session: AsyncSession):\n\t\t\"\"\"\n\t\tЧтение всех данных по станции станцией.\n\t\t\"\"\"\n\t\tresponse = await ac.get(\n\t\t\t\"/v1/stations/me\",\n\t\t\theaders=self.station.headers\n\t\t)\n\t\tassert response.status_code == 200\n\t\tschemas_stations.StationForStation(**response.json())  # Validation error\n\n\tasync def test_read_station_me_errors(self, ac: AsyncClient, session: AsyncSession):\n\t\t\"\"\"\n\t\t- Отсутствие данных по станции;\n\t\t- stations auth auto test\n\t\t\"\"\"\n\t\tawait auth.url_auth_stations_test(\n\t\t\t\"/v1/stations/me\", \"get\", self.station, session, ac\n\t\t)\n\t\tawait change_station_params(self.station, session, status=StationStatusEnum.AWAITING)\n\n\t\tawait session.execute(\n\t\t\tdelete(stations.StationSettings).where(stations.StationSettings.station_id == self.station.id)\n\t\t)\n\t\tawait session.commit()\n\n\t\tnon_existing_data_r = await ac.get(\n\t\t\t\"/v1/stations/me\",\n\t\t\theaders=self.station.headers\n\t\t)\n\n\t\tassert non_existing_data_r.status_code == 404\n\n", "repo_name": "Dahaka1/lfs", "sub_path": "tests/test_stations.py", "file_name": "test_stations.py", "file_ext": "py", "file_size_in_byte": 25048, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "tests.additional.users.UserData", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tests.additional.users", "line_number": 26, "usage_type": "name"}, {"api_name": "tests.additional.users.UserData", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tests.additional.users", "line_number": 27, "usage_type": "name"}, {"api_name": "tests.additional.users.UserData", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tests.additional.users", "line_number": 28, "usage_type": "name"}, {"api_name": "tests.additional.users.UserData", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tests.additional.users", "line_number": 29, "usage_type": "name"}, {"api_name": "tests.additional.users.UserData", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tests.additional.users", "line_number": 30, "usage_type": "name"}, {"api_name": "tests.additional.stations.StationData", "line_number": 31, "usage_type": "name"}, {"api_name": "httpx.AsyncClient", "line_number": 33, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 33, "usage_type": "name"}, {"api_name": "app.static.enums.RegionEnum.NORTHWEST", "line_number": 44, "usage_type": "attribute"}, {"api_name": "app.static.enums.RegionEnum", "line_number": 44, "usage_type": "name"}, {"api_name": "tests.additional.stations.rand_serial", "line_number": 45, "usage_type": "call"}, {"api_name": "app.schemas.schemas_stations.Station", "line_number": 53, "usage_type": "call"}, {"api_name": "app.schemas.schemas_stations", "line_number": 53, "usage_type": "name"}, {"api_name": "tests.additional.stations.get_station_by_id", "line_number": 56, "usage_type": "call"}, {"api_name": "services.DEFAULT_STATION_WASHING_AGENTS_AMOUNT", "line_number": 58, "usage_type": "attribute"}, {"api_name": "services.DEFAULT_STATION_WASHING_MACHINES_AMOUNT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "services.DEFAULT_STATION_IS_ACTIVE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "services.DEFAULT_STATION_IS_PROTECTED", "line_number": 64, "usage_type": "attribute"}, {"api_name": "services.DEFAULT_STATION_STATUS", "line_number": 65, "usage_type": "attribute"}, {"api_name": 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"tests.additional.auth.url_auth_stations_test", "line_number": 633, "usage_type": "call"}, {"api_name": "tests.additional.auth", "line_number": 633, "usage_type": "name"}, {"api_name": "app.static.enums.StationParamsEnum.GENERAL", "line_number": 634, "usage_type": "attribute"}, {"api_name": "app.static.enums.StationParamsEnum", "line_number": 634, "usage_type": "name"}, {"api_name": "httpx.AsyncClient", "line_number": 638, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 638, "usage_type": "name"}, {"api_name": "app.schemas.schemas_stations.StationForStation", "line_number": 647, "usage_type": "call"}, {"api_name": "app.schemas.schemas_stations", "line_number": 647, "usage_type": "name"}, {"api_name": "httpx.AsyncClient", "line_number": 649, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 649, "usage_type": "name"}, {"api_name": "tests.additional.auth.url_auth_stations_test", "line_number": 654, "usage_type": "call"}, {"api_name": "tests.additional.auth", "line_number": 654, "usage_type": "name"}, {"api_name": "tests.additional.stations.change_station_params", "line_number": 657, "usage_type": "call"}, {"api_name": "app.static.enums.StationStatusEnum.AWAITING", "line_number": 657, "usage_type": "attribute"}, {"api_name": "app.static.enums.StationStatusEnum", "line_number": 657, "usage_type": "name"}, {"api_name": "sqlalchemy.delete", "line_number": 660, "usage_type": "call"}, {"api_name": "app.models.stations.StationSettings", "line_number": 660, "usage_type": "attribute"}, {"api_name": "app.models.stations", "line_number": 660, "usage_type": "name"}, {"api_name": "pytest.mark.usefixtures", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 24, "usage_type": "attribute"}]}
{"seq_id": "39316182852", "text": "'''\nMisfit of the Advection-Diffusion problem written in FEniCS-2019.1.0 and hIPPYlib-3.0\nhttps://hippylib.github.io/tutorials_v3.0.0/4_AdvectionDiffusionBayesian/\n-------------------------------------------------------------------------\nProject of Bayesian SpatioTemporal analysis for Inverse Problems (B-STIP)\nShiwei Lan @ ASU, Sept. 2020\n--------------------------------------------------------------------------\nCreated on Sep 23, 2020\n'''\n__author__ = \"Shiwei Lan\"\n__copyright__ = \"Copyright 2020, The Bayesian STIP project\"\n__license__ = \"GPL\"\n__version__ = \"0.1\"\n__maintainer__ = \"Shiwei Lan\"\n__email__ = \"slan@asu.edu; lanzithinking@outlook.com\"\n\nimport dolfin as dl\nimport ufl\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport sys\nimport os\nsys.path.append( os.environ.get('HIPPYLIB_BASE_DIR', \"../../\") )\nfrom hippylib import *\nfrom pde import TimeDependentAD\n\nsys.path.append( \"../\" )\nfrom util.common_colorbar import common_colorbar\n\nclass SpaceTimePointwiseStateObservation(Misfit):\n    \"\"\"\n    Misfit (negative loglikelihood) of Advection-Diffusion inverse problem\n    \"\"\"\n    def __init__(self, Vh, observation_times=None, targets=None, rel_noise = 0.01, d = None, **kwargs):\n        \"\"\"\n        Initialize the misfit\n        \"\"\"\n        # function space\n        self.Vh = Vh\n        self.mpi_comm = self.Vh.mesh().mpi_comm()\n        self.rank = dl.MPI.rank(self.mpi_comm)\n        # observation times\n        if observation_times is None:\n            t_init         = 0.\n            t_final        = 4.\n            t_1            = 1.\n            dt             = .1\n            observation_dt = .2\n            self.observation_times = np.arange(t_1, t_final+.5*dt, observation_dt)\n        else:\n            self.observation_times = observation_times\n        # observation locations\n        self.targets = np.loadtxt('targets.txt') if targets is None else targets\n        self.rel_noise = rel_noise\n        # obtain observations\n        if d is None:\n            d=self.get_observations(pde=kwargs.pop('pde',None), nref=kwargs.pop('nref',0), init=kwargs.pop('pde',None))\n            if self.rank == 0:\n                sep = \"\\n\"+\"#\"*80+\"\\n\"\n                print( sep, \"Generate synthetic observations at {0} locations for {1} time points\".format(self.targets.shape[0], len(self.observation_times)), sep )\n        # reset observation container for reference\n        self.prep_container()\n        self.d.axpy(1., d)\n        \n    def prep_container(self, Vh=None):\n        \"\"\"\n        Prepare storage of the observations\n        \"\"\"\n        if Vh is None:\n            Vh = self.Vh\n        # storage for observations\n        self.B = assemblePointwiseObservation(Vh, self.targets)\n        self.d = TimeDependentVector(self.observation_times)\n        self.d.initialize(self.B, 0)\n        ## TEMP Vars\n        self.u_snapshot = dl.Vector()\n        self.Bu_snapshot = dl.Vector()\n        self.d_snapshot  = dl.Vector()\n        self.B.init_vector(self.u_snapshot, 1)\n        self.B.init_vector(self.Bu_snapshot, 0)\n        self.B.init_vector(self.d_snapshot, 0)\n    \n    def get_observations(self, pde=None, nref=0, init=None):\n        \"\"\"\n        Get the observations at given locations and time points\n        \"\"\"\n        # pde for observations\n        if pde is None:\n            mesh = self.Vh.mesh()\n            for i in range(nref): mesh = dl.refine(mesh) # refine mesh to obtain observations\n            pde = TimeDependentAD(mesh)\n        elif nref>0:\n            mesh = pde.mesh\n            for i in range(nref): mesh = dl.refine(mesh) # refine mesh to obtain observations\n            pde = TimeDependentAD(mesh)\n        # initial condition\n        if init is None:\n            true_init = dl.Expression('min(0.5,exp(-100*(pow(x[0]-0.35,2) +  pow(x[1]-0.7,2))))', element=pde.Vh[STATE].ufl_element())\n            init = dl.interpolate(true_init, pde.Vh[STATE]).vector()\n        # prepare container for observations\n        self.prep_container(pde.Vh[STATE])\n        \n        utrue = pde.generate_vector(STATE)\n        x = [utrue, init, None]\n        pde.solveFwd(x[STATE], x)\n        self.observe(x, self.d)\n        MAX = self.d.norm(\"linf\", \"linf\")\n        noise_std_dev = self.rel_noise * MAX\n        parRandom.normal_perturb(noise_std_dev,self.d)\n        self.noise_variance = noise_std_dev*noise_std_dev\n        return self.d.copy()\n    \n    def observe(self, x, obs):\n        \"\"\"\n        Observation operator\n        \"\"\"\n        obs.zero()\n        \n        for t in self.observation_times:\n            x[STATE].retrieve(self.u_snapshot, t)\n            self.B.mult(self.u_snapshot, self.Bu_snapshot)\n            obs.store(self.Bu_snapshot, t)\n            \n    def cost(self, x):\n        \"\"\"\n        Compute misfit\n        \"\"\"\n        c = 0\n        for t in self.observation_times:\n            x[STATE].retrieve(self.u_snapshot, t)\n            self.B.mult(self.u_snapshot, self.Bu_snapshot)\n            self.d.retrieve(self.d_snapshot, t)\n            self.Bu_snapshot.axpy(-1., self.d_snapshot)\n            c += self.Bu_snapshot.inner(self.Bu_snapshot)\n            \n        return c/(2.*self.noise_variance)\n    \n    def grad(self, i, x, out):\n        \"\"\"\n        Compute the gradient of misfit\n        \"\"\"\n        out.zero()\n        if i == STATE:\n            for t in self.observation_times:\n                x[STATE].retrieve(self.u_snapshot, t)\n                self.B.mult(self.u_snapshot, self.Bu_snapshot)\n                self.d.retrieve(self.d_snapshot, t)\n                self.Bu_snapshot.axpy(-1., self.d_snapshot)\n                self.Bu_snapshot *= 1./self.noise_variance\n                self.B.transpmult(self.Bu_snapshot, self.u_snapshot) \n                out.store(self.u_snapshot, t)           \n        else:\n            pass\n    \n    def setLinearizationPoint(self, x, gauss_newton_approx=False):\n        pass\n    \n    def apply_ij(self, i,j, direction, out):\n        out.zero()\n        if i == STATE and j == STATE:\n            for t in self.observation_times:\n                direction.retrieve(self.u_snapshot, t)\n                self.B.mult(self.u_snapshot, self.Bu_snapshot)\n                self.Bu_snapshot *= 1./self.noise_variance\n                self.B.transpmult(self.Bu_snapshot, self.u_snapshot) \n                out.store(self.u_snapshot, t)\n        else:\n            pass    \n    \n    def applyWuu(self, du, out):\n        out.zero()\n        self.apply_ij(STATE, STATE, du, out)\n    \n    def applyWum(self, dm, out):\n        out.zero()\n    \n    def applyWmu(self, du, out):\n        out.zero()\n    \n    def applyWmm(self, dm, out):\n        out.zero()\n    \n    def plot_data(self, times, figsz=(12,5)):\n        \"\"\"\n        Plot the observations with its values u(x, t) at fixed locations for given time points\n        \"\"\"\n        n=len(times)\n        nrow=np.floor(np.sqrt(n)).astype('int')\n        ncol=np.ceil(np.sqrt(n)).astype('int')\n        fig,axes=plt.subplots(nrows=nrow,ncols=ncol,sharex=True,sharey=True,figsize=figsz)\n        sub_figs = [None]*len(axes.flat)\n        for i in range(n):\n            plt.axes(axes.flat[i])\n            dl.plot(self.Vh.mesh())\n            sub_figs[i]=plt.scatter(self.targets[:,0],self.targets[:,1], c=self.d.data[np.where(np.isclose(self.d.times,times[i]))[0][0]], zorder=2)\n#             plt.xlim(0,1); plt.ylim(0,1)\n#             plt.gca().set_aspect('equal', 'box')\n            plt.title('Time: {:.1f} s'.format(times[i],))\n        fig=common_colorbar(fig,axes,sub_figs)\n        return fig\n    \nif __name__ == '__main__':\n    np.random.seed(2020)\n#     # define pde\n    meshsz = (61,61)\n    eldeg = 1\n    pde = TimeDependentAD(mesh=meshsz, eldeg=eldeg)\n    Vh = pde.Vh[STATE]\n    # obtain function space\n#     mesh = dl.Mesh('ad_10k.xml')\n#     Vh = dl.FunctionSpace(mesh, \"Lagrange\", 2)\n    # set observation times\n    t_init         = 0.\n    t_final        = 4.\n    t_1            = 1.\n    dt             = .1\n    observation_dt = .2\n    observation_times = np.arange(t_1, t_final+.5*dt, observation_dt)\n    # set observation locations\n    targets = np.loadtxt('targets.txt')\n    # define misfit\n    rel_noise = .5\n    nref = 1\n    misfit = SpaceTimePointwiseStateObservation(Vh, observation_times, targets, rel_noise=rel_noise, nref=nref)\n#     # optional: refine mesh to obtain (new) observations\n#     rf_mesh = dl.refine(pde.mesh)\n#     rf_pde = TimeDependentAD(mesh=rf_mesh)\n#     rf_obs = SpaceTimePointwiseStateObservation(rf_pde.Vh[STATE], observation_times, targets, pde=rf_pde).d.copy()\n#     misfit.d.zero()\n#     misfit.d.axpy(1.,rf_obs)\n    # plot observations\n    plt_times=[1.,2.,3.,4.]\n    fig = misfit.plot_data(plt_times, (10,9))\n    plt.subplots_adjust(wspace=0.1, hspace=0.2)\n    plt.savefig(os.path.join(os.getcwd(),'properties/obs.png'),bbox_inches='tight')\n    ", "repo_name": "lanzithinking/DREAM-BUQ", "sub_path": "ad_diff/misfit.py", "file_name": "misfit.py", "file_ext": "py", "file_size_in_byte": 8786, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.path.append", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 24, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "dolfin.MPI.rank", "line_number": 42, "usage_type": "call"}, {"api_name": "dolfin.MPI", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 54, "usage_type": "call"}, {"api_name": "dolfin.Vector", "line_number": 77, "usage_type": "call"}, {"api_name": "dolfin.Vector", "line_number": 78, "usage_type": "call"}, {"api_name": "dolfin.Vector", "line_number": 79, "usage_type": "call"}, {"api_name": "dolfin.refine", "line_number": 91, "usage_type": "call"}, {"api_name": "pde.TimeDependentAD", "line_number": 92, "usage_type": "call"}, {"api_name": "pde.mesh", "line_number": 94, "usage_type": "attribute"}, {"api_name": "dolfin.refine", "line_number": 95, "usage_type": "call"}, {"api_name": "pde.TimeDependentAD", "line_number": 96, "usage_type": "call"}, {"api_name": "dolfin.Expression", "line_number": 99, "usage_type": "call"}, {"api_name": "pde.Vh", "line_number": 99, "usage_type": "attribute"}, {"api_name": "dolfin.interpolate", "line_number": 100, "usage_type": "call"}, {"api_name": "pde.Vh", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pde.Vh", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pde.generate_vector", "line_number": 104, "usage_type": "call"}, {"api_name": "pde.solveFwd", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "dolfin.plot", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.isclose", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "util.common_colorbar.common_colorbar", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pde.TimeDependentAD", "line_number": 208, "usage_type": "call"}, {"api_name": "pde.Vh", "line_number": 209, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path", "line_number": 236, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 236, "usage_type": "call"}]}
{"seq_id": "12577010060", "text": "import win32com.client\nimport pandas as pd\nfrom datetime import datetime\nfrom com.utils import *\nimport time\n \n# 크레온 플러스 공통 OBJECT\ncpCodeMgr = win32com.client.Dispatch('CpUtil.CpCodeMgr')\ncpStatus = win32com.client.Dispatch('CpUtil.CpCybos')\ncpOhlc = win32com.client.Dispatch('CpSysDib.StockChart')\n\ndef get_ohlc(code, qty):\n    \"\"\"인자로 받은 종목의 OHLC 가격 정보를 qty 개수만큼 반환한다.\"\"\"\n    cpOhlc.SetInputValue(0, code)           # 종목코드\n    cpOhlc.SetInputValue(1, ord('2'))        # 1:기간, 2:개수\n    cpOhlc.SetInputValue(4, qty)             # 요청개수\n    cpOhlc.SetInputValue(5, [0, 2, 3, 4, 5, 8]) # 0:날짜, 2~5:OHLC\n    cpOhlc.SetInputValue(6, ord('D'))        # D:일단위\n    cpOhlc.SetInputValue(9, ord('1'))        # 0:무수정주가, 1:수정주가\n    cpOhlc.BlockRequest()\n    count = cpOhlc.GetHeaderValue(3)   # 3:수신개수\n    columns = ['open', 'high', 'low', 'close', 'vol']\n    index = []\n    rows = []\n\n    for i in range(count): \n        index.append(cpOhlc.GetDataValue(0, i)) \n        rows.append([cpOhlc.GetDataValue(1, i), cpOhlc.GetDataValue(2, i),\n            cpOhlc.GetDataValue(3, i), cpOhlc.GetDataValue(4, i), cpOhlc.GetDataValue(5, i)]) \n    df = pd.DataFrame(rows, columns=columns, index=index) \n    return df\n\ndef getVolMax(volarr):\n    max = 0\n    for vol in volarr:\n        if max < vol:\n            max = vol\n    return max\n\ndef get_movingaverage(code, window):\n    \"\"\"인자로 받은 종목에 대한 이동평균가격을 반환한다.\"\"\"\n    try:\n        time_now = datetime.now()\n        str_today = time_now.strftime('%Y%m%d')\n        ohlc = get_ohlc(code, window)  # 120 + 16 날짜별 데이터 추출\n\n        if len(ohlc.index) < window:\n            return None, None, None, None\n\n        # if str_today == str(ohlc.iloc[0].name):\n        #     lastday = ohlc.iloc[1].name\n        # else:\n        #     lastday = ohlc.iloc[0].name\n        lastday = ohlc.iloc[0].name\n\n        closes = ohlc['close'].sort_index()         \n        vols = ohlc['vol'].sort_index()\n\n        ma20 = closes.rolling(20).mean()\n        ma60 = closes.rolling(60).mean()\n        ma120 = closes.rolling(120).mean()\n        bf3d_m20 = ma20.loc[ohlc.iloc[3].name]\n        bf3d_m60 = ma60.loc[ohlc.iloc[3].name]\n        bf3d_m120 = ma120.loc[ohlc.iloc[3].name]\n        bf7d_m20 = ma20.loc[ohlc.iloc[7].name]\n        bf7d_m60 = ma60.loc[ohlc.iloc[7].name]\n        bf7d_m120 = ma120.loc[ohlc.iloc[7].name]\n        bf15d_m20 = ma20.loc[ohlc.iloc[15].name]\n        bf15d_m60 = ma60.loc[ohlc.iloc[15].name]\n        bf15d_m120 = ma120.loc[ohlc.iloc[15].name]\n        \n        if round(bf3d_m20, 2) > round(bf3d_m60, 2) and round(bf3d_m60, 2) > round(bf3d_m120, 2) \\\n            and round(bf7d_m20, 2) > round(bf7d_m60, 2) and round(bf7d_m60, 2) > round(bf7d_m120, 2) \\\n            and round(bf15d_m20, 2) > round(bf15d_m60, 2) and round(bf15d_m60, 2) > round(bf15d_m120, 2):\n\n            vol30arr = vols.tail(30).array      # 최근 30일 거래량의 최대값 추가\n            return code, closes[lastday], vols[lastday], getVolMax(vol30arr)\n        else:\n            return None, None, None, None\n\n    except Exception as ex:\n        print(datetime.now().strftime('[%m/%d %H:%M:%S]'), 'get_movingavrg(' + str(window) + ') -> exception! ' + str(ex))\n        \n        return None\n\nclass CMarketTotal():\n    def __init__(self):\n        self.dataInfo = {}\n        self.targetItems = {}\n \n        self.targetItems['code'] = []\n        self.targetItems['name'] = []\n        self.targetItems['lastclose'] = []\n        self.targetItems['vol'] = []\n        self.targetItems['sprice'] = []\n        self.targetItems['lastmaxvol'] = []\n \n    def get_target_items(self):\n        codeList = cpCodeMgr.GetStockListByMarket(1)  # 거래소\n        codeList2 = cpCodeMgr.GetStockListByMarket(2)  # 코스닥\n        allcodelist = codeList + codeList2\n        #print('전 종목 코드 %d, 거래소 %d, 코스닥 %d' % (len(allcodelist), len(codeList), len(codeList2)))\n \n        objMarket = CpMarketEye()\n        rqCodeList = []\n        for i, code in enumerate(allcodelist):\n            rqCodeList.append(code)\n            if len(rqCodeList) == 200:\n                time.sleep(1)\n                objMarket.request(rqCodeList, self.dataInfo)\n                rqCodeList = []\n                continue\n \n        if len(rqCodeList) > 0:\n            objMarket.request(rqCodeList, self.dataInfo)\n\n        # print(self.dataInfo)\n        for key in self.dataInfo.keys():\n            finalcode, close, vol, vol30max = get_movingaverage(key, 136)\n            if finalcode:\n                self.targetItems['code'].append(finalcode)\n                self.targetItems['name'].append(cpCodeMgr.CodeToName(finalcode))\n                self.targetItems['lastclose'].append(close)\n                self.targetItems['vol'].append(vol)\n                self.targetItems['lastmaxvol'].append(vol30max)                \n\n        return self.targetItems\n        #slack.chat.post_message('#stock', ' '.join(self.targetItems))\n\nclass CpMarketEye:\n    def __init__(self):\n        self.objRq = win32com.client.Dispatch(\"CpSysDib.MarketEye\")\n        self.RpFiledIndex = 0\n \n    def request(self, codes, dataInfo):\n        # 0: 종목코드 4: 현재가 10:거래량 22 : 전일거래량, 23: 전일종가\n        rqField = [0, 4, 10, 22, 23]  # 요청 필드\n \n        self.objRq.SetInputValue(0, rqField)  # 요청 필드\n        self.objRq.SetInputValue(1, codes)  # 종목코드 or 종목코드 리스트\n        self.objRq.BlockRequest()\n \n        # 현재가 통신 및 통신 에러 처리\n        rqStatus = self.objRq.GetDibStatus()\n        if rqStatus != 0:\n            return False\n \n        cnt = self.objRq.GetHeaderValue(2) # 0 : 필드개수, 1: 필드명배열, 2: 종목개수\n        \n        for i in range(cnt):\n            code = self.objRq.GetDataValue(0, i)  # 코드\n            cur_price = self.objRq.GetDataValue(1, i)  # 현재가\n            trade_amt = self.objRq.GetDataValue(2, i)  # 거래량\n            bf_trade_amt = self.objRq.GetDataValue(3, i)  # 전일 거래량\n            bf_price = self.objRq.GetDataValue(4, i)  # 전일 종가\n\n            ## TODO : 전일 or 당일 오전 조회에 따른 판단 분기\n\n            # 1. 전일 종가 대비 11% 이상 급등 \n            per = 0\n            if bf_price > 0:\n                per = ((cur_price - bf_price) / bf_price) * 100.0\n\n            # 2. 전일 거래량 조건\n            if trade_amt > 2000000 and per > 11.0:\n                dataInfo[code] = (cur_price, trade_amt)\n            \n        return True\n\n", "repo_name": "mongma-n/python-stock-toy-project-with-creon", "sub_path": "trade/collector.py", "file_name": "collector.py", "file_ext": "py", "file_size_in_byte": 6641, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "win32com.client.client.Dispatch", "line_number": 8, "usage_type": "call"}, {"api_name": "win32com.client.client", "line_number": 8, "usage_type": "attribute"}, {"api_name": "win32com.client", "line_number": 8, "usage_type": "name"}, {"api_name": "win32com.client.client.Dispatch", "line_number": 9, "usage_type": "call"}, {"api_name": "win32com.client.client", "line_number": 9, "usage_type": "attribute"}, {"api_name": "win32com.client", "line_number": 9, "usage_type": "name"}, {"api_name": "win32com.client.client.Dispatch", "line_number": 10, "usage_type": "call"}, {"api_name": "win32com.client.client", "line_number": 10, "usage_type": "attribute"}, {"api_name": "win32com.client", "line_number": 10, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 109, "usage_type": "call"}, {"api_name": "win32com.client.client.Dispatch", "line_number": 132, "usage_type": "call"}, {"api_name": "win32com.client.client", "line_number": 132, "usage_type": "attribute"}, {"api_name": "win32com.client", "line_number": 132, "usage_type": "name"}]}
{"seq_id": "37443926896", "text": "import openrouteservice\r\nimport folium\r\nimport random\r\nimport json\r\nimport pprint\r\nimport os\r\n\r\ndef rand_coord_in_range(x, y):\r\n    x_int = int(x * 10000000)\r\n    y_int = int(y * 10000000)\r\n    rand = random.randrange(min(x_int, y_int), max(x_int, y_int))\r\n    return rand / 10000000\r\n\r\n\r\nLONDON_BBOX = [[-81.398485, 43.006537], [-81.122701, 42.945282]]\r\n\r\n\"\"\"\r\nclient_local = openrouteservice.Client(base_url='http://localhost:5000') # Specify your personal API key\r\n\r\npois = client_local.places(request='pois', bbox=LONDON_BBOX, filter_category_ids=[596], validate=False)\r\n\r\nstation_locations = [station['geometry']['coordinates'] for station in pois['features']]\r\n\"\"\"\r\n\r\n# get locations and prices from pre-made json file\r\n\r\nprice_file = open('station_prices_reduced_network.json')\r\n\r\nprice_data = json.load(price_file)\r\n\r\nprint(str(len(price_data)))\r\n\r\nstation_locations = [station['coordinates'] for station in price_data]\r\n\r\ntrue_costco_index = next((z for z, st in enumerate(price_data) if st['price'] == 159.9), -1)\r\n\r\nclient_local_ors = openrouteservice.Client(base_url='http://localhost:8080/ors')\r\n\r\ndef simulate_best_station():\r\n    vehicle_category = random.random() # generate a random between 0 and 1 and use tranches to determine vehicle type\r\n\r\n    if vehicle_category < .17:\r\n        # Vehicle is a truck\r\n        BASE_FILL_L = 95 * 0.8\r\n        FUEL_BURN_L_PER_100KM = 14\r\n    elif vehicle_category < (.17 + .47):\r\n        # Vehicle is an SUV\r\n        BASE_FILL_L = 60 * 0.8\r\n        FUEL_BURN_L_PER_100KM = 9\r\n    elif vehicle_category < (.17 + .47 + .29):\r\n        # Vehicle is a car\r\n        BASE_FILL_L = 45 * 0.8\r\n        FUEL_BURN_L_PER_100KM = 7\r\n    else:\r\n        # Vehicle is a van\r\n        BASE_FILL_L = 76 * 0.8\r\n        FUEL_BURN_L_PER_100KM = 12\r\n\r\n    starting_location = [rand_coord_in_range(LONDON_BBOX[0][0], LONDON_BBOX[1][0]), rand_coord_in_range(LONDON_BBOX[0][1], LONDON_BBOX[1][1])]\r\n\r\n    temp_station_locations = station_locations.copy()\r\n    temp_price_data = price_data.copy()\r\n\r\n    removed_costco = False\r\n    if random.random() < (1.0 if os.getenv('REMOVE_COSTCO', False) else 0.5):\r\n        temp_station_locations.pop(true_costco_index)\r\n        temp_price_data.pop(true_costco_index)\r\n        removed_costco = True\r\n\r\n    locations = [starting_location] + temp_station_locations\r\n\r\n    matrix = client_local_ors.distance_matrix(locations=locations, destinations=list(range(1, len(locations))), sources=[0], profile='driving-car', metrics=['distance'], validate=False)\r\n\r\n    closest_station_index = matrix['distances'][0].index(min(matrix['distances'][0]))\r\n\r\n    cost_matrix = []\r\n    fuel_burn_matrix = []\r\n\r\n    for i in range(len(temp_price_data)):\r\n        station = temp_price_data[i]\r\n        base_cost = BASE_FILL_L * station['price']\r\n        on_route_fuel_burn = matrix['distances'][0][i] / 1000 / 100 * FUEL_BURN_L_PER_100KM * 2 # the distance is in meters so we convert to km, then we see what portion of 100 that is multiplied by fuel burn. We multiply by 2 to consider the return fuel burn\r\n        on_route_fuel_cost = on_route_fuel_burn * station['price']\r\n        total_fuel_cost = on_route_fuel_cost + base_cost\r\n        cost_matrix.append(total_fuel_cost)\r\n        fuel_burn_matrix.append(on_route_fuel_burn + BASE_FILL_L)\r\n\r\n    lowest_cost_station_index = cost_matrix.index(min(cost_matrix))\r\n\r\n    # note that the map will show the last route decision where this function was run multiple times\r\n    # we use reversed here to reverse coords because the map uses lat, lon and the rest of the apis use lon,lat\r\n    m = folium.Map(location=list(reversed(starting_location)))\r\n\r\n    folium.Marker(location=list(reversed(starting_location)), popup=\"starting point\", icon=folium.Icon(color=\"green\")).add_to(m)\r\n\r\n    folium.Marker(location=list(reversed(temp_station_locations[closest_station_index])), popup=\"Nearest station \\n Price: \" + str(temp_price_data[closest_station_index]['price']), icon=folium.Icon(color=\"blue\")).add_to(m)\r\n    folium.Marker(location=list(reversed(temp_station_locations[lowest_cost_station_index])), popup=\"Cheapest station \\n Price: \" + str(temp_price_data[lowest_cost_station_index]['price']), icon=folium.Icon(color=\"red\")).add_to(m)\r\n\r\n\r\n    m.save('map.html')\r\n\r\n    return { 'nearest_price': temp_price_data[closest_station_index]['price'], 'cheapest_price': temp_price_data[lowest_cost_station_index]['price'], 'additional_burn_l': fuel_burn_matrix[lowest_cost_station_index] - fuel_burn_matrix[closest_station_index], 'total_savings': (cost_matrix[closest_station_index] - cost_matrix[lowest_cost_station_index]) / 100, 'nearest_is_best': closest_station_index == lowest_cost_station_index, 'costco_is_best': lowest_cost_station_index == true_costco_index and not removed_costco }\r\n\r\ntotal_additional_burn = 0\r\ntotal_savings = 0\r\nnearest_best = 0\r\ncostco_best = 0\r\n\r\nTOTAL_RUNS = 10000\r\n\r\nfor run in range(TOTAL_RUNS):\r\n    result = simulate_best_station()\r\n    total_additional_burn += result['additional_burn_l']\r\n    total_savings += result['total_savings']\r\n    if result['nearest_is_best']:\r\n        nearest_best += 1\r\n    if result['costco_is_best']:\r\n        costco_best += 1\r\n\r\nprint('The total additional fuel burn was ' + str(total_additional_burn) + ' which saved consumers $' + str(round(total_savings, 2)) + ' a lower-priced station was optimal for ' + str(round((1 - (nearest_best / TOTAL_RUNS)) * 100, 1)) + ' % of drivers. ' + str(round(costco_best / TOTAL_RUNS * 100, 1)) + ' % of drivers would choose costco.')\r\n", "repo_name": "rye761/GasSim", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 5536, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "random.randrange", "line_number": 11, "usage_type": "call"}, {"api_name": "json.load", "line_number": 29, "usage_type": "call"}, {"api_name": "openrouteservice.Client", "line_number": 37, "usage_type": "call"}, {"api_name": "random.random", "line_number": 40, "usage_type": "call"}, {"api_name": "random.random", "line_number": 65, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 65, "usage_type": "call"}, {"api_name": "folium.Map", "line_number": 92, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 94, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 94, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 96, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 96, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 97, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 97, "usage_type": "call"}]}
{"seq_id": "16080802316", "text": "from os import stat\nfrom fastapi import Request\nfrom fastapi import APIRouter\nfrom SQL.BankDBManager import BankDBManager\nfrom dto.Transaction import Transaction\nfrom datetime import datetime\nfrom fastapi import FastAPI, HTTPException\nbank_db_manager = BankDBManager()\n\nrouter = APIRouter(\n    prefix=\"/transactions\",\n    tags=[\"transactions\"]\n)\n\n\n@router.get('/balance')\ndef get_balance():\n    balance_as_array = bank_db_manager.get_balance()\n    return  balance_as_array[0]\n\n@router.get('/')\ndef get_transactions(category=\"\", date=\"\", amount=\"\"):\n    tranactions_from_db = bank_db_manager.get_transactions()\n    list_of_all_transactions: list[Transaction] = [\n        Transaction(**res) for res in tranactions_from_db]\n\n    if category != \"\":\n        list_of_all_transactions = [\n            t for t in list_of_all_transactions if t.category == category]\n\n    if amount != \"\":\n        list_of_all_transactions = [\n            t for t in list_of_all_transactions if t.amount >= int(amount)]\n\n    if date != \"\":\n        list_of_all_transactions = [\n            t for t in list_of_all_transactions if t.tr_date >= datetime.strptime(date, '%Y-%m-%d').date()]\n\n    return list_of_all_transactions\n\n\n@router.post('/')\nasync def add_transaction(request: Request):\n    transaction: Transaction = Transaction(**(await request.json()))\n    bank_db_manager.add_new_transaction(transaction)\n    return transaction\n\n\n@router.delete('/{transactionID}')\nasync def delete_transaction(transactionID: int):\n    bank_db_manager.delete_transaction(transactionID)\n", "repo_name": "207Levy/Bank", "sub_path": "Server/router/transaction_route.py", "file_name": "transaction_route.py", "file_ext": "py", "file_size_in_byte": 1545, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "SQL.BankDBManager.BankDBManager", "line_number": 8, "usage_type": "call"}, {"api_name": "fastapi.APIRouter", "line_number": 10, "usage_type": "call"}, {"api_name": "dto.Transaction.Transaction", "line_number": 24, "usage_type": "name"}, {"api_name": "dto.Transaction.Transaction", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "name"}, {"api_name": "fastapi.Request", "line_number": 43, "usage_type": "name"}, {"api_name": "dto.Transaction.Transaction", "line_number": 44, "usage_type": "name"}]}
{"seq_id": "40533504958", "text": "import tkinter as tk\nimport vlc\n\nclass Screen(tk.Frame):\n    def __init__(self, parent, *args, **kwargs):\n        tk.Frame.__init__(self, parent, bg = 'black')\n        self.settings = { # Inizialazing dictionary settings\n            \"width\" : 1024,\n            \"height\" : 576\n        }\n        self.settings.update(kwargs) # Changing the default settings\n        # Open the video source |temporary\n        self.video_source =  \"./Assets/male_5_10.mp4\"\n\n        # Canvas where to draw video output\n        self.canvas = tk.Canvas(self, width = self.settings['width'], height = self.settings['height'], bg = \"black\", highlightthickness = 0)\n        self.canvas.pack()\n\n        # Creating VLC player\n        self.instance = vlc.Instance()\n        self.player = self.instance.media_player_new()\n\n\n    def GetHandle(self):\n        # Getting frame ID\n        return self.winfo_id()\n\n    def play(self, _source):\n        # Function to start player from given source\n        Media = self.instance.media_new(_source)\n        Media.get_mrl()\n        self.player.set_media(Media)\n\n        #self.player.play()\n        self.player.set_hwnd(self.GetHandle())\n        self.player.play()", "repo_name": "asmanjitha/Computer_Aided_Smart_Adverticements", "sub_path": "GUI_Interactive_App/VideoScreen.py", "file_name": "VideoScreen.py", "file_ext": "py", "file_size_in_byte": 1171, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "tkinter.Frame", "line_number": 4, "usage_type": "attribute"}, {"api_name": "tkinter.Frame.__init__", "line_number": 6, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tkinter.Canvas", "line_number": 16, "usage_type": "call"}, {"api_name": "vlc.Instance", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "32925431759", "text": "from lib.io import *\nfrom lib.file import *\nfrom lib.hue import *\nfrom requests import get\nfrom urllib import parse\nfrom json import loads\nimport time\nendl = \"\\n\"\n\nclass dork:\n\n\tdef help():\n\t\tprint(\"\")\n\t\tio.row(25, [\"dork -h\", \"Attributes list for this command.\"])\n\t\tio.row(25, [\"dork -l\", \"List all available dork files.\"])\n\t\tio.row(25, [\"dork -r [FILE]\", \"List all dorks within a file.\"])\n\t\tio.row(25, [\"dork -u [FILE] [ID]\", \"Use dork within a file.\"])\n\t\tprint(\"\")\n\n\tdef list():\n\t\tfiles = file.list(\"dorks\")\n\t\tif len(files) != 0:\n\t\t\tprint(\"\")\n\t\t\tfor f in files:\n\t\t\t\tprint(f[0:-4])\n\t\t\tprint(\"\")\n\t\telse:\n\t\t\tio.error(\"There ain't files to show up.\")\n\n\tdef read(f):\n\t\tcontent = file.readfile(\"dorks/\" + f + \".txt\")\n\t\tif content != False:\n\t\t\tif len(content) != 0:\n\t\t\t\tprint(\"\")\n\t\t\t\tio.row(5, [fore.blue + \"ID\", \"DORK\"])\n\t\t\t\ti = 1\n\t\t\t\tfor d in content:\n\t\t\t\t\tio.row(5, [fore.yellow + str(i), fore.white + d])\n\t\t\t\t\tio.prevline(1)\n\t\t\t\t\ti += 1\n\t\t\t\tprint(endl)\n\t\t\telse: io.error(\"There ain't dorks to show up.\")\n\t\telse: io.error(\"File doesn't exist.\")\n\n\tdef search(query):\n\t\t_google = \"https://www.googleapis.com/customsearch/v1\"\n\t\t_params = {\n\t\t\t\"key\": \"AIzaSyANm8farYg7FBUl49FRSMDURp4a7VDEyEY\",\n\t\t\t\"cx\": \"017576662512468239146:omuauf_lfve\",\n\t\t\t\"q\": query\n\t\t}\n\t\t_data = get(_google, params=_params).text\n\t\t_data = loads(_data)\n\t\ttry:\n\t\t\tfor item in _data[\"items\"]:\n\t\t\t\tprint(item[\"link\"])\n\t\t\tprint(\"\")\n\t\t\treturn True\n\t\texcept: return False\n\n\tdef use(f, n):\n\t\tcontent = file.readfile(\"dorks/\" + f + \".txt\")\n\t\tif content != False:\n\t\t\tif len(content) != 0:\n\t\t\t\ti = 1\n\t\t\t\tfor _dork in content:\n\t\t\t\t\tif n == i:\n\t\t\t\t\t\tio.quote(\"Dork: \" + fore.magenta + _dork + fore.white)\n\t\t\t\t\t\tio.prevline(1)\n\t\t\t\t\t\tif not dork.search(_dork):\n\t\t\t\t\t\t\tio.prevline(2)\n\t\t\t\t\t\t\tio.error(\"No results gathered.\")\n\t\t\t\t\t\ti = -1\n\t\t\t\t\t\tbreak\n\t\t\t\t\telse: i += 1\n\t\t\t\tif i != -1: io.error(\"Doesn't exist dork with given ID.\")\n\t\t\telse: io.error(\"This file doesn't contain any dorks to use.\")\n\t\telse: io.error(\"File doesn't exist.\")\n\n\tdef all(f):\n\t\tcontent = file.readfile(\"dorks/\" + f + \".txt\")\n\t\tif content != False:\n\t\t\tif len(content) != 0:\n\t\t\t\tio.quote(\"Gathering dorks data...\")\n\t\t\t\ti = 0\n\t\t\t\tfor _dork in content:\n\t\t\t\t\tif dork.search(_dork): i += 1\n\t\t\t\t\ttime.sleep(5)\n\t\t\t\tif i == 0:\n\t\t\t\t\tio.prevline(2)\n\t\t\t\t\tio.error(\"No results gathered.\")\n\t\t\telse: io.error(\"This file doesn't contain any dorks to use.\")\n\t\telse: io.error(\"File doesn't exist.\")", "repo_name": "skollprog/tochi", "sub_path": "lib/dork.py", "file_name": "dork.py", "file_ext": "py", "file_size_in_byte": 2404, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "requests.get", "line_number": 52, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 88, "usage_type": "call"}]}
{"seq_id": "6121768640", "text": "#pip install slack_sdk, aiohttp, selenium, bs4, requests\r\n\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\nimport json\r\n\r\ndef get_asin(url):\r\n    asin = url.split('/')\r\n    for i, dp in enumerate(asin):\r\n        if dp == \"dp\":\r\n            return (asin[i+1])\r\n\r\ndef get_domain(url):\r\n    uri = url.split('/')\r\n    for i in uri:\r\n        if \"amazon\" in i:\r\n            return i\r\n    print(\"not supported, amazon only\")\r\n    exit()\r\n\r\ndef get_itemcode(url):\r\n    return get_asin(url)\r\n\r\ndef getprice_amazon(parse_html):\r\n    parse_price = parse_html.find(id=\"twister-plus-price-data-price\")\r\n    parse_curr = parse_html.find(id=\"twister-plus-price-data-price-unit\")\r\n\r\n    if parse_price == None:\r\n        item_price = \"none found\"\r\n        currency = \"none found\"\r\n    else:\r\n        item_price = parse_price.get(\"value\")\r\n        currency = parse_curr.get(\"value\")\r\n\r\n    return item_price, currency\r\n\r\ndef read_html(html_file):\r\n    with open(html_file, \"r\", encoding=\"utf-8\") as f:\r\n        content = f.read()\r\n\r\n    parse_html = BeautifulSoup(content,\"html.parser\", multi_valued_attributes=None)\r\n    item_price, currency = getprice_amazon(parse_html)\r\n\r\n    return item_price, currency\r\n\r\n\r\ndef send_request(s, prepared):\r\n    response = s.send(prepared)\r\n    return response\r\n\r\ndef write_response_tofile(response, item_code):\r\n    html_file = \"ASIN - \" + item_code + \".html\"\r\n\r\n    with open(html_file,\"w\", encoding=\"utf-8\") as f:\r\n        f.write(response.text)\r\n    return html_file\r\n\r\ndef pretty_print_POST(req):\r\n    print('{}\\n{}\\r\\n{}\\r\\n\\r\\n{}'.format(\r\n        '-----------START-----------',\r\n        req.method + ' ' + req.url,\r\n        '\\r\\n'.join('{}: {}'.format(k, v) for k, v in req.headers.items()),\r\n        req.body,\r\n    ))\r\n\r\ndef getprice(url):\r\n    domain = get_domain(url)\r\n    item_code = get_itemcode(url)\r\n\r\n    headers = {'authority' :'www.amazon.com', 'user-agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Mobile Safari/537.36'}\r\n    req = requests.Request(\"GET\", url, headers=headers)\r\n    prepared = req.prepare()\r\n    s = requests.Session()\r\n    pretty_print_POST(prepared)\r\n\r\n    response = send_request(s, prepared)\r\n    #print(response.text)\r\n    #print(response.status_code)\r\n    html_file = write_response_tofile(response, item_code)\r\n    price_curr =  read_html(html_file)\r\n    price = price_curr[0]\r\n    currency = price_curr[1]\r\n    price_curr_json = {\"price\": price,\r\n    \"currency\": currency,\r\n    \"item\": item_code}\r\n    return price_curr_json", "repo_name": "marcus081c/pricecheck", "sub_path": "getpriceamazon.py", "file_name": "getpriceamazon.py", "file_ext": "py", "file_size_in_byte": 2560, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 41, "usage_type": "call"}, {"api_name": "requests.Request", "line_number": 71, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 73, "usage_type": "call"}]}
{"seq_id": "26330479726", "text": "from datetime import datetime\nfrom flask import render_template, session, redirect, url_for\n\nfrom . import main\nfrom .forms import NewBlogForm, EditContentForm, EditBlogForm, ContentForm\nfrom .. import db\nfrom ..models import Blog, BlogContent\n\n\n@main.route('/', methods=['GET', 'POST'])\ndef index():\n    blogs = Blog.query.order_by(Blog.timestamp.desc()).all()\n    return render_template('index.html', blogs=blogs, current_time=datetime.utcnow(),\n                           name=session.get('name'), text=session.get('text'))\n\n\n@main.route('/blogs/new', methods=['GET', 'POST'])\ndef new_blog():\n    blogform = NewBlogForm()\n    if blogform.validate_on_submit():\n        blog = Blog(title=blogform.title.data,\n                    body=blogform.body.data,\n                    timestamp=datetime.utcnow()\n                    )\n        db.session.add(blog)\n        return redirect(url_for('.index'))\n    return render_template('newblog.html', blogform=blogform, current_time=datetime.utcnow())\n\n\n@main.route('/blogs/<int:id>', methods=['GET', 'POST'])\ndef blog(id):\n    content_form = ContentForm()\n    blog = Blog.query.get_or_404(id)\n    session['id'] = blog.id\n    if content_form.validate_on_submit():\n        new_content = BlogContent(body=content_form.body.data,\n                                  blog=blog,\n                                  timestamp=datetime.utcnow())\n        db.session.add(new_content)\n        return redirect(url_for('.blog', id=blog.id))\n    return render_template('blog.html', blog=blog, content_form=content_form)\n\n\n@main.route('/edit/<int:id>', methods=['GET', 'POST'])\ndef edit_blog(id):\n    blog = Blog.query.get_or_404(id)\n    form = EditBlogForm()\n    if form.validate_on_submit():\n        blog.title = form.title.data\n        blog.body = form.body.data\n        return redirect(url_for('.blog', id=blog.id))\n    form.title.data = blog.title\n    form.body.data = blog.body\n    return render_template('edit_blog.html', blog=blog, form=form)\n\n\n@main.route('/edit/contents/<int:id>', methods=['GET', 'POST'])\ndef edit_content(id):\n    content = BlogContent.query.get_or_404(id)\n    form = EditContentForm()\n    if form.validate_on_submit():\n        content.body = form.body.data\n        return redirect(url_for('.blog', id=session.get('id')))\n    form.body.data = content.body\n    return render_template('edit_blog.html', content=content, form=form)\n\n\n@main.route('/delete/content/<int:id>')\ndef delete_content(id):\n    content = BlogContent.query.get_or_404(id)\n    db.session.delete(content)\n\n    return redirect(url_for('.blog', id=session.get('id')))\n\n\n@main.route('/delete/blog/<int:id>')\ndef delete_blog(id):\n    blog = Blog.query.get_or_404(id)\n    db.session.delete(blog)\n\n    return redirect(url_for('.index'))", "repo_name": "JunliuHub/MyBlog", "sub_path": "src/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "models.Blog.query.order_by", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Blog.query", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.Blog", "line_number": 12, "usage_type": "name"}, {"api_name": "models.Blog.timestamp.desc", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Blog.timestamp", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 14, "usage_type": "name"}, {"api_name": "forms.NewBlogForm", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Blog", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "forms.ContentForm", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Blog.query.get_or_404", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Blog.query", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Blog", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 34, "usage_type": "name"}, {"api_name": "models.BlogContent", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Blog.query.get_or_404", "line_number": 46, "usage_type": "call"}, {"api_name": "models.Blog.query", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.Blog", "line_number": 46, "usage_type": "name"}, {"api_name": "forms.EditBlogForm", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 54, "usage_type": "call"}, {"api_name": "models.BlogContent.query.get_or_404", "line_number": 59, "usage_type": "call"}, {"api_name": "models.BlogContent.query", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.BlogContent", "line_number": 59, "usage_type": "name"}, {"api_name": "forms.EditContentForm", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "models.BlogContent.query.get_or_404", "line_number": 70, "usage_type": "call"}, {"api_name": "models.BlogContent.query", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.BlogContent", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 73, "usage_type": "name"}, {"api_name": "models.Blog.query.get_or_404", "line_number": 78, "usage_type": "call"}, {"api_name": "models.Blog.query", "line_number": 78, "usage_type": "attribute"}, {"api_name": "models.Blog", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 81, "usage_type": "call"}]}
{"seq_id": "12089140908", "text": "#!/usr/bin/python3\nimport socket\nimport threading\nimport json\nimport time\nimport traceback\nimport subprocess\nimport os\nimport sys\nimport hashlib\nimport ctypes\n\ndatadir = '/mnt/var'\nbaseurl = 'https://yoomoney.ru/'\n\nlibx11=ctypes.CDLL('libX11.so.6')\nlibxtst=ctypes.CDLL('libXtst.so.6')\ndis=libx11.XOpenDisplay(None)\n\ndef kpress(kcode, d=1, u=1):\n\tsy = libx11.XFlush\n\tif d: libxtst.XTestFakeKeyEvent(dis, kcode, True, 0)\n\tsy(dis)\n\tif u: libxtst.XTestFakeKeyEvent(dis, kcode, False, 0)\n\tsy(dis)\n\nif 'early' in sys.argv:\n\ttime.sleep(10)\n\tkpress(24)\n\tkpress(39)\n\tkpress(36)\n\ttime.sleep(50)\n\t#kpress(71)\n\t#time.sleep(3)\n\tkpress(36)\n\t#time.sleep(5)\n\tkpress(37, 1, 0)\n\tkpress(64, 1, 0)\n\tkpress(28)\n\tkpress(37, 0, 1)\n\tkpress(64, 0, 1)\n\ttime.sleep(15)\n\tkpress(56)\n\tkpress(28)\n\tkpress(36)\n\texit()\n\ntoken1 = '\\r\\nrqbxmvJKSsNevlZDlTkiBktCVNdYWp\\r\\n\\r\\n'\ntoken2 = '/QINdbNFKeGGUmeAnbRhksGOTTZrATR'\n\nquery = []\npayed = []\nbad = []\n\nclass st:\n\twindow = None\n\tcapture = None\n\tmywallet = None\n\tpaymethod = None\n\tid = None\n\tdetails = None\n\tamount = None\n\trenew = False\n\ndef popcode():\n\tyfile = '/mnt/uc/' + st.mywallet\n\tcl = open(yfile).readlines()\n\tc = cl[0].split(' ')[1].strip()\n\topen(yfile, 'w').writelines(cl[1:])\n\tprint(len(cl), 'codes')\n\tst.renew = len(cl) < 7\n\treturn c\n\ndef timer():\n\twhile True:\n\t\ttry:\n\t\t\t#if st.id and st.enter:\n\t\t\t#\ttime.sleep(3)\n\t\t\t#\tif st.id and st.enter:\n\t\t\t#\t\tkpress(71)\n\t\t\t#\t\ttime.sleep(2)\n\t\t\t#\t\tkpress(36)\n\t\t\t#\t\ttime.sleep(3)\n\t\t\t#\tif st.id and st.enter:\n\t\t\t#\t\tkpress(36)\n\t\t\t#\t\ttime.sleep(2)\n\t\t\t#\ttime.sleep(5)\n\t\t\tprocess()\n\t\t\tif st.window and st.window.poll() is not None:\n\t\t\t\tst.window = None\n\t\t\t\tfinish()\n\t\texcept:\n\t\t\tpass\n\t\ttime.sleep(2)\n\nthreading.Thread(target=timer).start()\n\ndef screenrec(id):\n\tos.system('xscreensaver-command -deactivate')\n\tst.capture = subprocess.Popen(['/usr/bin/ffmpeg', '-f', 'x11grab', '-draw_mouse', '1', '-framerate', '25', '-video_size', '1366x768',\n\t\t'-i', ':0+0,0', '-pix_fmt', 'yuv420p', '-c:v', 'libx264', '-preset', 'veryfast', '-q:v', '1', '-s', '1366x768', '-f', 'matroska', \n\t\t'-v', '-8', '/mnt/screenrec/'+id+'.mkv'])\n\ndef process():\n\tif st.id or not query: return\n\tq = query.pop()\n\ta, st.mywallet, st.paymethod, st.id, st.details, st.amount = q\n\tif int(float(st.amount)) == float(st.amount):\n\t\tst.amount = str(int(float(st.amount)))\n\tif os.access(datadir + '/kpress', os.R_OK):\n\t\tkpress(int(open(datadir + '/kpress').read()))\n\t\ttime.sleep(1)\n\tst.window = subprocess.Popen(['xmessage']+q)\n\tbrowser = 'c' + st.mywallet\n\tif st.paymethod == 'billing':\n\t\tsubprocess.Popen([browser, baseurl+'main'])\n\telse:\n\t\tscreenrec(st.id)\n\t\tsubprocess.Popen([browser, {'wallet': baseurl+'transfer/a2w', 'phone': baseurl+'phone'}[st.paymethod]])\n\tst.enter = True\n\ndef finish(renew=True):\n\tif renew and st.renew:\n\t\tbrowser = 'c' + st.mywallet\n\t\tsubprocess.Popen([browser, baseurl+'emergency-codes'])\n\telse:\n\t\tst.id = None\n\t\tst.details = None\n\t\ttry:\n\t\t\tif st.window:\n\t\t\t\tst.window.terminate()\n\t\t\t\tst.window = None\n\t\t\tst.capture.terminate()\n\t\texcept:\n\t\t\tpass\n\t\tprint('finish')\n\t\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\ns.bind(('127.0.0.1', 22222))\ns.listen(1)\nwhile True:\n\ttry:\n\t\tres = ''\n\t\tc, a = s.accept()\n\t\tc.settimeout(5)\n\t\treq = b''\n\t\twhile b'\\r\\n\\r\\n' not in req:\n\t\t\tp = c.recv(8192)\n\t\t\tif not p: break\n\t\t\treq += p\n\t\tq = req.decode()\n\t\t\n\t\tif token1 in q:\n\t\t\tq = q.replace(token1, '').split('\\t')\n\t\t\t\n\t\t\tif q[0] == 'new':\n\t\t\t\tquery.insert(0, q)\n\t\t\t\n\t\t\tif q[0] == 'search':\n\t\t\t\tif len(q) == 3:\n\t\t\t\t\tquery.append(['new', q[2], 'billing', '-1', '0', '0'])\n\t\t\t\telse:\n\t\t\t\t\tif q[1] in payed:\n\t\t\t\t\t\tres = 'READY'\n\t\t\t\t\tif q[1] in bad:\n\t\t\t\t\t\tres = 'bad'\n\t\t\n\t\tif token2 in q:\n\t\t\twhile b'<EOF>' not in req:\n\t\t\t\tp = c.recv(8192)\n\t\t\t\tif not p: break\n\t\t\t\treq += p\n\t\t\tq = json.loads(req.decode().split('\\r\\n\\r\\n')[1].replace('<EOF>', ''))\n\t\t\tif q['action'] == 'details' and st.id and q['paymethod'] == st.paymethod:\n\t\t\t\tres = {'details': st.details, 'amount': st.amount}\n\t\t\t\n\t\t\tif q['action'] == 'getcode' and st.id:\n\t\t\t\tif float(q['amount'].replace('\\xa0', '').replace('\\t', '').strip()) <= float(st.amount)*1.03 and q['details'].strip() in st.details:\n\t\t\t\t\tprint('MATCH')\n\t\t\t\t\tres = {'ok': 1, 'c': popcode()}\n\t\t\t\t\tst.details = st.amount = None\n\t\t\t\telse:\n\t\t\t\t\tprint(q)\n\t\t\t\n\t\t\tif q['action'] == 'confirm' and st.id and not st.details:\n\t\t\t\tpayed.append(st.id)\n\t\t\t\tres = {'closed': 1, \n\t\t\t\t\t'redir': os.access(datadir + '/actions-' + st.mywallet, os.R_OK) and len(open(datadir + '/actions-' + st.mywallet).read())}\n\t\t\t\tfinish()\n\t\t\t\t\n\t\t\tif q['action'] == 'bad':\n\t\t\t\tbad.append(st.id)\n\t\t\t\tres = {'saved': 1}\n\t\t\t\tfinish('codeused' in q)\n\t\t\t\n\t\t\tif q['action'] == 'acode':\n\t\t\t\tres = {'c': popcode()}\n\t\t\t\n\t\t\tif q['action'] == 'savecodes':\n\t\t\t\tyfile = '/mnt/uc/' + st.mywallet\n\t\t\t\topen(yfile, 'w').write(q['content'])\n\t\t\t\tres = {'saved': 1}\n\t\t\t\tos.system('e7z /mnt/uc; uz &')\n\t\t\t\tfinish(False)\n\t\t\t\topen(datadir + '/codes-' + st.mywallet, 'w').write(q['content'])\n\t\t\t\n\t\t\tif q['action'] == 'loaded':\n\t\t\t\tst.enter = False\n\t\t\t\tres = 1\n\t\t\t\tprint('loaded')\n\t\t\t\n\t\t\tif 'bal' in q and q['mywallet'].isdigit():\n\t\t\t\trv = 0\n\t\t\t\tw = q['mywallet']\n\t\t\t\tif os.access(datadir + '/actions-' + w, os.R_OK):\n\t\t\t\t\trv = 1\n\t\t\t\t\tassert q['bal'] is not None\n\t\t\t\t\topen(datadir + '/balance-' + w, 'w').write(str(q['bal']))\n\t\t\t\t\told = open(datadir + '/actions-' + w).read().split('\\n')\n\t\t\t\t\tnewact = ''# if ''.join(old) else q['action']\n\t\t\t\t\twhile old:\n\t\t\t\t\t\tif not old[-1].strip():\n\t\t\t\t\t\t\told.pop()\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\tcan = (q['action']+'\\r\\r').replace('\\n'.join(old)+'\\n\\r\\r', '')\n\t\t\t\t\t\tif '\\r\\r' not in can:\n\t\t\t\t\t\t\tnewact = can\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\told.pop()\n\t\t\t\t\topen(datadir + '/actions-' + w, 'w').write(q['action'])\n\t\t\t\t\tfor act in newact.split('\\n'):\n\t\t\t\t\t\tprint(act)\n\t\t\t\t\t\tif not act: continue\n\t\t\t\t\t\tfn = hashlib.sha256(act.encode()).hexdigest() + '.eml'\n\t\t\t\t\t\tact = act.split('\\t')\n\t\t\t\t\t\tamo = str(float(act[1]))\n\t\t\t\t\t\tif act[0] == 'in':\n\t\t\t\t\t\t\topen(datadir + '/incoming-' + w + '/' + fn, 'w').write('in\\n'+amo)\n\t\t\t\t\t\tif act[0] == 'out':\n\t\t\t\t\t\t\topen(datadir + '/transactions-' + w + '/' + fn, 'w').write('out\\n'+amo)\n\t\t\t\t\t\n\t\t\t\tres = {'saved': rv}\n\t\t\t\tif st.paymethod == 'billing' and st.mywallet == w:\n\t\t\t\t\tfinish(False)\n\t\t\n\t\tif res: res = json.dumps(res)\n\t\tc.sendall(('HTTP/1.1 200 OK\\r\\nAccess-Control-Allow-Origin: *\\r\\nConnection: close\\r\\nContent-Length: '+str(len(res))+\n\t\t\t'\\r\\n\\r\\n'+res).encode())\n\t\tc.close()\n\texcept Exception:\n\t\ttraceback.print_exc()\n", "repo_name": "AsgardB/parovoz", "sub_path": "apm/listen.py", "file_name": "listen.py", "file_ext": "py", "file_size_in_byte": 6431, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "ctypes.CDLL", "line_number": 16, "usage_type": "call"}, {"api_name": "ctypes.CDLL", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 96, "usage_type": "call"}, {"api_name": "os.system", "line_number": 99, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 100, "usage_type": "call"}, {"api_name": "os.access", "line_number": 110, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 110, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 113, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 116, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 119, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 125, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 138, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 138, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 138, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 139, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 139, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 174, "usage_type": "call"}, {"api_name": "os.access", "line_number": 189, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 204, "usage_type": "call"}, {"api_name": "os.access", "line_number": 216, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 216, "usage_type": "attribute"}, {"api_name": "hashlib.sha256", "line_number": 235, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 247, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 252, "usage_type": "call"}]}
{"seq_id": "26415077614", "text": "import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\n\r\nexcel_file = \"血常规数据整合.xlsx\"\r\ndata = pd.read_excel(excel_file)\r\n\r\nX = data[['WBC', 'LY','GR','MO','RBC','Hgb','HCT','MCV','MCH','RDW','PLT','PCT','MPV','PDW']].values\r\nY = data['result'].values\r\n\r\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=50)\r\n\r\nk = 10\r\nclsf = KNeighborsClassifier(n_neighbors=k)\r\nclsf.fit(X_train,Y_train)\r\n\r\nY_pred = clsf.predict(X_test)\r\nprint(Y_pred)\r\nprint(Y_test)\r\n\r\nfrom sklearn.metrics import accuracy_score\r\nacc = accuracy_score(Y_test,Y_pred)\r\nprint(acc)\r\n", "repo_name": "KaFuuchao0313/BloodExamProject", "sub_path": "py/KNN_basic.py", "file_name": "KNN_basic.py", "file_ext": "py", "file_size_in_byte": 689, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pandas.read_excel", "line_number": 7, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 23, "usage_type": "call"}]}
{"seq_id": "16852461173", "text": "from numpy import array\nfrom os import listdir\nfrom pickle import load\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.image import load_img, img_to_array\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.utils import to_categorical, plot_model\nfrom keras.models import Model, load_model\nfrom keras.layers import Input, Dense, LSTM, Embedding, Dropout\nfrom keras.layers.merge import add\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.applications.vgg16 import preprocess_input\n\ndef load_doc(filename):\n    with open(filename, 'r') as f:\n        text = f.read()\n    return text\n\ndef load_set(filename):\n    doc = load_doc(filename)\n    dataset = set()\n    for line in doc.split('\\n'):\n        if len(line) < 1:\n            continue\n        key = line.split('.')[0]\n        dataset.add(key)\n    return dataset\n\n# load clean descriptions into memory\ndef load_clean_descriptions(filename, dataset):\n    doc = load_doc(filename)\n    texts = {}\n    for line in doc.split('\\n'):\n        if len(line) == 0:\n            continue\n        tokens = line.split()\n        image_id, text = tokens[0], tokens[1:]\n        if image_id in dataset:\n            desc = 'BOS ' + ' '.join(text) + ' EOS'\n            texts.setdefault(image_id, []).append(desc)\n    return texts\n\n#load photo features\ndef load_photo_features(filename, dataset):\n    with open(filename, 'rb') as f:\n        all_features = load(f)\n    features = {key: all_features[key] for key in dataset}\n    return features\n\n# convert a dictionary of clean descriptions to list\ndef to_lines(descriptions):\n    all_desc = []\n    for key in descriptions.keys():\n        all_desc += descriptions[key]\n    return all_desc\n\n# fit a tokenizer given caption descriptions\ndef create_tokenizer(descriptions):\n    lines = to_lines(descriptions)\n    tokenizer = Tokenizer()\n    tokenizer.fit_on_texts(lines)\n    return tokenizer\n\ndef max_length(descriptions):\n    lines = to_lines(descriptions)\n    return max(map(lambda d: len(d.split()), lines))\n\ndef create_sequences(tokenizer, max_length, descriptions, photos):\n    X1, X2, y = list(), list(), list()\n    # walk through each image identifier\n    for key, desc_list in descriptions.items():\n        # walk through each description for the image\n        for desc in desc_list:\n            # encode the sequence\n            seq = tokenizer.texts_to_sequences([desc])[0]\n            # split one sequence into multiple X,y pairs\n            for i in range(1, len(seq)):\n                # split into input and output pair\n                in_seq, out_seq = seq[:i], seq[i]\n                # pad input sequence\n                in_seq = pad_sequences([in_seq], maxlen=max_length)[0]\n                # encode output sequence\n                out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]\n                # store\n                X1.append(photos[key][0])\n                X2.append(in_seq)\n                y.append(out_seq)\n    return array(X1), array(X2), array(y)\n\ndef create_sequences_single(tokenizer, max_length, desc_list, photo):\n    X1, X2, y = [], [], []\n    vocab_size = len(tokenizer.word_index)+1\n    for desc in desc_list:\n        seq = tokenizer.texts_to_sequences([desc])[0]\n        for i in range(1, len(seq)):\n            in_seq, out_seq = seq[:i], seq[i]\n            in_seq = pad_sequences([in_seq], maxlen = max_length)[0]\n            out_seq = to_categorical([out_seq], num_classes = vocab_size)[0]\n            X1.append(photo[0])\n            X2.append(in_seq)\n            y.append(out_seq)\n    return [array(X1), array(X2), array(y)]\n\ndef define_model(vocab_size, max_length, filename = None):\n    # feature extractor model\n    inputs1 = Input(shape=(4096,))\n    fe1 = Dropout(0.5)(inputs1)\n    fe2 = Dense(256, activation='relu')(fe1)\n    # sequence model\n    inputs2 = Input(shape=(max_length,))\n    se1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2)\n    se2 = Dropout(0.5)(se1)\n    se3 = LSTM(256)(se2)\n\n    # decoder model\n    decoder1 = add([fe2, se3])\n    decoder2 = Dense(256, activation='relu')(decoder1)\n    outputs = Dense(vocab_size, activation='softmax')(decoder2)\n    \n    \n    # tie it together [image, seq] [word]\n    model = Model(inputs=[inputs1, inputs2], outputs=outputs)\n    if filename:\n        model = load_model(filename)\n    model.compile(loss='categorical_crossentropy', optimizer='adam')\n    \n    # summarize model\n    # print(model.summary())\n    # plot_model(model, to_file='model.png', show_shapes=True)\n    return model\n\n# train dataset\n\n# load training dataset (6K)\nfilename = 'Flickr8k_text/Flickr_8k.trainImages.txt'\ntrain = load_set(filename)\nprint('Dataset: %d' % len(train))\n# descriptions\ntrain_descriptions = load_clean_descriptions('descriptions.txt', train)\nprint('Descriptions: train=%d' % len(train_descriptions))\n# photo features\ntrain_features = load_photo_features('features.pkl', train)\nprint('Photos: train=%d' % len(train_features))\n# prepare tokenizer\ntokenizer = create_tokenizer(train_descriptions)\nvocab_size = len(tokenizer.word_index)+1\nprint('Vocabulary Size: %d' % vocab_size)\n\n# determine max sequence length\nmax_length = max_length(train_descriptions)\nprint('Description Length: %d' % max_length)\n'''\n# prepare sequences\nX1train, X2train, ytrain = create_sequences(tokenizer, max_length, train_descriptions, train_features)\n'''\ndef data_generator(descriptions, tokenizer, max_length):\n    directory = 'Flicker8k_Dataset'\n    while True:\n        for image_id in train:\n            image = train_features[image_id]\n            desc = descriptions[image_id]\n            in_img, in_seq, out_word = create_sequences_single(tokenizer, max_length, desc, image)\n            yield[[in_img, in_seq], out_word]\n# dev dataset\n\n# load test set\nfilename = 'Flickr8k_text/Flickr_8k.devImages.txt'\ntest = load_set(filename)\nprint('Dataset: %d' % len(test))\n# descriptions\ntest_descriptions = load_clean_descriptions('descriptions.txt', test)\nprint('Descriptions: test=%d' % len(test_descriptions))\n# photo features\ntest_features = load_photo_features('features.pkl', test)\nprint('Photos: test=%d' % len(test_features))\n# prepare sequences\nX1test, X2test, ytest = create_sequences(tokenizer, max_length, test_descriptions, test_features)\n\n# fit model\n\nmodel = define_model(vocab_size, max_length, 'model-ep010-loss3.900-val_loss3.950.h5')\n\nfilepath = 'model-ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5'\ncheckpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n\n# fit model\nmodel.fit_generator(data_generator(train_descriptions, tokenizer, max_length), epochs=80, steps_per_epoch=1200, callbacks=[checkpoint], validation_data=([X1test, X2test], ytest))\n\n\n\n\n\n\n\n", "repo_name": "tyge318/Keras-LSTM-Exercise", "sub_path": "Captions/caption_train.py", "file_name": "caption_train.py", "file_ext": "py", "file_size_in_byte": 6722, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pickle.load", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.preprocessing.text.Tokenizer", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.merge.add", "line_number": 116, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 117, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 118, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 122, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 184, "usage_type": "call"}]}
{"seq_id": "14194124988", "text": "import os\nfrom os.path import join as pjoin\nimport json\nfilePath = './static/ocr/'\nfor i,j,k in os.walk(filePath):\n    if i==\"./static/ocr/\" :\n        continue\n    else:\n        path1 = i\n        path2 = path1[12:]\n        path3 = path1+path2\n        jsonpath =path3+\".json\"\n        jpgpath = path3+\".jpg\"\n        fr = open(pjoin(jsonpath))\n        model=json.load(fr)\n        fr.close()\n        string = {\"jpgpath\":jpgpath}\n\n        for i in string:\n                model[i] = string[i]\n        jsObj = json.dumps(model)\n\n        with open(pjoin(jsonpath), \"w\") as fw:\n                fw.write(jsObj)\n                fw.close()", "repo_name": "jingyoushui/Search", "sub_path": "pdfprocess.py", "file_name": "pdfprocess.py", "file_ext": "py", "file_size_in_byte": 628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.walk", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}]}
{"seq_id": "34432829666", "text": "import h5py\nimport numpy as np\nimport sys\n\n#my own toolkit\nimport HiCutils\nimport utils\nimport convert\n\n### YOU ARE SUPPOSED TO ONLY MODIFY VALUE HERE ###\n#input file\nbedfilename='/users/invites/carron/Documents/Boost-HiC/test_dataset/mouse10kb_fend.bed'\nmatrixfilename='/users/invites/carron/Documents/Boost-HiC/test_dataset/chr16ES_10000.matrix'\nOperation='Sample'\nrepositoryout='/run/media/carron/0ac0fffa-3350-431d-b1d1-865f8a21db21/data/Hi-C/Mouse/boyan/test/'\n\n#default parameter\nresolution=10000 #default : 10kb\nachr=\"chr16\"\nalpha=0.2 #AFTER a lot of test : 0.24 is always a good and safe compromise, you must use this value\n###\n\n\ndef BoostHiC(amat):\n\tnormmat=HiCutils.SCN(np.copy(amat))\n\tFFmat=np.power(HiCutils.fastFloyd(1/np.power(normmat.copy(),alpha)),-1/alpha) #to dist, FF, to contact in one line\n\tboostedmat=HiCutils.adjustPdS(normmat,FFmat)\n\treturn boostedmat\n\ndef Sample(amat,repositoryout):\n\tpercentofsample=[0.1,1.,10.]\n\tfor j in percentofsample:\n\t\tprint(\"Value of sample\",j)\n\t\tchrmat_s=np.copy(amat)\n\t\tchrmat=HiCutils.downsample_basic(chrmat_s,j)\n\t\tfh5 = h5py.File(repositoryout+\"inputmat_sampleat_\"+str(j)+\"_percent.hdf5\", \"w\")\n\t\tfh5['data'] = chrmat\n\t\tfh5.close()\n\n\n\n### CODE EXECUTION ###\n\n# load the data\nprint(\"LOADING MATRIX\")\nD=convert.loadabsdatafile(bedfilename)\nbeginfend=D[achr][0]\nendfend=D[achr][1]\nprint(\"Data fend :\",beginfend,endfend)\nbasemat=convert.loadmatrixselected(matrixfilename,beginfend,endfend)\n\n#matrix filtering\nprint(\"FILTERING\")\npos_out=HiCutils.get_outliers(basemat)\nbasematfilter=basemat[np.ix_(~pos_out, ~pos_out)]\nbasematfilter=np.copy(basematfilter)\n#basematfilter=basematfilter[0:1000,0:1000]\nprint(len(basemat),len(basematfilter))\nfh5 = h5py.File(repositoryout+\"inputmat.hdf5\", \"w\")\nfh5['data'] = basemat\nfh5.close()\nfh5 = h5py.File(repositoryout+\"inputmat_filtered.hdf5\", \"w\")\nfh5['data']=basematfilter\nfh5.close()\nutils.savematrixasfilelist3(pos_out,repositoryout+\"filteredbin.txt\")\n\nif Operation==\"Boost\":\n\tprint(\"Boost Hic\")\n\tboosted=BoostHiC(basematfilter)\n\t#save\n\tfh5 = h5py.File(repositoryout+\"boostedmat.hdf5\", \"w\")\n\tfh5['data']=boosted\n\tfh5.close()\nelif Operation==\"Sample\":\n\tprint(\"SAMPLING\")\n\tSample(basematfilter,repositoryout)\n\n\n\n\n", "repo_name": "LeopoldC/Boost-HiC", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2200, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "46", "api": [{"api_name": "HiCutils.SCN", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 26, "usage_type": "call"}, {"api_name": "HiCutils.fastFloyd", "line_number": 26, "usage_type": "call"}, {"api_name": "HiCutils.adjustPdS", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 34, "usage_type": "call"}, {"api_name": "HiCutils.downsample_basic", "line_number": 35, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 36, "usage_type": "call"}, {"api_name": "convert.loadabsdatafile", "line_number": 46, "usage_type": "call"}, {"api_name": "convert.loadmatrixselected", "line_number": 50, "usage_type": "call"}, {"api_name": "HiCutils.get_outliers", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 56, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 59, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.savematrixasfilelist3", "line_number": 65, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 71, "usage_type": "call"}]}
{"seq_id": "70936434701", "text": "import sqlite3\nimport re\nimport datetime\nimport random\ndef login_server(username=None,password=None):\n   conn = sqlite3.connect('test.sqlite')\n   cursor = conn.cursor()\n   cursor.execute('select * from user where username=? and password=?',(username,password))\n   data=cursor.fetchall()\n   conn.close()\n   if data:\n       return data[0]\n   else:\n       return '账号或密码错误'\n\ndef register_server(**data):\n   conn = sqlite3.connect('test.sqlite')\n   cursor = conn.cursor()\n   reg= re.compile(r'^1[34578][0-9]{9}$')\n   if not reg.match(data['telephone'][0]):\n       return \"请填写正确的手机号码\"\n   try:\n       cursor.execute('insert into user (username,password,createtime,name,\\\n                      telephone,QQ,email)values(?,?,?,?,?,?,?)',\\\n                      (data['username'][0],data['password'][0],\\\n                       datetime.date.today(),data['name'][0],\\\n                       data['telephone'][0],data['QQ'][0],data['email'][0]))\n   except sqlite3.IntegrityError:\n       conn.close()\n       return \"用户名重复\"\n   conn.commit()\n   conn.close()\n   return \"注册成功\"\n\ndef pre_server(**data):\n   conn = sqlite3.connect('test.sqlite')\n   cursor = conn.cursor()\n   reg= re.compile(r'^1[34578][0-9]{9}$')\n   if not reg.match(data['telephone'][0]):\n       return \"请填写正确的手机号码\"\n   try:\n       cursor.execute('insert into pre (name,telephone,createtime,\\\n                      QQ,email)values(?,?,?,?,?)',\\\n                      (data['name'][0],data['telephone'][0],\\\n                       datetime.date.today(),data['QQ'][0],\\\n                       data['email'][0]))\n   except sqlite3.IntegrityError:\n       conn.close()\n       return \"手机号码重复\"\n   conn.commit()\n   conn.close()\n   return \"预报名成功\"\n\ndef score_server(user_id):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select real_score,virtual_score from user where id=?',(user_id,))\n    data=cursor.fetchall()\n    conn.close()\n    return data[0]\n\ndef update_user_question(**data):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('insert into user_questions (user_id,question_id,result,\\\n                choice) values(?,?,?,?)',\\\n                    (data['userId'][0],data['questionId'][0],\\\n                    data['answerResult'][0],data['userChoice'][0]))\n    conn.commit()\n    conn.close()\n\ndef get_user_question(user_id):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select question_id from user_questions where user_id=?',(user_id,))\n    done=[x[0] for x in cursor.fetchall()]\n    cursor.execute('select id from questions')\n    questions= [x[0] for x in cursor.fetchall()]\n    conn.close()\n    undone=[]\n    for q in questions:\n        if q not in done:\n            undone.append(q)\n    r=random.choice(undone)\n    return [r,get_question(r),len(undone)-1]\n\n\ndef get_question(question_id):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select title,A,B,C,D,explain,answer from questions where id=?',(question_id,))\n    data,=cursor.fetchall()\n    conn.close()\n    return data\n\ndef score_detail(user_id):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select createtime,name,telephone,status,id\\\n                   from re where user_id=?',(user_id,))\n    data=cursor.fetchall()\n    conn.close()\n    return data\n\ndef recommend_server(user_id,**data):\n   conn = sqlite3.connect('test.sqlite')\n   cursor = conn.cursor()\n   reg= re.compile(r'^1[34578][0-9]{9}$')\n   if not reg.match(data['re_telephone'][0]):\n       return \"请填写正确的手机号码\"\n   try:\n       cursor.execute('insert into re (createtime,name,telephone,email,\\\n                      user_id)values(?,?,?,?,?)',\\\n                      (datetime.date.today(),data['re_name'][0],data['re_telephone'][0],\\\n                       data['re_email'][0],user_id))\n   except sqlite3.IntegrityError:\n       conn.close()\n       return \"此人已被推荐\"\n   conn.commit()\n   cursor.execute('update user set virtual_score=virtual_score+150 where id=?',\\\n                  (user_id,))\n   conn.commit()\n   conn.close()\n   return \"恭喜获得150个推荐积分！\"\n\ndef auth_server(user_id):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select role from user where id=?',(user_id,))\n    data=cursor.fetchall()\n    conn.close()\n    return data[0]\n\ndef all_user_s():\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select createtime,name,telephone,real_score,\\\n                   virtual_score,id from user where role=0')\n    data=cursor.fetchall()\n    conn.close()\n    return data\n\ndef all_pre_s():\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select createtime,name,telephone,QQ,\\\n                   email from pre')\n    data=cursor.fetchall()\n    conn.close()\n    return data\n\ndef zero_server(user_id):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('update user set virtual_score=0 ,real_score=0 where id=?',\\\n                   (user_id,))\n    cursor.execute('delete from re where user_id=?',(user_id,))\n    conn.commit()\n    conn.close()\n\ndef get_user_by_id(user_id):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select name,telephone,QQ,email,virtual_score,\\\n                   real_score from user where id=?',\\\n                   (user_id,))\n    data=cursor.fetchall()\n    conn.close()\n    return data[0]\n\ndef get_userid_by_re(re_id):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select user_id from re where id=?',(re_id,))\n    data=cursor.fetchall()\n    conn.close()\n    return data[0]\n\ndef change_status_server(re_id,user_id,status,s):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('update re set status=? where id=?',(s,re_id))\n    if status==1:\n        cursor.execute('update user set virtual_score=virtual_score-50,\\\n                         real_score=real_score+50 where id=?',(user_id,))\n    if status==2:\n        cursor.execute('update user set virtual_score=virtual_score-100,\\\n                         real_score=real_score+100 where id=?',(user_id,))\n    conn.commit()\n    conn.close()\n\ndef search_users_by_phone(phone):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select createtime,name,telephone,id from user \\\n                    where telephone like ?',\\\n                   ('{}%'.format(phone),))\n    data=cursor.fetchall()\n    conn.close()\n    return data\n\ndef search_res_by_phone(phone):\n    conn = sqlite3.connect('test.sqlite')\n    cursor = conn.cursor()\n    cursor.execute('select createtime,name,telephone,status,id from re \\\n                    where telephone like ?',\\\n                   ('{}%'.format(phone),))\n    data=cursor.fetchall()\n    conn.close()\n    return data\n", "repo_name": "luozx207/gdyxscore", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 7065, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sqlite3.connect", "line_number": 6, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sqlite3.IntegrityError", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 36, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sqlite3.IntegrityError", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 63, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 73, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 84, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 97, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 106, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 114, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 114, "usage_type": "attribute"}, {"api_name": "sqlite3.IntegrityError", "line_number": 116, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 127, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 135, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 144, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 153, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 162, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 172, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 180, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 193, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 203, "usage_type": "call"}]}
{"seq_id": "15474204709", "text": "from msilib.schema import Error\nfrom typing import Dict\nfrom flask import Flask, request\nfrom flask_cors import CORS\nfrom ObjectNotExistException import ObjectNotExistException\nimport jwt_utils\nfrom question import Question\nfrom answer import Answer\nfrom dbhelper import DBHelper\nimport bcrypt\napp = Flask(__name__)\nCORS(app)\n\nusername_mdp = {\"admin\": \"Vive l'ESIEE !\"}\n\n###\n# AUTHENTICATION\n###\n\ndef check_token(token):\n    try:\n        token = token.split()\n        if token[0] == \"Bearer\":\n            token = token[1]\n        return token\n    except Exception as e:\n        return e\n\n@app.route('/login', methods=['POST'])\ndef login():\n    try:\n        payload = request.get_json()\n        username = payload[\"username\"]\n        \n        dbHelper = DBHelper()\n    \n        new_user = False\n\n        password_hash = dbHelper.get_player_password_hash(username)\n        if password_hash is None:\n            new_user = True\n        else:\n            password_hash = password_hash.encode()\n        password = payload[\"password\"]\n        \n        if new_user:\n            password_hash = bcrypt.hashpw(password.encode(), bcrypt.gensalt())\n            dbHelper.add_player(username, password_hash.decode())\n\n        if bcrypt.checkpw(password.encode(), password_hash):\n            token = jwt_utils.build_token(username)\n            return {\"token\": token}, 200\n    except Exception as e:\n        return '', 401\n    return '', 401\n\n@app.route('/is-logged/<username>', methods=['GET'])\ndef is_logged(username):\n    if username == 'null':\n        return {\"isLogged\": False}, 401\n    try:\n        token = request.headers.get('Authorization')\n        token = check_token(token)\n    except AttributeError as e:\n        return {\"isLogged\": False}, 401\n    try:\n        # check if the token is valid\n        if jwt_utils.decode_token(token) == username:\n            return {\"isLogged\": True}, 200\n        else:\n            return {\"isLogged\": False}, 401\n    except jwt_utils.JwtError as e:\n        return {\"isLogged\": False}, 401\n    except Exception as e:\n        return '', 401\n\n\n###\n# QUESTIONS\n###\n\n@app.route('/questions', methods=['POST'])\ndef add_question():\n    try:\n        token = request.headers.get('Authorization')\n        token = check_token(token)\n        # check if the token is valid\n        payload = request.get_json()\n        if jwt_utils.decode_token(token) == \"admin\":\n\n            question = Question(\n                payload['title'], payload['text'], payload['image'], payload['position'])\n\n            dbHelper = DBHelper()\n            dbHelper.insert_question(question, payload['possibleAnswers'])\n            return '', 200\n        else:\n            return '', 401\n    except jwt_utils.JwtError as e:\n        return e.message, 401\n\n@app.route('/questions/<position>', methods=['GET'])\ndef get_question(position):\n    try:\n        dbHelper = DBHelper()\n        question = dbHelper.get_question(position)\n\n        if question is None:\n            return '', 404\n\n        ret = question.convertToJson()\n        return ret, 200\n\n    except ObjectNotExistException as e_custom:\n        return e_custom.message, 404\n    except Exception as e_base:\n        return e_base.message, 404\n\n@app.route('/questions', methods=['GET'])\ndef get_questions():\n    try:\n        dbHelper = DBHelper()\n        questions = dbHelper.get_questions()\n\n        if questions is None:\n            return '', 404\n        return {\"questions\": questions}, 200\n\n    except ObjectNotExistException as e_custom:\n        return e_custom.message, 404\n    except Exception as e_base:\n        return e_base.message, 404\n\n@app.route('/questions/<position>', methods=['DELETE'])\ndef delete_quetion(position):\n\n    try:\n        token = request.headers.get('Authorization')\n        token = check_token(token)\n        if jwt_utils.decode_token(token) == \"admin\":\n\n            dbHelper = DBHelper()\n            dbHelper.delete_question(int(position))\n            return '', 204\n        else:\n            return '', 401\n    except jwt_utils.JwtError as e:\n        return e.message, 401\n    except ObjectNotExistException as e_custom:\n        return e_custom.message, 404\n    except Exception as e_base:\n        return e_base.message, 404\n\n@app.route('/questions/<position>', methods=['PUT'])\ndef update_question(position):\n    try:\n        token = request.headers.get('Authorization')\n        token = check_token(token)\n        # check if the token is valid\n        if jwt_utils.decode_token(token) == \"admin\":\n\n            payload = request.get_json()\n            new_position = int(payload['position'])\n            question = Question(\n                payload['title'], payload['text'], payload['image'], int(position))\n            dbHelper = DBHelper()\n\n            dbHelper.update_question(\n                new_position, question, payload['possibleAnswers'])\n\n            return '', 200\n        else:\n            return '', 401\n    except ObjectNotExistException as e_custom:\n        return e_custom.message, 404\n    except jwt_utils.JwtError as e:\n        return e.message, 401\n\n\n###\n# PARTICIPATIONS\n###\n\n@app.route('/participations', methods=['POST'])\ndef set_participation():\n    try:\n        token = request.headers.get('Authorization')\n        token = check_token(token)\n        payload = request.get_json()\n        username = payload['username']\n        answersId = payload['answers']\n        # check if the token is valid\n\n        if jwt_utils.decode_token(token) == username:\n\n            dbHelper = DBHelper()\n\n            question_count = dbHelper.get_question_count()\n\n            if (question_count != len(answersId)):\n                return \"Bad request\", 400\n\n            correct_participation = dbHelper.get_correct_participation()\n            if correct_participation is None:\n                return \"Bad request\", 400\n                \n            score = 0\n\n            for i in range(question_count):\n                if correct_participation[i] == answersId[i]:\n                    score += 1\n\n            dbHelper.set_score(username, score)\n\n            result = {\"username\": username, \"score\": score}\n\n            return result, 200\n        else:\n            return '', 401\n    except jwt_utils.JwtError as e:\n        return e.message, 401\n\n@app.route('/participations', methods=['DELETE'])\ndef delete_participations():\n\n    try:\n        token = request.headers.get('Authorization')\n        token = check_token(token)\n        if jwt_utils.decode_token(token) == \"admin\":\n\n            dbHelper = DBHelper()\n            dbHelper.delete_participations()\n            return 'ok deleted', 204\n        else:\n            return '', 401\n\n    except jwt_utils.JwtError as e:\n        return e.message, 401\n    except Exception as e:\n        return e.message, 401\n\n\n###\n# GET INFO\n###\n\n@app.route('/quiz-info', methods=['GET'])\ndef get_quiz_info():\n    try:\n        dbHelper = DBHelper()\n        scores = dbHelper.get_players_score()\n        numberQuestions = dbHelper.get_question_count()\n\n        return {\"size\": numberQuestions, \"scores\": scores}, 200\n    except Exception as e:\n        return e.message, 401\n\n@app.route('/questions-count', methods=['GET'])\ndef get_question_count():\n    try:\n        dbHelper = DBHelper()\n        count = dbHelper.get_question_count()\n        return {\"count\": count}, 200\n    except Exception as e:\n        return e.message, 401\n\n@app.route('/get-last-score/<username>', methods=['GET'])\ndef get_last_score(username):\n\n    try:\n        token = request.headers.get('Authorization')\n        token = check_token(token)\n        if jwt_utils.decode_token(token) == username:\n            dbHelper = DBHelper()\n            score = dbHelper.get_last_score(username)\n            if score == -1:\n                return '', 404\n            return {\"score\": score}, 200\n        else:\n            return '', 401\n    except jwt_utils.JwtError as e:\n        return e.message, 401\n    except Exception as e:\n        return e.message, 401\n\n@app.route('/get-best-score/<username>', methods=['GET'])\ndef get_best_score(username):\n    try:\n        token = request.headers.get('Authorization')\n        token = check_token(token)\n        if jwt_utils.decode_token(token) == username:\n            dbHelper = DBHelper()\n            score = dbHelper.get_best_score(username)\n            if score == -1:\n                return '', 404\n            return {\"score\": score}, 200\n        else:\n            return '', 401\n    except jwt_utils.JwtError as e:\n        return e.message, 401\n    except Exception as e:\n        return e.message, 401\n\n@app.route('/questions/<position>/answers', methods=['GET'])\ndef get_answer(position):\n    try:\n        dbHelper = DBHelper()\n        answers = dbHelper.get_answer(position)\n        if answers is None:\n            return '', 404\n            \n        for key in answers:\n            answers[key].pop('questionID', None)\n            answers[key].pop('isCorrect', None)\n        return {\"answers\", answers}, 200\n    except Exception as e:\n        return e.message, 401\n    \nif __name__ == \"__main__\":\n    app.run()\n", "repo_name": "Sangerwan/quiz-app", "sub_path": "quiz-api/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 9050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "dbhelper.DBHelper", "line_number": 35, "usage_type": "call"}, {"api_name": "bcrypt.hashpw", "line_number": 47, "usage_type": "call"}, {"api_name": "bcrypt.gensalt", "line_number": 47, "usage_type": "call"}, {"api_name": "bcrypt.checkpw", "line_number": 50, "usage_type": "call"}, {"api_name": "jwt_utils.build_token", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "jwt_utils.decode_token", "line_number": 68, "usage_type": "call"}, {"api_name": "jwt_utils.JwtError", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request.headers.get", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 88, "usage_type": "name"}, {"api_name": "jwt_utils.decode_token", "line_number": 89, "usage_type": "call"}, {"api_name": "question.Question", "line_number": 91, "usage_type": "call"}, {"api_name": "dbhelper.DBHelper", "line_number": 94, "usage_type": "call"}, {"api_name": "jwt_utils.JwtError", "line_number": 99, "usage_type": "attribute"}, {"api_name": "dbhelper.DBHelper", "line_number": 105, "usage_type": "call"}, {"api_name": "question.convertToJson", "line_number": 111, "usage_type": "call"}, {"api_name": "ObjectNotExistException.ObjectNotExistException", "line_number": 114, "usage_type": "name"}, {"api_name": "dbhelper.DBHelper", "line_number": 122, "usage_type": "call"}, {"api_name": "ObjectNotExistException.ObjectNotExistException", "line_number": 129, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 138, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 138, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 138, "usage_type": "name"}, {"api_name": "jwt_utils.decode_token", "line_number": 140, "usage_type": "call"}, {"api_name": "dbhelper.DBHelper", "line_number": 142, "usage_type": "call"}, {"api_name": "jwt_utils.JwtError", "line_number": 147, "usage_type": "attribute"}, {"api_name": "ObjectNotExistException.ObjectNotExistException", "line_number": 149, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 157, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 157, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 157, "usage_type": "name"}, {"api_name": "jwt_utils.decode_token", "line_number": 160, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 162, "usage_type": "name"}, {"api_name": "question.Question", "line_number": 164, "usage_type": "call"}, {"api_name": "dbhelper.DBHelper", "line_number": 166, "usage_type": "call"}, {"api_name": "ObjectNotExistException.ObjectNotExistException", "line_number": 174, "usage_type": "name"}, {"api_name": "jwt_utils.JwtError", "line_number": 176, "usage_type": "attribute"}, {"api_name": "flask.request.headers.get", "line_number": 187, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 187, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 187, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 189, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 189, "usage_type": "name"}, {"api_name": "jwt_utils.decode_token", "line_number": 194, "usage_type": "call"}, {"api_name": "dbhelper.DBHelper", "line_number": 196, "usage_type": "call"}, {"api_name": "jwt_utils.JwtError", "line_number": 220, "usage_type": "attribute"}, {"api_name": "flask.request.headers.get", "line_number": 227, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 227, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 227, "usage_type": "name"}, {"api_name": "jwt_utils.decode_token", "line_number": 229, "usage_type": "call"}, {"api_name": "dbhelper.DBHelper", "line_number": 231, "usage_type": "call"}, {"api_name": "jwt_utils.JwtError", "line_number": 237, "usage_type": "attribute"}, {"api_name": "dbhelper.DBHelper", "line_number": 250, "usage_type": "call"}, {"api_name": "dbhelper.DBHelper", "line_number": 261, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 271, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 271, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 271, "usage_type": "name"}, {"api_name": "jwt_utils.decode_token", "line_number": 273, "usage_type": "call"}, {"api_name": "dbhelper.DBHelper", "line_number": 274, "usage_type": "call"}, {"api_name": "jwt_utils.JwtError", "line_number": 281, "usage_type": "attribute"}, {"api_name": "flask.request.headers.get", "line_number": 289, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 289, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 289, "usage_type": "name"}, {"api_name": "jwt_utils.decode_token", "line_number": 291, "usage_type": "call"}, {"api_name": "dbhelper.DBHelper", "line_number": 292, "usage_type": "call"}, {"api_name": "jwt_utils.JwtError", "line_number": 299, "usage_type": "attribute"}, {"api_name": "dbhelper.DBHelper", "line_number": 307, "usage_type": "call"}]}
{"seq_id": "12561448500", "text": "#!/usr/bin/env python\n# encoding: utf-8\n\n# Python modules\nimport datetime\nimport time\nimport configparser as ConfigParser\nimport json\nimport uuid\nimport zmq\n\n# Added modules\nimport pymongo\nfrom pymongo import MongoClient\nfrom pymongo import errors as PyError\n# Own Modules\n\nfrom logger import logger\nlogger = logger('server_init', stream_level='INFO')\n\n\nclass InitProcess(object):\n\n    def __init__(self, config_file, restart):\n        \"\"\" \"\"\"\n        self.config_file = config_file\n        self.dbs_names = ['process_profile',\n                          'current_profile',\n                          'stored_profile',\n                          'process_link',\n                          'current_link',\n                          'stored_link',\n                          'change_link',\n                          'context_link',\n                          'context',\n                          'activity',\n                          'process_tweet',\n                          'tweet_info',\n                          'stored_tweet',\n                          'rand_lvl2',\n                          'rand_lvl3',\n                          'full_link']\n\n        self.restart = restart.lower()\n\n    def init_values(self):\n        \"\"\" \"\"\"\n        self.read_config(self.config_file)\n        self.dbs = self.build_dbs(self.address_db, self.db_name)\n        logger.info(self.dbs)\n\n        self.loop_interval = int(self.loop_interval)\n        self.time_lvl2 = self.get_time_lvl2()\n        if self.restart == 'false':\n            self.build_index(**self.dbs)\n            self.twitter_key = self.get_keys(self.twitter_file)\n            self.client_id = self.get_clients()\n            lvl1_list = self.get_lvl1(self.lvl1_file)\n            self.set_lvl1 = set([int(i) for i in lvl1_list])\n            self.loop_number = 1\n            self.nbr_client = set()\n            self.record_lvl1(lvl1_list)\n            # self.max_limit = Limit(self.loop_interval, len(self.client_id)).calculing_limit()\n            self.start_time = datetime.datetime.now()\n            # self.write_start()\n            # self.stop_time = self.get_stop_time()\n            # These values are set up in the config file\n        elif self.restart == 'true':\n            self.client_id = self.get_clients()\n            lvl1_list = self.get_lvl1(self.lvl1_file)\n            self.set_lvl1 = set([int(i) for i in lvl1_list])\n            self.get_restart()\n\n        else:\n            raise('Need to enter \"true\" or \"false\"')\n\n        return {'loop_interval': self.loop_interval,\n                'databases': self.dbs,\n                'time_lvl2': self.time_lvl2,\n                'set_lvl1': self.set_lvl1,\n                'loop_number': self.loop_number,\n                'nbr_client': self.client_id}\n\n    def get_restart(self):\n        \"\"\" function to get the values from the db to restart \"\"\"\n        # self.nbr_client = set()\n        # self.set_lvl1 = set()\n        loops = set()\n        logger.info('Doing Profile for loop')\n        for profile in self.dbs['process_profile'].find({}, {'loop_number': True, '_id': False}):\n            # self.nbr_client.add(profile.pop('profile_client_id', None))\n            loop_number = profile['loop_number']\n            if loop_number:\n                loops.add(loop_number)\n            if len(loops) == 2:\n                logger.info('Get two loops from profile: {}'.format(loops))\n                break\n        logger.info('Doing Links for loop')\n        for links in self.dbs['process_link'].find({}, {'loop_number': True, '_id': False}):\n            # self.nbr_client.add(profile.pop('profile_client_id', None))\n            loop_number = links['loop_number']\n            if loop_number:\n                loops.add(loop_number)\n            if len(loops) == 2:\n                logger.info('Get two loops from links: {}'.format(loops))\n                break\n        logger.info('Doing rand_3 for loop')\n        for links in self.dbs['rand_lvl3'].find({}, {'loop_number': True, '_id': False}):\n            # self.nbr_client.add(profile.pop('profile_client_id', None))\n            loop_number = links['loop_number'] - 1\n            if loop_number:\n                loops.add(loop_number)\n            if len(loops) == 2:\n                logger.info('Get two loops from rand_lvl3: {}'.format(loops))\n                break\n        logger.info('Doing rand_2 for loop')\n        for links in self.dbs['rand_lvl2'].find({}, {'loop_number': True, '_id': False}):\n            # self.nbr_client.add(profile.pop('profile_client_id', None))\n            loop_number = links['loop_number'] - 1\n            if loop_number:\n                loops.add(loop_number)\n            if len(loops) == 2:\n                logger.info('Get two loops from rand_lvl2: {}'.format(loops))\n                break\n                # self.set_lvl1.add(profile.pop('id_str', None))\n        self.loop_number = min(int(s) for s in loops)\n        logger.info('loop_number: {}'.format(self.loop_number))\n\n    def write_start(self):\n        \"\"\" \"\"\"\n        json.dump({'loop_interval': self.loop_interval,\n                   'client_id': self.client_id,\n                   'time_lvl2': self.time_lvl2,\n                   'databases': str(self.dbs),\n                   'start_time': str(self.start_time),\n                   'lvl1': self.set_lvl1}, open('start_params.txt', 'w'))\n\n    def read_config(self, config_file):\n        Config = ConfigParser.ConfigParser()\n        Config.read(config_file)\n        for section in Config.sections():\n            for option in Config.options(section):\n                setattr(self, option, Config.get(section, option))\n\n    def build_dbs(self, address, db_name):\n        \"\" \"\"\n        if address is None:\n            c = MongoClient()\n        else:\n            c = MongoClient(address)\n        db = c[db_name]\n        return {k: db[k] for k in self.dbs_names}\n\n    def build_index(self, **kwargs):\n        \"\"\" \"\"\"\n        try:\n            kwargs['process_profile'].create_index([('id_str', pymongo.DESCENDING),\n                                                    ('loop_number', pymongo.ASCENDING)],\n                                                   unique=True)\n            kwargs['process_profile'].create_index('extra')\n            kwargs['process_profile'].create_index('doing')\n\n            kwargs['stored_profile'].create_index('id_str', unique=True)\n\n            kwargs['process_link'].create_index([('id_str', pymongo.ASCENDING),\n                                                ('type_link', pymongo.DESCENDING)],\n                                                unique=True)\n            kwargs['full_link'].create_index([('id_str', pymongo.ASCENDING),\n                                              ('type_link', pymongo.DESCENDING),\n                                              ('loop_number', pymongo.ASCENDING)],\n                                             unique=True)\n\n            kwargs['process_tweet'].create_index('id_str', unique=True)\n            kwargs['process_tweet'].create_index('processing')\n\n            kwargs['stored_link'].create_index('type_link')\n            kwargs['stored_link'].create_index('id_str')\n\n            kwargs['context_link'].create_index('loop_number')\n            kwargs['context_link'].create_index('loop_number')\n\n            kwargs['activity'].create_index('id_str')\n            kwargs['activity'].create_index('loop_number')\n\n            kwargs['context'].create_index('id_str')\n            kwargs['stored_tweet'].create_index('id_str', unique=True)\n            kwargs['tweet_info'].create_index('id_str', unique=True)\n            kwargs['rand_lvl2'].create_index('id_str', unique=True)\n            kwargs['rand_lvl3'].create_index('id_str', unique=True)\n\n        except PyError.ServerSelectionTimeoutError:\n            raise('Error in DBS connection, check if MongoDB is alive')\n\n    def get_keys(self, twitter_file):\n        \"\"\" \"\"\"\n        keydict = {}\n        with open(twitter_file, 'r') as f:\n            for line in f:\n                key, val = line.split(':')\n                keydict[key] = val[:-1]\n        return keydict\n\n    def get_clients(self):\n        \"\"\" \"\"\"\n        client_id = list()\n\n        context_status = zmq.Context()\n        timeout_start = time.time()\n\n        status_sock = context_status.socket(zmq.REP)\n        status_sock.setsockopt(zmq.RCVTIMEO, 2000)\n        # status_sock.setsockopt(zmq.RCVTIMEO, 2000)\n        status_sock.bind(\"tcp://0.0.0.0:{}\".format(self.status_port))\n        while time.time() < (timeout_start + 10):\n            try:\n                client_to_connect = status_sock.recv()\n                if client_to_connect.decode() == 'id_request':\n                    _id = str(uuid.uuid4())\n                    data = {'client_id': _id, 'dbs_names': self.dbs_names}\n                    status_sock.send_json(data)\n                    client_id.append(_id)\n                else:\n                    status_sock.send('too early'.encode('utf-8'))\n            except zmq.error.Again:\n                pass\n        # time.sleep(2)\n\n        logger.info('Get {} clients'.format(len(client_id)))\n        logger.info('Close the socket')\n        status_sock.close()\n        context_status.term()\n        logger.info('Socket closed')\n        return client_id\n\n    def get_time_lvl2(self):\n        \"\"\"docstring for time_restrain\"\"\"\n        try:\n            if int(self.time_lvl2) != 1:\n                if int(self.time_lvl2) < int(self.loop_interval):\n                    value = int(self.loop_interval) *4\n                else:\n                    value = int(self.loop_interval)* int(self.time_lvl2)\n            else:\n                value = int(self.time_lvl2)\n        except ValueError:  # In case of None or string or anything\n                            # not a number\n            value = int(self.loop_interval)* 4\n        return value\n\n    def get_lvl1(self, lvl1_file):\n        logger.info('Getting the lvl1 file - {}'.format(lvl1_file))\n        with open(lvl1_file, 'r') as f:\n            return [line[:-1] for line in f]\n\n    def record_lvl1(self, lvl1_list):\n        \"\"\" \"\"\"\n        for user in lvl1_list:\n            info_user = dict()\n            info_user['id_str'] = int(user)\n            info_user['loop_number'] = 1\n\n            self.dbs['process_profile'].insert_one(info_user)\n            self.dbs['process_tweet'].insert_one(info_user)\n            info_user['type_link'] = 'followers'\n            self.dbs['process_link'].insert_one(info_user)\n            info_user['type_link'] = 'friends'\n            try:\n                del info_user['_id']\n            except KeyError:\n                pass\n            self.dbs['process_link'].insert_one(info_user)\n\n\ndef main():\n    pass\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "Oliph/PhD-WebScience", "sub_path": "server_init.py", "file_name": "server_init.py", "file_ext": "py", "file_size_in_byte": 10699, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "logger.logger", "line_number": 19, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 50, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 50, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 64, "usage_type": "attribute"}, {"api_name": "logger.logger.info", "line_number": 89, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 89, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 96, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 96, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 98, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 98, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 105, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 105, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 107, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 107, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 114, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 114, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 116, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 116, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 123, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 123, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 127, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 127, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 131, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 139, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 148, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 150, "usage_type": "call"}, {"api_name": "pymongo.DESCENDING", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pymongo.ASCENDING", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pymongo.ASCENDING", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pymongo.DESCENDING", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pymongo.ASCENDING", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pymongo.DESCENDING", "line_number": 169, "usage_type": "attribute"}, {"api_name": "pymongo.ASCENDING", "line_number": 170, "usage_type": "attribute"}, {"api_name": "pymongo.errors.ServerSelectionTimeoutError", "line_number": 191, "usage_type": "attribute"}, {"api_name": "pymongo.errors", "line_number": 191, "usage_type": "name"}, {"api_name": "zmq.Context", "line_number": 207, "usage_type": "call"}, {"api_name": "time.time", "line_number": 208, "usage_type": "call"}, {"api_name": "zmq.REP", "line_number": 210, "usage_type": "attribute"}, {"api_name": "zmq.RCVTIMEO", "line_number": 211, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 214, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 218, "usage_type": "call"}, {"api_name": "zmq.error", "line_number": 224, "usage_type": "attribute"}, {"api_name": "logger.logger.info", "line_number": 228, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 228, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 229, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 229, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 232, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 232, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 251, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 251, "usage_type": "name"}]}
{"seq_id": "11303693181", "text": "import os, sys\nimport logging\nimport multiprocessing as mp\n\nfrom argparse import ArgumentParser\nfrom collections import OrderedDict\n\nsys.path.insert(0, os.path.dirname(os.getcwd()))\nfrom Core.CoreSystem import  Helper\n\n\nclass clsParameters():\n\n    def __init__(self, options):\n        self.strUser    = options.user_name\n        self.strProject = options.project_name.replace('.txt', '') ## A user can be confused the input. So I prevented from it using 'replace'.\n        self.strGroup   = options.group\n        self.intCore    = options.thread\n\n        self.strSampleList = 'User/{user}/{project}.txt'.format(user=options.user_name, project=options.project_name)\n\n\ndef SummaryRandomBarcode(sFile_path):\n\n    \"\"\"\n    /Tmp\n    190819_Nahye_24k_2_D0_2-24kLib_Classified_Indel_barcode.fastq* -> process target\n    190819_Nahye_24k_2_D0_2-24kLib_Indel_freq.txt*\n    190819_Nahye_24k_2_D0_2-24kLib_Indel_summary.txt*\n    190819_Nahye_24k_2_D0_2-24kLib_Summary.txt*\n    Pickle\n\n    dBarcode_cnt = {'ACGTACTC_sorting_barcode': {'ACATACAC_random': 5, 'CGTGTTGA_random': 3, ...}\n    \"\"\"\n    dictBarcodeCnt = {}\n    strClassCheck  = ''\n\n    strSample = sFile_path.split('/')[-1]\n    logging.info('Summary_random_barcode start : %s, %s' % (sFile_path, strSample))\n\n    for sFile in os.listdir(sFile_path+'/Tmp/'):\n        if '.fastq' in sFile:\n            with open(sFile_path+'/Tmp/'+sFile) as Input:\n                for i, strRow in enumerate(Input):\n\n                    # @D00235:683:CE1P6ANXX:6:1114:2135:5231 1:N:0:CTGAAGCT+CCTATCCT:Barcode_TTTGCTATCTCGACGTATGGACAGTG:total\n                    if i % 4 == 0:\n                        listBarClass = strRow.replace('\\n','').split('Barcode_')[1].split(':')\n                        strBarcode   = listBarClass[0]\n                        strClass     = listBarClass[1]\n\n                        if strClass == 'total':\n                            strClassCheck = 'total'\n\n                    if i % 4 == 1 and strClassCheck == 'total':\n                        strRow = strRow.replace('\\n','').upper()\n                        intBarcodeStart   = strRow.find(strBarcode)\n                        strRandom_barcode = strRow[intBarcodeStart-8:intBarcodeStart]\n\n                        try:\n                            _ = dictBarcodeCnt[strBarcode]\n                        except KeyError:\n                            dictBarcodeCnt[strBarcode] = {}\n                        try:\n                            dictBarcodeCnt[strBarcode][strRandom_barcode] += 1\n                        except KeyError:\n                            dictBarcodeCnt[strBarcode][strRandom_barcode] = 1\n                        #print(sBarcode, sRandom_barcode, iBarcode_start, sRow)\n\n                        strClassCheck = ''\n\n    if not os.path.isdir(sFile_path + '/Summary_Random_barcode'): os.mkdir(sFile_path + '/Summary_Random_barcode')\n    with open(sFile_path + '/Summary_Random_barcode/%s_all_random_barcode.txt' % strSample, 'w') as All_random,\\\n        open(sFile_path + '/Summary_Random_barcode/%s_Unique_RandomBarcodeNumber_In_SortingBarcode.txt' % strSample, 'w') as Random_sorting:\n\n        All_random.write('Sorting_barcode\\tUnique_RandomBarcodeNumber_In_SortingBarcode\\tRandomBarcode\\tEach_RandomBarcode_read_count\\n')\n        Random_sorting.write('Sorting_barcode\\tUnique_RandomBarcodeNumber_In_SortingBarcode\\n')\n\n        for sBarcode, dRandom_barcode_cnt in dictBarcodeCnt.items():\n            iRandom_barcode_num = len(dRandom_barcode_cnt.keys())\n            Random_sorting.write('\\t'.join(map(str, [sBarcode, iRandom_barcode_num]))+'\\n')\n\n            for sRandom_barcode, iCnt in dRandom_barcode_cnt.items():\n                All_random.write('\\t'.join(map(str, [sBarcode, iRandom_barcode_num, sRandom_barcode, iCnt]))+'\\n')\n\n    logging.info('Summary_random_barcode end: %s' % sFile_path)\n\n## on going\ndef CountGroup(InstParameters):\n    \"\"\"\n    Sorting_barcode Unique_RandomBarcodeNumber_In_SortingBarcode    RandomBarcode   Each_RandomBarcode_read_count\n    TATATCATAGCGTACTCATC    8       TGCGTTTG        3\n    TATATCATAGCGTACTCATC    8       CGCGTTTG        3\n    TATATCATAGCGTACTCATC    8       TAGTTTTG        1\n    TATATCATAGCGTACTCATC    8       ATAGTTTG        1\n    \"\"\"\n\n    sHeader = ''\n\n    with open(InstParameters.strSampleList) as Sample: ## tmp input\n\n        listSample = Sample.readlines()\n\n        setGroup = set([strRow.replace('\\n', '').split('\\t')[2].upper() for strRow in listSample])\n\n        for strGroup in setGroup:\n            if strGroup == 'CTRL': continue\n\n            for strRow in listSample:\n                if strGroup == strGroupOfSample:  ## matched group names -> Sum the counts\n                    listCol          = strRow.replace('\\n', '').split('\\t')\n                    strSample        = listCol[0]\n                    strRef           = listCol[1]\n                    strGroupOfSample = listCol[2]\n\n                    strProjectDir = './Output/{user}/{project}'.format(user=InstParameters.strUser,\n                                                                       project=InstParameters.strProject)\n                    strGroupDir = os.path.join(strProjectDir, 'Group_result')\n                    Helper.MakeFolderIfNot(strGroupDir)\n\n                    dTotal_RandomBarcode_cnt_in_SortingBarcode = OrderedDict() ## ('GECKO_6367_GATCTGCTC', ['GECKO_6367', 'GATCTGCTC', 2, 156, '0.0128']),\n                                                                               ## Unique key, only one list.\n\n                    with open('{project_dir}/{sample}_all_random_barcode.txt'.format(project_dir=strProjectDir,\n                                                                                     sample=strSample)) as RandomBarcode_SeqFreq:\n                        sHeader = RandomBarcode_SeqFreq.readline()\n\n                        for sRow in RandomBarcode_SeqFreq:\n                            lCol = sRow.replace('\\n', '').split('\\t')\n\n                            sSortingBarcode                             = lCol[0]\n                            #iTotal_RandomBarcode_cnt_in_SortingBarcode  = int(lCol[1])\n                            sSorting_and_Random_barcode_seq             = lCol[0] + '_' + lCol[2]  ## Unique name : Doench2014_1000_CTCTGGGGT\n                            iRandomBarcode_count                        = int(lCol[3])\n\n                            lCol[3] = iRandomBarcode_count\n\n                            try:\n                                _ = dTotal_RandomBarcode_cnt_in_SortingBarcode[sSorting_and_Random_barcode_seq]\n\n                                dTotal_RandomBarcode_cnt_in_SortingBarcode[sSorting_and_Random_barcode_seq][3] += iRandomBarcode_count\n\n                            except KeyError:\n                                dTotal_RandomBarcode_cnt_in_SortingBarcode[sSorting_and_Random_barcode_seq] = lCol  ## initial assignment\n                    #END for\n                    dRecal_total_kind_of_RandomBarcode = OrderedDict()\n                    for sSort_Rand_seq in dTotal_RandomBarcode_cnt_in_SortingBarcode:  ## sSorting_and_Random_barcode_seq\n                        sSortBarcode = sSort_Rand_seq.split('_')[0]\n                        try:\n                            dRecal_total_kind_of_RandomBarcode[sSortBarcode].append(dTotal_RandomBarcode_cnt_in_SortingBarcode[sSort_Rand_seq])\n                        except KeyError:\n                            dRecal_total_kind_of_RandomBarcode[sSortBarcode] = [dTotal_RandomBarcode_cnt_in_SortingBarcode[sSort_Rand_seq]]\n\n                    for sKey, llValue in dRecal_total_kind_of_RandomBarcode.items():\n                        ## sKey: TATATCATAGCGTACTCATC, llValue : [[TATATCATAGCGTACTCATC, 8, TGCGTTTG, 3],[],[] ...\n                        iKind_of_RandomBarcode = len(llValue)  ################## why do I make like this ?????\n                        for lValue in llValue:\n                            lValue[1] = iKind_of_RandomBarcode ## Recal using group total cnt.\n\n                        llValue = sorted(llValue, key=lambda x:x[3], reverse=True)\n                        dRecal_total_kind_of_RandomBarcode[sKey] = llValue\n\n                    strEachGroup = './Output/Group_result/%s' % strGroup\n                    Helper.MakeFolderIfNot(strEachGroup)\n\n                    with open(os.path.join(strEachGroup, 'Summary_all_random_barcode_in_group.txt'), 'w') as Sort_Random_cnt,\\\n                        open(os.path.join(strEachGroup, 'Summary_Unique_RandomBarcodeNumber_in_group.txt'), 'w') as Uniq_random_cnt:\n\n                        Sort_Random_cnt.write(sHeader)\n                        Uniq_random_cnt.write('Sorting_barcode\\tUnique_RandomBarcodeNumber_In_SortingBarcode\\n')\n\n                        for sSortBarcode, llCol in dRecal_total_kind_of_RandomBarcode.items():\n                            Uniq_random_cnt.write('\\t'.join(map(str, [sSortBarcode, len(llCol)]))+'\\n')\n                            for lCol in llCol:\n                                Sort_Random_cnt.write('\\t'.join(map(str, lCol))+'\\n')\n        #END: for\n    #END: with\n\n\ndef Main():\n\n    logging.info('Program Start')\n    logging.info('Make commands for a multiple processing')\n\n    parser = ArgumentParser(description='Script for counting the random barcodes')\n\n    parser.add_argument('-u', '--user_name', type=str, dest='user_name', help='The user name in the /user subdir')\n    parser.add_argument('-p', '--project_name', type=str, dest='project_name', help='The project name in the /user/user_name/ subdir')\n    parser.add_argument('-g', '--group',  type=str, dest='group', default='false', help='The group sum run of the barcodes, default: false')\n    parser.add_argument('-t', '--thread', type=int, dest='thread', default='15', help='The multicore number 1~15')\n    options = parser.parse_args()\n\n    InstParameters = clsParameters(options)\n\n    lPara = []\n\n    with open(InstParameters.strSampleList) as SampleList:\n\n        for strSample in SampleList:\n            if strSample[0] == '#' or strSample[0] in ['', ' ', '\\r', '\\n', '\\r\\n']: continue\n            strSample  = strSample.replace('\\n', '').replace('\\r', '').split('\\t')[0]\n            sFile_path = './Output/{user}/{project}/{sample}'.format(user=options.user_name,\n                                                                     project=options.project_name,\n                                                                     sample=strSample)\n            #print('sFile_path', sFile_path)\n            lPara.append(sFile_path)\n\n    ## single_test\n    #Summary_random_barcode(lPara[0])\n\n    logging.info('Multiple processing Start')\n    p = mp.Pool(options.thread)\n    p.map_async(SummaryRandomBarcode, lPara).get()\n    logging.info('Multiple processing End')\n\n    #logging.info('Count group Start')\n    #CountGroup(InstParameters)\n    #logging.info('Count group End')\n\n    #logging.info('Program End')\n\nMain()\n", "repo_name": "CRISPRJWCHOI/CRISPR_toolkit", "sub_path": "Indel_searcher_2/Summary_Random_barcode.py", "file_name": "Summary_Random_barcode.py", "file_ext": "py", "file_size_in_byte": 10848, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sys.path.insert", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 39, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 72, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "Core.CoreSystem.Helper.MakeFolderIfNot", "line_number": 119, "usage_type": "call"}, {"api_name": "Core.CoreSystem.Helper", "line_number": 119, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 121, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 146, "usage_type": "call"}, {"api_name": "Core.CoreSystem.Helper.MakeFolderIfNot", "line_number": 164, "usage_type": "call"}, {"api_name": "Core.CoreSystem.Helper", "line_number": 164, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 182, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 183, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 185, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 211, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 212, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 214, "usage_type": "call"}]}
{"seq_id": "23472306307", "text": "import logging\nlogger = logging.getLogger(__name__)\n\nimport os\nimport re\nimport yaml\nimport json\nfrom comment_parser import comment_parser\n\n\nclass Parser():\n    def __init__(self, threatmodel):\n\n        self.threatmodel = threatmodel\n\n        self.action_table = {}\n        self.action_table[\"mitigate\"] = self.threatmodel.add_mitigation\n        self.action_table[\"accept\"] = self.threatmodel.add_acceptance\n        self.action_table[\"transfer\"] = self.threatmodel.add_transfer\n        self.action_table[\"expose\"] = self.threatmodel.add_exposure\n        self.action_table[\"connect\"] = self.threatmodel.add_connection\n        self.action_table[\"review\"] = self.threatmodel.add_review\n        self.action_table[\"test\"] = self.threatmodel.add_test\n        self.action_table[\"threat\"] = self.threatmodel.add_threat\n        self.action_table[\"control\"] = self.threatmodel.add_control\n        self.action_table[\"component\"] = self.threatmodel.add_component\n\n        self.patterns = {}\n        self.patterns[\"mitigate\"] = r'@mitigates? (?P<component>.*?) against (?P<threat>.*?) with (?P<control>.*)'\n        self.patterns[\"accept\"] = r'@accepts? (?P<threat>.*?) to (?P<component>.*?) with (?P<details>.*)'\n        self.patterns[\"transfer\"] = r'@transfers? (?P<threat>.*?) from (?P<source_component>.*?) to (?P<destination_component>.*?) with (?P<details>.*)'\n        self.patterns[\"expose\"] = r'@exposes? (?P<component>.*?) to (?P<threat>.*?) with (?P<details>.*)'\n        self.patterns[\"connect\"] = r'@connects? (?P<source_component>.*?) (?P<direction>with|to) (?P<destination_component>.*?) with (?P<details>.*)'\n        self.patterns[\"review\"] = r'@reviews? (?P<component>.*?) (?P<details>.*)'\n        self.patterns[\"test\"] = r'@tests? (?P<control>.*?) for (?P<component>.*)'\n\n        self.patterns[\"threat\"] = r'@threat (?P<threat>.*)'\n        self.patterns[\"control\"] = r'@control (?P<control>.*)'\n        self.patterns[\"component\"] = r'@component (?P<component>.*)'\n\n        self.cwd = os.getcwd()\n\n    def run_action(self, data, source):\n        action = data.pop(\"action\")\n        self.action_table[action](data, source=source)\n\n    def is_extended(self, line):\n        return line[-1] == \":\"\n\n    def is_threatspec_line(self, line):\n        for key in self.patterns.keys():\n            if \"@{}\".format(key) in line:\n                return True\n        return False\n\n    def check_file(self, filename):\n        logger.debug(\"Parsing file {}\".format(filename))\n        if filename.startswith(self.cwd):\n            return filename.replace(self.cwd, \"\", 1).lstrip(os.path.sep)\n        return filename\n\n\nclass CommentParser(Parser):\n    def __init__(self, threatmodel, mime=None):\n        super().__init__(threatmodel)\n        self.mime = mime\n\n    def parse_comment(self, comment):\n        annotations = []\n\n        LINE = 0\n        EXTENDED = 1\n\n        state = LINE\n        extended_lines = []\n        data = None\n\n        line_number = 1\n\n        for line in comment.split(\"\\n\"):\n            stripped_line = self.strip(line)\n            if state == LINE:\n                for action in self.patterns.keys():\n                    if stripped_line.startswith(\"@\" + action):\n                        data = {\"action\": action, \"line\": line_number, \"annotation\": stripped_line}\n                        extended_lines = []\n                        pattern = self.patterns[action]\n                        if self.is_extended(stripped_line):\n                            state = EXTENDED\n                            stripped_line = stripped_line[0:-1]\n                        m = re.match(pattern, stripped_line, re.M | re.I)\n                        if m:\n                            data.update(m.groupdict())\n                            if state == LINE:\n                                annotations.append(data)\n                        else:\n                            raise Exception(\"Could not parse {} pattern:\\n{} for comment line:\\n{}\".format(action, pattern, line))\n\n            elif state == EXTENDED:\n                if stripped_line == \"\":\n                    state = LINE\n                    self.process_extended_lines(extended_lines, data, annotations)\n                    extended_lines = []\n                else:\n                    extended_lines.append(self.strip_stars(line))\n\n            line_number += 1\n\n        if len(extended_lines) > 0:\n            self.process_extended_lines(extended_lines, data, annotations)\n\n        return annotations\n\n    def process_extended_lines(self, extended_lines, data, annotations):\n        extended_text = \"\\n\".join(extended_lines)\n        data[\"annotation\"] += \"\\n\" + extended_text\n        data.update(yaml.load(extended_text, Loader=yaml.SafeLoader))\n        annotations.append(data)\n\n    def strip(self, line):\n        return self.strip_stars(line).strip()\n\n    def strip_stars(self, line):\n        if self.mime not in [\"text/html\", \"text/x-shellscript\", \"text/xml\"]:\n            return re.sub(r\"\\s*\\*+\", \"\", line)\n        return line\n\n      \nclass TextFileParser(CommentParser):\n    def parse_file(self, filename):\n        filename = self.check_file(filename)\n\n        with open(filename) as fh:\n            data = fh.read()\n\n        source = {\n            \"filename\": filename,\n            \"code\": \"\"\n        }\n\n        for data in self.parse_comment(data):\n            source[\"annotation\"] = data.pop(\"annotation\")\n            source[\"line\"] = data.pop(\"line\")\n            self.run_action(data, source)\n\n\nclass SourceFileParser(CommentParser):\n\n    def __init__(self, threatmodel, mime=None):\n        super().__init__(threatmodel)\n        self.mime = mime\n\n    def extract_comment_context(self, lines, commented_lines, start_line, num_lines, multiline=False):\n        count = 0\n        i = start_line\n        code = []\n\n        capture_first_line = not multiline\n\n        for line in lines[start_line - 1:]:\n            if count >= num_lines:\n                return \"\".join(code)\n\n            if capture_first_line:\n                code.append(line)\n                capture_first_line = False\n\n            if i not in commented_lines:\n                code.append(line)\n                count += 1\n            i += 1\n        return \"\".join(code)\n\n    def get_lines(self, filename):\n        try:\n            with open(filename) as fh:\n                return fh.readlines()\n        except UnicodeDecodeError:\n            return None\n\n    def parse_file(self, filename):\n        logger.debug(\"Parsing file {}\".format(filename))\n\n        lines = self.get_lines(filename)\n        if not lines:\n            return\n\n        commented_line_numbers = []\n        comments = []\n        try:\n            for comment in comment_parser.extract_comments(filename, self.mime):\n                comment_text = comment.text()\n                comment_line = comment.line_number()\n                if comment.is_multiline():\n                    offset = len(comment_text.split(\"\\n\"))\n                    commented_line_numbers += range(comment_line, comment_line + offset)\n                else:\n                    offset = 0\n                    commented_line_numbers.append(comment_line)\n                comments.append({\n                    \"text\": comment_text,\n                    \"line\": comment_line,\n                    \"offset\": offset,\n                    \"multiline\": comment.is_multiline()\n                })\n        except comment_parser.UnsupportedError as e:\n            print(e)\n            return\n\n        for comment in comments:\n            comment[\"text\"] = comment[\"text\"].strip()\n            num_lines = 5  # Get 5 lines of code\n            code = self.extract_comment_context(lines, commented_line_numbers, comment[\"line\"] + comment[\"offset\"], num_lines, comment[\"multiline\"])\n\n            source = {\n                \"code\": code,\n                \"filename\": filename\n            }\n\n            annotations = self.parse_comment(comment[\"text\"])\n            if annotations:\n                for data in annotations:\n                    source[\"line\"] = data.pop(\"line\")\n                    source[\"annotation\"] = data.pop(\"annotation\")\n                    self.run_action(data, source)\n\n\nclass YamlFileParser(Parser):\n    def parse_annotation(self, annotation, data={}):\n        stripped_line = annotation.strip()\n        for action in self.patterns.keys():\n            if stripped_line.startswith(\"@\" + action):\n                data[\"action\"] = action\n                pattern = self.patterns[action]\n                m = re.match(pattern, stripped_line, re.M | re.I)\n                if m:\n                    data.update(m.groupdict())\n                    return data\n                else:\n                    raise Exception(\"Could not parse {} pattern:\\n{} for comment line:\\n{}\".format(action, pattern, stripped_line))\n\n    def parse_key(self, data, parent, filename):\n        if isinstance(data, str):\n            annotation = self.parse_annotation(data)\n            source = {\n                \"annotation\": data,\n                \"code\": json.dumps(parent, indent=2),\n                \"filename\": filename,\n                \"line\": 0\n            }\n            self.run_action(annotation, source)\n        elif isinstance(data, list):\n            for v in data:\n                if not isinstance(v, str):\n                    raise Exception(\"Invalid value type for x-threatspec list in {}\".format(filename))\n                annotation = self.parse_annotation(v)\n                source = {\n                    \"annotation\": v,\n                    \"code\": json.dumps(parent, indent=2),\n                    \"filename\": filename,\n                    \"line\": 0\n                }\n                self.run_action(annotation, source)\n        elif isinstance(data, dict):\n            for k, v in data.items():\n                annotation = self.parse_annotation(k, v)\n                source = {\n                    \"annotation\": k,\n                    \"code\": json.dumps(parent, indent=2),\n                    \"filename\": filename,\n                    \"line\": 0\n                }\n                self.run_action(annotation, source)\n\n    def parse_data(self, data, parent, filename):\n        if isinstance(data, dict):\n            for k, v in data.items():\n                if k == \"x-threatspec\":\n                    self.parse_key(v, data, filename)\n                else:\n                    self.parse_data(v, data, filename)\n        elif isinstance(data, list):\n            for v in data:\n                self.parse_data(v, data, filename)\n\n    def parse_file(self, filename):\n        filename = self.check_file(filename)\n\n        with open(filename) as fh:\n            file_data = yaml.load(fh, Loader=yaml.SafeLoader)\n            self.parse_data(file_data, {}, filename)\n", "repo_name": "threatspec/threatspec", "sub_path": "threatspec/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 10716, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 293, "dataset": "github-code", "pt": "46", "api": [{"api_name": "logging.getLogger", "line_number": 2, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 91, "usage_type": "call"}, {"api_name": "re.M", "line_number": 91, "usage_type": "attribute"}, {"api_name": "re.I", "line_number": 91, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 117, "usage_type": "call"}, {"api_name": "yaml.SafeLoader", "line_number": 117, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 125, "usage_type": "call"}, {"api_name": "comment_parser.comment_parser.extract_comments", "line_number": 191, "usage_type": "call"}, {"api_name": "comment_parser.comment_parser", "line_number": 191, "usage_type": "name"}, {"api_name": "comment_parser.comment_parser.UnsupportedError", "line_number": 206, "usage_type": "attribute"}, {"api_name": "comment_parser.comment_parser", "line_number": 206, "usage_type": "name"}, {"api_name": "re.match", "line_number": 235, "usage_type": "call"}, {"api_name": "re.M", "line_number": 235, "usage_type": "attribute"}, {"api_name": "re.I", "line_number": 235, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 247, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 259, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 269, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 290, "usage_type": "call"}, {"api_name": "yaml.SafeLoader", "line_number": 290, "usage_type": "attribute"}]}
{"seq_id": "26129396775", "text": "from adestis_netbox_plugin_account_management.models import *\nfrom netbox.filtersets import NetBoxModelFilterSet\nfrom django.db.models import Q\n\nfrom dcim.models import *\nfrom utilities.filters import TreeNodeMultipleChoiceFilter\nfrom django.utils.translation import gettext as _\nimport django_filters\nfrom utilities.forms import (\n    DynamicModelMultipleChoiceField, MultipleChoiceField, StaticSelect, TagFilterField, BOOLEAN_WITH_BLANK_CHOICES,\n)\nfrom virtualization.models import VirtualMachine, ClusterGroup, Cluster\nfrom tenancy.models import *\n\n__all__ = (\n    'SystemFilterSet',\n)\n\n\nclass SystemFilterSet(NetBoxModelFilterSet):\n    \n    cluster_group_id = DynamicModelMultipleChoiceField(\n        queryset=ClusterGroup.objects.all(),\n        required=False,\n        label=_('Cluster group (name)')\n    )   \n    \n    cluster_id = DynamicModelMultipleChoiceField(\n        queryset=Cluster.objects.all(),\n        required=False,\n        label=_('Cluster (name)')\n    )   \n    \n    device_id = django_filters.ModelMultipleChoiceFilter(\n        queryset=Device.objects.all(),\n        label=_('Device (ID)'),\n    )\n    \n    device = django_filters.ModelMultipleChoiceFilter(\n        field_name='device__name',\n        queryset=Device.objects.all(),\n        to_field_name='name',\n        label=_('Device (name)'),\n    )\n    \n    virtual_machine_id = DynamicModelMultipleChoiceField(\n        queryset=VirtualMachine.objects.all(),\n        required=False,\n        label=_('Virtual machine (name)'))\n\n    group = TreeNodeMultipleChoiceFilter(\n        queryset=TenantGroup.objects.all(),\n        field_name='group',\n        lookup_expr='in',\n        to_field_name='group',\n        label=_('Tenant group (group)'),\n    )\n    \n    tenant_id = django_filters.ModelMultipleChoiceFilter(\n        queryset=Tenant.objects.all(),\n        label=_('Tenant (ID)'),\n    )\n    \n    tenant = django_filters.ModelMultipleChoiceFilter(\n        queryset=Tenant.objects.all(),\n        field_name='tenant__name',\n        to_field_name='tenant',\n        label=_('Tenant (name)'),\n    )\n\n    class Meta:\n        model = System\n        fields = ('id', 'tenant', 'group', 'cluster_group_id', 'cluster_id', 'device', 'virtual_machine_id', 'name', 'system_url', 'system_status') \n\n    def search(self, queryset, name, value):\n        if not value.strip():\n            return queryset\n        return queryset.filter(\n            Q(name__icontains=value) |\n            Q(system_url__icontains=value) |\n            Q(system_status__icontains=value)\n        )\n", "repo_name": "adestis/netbox-account-management", "sub_path": "adestis_netbox_plugin_account_management/filtersets/system.py", "file_name": "system.py", "file_ext": "py", "file_size_in_byte": 2528, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "netbox.filtersets.NetBoxModelFilterSet", "line_number": 20, "usage_type": "name"}, {"api_name": "utilities.forms.DynamicModelMultipleChoiceField", "line_number": 22, "usage_type": "call"}, {"api_name": "virtualization.models.ClusterGroup.objects.all", "line_number": 23, "usage_type": "call"}, {"api_name": "virtualization.models.ClusterGroup.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "virtualization.models.ClusterGroup", "line_number": 23, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 25, "usage_type": "call"}, {"api_name": "utilities.forms.DynamicModelMultipleChoiceField", "line_number": 28, "usage_type": "call"}, {"api_name": "virtualization.models.Cluster.objects.all", "line_number": 29, "usage_type": "call"}, {"api_name": "virtualization.models.Cluster.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "virtualization.models.Cluster", "line_number": 29, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 31, "usage_type": "call"}, {"api_name": "django_filters.ModelMultipleChoiceFilter", "line_number": 34, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 36, "usage_type": "call"}, {"api_name": "django_filters.ModelMultipleChoiceFilter", "line_number": 39, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 43, "usage_type": "call"}, {"api_name": "utilities.forms.DynamicModelMultipleChoiceField", "line_number": 46, "usage_type": "call"}, {"api_name": "virtualization.models.VirtualMachine.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "virtualization.models.VirtualMachine.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "virtualization.models.VirtualMachine", "line_number": 47, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 49, "usage_type": "call"}, {"api_name": "utilities.filters.TreeNodeMultipleChoiceFilter", "line_number": 51, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 56, "usage_type": "call"}, {"api_name": "django_filters.ModelMultipleChoiceFilter", "line_number": 59, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 61, "usage_type": "call"}, {"api_name": "django_filters.ModelMultipleChoiceFilter", "line_number": 64, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 80, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 81, "usage_type": "call"}]}
{"seq_id": "25740885459", "text": "import numpy as np\nimport pandas as pd\nfrom tqdm.notebook import trange\n\ndef vuln_sim(dists, sim_years, days_in_year=365, sample_count=1000000, clip=True, verbose=False):\n    samples = pd.DataFrame({key:val.rvs(sample_count) for key, val in dists.items()})\n    \n    variants = {}\n    active = []\n    vuln_days = []\n    t = trange(sim_years * days_in_year) if verbose else range(sim_years * days_in_year)\n    for i in t:\n        var_occur = np.random.choice(samples['occurence']) / days_in_year\n        if var_occur > np.random.uniform():\n            var_idx = len(variants)\n            variants[var_idx] = {\n                'start_day': i,\n                'identification': np.random.choice(samples['identification']),\n                'remediation': np.random.choice(samples['remediation']),\n            }\n            variants[var_idx]['duration'] = variants[var_idx]['identification'] + variants[var_idx]['remediation']\n            active.append(var_idx)\n\n        efficacy = np.random.choice(samples['variant']) if len(active) > 0 \\\n            else np.random.choice(samples['efficacy'])\n\n        if efficacy < np.random.uniform():\n            vuln_days.append(i)\n\n        for var_idx in [*active]:\n            var_end = 1 / variants[var_idx]['duration']\n            if var_end > np.random.uniform():\n                active.remove(var_idx)\n                variants[var_idx]['end_day'] = i\n                \n    vuln_vals = (pd.Series(vuln_days) // days_in_year).value_counts().reindex(np.arange(1000)).fillna(0) / days_in_year\n    if clip: vuln_vals = vuln_vals.clip(1/sim_years, 1 - (1/sim_years))\n    \n    return vuln_vals, variants", "repo_name": "Calvinxc1/BayesianPlayground", "sub_path": "tools/vuln_sim.py", "file_name": "vuln_sim.py", "file_ext": "py", "file_size_in_byte": 1634, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pandas.DataFrame", "line_number": 6, "usage_type": "call"}, {"api_name": "tqdm.notebook.trange", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "1436880719", "text": "import firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import db\nimport os\nimport json\nfrom datetime import datetime\n\nnow = datetime.now()\ndt_string = now.strftime(\"%d-%m-%Y_%H:%M:%S\")\n            \ncred = credentials.Certificate(\"vinterprosjekt-it2-firebase-adminsdk-wi0bg-f0efd8881e.json\")\nfirebase_admin.initialize_app(cred, {'databaseURL':\"https://vinterprosjekt-it2-default-rtdb.europe-west1.firebasedatabase.app/\"})\n\nref = db.reference(\"/bazaar\")\n\nwith open(\"bazaar_info.json\", \"r\") as f:\n\tfile_contents = json.load(f)\n# ref.set(file_contents)\n\nref.child(dt_string).set(file_contents)\n\nos.remove(\"bazaar_info.json\")\n\nref = db.reference(\"/auctions\")\n\nwith open(\"auctions_info.json\", \"r\") as f:\n\tfile_contents = json.load(f)\n# ref.set(file_contents)\n\nref.child(dt_string).set(file_contents)\n\nos.remove(\"auctions_info.json\")\n\n# data_push = ref.push().set(file_contents)\n\n# post_id = data_push.key\n", "repo_name": "johahold/prosjekt_vinterferie", "sub_path": "data_management/push_to_database.py", "file_name": "push_to_database.py", "file_ext": "py", "file_size_in_byte": 925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "datetime.datetime.now", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "name"}, {"api_name": "firebase_admin.credentials.Certificate", "line_number": 11, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 11, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 12, "usage_type": "call"}, {"api_name": "firebase_admin.db.reference", "line_number": 14, "usage_type": "call"}, {"api_name": "firebase_admin.db", "line_number": 14, "usage_type": "name"}, {"api_name": "json.load", "line_number": 17, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 22, "usage_type": "call"}, {"api_name": "firebase_admin.db.reference", "line_number": 24, "usage_type": "call"}, {"api_name": "firebase_admin.db", "line_number": 24, "usage_type": "name"}, {"api_name": "json.load", "line_number": 27, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 32, "usage_type": "call"}]}
{"seq_id": "39277673078", "text": "from __future__ import absolute_import, unicode_literals\n\nimport json\nimport logging\n\nfrom django.conf import settings\nfrom django.db.models import Q, Count\nfrom django.core.management.base import BaseCommand\n\nfrom muses.collection.models import Item\nfrom muses.naive_classification.helpers_os import predict_image_path_dict\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass Command(BaseCommand):\n    \"\"\"Classify.\"\"\"\n\n    help = \"Classify items with our AI.\"\n\n    requires_system_checks = False\n\n    def add_arguments(self, parser):\n        parser.add_argument('--update-existing',\n                            action='store_true',\n                            dest='update_existing',\n                            default=False,\n                            help=\"Update existing classifications.\")\n\n    def handle(self, *args, **options):\n        \"\"\"Handle.\n\n        :param args:\n        :param options:\n        :return:\n        \"\"\"\n        update_existing = bool(options['update_existing'])\n\n        filters = []\n        if not update_existing:\n            for field in ['classified_as']:\n                filters.append(\n                    Q(**{\"{}__isnull\".format(field): True})\n                    | Q(**{\"{}__exact\".format(field): ''})\n                )\n\n        items = Item \\\n            .objects \\\n            .filter(*filters) \\\n            .prefetch_related('images') \\\n            .annotate(num_images=Count('images')) \\\n            .filter(num_images__gt=0)\n\n        for item in items:\n            paths = []\n            for image in item.images.all():\n                try:\n                    paths.append(image.image.path)\n                except Exception as err:\n                    LOGGER.warning(err)\n\n            conf = settings \\\n                .MUSES_CONFIG['classification']['naive_classification']\n            model_path = conf['model_path']\n\n            try:\n                classification = predict_image_path_dict(\n                    paths,\n                    model_path=model_path\n                )\n            except Exception as err:\n                LOGGER.warning(err)\n                continue\n\n            top_results = list(classification.items())[:5]\n            if top_results:\n                try:\n                    item.classified_as = top_results\n                    item.save()\n                except Exception as err:\n                    pass\n", "repo_name": "Aincient/cleo", "sub_path": "src/muses/collection/management/commands/muses_classify.py", "file_name": "muses_classify.py", "file_ext": "py", "file_size_in_byte": 2379, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 44, "usage_type": "call"}, {"api_name": "muses.collection.models.Item.objects.filter", "line_number": 47, "usage_type": "call"}, {"api_name": "muses.collection.models.Item.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "muses.collection.models.Item", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 51, "usage_type": "call"}, {"api_name": "django.conf.settings.MUSES_CONFIG", "line_number": 62, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 62, "usage_type": "name"}, {"api_name": "muses.naive_classification.helpers_os.predict_image_path_dict", "line_number": 67, "usage_type": "call"}]}
{"seq_id": "29946361664", "text": "\"\"\"Provides a ChatBot UI for a Github Repository. Powered by Llama Index and Panel\"\"\"\nimport os\nimport pickle\nfrom pathlib import Path\n\nimport nest_asyncio\nimport panel as pn\nimport param\nfrom llama_index import VectorStoreIndex, download_loader\n\nfrom llama_hub.github_repo import GithubClient, GithubRepositoryReader\n\n# needed because both Panel and GithubRepositoryReader starts up the ioloop\nnest_asyncio.apply()\n\nCACHE_PATH = Path(\".cache/panel_chatbot\")\nCACHE_PATH.mkdir(parents=True, exist_ok=True)\n\nCHAT_GPT_LOGO = \"https://upload.wikimedia.org/wikipedia/commons/thumb/0/04/ChatGPT_logo.svg/512px-ChatGPT_logo.svg.png\"\nCHAT_GPT_URL = \"https://chat.openai.com/\"\nLLAMA_INDEX_LOGO = (\n    \"https://cdn-images-1.medium.com/max/280/1*_mrG8FG_LiD23x0-mEtUkw@2x.jpeg\"\n)\nPANEL_LOGO = {\n    \"default\": \"https://panel.holoviz.org/_static/logo_horizontal_light_theme.png\",\n    \"dark\": \"https://panel.holoviz.org/_static/logo_horizontal_dark_theme.png\",\n}\n\nGITHUB_LOGO = \"https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png\"\nGITHUB_URL = \"https://github.com/\"\nLLAMA_INDEX_URL = \"https://www.llamaindex.ai/\"\nPANEL_URL = \"https://panel.holoviz.org/index.html\"\nGITHUB_COPILOT_LOGO = (\n    \"https://plugins.jetbrains.com/files/17718/447537/icon/pluginIcon.svg\"\n)\n\nINDEX_NOT_LOADED = \"No repository loaded\"\nINDEX_LOADED = \"Repository loaded\"\nLOADING_EXISTING_DOCS = \"Loading existing docs\"\nLOADING_NEW_DOCS = \"Downloading documents\"\nLOADING_EXISTING_INDEX = \"Loading existing index\"\nLOADING_NEW_INDEX = \"Creating index\"\nCUTE_LLAMA = \"https://raw.githubusercontent.com/run-llama/llama-hub/main/llama_hub/llama_packs/panel_chatbot/llama_by_sophia_yang.png\"\nCUTE_LLAMA_URL = \"https://x.com/sophiamyang/status/1729810715467252080?s=20\"\n\npn.chat.ChatMessage.default_avatars.update(\n    {\n        \"assistant\": GITHUB_COPILOT_LOGO,\n        \"user\": \"🦙\",\n    }\n)\npn.chat.ChatMessage.show_reaction_icons = False\n\nACCENT = \"#ec4899\"\n\nCSS_FIXES_TO_BE_UPSTREAMED_TO_PANEL = \"\"\"\n#sidebar {\n    padding-left: 5px !important;\n    background-color: var(--panel-surface-color);\n}\n.pn-wrapper {\n    height: calc( 100vh - 150px);\n}\n.bk-active.bk-btn-primary {border-color: var(--accent-fill-active)}\n.bk-btn-primary:hover {border-color: var(--accent-fill-hover)}\n.bk-btn-primary {border-color: var(--accent-fill-rest)}\na {color: var(--accent-fill-rest) !important;}\na:hover {color: var(--accent-fill-hover) !important;}\n\"\"\"\n\n\ndef _split_and_clean(cstext):\n    return cstext.split(\",\")\n\n\nclass IndexLoader(pn.viewable.Viewer):\n    \"\"\"The IndexLoader enables the user to interactively create a VectorStoreIndex from a\n    github repository of choice\"\"\"\n\n    value: VectorStoreIndex = param.ClassSelector(class_=VectorStoreIndex)\n\n    status = param.String(constant=True, doc=\"A status message\")\n\n    owner: str = param.String(\n        default=\"holoviz\", doc=\"The repository owner. For example 'holoviz'\"\n    )\n    repo: str = param.String(\n        default=\"panel\", doc=\"The repository name. For example 'panel'\"\n    )\n    filter_directories: str = param.String(\n        default=\"examples,docs,panel\",\n        label=\"Folders\",\n        doc=\"A comma separated list of folders to include. For example 'examples,docs,panel'\",\n    )\n    filter_file_extensions: str = param.String(\n        default=\".py,.md,.ipynb\",\n        label=\"File Extensions\",\n        doc=\"A comma separated list of file extensions to include. For example '.py,.md,.ipynb'\",\n    )\n\n    _load = param.Event(\n        label=\"LOAD\",\n        doc=\"Loads the repository index from the cache if it exists and otherwise from scratch\",\n    )\n    _reload = param.Event(\n        default=False,\n        label=\"RELOAD ALL\",\n        doc=\"Loads the repository index from scratch\",\n    )\n\n    def __init__(self):\n        super().__init__()\n\n        if self.index_exists:\n            pn.state.execute(self.load)\n        else:\n            self._update_status(INDEX_NOT_LOADED)\n\n        self._layout = pn.Column(\n            self.param.owner,\n            self.param.repo,\n            self.param.filter_directories,\n            self.param.filter_file_extensions,\n            pn.pane.HTML(self.github_url),\n            pn.widgets.Button.from_param(\n                self.param._load,\n                button_type=\"primary\",\n                disabled=self._is_loading,\n                loading=self._is_loading,\n            ),\n            pn.widgets.Button.from_param(\n                self.param._reload,\n                button_type=\"primary\",\n                button_style=\"outline\",\n                disabled=self._is_loading,\n                loading=self._is_loading,\n            ),\n            pn.pane.Markdown(\"### Status\", margin=(3, 5)),\n            pn.pane.Str(self.param.status),\n        )\n\n    def __panel__(self):\n        return self._layout\n\n    @property\n    def _unique_id(self):\n        uid = (\n            self.owner\n            + self.repo\n            + self.filter_directories\n            + self.filter_file_extensions\n        )\n        uid = uid.replace(\",\", \"\").replace(\".\", \"\")\n        return uid\n\n    @property\n    def _cached_docs_path(self):\n        return CACHE_PATH / f\"docs_{self._unique_id}.pickle\"\n\n    @property\n    def _cached_index_path(self):\n        return CACHE_PATH / f\"index_{self._unique_id}.pickle\"\n\n    async def _download_docs(self):\n        download_loader(\"GithubRepositoryReader\")\n\n        github_client = GithubClient(os.getenv(\"GITHUB_TOKEN\"))\n\n        filter_directories = _split_and_clean(self.filter_directories)\n        filter_file_extensions = _split_and_clean(self.filter_file_extensions)\n\n        loader = GithubRepositoryReader(\n            github_client,\n            owner=self.owner,\n            repo=self.repo,\n            filter_directories=(\n                filter_directories,\n                GithubRepositoryReader.FilterType.INCLUDE,\n            ),\n            filter_file_extensions=(\n                filter_file_extensions,\n                GithubRepositoryReader.FilterType.INCLUDE,\n            ),\n            verbose=True,\n            concurrent_requests=10,\n        )\n        return loader.load_data(branch=\"main\")\n\n    async def _get_docs(self):\n        docs_path = self._cached_docs_path\n        index_path = self._cached_index_path\n\n        if docs_path.exists():\n            self._update_status(LOADING_EXISTING_DOCS)\n            with docs_path.open(\"rb\") as f:\n                return pickle.load(f)\n\n        self._update_status(LOADING_NEW_DOCS)\n        docs = await self._download_docs()\n\n        with docs_path.open(\"wb\") as f:\n            pickle.dump(docs, f, pickle.HIGHEST_PROTOCOL)\n\n        if index_path.exists():\n            index_path.unlink()\n\n        return docs\n\n    async def _create_index(self, docs):\n        return VectorStoreIndex.from_documents(docs, use_async=True)\n\n    async def _get_index(self, index):\n        index_path = self._cached_index_path\n\n        if index_path.exists():\n            self._update_status(LOADING_EXISTING_INDEX)\n            with index_path.open(\"rb\") as f:\n                return pickle.load(f)\n\n        self._update_status(LOADING_NEW_INDEX)\n        index = await self._create_index(index)\n\n        with index_path.open(\"wb\") as f:\n            pickle.dump(index, f, pickle.HIGHEST_PROTOCOL)\n        return index\n\n    @param.depends(\"status\")\n    def _is_loading(self):\n        return self.status not in [INDEX_LOADED, INDEX_NOT_LOADED]\n\n    @param.depends(\"status\")\n    def _is_not_loading(self):\n        return self.status in [INDEX_LOADED, INDEX_NOT_LOADED]\n\n    @param.depends(\"_load\", watch=True)\n    async def load(self):\n        \"\"\"Loads the repository index either from the cache or by downloading from\n        the repository\"\"\"\n        self._update_status(\"Loading ...\")\n        self.value = None\n\n        docs = await self._get_docs()\n        self.value = await self._get_index(docs)\n        self._update_status(INDEX_LOADED)\n\n    @param.depends(\"_reload\", watch=True)\n    async def reload(self):\n        self._update_status(\"Deleteing cached index ...\")\n        if self._cached_docs_path.exists():\n            self._cached_docs_path.unlink()\n        if self._cached_index_path.exists():\n            self._cached_index_path.unlink()\n\n        await self.load()\n\n    def _update_status(self, text):\n        with param.edit_constant(self):\n            self.status = text\n        print(text)\n\n    @param.depends(\"owner\", \"repo\")\n    def github_url(self):\n        \"\"\"Returns a html string with a link to the github repository\"\"\"\n        text = f\"{self.owner}/{self.repo}\"\n        href = f\"https://github.com/{text}\"\n        return f\"<a href='{href}' target='_blank'>{text}</a>\"\n\n    @property\n    def index_exists(self):\n        \"\"\"Returns True if the index already exists\"\"\"\n        return self._cached_docs_path.exists() and self._cached_index_path.exists()\n\n\ndef powered_by():\n    \"\"\"Returns a component describing the frameworks powering the chat ui\"\"\"\n    params = {\"height\": 40, \"sizing_mode\": \"fixed\", \"margin\": (0, 10)}\n    return pn.Column(\n        pn.pane.Markdown(\"### AI Powered By\", margin=(10, 5, 10, 0)),\n        pn.Row(\n            pn.pane.Image(LLAMA_INDEX_LOGO, link_url=LLAMA_INDEX_URL, **params),\n            pn.pane.Image(CHAT_GPT_LOGO, link_url=CHAT_GPT_URL, **params),\n            pn.pane.Image(PANEL_LOGO[pn.config.theme], link_url=PANEL_URL, **params),\n            align=\"center\",\n        ),\n    )\n\n\nasync def chat_component(index: VectorStoreIndex, index_loader: IndexLoader):\n    \"\"\"Returns the chat component powering the main area of the application\"\"\"\n    if not index:\n        return pn.Column(\n            pn.chat.ChatMessage(\n                \"You are a now a *GitHub Repository assistant*.\",\n                user=\"System\",\n            ),\n            pn.chat.ChatMessage(\n                \"Please **load a GitHub Repository** to start chatting with me. This can take from seconds to minutes!\",\n                user=\"Assistant\",\n            ),\n        )\n\n    chat_engine = index.as_chat_engine(chat_mode=\"context\", verbose=True)\n\n    async def generate_response(contents, user, instance):\n        response = await chat_engine.astream_chat(contents)\n        text = \"\"\n        async for token in response.async_response_gen():\n            text += token\n            yield text\n\n    chat_interface = pn.chat.ChatInterface(\n        callback=generate_response,\n        sizing_mode=\"stretch_both\",\n    )\n    chat_interface.send(\n        pn.chat.ChatMessage(\n            \"You are a now a *GitHub Repository Assistant*.\", user=\"System\"\n        ),\n        respond=False,\n    )\n    chat_interface.send(\n        pn.chat.ChatMessage(\n            f\"Hello! you can ask me anything about {index_loader.github_url()}.\",\n            user=\"Assistant\",\n        ),\n        respond=False,\n    )\n    return chat_interface\n\n\ndef settings_components(index_loader: IndexLoader):\n    \"\"\"Returns a list of the components to add to the sidebar\"\"\"\n    return [\n        pn.pane.Image(\n            CUTE_LLAMA,\n            height=250,\n            align=\"center\",\n            margin=(10, 5, 25, 5),\n            link_url=CUTE_LLAMA_URL,\n        ),\n        \"## Github Repository\",\n        index_loader,\n        powered_by(),\n    ]\n\n\ndef create_chat_ui():\n    \"\"\"Returns the Chat UI\"\"\"\n    pn.extension(\n        sizing_mode=\"stretch_width\", raw_css=[CSS_FIXES_TO_BE_UPSTREAMED_TO_PANEL]\n    )\n\n    index_loader = IndexLoader()\n\n    pn.state.location.sync(\n        index_loader,\n        parameters={\n            \"owner\": \"owner\",\n            \"repo\": \"repo\",\n            \"filter_directories\": \"folders\",\n            \"filter_file_extensions\": \"file_extensions\",\n        },\n    )\n\n    bound_chat_interface = pn.bind(\n        chat_component, index=index_loader.param.value, index_loader=index_loader\n    )\n\n    return pn.template.FastListTemplate(\n        title=\"Chat with GitHub\",\n        sidebar=settings_components(index_loader),\n        main=[bound_chat_interface],\n        accent=ACCENT,\n        main_max_width=\"1000px\",\n        main_layout=None,\n    )\n\n\nif pn.state.served:\n    create_chat_ui().servable()\n", "repo_name": "run-llama/llama-hub", "sub_path": "llama_hub/llama_packs/panel_chatbot/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 12046, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2565, "dataset": "github-code", "pt": "46", "api": [{"api_name": "nest_asyncio.apply", "line_number": 14, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "panel.chat.ChatMessage.default_avatars.update", "line_number": 46, "usage_type": "call"}, {"api_name": "panel.chat", "line_number": 46, "usage_type": "attribute"}, {"api_name": "panel.chat", "line_number": 52, "usage_type": "attribute"}, {"api_name": "panel.viewable", "line_number": 76, "usage_type": "attribute"}, {"api_name": "llama_index.VectorStoreIndex", "line_number": 80, "usage_type": "name"}, {"api_name": "param.ClassSelector", "line_number": 80, "usage_type": "call"}, {"api_name": "param.String", "line_number": 82, "usage_type": "call"}, {"api_name": "param.String", "line_number": 84, "usage_type": "call"}, {"api_name": "param.String", "line_number": 87, "usage_type": "call"}, {"api_name": "param.String", "line_number": 90, "usage_type": "call"}, {"api_name": "param.String", "line_number": 95, "usage_type": "call"}, {"api_name": "param.Event", "line_number": 101, "usage_type": "call"}, {"api_name": "param.Event", "line_number": 105, "usage_type": "call"}, {"api_name": "panel.state.execute", "line_number": 115, "usage_type": "call"}, {"api_name": "panel.state", "line_number": 115, "usage_type": "attribute"}, {"api_name": "panel.Column", "line_number": 119, "usage_type": "call"}, {"api_name": "panel.pane.HTML", "line_number": 124, "usage_type": "call"}, {"api_name": "panel.pane", "line_number": 124, "usage_type": "attribute"}, {"api_name": "panel.widgets.Button.from_param", "line_number": 125, "usage_type": "call"}, {"api_name": "panel.widgets", "line_number": 125, "usage_type": "attribute"}, {"api_name": "panel.widgets.Button.from_param", "line_number": 131, "usage_type": "call"}, {"api_name": "panel.widgets", "line_number": 131, "usage_type": "attribute"}, {"api_name": "panel.pane.Markdown", "line_number": 138, "usage_type": "call"}, {"api_name": "panel.pane", "line_number": 138, "usage_type": "attribute"}, {"api_name": "panel.pane.Str", "line_number": 139, "usage_type": "call"}, {"api_name": "panel.pane", "line_number": 139, "usage_type": "attribute"}, {"api_name": "llama_index.download_loader", "line_number": 165, "usage_type": "call"}, {"api_name": "llama_hub.github_repo.GithubClient", "line_number": 167, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 167, "usage_type": "call"}, {"api_name": "llama_hub.github_repo.GithubRepositoryReader", "line_number": 172, "usage_type": "call"}, {"api_name": "llama_hub.github_repo.GithubRepositoryReader.FilterType", "line_number": 178, "usage_type": "attribute"}, {"api_name": "llama_hub.github_repo.GithubRepositoryReader", "line_number": 178, "usage_type": "name"}, {"api_name": "llama_hub.github_repo.GithubRepositoryReader.FilterType", "line_number": 182, "usage_type": "attribute"}, {"api_name": "llama_hub.github_repo.GithubRepositoryReader", "line_number": 182, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 196, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 202, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 202, "usage_type": "attribute"}, {"api_name": "llama_index.VectorStoreIndex.from_documents", "line_number": 210, "usage_type": "call"}, {"api_name": "llama_index.VectorStoreIndex", "line_number": 210, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 218, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 224, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 224, "usage_type": "attribute"}, {"api_name": "param.depends", "line_number": 227, "usage_type": "call"}, {"api_name": "param.depends", "line_number": 231, "usage_type": "call"}, {"api_name": "param.depends", "line_number": 235, "usage_type": "call"}, {"api_name": "param.depends", "line_number": 246, "usage_type": "call"}, {"api_name": "param.edit_constant", "line_number": 257, "usage_type": "call"}, {"api_name": "param.depends", "line_number": 261, "usage_type": "call"}, {"api_name": "panel.Column", "line_number": 277, "usage_type": "call"}, {"api_name": "panel.pane.Markdown", "line_number": 278, "usage_type": "call"}, {"api_name": "panel.pane", "line_number": 278, "usage_type": "attribute"}, {"api_name": "panel.Row", "line_number": 279, "usage_type": "call"}, {"api_name": "panel.pane.Image", "line_number": 280, "usage_type": "call"}, {"api_name": "panel.pane", "line_number": 280, "usage_type": "attribute"}, {"api_name": "panel.pane.Image", "line_number": 281, "usage_type": "call"}, {"api_name": "panel.pane", "line_number": 281, "usage_type": "attribute"}, {"api_name": "panel.pane.Image", "line_number": 282, "usage_type": "call"}, {"api_name": "panel.pane", "line_number": 282, "usage_type": "attribute"}, {"api_name": "panel.config", "line_number": 282, "usage_type": "attribute"}, {"api_name": "llama_index.VectorStoreIndex", "line_number": 288, "usage_type": "name"}, {"api_name": "panel.Column", "line_number": 291, "usage_type": "call"}, {"api_name": "panel.chat.ChatMessage", "line_number": 292, "usage_type": "call"}, {"api_name": "panel.chat", "line_number": 292, "usage_type": "attribute"}, {"api_name": "panel.chat.ChatMessage", "line_number": 296, "usage_type": "call"}, {"api_name": "panel.chat", "line_number": 296, "usage_type": "attribute"}, {"api_name": "panel.chat.ChatInterface", "line_number": 311, "usage_type": "call"}, {"api_name": "panel.chat", "line_number": 311, "usage_type": "attribute"}, {"api_name": "panel.chat.ChatMessage", "line_number": 316, "usage_type": "call"}, {"api_name": "panel.chat", "line_number": 316, "usage_type": "attribute"}, {"api_name": "panel.chat.ChatMessage", "line_number": 322, "usage_type": "call"}, {"api_name": "panel.chat", "line_number": 322, "usage_type": "attribute"}, {"api_name": "panel.pane.Image", "line_number": 334, "usage_type": "call"}, {"api_name": "panel.pane", "line_number": 334, "usage_type": "attribute"}, {"api_name": "panel.extension", "line_number": 349, "usage_type": "call"}, {"api_name": "panel.state.location.sync", "line_number": 355, "usage_type": "call"}, {"api_name": "panel.state", "line_number": 355, "usage_type": "attribute"}, {"api_name": "panel.bind", "line_number": 365, "usage_type": "call"}, {"api_name": "panel.template.FastListTemplate", "line_number": 369, "usage_type": "call"}, {"api_name": "panel.template", "line_number": 369, "usage_type": "attribute"}, {"api_name": "panel.state", "line_number": 379, "usage_type": "attribute"}]}
{"seq_id": "28588191341", "text": "# - *- coding: utf- 8 - *-\nimport time\n\nfrom pepper.robot import Pepper\ntry:\n    import urllib\nexcept:\n    import urllib.request as urllib\nimport base64\nimport json\nfrom PIL import Image\nimport random\n\n\ndef uploadPhotoToWeb(photo):\n    \"\"\"we need to upload photo to web as we (me) are not able to open it from local folder\"\"\"\n    f = open(photo, \"rb\")  # open our image file as read only in binary mode\n    image_data = f.read()  # read in our image file\n    b64_image = base64.standard_b64encode(image_data)\n    client_id = \"af482612ae6d1c1\"  # this the id which we've got after registrating the app on imgur\n    headers = {'Authorization': 'Client-ID ' + client_id}\n    data = {'image': b64_image, 'title': 'test'}\n    request = urllib.Request(url=\"https://api.imgur.com/3/upload.json\", data=urllib.urlencode(data),\n                              headers=headers)\n    response = urllib.urlopen(request).read()\n    parse = json.loads(response)\n    return parse['data']['link'] #returns a url of the photo\n\n\ndef getRandName():\n    \"\"\"returns a random name for the picture in order not to replace the old photo\"\"\"\n    randNum = random.randint(0, 1000)\n    return \"demoPictures/photo\" + str(randNum) + \".png\"\n\n\nclass PepperDemo:\n    def __init__(self, ip_address, port=9559):\n        self.robot = None\n        self.robot = Pepper(ip_address, port)\n        self.robot.set_czech_language()\n        self.photoName = None\n        self.greetings = [\"Good afternoon\", \"Hello\", \"Hi\", \"Hello everobody\", \"Welcome\"]\n        self.asks = [\"May I photograph you?\",\"May I take your picture?\", \"Do you want to make your picture?\"]\n\n\n    def wantToTakePic(self):\n        \"\"\"recognise answer with google speech reco\"\"\"\n        answers = {\"no\": [\"no\", \"no way\", \"not\", \"no no\", \" i don't\", \"i dont know\", \"not today\", \"later\", \"tommorow\"],\n                   \"yes\": [\"yes\", \"definitely\", \"yep\", \"ok\", \"okey dokey\", \"sure\", \"all yes\", \"you must\",\n                          \"absolutely\", \"i want\", \"i think so\", \"i agree\", \"if you want\", \"if you insist\", \"probably\", \"maybe\",\n                          \"yes sir\"]}\n        recorded = self.robot.recognize_google(lang=\"en-US\")\n        answer = self.getAnswer(answers, recorded)\n        if answer == \"no\":\n            return False\n        elif answer == \"yes\":\n            return True\n        else:\n            return None\n\n\n    def getAnswer(self, dic, recorded):\n        \"\"\"looks for a recorded answer in a dictionar\"\"\"\n        for x in dic.keys():\n            if dic[x] in recorded.lower():\n                return x\n        return None\n\n    def welcomeAndAsk(self):\n        self.robot.say(random.choice(self.greetings))\n        self.robot.greet()\n        self.robot.say(random.choice(self.asks))\n\n    def takePicture(self):\n        self.robot.subscribe_camera(\"camera_top\", 2, 30)\n        img = self.robot.get_camera_frame(show=False)\n        self.robot.unsubscribe_camera()\n        self.robot.play_sound(\"/home/nao/camera1.ogg\")\n        im = Image.fromarray(img)\n        self.photoName = getRandName()\n        im.save(self.photoName)\n\n    def showPicture(self):\n        link = uploadPhotoToWeb(self.photoName)\n        self.robot.show_image(link)\n        time.sleep(5)\n        self.robot.reset_tablet()\n\n    def recogniseAnswerAndDecide(self):\n        isTakePic = self.wantToTakePic()\n        if isTakePic:\n            self.robot.say(\"Perfect. On your marks. 3, 2, 1 .\")\n            self.takePicture()\n            self.showPicture()\n        elif isTakePic is None:\n            self.robot.say(\"Sorry, I did not understand you. Please repeat.\")\n            self.recogniseAnswerAndDecide()\n        else:\n            self.robot.say(\"Maybe next time\")\n\n    def dealWithRecoErrors(self):\n        \"\"\"there is a modifiable grammar error sometimes occurred.\n        In order to deal with it you should change language to english and back\"\"\"\n        self.robot.set_english_language()\n        self.robot.set_czech_language()\n\n    def run(self):\n        self.dealWithRecoErrors()\n        self.welcomeAndAsk()\n        self.recogniseAnswerAndDecide()\n\nif __name__ == \"__main__\":\n    pepperDemo = PepperDemo(\"10.37.1.232\")\n    pepperDemo.run()\n\n", "repo_name": "incognite-lab/Pepper-Controller", "sub_path": "demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 4158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "46", "api": [{"api_name": "base64.standard_b64encode", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 23, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 23, "usage_type": "name"}, {"api_name": "urllib.request.urlencode", "line_number": 23, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 25, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 26, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "pepper.robot.Pepper", "line_number": 39, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 70, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 72, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 79, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 79, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 86, "usage_type": "call"}]}
{"seq_id": "23186544917", "text": "from bson import ObjectId\nfrom dataclasses import field\nfrom typing import Optional\n\nfrom aenum import Enum\nfrom isodate import parse_duration\n\nfrom extensions.authorization.models.authorized_user import AuthorizedUser\nfrom extensions.authorization.models.invitation import Invitation, InvitationType\nfrom extensions.authorization.models.role.default_roles import DefaultRoles\nfrom extensions.authorization.models.role.role import RoleName\nfrom extensions.authorization.validators import (\n    check_role_id_valid_for_organization,\n    is_common_role,\n)\nfrom extensions.common.sort import SortField\nfrom sdk.auth.use_case.auth_request_objects import BaseAuthRequestObject\nfrom sdk.common.exceptions.exceptions import InvalidRequestException, PermissionDenied\nfrom sdk.common.localization.utils import Language\nfrom sdk.common.usecase.request_object import RequestObject\nfrom sdk.common.utils import inject\nfrom sdk.common.utils.convertible import (\n    convertibleclass,\n    meta,\n    required_field,\n    positive_integer_field,\n    default_field,\n)\nfrom sdk.common.utils.inject import autoparams\nfrom sdk.common.utils.validators import (\n    validate_id,\n    validate_email_list,\n    validate_object_id,\n    validate_object_ids,\n    must_be_present,\n    must_not_be_present,\n    validate_email,\n    incorrect_language_to_default,\n    must_be_at_least_one_of,\n    not_empty,\n)\nfrom sdk.phoenix.config.server_config import Client\n\nINVITATION_PERMISSIONS_PER_ROLE: dict[str, list[str]] = {\n    RoleName.ADMINISTRATOR: [\n        RoleName.ADMINISTRATOR,\n        RoleName.CLINICIAN,\n        RoleName.SUPERVISOR,\n        RoleName.SUPPORT,\n        RoleName.USER,\n    ],\n    RoleName.CLINICIAN: [RoleName.USER, RoleName.PROXY],\n}\n\n\ndef validate_role_id(field_value: str) -> bool:\n    if validate_default_role_id(field_value):\n        return True\n    if validate_id(field_value):\n        return True\n    return False\n\n\n@autoparams(\"default_roles\")\ndef validate_default_role_id(value: str, default_roles: DefaultRoles) -> bool:\n    return value in default_roles\n\n\n@convertibleclass\nclass SendInvitationRequestObject(RequestObject):\n    INVITATION = \"invitation\"\n    CLIENT = \"client\"\n    SENDER = \"sender\"\n    LANGUAGE = \"language\"\n    EXTRA_INFO = \"extraInfo\"\n\n    invitation: Invitation = required_field()\n    client: Client = required_field()\n    sender: AuthorizedUser = required_field()\n    language: str = field(\n        default=Language.EN, metadata=meta(value_to_field=incorrect_language_to_default)\n    )\n    extraInfo: dict = default_field()\n\n\n@convertibleclass\nclass SendInvitationsRequestObject(BaseAuthRequestObject):\n    DEPLOYMENT_IDS = \"deploymentIds\"\n    ORGANIZATION_ID = \"organizationId\"\n    EMAILS = \"emails\"\n    ROLE_ID = \"roleId\"\n    PATIENT_ID = \"patientId\"\n    EXPIRES_IN = \"expiresIn\"\n    SUBMITTER = \"submitter\"\n\n    emails: list[str] = required_field(metadata=meta(validate_email_list))\n    roleId: str = required_field(metadata=meta(validate_role_id))\n    organizationId: str = default_field(metadata=meta(validate_object_id))\n    deploymentIds: list[str] = default_field(metadata=meta(validate_object_ids))\n    patientId: str = default_field(metadata=meta(validate_object_id))\n    expiresIn: str = field(default=\"P1W\", metadata=meta(parse_duration))\n    submitter: AuthorizedUser = required_field()\n\n    @classmethod\n    def validate(cls, instance):\n        super().validate(instance)\n        is_org_role = check_role_id_valid_for_organization(\n            instance.roleId, instance.organizationId\n        )\n        is_proxy_role = instance.roleId == RoleName.PROXY\n        is_common_role = instance.roleId in RoleName.common_roles()\n        if is_common_role:\n            must_be_at_least_one_of(\n                organizationId=instance.organizationId,\n                deploymentIds=instance.deploymentIds,\n            )\n            must_not_be_present(patientId=instance.patientId)\n\n            if instance.deploymentIds and len(instance.deploymentIds) > 1:\n                must_be_present(organizationId=instance.organizationId)\n        elif is_org_role:\n            must_be_present(organizationId=instance.organizationId)\n            must_not_be_present(deploymentIds=instance.deploymentIds)\n            must_not_be_present(patientId=instance.patientId)\n        elif is_proxy_role:\n            must_be_present(patientId=instance.patientId)\n            must_not_be_present(deploymentIds=instance.deploymentIds)\n            must_not_be_present(organizationId=instance.organizationId)\n        else:\n            must_not_be_present(patientId=instance.patientId)\n            must_be_present(deploymentIds=instance.deploymentIds)\n            if len(instance.deploymentIds) == 0:\n                msg = f\"Must be invited to at least one deployment\"\n                raise InvalidRequestException(msg)\n\n            multiple_deployment_role = (\n                instance.roleId in cls.multiple_deployment_roles()\n            )\n\n            if multiple_deployment_role and len(instance.deploymentIds) > 1:\n                must_be_present(organizationId=instance.organizationId)\n\n            if not multiple_deployment_role and len(instance.deploymentIds) > 1:\n                msg = f\"Role {instance.roleId} can only be invited to one deployment\"\n                raise InvalidRequestException(msg)\n\n    def check_permission(self, submitter: AuthorizedUser):\n        submitter_role = submitter.get_role()\n\n        if submitter.is_super_admin():\n            return\n\n        if is_common_role(submitter_role.id):\n            if not self.roleId == RoleName.PROXY:\n                if not submitter.role_assignment.is_org():\n                    if not self.deploymentIds:\n                        raise PermissionDenied\n                    else:\n                        self.validate_resource_access(\n                            \"deployment\", self.deploymentIds, submitter\n                        )\n                else:\n                    if self.deploymentIds:\n                        self.validate_resource_access(\n                            \"deployment\", self.deploymentIds, submitter\n                        )\n                    elif self.organizationId:\n                        self.validate_resource_access(\n                            \"organization\", [self.organizationId], submitter\n                        )\n\n            allowed_roles = INVITATION_PERMISSIONS_PER_ROLE.get(submitter_role.id) or []\n            if ObjectId.is_valid(self.roleId):\n                allowed_roles.append(self.roleId)\n            if self.roleId not in allowed_roles:\n                raise PermissionDenied\n\n    @staticmethod\n    def validate_resource_access(\n        resource_name: str, resources: list[str], submitter: AuthorizedUser\n    ):\n        allowed_resources = []\n\n        if resource_name == \"organization\":\n            allowed_resources = submitter.organization_ids()\n        elif resource_name == \"deployment\":\n            allowed_resources = submitter.deployment_ids()\n\n        if not all(resource_id in allowed_resources for resource_id in resources):\n            raise PermissionDenied\n\n    @staticmethod\n    def multiple_deployment_roles():\n        org_keys = inject.instance(DefaultRoles).organization.keys()\n        return (set(org_keys) - set(RoleName.org_roles())).union(\n            set(RoleName.common_roles())\n        )\n\n    @property\n    def deployment_id(self) -> Optional[str]:\n        if self.deploymentIds:\n            return self.deploymentIds[0]\n\n\n@convertibleclass\nclass ResendInvitationsRequestObject(BaseAuthRequestObject):\n    INVITATION_CODE = \"invitationCode\"\n    EMAIL = \"email\"\n\n    email: str = required_field(metadata=meta(validate_email))\n    invitationCode: str = required_field()\n\n\n@convertibleclass\nclass ResendInvitationsListRequestObject(BaseAuthRequestObject):\n    INVITATIONS_LIST = \"invitationsList\"\n\n    @convertibleclass\n    class InvitationItem:\n        INVITATION_CODE = \"invitationCode\"\n        EMAIL = \"email\"\n\n        email: str = required_field(metadata=meta(validate_email))\n        invitationCode: str = required_field(metadata=meta(not_empty))\n\n    invitationsList: list[InvitationItem] = required_field(metadata=meta(not_empty))\n\n\n@convertibleclass\nclass GetInvitationLinkRequestObject(BaseAuthRequestObject):\n    DEPLOYMENT_ID = \"deploymentId\"\n    ROLE_ID = \"roleId\"\n    EXPIRES_IN = \"expiresIn\"\n    RETRIEVE_SHORTENED = \"retrieveShortened\"\n    SENDER_ID = \"senderId\"\n\n    deploymentId: str = required_field()\n    roleId: str = required_field()\n    expiresIn: str = field(default=\"P1W\", metadata=meta(parse_duration))\n    retrieveShortened: bool = field(default=False)\n    senderId: str = required_field(metadata=meta(validate_object_id))\n\n\n@convertibleclass\nclass DeleteInvitationRequestObject(RequestObject):\n    INVITATION_ID = \"invitationId\"\n\n    invitationId: str = required_field(metadata=meta(validate_object_id))\n\n\n@convertibleclass\nclass DeleteInvitationsListRequestObject(RequestObject):\n    INVITATION_ID_LIST = \"invitationIdList\"\n    INVITATION_TYPE = \"invitationType\"\n\n    invitationIdList: list[str] = required_field(\n        metadata=meta(lambda x: all(map(validate_object_id, x)))\n    )\n    invitationType: InvitationType = field(default=InvitationType.PERSONAL)\n\n\n@convertibleclass\nclass RetrieveInvitationsRequestObject(BaseAuthRequestObject):\n    EMAIL = \"email\"\n    ROLE_TYPE = \"roleType\"\n    SKIP = \"skip\"\n    LIMIT = \"limit\"\n    SUBMITTER = \"submitter\"\n    INVITATION_TYPE = \"invitationType\"\n    SORT_FIELDS = \"sortFields\"\n\n    class RoleType(Enum):\n        MANAGER = \"Manager\"\n        USER = \"User\"\n\n    email: str = default_field()\n    roleType: RoleType = required_field()\n    skip: int = positive_integer_field(default=None, metadata=meta(required=True))\n    limit: int = positive_integer_field(default=None, metadata=meta(required=True))\n    submitter: AuthorizedUser = required_field()\n    invitationType: InvitationType = default_field()\n    sortFields: list[SortField] = default_field()\n\n    def post_init(self):\n        if not self.sortFields:\n            self.sortFields = [\n                SortField.from_dict(\n                    {\n                        SortField.FIELD: Invitation.CREATE_DATE_TIME,\n                        SortField.DIRECTION: SortField.Direction.DESC.value,\n                    }\n                )\n            ]\n\n\n@convertibleclass\nclass InvitationValidityRequestObject(RequestObject):\n    INVITATION_CODE = \"invitationCode\"\n\n    invitationCode: str = required_field()\n", "repo_name": "meenu-gupta/huma-server-sdk", "sub_path": "extensions/authorization/router/invitation_request_objects.py", "file_name": "invitation_request_objects.py", "file_ext": "py", "file_size_in_byte": 10478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "extensions.authorization.models.role.role.RoleName.ADMINISTRATOR", "line_number": 45, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 45, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.CLINICIAN", "line_number": 52, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 52, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.ADMINISTRATOR", "line_number": 46, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 46, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.CLINICIAN", "line_number": 47, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 47, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.SUPERVISOR", "line_number": 48, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 48, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.SUPPORT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 49, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.USER", "line_number": 50, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 50, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.USER", "line_number": 52, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName.PROXY", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sdk.common.utils.validators.validate_id", "line_number": 59, "usage_type": "call"}, {"api_name": "extensions.authorization.models.role.default_roles.DefaultRoles", "line_number": 65, "usage_type": "name"}, {"api_name": "sdk.common.utils.inject.autoparams", "line_number": 64, "usage_type": "call"}, {"api_name": "sdk.common.usecase.request_object.RequestObject", "line_number": 70, "usage_type": "name"}, {"api_name": "extensions.authorization.models.invitation.Invitation", "line_number": 77, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 77, "usage_type": "call"}, {"api_name": "sdk.phoenix.config.server_config.Client", "line_number": 78, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 78, "usage_type": "call"}, {"api_name": "extensions.authorization.models.authorized_user.AuthorizedUser", "line_number": 79, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 79, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 80, "usage_type": "call"}, {"api_name": "sdk.common.localization.utils.Language.EN", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sdk.common.localization.utils.Language", "line_number": 81, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 81, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.incorrect_language_to_default", "line_number": 81, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.default_field", "line_number": 83, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 69, "usage_type": "name"}, {"api_name": "sdk.auth.use_case.auth_request_objects.BaseAuthRequestObject", "line_number": 87, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 96, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 96, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.validate_email_list", "line_number": 96, "usage_type": "argument"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 97, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 97, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.default_field", "line_number": 98, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 98, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.validate_object_id", "line_number": 98, "usage_type": "argument"}, {"api_name": "sdk.common.utils.convertible.default_field", "line_number": 99, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 99, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.validate_object_ids", "line_number": 99, "usage_type": "argument"}, {"api_name": "sdk.common.utils.convertible.default_field", "line_number": 100, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 100, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.validate_object_id", "line_number": 100, "usage_type": "argument"}, {"api_name": "dataclasses.field", "line_number": 101, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 101, "usage_type": "call"}, {"api_name": "isodate.parse_duration", "line_number": 101, "usage_type": "argument"}, {"api_name": "extensions.authorization.models.authorized_user.AuthorizedUser", "line_number": 102, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 102, "usage_type": "call"}, {"api_name": "extensions.authorization.validators.check_role_id_valid_for_organization", "line_number": 107, "usage_type": "call"}, {"api_name": "extensions.authorization.models.role.role.RoleName.PROXY", "line_number": 110, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 110, "usage_type": "name"}, {"api_name": "extensions.authorization.validators.is_common_role", "line_number": 111, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.common_roles", "line_number": 111, "usage_type": "call"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 111, "usage_type": "name"}, {"api_name": "extensions.authorization.validators.is_common_role", "line_number": 112, "usage_type": "name"}, {"api_name": "sdk.common.utils.validators.must_be_at_least_one_of", "line_number": 113, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_not_be_present", "line_number": 117, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_be_present", "line_number": 120, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_be_present", "line_number": 122, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_not_be_present", "line_number": 123, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_not_be_present", "line_number": 124, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_be_present", "line_number": 126, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_not_be_present", "line_number": 127, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_not_be_present", "line_number": 128, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_not_be_present", "line_number": 130, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_be_present", "line_number": 131, "usage_type": "call"}, {"api_name": "sdk.common.exceptions.exceptions.InvalidRequestException", "line_number": 134, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.must_be_present", "line_number": 141, "usage_type": "call"}, {"api_name": "sdk.common.exceptions.exceptions.InvalidRequestException", "line_number": 145, "usage_type": "call"}, {"api_name": "extensions.authorization.models.authorized_user.AuthorizedUser", "line_number": 147, "usage_type": "name"}, {"api_name": "extensions.authorization.validators.is_common_role", "line_number": 153, "usage_type": "call"}, {"api_name": "extensions.authorization.models.role.role.RoleName.PROXY", "line_number": 154, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 154, "usage_type": "name"}, {"api_name": "sdk.common.exceptions.exceptions.PermissionDenied", "line_number": 157, "usage_type": "name"}, {"api_name": "bson.ObjectId.is_valid", "line_number": 173, "usage_type": "call"}, {"api_name": "bson.ObjectId", "line_number": 173, "usage_type": "name"}, {"api_name": "sdk.common.exceptions.exceptions.PermissionDenied", "line_number": 176, "usage_type": "name"}, {"api_name": "extensions.authorization.models.authorized_user.AuthorizedUser", "line_number": 180, "usage_type": "name"}, {"api_name": "sdk.common.exceptions.exceptions.PermissionDenied", "line_number": 190, "usage_type": "name"}, {"api_name": "sdk.common.utils.inject.instance", "line_number": 194, "usage_type": "call"}, {"api_name": "extensions.authorization.models.role.default_roles.DefaultRoles", "line_number": 194, "usage_type": "argument"}, {"api_name": "sdk.common.utils.inject", "line_number": 194, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.org_roles", "line_number": 195, "usage_type": "call"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 195, "usage_type": "name"}, {"api_name": "extensions.authorization.models.role.role.RoleName.common_roles", "line_number": 196, "usage_type": "call"}, {"api_name": "extensions.authorization.models.role.role.RoleName", "line_number": 196, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 200, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 86, "usage_type": "name"}, {"api_name": "sdk.auth.use_case.auth_request_objects.BaseAuthRequestObject", "line_number": 206, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 210, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 210, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.validate_email", "line_number": 210, "usage_type": "argument"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 211, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 205, "usage_type": "name"}, {"api_name": "sdk.auth.use_case.auth_request_objects.BaseAuthRequestObject", "line_number": 215, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 223, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 223, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.validate_email", "line_number": 223, "usage_type": "argument"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 224, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 224, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.not_empty", "line_number": 224, "usage_type": "argument"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 218, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 226, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 226, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.not_empty", "line_number": 226, "usage_type": "argument"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 214, "usage_type": "name"}, {"api_name": "sdk.auth.use_case.auth_request_objects.BaseAuthRequestObject", "line_number": 230, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 237, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 238, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 239, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 239, "usage_type": "call"}, {"api_name": "isodate.parse_duration", "line_number": 239, "usage_type": "argument"}, {"api_name": "dataclasses.field", "line_number": 240, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 241, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 241, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.validate_object_id", "line_number": 241, "usage_type": "argument"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 229, "usage_type": "name"}, {"api_name": "sdk.common.usecase.request_object.RequestObject", "line_number": 245, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 248, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 248, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.validate_object_id", "line_number": 248, "usage_type": "argument"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 244, "usage_type": "name"}, {"api_name": "sdk.common.usecase.request_object.RequestObject", "line_number": 252, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 256, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 257, "usage_type": "call"}, {"api_name": "sdk.common.utils.validators.validate_object_id", "line_number": 257, "usage_type": "argument"}, {"api_name": "extensions.authorization.models.invitation.InvitationType", "line_number": 259, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 259, "usage_type": "call"}, {"api_name": "extensions.authorization.models.invitation.InvitationType.PERSONAL", "line_number": 259, "usage_type": "attribute"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 251, "usage_type": "name"}, {"api_name": "sdk.auth.use_case.auth_request_objects.BaseAuthRequestObject", "line_number": 263, "usage_type": "name"}, {"api_name": "aenum.Enum", "line_number": 272, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.default_field", "line_number": 276, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 277, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.positive_integer_field", "line_number": 278, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 278, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.positive_integer_field", "line_number": 279, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.meta", "line_number": 279, "usage_type": "call"}, {"api_name": "extensions.authorization.models.authorized_user.AuthorizedUser", "line_number": 280, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 280, "usage_type": "call"}, {"api_name": "extensions.authorization.models.invitation.InvitationType", "line_number": 281, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.default_field", "line_number": 281, "usage_type": "call"}, {"api_name": "extensions.common.sort.SortField", "line_number": 282, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.default_field", "line_number": 282, "usage_type": "call"}, {"api_name": "extensions.common.sort.SortField.from_dict", "line_number": 287, "usage_type": "call"}, {"api_name": "extensions.common.sort.SortField", "line_number": 287, "usage_type": "name"}, {"api_name": "extensions.common.sort.SortField.FIELD", "line_number": 289, "usage_type": "attribute"}, {"api_name": "extensions.common.sort.SortField", "line_number": 289, "usage_type": "name"}, {"api_name": "extensions.common.sort.SortField.DIRECTION", "line_number": 290, "usage_type": "attribute"}, {"api_name": "extensions.common.sort.SortField", "line_number": 290, "usage_type": "name"}, {"api_name": "extensions.authorization.models.invitation.Invitation.CREATE_DATE_TIME", "line_number": 289, "usage_type": "attribute"}, {"api_name": "extensions.authorization.models.invitation.Invitation", "line_number": 289, "usage_type": "name"}, {"api_name": "extensions.common.sort.SortField.Direction", "line_number": 290, "usage_type": "attribute"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 262, "usage_type": "name"}, {"api_name": "sdk.common.usecase.request_object.RequestObject", "line_number": 297, "usage_type": "name"}, {"api_name": "sdk.common.utils.convertible.required_field", "line_number": 300, "usage_type": "call"}, {"api_name": "sdk.common.utils.convertible.convertibleclass", "line_number": 296, "usage_type": "name"}]}
{"seq_id": "73226820938", "text": "# -*- coding:utf-8 -*-\nimport requests, os\nfrom flask import render_template, request, flash, redirect, url_for, send_from_directory, send_file\nfrom flask_login import current_user, login_required\nfrom oauth2client.service_account import ServiceAccountCredentials\nfrom pandas import DataFrame\nfrom pydrive.auth import GoogleAuth\nfrom reportlab.lib import styles\nfrom reportlab.lib.enums import TA_RIGHT, TA_CENTER\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfbase import pdfmetrics\nfrom reportlab.pdfbase.ttfonts import TTFont\nfrom reportlab.platypus import SimpleDocTemplate, Paragraph, Image, TableStyle, Table, Spacer\nfrom sqlalchemy import cast, Date\nfrom werkzeug.utils import secure_filename\nfrom . import purchase_tracker_bp as purchase_tracker\nfrom .forms import *\nfrom datetime import datetime\nfrom pytz import timezone\nfrom pydrive.drive import GoogleDrive\nfrom .models import PurchaseTrackerAccount, PurchaseTrackerForm\nfrom flask_mail import Message\nfrom ..main import mail\nfrom ..roles import finance_procurement_permission\n\n# Upload images for Google Drive\n\n\nFOLDER_ID = \"1JYkU2kRvbvGnmpQ1Tb-TcQS-vWQKbXvy\"\n\njson_keyfile = requests.get(os.environ.get('GOOGLE_APPLICATION_CREDENTIALS')).json()\n\nbangkok = timezone('Asia/Bangkok')\n\nALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}\n\n\n@purchase_tracker.route('/official/')\n@login_required\ndef landing_page():\n    return render_template('purchase_tracker/first_page.html')\n\n\n@purchase_tracker.route('/personnel/personnel_index')\ndef staff_index():\n    return render_template('purchase_tracker/personnel/personnel_index.html')\n\n\n@purchase_tracker.route('/personnel/personnel_index/e-form/method/select/<int:account_id>')\ndef select_form(account_id):\n    account = PurchaseTrackerAccount.query.get(account_id)\n    return render_template('purchase_tracker/personnel/alternative_form.html', account=account)\n\n\n@purchase_tracker.route('/main')\ndef index():\n    return render_template('purchase_tracker/index.html')\n\n\n@purchase_tracker.route('/account/create', methods=['GET', 'POST'])\n@login_required\ndef add_account():\n    form = CreateAccountForm()\n    if request.method == 'POST':\n        filename = ''\n        account = PurchaseTrackerAccount()\n        form.populate_obj(account)\n        account.creation_date = bangkok.localize(datetime.now())\n        account.staff = current_user\n        drive = initialize_gdrive()\n        if form.upload.data:\n            if not filename or (form.upload.data.filename != filename):\n                upfile = form.upload.data\n                filename = secure_filename(upfile.filename)\n                upfile.save(filename)\n                file_drive = drive.CreateFile({'title': filename,\n                                               'parents': [{'id': FOLDER_ID, \"kind\": \"drive#fileLink\"}]})\n                file_drive.SetContentFile(filename)\n                try:\n                    file_drive.Upload()\n                    permission = file_drive.InsertPermission({'type': 'anyone',\n                                                              'value': 'anyone',\n                                                              'role': 'reader'})\n                except:\n                    flash('Failed to upload the attached file to the Google drive.', 'danger')\n                else:\n                    flash('The attached file has been uploaded to the Google drive', 'success')\n                    account.url = file_drive['id']\n\n        db.session.add(account)\n        db.session.commit()\n        flash(u'บันทึกข้อมูลสำเร็จ.', 'success')\n        return render_template('purchase_tracker/personnel/personnel_index.html')\n        # Check Error\n    else:\n        for er in form.errors:\n            flash(er, 'danger')\n    return render_template('purchase_tracker/create_account.html', form=form)\n\n\ndef initialize_gdrive():\n    gauth = GoogleAuth()\n    scopes = ['https://www.googleapis.com/auth/drive']\n    gauth.credentials = ServiceAccountCredentials.from_json_keyfile_dict(json_keyfile, scopes)\n    return GoogleDrive(gauth)\n\n\n@purchase_tracker.route('/track/')\n@purchase_tracker.route('/track/<int:account_id>', methods=['GET'])\ndef track(account_id=None):\n    list_type = request.args.get('list_type')\n    if list_type == \"myAccount\" or list_type is None:\n        accounts = PurchaseTrackerAccount.query.filter_by(staff_id=current_user.id).all()\n    elif list_type == \"ourAccount\":\n        org = current_user.personal_info.org\n        accounts = [account for account in PurchaseTrackerAccount.query.all()\n                    if account.staff.personal_info.org == org]\n    return render_template('purchase_tracker/tracking.html',\n                           account_id=account_id, accounts=accounts, list_type=list_type)\n\n\n@purchase_tracker.route('/track/<int:account_id>/view')\ndef view_info_track(account_id=None):\n    from sqlalchemy import desc\n    if account_id:\n        account = PurchaseTrackerAccount.query.get(account_id)\n        # better make use of the relationship!\n        activities = [a.to_list() for a in account.records.all()]\n    else:\n        flash(u'ข้อมูลจะปรากฎเมื่อหน่วยงานคลังและพัสดุอัพเดตเรียบร้อย', 'warning')\n        activities = []\n    # activities = [a.to_list() for a in PurchaseTrackerStatus.query.filter_by(account_id=account_id)\n    #     .order_by(PurchaseTrackerStatus.start_date)]\n    if not activities:\n        default_date = datetime.now().isoformat()\n    else:\n        default_date = activities[-1][3]\n    return render_template('purchase_tracker/view_info_track.html',\n                           account_id=account_id, account=account, desc=desc,\n                           PurchaseTrackerStatus=PurchaseTrackerStatus,\n                           activities=activities, default_date=default_date)\n\n\n@purchase_tracker.route('/account/<int:account_id>/edit', methods=['GET', 'POST'])\n@login_required\ndef edit_account(account_id):\n    account = PurchaseTrackerAccount.query.get(account_id)\n    form = CreateAccountForm(obj=account)\n    if request.method == 'POST':\n        filename = ''\n        form.populate_obj(account)\n        account.creation_date = bangkok.localize(datetime.now())\n        account.staff = current_user\n        drive = initialize_gdrive()\n        if form.upload.data:\n            if not filename or (form.upload.data.filename != filename):\n                upfile = form.upload.data\n                filename = secure_filename(upfile.filename)\n                upfile.save(filename)\n                file_drive = drive.CreateFile({'title': filename,\n                                               'parents': [{'id': FOLDER_ID, \"kind\": \"drive#fileLink\"}]})\n                file_drive.SetContentFile(filename)\n                try:\n                    file_drive.Upload()\n                    permission = file_drive.InsertPermission({'type': 'anyone',\n                                                              'value': 'anyone',\n                                                              'role': 'reader'})\n                except:\n                    flash('Failed to upload the attached file to the Google drive.', 'danger')\n                else:\n                    flash('The attached file has been uploaded to the Google drive', 'success')\n                    purchase_tracker.url = file_drive['id']\n\n        db.session.add(account)\n        db.session.commit()\n        flash(u'บันทึกข้อมูลสำเร็จ.', 'success')\n        return redirect(url_for('purchase_tracker.track'))\n        # Check Error\n    else:\n        for er in form.errors:\n            flash(er, 'danger')\n    return render_template('purchase_tracker/edit_account.html', form=form, account_id=account_id)\n\n\n@purchase_tracker.route('/accounts/<int:account_id>/cancel', methods=['GET'])\n@login_required\ndef cancel_account(account_id):\n    account = PurchaseTrackerAccount.query.get(account_id)\n    if not account.cancelled_datetime:\n        account.cancelled_datetime = datetime.now(tz=bangkok)\n        account.cancelled_by = current_user\n        db.session.add(account)\n        db.session.commit()\n        flash(u'ยกเลิกบัญชีเรียบร้อยแล้ว', 'success')\n    else:\n        flash(u'บัญชีนี้ถูกยุติการดำเนินการแล้ว', 'warning')\n    next = request.args.get('next')\n    if next:\n        return redirect(next)\n    return redirect(url_for('purchase_tracker.track', account_id=account_id))\n\n\n@purchase_tracker.route('/accounts/<int:account_id>/close', methods=['GET'])\n@login_required\ndef close_account(account_id):\n    account = PurchaseTrackerAccount.query.get(account_id)\n    if not account.end_datetime:\n        account.end_datetime = datetime.now(tz=bangkok)\n        db.session.add(account)\n        db.session.commit()\n        flash(u'ปิดบัญชีเรียบร้อยแล้ว', 'success')\n    else:\n        flash(u'บัญชีนี้ถูกปิดการดำเนินการแล้ว', 'warning')\n    return redirect(url_for('purchase_tracker.update_status', account_id=account_id))\n\n\n@purchase_tracker.route('/supplies/')\n@finance_procurement_permission.require()\ndef supplies():\n    from sqlalchemy import desc\n    accounts = PurchaseTrackerAccount.query.all()\n    return render_template('purchase_tracker/procedure_supplies.html',\n                           accounts=accounts,\n                           desc=desc,\n                           PurchaseTrackerStatus=PurchaseTrackerStatus)\n\n\n@purchase_tracker.route('/description')\ndef description():\n    return render_template('purchase_tracker/description.html')\n\n\n@purchase_tracker.route('/contact')\ndef contact():\n    return render_template('purchase_tracker/contact_us.html')\n\n\ndef send_mail(recp, title, message):\n    message = Message(subject=title, body=message, recipients=recp)\n    mail.send(message)\n\n\n@purchase_tracker.route('/account/<int:account_id>/update', methods=['GET', 'POST'])\n@finance_procurement_permission.require()\n@login_required\ndef update_status(account_id):\n    form = StatusForm()\n    account = PurchaseTrackerAccount.query.get(account_id)\n    if request.method == 'POST':\n        if form.validate_on_submit():\n            status = PurchaseTrackerStatus()\n            form.populate_obj(status)\n            status.account_id = account_id\n            status.status_date = bangkok.localize(datetime.now())\n            status.creation_date = bangkok.localize(datetime.now())\n            status.cancel_datetime = bangkok.localize(datetime.now())\n            status.update_datetime = bangkok.localize(datetime.now())\n            status.staff = current_user\n            if not form.other_activity.data and not form.activity.data:\n                flash(u'กรุณาเลือกหัวข้อกิจกรรมหรือใส่กิจกรรมอื่นๆ.', 'danger')\n                return redirect(\n                    url_for('purchase_tracker.update_status', account_id=account_id, form=form, account=account))\n            db.session.add(status)\n            db.session.commit()\n            title = u'แจ้งเตือนการปรับเปลี่ยนสถานะการจัดซื้อพัสดุและครุภัณฑ์หมายเลข {}'.format(status.account.number)\n            message = u'เรียน {}\\n\\nสถานะการจัดซื้อพัสดุและครุภัณฑ์หมายเลข {} คือ {}' \\\n                .format(current_user.personal_info.fullname, status.account.number,\n                        status.other_activity or status.activity.activity)\n            message += u'\\n\\n======================================================'\n            message += u'\\nอีเมลนี้ส่งโดยระบบอัตโนมัติ กรุณาอย่าตอบกลับ ' \\\n                       u'หากมีปัญหาใดๆเกี่ยวกับเว็บไซต์กรุณาติดต่อหน่วยข้อมูลและสารสนเทศ '\n            message += u'\\nThis email was sent by an automated system. Please do not reply.' \\\n                       u' If you have any problem about website, please contact the IT unit.'\n            send_mail([u'{}@mahidol.ac.th'.format(account.staff.email)], title, message)\n            flash(u'อัพเดตข้อมูลเรียบร้อย', 'success')\n            form.activity.data = \"\"\n            form.other_activity.data = \"\"\n            form.comment.data = \"\"\n        # Check Error\n        else:\n            flash(form.errors, 'danger')\n\n    activities = [a.to_list() for a in PurchaseTrackerStatus.query.filter_by(account_id=account_id)\n        .order_by(PurchaseTrackerStatus.start_date)]\n    if not activities:\n        default_date = datetime.now().isoformat()\n    else:\n        default_date = activities[-1][3]\n    return render_template('purchase_tracker/update_record.html',\n                           account_id=account_id, form=form, activities=activities, account=account,\n                           default_date=default_date)\n\n\n@purchase_tracker.route('/account/<int:account_id>/status/<int:status_id>/edit', methods=['GET', 'POST'])\n@finance_procurement_permission.require()\n@login_required\ndef edit_update_status(account_id, status_id):\n    status = PurchaseTrackerStatus.query.get(status_id)\n    form = StatusForm(obj=status)\n    if request.method == 'POST':\n        if form.validate_on_submit():\n            form.populate_obj(status)\n            status.account_id = account_id\n            status.status_date = bangkok.localize(datetime.now())\n            status.creation_date = bangkok.localize(datetime.now())\n            status.cancel_datetime = bangkok.localize(datetime.now())\n            status.update_datetime = bangkok.localize(datetime.now())\n            status.staff = current_user\n            db.session.add(status)\n            db.session.commit()\n            title = u'แจ้งเตือนการแก้ไขปรับเปลี่ยนสถานะการจัดซื้อพัสดุและครุภัณฑ์หมายเลข {}'.format(\n                status.account.number)\n            message = u'เรียน {}\\n\\nสถานะการจัดซื้อพัสดุและครุภัณฑ์หมายเลข {} คือ {}' \\\n                .format(current_user.personal_info.fullname, status.account.number,\n                        status.other_activity or status.activity.activity)\n            message += u'\\n\\n======================================================'\n            message += u'\\nอีเมลนี้ส่งโดยระบบอัตโนมัติ กรุณาอย่าตอบกลับ ' \\\n                       u'หากมีปัญหาใดๆเกี่ยวกับเว็บไซต์กรุณาติดต่อหน่วยข้อมูลและสารสนเทศ '\n            message += u'\\nThis email was sent by an automated system. Please do not reply.' \\\n                       u' If you have any problem about website, please contact the IT unit.'\n            send_mail([u'{}@mahidol.ac.th'.format(status.account.staff.email)], title, message)\n            flash(u'แก้ไขข้อมูลเรียบร้อย', 'success')\n        return redirect(url_for('purchase_tracker.update_status', status_id=status.id, account_id=account_id))\n    return render_template('purchase_tracker/edit_update_record.html',\n                           account_id=account_id, form=form)\n\n\n@purchase_tracker.route('/account/<int:account_id>/status/<int:status_id>/delete')\n@finance_procurement_permission.require()\n@login_required\ndef delete_update_status(account_id, status_id):\n    if account_id:\n        status = PurchaseTrackerStatus.query.get(status_id)\n        flash(u'The update status has been removed.')\n        db.session.delete(status)\n        db.session.commit()\n        return redirect(url_for('purchase_tracker.update_status', account_id=account_id))\n\n\n@purchase_tracker.route('/create/<int:account_id>/activity', methods=['GET', 'POST'])\n@finance_procurement_permission.require()\n@login_required\ndef add_activity(account_id):\n    activity = db.session.query(PurchaseTrackerActivity)\n    form = CreateActivityForm()\n    if request.method == 'POST':\n        if form.validate_on_submit():\n            new_activity = PurchaseTrackerActivity()\n            form.populate_obj(new_activity)\n            db.session.add(new_activity)\n            db.session.commit()\n            flash(u'บันทึกการเพิ่มกิจกรรมใหม่สำเร็จ.', 'success')\n            return redirect(url_for('purchase_tracker.update_status', account_id=account_id))\n        # Check Error\n        else:\n            for er in form.errors:\n                flash(er, 'danger')\n    return render_template('purchase_tracker/create_activity.html', form=form, activity=activity, account_id=account_id)\n\n\n@purchase_tracker.route('/dashboard', methods=['GET', 'POST'])\ndef show_info_page():\n    start_date = None\n    end_date = None\n    account_query = PurchaseTrackerAccount.query.all()\n    form = ReportDateForm()\n    if request.method == 'POST':\n        if form.validate_on_submit():\n            start_date = datetime.strptime(form.start_date.data, '%d-%m-%Y')\n            end_date = datetime.strptime(form.end_date.data, '%d-%m-%Y')\n            account_query = PurchaseTrackerAccount.query.filter(\n                cast(PurchaseTrackerAccount.booking_date, Date) >= start_date) \\\n                .filter(cast(PurchaseTrackerAccount.booking_date, Date) <= end_date)\n        else:\n            flash(form.errors, 'danger')\n    return render_template('purchase_tracker/info_page.html', account_query=account_query, form=form,\n                           start_date=start_date, end_date=end_date)\n\n\n@purchase_tracker.route('/dashboard/info/download', methods=['GET'])\ndef dashboard_info_download():\n    records = []\n    start_date = request.args.get('start_date')\n    end_date = request.args.get('end_date')\n    if start_date and end_date:\n        accounts = PurchaseTrackerAccount.query.filter(cast(PurchaseTrackerAccount.booking_date, Date) >= start_date) \\\n            .filter(cast(PurchaseTrackerAccount.booking_date, Date) <= end_date)\n    else:\n        accounts = PurchaseTrackerAccount.query.all()\n\n    for account in accounts:\n        for record in account.records:\n            records.append({\n                u'เลขที่หนังสือ': u\"{}\".format(account.number),\n                u'วันที่หนังสือ': u\"{}\".format(account.booking_date),\n                u'ชื่อ': u\"{}\".format(account.subject),\n                u'วงเงินหลักการ': u\"{:,.2f}\".format(account.amount),\n                u'รูปแบบหลักการ': u\"{}\".format(account.formats),\n                u'ผู้สร้าง account โดย': u\"{}\".format(account.staff.personal_info.fullname),\n                u'หน่วยงาน/ภาควิชา': u\"{}\".format(account.staff.personal_info.org.name),\n                u'กิจกรรม': u\"{}\".format(record.other_activity or record.activity.activity),\n                u'ผู้รับผิดชอบ': u\"{}\".format(record.staff.personal_info.fullname),\n                u'วันเริ่มกิจกรรม': u\"{}\".format(record.start_date),\n                u'วันสิ้นสุดกิจกรรม': u\"{}\".format(record.end_date),\n                u'หมายเหตุเพิ่มเติม': u\"{}\".format(record.comment),\n                u'เวลาดำเนินกิจกรรม': u\"{}\".format(record.weekdays),\n            })\n    df = DataFrame(records)\n    df.to_excel('account_summary.xlsx')\n    return send_file(os.path.join(os.getcwd(), 'account_summary.xlsx'))\n\n\n# @purchase_tracker.route('/personnel/personnel_index/e-form/create/<string:form_code>/<int:account_id>', methods=['GET', 'POST'])\n# @login_required\n# def create_form(account_id, form_code):\n#     account = PurchaseTrackerAccount.query.get(account_id)\n#     MTPCform = create_MTPCForm(acnt=account)\n#     form = MTPCform()\n#     if form.validate_on_submit():\n#         new_form = PurchaseTrackerForm()\n#         form.populate_obj(new_form)\n#         new_form.staff = current_user\n#         db.session.add(new_form)\n#         db.session.commit()\n#         flash(u'บันทึกข้อมูลสำเร็จ.', 'success')\n#         form_letter(new_form, account)\n#         return send_file('e-form.pdf')\n#     # Check Error\n#     else:\n#         for er in form.errors:\n#             flash(\"{}:{}\".format(er,form.errors[er]), 'danger')\n#     return render_template('purchase_tracker/personnel/create_form_{}.html'.format(form_code), form=form, account=account)\n#\n#\n# sarabun_font = TTFont('Sarabun', 'app/static/fonts/THSarabunNew.ttf')\n# pdfmetrics.registerFont(sarabun_font)\n# style_sheet = getSampleStyleSheet()\n# style_sheet.add(ParagraphStyle(name='ThaiStyle', fontName='Sarabun'))\n# style_sheet.add(ParagraphStyle(name='ThaiStyleNumber', fontName='Sarabun', alignment=TA_RIGHT))\n# style_sheet.add(ParagraphStyle(name='ThaiStyleCenter', fontName='Sarabun', alignment=TA_CENTER))\n#\n#\n# def form_letter(form, account):\n#     logo = Image('app/static/img/logo-MU.jpg', 60, 60)\n#\n#     def all_page_setup(canvas, doc):\n#         canvas.saveState()\n#         logo_image = ImageReader('app/static/img/logo-MU.jpg')\n#         canvas.drawImage(logo_image, 10, 700, width=70, height=70)\n#         canvas.restoreState()\n#\n#     doc = SimpleDocTemplate(\"app/e-form.pdf\",\n#                             pagesize=letter,\n#                             rightMargin=72,\n#                             leftMargin=72,\n#                             topMargin=72,\n#                             bottomMargin=18)\n#\n#\n#     data = [ Paragraph(u'<font size=16>ภาควิชา / ศูนย์ {}</font>'.format(account.staff.personal_info.org.name), style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>ที่ {}</font>'.format(form.account.number), style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>วันที่ {}</font>'.format(form.account.creation_date), style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>เรื่อง {}</font>'.format(form.account.subject), style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>ข้าพเจ้า {}</font>'.format(form.name), style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>เหตุผลและความจำเป็นเร่งด่วนที่ต้องซื้อหรือจ้าง {}</font>'.format(form.reason),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>รายละเอียดของพัสดุที่ซื้อหรือจ้าง {}</font>'.format(form.account.desc),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>วงเงินที่ซื้อหรือจ้างในครั้งนี้เป็นเงินเท่าไหร่ {}</font>'.format(form.account.amount),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>จาก {}</font>'.format(form.account.amount),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>ตามใบส่งของ/ใบเสร็จรับเงินเล่มที่ {}</font>'.format(form.book),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>เลขที่ {}</font>'.format(form.number),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>วันที่ {}</font>'.format(form.receipt_date),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>โดยขอเบิกจ่ายจากเงิน {}</font>'.format(form.disbursement_method),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>ประจำปีงบประมาณ {}</font>'.format(form.financial_year),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>วันที่ {}</font>'.format(form.receipt_date),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>รหัสศูนย์ต้นทุน {}</font>'.format(form.cost_center),\n#                        style=style_sheet['ThaiStyle']),\n#              Paragraph(u'<font size=16>รหัสใบสั่งงานภายใน {}</font>'.format(form.internal_order),\n#                        style=style_sheet['ThaiStyle']),\n#              ]\n#     data.append(Spacer(1, 12))\n#\n#     doc.build(data, onLaterPages=all_page_setup, onFirstPage=all_page_setup)\n\n", "repo_name": "MUMT-IT/mis2018", "sub_path": "app/purchase_tracker/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 25624, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "46", "api": [{"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 33, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 48, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query.get", "line_number": 53, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 71, "usage_type": "name"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 100, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 63, "usage_type": "name"}, {"api_name": "pydrive.auth.GoogleAuth", "line_number": 104, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials.from_json_keyfile_dict", "line_number": 106, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials", "line_number": 106, "usage_type": "name"}, {"api_name": "pydrive.drive.GoogleDrive", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 113, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.filter_by", "line_number": 115, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 115, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 115, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 115, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 115, "usage_type": "name"}, {"api_name": "flask_login.current_user.personal_info", "line_number": 117, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 117, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.all", "line_number": 118, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 120, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query.get", "line_number": 128, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 128, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 128, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 132, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 137, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 137, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 140, "usage_type": "call"}, {"api_name": "sqlalchemy.desc", "line_number": 141, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.get", "line_number": 149, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 149, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 149, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 151, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 154, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 154, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 155, "usage_type": "name"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 160, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 171, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 173, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 178, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 179, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 179, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 183, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 184, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 147, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.get", "line_number": 190, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 190, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 190, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 192, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 192, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 193, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 196, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 198, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 199, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 199, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 199, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 201, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 202, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 202, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 188, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.get", "line_number": 208, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 208, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 208, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 210, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 210, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 213, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 215, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 216, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 216, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 206, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.all", "line_number": 223, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 223, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 223, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 224, "usage_type": "call"}, {"api_name": "sqlalchemy.desc", "line_number": 226, "usage_type": "name"}, {"api_name": "roles.finance_procurement_permission.require", "line_number": 220, "usage_type": "call"}, {"api_name": "roles.finance_procurement_permission", "line_number": 220, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 232, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 237, "usage_type": "call"}, {"api_name": "flask_mail.Message", "line_number": 241, "usage_type": "call"}, {"api_name": "main.mail.send", "line_number": 242, "usage_type": "call"}, {"api_name": "main.mail", "line_number": 242, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.get", "line_number": 250, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 250, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 250, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 251, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 251, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 256, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 256, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 257, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 257, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 258, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 258, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 259, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 259, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 260, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 262, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 263, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 264, "usage_type": "call"}, {"api_name": "flask_login.current_user.personal_info", "line_number": 269, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 269, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 277, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 283, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 288, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 288, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 291, "usage_type": "call"}, {"api_name": "roles.finance_procurement_permission.require", "line_number": 246, "usage_type": "call"}, {"api_name": "roles.finance_procurement_permission", "line_number": 246, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 247, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 302, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 302, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 306, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 306, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 307, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 307, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 308, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 308, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 309, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 309, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 310, "usage_type": "name"}, {"api_name": "flask_login.current_user.personal_info", "line_number": 316, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 316, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 324, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 325, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 325, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 326, "usage_type": "call"}, {"api_name": "roles.finance_procurement_permission.require", "line_number": 297, "usage_type": "call"}, {"api_name": "roles.finance_procurement_permission", "line_number": 297, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 298, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 336, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 339, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 339, "usage_type": "call"}, {"api_name": "roles.finance_procurement_permission.require", "line_number": 331, "usage_type": "call"}, {"api_name": "roles.finance_procurement_permission", "line_number": 331, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 332, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 348, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 348, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 354, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 355, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 355, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 359, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 360, "usage_type": "call"}, {"api_name": "roles.finance_procurement_permission.require", "line_number": 343, "usage_type": "call"}, {"api_name": "roles.finance_procurement_permission", "line_number": 343, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 344, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.all", "line_number": 367, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 367, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 367, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 369, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 369, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 371, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 371, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 372, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 372, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.filter", "line_number": 373, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 373, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 373, "usage_type": "name"}, {"api_name": "sqlalchemy.cast", "line_number": 374, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 374, "usage_type": "argument"}, {"api_name": "models.PurchaseTrackerAccount.booking_date", "line_number": 374, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 374, "usage_type": "name"}, {"api_name": "sqlalchemy.cast", "line_number": 375, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 375, "usage_type": "argument"}, {"api_name": "models.PurchaseTrackerAccount.booking_date", "line_number": 375, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 375, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 377, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 378, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 385, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 385, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 385, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 386, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 386, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 386, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.filter", "line_number": 388, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 388, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 388, "usage_type": "name"}, {"api_name": "sqlalchemy.cast", "line_number": 388, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 388, "usage_type": "argument"}, {"api_name": "models.PurchaseTrackerAccount.booking_date", "line_number": 388, "usage_type": "attribute"}, {"api_name": "sqlalchemy.cast", "line_number": 389, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 389, "usage_type": "argument"}, {"api_name": "models.PurchaseTrackerAccount.booking_date", "line_number": 389, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 389, "usage_type": "name"}, {"api_name": "models.PurchaseTrackerAccount.query.all", "line_number": 391, "usage_type": "call"}, {"api_name": "models.PurchaseTrackerAccount.query", "line_number": 391, "usage_type": "attribute"}, {"api_name": "models.PurchaseTrackerAccount", "line_number": 391, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 410, "usage_type": "call"}, {"api_name": "flask.send_file", "line_number": 412, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 412, "usage_type": "call"}, {"api_name": "os.path", "line_number": 412, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 412, "usage_type": "call"}]}
{"seq_id": "3389745396", "text": "import numpy as np\nfrom flask import Flask, request, jsonify, render_template\nimport pickle\n\n# create the flask app\napp = Flask(__name__)\n\n# load the pickle model\nmodel = pickle.load(open(\"model.pkl\", \"rb\"))\n\n\n# defining the homepage\n@app.route(\"/\")\ndef home():\n    return render_template(\"index.html\")\n\n\n# defining the predict page\n@app.route(\"/predict\", methods=[\"POST\"])\n@app.route(\"/predict\", methods=[\"POST\"])\ndef predict():\n    input = request.get_json()\n    features = [np.array(list(input.values()))]\n    prediction = model.predict(features)[0]\n    print(features)\n    if prediction == 0:\n        prediction = \"Drizzle\"\n    elif prediction == 1:\n        prediction = \"Foggy\"\n    elif prediction == 2:\n        prediction = \"Rain\"\n    elif prediction == 3:\n        prediction = \"Snow\"\n    elif prediction == 4:\n        prediction = \"Sunny\"\n\n    return jsonify({\"prediction_text\": \"{}\".format(prediction)})\n\n\nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\", port=5000)\n", "repo_name": "Sahilgupta4103/WeatherPred-webApp", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 980, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 37, "usage_type": "call"}]}
{"seq_id": "7632612459", "text": "#!/usr/bin/env python\n\n\"\"\"\nCalculate enrichment statistics for two sets of fasta files\nInputs:\n   two fasta files to compare\n   file containing patterns to check\nOutputs:\n   pickled dictionary of pattern enrichments\nBen Ober-Reynolds\n\"\"\"\n\n\nimport os\nimport sys\nimport re\nimport time\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport pickle\nfrom Bio import SeqIO\nfrom joblib import Parallel, delayed\n\n\ndef main():\n\n    # set up command line argument parser\n    parser = argparse.ArgumentParser(description='Calculate motif densities \\\n        for a target and a background set of fastas.')\n    group = parser.add_argument_group('required arguments:')\n    group.add_argument('-fi', '--fasta_of_interest', required=True,\n        help='file containing clusters of interest')\n    group.add_argument('-fb', '--background_fasta', required=True,\n        help='file containing background clusters')\n    group.add_argument('-pf', '--pattern_file', required=True,\n        help='file containing patterns to check for. Format: \\\n        {pattern name}\\\\t{regex_pattern}')\n    group = parser.add_argument_group('optional arguments')\n    group.add_argument('-od', '--output_directory', default=\".\",\n        help='output directory for statistics file and figures. \\\n        Default is current directory')\n    group.add_argument('-op', '--output_prefix', default=\"enrichment\",\n        help='output prefix for results file and figures')\n    group.add_argument('-isn', '--interesting_seq_name', \n        default=\"Sequences of Interest\",\n        help='The name of the sequence of interest pool. Default is \\\n        \"Sequences of Interest\"')\n    group.add_argument('-bsn', '--background_seq_name', \n        default=\"Background Sequences\", help='The name of the background \\\n        sequence pool. Default is \"Background Sequences\"')\n    group.add_argument('-rc', '--reverse_comp', default=\"y\",\n        help='also calculate enrichment in reverse complement of each pool \\\n        [y/n]? Default is y.')\n    group.add_argument('-nb', '--num_bootstraps', type=int, default=1000,\n        help='number of times to resample pools for enrichment calculation. \\\n        Default is 1000.')\n    group.add_argument('-n', '--num_cores', type=int, default=1,\n        help='number of cores to use for bootstrapping.')\n\n    # print help if no arguments provided\n    if len(sys.argv) <= 1:\n        parser.print_help()\n        sys.exit()\n\n    # parse command line arguments\n    args = parser.parse_args()\n    numCores = args.num_cores\n\n    # Pre-defined variables, constants, and settings\n    input_file_format = 'fasta'\n    rev_c_tag = \"Rev-Comp\"\n    output_prefix = time.strftime(\"%Y%m%d\") + \"_\" + args.output_prefix\n    pickle_file_ext = \"p\"\n\n    # Do some error checking before running this long script:\n    output_dir = args.output_directory\n    if not os.path.isdir(output_dir):\n        print(\"Error: invalid output directory. Exiting...\")\n        sys.exit()\n    \n    # Read in files:\n    seqs_of_interest = read_fasta(args.fasta_of_interest, input_file_format)\n    background_seqs = read_fasta(args.background_fasta, input_file_format)\n    pattern_dict = read_pattern_file(args.pattern_file)\n\n    # Find smallest pool size:\n    pool_size = min([len(seqs_of_interest), len(background_seqs)])\n\n    # seq pool dict:\n    seq_pool_dict = {args.interesting_seq_name: seqs_of_interest, \n    args.background_seq_name: background_seqs}\n\n    # Results dictionary:\n    density_result_dict = {}\n    for pname in pattern_dict.keys():\n        density_result_dict[pname] = {}\n\n    # compare to reverse complement?\n    if args.reverse_comp == 'y':\n        interesting_seq_rc_name = args.interesting_seq_name + \" \" + rev_c_tag\n        background_seq_rc_name = args.background_seq_name + \" \" + rev_c_tag\n        rc_seqs_of_interest = reverse_comp(seqs_of_interest)\n        rc_background_seqs = reverse_comp(background_seqs)\n        seq_pool_dict[interesting_seq_rc_name] = rc_seqs_of_interest\n        seq_pool_dict[background_seq_rc_name] = rc_background_seqs\n\n    # calculate motif density for each pattern\n    if numCores > 1:\n        with Parallel(n_jobs=numCores, verbose=10) as parallel: \n            for pname in pattern_dict.keys():\n                for pool_name in seq_pool_dict.keys():\n                    densities = []\n                    print(\"Calculating density of pattern '{}' in pool '{}'\\\n                        \".format(pname, pool_name))\n                    densities = parallel(delayed(calc_resampled_motif_density)\\\n                        (seq_pool_dict[pool_name], pool_size, pattern_dict[pname])\n                    for i in range(args.num_bootstraps))\n                    density_result_dict[pname][pool_name] = densities\n    else:\n        for pname in pattern_dict.keys():\n            for pool_name in seq_pool_dict.keys():\n                densities = []\n                print(\"Calculating density of pattern '{}' in pool '{}'\\\n                    \".format(pname, pool_name))\n                densities = [calc_resampled_motif_density(\n                    seq_pool_dict[pool_name], pool_size, pattern_dict[pname])\n                    for i in range(args.num_bootstraps)]\n                density_result_dict[pname][pool_name] = densities\n\n    # Dump results to pickle for latter replotting\n    with open(output_dir + '/' + output_prefix + '.' + pickle_file_ext, 'wb') as f:\n        pickle.dump(density_result_dict, f)\n\n\ndef read_fasta(filename, input_file_format):\n    \"\"\"\n    Read in a fasta file, and return sequences as a list.\n    Input: fasta filename\n    Output: sequence array \n    \"\"\"\n    fasta_list = []\n    with open(filename, 'r') as f:\n        for seq_rec in SeqIO.parse(f, input_file_format):\n            seq_rec = seq_rec.upper()\n            fasta_list.append(str(seq_rec.seq))\n    return np.array(fasta_list)\n\n\ndef read_pattern_file(filename):\n    \"\"\"\n    Read in a pattern file. Note that pattern files must be two-column,\n    tab-delimited files with the first column being the pattern name, and\n    the second column the regular expression defining that pattern.\n    \"\"\"\n    pattern_dict = {}\n    with open(filename, 'r') as f:\n        for line in f:\n            pname, reg_exp = line.strip().split('\\t')\n            reg_exp = re.compile(reg_exp)\n            pattern_dict[pname] = reg_exp\n    return pattern_dict\n\n\ndef reverse_comp(fasta_array):\n    \"\"\"\n    Reverse complement a list of sequences\n    Input: list of sequences\n    Output: reverse complement of same sequence list\n    \"\"\"\n    trans_table = str.maketrans('AGCT', 'TCGA')\n    rev_list = []\n    for seq in fasta_array:\n        rev_list.append(seq.translate(trans_table)[::-1])\n    return np.array(rev_list)\n\n\ndef calc_resampled_motif_density(seq_array, samp_size, regex):\n    \"\"\"\n    Calculate the length-normalized density of a specific regular\n    expression pattern in a resampled sequence pool.\n    Inputs: list of sequences, number of seqs to draw, regular expression pattern\n    Output: length-normalized motif density\n    \"\"\"\n    resampled_pool = np.random.choice(seq_array, size=samp_size, replace=True)\n    total_seq_space = 0\n    patterns_found = 0\n    for seq in resampled_pool:\n        patterns_found += len(re.findall(regex, seq))\n        total_seq_space += len(seq)\n    return patterns_found/total_seq_space\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "boberrey/sequence_analysis", "sub_path": "pattern_enrichment.py", "file_name": "pattern_enrichment.py", "file_ext": "py", "file_size_in_byte": 7332, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 64, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 80, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 110, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 116, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 133, "usage_type": "call"}, {"api_name": "Bio.SeqIO.parse", "line_number": 144, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 144, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 185, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 189, "usage_type": "call"}]}
{"seq_id": "38213118948", "text": "import re\nfrom collections import Counter\n\nimport gensim.parsing.preprocessing as proc\nfrom gensim.utils import deaccent, to_unicode\n\n####################\n# Global variables\n####################\n\n# Load list of stopwords\nwith open('stopwords-es.txt', 'r') as file:\n    stopwords = list()\n    for line in file:\n        stop_word = deaccent(line.strip('\\n'))\n        stopwords.append(stop_word)\nstopwords = frozenset(stopwords)\n\n# regexp for matching @usernames and #hashtags\ntw_handles = r\"([@][A-z]+)|([#][A-z]+)\"\n# for matching URLs\nurls = r\"((\\w+:\\/\\/)[-a-zA-Z0-9:@;?&=\\/%\\+\\.\\*!'\\(\\),\\$_\\{\\}\\^~\\[\\]`#|]+)\"\npunctuation_es = r'([!¡\"\\#\\$%\\&\\'\\(\\)\\*\\+,\\-\\./:;<=>\\?\\¿@\\[\\\\\\]\\^_`\\{\\|\\}\\~])+'\n# Master Regexp\nmulti_pattern = '|'.join([tw_handles, urls, punctuation_es])\nnon_plain_re = re.compile(multi_pattern, re.UNICODE)\n####################\n\ndef remove_non_plain(document):\n    \"\"\"\n    Replaces urls, @usernames, #tags, emojis and numbers\n    with a ' ' (space). Also removes accents and punctuation\n    to finally remove redundant whitespace and lowercase all\n    characters\n    :param document: string\n    :return: processed unicode string\n    \"\"\"\n    document = to_unicode(document)\n    document = non_plain_re.sub(' ', document)\n    document = proc.strip_non_alphanum(document)\n    document = proc.strip_numeric(document)\n    document = proc.strip_multiple_whitespaces(document)\n    document = deaccent(document)\n    return document.lower()\n\ndef process(document):\n    \"\"\"\n    Tokenize a document (a tweet) removing:\n    - punctuation\n    - URLs and Twitter handles\n    - Uppercases\n    - Stopword\n    - Whitespace\n    :param document: string\n    :returns: a list of strings\n    \"\"\"\n    wordbag = list()\n    for token in set(remove_non_plain(document).split()):\n        if token not in stopwords and token != '' \\\n           and token != 'rt':\n            wordbag.append(token)\n    return wordbag\n\ndef init_counter(corpus):\n    \"\"\"\n    Creates a collections.Counter object from a corpus.\n    :param corpus: iterator or iterable of lists of strings\n    :returns: Counter\n    \"\"\"\n    ctr = Counter()\n    for worbag in corpus:\n        ctr.update(worbag)\n\n    return ctr\n", "repo_name": "CeMasChile/Twitter", "sub_path": "codigo/npl_utils.py", "file_name": "npl_utils.py", "file_ext": "py", "file_size_in_byte": 2172, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "gensim.utils.deaccent", "line_number": 15, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 26, "usage_type": "call"}, {"api_name": "re.UNICODE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "gensim.utils.to_unicode", "line_number": 38, "usage_type": "call"}, {"api_name": "gensim.parsing.preprocessing.strip_non_alphanum", "line_number": 40, "usage_type": "call"}, {"api_name": "gensim.parsing.preprocessing", "line_number": 40, "usage_type": "name"}, {"api_name": "gensim.parsing.preprocessing.strip_numeric", "line_number": 41, "usage_type": "call"}, {"api_name": "gensim.parsing.preprocessing", "line_number": 41, "usage_type": "name"}, {"api_name": "gensim.parsing.preprocessing.strip_multiple_whitespaces", "line_number": 42, "usage_type": "call"}, {"api_name": "gensim.parsing.preprocessing", "line_number": 42, "usage_type": "name"}, {"api_name": "gensim.utils.deaccent", "line_number": 43, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 70, "usage_type": "call"}]}
{"seq_id": "25014921855", "text": "from ocr import *\nimport glob\nimport matplotlib.pyplot as plt\n\nweight_path = r'.\\weights\\ocr\\yolov3-ocr_final.weights'\ncfg_path = r'.\\weights\\ocr\\yolov3-ocr.cfg'\n\ndef show_image(image):\n    fig, ax1 = plt.subplots(1)\n    ax1.imshow(image)\n    plt.show()\n\nimage_paths = glob.glob(\"plates1/*.jpg\")\nimage_paths = glob.glob(\"m/*.jpg\")\nimage_paths = ['need contrast\\\\new\\\\534_0_plate.jpg']\nid = 0\nfor img_path in image_paths:\n    platereader = PlateReader()\n    img, _, _, _ = platereader.load_image(img_path)\n    #segmentedImage, plate_label = platereader.retrieve_label_and_segmentedImage_from(img_path, weight_path, cfg_path)\n    segmentedImage, plate_label, sum_confidences = platereader.final_retrieve_label_and_segmentedImage_from_im(img, weight_path, cfg_path)\n    #cv2.imshow(plate_label, segmentedImage)\n    #show_image(segmentedImage)\n    #cv2.imwrite(img_path, segmentedImage)\n    cv2.imwrite(img_path[:-4]+str(id)+\"_plate_\"+plate_label+\".jpg\", segmentedImage)\n    id += 1\n", "repo_name": "FerdaousAzh/Plate-detection.", "sub_path": "plate_ocr.py", "file_name": "plate_ocr.py", "file_ext": "py", "file_size_in_byte": 979, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 13, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "72142295485", "text": "#данный скрипт выдаёт словарь со всеми значениями и контекстами к ним (training set)\r\n\r\ndef scrape_txt():\r\n    from Zvezda import define_microcontext\r\n    from collections import Counter\r\n\r\n    with open(\"static/file.txt\") as f:\r\n        text = f.read()\r\n        word_meanings = {}\r\n\r\n    stopwords_r = open('static/stopwords_r.txt')\r\n    stopwords_r = stopwords_r.read()\r\n    stopwords_r = stopwords_r.split(', ')\r\n\r\n    for item in text.split('\\n\\n'):\r\n        text, meaning = item.split('значение: ')\r\n        found, context = define_microcontext(text) #возвращается найденная лемма \"звезда\" и контекст\r\n\r\n        context = list(filter(lambda x: x not in stopwords_r, context))#убираем стоп-слова из контекста\r\n\r\n        text = context\r\n\r\n        context = Counter(context) #Частотный словарь\r\n\r\n        context = {k: round(v / len(text), 3) for k, v in context.items()} #Присвоение MLE-коэффицента каждому слову контекста\r\n\r\n        word_meanings[meaning] = context\r\n\r\n    return word_meanings\r\n\r\n\r\n#for k,v in scrape_txt().items():\r\n#     print(k+':',v)\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "SilenosChestra/Finished_Projects", "sub_path": "Zvezda_Disambiguation/FlaskApplication/Scraper_txt.py", "file_name": "Scraper_txt.py", "file_ext": "py", "file_size_in_byte": 1262, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "Zvezda.define_microcontext", "line_number": 17, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 23, "usage_type": "call"}]}
{"seq_id": "17586935942", "text": "from scapy.all import IP, TCP, sniff\nimport logging    # first of all import the module\n\n\n\"\"\"\nRepresents a network packet with source and destination IP addresses\n\"\"\"\nclass Packet:\n    def __init__(self,rules):\n        self.rules = rules\n    #True, \"12.168.3.1009\", \"0\", \"10.0.0.100\", \"0\"\n    def get_summary(self, pkt):\n        try:\n            if TCP in pkt and IP in pkt:\n                for rule in self.rules:\n                    #print(\"src\",pkt[IP].src, pkt[TCP].sport,\"dst\", pkt[IP].dst, pkt[TCP].dport)\n                    #Check if the ip address and port number matches\n                    if (pkt[IP].src == rule[1] and pkt[IP].dst == rule[3]): \n\n                        if(True): #pkt[TCP].sport == rule[2]  ports changes a lot\n                            \n                            #If they matches check if that rule is to be allowed or blocked\n                            if rule[0] == True: \n                                log = \"Status: \", \"BLOCK\", \"Src IP: \",pkt[IP].src, \"Src Port:\", pkt[TCP].sport,\"Dst IP: \", pkt[IP].dst,\"Dst Port:\" ,pkt[TCP].dport\n                                logging.basicConfig(filename='firewall.log', filemode='w', format=\"%(asctime)s %(message)s\")\n                                logging.warning(log)\n                                print(log)\n                                \n                            elif rule[0] == False:\n                                log = \"Status: \", \"ALLOW\", \"Src IP: \",pkt[IP].src, \"Src Port:\", pkt[TCP].sport,\"Dst IP: \", pkt[IP].dst,\"Dst Port:\" ,pkt[TCP].dport\n                                logging.basicConfig(filename='firewall.log', filemode='w', format=\"%(asctime)s %(message)s\")\n                                logging.warning(log)\n                                print(log)\n                                \n                    else:\n                        print(\"Src IP: \",pkt[IP].src, \"Src Port:\", pkt[TCP].sport,\"Dst IP: \", pkt[IP].dst,\"Dst Port:\" ,pkt[TCP].dport)     \n            else:\n                pass\n                # return \"Waiting for pkt....\"\n        except Exception as e:\n            print(f\"[ERR] Error could not get pkt: {e}\")\n            False\n\n# capture = sniff(5)\n# capture.summary()    \n", "repo_name": "kudzaiprichard/python-packet-filter-firewall", "sub_path": "firewall/packet.py", "file_name": "packet.py", "file_ext": "py", "file_size_in_byte": 2200, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "46", "api": [{"api_name": "scapy.all.TCP", "line_number": 14, "usage_type": "name"}, {"api_name": "scapy.all.IP", "line_number": 14, "usage_type": "name"}, {"api_name": "scapy.all.IP", "line_number": 18, "usage_type": "name"}, {"api_name": "scapy.all.IP", "line_number": 24, "usage_type": "name"}, {"api_name": "scapy.all.TCP", "line_number": 24, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 26, "usage_type": "call"}, {"api_name": "scapy.all.IP", "line_number": 30, "usage_type": "name"}, {"api_name": "scapy.all.TCP", "line_number": 30, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 32, "usage_type": "call"}, {"api_name": "scapy.all.IP", "line_number": 36, "usage_type": "name"}, {"api_name": "scapy.all.TCP", "line_number": 36, "usage_type": "name"}]}
{"seq_id": "35707862664", "text": "\"\"\"initial\n\nRevision ID: 2bbb456c6a28\nRevises:\nCreate Date: 2023-11-04 14:46:55.786593\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op  # pylint: disable=no-name-in-module\n\n# revision identifiers, used by Alembic.\nrevision = '2bbb456c6a28'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table(\n        'user',\n        sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n        sa.Column('email', sa.String(length=64), nullable=False),\n        sa.Column(\n            'created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False\n        ),\n        sa.Column(\n            'updated_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False\n        ),\n        sa.PrimaryKeyConstraint('id', name=op.f('pk_user')),\n        sa.UniqueConstraint('id', name=op.f('uq_user_id')),\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('user')\n    # ### end Alembic commands ###\n", "repo_name": "tanasecucliciu/fastapi-async-demo-project", "sub_path": "src/app/alembic/versions/2bbb456c6a28_initial.py", "file_name": "2bbb456c6a28_initial.py", "file_ext": "py", "file_size_in_byte": 1127, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "alembic.op.create_table", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op.f", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 38, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 38, "usage_type": "name"}]}
{"seq_id": "11744648565", "text": "# _*_ coding:utf-8 _*_\n\nfrom django.conf.urls import url\n\nfrom .views import MessageDraftView, MessageInfoView, MessageManagementView, MessageSearchView,\\\n    MessageDraftFileUploadView, MessageCategoryManageView, ItemsMakeCountView, ItemReceiverCountView,\\\n    ItemsMakeSearchView,ItemReceiverSearchView\n\n\n__author__ = 'haoSev7'\n__date__ = '2017/5/26 21:59'\n\n\nurlpatterns = [\n    # 宣传管理信息申请表\n    url(r'^messagedraft/$', MessageDraftView.as_view(), name='message_draft'),\n    # 宣传信息统计页面\n    url(r'^messageinfo/$', MessageInfoView.as_view(), name='message_info'),\n    # 宣传信息管理页面\n    url(r'^management/$', MessageManagementView.as_view(), name='management'),\n    # 宣传信息查询页面\n    url(r'^messagesearch/$', MessageSearchView.as_view(), name='messagesearch'),\n    # 宣传信息申请表 附件上传\n    url(r'^messagedraftupload/$', MessageDraftFileUploadView.as_view(), name='message_upload'),\n    # 宣传信息类别管理页面\n    url(r'^messagecategory/$', MessageCategoryManageView.as_view(), name='management_category'),\n    # 宣传物资制作查询页面\n    url(r'^itemmakesearch/$', ItemsMakeSearchView.as_view(), name='itemmakesearch'),\n    # 宣传物资制作统计页面\n    url(r'^itemmakecount/$', ItemsMakeCountView.as_view(), name='item_make_count'),\n    # 宣传物资制作查询页面\n    url(r'^itemreceiversearch/$', ItemReceiverSearchView.as_view(), name='itemreceiversearch'),\n    # 宣传物资领用统计页面\n    url(r'^itemreceivercount/$', ItemReceiverCountView.as_view(), name='item_receiver_count'),\n]", "repo_name": "lhr520czx/Aproject", "sub_path": "apps/xuanchuan/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1599, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "views.MessageDraftView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "views.MessageDraftView", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "views.MessageInfoView.as_view", "line_number": 18, "usage_type": "call"}, {"api_name": "views.MessageInfoView", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "views.MessageManagementView.as_view", "line_number": 20, "usage_type": "call"}, {"api_name": "views.MessageManagementView", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "views.MessageSearchView.as_view", "line_number": 22, "usage_type": "call"}, {"api_name": "views.MessageSearchView", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "views.MessageDraftFileUploadView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "views.MessageDraftFileUploadView", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "views.MessageCategoryManageView.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "views.MessageCategoryManageView", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "views.ItemsMakeSearchView.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "views.ItemsMakeSearchView", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "views.ItemsMakeCountView.as_view", "line_number": 30, "usage_type": "call"}, {"api_name": "views.ItemsMakeCountView", "line_number": 30, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "views.ItemReceiverSearchView.as_view", "line_number": 32, "usage_type": "call"}, {"api_name": "views.ItemReceiverSearchView", "line_number": 32, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "views.ItemReceiverCountView.as_view", "line_number": 34, "usage_type": "call"}, {"api_name": "views.ItemReceiverCountView", "line_number": 34, "usage_type": "name"}]}
{"seq_id": "37895612584", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May  2 02:09:45 2023\n\n@author: bat_j\n\"\"\"\n\nfrom airflow import DAG\nfrom airflow.operators.python import PythonOperator\nfrom datetime import datetime, timedelta\nfrom python_scripts.Raw_Data_Processing import Raw_Data_Processing\nfrom python_scripts.Feature_engineering import Feature_engineering\nfrom python_scripts.unit_test import moving_mean_unit_test, moving_median_unit_test\nfrom python_scripts.Applying_ML import ML_model\n\ndefault_args = {\n    \"owner\": \"airflow\",\n    \"retries\": 5,\n    \"retry_delay\": timedelta(minutes=2),\n    \"start_date\": datetime(2023,5,11)\n    }\n\n\nwith DAG(\n        default_args = default_args,\n        dag_id = 'Data_Pipline_V1',\n        description = 'DAG for the assessment',\n        schedule_interval = '@daily'\n    ) as dag:\n    \n    Data_Processing = PythonOperator(\n        task_id = 'data_processing',\n        python_callable = Raw_Data_Processing  \n        )\n    \n    Feature_Engineering = PythonOperator(\n        task_id = 'feature_engineering',\n        python_callable = Feature_engineering\n        )\n    \n    Unit_test_1 = PythonOperator(\n        task_id = 'unit_test_mean',\n        python_callable = moving_mean_unit_test\n        )\n    \n    Unit_test_2 = PythonOperator(\n        task_id = 'unit_test_median',\n        python_callable = moving_median_unit_test  \n        )\n    \n    Machine_learning_model = PythonOperator(\n        task_id = 'Machine_learning_model',\n        python_callable = ML_model  \n        )\n    \n    Data_Processing >> Feature_Engineering\n    Feature_Engineering >> Unit_test_1\n    Feature_Engineering >> Unit_test_2\n    Unit_test_1 >> Machine_learning_model\n    Unit_test_2 >> Machine_learning_model", "repo_name": "Om-patel2398/Stock_Market_Prediction", "sub_path": "dags/Trial_1.py", "file_name": "Trial_1.py", "file_ext": "py", "file_size_in_byte": 1701, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "call"}, {"api_name": "airflow.DAG", "line_number": 24, "usage_type": "call"}, {"api_name": "airflow.operators.python.PythonOperator", "line_number": 31, "usage_type": "call"}, {"api_name": "python_scripts.Raw_Data_Processing.Raw_Data_Processing", "line_number": 33, "usage_type": "name"}, {"api_name": "airflow.operators.python.PythonOperator", "line_number": 36, "usage_type": "call"}, {"api_name": "python_scripts.Feature_engineering.Feature_engineering", "line_number": 38, "usage_type": "name"}, {"api_name": "airflow.operators.python.PythonOperator", "line_number": 41, "usage_type": "call"}, {"api_name": "python_scripts.unit_test.moving_mean_unit_test", "line_number": 43, "usage_type": "name"}, {"api_name": "airflow.operators.python.PythonOperator", "line_number": 46, "usage_type": "call"}, {"api_name": "python_scripts.unit_test.moving_median_unit_test", "line_number": 48, "usage_type": "name"}, {"api_name": "airflow.operators.python.PythonOperator", "line_number": 51, "usage_type": "call"}, {"api_name": "python_scripts.Applying_ML.ML_model", "line_number": 53, "usage_type": "name"}]}
{"seq_id": "38448267706", "text": "from collections import deque\nfrom copy import deepcopy\n\ndef solution(name):\n    answer = 0\n    queue = deque()\n    visited = [[False] for _ in name]\n    dd = [1, -1]\n    init = 'A' * len(name)\n    \n    # position, movements\n    queue.append([0, upAndDown(name[0]), name[0] + init[1:]])\n    visited[0] = True\n    result = 987654321\n    \n    while queue:\n        p, m, copied_name = queue.popleft()\n        \n        if copied_name == name:\n            result = min(result, m)\n            break\n            \n        for i in range(2):\n            _p = p + dd[i]\n            \n            if _p == -1: _p = len(name) - 1\n            elif _p == len(name):\n                _p = 0\n            \n            if not visited[_p]: continue\n            visited[_p] = True\n            if _p == len(name) - 1:\n                queue.append([_p, m + upAndDown(name[_p]) + 1, copied_name[0:_p] + name[_p]])\n            else:\n                queue.append([_p, m + upAndDown(name[_p]) + 1, copied_name[0:_p] + name[_p] + copied_name[_p + 1:]])\n            \n    answer = result\n    return answer\n\ndef upAndDown(alpha):\n    # starts with 'A'\n    # go up\n    up = ord('Z') - ord(alpha) + 1\n    \n    # go down\n    down = ord(alpha) - ord('A')\n    \n    return min(up, down)\n\nprint(solution(\"JEROEN\"))", "repo_name": "nayoon-kim/Algorithm", "sub_path": "조이스틱.py", "file_name": "조이스틱.py", "file_ext": "py", "file_size_in_byte": 1275, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.deque", "line_number": 6, "usage_type": "call"}]}
{"seq_id": "73685995002", "text": "from wordcloud import WordCloud, STOPWORDS\nimport numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n\n\nfile_path = \"Classic Computer Science Problems in Python by David Kopec (z-lib.org).txt\"\nfile_handle = open(file_path).read()\nstopwords = set(STOPWORDS)\nstopwords.add(\"will\")\n\nbook = WordCloud(max_words=5000, stopwords=stopwords)\nbook.generate(file_handle)\nplt.imshow(book, interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\nimage_file = \"griffin-silhouette-ancient-mythology-fantasy-vector-18016755.jpg\"\n\n# create mask\na_mask = np.array(Image.open(image_file))\n\nwc = WordCloud(background_color=\"black\", max_words=2000, mask=a_mask,\n               stopwords=stopwords)\nwc.generate(file_handle)\n\nplt.figure(figsize=(8,6), dpi=120)\nplt.imshow(wc, interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()\n\n", "repo_name": "gargnityansh/WordCloud", "sub_path": "wordcloud.py", "file_name": "wordcloud.py", "file_ext": "py", "file_size_in_byte": 821, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "wordcloud.STOPWORDS", "line_number": 10, "usage_type": "argument"}, {"api_name": "wordcloud.WordCloud", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 22, "usage_type": "name"}, {"api_name": "wordcloud.WordCloud", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}]}
{"seq_id": "42921966534", "text": "# This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. \n# If a copy of the MPL was not distributed with this file, \n# You can obtain one at https://mozilla.org/MPL/2.0/.\n# This file is part of NF Compose\n# [2019] - [2023] © NeuroForge GmbH & Co. KG\n\n\nfrom typing import Any\nfrom django.http.response import HttpResponse\n\nfrom django.shortcuts import render\nfrom rest_framework.decorators import permission_classes\nfrom rest_framework.permissions import IsAuthenticated\nfrom django.contrib.auth.decorators import login_required\n\nfrom skipper.settings import LOGIN_URL\n\nversion: str\nwith open('skipper/static-private/version.txt') as version_file:\n    version = version_file.read().replace('\\n', '')\n\nlicenses: str\nwith open('skipper/static-private/OPENSOURCE_LICENSES.html') as licenses_file:\n    licenses = licenses_file.read().replace('\\n', '')\n\n@permission_classes([IsAuthenticated])\n@login_required(login_url='/' + LOGIN_URL)\ndef licensing_view(request: Any) -> Any:\n    if request.method == 'GET':\n        return render(request, 'skipper/licensing.html', {\n            'name': 'Licensing',\n            'version': version\n        })\n\n@permission_classes([IsAuthenticated])\n@login_required(login_url='/' + LOGIN_URL)\ndef licensing_oss_view(request: Any) -> Any:\n    return HttpResponse(content=licenses, content_type='text/html')", "repo_name": "neuroforgede/nfcompose", "sub_path": "skipper/skipper/common/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1364, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "41", "api": [{"api_name": "typing.Any", "line_number": 28, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 26, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 27, "usage_type": "call"}, {"api_name": "skipper.settings.LOGIN_URL", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 37, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponse", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 35, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 35, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 36, "usage_type": "call"}, {"api_name": "skipper.settings.LOGIN_URL", "line_number": 36, "usage_type": "name"}]}
{"seq_id": "32718667329", "text": "import configs\nimport datasets\nimport numpy as np\nfrom torchvision import datasets as Datasets\n\n\n__all__ = ['MNIST']\n\n\nclass MNIST(datasets.BaseDataset):\n\n    def __init__(self, cfg, **kwargs):\n        super(MNIST, self).__init__(cfg, **kwargs)\n\n    @staticmethod\n    def more(cfg):\n        cfg.path = configs.env.getdir(cfg.path)\n        cfg.source.elements = cfg.source.width * cfg.source.height * cfg.source.time\n        out = dict()\n        out['elements'] = 1\n        cfg.out = configs.BaseConfig(out)\n        cfg.cross_folder = 0\n        cfg.index_cross = 0\n        return cfg\n\n    def load(self):\n        train = Datasets.MNIST(self.cfg.path, train=True, download=True)\n        test = Datasets.MNIST(self.cfg.path, train=False, download=True)\n\n        source_train, source_test = train.data.numpy()[:, np.newaxis, :, :], test.data.numpy()[:, np.newaxis, :, :]\n        source_train, source_test = source_train.astype(np.float32), source_test.astype(np.float32)\n        target_train, target_test = train.targets.numpy()[:, np.newaxis], test.targets.numpy()[:, np.newaxis]\n        count_train, count_test = len(train), len(test)\n        count = count_train + count_test\n\n        return {'source_train': source_train, 'target_train': target_train,\n                'source_test': source_test, 'target_test': target_test}, \\\n               {'count_train': count_train, 'count_test': count_test, 'count': count}\n\n    def __getitem__(self, index):\n        if index < self.cfg.data_count['count_train']:\n            source, target = self.data['source_train'][index], self.data['target_train'][index]\n        else:\n            index = index - self.cfg.data_count['count_train']\n            source, target = self.data['source_test'][index], self.data['target_test'][index]\n        return {'source': source, 'target': target}, index\n\n    def split(self, index_cross=None):\n        self._reset_norm(split_items=[['source_train', 'target_train'], ['source_test', 'target_test']])\n\n        trainset = datasets.BaseSplit(self, [[0, self.cfg.data_count['count_train']]])\n        testset = datasets.BaseSplit(self, [[self.cfg.data_count['count_train'], self.cfg.data_count['count']]])\n\n        return trainset, testset\n\n\nif __name__ == \"__main__\":\n    datasets.BaseTest(MNIST).run()\n", "repo_name": "whiplash003/pytorch_template", "sub_path": "datasets/MNIST.py", "file_name": "MNIST.py", "file_ext": "py", "file_size_in_byte": 2272, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "datasets.BaseDataset", "line_number": 10, "usage_type": "attribute"}, {"api_name": "configs.env.getdir", "line_number": 17, "usage_type": "call"}, {"api_name": "configs.env", "line_number": 17, "usage_type": "attribute"}, {"api_name": "configs.BaseConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 28, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.newaxis", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 32, "usage_type": "attribute"}, {"api_name": "datasets.BaseSplit", "line_number": 51, "usage_type": "call"}, {"api_name": "datasets.BaseSplit", "line_number": 52, "usage_type": "call"}, {"api_name": "datasets.BaseTest", "line_number": 58, "usage_type": "call"}]}
{"seq_id": "27681564222", "text": "from datetime import timedelta, date\n\nfrom django.contrib.auth.models import User\nfrom django.test import TestCase\nfrom django.urls import reverse\nfrom django.utils import timezone\nfrom django.utils.crypto import get_random_string\nfrom activity.models import Activity\nfrom member.models import Club, Membership\nfrom note.models import NoteUser\n\n\nclass TestPermissionDenied(TestCase):\n    \"\"\"\n    Load some protected pages and check that we have 403 errors.\n    \"\"\"\n    fixtures = ('initial',)\n\n    def setUp(self) -> None:\n        # Create sample user with no rights\n        self.user = User.objects.create(\n            username=\"toto\",\n        )\n        NoteUser.objects.create(user=self.user)\n        self.client.force_login(self.user)\n\n    def test_consos(self):\n        response = self.client.get(reverse(\"note:consos\"))\n        self.assertEqual(response.status_code, 403)\n\n    def test_create_activity(self):\n        response = self.client.get(reverse(\"activity:activity_create\"))\n        self.assertEqual(response.status_code, 403)\n\n    def test_activity_entries(self):\n        activity = Activity.objects.create(\n            name=\"\",\n            description=\"\",\n            creater=self.user,\n            activity_type_id=4,\n            organizer_id=1,\n            attendees_club_id=1,\n            date_start=timezone.now(),\n            date_end=timezone.now(),\n        )\n        response = self.client.get(reverse(\"activity:activity_entry\", kwargs=dict(pk=activity.pk)))\n        self.assertEqual(response.status_code, 403)\n\n    def test_invite_activity(self):\n        activity = Activity.objects.create(\n            name=\"\",\n            description=\"\",\n            creater=self.user,\n            activity_type_id=4,\n            organizer_id=1,\n            attendees_club_id=1,\n            date_start=timezone.now(),\n            date_end=timezone.now(),\n        )\n        response = self.client.get(reverse(\"activity:activity_invite\", kwargs=dict(pk=activity.pk)))\n        self.assertEqual(response.status_code, 403)\n\n    def test_create_club(self):\n        response = self.client.get(reverse(\"member:club_create\"))\n        self.assertEqual(response.status_code, 403)\n\n    def test_add_member_club(self):\n        club = Club.objects.create(name=get_random_string(127))\n        response = self.client.get(reverse(\"member:club_add_member\", kwargs=dict(club_pk=club.pk)))\n        self.assertEqual(response.status_code, 403)\n\n    def test_renew_membership(self):\n        club = Club.objects.create(name=get_random_string(127))\n        membership = Membership.objects.create(user=self.user, club=club)\n        response = self.client.get(reverse(\"member:club_renew_membership\", kwargs=dict(pk=membership.pk)))\n        self.assertEqual(response.status_code, 403)\n\n\n    def test_create_invoice(self):\n        response = self.client.get(reverse(\"treasury:invoice_create\"))\n        self.assertEqual(response.status_code, 403)\n\n    def test_list_invoices(self):\n        response = self.client.get(reverse(\"treasury:invoice_list\"))\n        self.assertEqual(response.status_code, 403)\n\n    def test_create_remittance(self):\n        response = self.client.get(reverse(\"treasury:remittance_create\"))\n        self.assertEqual(response.status_code, 403)\n\n    def test_list_remittance(self):\n        response = self.client.get(reverse(\"treasury:remittance_list\"))\n        self.assertEqual(response.status_code, 403)\n\n\n\nclass TestLoginRedirect(TestCase):\n    def test_consos_page(self):\n        response = self.client.get(reverse(\"note:consos\"))\n        self.assertRedirects(response, reverse(\"login\") + \"?next=\" + reverse(\"note:consos\"), 302, 200)\n", "repo_name": "jbdoderlein/notes-ker-lann", "sub_path": "apps/permission/tests/test_permission_denied.py", "file_name": "test_permission_denied.py", "file_ext": "py", "file_size_in_byte": 3637, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.test.TestCase", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 21, "usage_type": "name"}, {"api_name": "note.models.NoteUser.objects.create", "line_number": 24, "usage_type": "call"}, {"api_name": "note.models.NoteUser.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "note.models.NoteUser", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 32, "usage_type": "call"}, {"api_name": "activity.models", "line_number": 36, "usage_type": "name"}, {"api_name": "activity.models.Activity.objects.create", "line_number": 36, "usage_type": "call"}, {"api_name": "activity.models.Activity.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "activity.models.Activity", "line_number": 36, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 43, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 43, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 44, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 44, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 46, "usage_type": "call"}, {"api_name": "activity.models.pk", "line_number": 46, "usage_type": "attribute"}, {"api_name": "activity.models", "line_number": 46, "usage_type": "name"}, {"api_name": "activity.models", "line_number": 50, "usage_type": "name"}, {"api_name": "activity.models.Activity.objects.create", "line_number": 50, "usage_type": "call"}, {"api_name": "activity.models.Activity.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "activity.models.Activity", "line_number": 50, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 57, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 57, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 58, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 58, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 60, "usage_type": "call"}, {"api_name": "activity.models.pk", "line_number": 60, "usage_type": "attribute"}, {"api_name": "activity.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 64, "usage_type": "call"}, {"api_name": "member.models.Club.objects.create", "line_number": 68, "usage_type": "call"}, {"api_name": "member.models.Club.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "member.models.Club", "line_number": 68, "usage_type": "name"}, {"api_name": "django.utils.crypto.get_random_string", "line_number": 68, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 69, "usage_type": "call"}, {"api_name": "member.models.Club.objects.create", "line_number": 73, "usage_type": "call"}, {"api_name": "member.models.Club.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "member.models.Club", "line_number": 73, "usage_type": "name"}, {"api_name": "django.utils.crypto.get_random_string", "line_number": 73, "usage_type": "call"}, {"api_name": "member.models.Membership.objects.create", "line_number": 74, "usage_type": "call"}, {"api_name": "member.models.Membership.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "member.models.Membership", "line_number": 74, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 75, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 80, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 84, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 88, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 92, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 97, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 99, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 100, "usage_type": "call"}]}
{"seq_id": "72873454203", "text": "import openpyxl\nfrom work_on_OS import date_picker\nfrom openpyxl.styles import PatternFill\nfrom tkinter import Tk\nfrom tkinter.filedialog import askopenfilename\n\n\nTk().withdraw()\nfile_mame = askopenfilename(title=\"Выбор файла\", filetypes=[(\"Excel - файлы\", \".xlsx\")])\n\n\n\ndef open_file():\n    name = file_mame\n    wb = openpyxl.load_workbook(name)\n    sheet = wb.active\n    return sheet\n\n\ndef validation_of_excel_file():\n    sheet = open_file()\n    for i in range(len(sheet['1'])):\n        if \"Я подтверждаю, что являюсь совершеннолетним гражданином \" \\\n           \"РФ и потребителем никотинсодержащей продукции\" in str(sheet['1'][i].value):\n            return 0\n    return 1\n\n\ndef calculating_the_number_of_items():\n    list_of_purchased_products = []\n    list_of_presented_products = []\n    capsules = \"Капсулы\"\n    capsules_present = \"Сменные капсулы Logic Compact\"\n    count_of_capsules_buy = 0\n    count_of_capsules_present = 0\n    device = \"Logic Compact девайс\"\n    device_present = \"Электронный испаритель Logic Compact\"\n    count_of_device_buy = 0\n    count_of_device_present = 0\n    case_present = \"Чехол\"\n    count_of_case_present = 0\n    sheet = open_file()\n    acc = 1\n    while str(sheet['1'][acc].value).find(\"Подарки\") == -1:\n        acc = acc + 1\n    zero_point = acc - 14\n    for i in range(len(sheet['A'])):\n        print(f\"Проверка {i+1} строки из {len(sheet['A'])}...\")\n        for j in range(32+zero_point, len(sheet[i + 1])):\n            if str(sheet[i + 2][j].value).find(capsules) != -1:\n                count_of_capsules_buy += int(sheet[i + 2][j+1].value)\n            if str(sheet[i + 2][j].value).find(device) != -1:\n                count_of_device_buy += int(sheet[i + 2][j+1].value)\n        for j in range(acc, 31+zero_point):\n            if str(sheet[i + 2][j].value).find(capsules_present) != -1:\n                count_of_capsules_present += int(sheet[i + 2][j+1].value)\n            if str(sheet[i + 2][j].value).find(device_present) != -1:\n                count_of_device_present += int(sheet[i + 2][j+1].value)\n            if str(sheet[i + 2][j].value).find(case_present) != -1:\n                count_of_case_present += int(sheet[i + 2][j+1].value)\n        one_action_buy = []\n        one_action_present = []\n        one_action_buy.append(count_of_capsules_buy)\n        one_action_buy.append(count_of_device_buy)\n        one_action_present.append(count_of_capsules_present)\n        one_action_present.append(count_of_device_present)\n        one_action_present.append(count_of_case_present)\n        count_of_capsules_buy = 0\n        count_of_device_buy = 0\n        count_of_capsules_present = 0\n        count_of_device_present = 0\n        count_of_case_present = 0\n        list_of_purchased_products.append(one_action_buy)\n        list_of_presented_products.append(one_action_present)\n    return list_of_purchased_products, list_of_presented_products, zero_point\n\n\n\ndef paint_a_cell(cell, color):\n    cell.fill = PatternFill(start_color= color,\n                            end_color= color,\n                            fill_type='solid')\n\n\ndef main_function():\n    date = date_picker()\n    acc = 0\n    four_capsules = 0\n    core_device = 0\n    one_capsules = 0\n    two_capsules = 0\n    three_capsules = 0\n    case = 0\n    name = file_mame\n    wb = openpyxl.load_workbook(name)\n    sheet = wb.active\n    items_and_presents = calculating_the_number_of_items()\n    items = items_and_presents[0]\n    presents = items_and_presents[1]\n    zero_point = items_and_presents[2]\n    print(\"Завершение...\")\n    for i in range(len(items)-1):\n        if items[i][1] == 1:  # Уст обычное + 4 пачке капсул\n            if presents[i][0] == 4:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"0000FF00\")\n                four_capsules += 1\n            else:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"FFFF0000\")\n                acc += 1\n        elif items[i][0] == 2:  # 2+1\n            if presents[i][0] == 1:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"0000FF00\")\n                one_capsules += 1\n            else:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"FFFF0000\")\n                acc += 1\n        elif items[i][0] == 3:  # 3 пачке капсул + уст обычное\n            if presents[i][1] == 1:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"0000FF00\")\n                core_device += 1\n            else:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"FFFF0000\")\n                acc += 1\n        elif items[i][0] == 6:  # 6+3\n            if presents[i][0] == 3:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"0000FF00\")\n                three_capsules += 1\n            else:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"FFFF0000\")\n                acc += 1\n        elif items[i][0] == 4:  # 4+2\n            if presents[i][0] == 2:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"0000FF00\")\n                two_capsules += 1\n            elif presents[i][2] == 1:\n                paint_a_cell(sheet[i + 2][24 + zero_point], \"0000FF00\")\n                case += 1\n            else:\n                paint_a_cell(sheet[i + 2][24+zero_point], \"FFFF0000\")\n                acc += 1\n        elif items[i][0] == 0 and items[i][1] == 0:\n            if presents[i][0] == 0 and presents[i][1] == 0:\n                pass\n            else:\n                pass\n        else:\n            paint_a_cell(sheet[i + 2][24+zero_point], \"FFFF0000\")\n            acc += 1\n    for i in range(len(sheet['AG'])-1):\n        if str(sheet[i+2][32+zero_point].value).find(\"NEW Кардхол\") != -1:\n            paint_a_cell(sheet[i + 2][24+zero_point], \"FFFFFF00\")\n\n    wb.save(\"Reports/\" + date + '_report.xlsx')\n    return four_capsules, core_device, one_capsules, two_capsules, three_capsules, date, acc, case\n", "repo_name": "IlyaStarkov/LogicApp", "sub_path": "work_on_excel.py", "file_name": "work_on_excel.py", "file_ext": "py", "file_size_in_byte": 6044, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "tkinter.Tk", "line_number": 8, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 9, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 15, "usage_type": "call"}, {"api_name": "openpyxl.styles.PatternFill", "line_number": 80, "usage_type": "call"}, {"api_name": "work_on_OS.date_picker", "line_number": 86, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 95, "usage_type": "call"}]}
{"seq_id": "32743918266", "text": "import random\nimport laspy as lp\nimport numpy as np\n\n\ndef one_hot_encode(labels):\n    targets = np.array(list(map(label_mapper, labels)))\n    nb_classes = 5\n    one_hot_targets = np.eye(nb_classes)[targets]\n    return one_hot_targets\n\n\ndef group_into_grids(points, grid_dim):\n    pt_cloud_grids = []\n    sorted_by_x = points[np.argsort(points[:, 0])]\n    splits_along_x = np.array_split(sorted_by_x, grid_dim)\n    for split in splits_along_x:\n        sorted_by_y = split[np.argsort(split[:, 1])]\n        splits_along_y = np.array_split(sorted_by_y, grid_dim)\n        pt_cloud_grids.extend(splits_along_y)\n    return pt_cloud_grids\n\n\ndef label_mapper(label):\n    \"\"\"\n    for dales\n    Args:\n        label:\n\n    Returns:\n\n    \"\"\"\n    label_map = {1.0: 0, 2.0: 1, 6.0: 2, 9.0: 3, 26.0: 4}\n    return label_map[label]\n\n\ndef norm_pts(x):\n    \"\"\"\n    Normalise points to a\n    Unit Sphere\n    Args:\n        x: input arr\n\n    Returns: points normalised\n    to Unit Sphere\n\n    \"\"\"\n    x = x / 1000\n    x -= np.mean(x, axis=0)\n    dists = np.linalg.norm(x, axis=1)\n    return x / np.max(dists)\n\ndef normalise_intensities(intensities):\n    intensities = np.clip(intensities, a_min=1, a_max=600)\n    intensities = (intensities - 1)/600\n    return intensities\n\ndef generate_samples(points, sample_size):\n    inputs = []\n    labels = []\n    for i in range(10):\n        indices = np.random.randint(points.shape[0], size=sample_size)\n        sample = points[indices, :]\n        inp = sample[:, :-1]\n        label = one_hot_encode(sample[:, -1])\n        inputs.append(inp)\n        labels.append(label)\n    return inputs, labels\n\n\norig_point_cloud = lp.read('C_37EZ1.las')\norig_data = np.vstack((orig_point_cloud.x, orig_point_cloud.y, orig_point_cloud.z, orig_point_cloud.intensity,\n                  orig_point_cloud.classification)).transpose()\n\n# globally normalise intensities\norig_data[:, 3] = normalise_intensities(orig_data[:, 3])\n\norig_grids = group_into_grids(orig_data, 10)\n\norig_grids = random.sample(orig_grids, 30)\n\ninput_samples = []\nlabel_samples = []\ncount = 0\nfor orig_grid in orig_grids:\n\n    grids = group_into_grids(orig_grid, 10)\n    for grid in grids:\n        grid[:, :3] = norm_pts(grid[:, :3])\n        inputs, labels = generate_samples(grid, 2048)\n        input_samples.extend(inputs)\n        label_samples.extend(labels)\n    count += 1\n    print(f'{count} files processed')\n\nnp.save('inputs.npy', np.asarray(input_samples))\nnp.save('labels.npy', np.asarray(label_samples))\n", "repo_name": "patchy631/3D-supervised-seg", "sub_path": "data_generator.py", "file_name": "data_generator.py", "file_ext": "py", "file_size_in_byte": 2483, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.array", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array_split", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array_split", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 62, "usage_type": "attribute"}, {"api_name": "laspy.read", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 72, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 97, "usage_type": "call"}]}
{"seq_id": "11384035136", "text": "import jwt\n\nfrom django.urls import reverse\nfrom django.contrib.sites.shortcuts import get_current_site\n\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom rest_framework.permissions import AllowAny\n\nfrom rest_framework_simplejwt.tokens import RefreshToken\nfrom rest_framework_simplejwt.views import TokenObtainPairView, TokenRefreshView\n\nfrom watchLuxuryAPI.settings import SECRET_KEY, SIMPLE_JWT\nfrom watchLuxuryAPI.utils import response_code as rescode\n\nfrom users.serializers import UserSerializer\nfrom users.models import User\n\nfrom .serializers import LoginSerializer\nfrom .utils import EmailUtil\n\n\nclass LoginView(TokenObtainPairView):\n    permission_classes = (AllowAny, )\n    serializer_class = LoginSerializer\n\n    def post(self, request, *args, **kwargs):\n        serializer = self.get_serializer(data=request.data)\n\n        try:\n            serializer.is_valid()\n        except Exception:\n            return Response({\n                'code': rescode.API_INVALID_LOGIN,\n                'msg': 'Invalid login info',\n            }, status=status.HTTP_401_UNAUTHORIZED)\n\n        return Response({\n            'code': rescode.API_SUCCESS,\n            'msg': 'User authenticated',\n            'data': serializer.validated_data,\n        }, status=status.HTTP_200_OK)\n    \n\nclass RefreshView(TokenRefreshView):\n    permission_classes = (AllowAny, )\n\n    def post(self, request, *args, **kwargs):\n        serializer = self.get_serializer(data=request.data)\n\n        try:\n            serializer.is_valid()\n        except Exception:\n            return Response({\n                'code': rescode.API_GENERIC_ERROR,\n                'msg': 'Request failed',\n            }, status=status.HTTP_400_BAD_REQUEST)\n\n        return Response({\n            'code': rescode.API_SUCCESS,\n            'msg': 'Access token refreshed',\n            'data': serializer.validated_data,\n        }, status=status.HTTP_200_OK)\n\n\nclass RegisterView(APIView):\n    permission_classes = (AllowAny, )\n\n    def post(self, request):\n        print(request.data)\n\n        serializer = UserSerializer(data=request.data)\n        if serializer.is_valid():\n            user = serializer.save()\n\n            token = RefreshToken.for_user(user).access_token\n            current_site = get_current_site(request).domain\n            relative_link = reverse('verify_email')\n            EmailUtil.send_email(\n                'Verify your email',\n                f\"\"\"Hi {user.username}. Use the link below to verify your email \\n\n                http://{current_site}{relative_link}?token={str(token)}\n                \"\"\",\n                user.email\n            )\n\n            return Response({\n                'code': rescode.API_SUCCESS,\n                'msg': f'Created user',\n                'data': serializer.data\n            }, status=status.HTTP_201_CREATED)\n\n        print(serializer.errors)\n        return Response({\n            'code': rescode.API_GENERIC_ERROR,\n            'msg': 'Request failed',\n        }, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass VerifyEmailView(APIView):\n    permission_classes = (AllowAny, )\n\n    def get(self, request):\n        token = request.query_params.get('token')\n\n        if not token:\n            return Response({\"msg\": \"Please provide an access token\"}, status=status.HTTP_400_BAD_REQUEST)\n\n        try:\n            payload = jwt.decode(token, SECRET_KEY, [SIMPLE_JWT[\"ALGORITHM\"]])\n            user = User.objects.get(id=payload['user_id'])\n            if not user.is_active:\n                user.is_active = True\n                user.save()\n            return Response({\"msg\": \"User account successfully activated!\"}, status=status.HTTP_200_OK)\n        except jwt.exceptions.ExpiredSignatureError:\n            return Response({\"msg\": \"Activation link expired.\"}, status=status.HTTP_400_BAD_REQUEST)\n        except jwt.exceptions.InvalidSignatureError:\n            return Response({\"msg\": \"Invalid activation link.\"}, status=status.HTTP_400_BAD_REQUEST)\n", "repo_name": "TaPDuy/watch-luxury-backend", "sub_path": "authentication/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4036, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "rest_framework_simplejwt.views.TokenObtainPairView", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 25, "usage_type": "name"}, {"api_name": "serializers.LoginSerializer", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 34, "usage_type": "call"}, {"api_name": "watchLuxuryAPI.utils.response_code.API_INVALID_LOGIN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "watchLuxuryAPI.utils.response_code", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 39, "usage_type": "call"}, {"api_name": "watchLuxuryAPI.utils.response_code.API_SUCCESS", "line_number": 40, "usage_type": "attribute"}, {"api_name": "watchLuxuryAPI.utils.response_code", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 43, "usage_type": "name"}, {"api_name": "rest_framework_simplejwt.views.TokenRefreshView", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 55, "usage_type": "call"}, {"api_name": "watchLuxuryAPI.utils.response_code.API_GENERIC_ERROR", "line_number": 56, "usage_type": "attribute"}, {"api_name": "watchLuxuryAPI.utils.response_code", "line_number": 56, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 58, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 58, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 60, "usage_type": "call"}, {"api_name": "watchLuxuryAPI.utils.response_code.API_SUCCESS", "line_number": 61, "usage_type": "attribute"}, {"api_name": "watchLuxuryAPI.utils.response_code", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 64, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 64, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 67, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 68, "usage_type": "name"}, {"api_name": "users.serializers.UserSerializer", "line_number": 73, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.tokens.RefreshToken.for_user", "line_number": 77, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.tokens.RefreshToken", "line_number": 77, "usage_type": "name"}, {"api_name": "django.contrib.sites.shortcuts.get_current_site", "line_number": 78, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 79, "usage_type": "call"}, {"api_name": "utils.EmailUtil.send_email", "line_number": 80, "usage_type": "call"}, {"api_name": "utils.EmailUtil", "line_number": 80, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 88, "usage_type": "call"}, {"api_name": "watchLuxuryAPI.utils.response_code.API_SUCCESS", "line_number": 89, "usage_type": "attribute"}, {"api_name": "watchLuxuryAPI.utils.response_code", "line_number": 89, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 92, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 92, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 95, "usage_type": "call"}, {"api_name": "watchLuxuryAPI.utils.response_code.API_GENERIC_ERROR", "line_number": 96, "usage_type": "attribute"}, {"api_name": "watchLuxuryAPI.utils.response_code", "line_number": 96, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 98, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 98, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 101, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 102, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 108, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 108, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 108, "usage_type": "name"}, {"api_name": "jwt.decode", "line_number": 111, "usage_type": "call"}, {"api_name": "watchLuxuryAPI.settings.SECRET_KEY", "line_number": 111, "usage_type": "argument"}, {"api_name": "watchLuxuryAPI.settings.SIMPLE_JWT", "line_number": 111, "usage_type": "name"}, {"api_name": "users.models.User.objects.get", "line_number": 112, "usage_type": "call"}, {"api_name": "users.models.User.objects", "line_number": 112, "usage_type": "attribute"}, {"api_name": "users.models.User", "line_number": 112, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 116, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 116, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 116, "usage_type": "name"}, {"api_name": "jwt.exceptions", "line_number": 117, "usage_type": "attribute"}, {"api_name": "rest_framework.response.Response", "line_number": 118, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 118, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 118, "usage_type": "name"}, {"api_name": "jwt.exceptions", "line_number": 119, "usage_type": "attribute"}, {"api_name": "rest_framework.response.Response", "line_number": 120, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 120, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 120, "usage_type": "name"}]}
{"seq_id": "12047585505", "text": "import os\nimport setuptools\n\nshort_description = 'LaunchPanel is a simple interface designed to expose LaunchPad Actions to the user in an intuitive way.'\nif os.path.exists('README.md'):\n    with open('README.md', 'r') as fh:\n        long_description = fh.read()\n\nelse:\n    long_description = short_description\n\nsetuptools.setup(\n    name='launchpanel',\n    version='2.0.1',\n    author='Mike Malinowski',\n    author_email='mike.malinowski@outlook.com',\n    description=short_description,\n    long_description=long_description,\n    long_description_content_type='text/markdown',\n    url='https://github.com/mikemalinowski/launchpad',\n    packages=setuptools.find_packages(),\n    classifiers=[\n        'Programming Language :: Python',\n        'License :: OSI Approved :: MIT License',\n        'Operating System :: OS Independent',\n    ],\n    package_data={\n        '': ['_resources/*.png', '_resources/*.ui', '_resources/*.qss'],\n    },\n    install_requires=['qute', 'scribble', 'factories', 'launchpad'],\n    keywords=\"launch launchpad pad action actions launchpanel panel\",\n)\n", "repo_name": "mikemalinowski/launchpanel", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1077, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.exists", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 12, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 21, "usage_type": "call"}]}
{"seq_id": "9176163934", "text": "\"\"\" Tasks for the grades app \"\"\"\nimport json\n\nfrom celery import shared_task\nfrom requests.exceptions import HTTPError\nfrom rest_framework.exceptions import ValidationError\nfrom user_tasks.tasks import UserTask\n\nfrom registrar.apps.core.jobs import post_job_failure, post_job_success\nfrom registrar.apps.core.tasks import get_program\n\nfrom . import lms_interop as lms\nfrom .constants import GradeReadStatus\nfrom .serializers import serialize_course_run_grades_to_csv\n\n\n@shared_task(base=UserTask, bind=True)\n# pylint: disable=unused-argument\ndef get_course_run_grades(self, job_id, user_id, file_format, program_key, internal_course_key):\n    \"\"\"\n    A user task that reads course run grade data from the LMS, and writes it to\n    a JSON- or CSV-formatted result file.\n    \"\"\"\n    program = get_program(job_id, program_key)\n    if not program:\n        return\n    try:\n        any_successes, any_failures, grades = lms.get_course_run_grades(\n            program.discovery_uuid,\n            internal_course_key,\n        )\n    except HTTPError as err:\n        post_job_failure(\n            job_id,\n            f\"HTTP error {err.response.status_code} when getting grades at {err.request.url}\"\n        )\n        return\n    except ValidationError as err:\n        post_job_failure(\n            job_id,\n            f\"Invalid grade data from LMS: {err}\",\n        )\n        return\n\n    if any_successes and any_failures:\n        code_str = str(GradeReadStatus.MULTI_STATUS.value)\n    elif not any_successes and not any_failures:\n        code_str = str(GradeReadStatus.NO_CONTENT.value)\n    elif any_successes:\n        code_str = str(GradeReadStatus.OK.value)\n    else:\n        code_str = str(GradeReadStatus.UNPROCESSABLE_ENTITY.value)\n\n    if file_format == 'json':\n        serialized = json.dumps(grades, indent=4)\n    elif file_format == 'csv':\n        serialized = serialize_course_run_grades_to_csv(grades)\n    else:\n        raise ValueError(f'Invalid file_format: {file_format}')\n    post_job_success(job_id, serialized, file_format, text=code_str)\n", "repo_name": "openedx/registrar", "sub_path": "registrar/apps/grades/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 2045, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "41", "api": [{"api_name": "registrar.apps.core.tasks.get_program", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.exceptions.HTTPError", "line_number": 32, "usage_type": "name"}, {"api_name": "registrar.apps.core.jobs.post_job_failure", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 38, "usage_type": "name"}, {"api_name": "registrar.apps.core.jobs.post_job_failure", "line_number": 39, "usage_type": "call"}, {"api_name": "constants.GradeReadStatus.MULTI_STATUS", "line_number": 46, "usage_type": "attribute"}, {"api_name": "constants.GradeReadStatus", "line_number": 46, "usage_type": "name"}, {"api_name": "constants.GradeReadStatus.NO_CONTENT", "line_number": 48, "usage_type": "attribute"}, {"api_name": "constants.GradeReadStatus", "line_number": 48, "usage_type": "name"}, {"api_name": "constants.GradeReadStatus.OK", "line_number": 50, "usage_type": "attribute"}, {"api_name": "constants.GradeReadStatus", "line_number": 50, "usage_type": "name"}, {"api_name": "constants.GradeReadStatus.UNPROCESSABLE_ENTITY", "line_number": 52, "usage_type": "attribute"}, {"api_name": "constants.GradeReadStatus", "line_number": 52, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "serializers.serialize_course_run_grades_to_csv", "line_number": 57, "usage_type": "call"}, {"api_name": "registrar.apps.core.jobs.post_job_success", "line_number": 60, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 17, "usage_type": "call"}, {"api_name": "user_tasks.tasks.UserTask", "line_number": 17, "usage_type": "name"}]}
{"seq_id": "41003057636", "text": "# import standard plotting and animation\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom IPython.display import clear_output\n\n# import autograd functionality\nfrom autograd import grad as compute_grad   # The only autograd function you may ever need\nimport autograd.numpy as np\nfrom autograd import hessian as compute_hess\nimport math\nimport time\nfrom matplotlib import gridspec\nimport copy\n\nclass Visualizer:\n    '''\n    Visualize an input cost function based on data.\n    '''\n    \n    #### initialize ####\n    def __init__(self,data):\n        # grab input\n        data = data.T\n        self.x = data[:,:-1]\n        self.y = data[:,-1]\n    \n    # sigmoid\n    def sigmoid(self,t):\n        return 1/(1 + np.exp(-t))\n    \n    # sigmoid non-convex least squares\n    def sigmoid_least_squares(self,w):\n        cost = 0\n        for p in range(0,len(self.y)):\n            x_p = self.x[p,:]\n            y_p = self.y[p]\n            a_p = w[0] + np.sum([u*v for (u,v) in zip(x_p,w[1:])])\n            cost +=(self.sigmoid(a_p) - y_p)**2\n        return cost\n\n    ###### function plotting functions #######\n    def plot_costs(self,**kwargs):    \n        # construct figure\n        fig, axs = plt.subplots(1, 2, figsize=(6,3))\n\n        # create subplot with 2 panels\n        gs = gridspec.GridSpec(1, 2, width_ratios=[.75,1]) \n        ax1 = plt.subplot(gs[0]);\n        self.scatter_pts(ax1)\n        ax2 = plt.subplot(gs[1],projection='3d'); \n\n        \n        # pull user-defined args\n        viewmax = 3\n        if 'viewmax' in kwargs:\n            viewmax = kwargs['viewmax']\n        view = [20,100]\n        if 'view' in kwargs:\n            view = kwargs['view']\n        label_axes = True\n        if 'label_axes' in kwargs:\n            label_axes = kwargs['label_axes']\n        \n        # make contour plot in each panel\n        g = self.sigmoid_least_squares\n        self.surface_plot(g,ax2,viewmax,view)\n        \n        if label_axes == True:\n            ax2.set_xlabel(r'$w_0$',fontsize = 12)\n            ax2.set_ylabel(r'$w_1$',fontsize = 12,rotation = 0)\n        plt.show()\n        \n    ### visualize the surface plot of cost function ###\n    def surface_plot(self,g,ax,wmax,view):\n        ##### Produce cost function surface #####\n        r = np.linspace(-wmax,wmax,300)\n\n        # create grid from plotting range\n        w1_vals,w2_vals = np.meshgrid(r,r)\n        w1_vals.shape = (len(r)**2,1)\n        w2_vals.shape = (len(r)**2,1)\n        w_ = np.concatenate((w1_vals,w2_vals),axis = 1)\n        g_vals = []\n        for i in range(len(r)**2):\n            g_vals.append(g(w_[i,:]))\n        g_vals = np.asarray(g_vals)\n        \n        w1_vals.shape = (np.size(r),np.size(r))\n        w2_vals.shape = (np.size(r),np.size(r))\n        \n        ### is this a counting cost?  if so re-calculate ###\n        levels = np.unique(g_vals)\n        if np.size(levels) < 30:\n            # plot each level of the counting cost\n            levels = np.unique(g_vals)\n            for u in levels:\n                # make copy of cost and nan out all non level entries\n                z = g_vals.copy()\n                ind = np.argwhere(z != u)\n                ind = [v[0] for v in ind]\n                z[ind] = np.nan\n\n                # plot the current level\n                z.shape = (len(r),len(r)) \n                ax.plot_surface(w1_vals,w2_vals,z,alpha = 1,color = '#696969',zorder = 0,shade = True,linewidth=0)\n\n        else: # smooth cost function, plot usual\n            # reshape and plot the surface, as well as where the zero-plane is\n            g_vals.shape = (np.size(r),np.size(r))\n\n            # plot cost surface\n            ax.plot_surface(w1_vals,w2_vals,g_vals,alpha = 1,color = 'w',rstride=25, cstride=25,linewidth=1,edgecolor = 'k',zorder = 2)  \n        \n        ### clean up panel ###\n        ax.xaxis.pane.fill = False\n        ax.yaxis.pane.fill = False\n        ax.zaxis.pane.fill = False\n\n        ax.xaxis.pane.set_edgecolor('white')\n        ax.yaxis.pane.set_edgecolor('white')\n        ax.zaxis.pane.set_edgecolor('white')\n\n        ax.xaxis._axinfo[\"grid\"]['color'] =  (1,1,1,0)\n        ax.yaxis._axinfo[\"grid\"]['color'] =  (1,1,1,0)\n        ax.zaxis._axinfo[\"grid\"]['color'] =  (1,1,1,0)\n\n        ax.view_init(view[0],view[1])\n        \n        \n    # scatter points\n    def scatter_pts(self,ax):\n        if np.shape(self.x)[1] == 1:\n            # set plotting limits\n            xmax = max(copy.deepcopy(self.x))\n            xmin = min(copy.deepcopy(self.x))\n            xgap = (xmax - xmin)*0.4\n            xmin -= xgap\n            xmax += xgap\n            \n            ymax = max(copy.deepcopy(self.y))\n            ymin = min(copy.deepcopy(self.y))\n            ygap = (ymax - ymin)*0.4\n            ymin -= ygap\n            ymax += ygap    \n\n            # initialize points\n            ax.scatter(self.x,self.y,color = 'k', edgecolor = 'w',linewidth = 0.9,s = 40)\n\n            # clean up panel\n            ax.set_xlim([xmin,xmax])\n            ax.set_ylim([ymin,ymax])\n            \n            # label axes\n            ax.set_xlabel(r'$x$', fontsize = 12)\n            ax.set_ylabel(r'$y$', rotation = 0,fontsize = 12)\n            \n            ax.axhline(y=0, color='k',zorder = 0,linewidth = 0.5)\n            ax.axvline(x=0, color='k',zorder = 0,linewidth = 0.5)\n            \n        if np.shape(self.x)[1] == 2:\n            # set plotting limits\n            xmax1 = copy.deepcopy(max(self.x[:,0]))\n            xmin1 = copy.deepcopy(min(self.x[:,0]))\n            xgap1 = (xmax1 - xmin1)*0.35\n            xmin1 -= xgap1\n            xmax1 += xgap1\n            \n            xmax2 = copy.deepcopy(max(self.x[:,0]))\n            xmin2 = copy.deepcopy(min(self.x[:,0]))\n            xgap2 = (xmax2 - xmin2)*0.35\n            xmin2 -= xgap2\n            xmax2 += xgap2\n            \n            ymax = max(self.y)\n            ymin = min(self.y)\n            ygap = (ymax - ymin)*0.2\n            ymin -= ygap\n            ymax += ygap    \n\n            # initialize points\n            ax.scatter(self.x[:,0],self.x[:,1],self.y,s = 40,color = 'k', edgecolor = 'w',linewidth = 0.9)\n\n            # clean up panel\n            ax.set_xlim([xmin1,xmax1])\n            ax.set_ylim([xmin2,xmax2])\n            ax.set_zlim([ymin,ymax])\n            \n            ax.set_xticks(np.arange(round(xmin1) +1, round(xmax1), 1.0))\n            ax.set_yticks(np.arange(round(xmin2) +1, round(xmax2), 1.0))\n\n            # label axes\n            ax.set_xlabel(r'$x_1$', fontsize = 12,labelpad = 5)\n            ax.set_ylabel(r'$x_2$', rotation = 0,fontsize = 12,labelpad = 5)\n            ax.set_zlabel(r'$y$', rotation = 0,fontsize = 12,labelpad = -3)\n\n            # clean up panel\n            ax.xaxis.pane.fill = False\n            ax.yaxis.pane.fill = False\n            ax.zaxis.pane.fill = False\n\n            ax.xaxis.pane.set_edgecolor('white')\n            ax.yaxis.pane.set_edgecolor('white')\n            ax.zaxis.pane.set_edgecolor('white')\n\n            ax.xaxis._axinfo[\"grid\"]['color'] =  (1,1,1,0)\n            ax.yaxis._axinfo[\"grid\"]['color'] =  (1,1,1,0)\n            ax.zaxis._axinfo[\"grid\"]['color'] =  (1,1,1,0)        \n   ", "repo_name": "jermwatt/machine_learning_refined", "sub_path": "notes/13_Multilayer_perceptrons/chapter_13_library/LS_sigmoid.py", "file_name": "LS_sigmoid.py", "file_ext": "py", "file_size_in_byte": 7169, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1452, "dataset": "github-code", "pt": "41", "api": [{"api_name": "autograd.numpy.exp", "line_number": 30, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 30, "usage_type": "name"}, {"api_name": "autograd.numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "autograd.numpy.linspace", "line_number": 77, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 77, "usage_type": "name"}, {"api_name": "autograd.numpy.meshgrid", "line_number": 80, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 80, "usage_type": "name"}, {"api_name": "autograd.numpy.concatenate", "line_number": 83, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 83, "usage_type": "name"}, {"api_name": "autograd.numpy.asarray", "line_number": 87, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 87, "usage_type": "name"}, {"api_name": "autograd.numpy.size", "line_number": 89, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 89, "usage_type": "name"}, {"api_name": "autograd.numpy.size", "line_number": 90, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 90, "usage_type": "name"}, {"api_name": "autograd.numpy.unique", "line_number": 93, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 93, "usage_type": "name"}, {"api_name": "autograd.numpy.size", "line_number": 94, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 94, "usage_type": "name"}, {"api_name": "autograd.numpy.unique", "line_number": 96, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 96, "usage_type": "name"}, {"api_name": "autograd.numpy.argwhere", "line_number": 100, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 100, "usage_type": "name"}, {"api_name": "autograd.numpy.nan", "line_number": 102, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 102, "usage_type": "name"}, {"api_name": "autograd.numpy.size", "line_number": 110, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 110, "usage_type": "name"}, {"api_name": "autograd.numpy.shape", "line_number": 133, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 133, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 135, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 136, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 141, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 142, "usage_type": "call"}, {"api_name": "autograd.numpy.shape", "line_number": 161, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 161, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 163, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 164, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 169, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 170, "usage_type": "call"}, {"api_name": "autograd.numpy.arange", "line_number": 189, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 189, "usage_type": "name"}, {"api_name": "autograd.numpy.arange", "line_number": 190, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 190, "usage_type": "name"}]}
{"seq_id": "40516794492", "text": "import numpy as np\nimport os\nimport nibabel\nimport torch\nimport pandas as pd\nfrom sklearn.base import BaseEstimator\nfrom sklearn.base import TransformerMixin\nfrom collections import OrderedDict\nfrom nilearn.masking import unmask\n\ndef bin_age(age_real: torch.Tensor):\n    bins = [i for i in range(4, 92, 2)]\n    age_binned = age_real.clone()\n    for value in bins[::-1]:\n        age_binned[age_real <= value] = value\n    return age_binned.long()\n\ndef read_data(path, dataset, fast):\n    print(f\"Read {dataset.upper()}\")\n    df = pd.read_csv(os.path.join(path, dataset + \".tsv\"), sep=\"\\t\")\n    df.loc[df[\"split\"] == \"external_test\", \"site\"] = np.nan\n\n    y_arr = df[[\"age\", \"site\"]].values\n\n    x_arr = np.zeros((10, 3659572))\n    if not fast:\n        x_arr = np.load(os.path.join(path, dataset + \".npy\"), mmap_mode=\"r\")\n    \n    print(\"- y size [original]:\", y_arr.shape)\n    print(\"- x size [original]:\", x_arr.shape)\n    return x_arr, y_arr\n\nclass OpenBHB(torch.utils.data.Dataset):\n    def __init__(self, root, train=True, internal=True, transform=None, \n                 label=\"cont\", fast=False, load_feats=None):\n        self.root = root\n\n        if train and not internal:\n            raise ValueError(\"Invalid configuration train=True and internal=False\")\n        \n        self.train = train\n        self.internal = internal\n        \n        dataset = \"train\"\n        if not train:\n            if internal:\n                dataset = \"internal_test\"\n            else:\n                dataset = \"external_test\"\n        \n        self.X, self.y = read_data(root, dataset, fast)\n        self.T = transform\n        self.label = label\n        self.fast = fast\n\n        self.bias_feats = None\n        if load_feats:\n            print(\"Loading biased features\", load_feats)\n            self.bias_feats = torch.load(load_feats, map_location=\"cpu\")\n        \n        print(f\"Read {len(self.X)} records\")\n\n    def __len__(self):\n        return len(self.y)\n\n    def __getitem__(self, index):\n        if not self.fast:\n            x = self.X[index]\n        else:\n            x = self.X[0]\n\n        y = self.y[index]\n\n        if self.T is not None:\n            x = self.T(x)\n        \n        # sample, age, site\n        age, site = y[0], y[1]\n        if self.label == \"bin\":\n            age = bin_age(torch.tensor(age))\n        \n        if self.bias_feats is not None:\n            return x, age, self.bias_feats[index]\n        else:\n            return x, age, site\n\nclass FeatureExtractor(BaseEstimator, TransformerMixin):\n    \"\"\" Select only the requested data associatedd features from the the\n    input buffered data.\n    \"\"\"\n    MODALITIES = OrderedDict([\n        (\"vbm\", {\n            \"shape\": (1, 121, 145, 121),\n            \"size\": 519945}),\n        (\"quasiraw\", {\n            \"shape\": (1, 182, 218, 182),\n            \"size\": 1827095}),\n        (\"xhemi\", {\n            \"shape\": (8, 163842),\n            \"size\": 1310736}),\n        (\"vbm_roi\", {\n            \"shape\": (1, 284),\n            \"size\": 284}),\n        (\"desikan_roi\", {\n            \"shape\": (7, 68),\n            \"size\": 476}),\n        (\"destrieux_roi\", {\n            \"shape\": (7, 148),\n            \"size\": 1036})\n    ])\n    MASKS = {\n        \"vbm\": {\n            \"path\": None,\n            \"thr\": 0.05},\n        \"quasiraw\": {\n            \"path\": None,\n            \"thr\": 0}\n    }\n\n    def __init__(self, dtype, mock=False):\n        \"\"\" Init class.\n        Parameters\n        ----------\n        dtype: str\n            the requested data: 'vbm', 'quasiraw', 'vbm_roi', 'desikan_roi',\n            'destrieux_roi' or 'xhemi'.\n        \"\"\"\n        if dtype not in self.MODALITIES:\n            raise ValueError(\"Invalid input data type.\")\n        self.dtype = dtype\n\n        data_types = list(self.MODALITIES.keys())\n        index = data_types.index(dtype)\n        \n        cumsum = np.cumsum([item[\"size\"] for item in self.MODALITIES.values()])\n        \n        if index > 0:\n            self.start = cumsum[index - 1]\n        else:\n            self.start = 0\n        self.stop = cumsum[index]\n        \n        self.masks = dict((key, val[\"path\"]) for key, val in self.MASKS.items())\n        self.masks[\"vbm\"] = \"./data/masks/cat12vbm_space-MNI152_desc-gm_TPM.nii.gz\"\n        self.masks[\"quasiraw\"] = \"./data/masks/quasiraw_space-MNI152_desc-brain_T1w.nii.gz\"\n\n        self.mock = mock\n        if mock:\n            return\n\n        for key in self.masks:\n            if self.masks[key] is None or not os.path.isfile(self.masks[key]):\n                raise ValueError(\"Impossible to find mask:\", key, self.masks[key])\n            arr = nibabel.load(self.masks[key]).get_fdata()\n            thr = self.MASKS[key][\"thr\"]\n            arr[arr <= thr] = 0\n            arr[arr > thr] = 1\n            self.masks[key] = nibabel.Nifti1Image(arr.astype(int), np.eye(4))\n\n    def fit(self, X, y):\n        return self\n\n    def transform(self, X):\n        if self.mock:\n            #print(\"transforming\", X.shape)\n            data = X.reshape(self.MODALITIES[self.dtype][\"shape\"])\n            #print(\"mock data:\", data.shape)\n            return data\n        \n        # print(X.shape)\n        select_X = X[self.start:self.stop]\n        if self.dtype in (\"vbm\", \"quasiraw\"):\n            im = unmask(select_X, self.masks[self.dtype])\n            select_X = im.get_fdata()\n            select_X = select_X.transpose(2, 0, 1)\n        select_X = select_X.reshape(self.MODALITIES[self.dtype][\"shape\"])\n        # print('transformed.shape', select_X.shape)\n        return select_X\n\n\nif __name__ == '__main__':\n    import sys\n    from torchvision import transforms\n    from .transforms import Crop, Pad\n\n    selector = FeatureExtractor(\"vbm\")\n\n    T_pre = transforms.Lambda(lambda x: selector.transform(x))\n    T_train = transforms.Compose([\n        T_pre,\n        Crop((1, 121, 128, 121), type=\"random\"),\n        Pad((1, 128, 128, 128)),\n        transforms.Lambda(lambda x: torch.from_numpy(x)),\n        transforms.Normalize(mean=0.0, std=1.0)\n    ])\n\n    train_loader = torch.utils.data.DataLoader(OpenBHB(sys.argv[1], train=True, internal=True, transform=T_train),\n                                               batch_size=3, shuffle=True, num_workers=8,\n                                               persistent_workers=True)\n    \n    x, y1, y2 = next(iter(train_loader))\n    print(x.shape, y1, y2)", "repo_name": "EIDOSLAB/contrastive-brain-age-prediction", "sub_path": "src/data/openbhb.py", "file_name": "openbhb.py", "file_ext": "py", "file_size_in_byte": 6325, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "torch.Tensor", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.utils", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.base.BaseEstimator", "line_number": 87, "usage_type": "name"}, {"api_name": "sklearn.base.TransformerMixin", "line_number": 87, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "nibabel.load", "line_number": 154, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 158, "usage_type": "call"}, {"api_name": "nilearn.masking.unmask", "line_number": 173, "usage_type": "call"}, {"api_name": "torchvision.transforms.Lambda", "line_number": 188, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 188, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 189, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 189, "usage_type": "name"}, {"api_name": "transforms.Crop", "line_number": 191, "usage_type": "call"}, {"api_name": "transforms.Pad", "line_number": 192, "usage_type": "call"}, {"api_name": "torchvision.transforms.Lambda", "line_number": 193, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 193, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 193, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 194, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 194, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 197, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 197, "usage_type": "attribute"}]}
{"seq_id": "73305640444", "text": "from django.shortcuts import render\nimport pickle\nimport numpy as np\nimport pandas as pd\n\n## for Ipl\nmodel_path_ipl='model/pipetipl.pkl'\npipe_ipl=pickle.load(open(model_path_ipl,'rb'))\ndef ipl_pridict(request):\n    if request.method=='POST':\n       batting_team=request.POST['batting_team']\n       bowling_team=request.POST['bowling_team']\n       city=request.POST['city']\n       current_score=request.POST['current_score']\n       overs=request.POST['overs']\n       wickets=request.POST['wickets']\n       last_five=request.POST['last_five']\n       fields=[batting_team,bowling_team,city,current_score,overs,wickets,last_five]\n       if batting_team==bowling_team:\n           return render(request,'index.html',{'Error':'Batting Team And Bowling Team Can not Be same'})\n       if not None in fields:\n            overs=float(overs)\n            if overs>=5 and overs<=19:\n                wickets=float(wickets)\n                current_score=float(current_score)\n                last_five=float(last_five)\n                balls_left=120-(overs*6)\n                wickets_left=10-wickets\n                crr=current_score/overs\n                input=pd.DataFrame([[batting_team,bowling_team,city,current_score,balls_left,wickets_left,crr,last_five]],columns=['batting_team','bowling_team','city','current_score','balls_left','wickets_left','crr','last_five'])\n                result=pipe_ipl.predict(input)[0]\n                result=np.round(result,0)\n                result_dict={'Batting_team':batting_team,\n                             'Bowling_team':bowling_team,\n                             'Current_Runs':current_score,\n                             'overs':overs,\n                             'wickets':wickets,\n                             'result':result}\n            return render(request,'index.html',{'dict':result_dict})\n       return render(request,'index.html',{'Error':'Please Type Correct Input'})          \n    return render(request,'index.html')\n\n\n\n\n", "repo_name": "Rammy12/IplScorePridictor", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1964, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pickle.load", "line_number": 8, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}]}
{"seq_id": "26247681247", "text": "\nimport serial\nimport time\nimport datetime\n\nlist_quality = [\n    \"160x120\",\n    \"176x144\",\n    \"320x240\",\n    \"352x288\",\n    \"640x480\",\n    \"800x600\",\n    \"1024x768\",\n    \"1280x1024\"\n]\n\nitem_list = []\nser1 = serial.Serial('/dev/ttyUSB0', 921600, timeout=1.5)\n\n\nclass Cameras:\n    def __init__(self, num, serial_in, list_items, list_qual):\n        self.num = num\n        self.serial_in = serial_in\n        self.list_items = list_items\n        self.list_qual = list_qual\n\n    def take_photo(self):\n        data_in = []\n        self.serial_in.write(b'1')\n        while self.serial_in.inWaiting() == 0:\n            pass\n        while True:\n            try:\n                data_in.append(int(self.serial_in.read(1)[-1]).to_bytes(1, 'little'))\n            except:\n                print(\"Bytes received = \" + str(data_in.__len__()))\n                break\n        stamp = str(datetime.datetime.time(datetime.datetime.today()))\n        stamp = str(stamp[0:2] + \"_\" + stamp[3:5] + \"_\" + stamp[6:8])\n        photo = open('photos/testing_' + stamp + '.jpg', 'wb')\n        for i in range(data_in.__len__()):\n            photo.write(data_in[i])\n        photo.close()\n        data_in.clear()\n\n    def change_q(self, qual_num):\n        qual = qual_num + 2\n        self.serial_in.write(str(qual).encode())\n\n\nif __name__ == \"__main__\":\n    cam1 = Cameras(0, ser1, item_list, list_quality)\n    cam1.change_q(7)\n    time.sleep(3)\n    while True:\n        cam1.take_photo()\n        for i in range(30):\n            time.sleep(60)\n            print(i+1)\n", "repo_name": "NikolayKadiev/Modules-and-stuff", "sub_path": "OV2640_mini_2mp_plus/ardu_cam_timer.py", "file_name": "ardu_cam_timer.py", "file_ext": "py", "file_size_in_byte": 1531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "serial.Serial", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime.time", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 59, "usage_type": "call"}]}
{"seq_id": "18560344509", "text": "from typing import *\nimport pastel\n\nclass Logger:\n  def __init__(self, level:int=1):\n    self.level = level\n    self.debug = self._make_log_function(\n      level=4,\n      style_normal='options=dark',\n      style_value_primary='options=dark;fg=cyan',\n      style_value_secondary='options=dark;fg=blue'\n    )\n    self.info = self._make_log_function(\n      level=3,\n      style_normal='fg=default',\n      style_value_primary='fg=cyan',\n      style_value_secondary='fg=blue'\n    )\n    self.warn = self._make_log_function(\n      level=2,\n      style_normal='fg=yellow',\n      style_value_primary='fg=default',\n      style_value_secondary='options=bold'\n    )\n    self.error = self._make_log_function(\n      level=1,\n      style_normal='fg=red',\n      style_value_primary='fg=default',\n      style_value_secondary='options=bold'\n    )\n    self.success = self._make_log_function(\n      level=3,\n      style_normal='fg=green',\n      style_value_primary='fg=default',\n      style_value_secondary='options=bold'\n    )\n\n  def _make_log_function(self, level, style_normal, style_value_primary, style_value_secondary):\n    def _function(msg: str, data1=None, data2=None):\n      msg = msg.format(\n        f'<{style_value_primary}>{data1}</{style_value_primary}>' if data1 is not None else '',\n        f'<{style_value_secondary}>{data2}</{style_value_secondary}>' if data2 is not None else ''\n      )\n      if self.level >= level:\n        print(pastel.colorize(f'<{style_normal}>{msg}</{style_normal}>'))\n    return _function\n", "repo_name": "ewen-lbh/check-availability", "sub_path": "check_availability/log.py", "file_name": "log.py", "file_ext": "py", "file_size_in_byte": 1511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pastel.colorize", "line_number": 45, "usage_type": "call"}]}
{"seq_id": "36066385686", "text": "\"\"\"\n<SCRIPTNAME> API TEST </SCRIPTNAME>\n<DESCRIPTION>\n    完成指定接口的参数测试，运算效率测试\n</DESCRIPTION>\n<AUTHOR>\n    Bingo\n</AUTHOR>\n<HISTORY>\n</HISTORY>\n\"\"\"\nimport requests\nimport json, random, copy\nfrom optparse import OptionParser\n\ndef initData(p,typ):\n\n    numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n    character = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',\n                 'u', 'v', 'w', 's', 'y', 'z']\n    special = ['~', '`', '!', '@', '#', '$', '%', '^', '&', '*', '(', ')', '_', '-', '+', '=', '{', '}', '|', '\\\\', '[',\n               ']', '\\'', ':', '\"', ';', '<', '>', '?', '?', ',', '.', '/', ' ']\n    if typ == 'string':\n        return random.choice(character)\n    if typ == 'integer':\n        return int(random.choice(numbers))\n\ndef getLog(way,*args):\n    if way == True:   #如果方式为0，直接输出到控制台\n        for message in args:\n            print(message)\n    else:\n        for message in args:\n            with open(\"Result.txt\", \"a+\") as f:\n                f.write(message)\n                f.write('\\r\\n')\n\ndef transHttp(ConfigHttp,method):\n    global maxtime,resdict\n    if method == \"get\":\n        resp = ConfigHttp.get()\n    else:\n        resp = ConfigHttp.post()\n    tim = resp.elapsed.total_seconds() * 1000  # 接口效率\n    if int(tim) > maxtime:\n        maxtime = int(tim)\n        resdict = ConfigHttp.data\n    return json.loads(str(resp.content, 'utf-8'))\n\nclass ConfigHttp:\n    def __init__(self):\n        global timeout\n        timeout = 2\n        self.headers = {}\n        self.params = {}\n        self.data = {}\n        self.host = None\n        self.url = None\n        self.files = {}\n        self.response={}\n        self.path = None\n\n    def set_url(self, url):\n        self.url = self.host + url\n\n    def set_headers(self, header):\n        self.headers = header\n\n    def set_params(self, param):\n        self.params = param\n\n    def set_host(self, host):\n        self.host = host\n\n    def set_data(self, data):\n        self.data = data\n\n    def set_files(self, file):\n        self.files = file\n\n        # defined http get method\n    def get(self):\n        try:\n            # response = requests.get(self.url, params=self.params, headers=self.headers, timeout=float(timeout))\n            response = requests.get(self.url, params=self.data, headers=self.headers, timeout=float(timeout))\n\n            # response.raise_for_status()\n\n            #return json.loads(str(response.content, 'utf-8'))\n            return response  #返回response，提取其中的响应时间，做效率判定\n        except TimeoutError:\n            # self.logger.error(\"Time out!\")\n            return None\n\n\n    # defined http post method\n    def post(self):\n        try:\n            response = requests.post(self.url, headers=self.headers, data=self.data, files=self.files,\n                                     timeout=float(timeout))\n            # response.raise_for_status()\n            #return json.loads(str(response.content, 'utf-8'))\n            return response\n        except TimeoutError:\n            # self.logger.error(\"Time out!\")\n            return None\n\nspecial = ['~', '`', '!', '@', '#', '$', '%', '^', '&', '*', '(', ')', '_', '-', '+', '=', '{', '}', '|', '\\\\', '[',\n               ']', '\\'', ':', '\"', ';', '<', '>', '?', '?', ',', '.', '/', ' ']\nstatus = {\"success\": 2000, \"fail\":5000,\"missarg\":4010}\nargType = ['string', 'integer']\nlimiTime = 500  #接口返回效率限制，超过该值将print提醒\ngeShi = {\"tab\": \"      \"}\n\n\nif __name__ == '__main__':\n\n    optparser = OptionParser()\n    #  add the private options here:\n    optparser.add_option(\"-c\", action=\"store_true\", dest=\"way\", help=\"Log打印直接输出到控制台\")\n    optparser.add_option(\"-t\", action=\"store_false\", dest=\"way\", help=\"Log打印输入至result.txt\")\n    optparser.add_option(\"-p\",\"--path\", type=str, dest=\"path\", help=\"要测试的接口路径，例如：/search/goods\")\n\n    (options, args) = optparser.parse_args()\n    way = options.way\n    path = options.path\n\n    # 需要输入way,path参数，否则退出\n    if way == None or path == None:\n        optparser.print_help()\n        exit()\n\n\n    API = ConfigHttp()\n    url = \"http://172.16.31.42:7211/v2/api-docs\"\n    res_html = requests.get(url)\n    res_dict = json.loads(res_html.text)\n    host = res_dict[\"host\"]\n    API.set_host(\"http://\"+host)\n    header = {\"x-uc-userdata\": \"{id:1}\"}\n    API.set_headers(header)\n    paths = res_dict[\"paths\"]\n    path_will_test = []\n    # path_will_test.append(\"/search/goods/associativeWords\")\n    # path_will_test.append(\"/search/goods\")\n    path_will_test.append(path)\n\n    for i, j in paths.items():\n        if i in path_will_test:\n            global maxtime,resdict\n            maxtime = 0    #记录接口性能时间，只保留最长时间\n            resdict = {}   #记录耗时最长时的请求参数\n            API.path = i\n            API.set_url(i)  #初始化 url\n            api_methods = list(j.keys())[0]\n            dict_values = list(j.values())[0]\n            getLog(way,dict_values[\"summary\"], \"路径：\"+API.path, \"方法：\"+api_methods)\n\n            API.data = {}\n\n            # 初始化生成正确的data，暂时不处理header参数，并进行正确参数测试\n            for parameter in dict_values[\"parameters\"]:\n                if parameter['in'] != 'header':\n                    API.data[parameter['name']] = initData(parameter, parameter['type'])\n            dataRight = copy.deepcopy(API.data) #基准正确参数保留\n            res = transHttp(API, api_methods)\n            if res['statusCode'] == status[\"success\"]:\n                getLog(way, \"pass————冒烟测试\")\n\n            else:\n                getLog(way, \"fail————冒烟测试\", geShi[\"tab\"]+\"错误返回：\"+json.dumps(res))\n                getLog(way, geShi[\"tab\"] + \"请求数据：\" + str(API.data))\n\n            getLog(way,\"<<<<<<<参数类型异常测试>>>>>>\")\n            for parameter in dict_values[\"parameters\"]:\n                if parameter['in'] != 'header':\n                    for typ in argType:\n                        if typ != parameter['type']:\n                            API.data[parameter['name']] = initData(parameter, typ)\n                            res = transHttp(API, api_methods)\n                            if res['statusCode'] == status[\"fail\"]:\n                                getLog(way,\"pass————\"+ parameter[\"name\"]+\"参数要求类型为\"+parameter['type']+\"，使用\"+typ+\"类型测试\")\n                            else:\n                                getLog(way,\"fail————\"+parameter[\"name\"]+\"参数要求类型为\"+parameter['type']+\"，使用\"+typ+\"类型测试\", geShi[\"tab\"]+\"错误返回：\" + json.dumps(res))\n                                getLog(way,geShi[\"tab\"]+\"请求数据：\" + str(API.data))\n                API.data = copy.deepcopy(dataRight)  #重置数据，进行下一个参数修改测试\n\n            getLog(way,\"<<<<<<参数必要性测试>>>>>>：\")\n            for parameter in dict_values[\"parameters\"]:\n                if parameter['in'] != 'header':\n                    API.data.pop(parameter['name'])\n                    res = transHttp(API, api_methods)\n                    if parameter['required'] == True:\n                        if res['statusCode'] == status[\"missarg\"]:\n                            getLog(way,\"pass————\"+parameter[\"name\"] + \"参数要求为必要，缺失类型测试\")\n                        else:\n                            getLog(way,\"fail————\"+parameter[\"name\"] + \"参数要求为必要，缺失类型测试\",\n                                   geShi[\"tab\"]+\"错误返回：\" + json.dumps(res))\n                            getLog(way,geShi[\"tab\"]+\"请求数据：\" + str(API.data))\n                    else:\n                        if res['statusCode'] == status[\"success\"]:\n                            getLog(way,\"pass————\"+parameter[\"name\"] + \"参数要求为非必要，缺失类型测试\")\n                        else:\n                            getLog(way,\"fail————\"+parameter[\"name\"] + \"参数要求为非必要，缺失类型测试\",\n                                   geShi[\"tab\"]+\"错误返回：\" + json.dumps(res))\n                            getLog(way,geShi[\"tab\"]+\"请求数据：\" + str(API.data))\n                API.data = copy.deepcopy(dataRight)  #重置数据，进行下一个参数修改测试\n            for parameter in dict_values[\"parameters\"]:\n                if parameter['in'] != 'header':\n                    if parameter['required'] == False:\n                        API.data.pop(parameter['name'])\n            res = transHttp(API, api_methods)\n            if res['statusCode'] != status[\"success\"]:\n                getLog(way,\"fail——非必要参数全部缺失测试：\",\n                       \"错误返回：\" + json.dumps(res))\n                getLog(way,\"请求数据：\" + str(API.data))\n            API.data = copy.deepcopy(dataRight)  # 重置数据，进行下一个参数修改测试\n\n            getLog(\"<<<<<<参数特殊字符遍历测试>>>>>>\") #当参数类型为string则进行该测试\n            for parameter in dict_values[\"parameters\"]:\n                tag = 0  #作为全部通过标志\n                if parameter['in'] != 'header':\n                    if parameter['type'] ==\"string\":\n                        for c in special:\n                            API.data[parameter['name']] = c\n                            res = transHttp(API, api_methods)\n                            if res['statusCode'] != status[\"success\"]:\n                                tag = 1\n                                getLog(\n                                    way,\"fail————\"+parameter[\"name\"] + \"特殊字符<\"+c + \">测试\",\n                                    geShi[\"tab\"]+\"错误返回：\" + json.dumps(res))\n                                getLog(way,geShi[\"tab\"]+\"请求数据：\" + str(API.data))\n                        if tag == 0:\n                            getLog(way,\"pass————\"+parameter[\"name\"] + \"特殊字符测试\")\n                API.data = copy.deepcopy(dataRight)  # 重置数据，进行下一个参数修改测试\n\n            getLog(\"<<<<<<接口性能测试>>>>>>\")  # 当参数类型为string则进行该测试\n            if maxtime > limiTime:\n                getLog(\n                    way,\"fail————接口耗时最长为：\"+str(maxtime)+\"ms,超过设定限定值\"+str(limiTime)+\"ms\",\n                    geShi[\"tab\"] + \"请求数据：\" + json.dumps(resdict))\n            else:\n                getLog(way,\"pass————接口耗时最长为：\"+str(maxtime)+\"ms,小于设定限定值\"+str(limiTime)+\"ms\")\n\n\n\n\n\n\n\n", "repo_name": "bingosong17/test1", "sub_path": "autotestServer/code/spider.py", "file_name": "spider.py", "file_ext": "py", "file_size_in_byte": 10827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "random.choice", "line_number": 24, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 85, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 99, "usage_type": "call"}, {"api_name": "optparse.OptionParser", "line_number": 118, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 136, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 137, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 165, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 171, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 184, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 186, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 198, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 205, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 207, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 215, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 217, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 231, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 235, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 241, "usage_type": "call"}]}
{"seq_id": "2232916424", "text": "import talib\nimport numpy as np\nfrom binance.client import Client\nimport time\nfrom tradingview_ta import TA_Handler, Interval, Exchange\ntesla = TA_Handler(\n    symbol=\"TSLA\",\n    screener=\"america\",\n    exchange=\"NASDAQ\",\n    interval=Interval.INTERVAL_1_MINUTE\n)\n\napi_key = ''\napi_secret = ''\n\nclient = Client(api_key, api_secret, testnet=True)\n\nsymbol = 'BTCUSDT'\nquantity = 0.001\n\ndef get_indicators(symbol):\n    candles = client.get_klines(symbol=symbol, interval=Client.KLINE_INTERVAL_1MINUTE)\n    print(candles)\n    closes = np.array([float(candle[4]) for candle in candles])\n    rsi = talib.RSI(closes, timeperiod=14)\n    adx = talib.ADX(closes, closes, closes, timeperiod=14)\n    upper, middle, lower = talib.BBANDS(closes, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)\n    return rsi[-1], adx[-1], closes[-1], upper[-1], middle[-1], lower[-1]\n\ndef place_order(symbol, side, quantity):\n    try:\n        order = client.create_order(symbol=symbol, side=side, type=Client.ORDER_TYPE_MARKET, quantity=quantity)\n        print(order)\n    except Exception as e:\n        print(e)\n\nwhile True:\n    try:\n        rsi, adx, close, upper, middle, lower = get_indicators(symbol)\n        print(\"rsi:\", rsi)\n        print(\"adx:\", adx)\n        print(\"close:\", close)\n        print(\"upper:\", upper)\n        print(\"middle:\", middle)\n        print(\"lower:\", lower)\n        if rsi < 30 and adx > 25 and close < lower:\n            print('Placing buy order')\n            place_order(symbol, Client.SIDE_BUY, quantity)\n        elif rsi > 70 and adx > 25 and close > upper:\n            print('Placing sell order')\n            place_order(symbol, Client.SIDE_SELL, quantity)\n    except Exception as e:\n        print(e)\n\n    time.sleep(60)", "repo_name": "RezaHedayatkhah/CryptoTrader", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "tradingview_ta.TA_Handler", "line_number": 6, "usage_type": "call"}, {"api_name": "tradingview_ta.Interval.INTERVAL_1_MINUTE", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tradingview_ta.Interval", "line_number": 10, "usage_type": "name"}, {"api_name": "binance.client.Client", "line_number": 16, "usage_type": "call"}, {"api_name": "binance.client.Client.KLINE_INTERVAL_1MINUTE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "binance.client.Client", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "talib.RSI", "line_number": 25, "usage_type": "call"}, {"api_name": "talib.ADX", "line_number": 26, "usage_type": "call"}, {"api_name": "talib.BBANDS", "line_number": 27, "usage_type": "call"}, {"api_name": "binance.client.Client.ORDER_TYPE_MARKET", "line_number": 32, "usage_type": "attribute"}, {"api_name": "binance.client.Client", "line_number": 32, "usage_type": "name"}, {"api_name": "binance.client.Client.SIDE_BUY", "line_number": 48, "usage_type": "attribute"}, {"api_name": "binance.client.Client", "line_number": 48, "usage_type": "name"}, {"api_name": "binance.client.Client.SIDE_SELL", "line_number": 51, "usage_type": "attribute"}, {"api_name": "binance.client.Client", "line_number": 51, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}]}
{"seq_id": "73100410043", "text": "#!/usr/bin/env ipy\n\n##############################################################################\n# Written by:  Calen Chen <cachen@novell.com>\n#              Ray Wang <rawang@novell.com>\n# Date:        03/11/2008\n# Description: This is a test application sample for winforms control:\n#              TextBox\n##############################################################################\n\nimport clr\n\nclr.AddReference('System.Windows.Forms')\nclr.AddReference('System.Drawing')\n\nfrom System.Drawing import *\nfrom System.Windows.Forms import *\n\n\nclass TextBoxApp(Form):\n\n    def __init__(self):\n        self.Text = \"TextBox Control\"\n        self.Height = 335\n\n        self.label1 = Label()\n        self.label1.Text = \"Normal TextBox\"\n        self.label1.AccessibleName = \"explicitly set name for label\"\n        self.label1.Dock = DockStyle.Bottom\n    \n        self.textbox1 = TextBox()\n        self.textbox1.AcceptsTab = True\n        self.textbox1.AcceptsReturn = True\n        self.textbox1.Dock = DockStyle.Bottom\n        self.textbox1.Name = \"self.textbox1\"\n        self.textbox1.AccessibleName = \"explicitly set name for textbox\"\n        self.textbox1.TextChanged += self.textbox1_enter\n\n        # create a password entry textbox to display asterisks \n        # instead of the text typed\n        self.label2 = Label()\n        self.label2.Text = \"Multi-Line TextBox\"\n        self.label2.Dock = DockStyle.Bottom\n        self.label2.Height = 80\n\n        self.textbox2 = TextBox()\n        self.textbox2.Dock = DockStyle.Bottom\n        self.textbox2.Height = 100\n        self.textbox2.Multiline = True\n        self.textbox2.ScrollBars = ScrollBars.Both\n        self.textbox2.AcceptsTab = True\n        self.textbox2.AcceptsReturn = True\n        self.textbox2.WordWrap = False\n        self.textbox2.TextChanged += self.textbox2_enter\n\n        self.label3 = Label()\n        self.label3.Text = \"Password TextBox\"\n        self.label3.Dock = DockStyle.Bottom\n\n        self.textbox3 = TextBox()\n        self.textbox3.Dock = DockStyle.Bottom\n        self.textbox3.UseSystemPasswordChar = True\n        self.textbox3.TextChanged += self.textbox3_enter\n\n        self.label4 = Label()\n        self.label4.Text = \"non-Editable TextBox\"\n        self.label4.Dock = DockStyle.Bottom\n\n        self.textbox4 = TextBox()\n        self.textbox4.Enabled = False\n        self.textbox4.Dock = DockStyle.Bottom\n\n        self.Controls.Add(self.label1)\n        self.Controls.Add(self.textbox1)\n        self.Controls.Add(self.label2)\n        self.Controls.Add(self.textbox2)\n        self.Controls.Add(self.label3)\n        self.Controls.Add(self.textbox3)\n        self.Controls.Add(self.label4)\n        self.Controls.Add(self.textbox4)\n\n    def textbox1_enter(self, sender, event):\n        self.label1.Text = self.textbox1.Text\n\n    def textbox2_enter(self, sender, event):\n        self.label2.Text = self.textbox2.Text\n\n    def textbox3_enter(self, sender, event):\n        self.label3.Text = self.textbox3.Text\n\nform = TextBoxApp()\nApplication.Run(form)\n", "repo_name": "mono/uia2atk", "sub_path": "test/samples/winforms/textbox.py", "file_name": "textbox.py", "file_ext": "py", "file_size_in_byte": 3022, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "41", "api": [{"api_name": "clr.AddReference", "line_number": 13, "usage_type": "call"}, {"api_name": "clr.AddReference", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "42761456839", "text": "from rest_framework import serializers\n\nfrom .models import Group, Video, ImageModel\n\n\nclass ImageModelSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = ImageModel\n        fields = '__all__'\n\n    def update(self, instance, validated_data):\n        # Delete old image file before setting new one\n        if instance.image:\n            instance.image.delete()\n        return super().update(instance, validated_data)\n\n\nclass VideoReadSerializer(serializers.ModelSerializer):\n    aliases = serializers.ListField(source='get_aliases')\n    links = serializers.ListField(source='get_links')\n\n    class Meta:\n        model = Video\n        fields = '__all__'\n        extra_kwargs = {\n            'alias': {'write_only': True},\n            'links_arr': {'write_only': True},\n        }\n\n\nclass VideoWriteSerializer(serializers.ModelSerializer):\n    aliases = serializers.ListField()\n    links = serializers.ListField()\n\n    class Meta:\n        model = Video\n        fields = '__all__'\n        extra_kwargs = {\n            'alias': {'read_only': True},\n            'links_arr': {'read_only': True},\n        }\n\n    def validate(self, attrs):\n        attrs['alias'] = Group.build_alias(attrs.pop('aliases'))\n        attrs['links_arr'] = Group.build_links(attrs.pop('links'))\n        return attrs\n\n    def update(self, instance, validated_data):\n        old_order = instance.order\n        new_order = validated_data[\"order\"]\n        if old_order != new_order:\n            instance.reorder(old_order, new_order)\n        return super().update(instance, validated_data)\n\n    def create(self, validated_data):\n        instance = super().create(validated_data)\n        instance.created()\n        return instance\n\n    def to_representation(self, instance):\n        serializer = GroupReadSerializer(instance=instance.group, context=self.context)\n        return serializer.data\n\n\nclass GroupReadSerializer(serializers.ModelSerializer):\n    videos = VideoReadSerializer(many=True)\n    images = ImageModelSerializer(many=True)\n    aliases = serializers.ListField(source='get_aliases')\n    links = serializers.ListField(source='get_links')\n\n    class Meta:\n        model = Group\n        fields = '__all__'\n        extra_kwargs = {\n            'alias': {'write_only': True},\n            'links_arr': {'write_only': True},\n        }\n\n\nclass GroupWriteSerializer(serializers.ModelSerializer):\n    aliases = serializers.ListField()\n    links = serializers.ListField()\n\n    class Meta:\n        model = Group\n        fields = '__all__'\n        extra_kwargs = {\n            'alias': {'read_only': True},\n            'links_arr': {'read_only': True},\n        }\n\n    def validate(self, attrs):\n        attrs['alias'] = Group.build_alias(attrs.pop('aliases'))\n        attrs['links_arr'] = Group.build_links(attrs.pop('links'))\n        return attrs\n\n    def to_representation(self, instance):\n        serializer = GroupReadSerializer(instance, context=self.context)\n        return serializer.data\n", "repo_name": "Mefodii/ReganamKevolehc_BE", "sub_path": "watching/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 2984, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 6, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "models.ImageModel", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 20, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 21, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 21, "usage_type": "name"}, {"api_name": "models.Video", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 32, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 34, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 34, "usage_type": "name"}, {"api_name": "models.Video", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Group.build_alias", "line_number": 45, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 45, "usage_type": "name"}, {"api_name": "models.Group.build_links", "line_number": 46, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 66, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 66, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 69, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 69, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 70, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 70, "usage_type": "name"}, {"api_name": "models.Group", "line_number": 73, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 81, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 81, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 82, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 82, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 83, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 83, "usage_type": "name"}, {"api_name": "models.Group", "line_number": 86, "usage_type": "name"}, {"api_name": "models.Group.build_alias", "line_number": 94, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 94, "usage_type": "name"}, {"api_name": "models.Group.build_links", "line_number": 95, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 95, "usage_type": "name"}]}
{"seq_id": "28843383510", "text": "\"\"\"\n@file\n@brief Helpers to convert docstring to various format.\n\"\"\"\nimport os\nimport sys\nfrom collections import deque\nimport warnings\nimport pickle\nimport platform\nfrom html import escape as htmlescape\nfrom io import StringIO\nfrom docutils.parsers.rst import roles\nfrom docutils.languages import en as docutils_en\nfrom docutils import nodes\nfrom docutils.utils import Reporter\nfrom sphinx.application import Sphinx\nfrom sphinx.environment import BuildEnvironment\nfrom sphinx.errors import ExtensionError\nfrom sphinx.ext.extlinks import setup_link_roles\nfrom sphinx.transforms import SphinxTransformer\nfrom sphinx.writers.html import HTMLWriter\nfrom sphinx.util.build_phase import BuildPhase\nfrom sphinx.util.logging import prefixed_warnings\nfrom sphinx.project import Project\nfrom sphinx.errors import ApplicationError\nfrom sphinx.util.logging import getLogger\nfrom ..sphinxext.sphinx_doctree_builder import (\n    DocTreeBuilder, DocTreeWriter, DocTreeTranslator)\nfrom ..sphinxext.sphinx_md_builder import MdBuilder, MdWriter, MdTranslator\nfrom ..sphinxext.sphinx_latex_builder import (\n    EnhancedLaTeXBuilder, EnhancedLaTeXWriter, EnhancedLaTeXTranslator)\nfrom ..sphinxext.sphinx_rst_builder import RstBuilder, RstWriter, RstTranslator\nfrom ._single_file_html_builder import CustomSingleFileHTMLBuilder\n\n\ndef _get_LaTeXTranslator():\n    try:\n        from sphinx.writers.latex import LaTeXTranslator\n    except ImportError:  # pragma: no cover\n        # Since sphinx 1.7.3 (circular reference).\n        import sphinx.builders.latex.transforms\n        from sphinx.writers.latex import LaTeXTranslator\n    return LaTeXTranslator\n\n\ntry:\n    from sphinx.util.docutils import is_html5_writer_available\nexcept ImportError:\n    def is_html5_writer_available():\n        return True\n\nif is_html5_writer_available():\n    from sphinx.writers.html5 import HTML5Translator as HTMLTranslator\nelse:\n    from sphinx.writers.html import HTMLTranslator  # pragma: no cover\n\n\ndef update_docutils_languages(values=None):\n    \"\"\"\n    Updates ``docutils/languages/en.py`` with missing labels.\n    It Does it for languages *en*.\n\n    @param      values      consider values in this dictionaries first\n    \"\"\"\n    if values is None:\n        values = dict()\n    lab = docutils_en.labels\n    if 'versionmodified' not in lab:\n        lab['versionmodified'] = values.get(\n            'versionmodified', 'modified version')\n    if 'desc' not in lab:\n        lab['desc'] = values.get('desc', 'description')\n\n\nclass _AdditionalVisitDepart:\n    \"\"\"\n    Additional visitors and departors.\n    \"\"\"\n\n    def __init__(self, output_format):\n        self.output_format = output_format\n\n    def is_html(self):\n        \"\"\"\n        Tells if the translator is :epkg:`html` format.\n        \"\"\"\n        return self.base_class is HTMLTranslator\n\n    def is_rst(self):\n        \"\"\"\n        Tells if the translator is :epkg:`rst` format.\n        \"\"\"\n        return self.base_class is RstTranslator\n\n    def is_latex(self):\n        \"\"\"\n        Tells if the translator is :epkg:`latex` format.\n        \"\"\"\n        return self.base_class is _get_LaTeXTranslator()\n\n    def is_md(self):\n        \"\"\"\n        Tells if the translator is :epkg:`markdown` format.\n        \"\"\"\n        return self.base_class is _get_LaTeXTranslator()\n\n    def is_doctree(self):\n        \"\"\"\n        Tells if the translator is doctree format.\n        \"\"\"\n        return self.base_class is _get_LaTeXTranslator()\n\n    def add_secnumber(self, node):\n        \"\"\"\n        Overwrites this method to catch errors due when\n        it is a single document being processed.\n        \"\"\"\n        if node.get('secnumber'):\n            self.base_class.add_secnumber(self, node)\n        elif len(node.parent['ids']) > 0:\n            self.base_class.add_secnumber(self, node)\n        else:\n            n = len(self.builder.secnumbers)\n            node.parent['ids'].append(\"custom_label_%d\" % n)\n            self.base_class.add_secnumber(self, node)\n\n    def eval_expr(self, expr):\n        rst = self.output_format == 'rst'\n        latex = self.output_format in ('latex', 'elatex')\n        texinfo = [('index', 'A_AdditionalVisitDepart', 'B_AdditionalVisitDepart',   # pylint: disable=W0612\n                    'C_AdditionalVisitDepart', 'D_AdditionalVisitDepart',\n                    'E_AdditionalVisitDepart', 'Miscellaneous')]\n        html = self.output_format == 'html'\n        md = self.output_format == 'md'\n        doctree = self.output_format in ('doctree', 'doctree.txt')\n        if not (rst or html or latex or md or doctree):\n            raise ValueError(  # pragma: no cover\n                f\"Unknown output format '{self.output_format}'.\")\n        try:\n            ev = eval(expr)\n        except Exception:  # pragma: no cover\n            raise ValueError(\n                f\"Unable to interpret expression '{expr}'\")\n        return ev\n\n    def visit_only(self, node):\n        ev = self.eval_expr(node.attributes['expr'])\n        if ev:\n            pass\n        else:\n            raise nodes.SkipNode\n\n    def depart_only(self, node):\n        ev = self.eval_expr(node.attributes['expr'])\n        if ev:\n            pass\n        else:\n            # The program should not necessarily be here.\n            pass\n\n    def visit_viewcode_anchor(self, node):\n        # Removed in sphinx 3.5\n        pass\n\n    def depart_viewcode_anchor(self, node):\n        # Removed in sphinx 3.5\n        pass\n\n    def unknown_visit(self, node):  # pragma: no cover\n        raise NotImplementedError(\n            \"[_AdditionalVisitDepart] Unknown node: '{0}' in '{1}'\".format(\n                node.__class__.__name__, self.__class__.__name__))\n\n\nclass HTMLTranslatorWithCustomDirectives(_AdditionalVisitDepart, HTMLTranslator):\n    \"\"\"\n    See @see cl HTMLWriterWithCustomDirectives.\n    \"\"\"\n\n    def __init__(self, document, builder, *args, **kwds):\n        HTMLTranslator.__init__(self, document, builder, *args, **kwds)\n        _AdditionalVisitDepart.__init__(self, 'html')\n        nodes_list = getattr(builder, '_function_node', None)\n        if nodes_list is not None:\n            for name, f1, f2 in nodes_list:\n                setattr(self.__class__, \"visit_\" + name, f1)\n                setattr(self.__class__, \"depart_\" + name, f2)\n        self.base_class = HTMLTranslator\n\n    def visit_field(self, node):\n        if not hasattr(self, '_fieldlist_row_index'):\n            # needed when a docstring starts with :param:\n            self._fieldlist_row_index = 0\n        return HTMLTranslator.visit_field(self, node)\n\n    def visit_pending_xref(self, node):\n        self.visit_Text(node)\n        raise nodes.SkipNode\n\n    def unknown_visit(self, node):  # pragma: no cover\n        raise NotImplementedError(\"[HTMLTranslatorWithCustomDirectives] Unknown node: '{0}' in '{1}'\".format(\n            node.__class__.__name__, self.__class__.__name__))\n\n\nclass RSTTranslatorWithCustomDirectives(_AdditionalVisitDepart, RstTranslator):\n    \"\"\"\n    See @see cl HTMLWriterWithCustomDirectives.\n    \"\"\"\n\n    def __init__(self, document, builder, *args, **kwds):\n        \"\"\"\n        constructor\n        \"\"\"\n        RstTranslator.__init__(self, document, builder, *args, **kwds)\n        _AdditionalVisitDepart.__init__(self, 'rst')\n        for name, f1, f2 in builder._function_node:\n            setattr(self.__class__, \"visit_\" + name, f1)\n            setattr(self.__class__, \"depart_\" + name, f2)\n        self.base_class = RstTranslator\n\n\nclass MDTranslatorWithCustomDirectives(_AdditionalVisitDepart, MdTranslator):\n    \"\"\"\n    See @see cl HTMLWriterWithCustomDirectives.\n    \"\"\"\n\n    def __init__(self, document, builder, *args, **kwds):\n        \"\"\"\n        constructor\n        \"\"\"\n        MdTranslator.__init__(self, document, builder, *args, **kwds)\n        _AdditionalVisitDepart.__init__(self, 'md')\n        for name, f1, f2 in builder._function_node:\n            setattr(self.__class__, \"visit_\" + name, f1)\n            setattr(self.__class__, \"depart_\" + name, f2)\n        self.base_class = MdTranslator\n\n\nclass DocTreeTranslatorWithCustomDirectives(DocTreeTranslator):\n    \"\"\"\n    See @see cl HTMLWriterWithCustomDirectives.\n    \"\"\"\n\n    def __init__(self, document, builder, *args, **kwds):\n        \"\"\"\n        constructor\n        \"\"\"\n        DocTreeTranslator.__init__(self, document, builder, *args, **kwds)\n        self.base_class = DocTreeTranslator\n\n\nclass LatexTranslatorWithCustomDirectives(_AdditionalVisitDepart, EnhancedLaTeXTranslator):\n    \"\"\"\n    See @see cl LatexWriterWithCustomDirectives.\n    \"\"\"\n\n    def __init__(self, document, builder, *args, **kwds):\n        \"\"\"\n        constructor\n        \"\"\"\n        if not hasattr(builder, \"config\"):\n            builder, document = document, builder\n        if not hasattr(builder, \"config\"):\n            raise TypeError(  # pragma: no cover\n                f\"Builder has no config: {type(builder)} - {type(document)}\")\n        EnhancedLaTeXTranslator.__init__(\n            self, document, builder, *args, **kwds)\n        _AdditionalVisitDepart.__init__(self, 'md')\n        for name, f1, f2 in builder._function_node:\n            setattr(self.__class__, \"visit_\" + name, f1)\n            setattr(self.__class__, \"depart_\" + name, f2)\n        self.base_class = EnhancedLaTeXTranslator\n\n\nclass _WriterWithCustomDirectives:\n    \"\"\"\n    Common class to @see cl HTMLWriterWithCustomDirectives and @see cl RSTWriterWithCustomDirectives.\n    \"\"\"\n\n    def _init(self, base_class, translator_class, app=None):\n        \"\"\"\n        @param      base_class  base class\n        @param      app         Sphinx application\n        \"\"\"\n        if app is None:\n            self.app = _CustomSphinx(srcdir=None, confdir=None, outdir=None, doctreedir=None,\n                                     buildername='memoryhtml')\n        else:\n            self.app = app\n        builder = self.app.builder\n        builder.fignumbers = {}\n        base_class.__init__(self, builder)\n        self.translator_class = translator_class\n        self.builder.secnumbers = {}\n        self.builder._function_node = []\n        self.builder.current_docname = None\n        self.base_class = base_class\n\n    def connect_directive_node(self, name, f_visit, f_depart):\n        \"\"\"\n        Adds custom node to the translator.\n\n        @param      name        name of the directive\n        @param      f_visit     visit function\n        @param      f_depart    depart function\n        \"\"\"\n        if self.builder.format != \"doctree\":\n            self.builder._function_node.append((name, f_visit, f_depart))\n\n    def add_configuration_options(self, new_options):\n        \"\"\"\n        Add new options.\n\n        @param      new_options     new options\n        \"\"\"\n        for k, v in new_options.items():\n            self.builder.config.values[k] = v\n\n    def write(self, document, destination):\n        \"\"\"\n        Processes a document into its final form.\n        Translates `document` (a Docutils document tree) into the Writer's\n        native format, and write it out to its `destination` (a\n        `docutils.io.Output` subclass object).\n\n        Normally not overridden or extended in subclasses.\n        \"\"\"\n        self.base_class.write(self, document, destination)\n\n\nclass HTMLWriterWithCustomDirectives(_WriterWithCustomDirectives, HTMLWriter):\n    \"\"\"\n    This :epkg:`docutils` writer extends the HTML writer with\n    custom directives implemented in this module,\n    @see cl RunPythonDirective, @see cl BlogPostDirective.\n\n    See `Write your own ReStructuredText-Writer <http://www.arnebrodowski.de/blog/write-your-own-restructuredtext-writer.html>`_.\n\n    This class needs to tell :epkg:`docutils` to call the added function\n    when directives *runpython* or *blogpost* are met.\n    \"\"\"\n\n    def __init__(self, builder=None, app=None):  # pylint: disable=W0231\n        \"\"\"\n        @param      builder builder\n        @param      app     Sphinx application\n        \"\"\"\n        _WriterWithCustomDirectives._init(\n            self, HTMLWriter, HTMLTranslatorWithCustomDirectives, app)\n\n    def translate(self):\n        self.visitor = visitor = self.translator_class(\n            self.document, self.builder)\n        self.document.walkabout(visitor)\n        self.output = visitor.astext()\n        for attr in ('head_prefix', 'stylesheet', 'head', 'body_prefix',\n                     'body_pre_docinfo', 'docinfo', 'body', 'fragment',\n                     'body_suffix', 'meta', 'title', 'subtitle', 'header',\n                     'footer', 'html_prolog', 'html_head', 'html_title',\n                     'html_subtitle', 'html_body', ):\n            setattr(self, attr, getattr(visitor, attr, None))\n        self.clean_meta = ''.join(visitor.meta[2:])\n\n\nclass RSTWriterWithCustomDirectives(_WriterWithCustomDirectives, RstWriter):\n    \"\"\"\n    This :epkg:`docutils` writer extends the :epkg:`RST` writer with\n    custom directives implemented in this module.\n    \"\"\"\n\n    def __init__(self, builder=None, app=None):  # pylint: disable=W0231\n        \"\"\"\n        @param      builder builder\n        @param      app     Sphinx application\n        \"\"\"\n        _WriterWithCustomDirectives._init(\n            self, RstWriter, RSTTranslatorWithCustomDirectives, app)\n\n    def translate(self):\n        visitor = self.translator_class(self.document, self.builder)\n        self.document.walkabout(visitor)\n        self.output = visitor.body\n\n\nclass MDWriterWithCustomDirectives(_WriterWithCustomDirectives, MdWriter):\n    \"\"\"\n    This :epkg:`docutils` writer extends the :epkg:`MD` writer with\n    custom directives implemented in this module.\n    \"\"\"\n\n    def __init__(self, builder=None, app=None):  # pylint: disable=W0231\n        \"\"\"\n        @param      builder builder\n        @param      app     Sphinx application\n        \"\"\"\n        _WriterWithCustomDirectives._init(\n            self, MdWriter, MDTranslatorWithCustomDirectives, app)\n\n    def translate(self):\n        visitor = self.translator_class(self.document, self.builder)\n        self.document.walkabout(visitor)\n        self.output = visitor.body\n\n\nclass DocTreeWriterWithCustomDirectives(_WriterWithCustomDirectives, DocTreeWriter):\n    \"\"\"\n    This :epkg:`docutils` writer creates a doctree writer with\n    custom directives implemented in this module.\n    \"\"\"\n\n    def __init__(self, builder=None, app=None):  # pylint: disable=W0231\n        \"\"\"\n        @param      builder builder\n        @param      app     Sphinx application\n        \"\"\"\n        _WriterWithCustomDirectives._init(\n            self, DocTreeWriter, DocTreeTranslatorWithCustomDirectives, app)\n\n    def translate(self):\n        visitor = self.translator_class(self.document, self.builder)\n        self.document.walkabout(visitor)\n        self.output = visitor.body\n\n\nclass LatexWriterWithCustomDirectives(_WriterWithCustomDirectives, EnhancedLaTeXWriter):\n    \"\"\"\n    This :epkg:`docutils` writer extends the :epkg:`Latex` writer with\n    custom directives implemented in this module.\n    \"\"\"\n\n    def __init__(self, builder=None, app=None):  # pylint: disable=W0231\n        \"\"\"\n        @param      builder builder\n        @param      app     Sphinx application\n        \"\"\"\n        _WriterWithCustomDirectives._init(\n            self, EnhancedLaTeXWriter, LatexTranslatorWithCustomDirectives, app)\n        if not hasattr(self.builder, \"config\"):\n            raise TypeError(  # pragma: no cover\n                f\"Builder has no config: {type(self.builder)}\")\n\n    def translate(self):\n        if not hasattr(self.builder, \"config\"):\n            raise TypeError(  # pragma: no cover\n                f\"Builder has no config: {type(self.builder)}\")\n        # The instruction\n        # visitor = self.builder.create_translator(self.document, self.builder)\n        # automatically adds methods visit_ and depart_ for translator\n        # based on the list of registered extensions. Might be worth using it.\n        theme = self.builder.themes.get('manual')\n        if theme is None:\n            raise RuntimeError(  # pragma: no cover\n                \"theme cannot be None.\")\n        visitor = self.translator_class(\n            self.document, self.builder, theme=theme)\n        self.document.walkabout(visitor)\n        self.output = visitor.body\n\n\nclass _MemoryBuilder:\n    \"\"\"\n    Builds :epkg:`HTML` output in memory.\n    The API is defined by the page\n    :epkg:`builderapi`.\n    \"\"\"\n\n    def _init(self, base_class, app, env=None):\n        \"\"\"\n        Constructs the builder.\n        Most of the parameter are static members of the class and cannot\n        be overwritten (yet).\n\n        :param base_class: base builder class\n        :param app: :epkg:`Sphinx application`\n        :param env: Environment\n        \"\"\"\n        if \"IMPOSSIBLE:TOFIND\" in app.srcdir:\n            import sphinx.util.osutil\n            from .conf_path_tools import custom_ensuredir\n            sphinx.util.osutil.ensuredir = custom_ensuredir\n            sphinx.builders.ensuredir = custom_ensuredir\n\n        try:\n            base_class.__init__(self, app=app, env=env)\n        except TypeError:\n            # older version of sphinx\n            base_class.__init__(self, app=app)\n        self.built_pages = {}\n        self.base_class = base_class\n\n    def iter_pages(self):\n        \"\"\"\n        Enumerate created pages.\n\n        @return     iterator on tuple(name, content)\n        \"\"\"\n        for k, v in self.built_pages.items():\n            yield k, v.getvalue()\n\n    def create_translator(self, *args):\n        \"\"\"\n        Returns an instance of translator.\n        This method returns an instance of ``default_translator_class`` by default.\n        Users can replace the translator class with ``app.set_translator()`` API.\n        \"\"\"\n        translator_class = self.translator_class\n        return translator_class(*args)\n\n    def _write_serial(self, docnames):\n        \"\"\"\n        Overwrites *_write_serial* to avoid writing on disk.\n        \"\"\"\n        from sphinx.util.logging import pending_warnings\n        try:\n            from sphinx.util.display import status_iterator\n        except ImportError:\n            from sphinx.util import status_iterator\n        with pending_warnings():\n            for docname in status_iterator(docnames, 'writing output... ', \"darkgreen\",\n                                           len(docnames), self.app.verbosity):\n                doctree = self.env.get_and_resolve_doctree(docname, self)\n                self.write_doc_serialized(docname, doctree)\n                self.write_doc(docname, doctree)\n\n    def _write_parallel(self, docnames, nproc):\n        \"\"\"\n        Not supported.\n        \"\"\"\n        raise NotImplementedError(\n            \"Use parallel=0 when creating the sphinx application.\")\n\n    def assemble_doctree(self, *args, **kwargs):\n        \"\"\"\n        Overwrites *assemble_doctree* to control the doctree.\n        \"\"\"\n        from sphinx.util.nodes import inline_all_toctrees\n        from sphinx.util.console import darkgreen\n        master = self.config.master_doc\n        if hasattr(self, \"doctree_\"):\n            tree = self.doctree_\n        else:\n            raise AttributeError(  # pragma: no cover\n                \"Attribute 'doctree_' is not present. Call method finalize().\")\n        tree = inline_all_toctrees(\n            self, set(), master, tree, darkgreen, [master])\n        tree['docname'] = master\n        self.env.resolve_references(tree, master, self)\n        self.fix_refuris(tree)\n        return tree\n\n    def fix_refuris(self, tree):\n        \"\"\"\n        Overwrites *fix_refuris* to control the reference names.\n        \"\"\"\n        fname = \"__\" + self.config.master_doc + \"__\"\n        for refnode in tree.traverse(nodes.reference):\n            if 'refuri' not in refnode:\n                continue\n            refuri = refnode['refuri']\n            hashindex = refuri.find('#')\n            if hashindex < 0:\n                continue\n            hashindex = refuri.find('#', hashindex + 1)\n            if hashindex >= 0:\n                refnode['refuri'] = fname + refuri[hashindex:]\n\n    def get_target_uri(self, docname, typ=None):\n        \"\"\"\n        Overwrites *get_target_uri* to control the page name.\n        \"\"\"\n        if docname in self.env.all_docs:\n            # all references are on the same page...\n            return self.config.master_doc + '#document-' + docname\n        elif docname in (\"genindex\", \"search\"):\n            return self.config.master_doc + '-#' + docname\n        else:\n            docs = \", \".join(  # pragma: no cover\n                sorted(f\"'{_}'\" for _ in self.env.all_docs))\n            raise ValueError(  # pragma: no cover\n                f\"docname='{docname}' should be in 'self.env.all_docs' which contains:\\n{docs}\")\n\n    def get_outfilename(self, pagename):\n        \"\"\"\n        Overwrites *get_target_uri* to control file names.\n        \"\"\"\n        return f\"{self.outdir}/{pagename}.m.html\".replace(\"\\\\\", \"/\")\n\n    def handle_page(self, pagename, addctx, templatename='page.html',\n                    outfilename=None, event_arg=None):\n        \"\"\"\n        Overrides *handle_page* to write into stream instead of files.\n        \"\"\"\n        from sphinx.util.osutil import relative_uri\n        ctx = self.globalcontext.copy()\n        if hasattr(self, \"warning\"):\n            ctx['warn'] = self.warning\n        elif hasattr(self, \"warn\"):\n            ctx['warn'] = self.warn\n        # current_page_name is backwards compatibility\n        ctx['pagename'] = ctx['current_page_name'] = pagename\n        ctx['encoding'] = self.config.html_output_encoding\n        default_baseuri = self.get_target_uri(pagename)\n        # in the singlehtml builder, default_baseuri still contains an #anchor\n        # part, which relative_uri doesn't really like...\n        default_baseuri = default_baseuri.rsplit('#', 1)[0]\n\n        def pathto(otheruri, resource=False, baseuri=default_baseuri):\n            if resource and '://' in otheruri:\n                # allow non-local resources given by scheme\n                return otheruri\n            elif not resource:\n                otheruri = self.get_target_uri(otheruri)\n            uri = relative_uri(baseuri, otheruri) or '#'\n            if uri == '#' and not self.allow_sharp_as_current_path:\n                uri = baseuri\n            return uri\n        ctx['pathto'] = pathto\n\n        def css_tag(css):\n            attrs = []\n            for key in sorted(css.attributes):\n                value = css.attributes[key]\n                if value is not None:\n                    attrs.append('%s=\"%s\"' % (key, htmlescape(    # pylint: disable=W1505\n                        value, True)))  # pylint: disable=W1505\n            attrs.append(f'href=\"{pathto(css.filename, resource=True)}\"')\n            return f\"<link {' '.join(attrs)} />\"\n        ctx['css_tag'] = css_tag\n\n        def hasdoc(name):\n            if name in self.env.all_docs:\n                return True\n            elif name == 'search' and self.search:\n                return True\n            elif name == 'genindex' and self.get_builder_config('use_index', 'html'):\n                return True\n            return False\n        ctx['hasdoc'] = hasdoc\n\n        ctx['toctree'] = lambda **kw: self._get_local_toctree(pagename, **kw)\n        self.add_sidebars(pagename, ctx)\n        ctx.update(addctx)\n\n        self.update_page_context(pagename, templatename, ctx, event_arg)\n        newtmpl = self.app.emit_firstresult('html-page-context', pagename,\n                                            templatename, ctx, event_arg)\n        if newtmpl:\n            templatename = newtmpl\n\n        try:\n            output = self.templates.render(templatename, ctx)\n        except UnicodeError:  # pragma: no cover\n            logger = getLogger(\"MockSphinxApp\")\n            logger.warning(\"[_CustomSphinx] A unicode error occurred when rendering the page %s. \"\n                           \"Please make sure all config values that contain \"\n                           \"non-ASCII content are Unicode strings.\", pagename)\n            return\n\n        if not outfilename:\n            outfilename = self.get_outfilename(pagename)\n        # outfilename's path is in general different from self.outdir\n        # ensuredir(path.dirname(outfilename))\n        if outfilename not in self.built_pages:\n            self.built_pages[outfilename] = StringIO()\n        self.built_pages[outfilename].write(output)\n\n\nclass MemoryHTMLBuilder(_MemoryBuilder, CustomSingleFileHTMLBuilder):\n    \"\"\"\n    Builds :epkg:`HTML` output in memory.\n    The API is defined by the page\n    :epkg:`builderapi`.\n    \"\"\"\n    name = 'memoryhtml'\n    format = 'html'\n    out_suffix = None  # \".memory.html\"\n    supported_image_types = ['application/pdf', 'image/png', 'image/jpeg']\n    default_translator_class = HTMLTranslatorWithCustomDirectives\n    translator_class = HTMLTranslatorWithCustomDirectives\n    _writer_class = HTMLWriterWithCustomDirectives\n    supported_remote_images = True\n    supported_data_uri_images = True\n    html_scaled_image_link = True\n\n    def __init__(self, app, env=None):  # pylint: disable=W0231\n        \"\"\"\n        Construct the builder.\n        Most of the parameter are static members of the class and cannot\n        be overwritten (yet).\n\n        :param app: :epkg:`Sphinx application`\n        \"\"\"\n        _MemoryBuilder._init(self, CustomSingleFileHTMLBuilder, app, env=env)\n\n\nclass MemoryRSTBuilder(_MemoryBuilder, RstBuilder):\n\n    \"\"\"\n    Builds :epkg:`RST` output in memory.\n    The API is defined by the page\n    :epkg:`builderapi`.\n    The writer simplifies the :epkg:`RST` syntax by replacing\n    custom roles into true :epkg:`RST` syntax.\n    \"\"\"\n\n    name = 'memoryrst'\n    format = 'rst'\n    out_suffix = None  # \".memory.rst\"\n    supported_image_types = ['application/pdf', 'image/png', 'image/jpeg']\n    default_translator_class = RSTTranslatorWithCustomDirectives\n    translator_class = RSTTranslatorWithCustomDirectives\n    _writer_class = RSTWriterWithCustomDirectives\n    supported_remote_images = True\n    supported_data_uri_images = True\n    html_scaled_image_link = True\n\n    def __init__(self, app, env=None):  # pylint: disable=W0231\n        \"\"\"\n        Construct the builder.\n        Most of the parameter are static members of the class and cannot\n        be overwritten (yet).\n\n        :param app: :epkg:`Sphinx application`\n        \"\"\"\n        _MemoryBuilder._init(self, RstBuilder, app, env=env)\n\n    def handle_page(self, pagename, addctx, templatename=None,\n                    outfilename=None, event_arg=None):\n        \"\"\"\n        Override *handle_page* to write into stream instead of files.\n        \"\"\"\n        if templatename is not None:\n            raise NotImplementedError(\n                \"templatename must be None.\")  # pragma: no cover\n        if not outfilename:\n            outfilename = self.get_outfilename(pagename)\n        if outfilename not in self.built_pages:\n            self.built_pages[outfilename] = StringIO()\n        self.built_pages[outfilename].write(self.writer.output)\n\n\nclass MemoryMDBuilder(_MemoryBuilder, MdBuilder):\n    \"\"\"\n    Builds :epkg:`MD` output in memory.\n    The API is defined by the page\n    :epkg:`builderapi`.\n    \"\"\"\n    name = 'memorymd'\n    format = 'md'\n    out_suffix = None  # \".memory.rst\"\n    supported_image_types = ['application/pdf', 'image/png', 'image/jpeg']\n    default_translator_class = MDTranslatorWithCustomDirectives\n    translator_class = MDTranslatorWithCustomDirectives\n    _writer_class = MDWriterWithCustomDirectives\n    supported_remote_images = True\n    supported_data_uri_images = True\n    html_scaled_image_link = True\n\n    def __init__(self, app, env=None):  # pylint: disable=W0231\n        \"\"\"\n        Construct the builder.\n        Most of the parameter are static members of the class and cannot\n        be overwritten (yet).\n\n        :param app: :epkg:`Sphinx application`\n        \"\"\"\n        _MemoryBuilder._init(self, MdBuilder, app, env=env)\n\n    def handle_page(self, pagename, addctx, templatename=None,\n                    outfilename=None, event_arg=None):\n        \"\"\"\n        Override *handle_page* to write into stream instead of files.\n        \"\"\"\n        if templatename is not None:\n            raise NotImplementedError(\n                \"templatename must be None.\")  # pragma: no cover\n        if not outfilename:\n            outfilename = self.get_outfilename(pagename)\n        if outfilename not in self.built_pages:\n            self.built_pages[outfilename] = StringIO()\n        self.built_pages[outfilename].write(self.writer.output)\n\n\nclass MemoryDocTreeBuilder(_MemoryBuilder, DocTreeBuilder):\n    \"\"\"\n    Builds doctree output in memory.\n    The API is defined by the page\n    :epkg:`builderapi`.\n    \"\"\"\n    name = 'memorydoctree'\n    format = 'doctree'\n    out_suffix = None  # \".memory.rst\"\n    default_translator_class = DocTreeTranslatorWithCustomDirectives\n    translator_class = DocTreeTranslatorWithCustomDirectives\n    _writer_class = DocTreeWriterWithCustomDirectives\n    supported_remote_images = True\n    supported_data_uri_images = True\n    html_scaled_image_link = True\n\n    def __init__(self, app, env=None):  # pylint: disable=W0231\n        \"\"\"\n        Constructs the builder.\n        Most of the parameter are static members of the class and cannot\n        be overwritten (yet).\n\n        :param app: :epkg:`Sphinx application`\n        \"\"\"\n        _MemoryBuilder._init(self, DocTreeBuilder, app, env=env)\n\n    def handle_page(self, pagename, addctx, templatename=None,\n                    outfilename=None, event_arg=None):\n        \"\"\"\n        Override *handle_page* to write into stream instead of files.\n        \"\"\"\n        if templatename is not None:\n            raise NotImplementedError(\n                \"templatename must be None.\")  # pragma: no cover\n        if not outfilename:\n            outfilename = self.get_outfilename(pagename)\n        if outfilename not in self.built_pages:\n            self.built_pages[outfilename] = StringIO()\n        self.built_pages[outfilename].write(self.writer.output)\n\n\nclass MemoryLatexBuilder(_MemoryBuilder, EnhancedLaTeXBuilder):\n    \"\"\"\n    Builds :epkg:`Latex` output in memory.\n    The API is defined by the page\n    :epkg:`builderapi`.\n    \"\"\"\n    name = 'memorylatex'\n    format = 'tex'\n    out_suffix = None  # \".memory.tex\"\n    supported_image_types = ['image/png', 'image/jpeg', 'image/gif']\n    default_translator_class = LatexTranslatorWithCustomDirectives\n    translator_class = LatexTranslatorWithCustomDirectives\n    _writer_class = LatexWriterWithCustomDirectives\n    supported_remote_images = True\n    supported_data_uri_images = True\n    html_scaled_image_link = True\n\n    def __init__(self, app, env=None):  # pylint: disable=W0231\n        \"\"\"\n        Constructs the builder.\n        Most of the parameter are static members of the class and cannot\n        be overwritten (yet).\n\n        :param app: :epkg:`Sphinx application`\n        \"\"\"\n        _MemoryBuilder._init(self, EnhancedLaTeXBuilder, app, env=env)\n\n    def write_stylesheet(self):\n        from sphinx.highlighting import PygmentsBridge\n        highlighter = PygmentsBridge('latex', self.config.pygments_style)\n        rows = []\n        rows.append('\\\\NeedsTeXFormat{LaTeX2e}[1995/12/01]\\n')\n        rows.append('\\\\ProvidesPackage{sphinxhighlight}')\n        rows.append(\n            '[2016/05/29 stylesheet for highlighting with pygments]\\n\\n')\n        rows.append(highlighter.get_stylesheet())\n        self.built_pages['sphinxhighlight.sty'] = StringIO()\n        self.built_pages['sphinxhighlight.sty'].write(\"\".join(rows))\n\n    class EnhancedStringIO(StringIO):\n        def write(self, content):\n            if isinstance(content, str):\n                StringIO.write(self, content)\n            else:\n                for line in content:\n                    StringIO.write(self, line)\n\n    def _get_filename(self, targetname, encoding='utf-8', overwrite_if_changed=True):\n        if not isinstance(targetname, str):\n            raise TypeError(  # pragma: no cover\n                f\"targetname must be a string: {targetname}\")\n        destination = MemoryLatexBuilder.EnhancedStringIO()\n        self.built_pages[targetname] = destination\n        return destination\n\n\nclass _CustomBuildEnvironment(BuildEnvironment):\n    \"\"\"\n    Overrides some functionalities of\n    `BuildEnvironment <https://www.sphinx-doc.org/en/master/extdev/envapi.html>`_.\n    \"\"\"\n\n    def __init__(self, app):\n        \"\"\"\n        \"\"\"\n        BuildEnvironment.__init__(self, app)\n        self.doctree_ = {}\n\n    def get_doctree(self, docname):\n        \"\"\"Read the doctree for a file from the pickle and return it.\"\"\"\n        if hasattr(self, \"doctree_\") and docname in self.doctree_:\n            from sphinx.util.docutils import WarningStream\n            doctree = self.doctree_[docname]\n            doctree.settings.env = self\n            doctree.reporter = Reporter(self.doc2path(\n                docname), 2, 5, stream=WarningStream())\n            return doctree\n\n        if hasattr(self, \"doctree_\"):\n            available = list(sorted(self.doctree_))\n            if len(available) > 10:\n                available = available[10:]\n            raise KeyError(\n                \"Unable to find entry '{}' (has doctree: {})\\nFirst documents:\\n{}\"\n                \"\".format(\n                    docname, hasattr(self, \"doctree_\"),\n                    \"\\n\".join(available)))\n\n        raise KeyError(  # pragma: no cover\n            \"Doctree empty or not found for '{}' (has doctree: {})\"\n            \"\".format(\n                docname, hasattr(self, \"doctree_\")))\n        # return BuildEnvironment.get_doctree(self, docname)\n\n    def apply_post_transforms(self, doctree, docname):\n        \"\"\"Apply all post-transforms.\"\"\"\n        # set env.docname during applying post-transforms\n        self.temp_data['docname'] = docname\n\n        transformer = SphinxTransformer(doctree)\n        transformer.set_environment(self)\n        transformer.add_transforms(self.app.post_transforms)\n        transformer.apply_transforms()\n        self.temp_data.clear()\n\n\nclass _CustomSphinx(Sphinx):\n    \"\"\"\n    Custom :epkg:`Sphinx` application to avoid using disk.\n    \"\"\"\n\n    def __init__(self, srcdir, confdir, outdir, doctreedir, buildername=\"memoryhtml\",  # pylint: disable=W0231\n                 confoverrides=None, status=None, warning=None,\n                 freshenv=False, warningiserror=False,\n                 tags=None, verbosity=0, parallel=0, keep_going=False,\n                 new_extensions=None):\n        '''\n        Same constructor as :epkg:`Sphinx application`.\n        Additional parameters:\n\n        @param      new_extensions      extensions to add to the application\n\n        Some insights about domains:\n\n        ::\n\n            {'cpp': sphinx.domains.cpp.CPPDomain,\n             'hpp': sphinx.domains.cpp.CPPDomain,\n             'h': sphinx.domains.cpp.CPPDomain,\n             'js': sphinx.domains.javascript.JavaScriptDomain,\n             'std': sphinx.domains.std.StandardDomain,\n             'py': sphinx.domains.python.PythonDomain,\n             'rst': sphinx.domains.rst.ReSTDomain,\n             'c': sphinx.domains.c.CDomain}\n\n        And builders:\n\n        ::\n\n            {'epub': ('epub', 'EpubBuilder'),\n            'singlehtml': ('html', 'SingleFileHTMLBuilder'),\n            'qthelp': ('qthelp', 'QtHelpBuilder'),\n            'epub3': ('epub3', 'Epub3Builder'),\n            'man': ('manpage', 'ManualPageBuilder'),\n            'dummy': ('dummy', 'DummyBuilder'),\n            'json': ('html', 'JSONHTMLBuilder'),\n            'html': ('html', 'StandaloneHTMLBuilder'),\n            'xml': ('xml', 'XMLBuilder'),\n            'texinfo': ('texinfo', 'TexinfoBuilder'),\n            'devhelp': ('devhelp', 'DevhelpBuilder'),\n            'web': ('html', 'PickleHTMLBuilder'),\n            'pickle': ('html', 'PickleHTMLBuilder'),\n            'htmlhelp': ('htmlhelp', 'HTMLHelpBuilder'),\n            'applehelp': ('applehelp', 'AppleHelpBuilder'),\n            'linkcheck': ('linkcheck', 'CheckExternalLinksBuilder'),\n            'dirhtml': ('html', 'DirectoryHTMLBuilder'),\n            'latex': ('latex', 'LaTeXBuilder'),\n            'elatex': ('latex', 'EnchancedLaTeXBuilder'),\n            'text': ('text', 'TextBuilder'),\n            'changes': ('changes', 'ChangesBuilder'),\n            'websupport': ('websupport', 'WebSupportBuilder'),\n            'gettext': ('gettext', 'MessageCatalogBuilder'),\n            'pseudoxml': ('xml', 'PseudoXMLBuilder')}\n            'rst': ('rst', 'RstBuilder')}\n            'md': ('md', 'MdBuilder'),\n            'doctree': ('doctree', 'DocTreeBuilder')}\n        '''\n        # own purpose (to monitor)\n        self._logger = getLogger(\"_CustomSphinx\")\n        self._added_objects = []\n        self._added_collectors = []\n\n        # from sphinx.domains.cpp import CPPDomain\n        # from sphinx.domains.javascript import JavaScriptDomain\n        # from sphinx.domains.python import PythonDomain\n        # from sphinx.domains.std import StandardDomain\n        # from sphinx.domains.rst import ReSTDomain\n        # from sphinx.domains.c import CDomain\n\n        from sphinx.registry import SphinxComponentRegistry\n        self.phase = BuildPhase.INITIALIZATION\n        self.verbosity = verbosity\n        self.extensions = {}\n        self.builder = None\n        self.env = None\n        self.project = None\n        self.registry = SphinxComponentRegistry()\n        self.post_transforms = []\n        self.pdb = False\n\n        if doctreedir is None:\n            doctreedir = \"IMPOSSIBLE:TOFIND\"\n        if srcdir is None:\n            srcdir = \"IMPOSSIBLE:TOFIND\"\n        update_docutils_languages()\n\n        self.srcdir = os.path.abspath(srcdir)\n        self.confdir = os.path.abspath(\n            confdir) if confdir is not None else None\n        self.outdir = os.path.abspath(outdir) if confdir is not None else None\n        self.doctreedir = os.path.abspath(doctreedir)\n        self.parallel = parallel\n\n        if self.srcdir == self.outdir:\n            raise ApplicationError('Source directory and destination '  # pragma: no cover\n                                   'directory cannot be identical')\n\n        if status is None:\n            self._status = StringIO()\n            self.quiet = True\n        else:\n            self._status = status\n            self.quiet = False\n\n        from sphinx.events import EventManager\n        # logging.setup(self, self._status, self._warning)\n        self.events = EventManager(self)\n\n        # keep last few messages for traceback\n        # This will be filled by sphinx.util.logging.LastMessagesWriter\n        self.messagelog = deque(maxlen=10)\n\n        # say hello to the world\n        from sphinx import __display_version__\n        self.info(f'Running Sphinx v{__display_version__}')  # pragma: no cover\n\n        # notice for parallel build on macOS and py38+\n        if sys.version_info > (3, 8) and platform.system() == 'Darwin' and parallel > 1:\n            self._logger.info(  # pragma: no cover\n                \"For security reason, parallel mode is disabled on macOS and \"\n                \"python3.8 and above.  For more details, please read \"\n                \"https://github.com/sphinx-doc/sphinx/issues/6803\")\n\n        # status code for command-line application\n        self.statuscode = 0\n\n        # delayed import to speed up time\n        from sphinx.application import builtin_extensions\n        from sphinx.config import CONFIG_FILENAME, Config, Tags\n\n        # read config\n        self.tags = Tags(tags)\n        with warnings.catch_warnings():\n            warnings.simplefilter(\n                \"ignore\", (DeprecationWarning, PendingDeprecationWarning))\n            if self.confdir is None:\n                self.config = Config({}, confoverrides or {})\n            else:  # pragma: no cover\n                try:\n                    self.config = Config.read(\n                        self.confdir, confoverrides or {}, self.tags)\n                except AttributeError:\n                    try:\n                        self.config = Config(  # pylint: disable=E1121\n                            confdir, confoverrides or {}, self.tags)\n                    except TypeError:\n                        try:\n                            self.config = Config(confdir, CONFIG_FILENAME,  # pylint: disable=E1121\n                                                 confoverrides or {}, self.tags)\n                        except TypeError:\n                            # Sphinx==3.0.0\n                            self.config = Config({}, confoverrides or {})\n        self.sphinx__display_version__ = __display_version__\n\n        # create the environment\n        self.config.pre_init_values()\n\n        # set up translation infrastructure\n        self._init_i18n()\n\n        # check the Sphinx version if requested\n        if (self.config.needs_sphinx and self.config.needs_sphinx >\n                __display_version__):  # pragma: no cover\n            from sphinx.locale import _\n            from sphinx.application import VersionRequirementError\n            raise VersionRequirementError(\n                _('This project needs at least Sphinx v%s and therefore cannot '\n                  'be built with this version.') % self.config.needs_sphinx)\n\n        # set confdir to srcdir if -C given (!= no confdir); a few pieces\n        # of code expect a confdir to be set\n        if self.confdir is None:\n            self.confdir = self.srcdir\n\n        # load all built-in extension modules\n        for extension in builtin_extensions:\n            try:\n                with warnings.catch_warnings():\n                    warnings.filterwarnings(\n                        \"ignore\", category=DeprecationWarning)\n                    self.setup_extension(extension)\n            except Exception as e:  # pragma: no cover\n                if 'sphinx.builders.applehelp' not in str(e):  # pragma: no cover\n                    mes = \"Unable to run setup_extension '{0}'\\nWHOLE LIST\\n{1}\".format(\n                        extension, \"\\n\".join(builtin_extensions))\n                    raise ExtensionError(mes) from e\n\n        # load all user-given extension modules\n        for extension in self.config.extensions:\n            self.setup_extension(extension)\n\n        # /1 addition to the original code\n        # additional extensions\n        if new_extensions:\n            for extension in new_extensions:\n                if isinstance(extension, str):\n                    self.setup_extension(extension)\n                else:  # pragma: no cover\n                    # We assume it is a module.\n                    dirname = os.path.dirname(extension.__file__)\n                    sys.path.insert(0, dirname)\n                    self.setup_extension(extension.__name__)\n                    del sys.path[0]\n\n        # add default HTML builders\n        self.add_builder(MemoryHTMLBuilder)\n        self.add_builder(MemoryRSTBuilder)\n        self.add_builder(MemoryMDBuilder)\n        self.add_builder(MemoryLatexBuilder)\n        self.add_builder(MemoryDocTreeBuilder)\n\n        if isinstance(buildername, tuple):\n            if len(buildername) != 2:\n                raise ValueError(  # pragma: no cover\n                    \"The builder can be custom but it must be specifed \"\n                    \"as a 2-uple=(builder_name, builder_class).\")\n            self.add_builder(buildername[1])\n            buildername = buildername[0]\n\n        # /1 end of addition\n\n        # preload builder module (before init config values)\n        self.preload_builder(buildername)\n\n        # the config file itself can be an extension\n        if self.config.setup:\n            prefix = f\"while setting up extension {'conf.py'}:\"\n            if prefixed_warnings is not None:\n                with prefixed_warnings(prefix):\n                    if callable(self.config.setup):\n                        self.config.setup(self)\n                    else:  # pragma: no cover\n                        from sphinx.locale import _\n                        from sphinx.application import ConfigError\n                        raise ConfigError(\n                            _(\"'setup' as currently defined in conf.py isn't a Python callable. \"\n                              \"Please modify its definition to make it a callable function. This is \"\n                              \"needed for conf.py to behave as a Sphinx extension.\")\n                        )\n            elif callable(self.config.setup):\n                self.config.setup(self)\n\n        # now that we know all config values, collect them from conf.py\n        noallowed = []\n        rem = []\n        for k in confoverrides:\n            if k in {'initial_header_level', 'doctitle_xform', 'input_encoding',\n                     'outdir', 'warnings_log', 'extensions'}:\n                continue\n            if k == 'override_image_directive':\n                self.config.images_config[\"override_image_directive\"] = True\n                rem.append(k)\n                continue\n            if k not in self.config.values:\n                noallowed.append(k)\n        for k in rem:\n            del confoverrides[k]\n        if len(noallowed) > 0:\n            raise ValueError(  # pragma: no cover\n                \"The following configuration values are declared in any extension.\\n--???--\\n\"\n                \"{0}\\n--DECLARED--\\n{1}\".format(\n                    \"\\n\".join(sorted(noallowed)),\n                    \"\\n\".join(sorted(self.config.values))))\n\n        # now that we know all config values, collect them from conf.py\n        self.config.init_values()\n        self.events.emit('config-inited', self.config)\n\n        # /2 addition to the original code\n        # check extension versions if requested\n        # self.config.needs_extensions = self.config.extensions\n        if not hasattr(self.config, 'items'):\n\n            def _citems():\n                for k, v in self.config.values.items():\n                    yield k, v\n\n            self.config.items = _citems\n\n        # /2 end of addition\n\n        # create the project\n        self.project = Project(self.srcdir, self.config.source_suffix)\n        # set up the build environment\n        self._init_env(freshenv)\n        assert self.env is not None\n        # create the builder, initializes _MemoryBuilder\n        self.builder = self.create_builder(buildername)\n        # build environment post-initialisation, after creating the builder\n        if hasattr(self, \"_post_init_env\"):\n            self._post_init_env()\n        # set up the builder\n        self._init_builder()\n\n        if not isinstance(self.env, _CustomBuildEnvironment):\n            raise TypeError(  # pragma: no cover\n                f\"self.env is not _CustomBuildEnvironment: {type(self.env)!r} \"\n                f\"buildername='{buildername}'\")\n\n        # addition\n        self._extended_init_()\n\n        # verification\n        self._check_init_()\n\n    def _init_builder(self) -> None:\n        if not hasattr(self.builder, \"env\") or self.builder.env is None:\n            self.builder.set_environment(self.env)\n        self.builder.init()\n        self.events.emit('builder-inited')\n\n    def _check_init_(self):\n        pass\n\n    def _init_env(self, freshenv):\n        ENV_PICKLE_FILENAME = 'environment.pickle'\n        filename = os.path.join(self.doctreedir, ENV_PICKLE_FILENAME)\n        if freshenv or not os.path.exists(filename):\n            self.env = _CustomBuildEnvironment(self)\n            self._fresh_env_used = True\n            self.env.setup(self)\n            if (self.srcdir is not None and self.srcdir != \"IMPOSSIBLE:TOFIND\" and\n                    self.builder is not None):\n                self.env.find_files(self.config, self.builder)\n            return self.env\n\n        if \"IMPOSSIBLE:TOFIND\" not in self.doctreedir:  # pragma: no cover\n            from sphinx.application import ENV_PICKLE_FILENAME\n            filename = os.path.join(self.doctreedir, ENV_PICKLE_FILENAME)\n            try:\n                self.info('loading pickled environment... ')\n                with open(filename, 'rb') as f:\n                    self.env = pickle.load(f)\n                    self.env.setup(self)\n                self.info('done')\n                return self.env\n            except Exception as err:\n                self.info('failed: %r', err)\n                return self._init_env(freshenv=True)\n\n        if self.env is None:  # pragma: no cover\n            self.env = _CustomBuildEnvironment(self)\n            if hasattr(self.env, 'setup'):\n                self.env.setup(self)\n            return self.env\n\n        if not hasattr(self.env, 'project') or self.env.project is None:\n            raise AttributeError(  # pragma: no cover\n                \"self.env.project is not initialized.\")\n\n    def create_builder(self, name):\n        \"\"\"\n        Creates a builder, raises an exception if name is None.\n        \"\"\"\n        if name is None:\n            raise ValueError(  # pragma: no cover\n                \"Builder name cannot be None\")\n        try:\n            return self.registry.create_builder(self, name, env=self.env)\n        except TypeError:\n            # old version of sphinx\n            return self.registry.create_builder(self, name)\n\n    def _extended_init_(self):\n        \"\"\"\n        Additional initialization steps.\n        \"\"\"\n        if not hasattr(self, \"domains\"):\n            self.domains = {}\n        if not hasattr(self, \"_events\"):\n            self._events = {}\n\n        # Otherwise, role issue is missing.\n        setup_link_roles(self)\n\n    def _lookup_doctree(self, doctree, node_type):\n        for node in doctree.traverse(node_type):\n            yield node\n\n    def _add_missing_ids(self, doctree):\n        for i, node in enumerate(self._lookup_doctree(doctree, None)):\n            stype = str(type(node))\n            if ('section' not in stype and 'title' not in stype and\n                    'reference' not in stype):\n                continue\n            try:\n                node['ids'][0]\n            except IndexError:\n                node['ids'] = ['missing%d' % i]\n            except TypeError:  # pragma: no cover\n                pass\n\n    def finalize(self, doctree, external_docnames=None):\n        \"\"\"\n        Finalizes the documentation after it was parsed.\n\n        @param      doctree             doctree (or pub.document), available after publication\n        @param      external_docnames   other docnames the doctree references\n        \"\"\"\n        imgs = list(self._lookup_doctree(doctree, nodes.image))\n        for img in imgs:\n            img['save_uri'] = img['uri']\n\n        if not isinstance(self.env, _CustomBuildEnvironment):\n            raise TypeError(  # pragma: no cover\n                f\"self.env is not _CustomBuildEnvironment: '{type(self.env)}'\")\n        if not isinstance(self.builder.env, _CustomBuildEnvironment):\n            raise TypeError(  # pragma: no cover\n                \"self.builder.env is not _CustomBuildEnvironment: '{0}'\".format(\n                    type(self.builder.env)))\n        self.doctree_ = doctree\n        self.builder.doctree_ = doctree\n        self.env.doctree_[self.config.master_doc] = doctree\n        self.env.all_docs = {self.config.master_doc: self.config.master_doc}\n\n        if external_docnames:\n            for doc in external_docnames:\n                self.env.all_docs[doc] = doc\n\n        # This steps goes through many function including one\n        # modifying paths in image node.\n        # Look for node['candidates'] = candidates in Sphinx code.\n        # If a path startswith('/'), it is removed.\n        from sphinx.environment.collectors.asset import logger as logger_asset\n        logger_asset.setLevel(40)  # only errors\n        self._add_missing_ids(doctree)\n        self.events.emit('doctree-read', doctree)\n        logger_asset.setLevel(30)  # back to warnings\n\n        for img in imgs:\n            img['uri'] = img['save_uri']\n\n        self.events.emit('doctree-resolved', doctree,\n                         self.config.master_doc)\n        self.builder.write(None, None, 'all')\n\n    def debug(self, message, *args, **kwargs):\n        self._logger.debug(message, *args, **kwargs)\n\n    def info(self, message, *args):\n        self._logger.info(message, *args)\n\n    def warning(self, message='', name=None, type=None, subtype=None):\n        if \"is already registered\" not in message:  # pragma: no cover\n            self._logger.warning(\n                \"[_CustomSphinx] %s -- %s\", message, name,\n                type=type, subtype=subtype)\n\n    def add_builder(self, builder, override=False):\n        self._added_objects.append(('builder', builder))\n        if builder.name not in self.registry.builders:\n            self.debug('[_CustomSphinx]  adding builder: %r', builder)\n            self.registry.add_builder(builder, override=override)\n        else:\n            self.debug('[_CustomSphinx]  already added builder: %r', builder)\n\n    def setup_extension(self, extname):\n        self._added_objects.append(('extension', extname))\n\n        logger = getLogger('sphinx.application')\n        disa = logger.logger.disabled\n        logger.logger.disabled = True\n\n        # delayed import to speed up time\n        try:\n            with warnings.catch_warnings():\n                warnings.filterwarnings(\n                    \"ignore\", category=DeprecationWarning)\n                self.registry.load_extension(self, extname)\n        except Exception as e:  # pragma: no cover\n            raise ExtensionError(\n                f\"Unable to setup extension '{extname}'\") from e\n        finally:\n            logger.logger = disa\n\n    def add_directive(self, name, obj, content=None, arguments=None,  # pylint: disable=W0221,W0237\n                      override=True, **options):\n        self._added_objects.append(('directive', name))\n        if name == 'plot' and obj.__name__ == 'PlotDirective':\n\n            old_run = obj.run\n\n            def run(self):\n                \"\"\"Run the plot directive.\"\"\"\n                logger = getLogger(\"MockSphinxApp\")\n                logger.info('[MockSphinxApp] PlotDirective: %r', self.content)\n                try:\n                    res = old_run(self)\n                    logger.info('[MockSphinxApp] PlotDirective ok')\n                    return res\n                except OSError as e:  # pragma: no cover\n                    logger = getLogger(\"MockSphinxApp\")\n                    logger.info('[MockSphinxApp] PlotDirective failed: %s', e)\n                return []\n\n            obj.run = run\n\n        Sphinx.add_directive(self, name, obj, override=override, **options)\n\n    def add_domain(self, domain, override=True):\n        self._added_objects.append(('domain', domain))\n        Sphinx.add_domain(self, domain, override=override)\n        # For some reason, the directives are missing from the main catalog\n        # in docutils.\n        for k, v in domain.directives.items():\n            self.add_directive(f\"{domain.name}:{k}\", v)\n            if domain.name in ('py', 'std', 'rst'):\n                # We add the directive without the domain name as a prefix.\n                self.add_directive(k, v)\n        for k, v in domain.roles.items():\n            self.add_role(f\"{domain.name}:{k}\", v)\n            if domain.name in ('py', 'std', 'rst'):\n                # We add the role without the domain name as a prefix.\n                self.add_role(k, v)\n\n    def add_role(self, name, role, override=True):\n        self._added_objects.append(('role', name))\n        self.debug('[_CustomSphinx]  adding role: %r', (name, role))\n        roles.register_local_role(name, role)\n\n    def add_generic_role(self, name, nodeclass, override=True):\n        self._added_objects.append(('generic_role', name))\n        self.debug(\"[_CustomSphinx] adding generic role: '%r'\",\n                   (name, nodeclass))\n        role = roles.GenericRole(name, nodeclass)\n        roles.register_local_role(name, role)\n\n    def add_node(self, node, override=True, **kwds):\n        self._added_objects.append(('node', node))\n        self.debug('[_CustomSphinx]  adding node: %r', (node, kwds))\n        nodes._add_node_class_names([node.__name__])\n        for key, val in kwds.items():\n            try:\n                visit, depart = val\n            except ValueError:  # pragma: no cover\n                raise ExtensionError((\"Value for key '%r' must be a \"\n                                      \"(visit, depart) function tuple\") % key)\n            translator = self.registry.translators.get(key)\n            translators = []\n            if translator is not None:\n                translators.append(translator)\n            elif key == 'html':\n                from sphinx.writers.html import HTMLTranslator\n                translators.append(HTMLTranslator)\n                if is_html5_writer_available():\n                    from sphinx.writers.html5 import HTML5Translator\n                    translators.append(HTML5Translator)\n            elif key == 'latex':\n                translators.append(_get_LaTeXTranslator())\n            elif key == 'elatex':\n                translators.append(EnhancedLaTeXBuilder)\n            elif key == 'text':\n                from sphinx.writers.text import TextTranslator\n                translators.append(TextTranslator)\n            elif key == 'man':\n                from sphinx.writers.manpage import ManualPageTranslator\n                translators.append(ManualPageTranslator)\n            elif key == 'texinfo':\n                from sphinx.writers.texinfo import TexinfoTranslator\n                translators.append(TexinfoTranslator)\n\n            for translator in translators:\n                setattr(translator, 'visit_' + node.__name__, visit)\n                if depart:\n                    setattr(translator, 'depart_' + node.__name__, depart)\n\n    def add_event(self, name):\n        self._added_objects.append(('event', name))\n        Sphinx.add_event(self, name)\n\n    def add_config_value(self, name, default, rebuild, types_=(), types=()):  # pylint: disable=W0221,W0237\n        types_ = types or types_\n        self._added_objects.append(('config_value', name))\n        Sphinx.add_config_value(self, name, default, rebuild, types_)\n\n    def add_directive_to_domain(self, domain, name, obj, has_content=None,  # pylint: disable=W0221,W0237\n                                argument_spec=None, override=False, **option_spec):\n        self._added_objects.append(('directive_to_domain', domain, name))\n        Sphinx.add_directive_to_domain(self, domain, name, obj,\n                                       override=override, **option_spec)\n\n    def add_role_to_domain(self, domain, name, role, override=False):\n        self._added_objects.append(('roles_to_domain', domain, name))\n        Sphinx.add_role_to_domain(self, domain, name, role, override=override)\n\n    def add_transform(self, transform):\n        self._added_objects.append(('transform', transform))\n        Sphinx.add_transform(self, transform)\n\n    def add_post_transform(self, transform):\n        self._added_objects.append(('post_transform', transform))\n        Sphinx.add_post_transform(self, transform)\n\n    def add_js_file(self, filename, priority=500, **kwargs):  # pylint: disable=W0221\n        # loading_method=None: added in Sphinx 4.4\n        self._added_objects.append(('js', filename))\n        Sphinx.add_js_file(self, filename, priority=priority, **kwargs)\n\n    def add_css_file(self, filename, priority=500, **kwargs):\n        self._added_objects.append(('css', filename))\n        Sphinx.add_css_file(self, filename, priority=priority, **kwargs)\n\n    def add_latex_package(self, packagename, options=None, after_hyperref=False):\n        self._added_objects.append(('latex', packagename))\n        Sphinx.add_latex_package(\n            self, packagename=packagename, options=options,\n            after_hyperref=after_hyperref)\n\n    def add_object_type(self, directivename, rolename, indextemplate='',\n                        parse_node=None, ref_nodeclass=None, objname='',\n                        doc_field_types=None, override=False):\n        if doc_field_types is None:\n            doc_field_types = []\n        self._added_objects.append(('object', directivename, rolename))\n        Sphinx.add_object_type(self, directivename, rolename, indextemplate=indextemplate,\n                               parse_node=parse_node, ref_nodeclass=ref_nodeclass,\n                               objname=objname, doc_field_types=doc_field_types,\n                               override=override)\n\n    def add_env_collector(self, collector):\n        \"\"\"\n        See :epkg:`class Sphinx`.\n        \"\"\"\n        self.debug(\n            '[_CustomSphinx] adding environment collector: %r', collector)\n        coll = collector()\n        coll.enable(self)\n        self._added_collectors.append(coll)\n\n    def disconnect_env_collector(self, clname, exc=True):\n        \"\"\"\n        Disables a collector given its class name.\n\n        @param      cl      name\n        @param      exc     raises an exception if not found\n        @return             found collector\n        \"\"\"\n        found = None\n        foundi = None\n        for i, co in enumerate(self._added_collectors):\n            if clname == co.__class__.__name__:\n                found = co\n                foundi = i\n                break\n        if found is not None and not exc:\n            return None\n        if found is None:\n            raise ValueError(  # pragma: no cover\n                \"Unable to find a collector '{0}' in \\n{1}\".format(\n                    clname, \"\\n\".join(\n                        map(lambda x: x.__class__.__name__,\n                            self._added_collectors))))\n        for v in found.listener_ids.values():\n            self.disconnect(v)\n        del self._added_collectors[foundi]\n        return found\n", "repo_name": "sdpython/pyquickhelper", "sub_path": "src/pyquickhelper/helpgen/sphinxm_convert_doc_sphinx_helper.py", "file_name": "sphinxm_convert_doc_sphinx_helper.py", "file_ext": "py", "file_size_in_byte": 61727, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sphinx.writers.latex.LaTeXTranslator", "line_number": 44, "usage_type": "name"}, {"api_name": "sphinx.util.docutils.is_html5_writer_available", "line_number": 53, "usage_type": "call"}, {"api_name": "docutils.languages.en.labels", "line_number": 68, "usage_type": "attribute"}, {"api_name": "docutils.languages.en", "line_number": 68, "usage_type": "name"}, {"api_name": "sphinx.writers.html.HTMLTranslator", "line_number": 88, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_rst_builder.RstTranslator", "line_number": 94, "usage_type": "name"}, {"api_name": "docutils.nodes.SkipNode", "line_number": 152, "usage_type": "attribute"}, {"api_name": "docutils.nodes", "line_number": 152, "usage_type": "name"}, {"api_name": "sphinx.writers.html.HTMLTranslator", "line_number": 176, "usage_type": "name"}, {"api_name": "sphinx.writers.html.HTMLTranslator.__init__", "line_number": 182, "usage_type": "call"}, {"api_name": "sphinx.writers.html.HTMLTranslator", "line_number": 182, "usage_type": "name"}, {"api_name": "sphinx.writers.html.HTMLTranslator", "line_number": 189, "usage_type": "name"}, {"api_name": "sphinx.writers.html.HTMLTranslator.visit_field", "line_number": 195, "usage_type": "call"}, {"api_name": "sphinx.writers.html.HTMLTranslator", "line_number": 195, "usage_type": "name"}, {"api_name": "docutils.nodes.SkipNode", "line_number": 199, "usage_type": "attribute"}, {"api_name": "docutils.nodes", "line_number": 199, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_rst_builder.RstTranslator", "line_number": 206, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_rst_builder.RstTranslator.__init__", "line_number": 215, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_rst_builder.RstTranslator", "line_number": 215, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_rst_builder.RstTranslator", "line_number": 220, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_md_builder.MdTranslator", "line_number": 223, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_md_builder.MdTranslator.__init__", "line_number": 232, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_md_builder.MdTranslator", "line_number": 232, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_md_builder.MdTranslator", "line_number": 237, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_doctree_builder.DocTreeTranslator", "line_number": 240, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_doctree_builder.DocTreeTranslator.__init__", "line_number": 249, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_doctree_builder.DocTreeTranslator", "line_number": 249, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_doctree_builder.DocTreeTranslator", "line_number": 250, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_latex_builder.EnhancedLaTeXTranslator", "line_number": 253, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_latex_builder.EnhancedLaTeXTranslator.__init__", "line_number": 267, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_latex_builder.EnhancedLaTeXTranslator", "line_number": 267, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_latex_builder.EnhancedLaTeXTranslator", "line_number": 273, "usage_type": "name"}, {"api_name": "sphinx.writers.html.HTMLWriter", "line_number": 332, "usage_type": "name"}, {"api_name": "sphinx.writers.html.HTMLWriter", "line_number": 350, "usage_type": "argument"}, {"api_name": "sphinxext.sphinx_rst_builder.RstWriter", "line_number": 366, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_rst_builder.RstWriter", "line_number": 378, "usage_type": "argument"}, {"api_name": "sphinxext.sphinx_md_builder.MdWriter", "line_number": 386, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_md_builder.MdWriter", "line_number": 398, "usage_type": "argument"}, {"api_name": "sphinxext.sphinx_doctree_builder.DocTreeWriter", "line_number": 406, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_doctree_builder.DocTreeWriter", "line_number": 418, "usage_type": "argument"}, {"api_name": "sphinxext.sphinx_latex_builder.EnhancedLaTeXWriter", "line_number": 426, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_latex_builder.EnhancedLaTeXWriter", "line_number": 438, "usage_type": "argument"}, {"api_name": "sphinx.application.util", "line_number": 481, "usage_type": "attribute"}, {"api_name": "sphinx.application", "line_number": 481, "usage_type": "name"}, {"api_name": "conf_path_tools.custom_ensuredir", "line_number": 481, "usage_type": "name"}, {"api_name": "sphinx.application.builders", "line_number": 482, "usage_type": "attribute"}, {"api_name": "sphinx.application", "line_number": 482, "usage_type": "name"}, {"api_name": "conf_path_tools.custom_ensuredir", "line_number": 482, "usage_type": "name"}, {"api_name": "sphinx.util.logging.pending_warnings", "line_number": 519, "usage_type": "call"}, {"api_name": "sphinx.util.status_iterator", "line_number": 520, "usage_type": "call"}, {"api_name": "sphinx.util.nodes.inline_all_toctrees", "line_number": 545, "usage_type": "call"}, {"api_name": "sphinx.util.console.darkgreen", "line_number": 546, "usage_type": "name"}, {"api_name": "docutils.nodes.reference", "line_number": 557, "usage_type": "attribute"}, {"api_name": "docutils.nodes", "line_number": 557, "usage_type": "name"}, {"api_name": "sphinx.util.osutil.relative_uri", "line_number": 614, "usage_type": "call"}, {"api_name": "html.escape", "line_number": 625, "usage_type": "call"}, {"api_name": "sphinx.util.logging.getLogger", "line_number": 654, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 665, "usage_type": "call"}, {"api_name": "_single_file_html_builder.CustomSingleFileHTMLBuilder", "line_number": 669, "usage_type": "name"}, {"api_name": "{'sphinx.util.osutil': 'sphinx.util.osutil', 'custom_ensuredir': 'conf_path_tools.custom_ensuredir', 'pending_warnings': 'sphinx.util.logging.pending_warnings', 'status_iterator': 'sphinx.util.status_iterator', 'inline_all_toctrees': 'sphinx.util.nodes.inline_all_toctrees', 'darkgreen': 'sphinx.util.console.darkgreen', 'relative_uri': 'sphinx.util.osutil.relative_uri'}._init", "line_number": 694, "usage_type": "call"}, {"api_name": "_single_file_html_builder.CustomSingleFileHTMLBuilder", "line_number": 694, "usage_type": "argument"}, {"api_name": "sphinxext.sphinx_rst_builder.RstBuilder", "line_number": 697, "usage_type": "name"}, {"api_name": "{'sphinx.util.osutil': 'sphinx.util.osutil', 'custom_ensuredir': 'conf_path_tools.custom_ensuredir', 'pending_warnings': 'sphinx.util.logging.pending_warnings', 'status_iterator': 'sphinx.util.status_iterator', 'inline_all_toctrees': 'sphinx.util.nodes.inline_all_toctrees', 'darkgreen': 'sphinx.util.console.darkgreen', 'relative_uri': 'sphinx.util.osutil.relative_uri'}._init", "line_number": 726, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_rst_builder.RstBuilder", "line_number": 726, "usage_type": "argument"}, {"api_name": "io.StringIO", "line_number": 739, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_md_builder.MdBuilder", "line_number": 743, "usage_type": "name"}, {"api_name": "{'sphinx.util.osutil': 'sphinx.util.osutil', 'custom_ensuredir': 'conf_path_tools.custom_ensuredir', 'pending_warnings': 'sphinx.util.logging.pending_warnings', 'status_iterator': 'sphinx.util.status_iterator', 'inline_all_toctrees': 'sphinx.util.nodes.inline_all_toctrees', 'darkgreen': 'sphinx.util.console.darkgreen', 'relative_uri': 'sphinx.util.osutil.relative_uri'}._init", "line_number": 768, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_md_builder.MdBuilder", "line_number": 768, "usage_type": "argument"}, {"api_name": "io.StringIO", "line_number": 781, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_doctree_builder.DocTreeBuilder", "line_number": 785, "usage_type": "name"}, {"api_name": "{'sphinx.util.osutil': 'sphinx.util.osutil', 'custom_ensuredir': 'conf_path_tools.custom_ensuredir', 'pending_warnings': 'sphinx.util.logging.pending_warnings', 'status_iterator': 'sphinx.util.status_iterator', 'inline_all_toctrees': 'sphinx.util.nodes.inline_all_toctrees', 'darkgreen': 'sphinx.util.console.darkgreen', 'relative_uri': 'sphinx.util.osutil.relative_uri'}._init", "line_number": 809, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_doctree_builder.DocTreeBuilder", "line_number": 809, "usage_type": "argument"}, {"api_name": "io.StringIO", "line_number": 822, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_latex_builder.EnhancedLaTeXBuilder", "line_number": 826, "usage_type": "name"}, {"api_name": "{'sphinx.util.osutil': 'sphinx.util.osutil', 'custom_ensuredir': 'conf_path_tools.custom_ensuredir', 'pending_warnings': 'sphinx.util.logging.pending_warnings', 'status_iterator': 'sphinx.util.status_iterator', 'inline_all_toctrees': 'sphinx.util.nodes.inline_all_toctrees', 'darkgreen': 'sphinx.util.console.darkgreen', 'relative_uri': 'sphinx.util.osutil.relative_uri'}._init", "line_number": 851, "usage_type": "call"}, {"api_name": "sphinxext.sphinx_latex_builder.EnhancedLaTeXBuilder", "line_number": 851, "usage_type": "argument"}, {"api_name": "sphinx.highlighting.PygmentsBridge", "line_number": 855, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 862, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 865, "usage_type": "name"}, {"api_name": "io.StringIO.write", "line_number": 868, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 868, "usage_type": "name"}, {"api_name": "io.StringIO.write", "line_number": 871, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 871, "usage_type": "name"}, {"api_name": "{'PygmentsBridge': 'sphinx.highlighting.PygmentsBridge'}.EnhancedStringIO", "line_number": 877, "usage_type": "call"}, {"api_name": "sphinx.environment.BuildEnvironment", "line_number": 882, "usage_type": "name"}, {"api_name": "sphinx.environment.BuildEnvironment.__init__", "line_number": 891, "usage_type": "call"}, {"api_name": "sphinx.environment.BuildEnvironment", "line_number": 891, "usage_type": "name"}, {"api_name": "docutils.utils.Reporter", "line_number": 900, "usage_type": "call"}, {"api_name": "sphinx.util.docutils.WarningStream", "line_number": 901, "usage_type": "call"}, {"api_name": "sphinx.transforms.SphinxTransformer", "line_number": 925, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 932, "usage_type": "name"}, {"api_name": "sphinx.util.logging.getLogger", "line_number": 994, "usage_type": "call"}, {"api_name": "sphinx.util.build_phase.BuildPhase.INITIALIZATION", "line_number": 1006, "usage_type": "attribute"}, {"api_name": "sphinx.util.build_phase.BuildPhase", "line_number": 1006, "usage_type": "name"}, {"api_name": "sphinx.registry.SphinxComponentRegistry", "line_number": 1012, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 1022, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1022, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 1023, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1023, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 1025, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1025, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 1026, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1026, "usage_type": "attribute"}, {"api_name": "sphinx.errors.ApplicationError", "line_number": 1030, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 1034, "usage_type": "call"}, {"api_name": "sphinx.events.EventManager", "line_number": 1042, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 1046, "usage_type": "call"}, {"api_name": "sphinx.__display_version__", "line_number": 1050, "usage_type": "name"}, {"api_name": "sys.version_info", "line_number": 1053, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 1053, "usage_type": "call"}, {"api_name": "sphinx.config.Tags", "line_number": 1067, "usage_type": "call"}, {"api_name": "warnings.catch_warnings", "line_number": 1068, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 1069, "usage_type": "call"}, {"api_name": "sphinx.config.Config", "line_number": 1072, "usage_type": "call"}, {"api_name": "sphinx.config.Config.read", "line_number": 1075, "usage_type": "call"}, {"api_name": "sphinx.config.Config", "line_number": 1075, "usage_type": "name"}, {"api_name": "sphinx.config.Config", "line_number": 1079, "usage_type": "call"}, {"api_name": "sphinx.config.Config", "line_number": 1083, "usage_type": "call"}, {"api_name": "sphinx.config.CONFIG_FILENAME", "line_number": 1083, "usage_type": "name"}, {"api_name": "sphinx.config.Config", "line_number": 1087, "usage_type": "call"}, {"api_name": "sphinx.__display_version__", "line_number": 1088, "usage_type": "name"}, {"api_name": "sphinx.__display_version__", "line_number": 1098, "usage_type": "name"}, {"api_name": "sphinx.application.VersionRequirementError", "line_number": 1101, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 1102, "usage_type": "call"}, {"api_name": "sphinx.application.builtin_extensions", "line_number": 1111, "usage_type": "name"}, {"api_name": "warnings.catch_warnings", "line_number": 1113, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 1114, "usage_type": "call"}, {"api_name": "sphinx.application.builtin_extensions", "line_number": 1120, "usage_type": "name"}, {"api_name": "sphinx.errors.ExtensionError", "line_number": 1121, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 1135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1135, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 1136, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 1136, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 1138, "usage_type": "attribute"}, {"api_name": "sphinx.util.logging.prefixed_warnings", "line_number": 1163, "usage_type": "name"}, {"api_name": "sphinx.util.logging.prefixed_warnings", "line_number": 1164, "usage_type": "call"}, {"api_name": "sphinx.application.ConfigError", "line_number": 1170, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 1171, "usage_type": "call"}, {"api_name": "sphinx.project.Project", "line_number": 1218, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1252, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1252, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 1253, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1253, "usage_type": "attribute"}, {"api_name": "{'WarningStream': 'sphinx.util.docutils.WarningStream'}", "line_number": 1254, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1264, "usage_type": "attribute"}, {"api_name": "sphinx.application.ENV_PICKLE_FILENAME", "line_number": 1264, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 1268, "usage_type": "call"}, {"api_name": "{'WarningStream': 'sphinx.util.docutils.WarningStream'}", "line_number": 1277, "usage_type": "call"}, {"api_name": "sphinx.ext.extlinks.setup_link_roles", "line_number": 1309, "usage_type": "call"}, {"api_name": "docutils.nodes.image", "line_number": 1335, "usage_type": "attribute"}, {"api_name": "docutils.nodes", "line_number": 1335, "usage_type": "name"}, {"api_name": "sphinx.environment.collectors.asset.logger.setLevel", "line_number": 1360, "usage_type": "call"}, {"api_name": "sphinx.environment.collectors.asset.logger", "line_number": 1360, "usage_type": "name"}, {"api_name": "sphinx.environment.collectors.asset.logger.setLevel", "line_number": 1363, "usage_type": "call"}, {"api_name": "sphinx.environment.collectors.asset.logger", "line_number": 1363, "usage_type": "name"}, {"api_name": "sphinx.util.logging.getLogger", "line_number": 1395, "usage_type": "call"}, {"api_name": "warnings.catch_warnings", "line_number": 1401, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 1402, "usage_type": "call"}, {"api_name": "sphinx.errors.ExtensionError", "line_number": 1406, "usage_type": "call"}, {"api_name": "sphinx.util.logging.getLogger", "line_number": 1420, "usage_type": "call"}, {"api_name": "sphinx.util.logging.getLogger", "line_number": 1427, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx.add_directive", "line_number": 1433, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1433, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_domain", "line_number": 1437, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1437, "usage_type": "name"}, {"api_name": "docutils.parsers.rst.roles.register_local_role", "line_number": 1454, "usage_type": "call"}, {"api_name": "docutils.parsers.rst.roles", "line_number": 1454, "usage_type": "name"}, {"api_name": "docutils.parsers.rst.roles.GenericRole", "line_number": 1460, "usage_type": "call"}, {"api_name": "docutils.parsers.rst.roles", "line_number": 1460, "usage_type": "name"}, {"api_name": "docutils.parsers.rst.roles.register_local_role", "line_number": 1461, "usage_type": "call"}, {"api_name": "docutils.parsers.rst.roles", "line_number": 1461, "usage_type": "name"}, {"api_name": "docutils.nodes._add_node_class_names", "line_number": 1466, "usage_type": "call"}, {"api_name": "docutils.nodes", "line_number": 1466, "usage_type": "name"}, {"api_name": "sphinx.errors.ExtensionError", "line_number": 1471, "usage_type": "call"}, {"api_name": "sphinx.writers.html.HTMLTranslator", "line_number": 1479, "usage_type": "argument"}, {"api_name": "sphinx.util.docutils.is_html5_writer_available", "line_number": 1480, "usage_type": "call"}, {"api_name": "sphinx.writers.html5.HTML5Translator", "line_number": 1482, "usage_type": "name"}, {"api_name": "sphinxext.sphinx_latex_builder.EnhancedLaTeXBuilder", "line_number": 1486, "usage_type": "argument"}, {"api_name": "sphinx.writers.text.TextTranslator", "line_number": 1489, "usage_type": "name"}, {"api_name": "sphinx.writers.manpage.ManualPageTranslator", "line_number": 1492, "usage_type": "name"}, {"api_name": "sphinx.writers.texinfo.TexinfoTranslator", "line_number": 1495, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_event", "line_number": 1504, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1504, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_config_value", "line_number": 1509, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1509, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_directive_to_domain", "line_number": 1514, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1514, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_role_to_domain", "line_number": 1519, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1519, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_transform", "line_number": 1523, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1523, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_post_transform", "line_number": 1527, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1527, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_js_file", "line_number": 1532, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1532, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_css_file", "line_number": 1536, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1536, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_latex_package", "line_number": 1540, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1540, "usage_type": "name"}, {"api_name": "sphinx.application.Sphinx.add_object_type", "line_number": 1550, "usage_type": "call"}, {"api_name": "sphinx.application.Sphinx", "line_number": 1550, "usage_type": "name"}]}
{"seq_id": "12783295224", "text": "from pyspark import SparkContext,SparkConf\nfrom pyspark.sql import SQLContext\nfrom pyspark.sql import SparkSession\nfrom datetime import datetime, date, timedelta\n\nfrom imports.plotter import Plotter\n\nimport time\nimport sys\n\ndef main(argv):\n    plotter    = Plotter()\n\n    y = []\n    x = []\n\n    uri_conf = 'mongodb://localhost:27017/meteo_station.outside'\n    spark = SparkSession \\\n                .builder \\\n                .appName(\"Meteo_Station\") \\\n                .config(\"spark.mongodb.input.uri\", uri_conf) \\\n                .config(\"spark.executor.memory\", \"4g\") \\\n                .config(\"spark.executor.number\", \"4\") \\\n                .config(\"spark.executor.cores\", \"4\") \\\n                .config(\"spark.network.timeout\", \"800s\") \\\n                .config('spark.yarn.executor.memoryOverhead', \"4096m\") \\\n                .config(\"spark.driver.memoryOverhead\",) \\\n                .getOrCreate()\n\n    df = spark.read.format(\"com.mongodb.spark.sql.DefaultSource\").load()\n    df.printSchema()\n    df.registerTempTable('temp')\n    pressure = spark.sql('SELECT * FROM temp WHERE sensor = \\'BMP Pressure \\' ')\n    coll = pressure.collect()\n\n    for c in coll:\n        y.append(float(c['data']))\n\n        date = c['date']\n        xtime = c['time']\n        dtime = date + '_' + xtime\n\n        try:\n            xdtime = datetime.strptime(dtime, '%Y_%m_%d_%H:%M:%S')\n        except:\n            xdtime = datetime.strptime(dtime, '%Y_%m_%d_%H:%M:%S.%f')\n        \n        x.append(xdtime)\n\n    print('-->Data count\\t: ' + str(len(x)))\n\n    plotter.show_plotter(x, y, plotLabel = 'Pressure (BMP180)', yLabel = 'mm Hg')\n\nif (__name__ == '__main__'):\n    main(sys.argv[1:])", "repo_name": "DucklingDark/Meteo_Station", "sub_path": "python/mongo_spark.py", "file_name": "mongo_spark.py", "file_ext": "py", "file_size_in_byte": 1669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "imports.plotter.Plotter", "line_number": 12, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 18, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 18, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 41, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 55, "usage_type": "attribute"}]}
{"seq_id": "43149787832", "text": "import json\nimport urllib.request as urlr\n\nfrom geopandas.geodataframe import GeoDataFrame\nfrom src.helpers.database import Database\nimport pandas as pd\nimport geopandas as gpd\nfrom shapely.geometry import shapely \nfrom src.helpers.environment_variables import DATABASE_URL, SUPERUSER_DATABASE_URL\n\ndef download_epa_sld_data_as_geojson(filepath:str):\n    \"\"\"\n    - This function downloads a geojson from the EPA's Smart Location Database\n    - It uses a loop to iterate over all features in the table, which gets around\n    the issue of the EPA rest service not allowing all of the features to be queried at once\n    \"\"\"\n\n    # set variables for map service\n    # map service url\n    mapservice = (\n        r\"https://geodata.epa.gov/arcgis/rest/services/OA/SmartLocationDatabase/MapServer/15/\"\n    )\n    # map service query\n    query = r'query?where=GEOID10+like+%2734005%25%27+or+GEOID10+like+%2734007%25%27+or+GEOID10+like+%2734015%25%27+or+GEOID10+like+%2734021%25%27'\n\n    # geojson structure\n    dict = '{\"type\": \"FeatureCollection\"}'\n    geojson = json.loads(dict)\n    geojson[\"features\"] = []\n\n    # find total records allowed to be queried from the mapservice\n    maxurl = mapservice + r\"?f=json\"\n    response = urlr.urlopen(maxurl)\n    maxjson = json.load(response)\n    maxcount = int(maxjson[\"maxRecordCount\"])\n\n    # query all objectids, count total objectids\n    oidurl = mapservice + query + r\"&returnIdsOnly=true&f=json\"\n    print(oidurl)\n\n    response = urlr.urlopen(oidurl)\n    print(response)\n    oidjson = json.load(response)\n    print(oidjson) \n    idfield = oidjson[\"objectIdFieldName\"]\n    print(idfield)\n    idlist = oidjson[\"objectIds\"]\n    idlist.sort()\n    numrec = len(idlist)\n\n    # iterate through map service objectids by max record count\n    for i in range(0, numrec, maxcount):\n        torec = i + (maxcount - 1)\n        if torec > numrec:\n            torec = numrec - 1\n        fromid = idlist[i]\n        toid = idlist[torec]\n        where = r\"{}+%3E%3D+{}+and+{}+%3C%3D+{}\".format(idfield, fromid, idfield, toid)\n        # possible to adjust api parameters here to request only specified field (outfields) or even change projection (outsr)\n        urlstring = (\n            mapservice\n            + \"query?where={}&outfields=*&outsr=4326&returnGeometry=true&f=geojson\".format(where)\n        )\n        response = urlr.urlopen(urlstring)\n        testjson = json.load(response)\n        for feature in testjson[\"features\"]:\n            # append each feature to geojson dict\n            geojson[\"features\"].append(feature)\n\n    # write final geojson to location on hard drive\n    file_path = r\"/Users/fakram/Downloads/SLD.geojson\"\n\n    with open(file_path, \"w\") as outfile:\n        json.dump(geojson, outfile)\n\n    return file_path\n\nif __name__ == \"__main__\":\n    db = Database()\n\n    print(\"-\" * 80)\n    print(\"Importing data from SLD/Map Service\")\n    print(\"-\" * 80)\n\n            \n    filepath = download_epa_sld_data_as_geojson(\"./SLD.geojson\")\n    gdf = gpd.read_file(filepath)\n    explodedgdf = gdf.explode()\n    db.import_geodataframe(explodedgdf, new_tablename=\"sld\", schema=\"extract\")\n\n\n", "repo_name": "dvrpc/DVRPC-VisionEval-Inputs-Preparation", "sub_path": "src/step_01_extract/mapservice_to_geojson.py", "file_name": "mapservice_to_geojson.py", "file_ext": "py", "file_size_in_byte": 3124, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "41", "api": [{"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 33, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 33, "usage_type": "name"}, {"api_name": "json.load", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 41, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 41, "usage_type": "name"}, {"api_name": "json.load", "line_number": 43, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 64, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 64, "usage_type": "name"}, {"api_name": "json.load", "line_number": 65, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 74, "usage_type": "call"}, {"api_name": "src.helpers.database.Database", "line_number": 79, "usage_type": "call"}, {"api_name": "geopandas.read_file", "line_number": 87, "usage_type": "call"}]}
{"seq_id": "13924678442", "text": "import game\nimport pygame\nfrom kuposztok.Info.InfoActors import *\nimport kuposztok\n\nclass InfoStage(game.scene2d.MyStage):\n\n    def soundvaltread(self):\n        with open('../kuposztok/Save/options.txt', 'r') as beskinfile1:\n            self.soundvaltbe = int(beskinfile1.readline())\n            self.musica = int(beskinfile1.readline())\n            self.allstagebe = int(beskinfile1.readline())\n            beskinfile1.close()\n\n    def __init__(self):\n        super().__init__()\n        self.height = pygame.display.get_surface().get_height()\n        self.width = pygame.display.get_surface().get_width()\n        self.soundvaltread()\n        self.musicaselect  = self.musica\n        self.allstageben = self.allstagebe\n        self.bg = BgActor()\n        self.add_actor(self.bg)\n        self.soundvalt = self.soundvaltbe\n        pygame.mixer.init()\n        self.allstage = False\n        if self.allstageben == 0 or self.allstageben == 1:\n            self.allstage = True\n        if self.allstageben == 2:\n            self.allstage = False\n        if self.allstage == False:\n            pygame.mixer.music.load(\"../kuposztok/music/infomusica.wav\")\n        else:\n            if self.musicaselect == 0 or self.musicaselect == 1:\n                pygame.mixer.music.load(\"../kuposztok/music/gamemusica1.wav\")\n            if self.musicaselect == 2:\n                pygame.mixer.music.load(\"../kuposztok/music/gamemusica2.wav\")\n            if self.musicaselect == 3:\n                pygame.mixer.music.load(\"../kuposztok/music/gamemusica3.wav\")\n            if self.musicaselect == 4:\n                pygame.mixer.music.load(\"../kuposztok/music/gamemusica4.wav\")\n            if self.musicaselect == 5:\n                pygame.mixer.music.load(\"../kuposztok/music/gamemusica5.wav\")\n        pygame.mixer.music.play(-1)\n        if self.soundvalt == 0 or self.soundvalt == 1:\n            pygame.mixer.music.set_volume(0.5)\n        if self.soundvalt == 2:\n            pygame.mixer.music.set_volume(0.20)\n        if self.soundvalt == 3:\n            pygame.mixer.music.set_volume(0.07)\n        if self.soundvalt == 4:\n            pygame.mixer.music.stop()\n        self.text1 = game.scene2d.MyLabel(\"A játék lényege, hogy a karakterünkel minél több\")\n        self.text1.set_color(0, 0, 0)\n        self.text1.x = 100\n        self.text1.y = 0 + self.text1.get_height()\n        self.add_actor(self.text1)\n        self.text2 = game.scene2d.MyLabel(\"pontot érjünk el úgy, hogy a fákat kerülgetjük.\")\n        self.text2.set_color(0, 0, 0)\n        self.text2.x = 100\n        self.text2.y = 0 + self.text2.get_height() * 2\n        self.add_actor(self.text2)\n        self.text3 = game.scene2d.MyLabel(\"A játék ezek mellet tartalmaz még egyéb tárgyakat,\")\n        self.text3.set_color(0, 0, 0)\n        self.text3.x = 100\n        self.text3.y = 0 + self.text3.get_height() * 3\n        self.add_actor(self.text3)\n        self.text31 = game.scene2d.MyLabel(\"mint például az energiaital, vagy a trap:\")\n        self.text31.set_color(0, 0, 0)\n        self.text31.x = 100\n        self.text31.y = 0 + self.text31.get_height() * 4\n        self.add_actor(self.text31)\n        self.energy = Energy()\n        self.energy.x = self.text3.get_x()\n        self.energy.y = 0 + self.text3.get_height() * 5\n        self.energy.width = 150\n        self.energy.height = 150\n        self.add_actor(self.energy)\n        self.trap = Trap()\n        self.trap.x = self.text3.get_x() + self.text3.get_x() * 3\n        self.trap.y = 0 + self.text3.get_height() * 5\n        self.add_actor(self.trap)\n        self.text4 = game.scene2d.MyLabel(\"A játékost a billentyűzeten lévő 'w', 'a', 's', 'd' gombokkal\")\n        self.text4.set_color(0, 0, 0)\n        self.text4.x = 100\n        self.text4.y = self.height / 2 + self.text4.get_height()\n        self.add_actor(self.text4)\n        self.text5 = game.scene2d.MyLabel(\"tudjuk irányítani. Multiplayer módban az egyik karaktert\")\n        self.text5.set_color(0, 0, 0)\n        self.text5.x = 100\n        self.text5.y = self.height / 2 + self.text5.get_height() * 2\n        self.add_actor(self.text5)\n        self.text6 = game.scene2d.MyLabel(\"szintén ezekkel a gombokkal, míg a másikat pedig a \")\n        self.text6.set_color(0, 0, 0)\n        self.text6.x = 100\n        self.text6.y = self.height / 2 + self.text5.get_height() * 3\n        self.add_actor(self.text6)\n        self.text7 = game.scene2d.MyLabel(\"billyentyűzeten lévő nyilakkal. Visszalépés \")\n        self.text7.set_color(0, 0, 0)\n        self.text7.x = 100\n        self.text7.y = self.height / 2 + self.text7.get_height() * 4\n        self.add_actor(self.text7)\n        self.text71 = game.scene2d.MyLabel(\"az 'ESC' gombbal történik, valamint ez a gomb a menüben\")\n        self.text71.set_color(0, 0, 0)\n        self.text71.x = 100\n        self.text71.y = self.height / 2 + self.text71.get_height() * 5\n        self.add_actor(self.text71)\n        self.text72 = game.scene2d.MyLabel(\"a kilépést szolgálja.\")\n        self.text72.set_color(0, 0, 0)\n        self.text72.x = 100\n        self.text72.y = self.height / 2 + self.text72.get_height() * 6\n        self.add_actor(self.text72)\n        self.back = Back()\n        self.add_actor(self.back)\n        self.back.x = self.width - self.back.get_width()\n        self.back.y = self.height - self.back.get_height()\n\n        self.set_on_key_down_listener(self.katt)\n        self.back.set_on_mouse_down_listener(self.Back)\n\n    def katt(self, sender, event):\n        if event.key == pygame.K_ESCAPE:\n            self.screen.game.set_screen(kuposztok.Menu.MenuScreen.MenuScreen())\n\n    def Back(self, sender, event):\n        if event.button == 1:\n            self.screen.game.set_screen(kuposztok.Menu.MenuScreen.MenuScreen())\n", "repo_name": "csany2020c/MyGame", "sub_path": "kuposztok/Info/InfoStage.py", "file_name": "InfoStage.py", "file_ext": "py", "file_size_in_byte": 5757, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "game.scene2d", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.display.get_surface", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.display.get_surface", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.mixer.init", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.set_volume", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.set_volume", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.set_volume", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.stop", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 52, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 53, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 53, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 58, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 58, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 63, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 63, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 68, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 68, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 83, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 83, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 88, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 88, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 93, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 93, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 98, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 98, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 103, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 103, "usage_type": "attribute"}, {"api_name": "game.scene2d.MyLabel", "line_number": 108, "usage_type": "call"}, {"api_name": "game.scene2d", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 122, "usage_type": "attribute"}, {"api_name": "kuposztok.Menu.MenuScreen.MenuScreen", "line_number": 123, "usage_type": "call"}, {"api_name": "kuposztok.Menu", "line_number": 123, "usage_type": "attribute"}, {"api_name": "kuposztok.Menu.MenuScreen.MenuScreen", "line_number": 127, "usage_type": "call"}, {"api_name": "kuposztok.Menu", "line_number": 127, "usage_type": "attribute"}]}
{"seq_id": "5869381803", "text": "from django.urls import path\nfrom django.views.generic import TemplateView\nfrom . import views\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('register/', views.register, name='register'),\n    path('requirements/', views.requirements, name='requirements'),\n    path('types/', views.types, name='types'),\n    path('tips/', views.tips, name='tips'),\n    path('login/', views.log, name='login'),\n    path('donor_register/', views.donor_register.as_view(), name='donor_register'),\n    path('center_register/', views.center_register.as_view(), name='center_register'),\n    path('quiz/', views.quiz, name='quiz'),\n    path('edit_profile/<str:edit>',views.edit_profile,name ='edit_profile') ,\n    path('profiles/<str:email>',views.profiles,name ='profiles') ,\n    path('search/', views.search_results, name='search_results'),\n    path('create_event/', views.create_event.as_view(), name='create_event'),\n    path('appointment/', views.create_appointment.as_view(), name='appointment'),\n    path('events/', views.events, name='events'),\n    path('center/<str:name>', views.cent, name='center'),\n    path('logout/', views.pagelogout, name='logout'),\n    # path( \"/dashboard\",\n    #     TemplateView.as_view(template_name=\"layouts/dashboard.html\"),\n    #     name=\"dashboard\",),\n    # path('accounts/logout/', views.LogoutView.as_view(template_name=\"post_list.html\"), name='logout'),\n    # path(\"<int:myid>/\", views.quiz, name=\"quiz\"), \n    # path(\"quiz/\", views.quiz, name=\"quiz\"), \n    # path('<int:myid>/data/', views.quiz_data_view, name='quiz-data'),\n    # path('<int:myid>/save/', views.save_quiz_view, name='quiz-save'),\n]", "repo_name": "Anabella1109/Blood-donation", "sub_path": "mysite/blooddonation/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1639, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}]}
{"seq_id": "9969851121", "text": "from rest_framework.views import exception_handler\nfrom rest_framework.exceptions import (\n    AuthenticationFailed,\n    NotFound,\n    PermissionDenied,\n    ValidationError,\n    MethodNotAllowed\n)\nfrom rest_framework.response import Response\nfrom rest_framework import status\n\n\ndef custom_exception_handler(exc, context):\n    response = exception_handler(exc, context)\n    if isinstance(exc, AuthenticationFailed):\n        response = Response(\n            {\n                'status_code': status.HTTP_401_UNAUTHORIZED,\n                'message': 'Authentication failed',\n                'errors': []\n            },\n            status=status.HTTP_401_UNAUTHORIZED\n        )\n    elif isinstance(exc, ValidationError):\n        print(exc)\n        response = Response(\n            {\n                'status_code': status.HTTP_400_BAD_REQUEST,\n                'message': 'Bad request',\n                'errors': str(exc.detail)\n            },\n            status=status.HTTP_400_BAD_REQUEST\n        )\n    elif isinstance(exc, PermissionDenied):\n        response = Response(\n            {\n                'status_code': status.HTTP_403_FORBIDDEN,\n                'message': 'Permission denied',\n                'errors': []\n            },\n            status=status.HTTP_403_FORBIDDEN\n        )\n    elif isinstance(exc, NotFound):\n        response = Response(\n            {\n                'status_code': status.HTTP_404_NOT_FOUND,\n                'message': 'Not found',\n                'errors': []\n            },\n            status=status.HTTP_404_NOT_FOUND\n        )\n    elif isinstance(exc, MethodNotAllowed):\n        response = Response(\n            {\n                'status_code': status.HTTP_405_METHOD_NOT_ALLOWED,\n                'message': 'Method not allowed',\n                'errors': []\n            },\n            status=status.HTTP_405_METHOD_NOT_ALLOWED\n        )\n\n    return response", "repo_name": "QuangDat2412/base-project", "sub_path": "backend/utils/exception_handler.py", "file_name": "exception_handler.py", "file_ext": "py", "file_size_in_byte": 1893, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "rest_framework.views.exception_handler", "line_number": 14, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.AuthenticationFailed", "line_number": 15, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 18, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 24, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 28, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 32, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.PermissionDenied", "line_number": 34, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 35, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 41, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.NotFound", "line_number": 43, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 50, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 50, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.MethodNotAllowed", "line_number": 52, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 53, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_405_METHOD_NOT_ALLOWED", "line_number": 55, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_405_METHOD_NOT_ALLOWED", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 59, "usage_type": "name"}]}
{"seq_id": "13085376838", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nPrediction on video using\npytorch model\n\"\"\"\n# import the necessary packages\nfrom __future__ import print_function\nimport argparse\nimport json\nfrom collections import deque\nimport torch\nfrom torchvision import transforms\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport numpy as np\nimport cv2\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nprint(device)\n# construct the argument parser and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-m\", \"--model\", required=True,\n                help=\"path to trained serialized model\")\nap.add_argument(\"-w\", \"--weights\", required=True,\n                help=\"path to the parameters of the model\")\nap.add_argument(\"-i\", \"--input\", required=True,\n                help=\"path to our input video\")\nap.add_argument(\"-l\", \"--labels\", required=True,\n                help=\"path to the labels\")\nap.add_argument(\"-o\", \"--output\", required=True,\n                help=\"path to our output video\")\nap.add_argument(\"-s\", \"--size\", type=int, default=1,\n                help=\"size of queue for averaging\")\nargs = vars(ap.parse_args())\n\n# load dictionary of labels\nprint(\"[INFO] loading labels...\")\njson_file = open(args['labels'])\nlabel_dict = json.load(json_file)\n\n# load the trained model\nprint(\"[INFO] loading model...\")\nloc = torch.load(args[\"weights\"], map_location=device)\nmodel = torch.load(args[\"model\"], map_location=device)\nmodel.load_state_dict(loc[\"model\"])\nmodel.eval()\n\ndata_transforms = {\n    'val': transforms.Compose([transforms.ToTensor(),\n                               transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),\n}\n\ndef predict(image, model):\n    # Pass the image through our model\n    image_tensor = data_transforms['val'](image).float()\n    image_tensor = image_tensor.unsqueeze_(0)\n    images = Variable(image_tensor)\n    image_tensor = images.to(device)\n    torch.no_grad()\n    predict = F.softmax(model(image_tensor))\n    print(predict.detach().cpu().numpy())\n    return predict.cpu().detach().numpy()\n\n# initialize the image mean for mean subtraction along with the\n# predictions queue\nQ = deque(maxlen=args[\"size\"])\n\n# initialize the video stream, pointer to output video file, and\n# frame dimensions\nvs = cv2.VideoCapture(args[\"input\"])\nwriter = None\n(W, H) = (None, None)\n\n# loop over frames from the video file stream\nwhile True:\n    # read the next frame from the file\n    (grabbed, frame) = vs.read()\n\n    # if the frame was not grabbed, then we have reached the end\n    # of the stream\n    if not grabbed:\n        break\n\n    # if the frame dimensions are empty, grab them\n    if W is None or H is None:\n        (H, W) = frame.shape[:2]\n\n    # clone the output frame, then convert it from BGR to RGB\n    # ordering, resize the frame to a fixed 224x224, and then\n    # perform mean subtraction\n    output = frame.copy()\n    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\n    # make predictions on the frame and then update the predictions\n    # queue\n    preds = predict(frame, model)\n    Q.append(preds)\n\n    # perform prediction averaging over the current history of\n    # previous predictions\n    results = np.array(Q).mean(axis=0)\n    i = np.argmax(results)\n    label = label_dict[str(i)]\n\n    # draw the condition on the output frame\n    text = \"{}\".format(label)\n    cv2.putText(output, text, (35, 50), cv2.FONT_HERSHEY_SIMPLEX,\n                1.25, (0, 255, 0), 5)\n\n    # check if the video writer is None\n    if writer is None:\n        # initialize our video writer\n        fourcc = cv2.VideoWriter_fourcc(*\"MJPG\")\n        writer = cv2.VideoWriter(args[\"output\"], fourcc, 30,\n                                 (W, H), True)\n\n    # write the output frame to disk\n    writer.write(output)\n\n    # show the output image\n    cv2.imshow(\"Output\", output)\n    key = cv2.waitKey(1) & 0xFF\n\n    # if the `q` key was pressed, break from the loop\n    if key == ord(\"q\"):\n        break\n\n# release the file pointers\nprint(\"[INFO] cleaning up...\")\nwriter.release()\nvs.release()\n", "repo_name": "ReconAI/AM-Traffic-Phase2.Iteration2_Task-1-Pytorch", "sub_path": "predict_video_torch.py", "file_name": "predict_video_torch.py", "file_ext": "py", "file_size_in_byte": 4050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "torch.device", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 18, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "json.load", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 60, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 107, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 122, "usage_type": "call"}]}
{"seq_id": "74814177083", "text": "from roughsets.roughsets import *\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nimport numpy as np\r\n\r\n\r\n'''\r\nDetermina le soglie utilizzando tre metodi: OPTIMIZER, FORZA BRUTA e VISUALE (scrivo nel file rfilename le prime due coppie)\r\n'''\r\ndef determineThresholds(dtable, ncol, concept, rfilename, output, path, kind):\r\n\r\n    f = open(rfilename, \"w\")\r\n    f.write(\"beta,alpha\\n\")\r\n\r\n    nrs = RoughSets()\r\n    print(dtable)\r\n    output.write(str(dtable) + '\\n')\r\n\r\n\r\n    nrs.setInformationSystem(dtable, np.array([i for i in range(ncol)]))\r\n\r\n    b1 = (0.0, 0.5)\r\n    b2 = (0.5, 1.0)\r\n    xinit = [0.3, 0.7]\r\n    beta, alpha = nrs.calculateAlphaBeta(concept, b1, b2, xinit)\r\n\r\n    print(\"* Optimizer\")\r\n    output.write(\"* Optimizer \\n\")\r\n\r\n    print(\"BETA, ALFA: \", beta, alpha)\r\n    output.write(str(beta) + \",\" + str(alpha) + \"\\n\")\r\n    f.write(str(beta)+\",\"+str(alpha)+\"\\n\")\r\n\r\n    b = np.linspace(0.0, 0.5, 5)\r\n    a = np.linspace(0.5, 1.0, 5)\r\n\r\n    A, B = np.meshgrid(a, b)\r\n\r\n    HM=list()\r\n    for i in range(len(B)):\r\n        HR = list()\r\n        for j in range(len(A)):\r\n            HR.append(nrs.Entropy((B[i][j], A[i][j])))\r\n        HM.append(HR)\r\n\r\n    print(HM)\r\n    output.write(str(HM)+'\\n')\r\n\r\n    ind = np.unravel_index(np.argmin(np.array(HM), axis=None), np.array(HM).shape)\r\n    print(\"ARGMIN: \",ind)\r\n    output.write(\"* ARGMIN: \"+str(ind)+ '\\n')\r\n\r\n    print(\"MIN: \", np.min(HM))\r\n    output.write(\"MIN: \"+str(np.min(HM))+'\\n')\r\n\r\n    print(\"* Brute Force\")\r\n    output.write(\"* Brute Force \\n\")\r\n\r\n    print(\"BETA, ALFA: \", b[ind[0]], a[ind[1]])\r\n    output.write(\"BETA, ALFA: \"+str([ind[0]])+','+str(a[ind[1]])+\"\\n\")\r\n\r\n    f.write(str(b[ind[0]])+\",\"+str(a[ind[1]])+\"\\n\")\r\n    f.close()\r\n\r\n    print(\"* Visual\")\r\n    output.write('* Visual\\n')\r\n    ax = plt.axes(projection='3d')\r\n    ax.plot_surface(A, B, np.array(HM), rstride=1, cstride=1,\r\n                    cmap='viridis', edgecolor='none')\r\n    ax.set_title('surface')\r\n    ax.set_xlabel('alpha')\r\n    ax.set_ylabel('beta')\r\n    ax.set_zlabel('entropy')\r\n    #plt.show()\r\n    plt.savefig(path+'/'+str(kind)+'.png')\r\n\r\n\r\n\r\n'''\r\nApplica il 3WD probabilistico con le soglie date e calcola l'esagono di opposizione\r\n'''\r\ndef exagonOpposition(dtable, ncol, concept, beta, alpha, output):\r\n    nrs = RoughSets()\r\n    nrs.setInformationSystem(dtable, np.array([i for i in range(ncol)]))\r\n\r\n    regions = nrs.pcalculate3WD(concept, alpha, beta)\r\n    regions = list(regions)\r\n\r\n    POS = regions[0]\r\n    BND = regions[1]\r\n    NEG = regions[2]\r\n\r\n    print(\"POS: \", POS)\r\n    output.write(\"POS: \" + str(POS) + '\\n')\r\n    print(\"BND: \", BND)\r\n    output.write(\"BND: \" + str(BND) + '\\n')\r\n    print(\"NEG: \", NEG)\r\n    output.write(\"NEG: \" + str(NEG) + '\\n')\r\n    regs = {\"POS\": POS, \"BND\": BND, \"NEG\": NEG}\r\n    points = {\"A\": {}, \"B\": {}, \"C\": {}, \"D\": {}, \"E\": {}, \"F\": {}}\r\n\r\n    points[\"A\"] = POS\r\n    points[\"B\"] = NEG\r\n    points[\"F\"] = BND\r\n    points[\"D\"] = NEG.union(BND)\r\n    points[\"C\"] = POS.union(BND)\r\n    points[\"E\"] = POS.union(NEG)\r\n\r\n    return regs, points\r\n\r\n\r\ndef mainNew(path,i):\r\n\r\n    print(\"------------------------------\"+str(i)+\"/\"+str(i+1)+\"-------------------------------------\")\r\n    with open(path+'regions.txt', 'w') as output:\r\n        # Numero di attributi condizionali del primo dataset\r\n        NCOL1=6\r\n\r\n        data1 = pd.read_csv(path+\"decision_table_base.csv\")\r\n        data1=data1.drop(columns=\"usr_index\")\r\n        infoTable1 = data1.values\r\n\r\n        # filtro sulla colonna decisionale (il concetto da studiare deve essere cambiato in questa riga)\r\n        decisions_d1 = data1[data1.decision == 2]\r\n        decisions_d1 = np.array(list(decisions_d1.index))\r\n\r\n        determineThresholds(infoTable1, NCOL1, decisions_d1, path+\"thresholds_step1.csv\", output, path, 'base')\r\n\r\n        f=open(path+\"thresholds_step1.csv\", \"r\")\r\n        lfile=list()\r\n\r\n        for line in f:\r\n            lfile.append(line.strip())\r\n        f.close()\r\n\r\n        # uso solo i valori calcolati con forza bruta\r\n        beta1 = float(lfile[2].split(\",\")[0])\r\n        alpha1 = float(lfile[2].split(\",\")[1])\r\n\r\n        regions1, points1 = exagonOpposition(infoTable1, NCOL1, decisions_d1, beta1, alpha1, output)\r\n\r\n        for k, v in points1.items():\r\n            print(\"POINT: \", k, \"   VALUE: \", v)\r\n            output.write(\"POINT: \"+str(k)+\" VALUE \"+str(v)+'\\n')\r\n\r\n        for k, v in regions1.items():\r\n            print(\"REGION: \", k, \"   VALUE: \", v)\r\n            output.write(\"REGION: \" + str(k) + \" VALUE \" + str(v) + '\\n')\r\n        # Numero di attributi condizionali del secondo dataset\r\n        NCOL2=7\r\n\r\n        data2 = pd.read_csv(path+\"decision_table_t1.csv\")\r\n        data2=data2.drop(columns=\"usr_index\")\r\n        infoTable2 = data2.values\r\n\r\n        # filtro sulla colonna decisionale (il concetto da studiare deve essere cambiato in questa riga)\r\n        decisions_d2 = data2[data2.decision == 2]\r\n        decisions_d2 = np.array(list(decisions_d2.index))\r\n\r\n        determineThresholds(infoTable2, NCOL2, decisions_d2, path+\"thresholds_step2.csv\", output, path, 't1')\r\n\r\n\r\n        f=open(path+\"thresholds_step2.csv\", \"r\")\r\n        lfile=list()\r\n\r\n        for line in f:\r\n            lfile.append(line.strip())\r\n        f.close()\r\n\r\n        # uso solo i valori calcolati con forza bruta\r\n        beta2 = float(lfile[2].split(\",\")[0])\r\n        alpha2 = float(lfile[2].split(\",\")[1])\r\n\r\n        regions2, points2 = exagonOpposition(infoTable2, NCOL2, decisions_d2, beta2, alpha2, output)\r\n\r\n        for k, v in points2.items():\r\n            print(\"POINT: \", k, \"   VALUE: \", v)\r\n            output.write(\"POINT: \" + str(k) + \" VALUE \" + str(v) + '\\n')\r\n\r\n        for k, v in regions2.items():\r\n            print(\"REGION: \", str(k), \"   VALUE: \", str(v))\r\n            output.write(\"REGION: \" + str(k) + \" VALUE \" + str(v) + '\\n')\r\n        mu, xi = utilityMeasures((regions1, regions2), (points1, points2), ((beta1, alpha1),(beta2, alpha2)), (data1, data2), (decisions_d1, decisions_d2))\r\n\r\n        print(\"MU: \", mu)\r\n        output.write(\"MU: \" + str(mu) + '\\n')\r\n        print(\"XI: \", xi)\r\n        output.write(\"XI: \" + str(xi) + '\\n')\r\n\r\n\r\n\r\ndef communityProbability(community, concept):\r\n    return len(set(community).intersection(set(concept)))/len(set(concept))\r\n\r\n   \r\ndef calculateX(POS_T1, POS_T2, concept, data):\r\n    data1=data[0]\r\n    data2=data[1]\r\n\r\n    if len(POS_T2) > len(POS_T1):\r\n        probabilities = list()\r\n        communities = set()\r\n        irange=set(POS_T2).difference(set(POS_T1))\r\n        for i in list(irange):\r\n            ci=data1.iloc[i][\"community\"]\r\n            communities.add(ci)\r\n        for i in list(communities):\r\n            datax=data1[data1.community==i]\r\n            datax = list(datax.index)\r\n            prob=communityProbability(datax, concept)\r\n            probabilities.append(prob)\r\n        mini=0\r\n        if len(probabilities)!=0:\r\n            mini=min(probabilities)\r\n        return mini\r\n    else:\r\n        probabilities = list()\r\n        communities = set()\r\n        irange=set(POS_T1).difference(set(POS_T2))\r\n        for i in list(irange):\r\n            ci=data1.iloc[i][\"community\"]\r\n            communities.add(ci)\r\n        for i in list(communities):\r\n            datax=data1[data1.community==i]\r\n            datax = list(datax.index)\r\n            prob=communityProbability(datax, concept)\r\n            probabilities.append(prob)\r\n        maxi=0\r\n        if len(probabilities)!=0:\r\n            maxi=max(probabilities)\r\n        return maxi\r\n\r\ndef calculateY(NEG_T1, NEG_T2, concept, data):\r\n    data1=data[0]\r\n    data2=data[1]\r\n\r\n    if len(NEG_T2) > len(NEG_T1):\r\n        probabilities = list()\r\n        communities = set()\r\n        irange=set(NEG_T2).difference(set(NEG_T1))\r\n        for i in irange:\r\n            ci=data1.iloc[i][\"community\"]\r\n            communities.add(ci)\r\n        for i in list(communities):\r\n            datax=data1[data1.community==i]\r\n            datax = list(datax.index)\r\n            prob=communityProbability(datax, concept)\r\n            probabilities.append(prob)\r\n        maxi=0\r\n        if len(probabilities)!=0:\r\n            maxi=max(probabilities)\r\n        return maxi\r\n    else:\r\n        probabilities = list()\r\n        communities = set()\r\n        irange=set(NEG_T1).difference(set(NEG_T2))\r\n        for i in irange:\r\n            ci=data1.iloc[i][\"community\"]\r\n            communities.add(ci)\r\n        for i in list(communities):\r\n            datax=data1[data1.community==i]\r\n            datax = list(datax.index)\r\n            prob=communityProbability(datax, concept)\r\n            probabilities.append(prob)\r\n        mini=0\r\n        if len(probabilities)!=0:\r\n            mini=min(probabilities)\r\n        return mini\r\n\r\ndef utilityMeasures(regions, points, thresholds, datas, concepts):\r\n    regions1=regions[0]\r\n    regions2=regions[1]\r\n    points1=points[0]\r\n    points2=points[1]\r\n    beta1 = thresholds[0][0]\r\n    alpha1 = thresholds[0][1]\r\n    beta2 = thresholds[1][0]\r\n    alpha2 = thresholds[1][1]\r\n    data1 = datas[0]\r\n    data2 = datas[1]\r\n    concept1 = concepts[0]\r\n    concept2 = concepts[1]\r\n    \r\n    x=calculateX(regions1[\"POS\"], regions2[\"POS\"], concept1, datas)\r\n    y=calculateY(regions1[\"NEG\"], regions2[\"NEG\"], concept1, datas)\r\n\r\n    mu = None\r\n    xi = None\r\n    if abs(alpha1-x) > 0:\r\n        mu = (len(list(regions2[\"POS\"]))-len(list(regions1[\"POS\"])))/(abs(alpha1-x))\r\n    if abs(y-beta1) > 0:\r\n        xi = (len(list(regions2[\"NEG\"]))-len(list(regions1[\"NEG\"])))/(abs(y-beta1))\r\n    \r\n    return mu, xi\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    for i in range(5,23):\r\n        path = 'fludata_luigi/table_pairs/'+str(i)+'-'+str(i+1)+'/'\r\n        mainNew(path, i)\r\n", "repo_name": "luilom/Phd_Fact_Checking", "sub_path": "Prima Versione/script_roughsets_prof/testFlu_Luigi.py", "file_name": "testFlu_Luigi.py", "file_ext": "py", "file_size_in_byte": 9745, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 126, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}]}
{"seq_id": "74415442363", "text": "###### import ######\nfrom threading import Thread, Lock\nfrom enum import Enum\nimport sys\nfrom loguru import logger\nfrom pyloramac import *\nfrom ipaddress import IPv6Address, AddressValueError\nfrom scapy.all import *\nfrom time import *\n###### LOG Configuration ######\nLOG_FORMAT = \"<green>(\"+str(perf_counter_ns())+\"){time: HH:mm:ss.SSS}</green> | \"+\\\n             \"<level>{level: <8}</level> |\"+\\\n             \"{level.icon}  <cyan>{name}</cyan>\"+\\\n             \":<cyan>{function}</cyan>:<cyan>{line}</cyan> \"+\\\n             \"- <level>{message}</level>\"+\\\n             \"\\n{exception}\"\nLOG_CONFIG = {\n    \"handlers\": [\n        {\"sink\": sys.stdout, \"format\": lambda record: LOG_FORMAT, \"level\": \"INFO\"},\n        #{\"sink\":\"file_{time}.log\", \"format\": lambda record: LOG_FORMAT, \"level\": \"INFO\"},\n    ]\n}\n###### UDP Configuration ######\nUDP_CLIENT_PORT = 8765\nUDP_SERVER_PORT = 5678\n\nSEND_INTERVAL = 5 # s\nMAX_PAQUET_COUNT = 100\n###### MAIN APP ######\nclass Mode(Enum):\n    PINGPONG = 0\n    RECEIVER = 1\n    SENDER = 2\n\nclass Child:\n    def __init__(self, mode: Mode, first_data: IPv6):\n        self.mode = mode\n        self.addr = first_data.src\n        self.count = 0\n        self.sender = None\n        \n        if mode == Mode.PINGPONG:\n            self.receive(first_data)\n        if mode == Mode.SENDER:\n            self.sender = Thread(target=self.send)\n            self.sender.start()\n\n    def send(self):\n        while self.count < MAX_PAQUET_COUNT:\n            data = IPv6(src=NETWORK_STACK.node_ip_addr, dst=self.addr)/UDP(sport=UDP_SERVER_PORT, dport=UDP_CLIENT_PORT)/Raw(load=f\"hello {self.count}\")\n            logger.log(\"APP\", f\"Send {data[UDP][Raw].load.decode()} to {data.dst}\")\n            NETWORK_STACK.send(data)\n            sleep(SEND_INTERVAL)\n            self.count += 1\n\n    def receive(self, data):\n        logger.log(\"APP\", f\"Receive {data[UDP][Raw].load.decode()} from {data.src}\")\n        new_data = IPv6(src=NETWORK_STACK.node_ip_addr, dst=self.addr)/UDP(sport=UDP_SERVER_PORT, dport=UDP_CLIENT_PORT)/Raw(load=f\"PONG {self.count}\")\n        logger.log(\"APP\", f\"send {data[UDP][Raw].load.decode()} to {new_data.dst}\")\n        NETWORK_STACK.send(new_data)\n        self.count +=1\n\nclass LoRaRoot:\n    def __init__(self, mode:Mode):\n        self.childs = {}\n        self.mode = mode\n\n    def init(self, port:str):\n        NETWORK_STACK.init(port=port)\n        NETWORK_STACK.register_listener(self.on_data)\n\n    def on_data(self, data: IPv6):\n        if mode == Mode.RECEIVER:\n            logger.log(\"APP\", f\"Receive {data[UDP][Raw].load.decode()} from {data.src}\")\n        else:\n            child = self.childs.get(data.src, None)\n            if child is None:\n                child = Child(self.mode, data)\n                self.childs[data.src] = child\n            elif self.mode == Mode.PINGPONG:\n                child.receive(data)\n\nif __name__ == \"__main__\":\n    logger.configure(**LOG_CONFIG)\n    logger.enable(\"pyloramac\")\n    logger.level(\"APP\", no=26, color=\"<fg #00ABCC><i>\", icon=\"\\U0001F3D3\")\n\n    if len(sys.argv) != 3:\n        logger.error(\"Usage: lora-root.py <port> <mode>\")\n        sys.exit(1)\n    \n    port = sys.argv[1]\n    mode = Mode(int(sys.argv[2]))\n    logger.log(\"APP\", f\"Mode: {mode}\")\n\n    try:    \n        LoRaRoot(mode).init(port)\n    except KeyboardInterrupt:\n        logger.info(\"KeyboardInterrupt\")\n        logger.info(\"Exiting...\")\n        sys.exit(0)\n", "repo_name": "ArnaudPalgen/ma2_memoire", "sub_path": "code/example-udp-rpl-lora/lora-root-node/lora-root.py", "file_name": "lora-root.py", "file_ext": "py", "file_size_in_byte": 3402, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.stdout", "line_number": 19, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 30, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 45, "usage_type": "call"}, {"api_name": "loguru.logger.log", "line_number": 51, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 51, "usage_type": "name"}, {"api_name": "loguru.logger.log", "line_number": 57, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 57, "usage_type": "name"}, {"api_name": "loguru.logger.log", "line_number": 59, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 59, "usage_type": "name"}, {"api_name": "loguru.logger.log", "line_number": 74, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 74, "usage_type": "name"}, {"api_name": "loguru.logger.configure", "line_number": 84, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 84, "usage_type": "name"}, {"api_name": "loguru.logger.enable", "line_number": 85, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 85, "usage_type": "name"}, {"api_name": "loguru.logger.level", "line_number": 86, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 86, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 88, "usage_type": "attribute"}, {"api_name": "loguru.logger.error", "line_number": 89, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 89, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 90, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 93, "usage_type": "attribute"}, {"api_name": "loguru.logger.log", "line_number": 94, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 94, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 99, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 99, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 100, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 100, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 101, "usage_type": "call"}]}
{"seq_id": "27082832453", "text": "from utils.tree_util import create_binary_tree_from_array, inorder\r\n\r\n\r\ndef good_nodes(root):\r\n    count = 0\r\n    def util(head, max_till_now):\r\n        if not head:\r\n            return\r\n        nonlocal count\r\n        if head.val >= max_till_now:\r\n            count += 1\r\n            max_till_now = head.val\r\n        util(head.left, max_till_now)\r\n        util(head.right, max_till_now)\r\n    util(root, float('-inf'))\r\n    return count\r\n\r\narray =  [3,1,4,3,None,1,5]\r\nroot = create_binary_tree_from_array(array)\r\nprint('--')\r\nresult = good_nodes(root)\r\nprint(result)\r\n", "repo_name": "sumitpatra6/leetcode_daily_challenges", "sub_path": "august/count_good_nodes.py", "file_name": "count_good_nodes.py", "file_ext": "py", "file_size_in_byte": 569, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "utils.tree_util.create_binary_tree_from_array", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "43563789812", "text": "# -*- coding: utf-8 -*-\r\nfrom tkinter import *\r\nfrom tkinter import filedialog\r\nfrom tkinter import messagebox\r\nfrom tkinter import simpledialog\r\n\r\nfrom tkinter.colorchooser import *\r\nfrom library.settings_window import *\r\n\r\nimport tkinter.font as tkFont\r\nfrom PIL import Image, ImageTk\r\n\r\n\r\n#imports configparser and images list\r\nfrom library.read_config import *\r\n\r\nsavedFile = {1:\"\"}\r\nimgDict={}\r\n\r\n#======================================\r\n# Principale Window\r\n#======================================\r\n\r\nclass BlockNote_window:\r\n\r\n    def __init__(self,master,content,icons_menu):\r\n        self.master = master\r\n        self.content=content\r\n        self.icons_menu=icons_menu\r\n\r\n        #initiat style variables\r\n        self.all_variable_initiation()\r\n\r\n    def create_window(self):\r\n        self.master = Tk()\r\n        self.master.title(\"Editeur de Texte\")\r\n\r\n        # set the dimensions of the screen \r\n        # and where it is placed base on conteur values\r\n        self.master.geometry('%dx%d+%d+%d' % (int(config_window_width), int(config_window_height), int(config_window_left), int(config_window_top)))\r\n\r\n    def add_text(self):\r\n        self.content = Text(self.master,padx=10,pady=5, undo=True)\r\n        self.content.config(fg=str(BlockNote_window.get_config_default_font_color(self)) ,bg=str(BlockNote_window.get_config_default_bg_color(self)))\r\n        self.content.config(font=(config_font_style,config_font_size))\r\n        self.content.pack(expand=1,side=BOTTOM,fill='both')\r\n\r\n        #import text style from configuration.ini\r\n        self.change_text_style(self.weight_v, self.underline_v, self.slant_v)\r\n        \r\n        # justify text  -- problem in loads first time - fix later\r\n        self.content.tag_configure(\"%s\" % self.alignment, justify=self.alignment.lower())\r\n        self.content.tag_add(\"%s\" % self.alignment, 1.0, \"end\")\r\n\r\n    def generate(self):\r\n        self.master.mainloop()\r\n\r\n    #======================================\r\n    # Some variables initiation\r\n    #======================================\r\n\r\n    def all_variable_initiation(self) :\r\n\r\n        self.weight_v    = config_weight_text\r\n        self.slant_v     = config_slant_text\r\n        self.underline_v = eval(config_underline_text)\r\n        self.alignment   = config_alignment_text\r\n\r\n    #======================================\r\n    # BarMenu Actions\r\n    #======================================\r\n\r\n    # New NotePad winodw\r\n    def nouveau(self ,*args):\r\n        import os\r\n        import sys\r\n        # Get the path to current interpreter and run a new window\r\n        os.popen(sys.executable + ' main.py')\r\n\r\n\r\n    # Open an Existing file\r\n    def fopen(self, *args):\r\n        file = self.master.filename =  filedialog.askopenfilename(initialdir = \"/\",title = \"Select File\",filetypes = ((\"Text Files\",\"*.txt\"),(\"all files\",\"*.*\")))\r\n        \r\n        if file:\r\n            self.master.title('{} - {}'.format(os.path.basename(file), \"Editeur de texte\"))\r\n            self.content.delete(1.0, END)\r\n            with open(file) as _file:\r\n                self.content.insert(1.0, _file.read())\r\n\r\n\r\n    # Save As Method\r\n    def saveAs(self, *args):\r\n        # create save dialog\r\n        fichier=self.master.filename =  filedialog.asksaveasfilename(initialdir = \"/\",title = \"Enregistrer Sous\\\r\n        \",filetypes = ((\"Fichier Texte\",\"*.txt\"),(\"Tous les fichiers\",\"*.*\")))\r\n\r\n        if fichier:\r\n            fichier = fichier + \".txt\"\r\n            \r\n            savedFile[1] = fichier\r\n            f = open(fichier,\"w\")\r\n            s = self.content.get(\"1.0\",END)\r\n            f.write(s) \r\n            f.close()\r\n       \r\n    # Save Method \r\n    def save(self, *args):\r\n        if(savedFile[1] ==\"\"):\r\n            self.saveAs()            \r\n        else:\r\n            f = open(savedFile[1],\"w\")\r\n            s = self.content.get(\"1.0\",END)\r\n            f.write(s) \r\n            f.close() \r\n\r\n    # Exit Method\r\n    def quitter(self):\r\n        if messagebox.askokcancel(\"Are you sure?\", \"Please Confirm that you want to exit!\"):\r\n            self.master.quit()\r\n\r\n    # Undo text changes\r\n    def undo(self, event=None):\r\n        try:\r\n            self.content.edit_undo()\r\n        except:\r\n            print('Nothing to undo...')\r\n\r\n    # redo text changes\r\n    def redo(self, *args):\r\n        try:\r\n            self.content.edit_redo()\r\n        except:\r\n            print('Nothing to redo...')\r\n\r\n    # Copy text\r\n    def copy(self, *args):\r\n        try:\r\n            self.content.clipboard_clear()\r\n            self.content.clipboard_append(self.content.selection_get())\r\n        except:\r\n            print('Nothing to copy...')\r\n\r\n    # Cut text\r\n    def cut(self, *args):\r\n        try:\r\n            self.copy()\r\n            self.content.delete(\"sel.first\",\"sel.last\")  \r\n        except:\r\n            print('Nothing to cut...')\r\n\r\n    # Paste Text\r\n    def paste(self, *args):\r\n        try:\r\n            self.content.insert(INSERT, self.content.clipboard_get())\r\n        except:\r\n            print('Nothing to paste...')\r\n\r\n    # Delete Selected text\r\n    def clear(self,*args):\r\n        sel = self.content.get(SEL_FIRST, SEL_LAST)\r\n        if sel!='':\r\n            self.content.delete(SEL_FIRST, SEL_LAST)\r\n        else:\r\n            print(\"Noting to clear\")\r\n\r\n    # Delete All text\r\n    def clearall(self,*args):\r\n        try:\r\n            self.content.delete(1.0 , END)\r\n        except:\r\n            print('Nothing to clear...')\r\n\r\n    # Select All text\r\n    def selectAll(self, *args):\r\n        self.content.tag_add(SEL, '1.0', END)\r\n        self.content.mark_set(0.0, END)\r\n        self.content.see(INSERT)\r\n\r\n    def left_alignment(self , *args):\r\n        self.content.tag_configure(\"LEFT\",justify=LEFT)\r\n        self.content.tag_add(\"LEFT\", 1.0, \"end\")\r\n\r\n        conteur['text_styles']['alignment']  = \"LEFT\"\r\n        conteur.write(open('configuration.ini','w'))\r\n\r\n    def right_alignment(self , *args):\r\n        self.content.tag_configure(\"RIGHT\",justify=RIGHT)\r\n        self.content.tag_add(\"RIGHT\", 1.0, \"end\")\r\n\r\n        conteur['text_styles']['alignment']  = \"RIGHT\"\r\n        conteur.write(open('configuration.ini','w'))\r\n\r\n    def center_alignment(self , *args):\r\n        self.content.tag_configure(\"CENTER\",justify=CENTER)\r\n        self.content.tag_add(\"CENTER\", 1.0, \"end\")\r\n\r\n        conteur['text_styles']['alignment']  = \"CENTER\"\r\n        conteur.write(open('configuration.ini','w'))\r\n\r\n    def bold(self,*args):\r\n\r\n        if self.weight_v ==\"bold\":\r\n            self.weight_v = \"normal\"\r\n        else:\r\n            self.weight_v = \"bold\"\r\n        self.change_text_style(self.weight_v, self.underline_v, self.slant_v)\r\n      \r\n    def underline(self,*args):\r\n\r\n        if self.underline_v:\r\n            self.underline_v = 0\r\n        else:\r\n            self.underline_v = 1\r\n        self.change_text_style(self.weight_v, self.underline_v, self.slant_v)\r\n\r\n    def italic(self,*args):\r\n        if self.slant_v ==\"roman\":\r\n            self.slant_v = \"italic\"\r\n        else:\r\n            self.slant_v = \"roman\"\r\n        self.change_text_style(self.weight_v, self.underline_v, self.slant_v)\r\n\r\n    def change_text_style(self, boldness, underline, slant):\r\n        styling = tkFont.Font(family=config_font_style, size=config_font_size,weight= boldness,slant=slant, underline=underline)\r\n        self.content.configure(font=styling)\r\n\r\n        conteur['text_styles']['underline']  = str(underline)\r\n        conteur['text_styles']['weight']  = boldness\r\n        conteur['text_styles']['slant']  = slant\r\n        conteur.write(open('configuration.ini','w'))\r\n\r\n    def find(self, *args):\r\n        self.content.tag_remove('found', '1.0', END)\r\n        target = simpledialog.askstring('Search', 'words to search:')\r\n        simpledialog\r\n        if target:\r\n            idx = '1.0'\r\n            while 1:\r\n                idx = self.content.search(target, idx, nocase=1,\r\n                        stopindex=END)\r\n                if not idx:\r\n                    break\r\n                lastidx = '%s+%dc' % (idx, len(target))\r\n                self.content.tag_add('found', idx, lastidx)\r\n                idx = lastidx\r\n\r\n            self.content.tag_config('found',foreground='white', background=\"blue\")\r\n\r\n    def open_about(self, *args):\r\n        about_window = Toplevel(self.master)\r\n        about_window.grab_set()\r\n        about_window.title(\"About Me\")\r\n        about_window.geometry(\"250x150\")\r\n        about_window.resizable(False, False)\r\n        about_window.configure(background='white')\r\n\r\n\r\n        aboutme_icon = Image.open( other_icons.get('aboutme') )\r\n        aboutme_icon = aboutme_icon.resize((100,100))\r\n        aboutme_img = ImageTk.PhotoImage(aboutme_icon)\r\n        aboutme_label = Label(about_window, image=aboutme_img,borderwidth=0, bg=\"white\")\r\n        imgDict['aboutme'] = aboutme_img \r\n        aboutme_label.pack()\r\n\r\n        my_name= Label(about_window, text=\"SAIFEDDINE CHAGDALI\",fg=\"black\", bg=\"white\", font=('Calibri',10))\r\n        my_name.place(x=60, y=100)\r\n\r\n        git_link= Label(about_window, text=\"github.com/sifdin17/TextEditor\",fg=\"blue\", bg=\"white\",font=('Calibri',10))\r\n        git_link.place(x=40, y=120)\r\n\r\n\r\n\r\n    #======================================\r\n    # action that opens the setting window\r\n    #======================================\r\n\r\n    def settings_w(self, *args):\r\n\r\n        # New Settings Instance\r\n        preference_window = Settings_class(self.master,self.content,\"Settings_win\")\r\n        # Create all widgets\r\n        preference_window.create_settings_window()\r\n\r\n\r\n    #===========================\r\n    # BarMenu Creation\r\n    #===========================\r\n\r\n    def add_menu(self):\r\n        # 1 - Création de la barre des menus\r\n        menuBar = Menu(self.master)\r\n        \r\n        # 2 - Création du menu Fichier\r\n        global menuFichier\r\n        menuFichier = Menu(menuBar,tearoff=0)\r\n        menuBar.add_cascade(label = \"File\", menu=menuFichier)\r\n        menuFichier.add_command(label=\"New\", command=self.nouveau)\r\n        menuFichier.add_command(label=\"Open\", command=self.fopen)\r\n        menuFichier.add_command(label=\"Save\", command=self.save)\r\n        menuFichier.add_command(label=\"Save as\", command=self.saveAs)\r\n        menuFichier.add_separator()\r\n        menuFichier.add_command(label=\"Exit\", command = self.quitter) \r\n        self.master.config(menu = menuBar)\r\n        \r\n        #3 - Création du Menu Edition\r\n        global menuEdition\r\n        menuEdition= Menu(menuBar,tearoff=0)\r\n        menuBar.add_cascade(label = \"Edit\", menu=menuEdition)\r\n        menuEdition.add_command(label=\"Undo\", command = self.undo)\r\n        menuEdition.add_command(label=\"Redo\", command = self.redo)\r\n        menuEdition.add_separator()\r\n        menuEdition.add_command(label=\"Copy\", command=self.copy)\r\n        menuEdition.add_command(label=\"Cut\", command = self.cut)\r\n        menuEdition.add_command(label=\"Paste\", command=self.paste)\r\n        menuEdition.add_separator()\r\n        menuEdition.add_command(label=\"Delete\",command=self.clear)\r\n        menuEdition.add_command(label=\"Delete All\",command=self.clearall)\r\n        menuEdition.add_command(label=\"Select All\",command=self.selectAll)\r\n\r\n       \r\n        # Création du Menu Options\r\n        menuOutils = Menu(menuBar,tearoff=0)\r\n        menuBar.add_cascade(label = \"Tools\", menu = menuOutils)\r\n\r\n        global show_icons_menucheck, show_shortcuts_menucheck\r\n\r\n        show_icons_menucheck = IntVar()\r\n        show_icons_menucheck.set(config_show_icons)\r\n        menuOutils.add_checkbutton(label='Show Icons',onvalue=1, offvalue=0, variable=show_icons_menucheck, command=self.toggle_icons)\r\n\r\n        show_shortcuts_menucheck = IntVar()\r\n        show_shortcuts_menucheck.set(config_show_shortcuts)\r\n        menuOutils.add_checkbutton(label='Show Shortcuts',onvalue=True, offvalue=False, variable=show_shortcuts_menucheck, command=self.toggle_shortcuts)\r\n\r\n\r\n        menuOutils.add_separator()\r\n        menuOutils.add_command(label=\"Settings\", command=self.settings_w)\r\n        \r\n        # Création du Menu Aide\r\n        menuAide = Menu(menuBar,tearoff=0)\r\n        menuBar.add_cascade(label = \"Help\", menu = menuAide)\r\n        menuAide.add_command(label=\"About\",command=self.open_about)\r\n\r\n        if config_show_shortcuts:\r\n            BlockNote_window.add_shortcuts_to_menu(self)\r\n\r\n    #===========================\r\n    # BarMenu Checkbuttons Action\r\n    #===========================\r\n\r\n    def toggle_shortcuts(self):\r\n        if show_shortcuts_menucheck.get():\r\n            BlockNote_window.add_shortcuts_to_menu(self)\r\n        else:\r\n            BlockNote_window.remove_shortcuts_from_menu(self)\r\n\r\n        conteur['menu_settings']['show_shortcuts']  = str( show_shortcuts_menucheck.get() )\r\n        conteur.write(open('configuration.ini','w'))\r\n\r\n    def add_shortcuts_to_menu(self, *args):\r\n\r\n        menuFichier.entryconfig(0, accelerator='Ctrl+N')\r\n        menuFichier.entryconfig(1, accelerator='Ctrl+O')\r\n        menuFichier.entryconfig(2, accelerator='Ctrl+S')\r\n\r\n        menuEdition.entryconfig(0, accelerator='Ctrl+Z')\r\n        menuEdition.entryconfig(1, accelerator='Ctrl+Y')\r\n        menuEdition.entryconfig(3, accelerator='Ctrl+C')\r\n        menuEdition.entryconfig(4, accelerator='Ctrl+X')\r\n        menuEdition.entryconfig(5, accelerator='Ctrl+V')\r\n        menuEdition.entryconfig(9, accelerator='Ctrl+A')\r\n\r\n        # bind shortcuts to menuBar\r\n\r\n        self.content.bind('<Control-N>', self.nouveau)\r\n        self.content.bind('<Control-n>', self.nouveau)\r\n        self.content.bind('<Control-O>', self.fopen)\r\n        self.content.bind('<Control-o>', self.fopen)\r\n        self.content.bind('<Control-S>', self.save)\r\n        self.content.bind('<Control-s>', self.save)\r\n        self.content.bind('<Control-z>', self.undo)\r\n        self.content.bind('<Control-Z>', self.undo)\r\n        self.content.bind('<Control-y>', self.redo)\r\n        self.content.bind('<Control-Y>', self.redo)\r\n        self.content.bind('<Control-c>', self.copy)\r\n        self.content.bind('<Control-C>', self.copy)\r\n        self.content.bind('<Control-x>', self.cut)\r\n        self.content.bind('<Control-X>', self.cut)\r\n        self.content.bind('<Control-v>', self.paste)\r\n        self.content.bind('<Control-V>', self.paste)\r\n        self.content.bind('<Control-A>', self.selectAll)\r\n        self.content.bind('<Control-a>', self.selectAll)\r\n\r\n    def remove_shortcuts_from_menu(self, *args):\r\n        menuFichier.entryconfig(0, accelerator='')\r\n        menuFichier.entryconfig(1, accelerator='')\r\n        menuFichier.entryconfig(2, accelerator='')\r\n\r\n        menuEdition.entryconfig(0, accelerator='')\r\n        menuEdition.entryconfig(1, accelerator='')\r\n        menuEdition.entryconfig(3, accelerator='')\r\n        menuEdition.entryconfig(4, accelerator='')\r\n        menuEdition.entryconfig(5, accelerator='')\r\n        menuEdition.entryconfig(9, accelerator='')\r\n\r\n        \"\"\"# unbind shortcuts from menuBar \r\n        self.content.unbind('<Control-N>', self.nouveau)\r\n        self.content.unbind('<Control-n>', self.nouveau)\r\n        self.content.unbind('<Control-O>', self.fopen)\r\n        self.content.unbind('<Control-o>', self.fopen)\r\n        self.content.unbind('<Control-S>', self.save)\r\n        self.content.unbind('<Control-s>', self.save)\r\n        self.content.unbind('<Control-z>', self.undo)\r\n        self.content.unbind('<Control-Z>', self.undo)\r\n        self.content.unbind('<Control-y>', self.redo)\r\n        self.content.unbind('<Control-Y>', self.redo)\r\n        self.content.unbind('<Control-c>', self.copy)\r\n        self.content.unbind('<Control-C>', self.copy)\r\n        self.content.unbind('<Control-x>', self.cut)\r\n        self.content.unbind('<Control-X>', self.cut)\r\n        self.content.unbind('<Control-v>', self.paste)\r\n        self.content.unbind('<Control-V>', self.paste)\r\n        self.content.unbind('<Control-A>', self.selectAll)\r\n        self.content.unbind('<Control-a>', self.selectAll)\"\"\"\r\n\r\n    def add_icons_menu(self):\r\n        self.icons_menu=Frame(height=10,borderwidth=0, padx=5, pady=0)\r\n        self.icons_menu.pack(side=TOP,fill=X)\r\n\r\n    def add_icons(self):\r\n        i = 0\r\n        for path, bind_function in menu_icons_list.items():\r\n\r\n            load = Image.open(path)\r\n            load = load.resize((16,16))\r\n            img = ImageTk.PhotoImage(load)\r\n\r\n            label_name = bind_function+\"_label\" # problem d smya -- fix later !\r\n\r\n            label_name = Label(self.icons_menu,cursor=\"hand2\", image=img)\r\n            imgDict[path] = img # save image ref in imgDict -- keep track of the reference or else it wont work!!! \r\n            label_name.pack(side=LEFT,padx=2,pady=5)\r\n\r\n            bind_function = \"self.\"+bind_function \r\n            label_name.bind( \"<Button>\", eval( bind_function )) # convert string into variable name \r\n\r\n            #add space between labels - just for styling\r\n            i = i+1\r\n            if i in [3,6,8,11,14]:\r\n                label_name = str(i)+\"_label\"\r\n                label_name = Label(self.icons_menu,text=\"|\",fg=\"#cccccc\",width=0)\r\n                label_name.pack(side=LEFT,padx=5,pady=0)\r\n                \r\n    def toggle_icons(self):\r\n        if show_icons_menucheck.get():\r\n            self.add_icons_menu()\r\n            self.add_icons()\r\n        else:\r\n            self.icons_menu.pack_forget()\r\n\r\n        conteur['menu_settings']['show_icons']  = str( show_icons_menucheck.get() )\r\n        conteur.write(open('configuration.ini','w'))\r\n\r\n        \r\n    #===================================================\r\n    # reading default style colors from configuration.ini\r\n    #===================================================\r\n\r\n    def get_config_default_font_color(self):\r\n\r\n        print( eval(config_style_1) )\r\n        if eval(config_style_1):\r\n            return config_font_style_1\r\n        elif eval(config_style_2):\r\n            return config_font_style_2\r\n        elif eval(config_style_3):\r\n            return config_font_style_3\r\n        else:\r\n            return config_font_color\r\n\r\n    def get_config_default_bg_color(self):\r\n        if eval(config_style_1):\r\n            return config_bg_style_1\r\n        elif eval(config_style_2):\r\n            return config_bg_style_2\r\n        elif eval(config_style_3):\r\n            return config_bg_style_3\r\n        else:\r\n            return config_text_background\r\n\r\n", "repo_name": "sifdin17/TextEditor-Python-Tkinter", "sub_path": "library/main_window.py", "file_name": "main_window.py", "file_ext": "py", "file_size_in_byte": 18374, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "os.popen", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 83, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 83, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tkinter.filedialog.asksaveasfilename", "line_number": 95, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 95, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 119, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 119, "usage_type": "name"}, {"api_name": "tkinter.font.Font", "line_number": 225, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 225, "usage_type": "name"}, {"api_name": "tkinter.simpledialog.askstring", "line_number": 235, "usage_type": "call"}, {"api_name": "tkinter.simpledialog", "line_number": 235, "usage_type": "name"}, {"api_name": "tkinter.simpledialog", "line_number": 236, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 259, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 259, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 261, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 261, "usage_type": "name"}, {"api_name": "{'os': 'os', 'sys': 'sys'}.add_shortcuts_to_menu", "line_number": 346, "usage_type": "call"}, {"api_name": "{'os': 'os', 'sys': 'sys'}.add_shortcuts_to_menu", "line_number": 354, "usage_type": "call"}, {"api_name": "{'os': 'os', 'sys': 'sys'}.remove_shortcuts_from_menu", "line_number": 356, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 435, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 435, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 437, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 437, "usage_type": "name"}]}
{"seq_id": "12358786444", "text": "import csv, mechanize, urllib2\nfrom bs4 import BeautifulSoup\n\noutput = open('output.csv', 'w')\nwriter = csv.writer(output)\n\nbr = mechanize.Browser()\nbr.open('http://enr.sos.mo.gov/EnrNet/CountyResults.aspx')\n#print br.response().read()\n\n#get HTML so I can then target the countyForm via its HTML tags\nhtml = br.response().read()\n#transform HTML into a BeautifulSoup object so I can use soup.find on the countyForm\nsoup = BeautifulSoup(html, \"html.parser\")\n#print soup\n\nbr.select_form(nr=0) #nr = get first (only) form on the page\n\n#first dropdown menu:\nbr.form['ctl00$MainContent$cboElectionNames'] = ['750003566']\n#br.submit('ctl00$MainContent$btnElectionType')\n#why does the code not work when I have two br.submits?\n#used to get this error: \n#File \"countyScraper.py\", line 21, in <module>\n    #br.form['ctl00$MainContent$cboCounty'] = ['001']\n    #TypeError: 'NoneType' object does not support item assignment\n\n#second dropdown menu:\n\ncounty = soup.find('select',\n    {'name': 'ctl00$MainContent$cboCounty',\n    'id': 'cboCounty'\n})\n\nfor row in county.find_all('option'):\n    countyValue = row['value']\n\n    br.select_form(nr=0)\n    br.form['ctl00$MainContent$cboCounty'] = [countyValue] \n    #why don't I need to do this: ['countyValue']\n    br.submit('ctl00$MainContent$btnCountyChange')\n\n    html = br.response().read()\n\n    # print html\n    soup = BeautifulSoup(html, \"html.parser\")\n\n    #select the table\n    main_table = soup.find('table',\n        {'class': 'electtable'} \n    )\n\n    #extract info from table cells\n    for row in main_table.find_all('tr'):\n        data = [cell.text.encode('utf-8') for cell in row.find_all('td')]\n        \n    #select candidates of interest:\n        for cell in data:\n            candidates = ['Hilary Clinton', 'Bernie Sanders', 'Ted Cruz', 'John R. Kasich','Donald J. Trump']\n            if cell in candidates:\n                \n                writer.writerow(row.text)\n                #print row.text", "repo_name": "SheilaWangWang/advance_data_assignment", "sub_path": "practice/jen.py", "file_name": "jen.py", "file_ext": "py", "file_size_in_byte": 1946, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "csv.writer", "line_number": 5, "usage_type": "call"}, {"api_name": "mechanize.Browser", "line_number": 7, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "380714234", "text": "import pytest\nfrom selenium import webdriver\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom wheel.signatures import assertTrue\n\n\n@pytest.fixture\ndef driver(request):\n    # wd = webdriver.Firefox()\n    wd = webdriver.Chrome()\n    # wd = webdriver.Chrome(desired_capabilities={\"chromeOptions\": {\"args\": ['--start-fullscreen']}})\n    wd.implicitly_wait(2)\n    request.addfinalizer(wd.quit)\n    return wd\n\ndef test_left_menu(driver):\n\n    driver.get(\"http://localhost/litecart/admin\")\n\n    driver.find_element_by_name(\"username\").send_keys(\"admin\")\n    driver.find_element_by_name(\"password\").send_keys(\"admin\")\n    driver.find_element_by_name(\"login\").click()\n\n    WebDriverWait(driver, 5).until(EC.title_is(\"My Store\"))\n\n    menu = driver.find_element_by_id(\"box-apps-menu\")\n    menu_items = []\n    for menu_name in menu.find_elements_by_css_selector('.name'):\n        menu_items.append(menu_name.text)\n    for menu_item in menu_items:\n        driver.find_element_by_link_text(menu_item).click()\n        assertTrue(driver.find_element_by_tag_name('h1').is_displayed())\n\n        sub_menu_items = []\n        for sub_menu_name in driver.find_elements_by_css_selector('.docs .name'):\n            sub_menu_items.append(sub_menu_name.text)\n        for sub_menu_item in sub_menu_items:\n            driver.find_element_by_link_text(sub_menu_item).click()\n            assertTrue(driver.find_element_by_tag_name('h1').is_displayed())\n", "repo_name": "dmchu/selenium_gr_5", "sub_path": "tests/day_4/hw_day_4_task_7.py", "file_name": "hw_day_4_task_7.py", "file_ext": "py", "file_size_in_byte": 1547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 12, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 12, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.title_is", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 26, "usage_type": "name"}, {"api_name": "wheel.signatures.assertTrue", "line_number": 34, "usage_type": "call"}, {"api_name": "wheel.signatures.assertTrue", "line_number": 41, "usage_type": "call"}]}
{"seq_id": "37415633287", "text": "from .base import KnowledgeBase\nfrom .util import parametrized_performance_debugger\nfrom .concept import Concept\nfrom .search import Node\nfrom .abstracts import BaseRefinement\nimport copy\nfrom typing import Set, Generator\nfrom itertools import chain, tee\n\n\nclass Refinement(BaseRefinement):\n    \"\"\"\n     A top down/downward refinement operator refinement operator in ALC.\n    \"\"\"\n\n    def __init__(self, kb: KnowledgeBase):\n        super().__init__(kb)\n\n    @parametrized_performance_debugger()\n    def refine_atomic_concept(self, concept: Concept) -> Set:\n        \"\"\"\n        # (1) Create all direct sub concepts of C that are defined in TBOX.\n        # (2) Create negations of all leaf concepts in  the concept hierarchy.\n        # (3) Create ∀.r.T and ∃.r.T where r is the most general relation.\n\n        :param concept: Concept\n        :return: A set of refinements.\n        \"\"\"\n        # (1) Generate all direct_sub_concepts\n        sub_concepts = self.kb.get_direct_sub_concepts(concept)\n        # (2) Create negation of all leaf_concepts\n        negs = self.kb.negation_from_iterables(self.kb.get_leaf_concepts(concept))\n        # (3) Create ∃.r.T where r is the most general relation.\n        existential_rest = self.kb.most_general_existential_restrictions(concept)\n        universal_rest = self.kb.most_general_universal_restriction(concept)\n        a, b = tee(chain(sub_concepts, negs, existential_rest, universal_rest))\n\n        mem = set()\n        for i in a:\n            yield i\n            for j in copy.copy(b):\n                if (i == j) or ((i.str, j.str) in mem) or ((j.str, i.str) in mem):\n                    continue\n                mem.add((j.str, i.str))\n                mem.add((i.str, j.str))\n                mem.add((j.str, i.str))\n                yield self.kb.union(i, j)\n                yield self.kb.intersection(i, j)\n\n    def refine_complement_of(self, concept: Concept):\n        \"\"\"\n        :type concept: Concept\n        :param concept:\n        :return:\n        \"\"\"\n        parents = self.kb.get_direct_parents(self.kb.negation(concept))\n        return self.kb.negation_from_iterables(parents)\n\n    def refine_object_some_values_from(self, concept: Concept):\n        refs = set()\n        # rule 1: EXISTS r.D = > EXISTS r.E\n        for i in self.refine(concept.filler):\n            refs.update(self.kb.existential_restriction(i, concept.role))\n        return refs\n\n    def refine_object_all_values_from(self, C: Concept):\n        \"\"\"\n\n        :param C:\n        :return:\n        \"\"\"\n        refs = set()\n        # rule 1: Forall r.D = > Forall r.E\n        for i in self.refine(C.filler):\n            refs.add(self.kb.universal_restriction(i, C.role))\n        return refs\n\n    def refine_object_union_of(self, C: Concept):\n        \"\"\"\n\n        :param C:\n        :return:\n        \"\"\"\n\n        result = set()\n        concept_A = C.concept_a\n        concept_B = C.concept_b\n        for ref_concept_A in self.refine(concept_A):\n            result.add(self.kb.union(ref_concept_A, concept_B))\n\n        for concept_B in self.refine(concept_A):\n            result.add(self.kb.union(concept_B, concept_A))\n\n        return result\n\n    def refine_object_intersection_of(self, C: Concept):\n        \"\"\"\n\n        :param C:\n        :return:\n        \"\"\"\n\n        result = set()\n        concept_A = C.concept_a\n        concept_B = C.concept_b\n        for ref_concept_A in self.refine(concept_A):\n            result.add(self.kb.intersection(ref_concept_A, concept_B))\n\n        for concept_B in self.refine(concept_A):\n            result.add(self.kb.intersection(concept_B, concept_A))\n\n        return result\n\n    def refine(self, concept: Concept):\n        assert isinstance(concept, Concept)\n\n        if concept.is_atomic:\n            yield from self.refine_atomic_concept(concept)\n        elif concept.form == 'ObjectComplementOf':\n            yield from self.refine_complement_of(concept)\n        elif concept.form == 'ObjectSomeValuesFrom':\n            yield from self.refine_object_some_values_from(concept)\n        elif concept.form == 'ObjectAllValuesFrom':\n            yield from self.refine_object_all_values_from(concept)\n        elif concept.form == 'ObjectUnionOf':\n            yield from self.refine_object_union_of(concept)\n        elif concept.form == 'ObjectIntersectionOf':\n            yield from self.refine_object_intersection_of(concept)\n        else:\n            raise ValueError\n\n\nclass ModifiedCELOERefinement(BaseRefinement):\n    \"\"\"\n     A top down/downward refinement operator refinement operator in ALC.\n    \"\"\"\n\n    def __init__(self, kb, max_child_length):\n        super().__init__(kb)\n\n        self.topRefinementsCumulative = dict()\n        self.topRefinementsLength = 0\n        self.max_child_length = max_child_length\n\n        self.combos = dict()\n        self.topRefinements = dict()\n        self.topARefinements = dict()\n        self.concepts_to_nodes = dict()\n\n    def set_concepts_node_mapping(self, m: dict):\n        \"\"\"\n\n        @param m:\n        @return:\n        \"\"\"\n        self.concepts_to_nodes = m\n\n    def refine_atomic_concept(self, node: Node, max_length: int = None, current_domain: Concept = None) -> Set:\n        \"\"\"\n        Refinement operator implementation in CELOE-DL-learner,\n        distinguishes the refinement of atomic concepts and start concept(they called Top concept)\n            1) An atomic concept is refined by returning all concepts that are subclass of A.\n            2) Top concept is refined by\n                        (2.1) Generate all direct_sub_concepts.\n                        (2.2) Create negation of all leaf_concepts\n                        (2.3) Create ∃.r.T where r is the most general relation.\n                        (2.4) Union direct_sub_concepts and negated_all_leaf_concepts\n                        (2.5) Create unions of each of direct_sub_concept with all other direct_sub_concepts\n                        (2.6) Create unions of all of direct_sub_concepts, and negated_all_leaf_concepts\n                        (2.7) Create \\forall.r.T and \\exists.r.T where r is the most general relation.\n                        (Currently we are not able to identify general relations.).\n\n\n        @param node:\n        @param max_length:\n        @param current_domain:\n        @return:\n        \"\"\"\n\n        iter_container = []\n        # (2.1) Generate all direct_sub_concepts\n        sub_concepts, temp_sub_concepts = tee(self.kb.get_direct_sub_concepts(node.concept))\n        iter_container.append(sub_concepts)\n\n        if max_length >= 2 and (len(node.concept) + 1 <= self.max_child_length):\n            # (2.2) Create negation of all leaf_concepts\n            iter_container.append(self.kb.negation_from_iterables(self.kb.get_leaf_concepts(node.concept)))\n\n        if max_length >= 3 and (len(node.concept) + 2 <= self.max_child_length):\n            # (2.3) Create ∀.r.T and ∃.r.T where r is the most general relation.\n            iter_container.append(self.kb.most_general_existential_restrictions(node.concept))\n            iter_container.append(self.kb.most_general_universal_restriction(node.concept))\n\n        a, b = tee(chain.from_iterable(iter_container))\n\n        # Compute all possible combinations of the disjunction\n        mem = set()\n        for i in a:\n            yield i\n            for j in copy.copy(b):\n                if (i == j) or ((i.str, j.str) in mem) or ((j.str, i.str) in mem):\n                    continue\n                mem.add((j.str, i.str))\n                mem.add((i.str, j.str))\n                length = len(i) + len(j)\n\n                if (max_length >= length) and (self.max_child_length >= length + 1):\n\n                    # if i or j is not == T\n                    if i.str != 'T' and j.str != 'T':\n                        if (i.instances.union(j.instances)) != self.kb.thing.instances:\n                            yield self.kb.union(i, j)\n\n                    if not i.instances.isdisjoint(j.instances):\n                        yield self.kb.intersection(i, j)\n                else:\n                    continue\n\n    def refine_complement_of(self, node: Node, maxlength: int, current_domain: Concept = None) -> Generator:\n        \"\"\"\n                @param current_domain:\n        @param node:\n        @param maxlength:\n        @return:\n        \"\"\"\n        parents = self.kb.get_direct_parents(self.kb.negation(node.concept))\n        yield from self.kb.negation_from_iterables(parents)\n\n    def refine_object_some_values_from(self, node: Node, maxlength: int, current_domain: Concept = None) -> Generator:\n        \"\"\"\n        @param current_domain:\n        @param node:\n        @param maxlength:\n        @return:\n        \"\"\"\n        assert isinstance(node.concept.filler, Concept)\n        # rule 1: EXISTS r.D = > EXISTS r.E\n        for i in self.refine(self.getNode(node.concept.filler, parent_node=node), maxlength=maxlength - 2,\n                             current_domain=current_domain):\n            yield self.kb.existential_restriction(i, node.concept.role)\n\n    def refine_object_all_values_from(self, node: Node, maxlength: int, current_domain: Concept = None):\n        \"\"\"\n\n        @param current_domain:\n        @param node:\n        @param maxlength:\n        @return:\n        \"\"\"\n\n        # rule 1: Forall r.D = > Forall r.E\n        for i in self.refine(self.getNode(node.concept.filler, parent_node=node), maxlength=maxlength - 2,\n                             current_domain=current_domain):\n            yield self.kb.universal_restriction(i, node.concept.role)\n        # if (node.concept.filler.str != 'Nothing') and node.concept.filler.isatomic and (len(refs) == 0):\n        #    refs.update(self.kb.universal_restriction(i, node.concept.role))# TODO find a way to include nothig concept\n\n    def getNode(self, c: Concept, parent_node=None, root=False):\n\n        if c in self.concepts_to_nodes:\n            return self.concepts_to_nodes[c]\n\n        if parent_node is None and root is False:\n            print(c)\n            raise ValueError\n\n        n = Node(concept=c, parent_node=parent_node, root=root)\n        self.concepts_to_nodes[c] = n\n        return n\n\n    def refine_object_union_of(self, node: Node, maxlength: int, current_domain: Concept):\n        \"\"\"\n        Given a node corresponding a concepts that comprises union operation.\n        1) Obtain two concepts A, B\n        2) Refine A and union refiements with B.\n        3) Repeat (2) for B.\n        @param current_domain:\n        @param node:\n        @param maxlength:\n        @return:\n        \"\"\"\n        concept_A = node.concept.concept_a\n        concept_B = node.concept.concept_b\n\n        for ref_concept_A in self.refine(self.getNode(concept_A, parent_node=node),\n                                         maxlength=maxlength - len(concept_A) + len(concept_B),\n                                         current_domain=current_domain):\n            if maxlength >= len(concept_B) + len(ref_concept_A):\n                yield self.kb.union(concept_B, ref_concept_A)\n\n        for ref_concept_B in self.refine(self.getNode(concept_B, parent_node=node),\n                                         maxlength=maxlength - len(concept_A) + len(concept_B),\n                                         current_domain=current_domain):\n            if maxlength >= len(concept_A) + len(ref_concept_B):\n                yield self.kb.union(concept_A, ref_concept_B)\n\n    def refine_object_intersection_of(self, node: Node, maxlength: int, current_domain: Concept):\n        \"\"\"\n        @param node:\n        @param maxlength:\n        @param current_domain:\n        @return:\n        \"\"\"\n\n        concept_A = node.concept.concept_a\n        concept_B = node.concept.concept_b\n        for ref_concept_A in self.refine(self.getNode(concept_A, parent_node=node),\n                                         maxlength=maxlength - len(concept_A) + len(concept_B),\n                                         current_domain=current_domain):\n            if maxlength >= len(concept_B) + len(ref_concept_A):\n                yield self.kb.intersection(concept_B, ref_concept_A)\n\n        for ref_concept_B in self.refine(self.getNode(concept_B, parent_node=node),\n                                         maxlength=maxlength - len(concept_A) + len(concept_B),\n                                         current_domain=current_domain):\n            if maxlength >= len(concept_A) + len(ref_concept_B):\n                # if concept_A.instances.isdisjoint(ref_concept_B.instances):\n                #    continue\n                yield self.kb.intersection(concept_A, ref_concept_B)\n\n    def refine(self, node, maxlength, current_domain):\n        \"\"\"\n\n        @param node:\n        @param maxlength:\n        @param current_domain:\n        @return:\n        \"\"\"\n        assert isinstance(node, Node)\n        if node.concept.is_atomic:\n            return self.refine_atomic_concept(node, maxlength, current_domain)\n        elif node.concept.form == 'ObjectComplementOf':\n            return self.refine_complement_of(node, maxlength, current_domain)\n        elif node.concept.form == 'ObjectSomeValuesFrom':\n            return self.refine_object_some_values_from(node, maxlength, current_domain)\n        elif node.concept.form == 'ObjectAllValuesFrom':\n            return self.refine_object_all_values_from(node, maxlength, current_domain)\n        elif node.concept.form == 'ObjectUnionOf':\n            return self.refine_object_union_of(node, maxlength, current_domain)\n        elif node.concept.form == 'ObjectIntersectionOf':\n            return self.refine_object_intersection_of(node, maxlength, current_domain)\n        else:\n            raise ValueError\n", "repo_name": "AbhratanuSurai/KnowledgeGraph-CarcinogenicityPrediction", "sub_path": "CarciogenecityPrediction(Py)/venv/Lib/site-packages/ontolearn/refinement_operators.py", "file_name": "refinement_operators.py", "file_ext": "py", "file_size_in_byte": 13636, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "abstracts.BaseRefinement", "line_number": 11, "usage_type": "name"}, {"api_name": "base.KnowledgeBase", "line_number": 16, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 20, "usage_type": "name"}, {"api_name": "itertools.tee", "line_number": 36, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 36, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 41, "usage_type": "call"}, {"api_name": "util.parametrized_performance_debugger", "line_number": 19, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 20, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 50, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 59, "usage_type": "name"}, {"api_name": "concept.filler", "line_number": 62, "usage_type": "attribute"}, {"api_name": "concept.role", "line_number": 63, "usage_type": "attribute"}, {"api_name": "concept.Concept", "line_number": 66, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 78, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 96, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 114, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 115, "usage_type": "argument"}, {"api_name": "concept.is_atomic", "line_number": 117, "usage_type": "attribute"}, {"api_name": "concept.form", "line_number": 119, "usage_type": "attribute"}, {"api_name": "concept.form", "line_number": 121, "usage_type": "attribute"}, {"api_name": "concept.form", "line_number": 123, "usage_type": "attribute"}, {"api_name": "concept.form", "line_number": 125, "usage_type": "attribute"}, {"api_name": "concept.form", "line_number": 127, "usage_type": "attribute"}, {"api_name": "abstracts.BaseRefinement", "line_number": 133, "usage_type": "name"}, {"api_name": "search.Node", "line_number": 158, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 158, "usage_type": "name"}, {"api_name": "itertools.tee", "line_number": 182, "usage_type": "call"}, {"api_name": "itertools.tee", "line_number": 194, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 194, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 194, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 200, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 158, "usage_type": "name"}, {"api_name": "search.Node", "line_number": 219, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 219, "usage_type": "name"}, {"api_name": "typing.Generator", "line_number": 219, "usage_type": "name"}, {"api_name": "search.Node", "line_number": 229, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 229, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 236, "usage_type": "argument"}, {"api_name": "typing.Generator", "line_number": 229, "usage_type": "name"}, {"api_name": "search.Node", "line_number": 242, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 242, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 258, "usage_type": "name"}, {"api_name": "search.Node", "line_number": 267, "usage_type": "call"}, {"api_name": "search.Node", "line_number": 271, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 271, "usage_type": "name"}, {"api_name": "search.Node", "line_number": 297, "usage_type": "name"}, {"api_name": "concept.Concept", "line_number": 297, "usage_type": "name"}, {"api_name": "search.Node", "line_number": 329, "usage_type": "argument"}]}
{"seq_id": "19164341180", "text": "from pydantic import BaseModel\nfrom sqlalchemy import (\n    delete,\n    select,\n    update,\n)\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom src.app.configs.db import Base\n\n\nclass BaseServices:\n    MODEL = Base\n    SCHEMA = BaseModel\n\n    @classmethod\n    async def get_all(cls, session: AsyncSession):\n        \"\"\"Получает все записи из базы данных\"\"\"\n        result = await session.execute(select(cls.MODEL))\n        return result.scalars().all()\n\n    @classmethod\n    async def add(cls, session: AsyncSession, data: SCHEMA):\n        \"\"\"Добавляет запись в базу данных\"\"\"\n        new_obj = cls.MODEL(**data.dict())\n        session.add(new_obj)\n        return new_obj\n\n    @classmethod\n    async def update(cls, session: AsyncSession, record_id: int, data: dict) -> None:\n        \"\"\"Обновляет запись в базе данных\"\"\"\n        await session.execute(update(cls.MODEL).where(cls.MODEL.id == record_id).values(**data))\n\n    @classmethod\n    async def delete(cls, session: AsyncSession, record_id: int):\n        \"\"\"Удаление записи в БД\"\"\"\n        return await session.execute(delete(cls.MODEL).where(cls.MODEL.id == record_id).returning(cls.MODEL))\n\n    @classmethod\n    async def get_by(cls, session: AsyncSession, param_name: str, param_value: any):\n        \"\"\"Удаление записи в БД\"\"\"\n        result = await session.execute(select(cls.MODEL).where(getattr(cls.MODEL, param_name) == param_value))\n        return result.scalars().all()\n", "repo_name": "VovanchikVP/investor", "sub_path": "src/app/services/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 1551, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "src.app.configs.db.Base", "line_number": 13, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 14, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 17, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlalchemy.update", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlalchemy.delete", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 40, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 42, "usage_type": "call"}]}
{"seq_id": "726895572", "text": "import xml.sax.saxutils\nfrom dublincore import dublinCoreMetadata\n\nclass DublinCore(dublinCoreMetadata):\n\tdef __init__(self):\n\t\tdublinCoreMetadata.__init__(self)\n\t\tself.Relation = []\n\t\n\tdef makeXML(self, schemaLocation, encapsulatingTag='metadata'):\n\t\t\"\"\"\n\t\tThis method transforms the class attribute data into standards\n\t\tcompliant XML according to the guidlines laid out in \n\t\t\"Guidelines for implementing Dublin Core in XML\" available online \n\t\tat http://www.dublincore.org/documents/2003/04/02/dc-xml-guidelines/\n\t\t\n\t\tThis method takes one mandatory argument, one optional argument and\n\t\treturns a string. The mandatory argument is the location of the XML\n\t\tschema which should be a fully qualified URL. The option arguments\n\t\tis the root tag with which to enclose and encapsulate all of the\n\t\tDC elements. The default is \"metadata\" but it can be overridden if\n\t\tneeded.\n\t\t\n\t\tThe output can be directed to a file or standard output. This RDF \n\t\tdata should be suitable for marking most documents including webpages.\n\t\t\"\"\"\n\t\t#set XML declaration\n\t\txmlOut = '<?xml version=\"1.0\"?>\\n'\n\t\t\n\t\t#open encapsulating element tag and deal with namespace and schema declarations\n\t\txmlOut += '''\\n<%s\n    xmlns=\"http://example.org/myapp/\"\n    xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"\n    xsi:schemaLocation=\"%s\"\n    xmlns:dc=\"http://purl.org/dc/elements/1.1/\">\\n\\n''' % (encapsulatingTag, schemaLocation)\n\t\t\n\t\t#if the Title element is set, make the dc:title tag\n\t\tif self.Title:\n\t\t\txmlOut += '\\t<dc:title>%s</dc:title>\\n' % xml.sax.saxutils.escape(self.Title)\n\t\t\n\t\t#if the creator element is set, make the dc:title tag\n\t\tif self.Creator:\n\t\t\txmlOut += '\\t<dc:creator>%s</dc:creator>\\n' % xml.sax.saxutils.escape(self.Creator)\n\t\t\n\t\t#if the subject element is set, make the dc:subject tag\n\t\tif self.Subject:\n\t\t\txmlOut += '\\t<dc:subject>%s</dc:subject>\\n' % xml.sax.saxutils.escape(self.Subject)\n\t\t\n\t\t#if the description element is set, make the dc:description tag\n\t\tif self.Description:\n\t\t\txmlOut += '\\t<dc:description>%s</dc:description>\\n' % xml.sax.saxutils.escape(self.Description)\n\t\t\t\n\t\t#if the publisher element is set, make the dc:publisher tag\n\t\tif self.Publisher:\n\t\t\txmlOut += '\\t<dc:publisher>%s</dc:publisher>\\n' % xml.sax.saxutils.escape(self.Publisher)\n\t\t\t\n\t\t#if the contributor element is set, make the dc:contributor tag\n\t\tif self.Contributor:\n\t\t\txmlOut += '\\t<dc:contributor>%s</dc:contributor>\\n' % xml.sax.saxutils.escape(self.Contributor)\n\t\t\t\n\t\t#if the date element is set, make the dc:date tag\n\t\tif self.Date:\n\t\t\txmlOut += '\\t<dc:date>%s</dc:date>\\n' % xml.sax.saxutils.escape(self.Date)\n\t\t\t \n\t\t#if the type element is set, make the dc:type tag\n\t\tif self.Type:\n\t\t\txmlOut += '\\t<dc:type>%s</dc:type>\\n' % xml.sax.saxutils.escape(self.Type)\n\t\t\t\n\t\t#if the format element is set, make the dc:format tag\n\t\tif self.Format:\n\t\t\txmlOut += '\\t<dc:format>%s</dc:format>\\n' % xml.sax.saxutils.escape(self.Format)\n\t\t\n\t\t#if the identifier element is set, make the dc:identifier tag\n\t\tif self.Identifier:\n\t\t\txmlOut += '\\t<dc:identifier>%s</dc:identifier>\\n' % xml.sax.saxutils.escape(self.Identifier)\n\t\t\t\n\t\t#if the source element is set, deal with it properly\n\t\tif self.Source:\n\t\t\txmlOut += '\\t<dc:source>%s</dc:source>\\n' % xml.sax.saxutils.escape(self.Source)\n\t\t\n\t\t#if the language element is set, make the dc:language tag\n\t\tif self.Language:\n\t\t\txmlOut += '\\t<dc:language>%s</dc:language>\\n' % xml.sax.saxutils.escape(self.Language)\n\t\t\t\n\t\t#if the relation element is set, make the dc:relation tag\n\t\tif self.Relation:\n\t\t\tfor relation in self.Relation:\n\t\t\t\txmlOut += '\\t<dc:relation>%s</dc:relation>\\n' % xml.sax.saxutils.escape(relation)\n\t\t\t\n\t\t#if the coverage element is set, make the dc:coverage tag\n\t\tif self.Coverage:\n\t\t\txmlOut += '\\t<dc:coverage>%s</dc:coverage>\\n' % xml.sax.saxutils.escape(self.Coverage)\n\t\t\t\n\t\t#if the rights element is set, make the dc:rights tag\n\t\tif self.Rights:\n\t\t\txmlOut += '\\t<dc:rights>%s</dc:rights>\\n' % xml.sax.saxutils.escape(self.Rights)\n\t\t\t\n\t\t#close encapsulating element tag\n\t\txmlOut += '</metadata>\\n'\n\t\t\n\t\treturn xmlOut", "repo_name": "mmccollow/TSV-Convert", "sub_path": "DublinCore.py", "file_name": "DublinCore.py", "file_ext": "py", "file_size_in_byte": 4066, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "dublincore.dublinCoreMetadata", "line_number": 4, "usage_type": "name"}, {"api_name": "dublincore.dublinCoreMetadata.__init__", "line_number": 6, "usage_type": "call"}, {"api_name": "dublincore.dublinCoreMetadata", "line_number": 6, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 38, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 38, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 38, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 42, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 42, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 42, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 46, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 46, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 46, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 50, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 50, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 50, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 54, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 54, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 54, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 58, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 58, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 58, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 62, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 62, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 62, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 66, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 66, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 66, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 70, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 70, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 70, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 74, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 74, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 74, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 78, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 78, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 78, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 82, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 82, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 82, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 87, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 87, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 87, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 91, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 91, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 91, "usage_type": "name"}, {"api_name": "xml.sax.saxutils.sax.saxutils.escape", "line_number": 95, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.sax", "line_number": 95, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils", "line_number": 95, "usage_type": "name"}]}
{"seq_id": "4034457695", "text": "from django.urls import path,include\nfrom . import views\nurlpatterns = [\n\n    # Page Url\n    path(\"\",views.IndexPage,name=\"index\"),\n    path(\"loginpage/\",views.LoginPage,name=\"loginpage\"),\n    path(\"catpage/\",views.CatPage,name=\"catpage\"),\n    path(\"signuppage/\",views.SingupPage,name=\"signuppage\"),\n    path(\"signuppagechef/\",views.SingupChefPage,name=\"signupchefpage\"),\n    path(\"insert/\",views.Insertdata,name=\"insert\"),\n    path(\"login/\",views.LoginUser,name=\"login\"),\n    path(\"loginchef/\",views.LoginChefPage,name=\"loginchef\"),\n    path(\"show/\",views.Showpage,name=\"show\"),\n    path(\"welcomeback/\",views.customerhome,name=\"welcomeback\"),\n    path(\"display/<int:pk>\",views.DisplayData,name=\"display\"),\n    path(\"edit/<int:pk>\",views.EditPage,name=\"edit\"),\n    path(\"update/<int:pk>\",views.UpdateData,name=\"update\"),\n    path(\"chefedit/<int:pk>\",views.ChefAlldata,name=\"chefedit\"),\n    path(\"chefeditprofile/\",views.ChefEditProfilePage,name=\"chefeditprofile\"),\n    path(\"ChefProductadd/\",views.ChefProduct_add,name=\"ChefProductadd\"),\n    path(\"allorders/\",views.allorder,name=\"allorders\"),\n    path(\"add/<int:pk>\",views.Addproduct,name=\"add\"),\n    path(\"shoppage/\",views.CustomerShop_page,name=\"shoppage\"),\n    path(\"showallproduct/\",views.ShowProduct,name=\"showproduct\"),\n    path(\"showallproductbycategory/<int:pk>\",views.OpenproductsbyCategory,name=\"productbycategory\"),\n    path(\"productdes/<int:pk>\",views.ProductDescription,name=\"productdes\"),\n    path(\"shopsinglepage/\",views.ShopSinglePage,name=\"shopsinglepage\"),\n    path(\"chefproductpage/\",views.Product_Page,name=\"chefproductpage\"),\n    path(\"chefshowproductpage/<int:pk>\",views.ChefShowProduct_Page,name=\"chefshowproductpage\"),\n    path(\"editproduct/<int:pk>\",views.EditProduct,name=\"editproduct\"),\n    path(\"updateproduct/<int:pk>\",views.UpdateProduct,name=\"updateproduct\"),\n    path(\"deleteproduct/<int:pk>\",views.DeleteProduct,name=\"deleteproduct\"),\n    path(\"logoutchef/\",views.LogoutChef,name=\"logoutchef\"),\n    path(\"logoutcustomer/\",views.LogoutCustomer,name=\"logoutcustomer\"),\n    path(\"adminindexpage/\",views.Openadminindexpage,name=\"openadminindexpage\"),\n    path(\"loginadmin/\",views.LoginadminPage,name=\"loginadmin\"),\n    path(\"adminlogin/\",views.AdminLogin,name=\"adminlogin\"),\n    path(\"admintablepage/\",views.AdminUserTablePage,name=\"admintablepage\"),\n    path(\"adminchefeditpage/<int:pk>\",views.AdminChefEditPage,name=\"adminchefeditpage\"),\n    path(\"adminupdate/<int:pk>\",views.AdminUpdatePage,name=\"adminupdate\"),\n    path(\"admindelete/<int:pk>\",views.AdminDeletePage,name=\"admindelete\"),\n    path(\"verifyotp/\",views.VerifyOtp,name=\"verifyotp\"),\n    path(\"addtocart/<int:pk>\",views.Addtocart,name=\"addtocart\"),\n    path(\"cartpro/<int:pk>\",views.CartProduct,name=\"cartpro\"),\n    path(\"addcat/<int:pk>\",views.AddCaategory,name=\"addcat\"),\n    path(\"showcatpro/<int:pk>\",views.ShowCatPro,name=\"showcatpro\"),\n    path(\"menupage/\",views.MenuCard,name=\"menupage\"),\n######################################################## Paytm URLS #########################3\n    path(\"checkout/<int:pk>\",views.Proceedtocheckout,name=\"checkout\"),\n    path('pay/',views.initiate_payment, name='pay'),\n    path('callback/', views.callback, name='callback'),\n    \n    path('chefdata/<int:pk>',views.Chefdetail,name='chefdata'),\n    path('feedback/<int:pk>',views.feedback_submit,name='feedback'),\n    \n    \n]\n\n\n\n", "repo_name": "sunitsai/PoojanProject", "sub_path": "app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 3367, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 48, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 51, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 52, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 55, "usage_type": "call"}]}
{"seq_id": "71935345421", "text": "#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport argparse\nimport collections\nimport sys\nimport yaml\n\n\nclass UnsortableList(list):\n    def sort(self, *args, **kwargs):\n        pass\n\n\nclass UnsortableOrderedDict(collections.OrderedDict):\n    def items(self, *args, **kwargs):\n        return UnsortableList(\n            collections.OrderedDict.items(self, *args, **kwargs))\n\n\nyaml.add_representer(UnsortableOrderedDict,\n                     yaml.representer.SafeRepresenter.represent_dict)\n\n\ndef kvarg(v):\n    if '=' not in v:\n        raise ValueError('arguments must be of the form k=v')\n\n    k, v = v.split('=')\n\n    if v.lower() in ('true', 'false'):\n        v = v.lower() == 'true'\n    elif v.isdigit():\n        v = int(v)\n\n    return (k, v)\n\n\ndef parse_args():\n    p = argparse.ArgumentParser()\n    p.add_argument('--output', '-o')\n\n    p.add_argument('--tag', '-t',\n                   action='append',\n                   default=[])\n    p.add_argument('--network', '-n',\n                   action='append',\n                   default=[])\n    p.add_argument('--remove-network',\n                   action='append',\n                   default=[])\n    p.add_argument('--service', '-s',\n                   action='append',\n                   default=[])\n    p.add_argument('--remove-service', '-r',\n                   action='append',\n                   default=[])\n\n    p.add_argument('--extra', '-x',\n                   type=kvarg,\n                   action='append',\n                   default=[])\n\n    p.add_argument('name')\n    p.add_argument('roles',\n                   default=[],\n                   nargs='+')\n\n    return p.parse_args()\n\n\ndef uniqify(l, exclude=None):\n    if exclude is None:\n        exclude = []\n\n    seen = set()\n    return [x for x in l if not (x in seen or seen.add(x))\n            and x not in exclude]\n\n\ndef main():\n    args = parse_args()\n\n    extraconfig = [\n        ('HostnameFormatDefault',\n         '%stackname%-{}-%index%'.format(args.name.lower())),\n        ('CountDefault', 1),\n    ]\n\n    services = []\n    networks = []\n    default_route_networks = []\n    tags = []\n    for role_file in args.roles:\n        with open(role_file) as fd:\n            role_spec = yaml.load(fd)\n            for role in role_spec:\n                services.extend(role.get('ServicesDefault', []))\n                networks.extend(role.get('networks', []))\n                tags.extend(role.get('tags', []))\n                default_route_networks.extend(\n                    role.get('default_route_networks', []))\n\n    services.extend(args.service)\n    networks.extend(args.network)\n    tags.extend(args.tag)\n\n    for kv in args.extra:\n        extraconfig.append(kv)\n\n    new_networks = sorted(uniqify(networks, exclude=args.remove_network))\n    new_services = sorted(uniqify(services, exclude=args.remove_service))\n\n    new_role = [\n        UnsortableOrderedDict(\n            (('name', args.name),) + tuple(extraconfig) + (\n                ('tags', sorted(uniqify(tags))),\n                ('networks', new_networks),\n                ('default_route_networks',\n                 sorted(uniqify(default_route_networks))),\n                ('ServicesDefault', new_services),\n            ))\n    ]\n\n    with (open(args.output, 'w') if args.output else sys.stdout) as fd:\n        print(yaml.dump(new_role, default_flow_style=False), file=fd)\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "CCI-MOC/rhosp-director-config", "sub_path": "scripts/merge-roles.py", "file_name": "merge-roles.py", "file_ext": "py", "file_size_in_byte": 3407, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "46", "api": [{"api_name": "collections.OrderedDict", "line_number": 16, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict.items", "line_number": 19, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 19, "usage_type": "attribute"}, {"api_name": "yaml.add_representer", "line_number": 22, "usage_type": "call"}, {"api_name": "yaml.representer", "line_number": 23, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 41, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 126, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 127, "usage_type": "call"}]}
{"seq_id": "1241479120", "text": "from lib.booking import Booking\n\n\nclass BookingRepository:\n    def __init__(self, connection):\n        self._connection = connection\n\n    def all(self):\n        rows = self._connection.execute(\"SELECT * FROM bookings\")\n        bookings = [Booking(row['id'], row['user_id'], row['space_id'], row['date'], row['confirmed']) for row in rows]\n        return bookings\n    \n    def create(self, booking):\n        self._connection.execute(\"INSERT INTO bookings (user_id, space_id, date, confirmed) VALUES (%s, %s, %s, False)\", [booking.user_id, booking.space_id, booking.date])\n    \n    def find_by_user_id(self, user_id):\n        rows = self._connection.execute(\"SELECT * FROM BOOKINGS WHERE user_id = %s\", [user_id])\n        bookings = [Booking(row['id'], row['user_id'], row['space_id'], row['date'], row['confirmed']) for row in rows]\n        return bookings\n    \n    def find_by_space_id(self, space_id):\n        rows = self._connection.execute(\"SELECT * FROM BOOKINGS WHERE space_id = %s\", [space_id])\n        bookings = [Booking(row['id'], row['user_id'], row['space_id'], row['date'], row['confirmed']) for row in rows]\n        return bookings\n\n    def show_unavailable_dates_for_space(self, space_id):\n        bookings = self.find_by_space_id(space_id)\n        dates = [booking.date for booking in bookings]\n        return dates\n    \n    def delete(self, booking_id):\n        self._connection.execute(\"DELETE FROM bookings WHERE id = %s\", [booking_id])\n\n    def find_confirmed_bookings_by_user_id(self, user_id):\n        rows = self._connection.execute(\"SELECT * FROM BOOKINGS WHERE confirmed = True and user_id = %s\", [user_id])\n        bookings = [Booking(row['id'], row['user_id'], row['space_id'], row['date'], row['confirmed']) for row in rows]\n        return bookings\n    \n    def find_all_unconfirmed_booking_requests(self, user_id):\n\n        sql_query = \"\"\"\n                SELECT\n                    bookings.id as booking_id,\n                    bookings.user_id,\n                    bookings.space_id,\n                    bookings.date,\n                    bookings.confirmed,\n                    spaces.name,\n                    spaces.owner_id\n                FROM\n                    bookings\n                JOIN\n                    spaces ON bookings.space_id = spaces.id\n                WHERE spaces.owner_id = %s \n                and confirmed = False \n                and spaces.owner_id != bookings.user_id\n                    \"\"\"\n\n        rows = self._connection.execute(sql_query, [user_id])\n        return rows\n    \n    def update_booking_to_confirmed(self, booking_id):\n        self._connection.execute(\"UPDATE bookings SET confirmed = True WHERE id = %s\", [booking_id])\n", "repo_name": "jmiller84/bnb_team_2", "sub_path": "lib/booking_repository.py", "file_name": "booking_repository.py", "file_ext": "py", "file_size_in_byte": 2697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "lib.booking.Booking", "line_number": 10, "usage_type": "call"}, {"api_name": "lib.booking.Booking", "line_number": 18, "usage_type": "call"}, {"api_name": "lib.booking.Booking", "line_number": 23, "usage_type": "call"}, {"api_name": "lib.booking.Booking", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "71864565901", "text": "import platform\nfrom PySide6 import QtCore, QtGui, QtWidgets\nfrom src.Controller.PathHandler import resource_path\n\n\nclass BatchMLResultsWindow(QtWidgets.QDialog):\n    \"\"\"\n    This class creates a GUI window for displaying a summary of batch\n    processing. It inherits from QDialog.\n    \"\"\"\n\n    def __init__(self):\n        QtWidgets.QDialog.__init__(self)\n        self.ml_model = None\n        self.params = None\n        self.scaling = None\n\n        # # Set maximum width, icon, and title\n        self.setFixedSize(450, 450)\n        self.setWindowTitle(\"Machine Learning Model Training Results\")\n        window_icon = QtGui.QIcon()\n        window_icon.addPixmap(\n            QtGui.QPixmap(resource_path(\"res/images/icon.ico\")),\n            QtGui.QIcon.Normal, QtGui.QIcon.Off)\n        self.setWindowIcon(window_icon)\n\n        # Create widgets\n        self.summary_label = QtWidgets.QLabel()\n        self.summary_label.setWordWrap(True)\n        self.scroll_area = QtWidgets.QScrollArea()\n        self.export_risk_table_button = QtWidgets. \\\n            QPushButton(\"Export 'Risk table'\"\n                        \" to Text File\")\n        self.save_ml_model_button = QtWidgets.QPushButton(\"Save model\")\n        self.save_ml_parameters_button = QtWidgets.QPushButton(\"Save model\"\n                                                               \" parameters\")\n        self.results_table = QtWidgets.QTableWidget(0, 0)\n\n        # # Get stylesheet\n        if platform.system() == 'Darwin':\n            self.stylesheet_path = \"res/stylesheet.qss\"\n        else:\n            self.stylesheet_path = \"res/stylesheet-win-linux.qss\"\n        self.stylesheet = open(resource_path(self.stylesheet_path)).read()\n\n        # Set stylesheet\n        self.summary_label.setStyleSheet(self.stylesheet)\n        self.scroll_area.setStyleSheet(self.stylesheet)\n        self.export_risk_table_button.setStyleSheet(self.stylesheet)\n        self.save_ml_model_button.setStyleSheet(self.stylesheet)\n        self.save_ml_parameters_button.setStyleSheet(self.stylesheet)\n\n        # Make QLabel wrap text\n        self.summary_label.setWordWrap(True)\n\n        # Set scroll area properties\n        self.scroll_area.setVerticalScrollBarPolicy(\n            QtCore.Qt.ScrollBarAsNeeded)\n        self.scroll_area.setHorizontalScrollBarPolicy(\n            QtCore.Qt.ScrollBarAlwaysOff)\n        self.scroll_area.setWidgetResizable(True)\n        self.scroll_area.setWidget(self.summary_label)\n\n        # Create layout\n        self.layout = QtWidgets.QVBoxLayout()\n        self.layout.addWidget(self.scroll_area)\n        self.layout.addStretch(1)\n        self.layout.addWidget(self.results_table)\n        self.layout.addWidget(self.export_risk_table_button)\n        self.layout.addWidget(self.save_ml_model_button)\n        self.layout.addWidget(self.save_ml_parameters_button)\n\n        # Connect buttons to functions\n        self.export_risk_table_button.clicked.connect(\n            self.export_risk_table_clicked)\n        self.save_ml_model_button.clicked.connect(\n            self.save_ml_model_clicked)\n        self.save_ml_parameters_button.clicked.connect(\n            self.save_ml_model_paramaters_clicked)\n\n        # Set layout of window\n        self.setLayout(self.layout)\n\n    def set_results_values(self, results_values):\n        \"\"\"\n        Sets the summary text.\n        :param batch_summary: List where first index is a dictionary where key\n                              is a patient, and value is a dictionary of\n                              process name and status key-value pairs, and\n                              second index is a batch ROI name cleaning summary\n        \"\"\"\n        # Create summary text\n        summary_text = \"\"\n        for key, value in results_values.items():\n            summary_text += f\"{key.upper()}: {value}\\n\"\n\n        # Set summary text\n        # self.summary_label.setWordWrap(True)\n        self.summary_label.setText(summary_text)\n\n    def set_ml_model(self, ml_model):\n        self.ml_model = ml_model\n\n    def set_df_parameters(self, params):\n        self.params = params\n\n    def set_df_scaling(self, scaling):\n        self.scaling = scaling\n\n    def export_risk_table_clicked(self):\n        \"\"\"\n        Function to handle the export button being clicked. Opens a file\n        save dialog and saves the summary text to this text file.\n        \"\"\"\n        file_path = QtWidgets.QFileDialog. \\\n            getExistingDirectory(self,\n                                 \"Open Directory\",\n                                 \"\",\n                                 QtWidgets.QFileDialog.ShowDirsOnly |\n                                 QtWidgets.QFileDialog.DontResolveSymlinks)\n        if file_path:\n            self.ml_model.save_confusion_matrix(f'{file_path}/')\n\n    def save_ml_model_clicked(self):\n        \"\"\"\n        Function to handle the ok button being clicked. Closes this\n        window.\n        :return: True when the window has closed.\n        \"\"\"\n        file_path = QtWidgets.QFileDialog. \\\n            getExistingDirectory(self,\n                                 \"Open Directory\",\n                                 \"\",\n                                 QtWidgets.QFileDialog.ShowDirsOnly |\n                                 QtWidgets.QFileDialog.DontResolveSymlinks)\n        if file_path:\n            self.ml_model.save_ml_model(self.params, f'{file_path}/', self.scaling)\n\n    def save_ml_model_paramaters_clicked(self):\n        file_path = QtWidgets.QFileDialog. \\\n            getExistingDirectory(self,\n                                 \"Open Directory\", \"\",\n                                 QtWidgets.QFileDialog.ShowDirsOnly |\n                                 QtWidgets.QFileDialog.DontResolveSymlinks)\n        if file_path:\n            self.ml_model.save_model_parameters(f'{file_path}/')\n", "repo_name": "didymo/OnkoDICOM", "sub_path": "src/View/batchprocessing/BatchMLResultsWindow.py", "file_name": "BatchMLResultsWindow.py", "file_ext": "py", "file_size_in_byte": 5800, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 52, "dataset": "github-code", "pt": "46", "api": [{"api_name": "PySide6.QtWidgets.QDialog", "line_number": 6, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 6, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QDialog.__init__", "line_number": 13, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QDialog", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 13, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QIcon", "line_number": 21, "usage_type": "call"}, {"api_name": "PySide6.QtGui", "line_number": 21, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QPixmap", "line_number": 23, "usage_type": "call"}, {"api_name": "PySide6.QtGui", "line_number": 23, "usage_type": "name"}, {"api_name": "src.Controller.PathHandler.resource_path", "line_number": 23, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QIcon", "line_number": 24, "usage_type": "attribute"}, {"api_name": "PySide6.QtGui", "line_number": 24, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 28, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 28, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QScrollArea", "line_number": 30, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 30, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QPushButton", "line_number": 31, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 31, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QPushButton", "line_number": 34, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 34, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QPushButton", "line_number": 35, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 35, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QTableWidget", "line_number": 37, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 37, "usage_type": "name"}, {"api_name": "platform.system", "line_number": 40, "usage_type": "call"}, {"api_name": "src.Controller.PathHandler.resource_path", "line_number": 44, "usage_type": "call"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 58, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 58, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 60, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 60, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QVBoxLayout", "line_number": 65, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 65, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 115, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QFileDialog", "line_number": 115, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 115, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFileDialog", "line_number": 119, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 119, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFileDialog", "line_number": 120, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 120, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 130, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QFileDialog", "line_number": 130, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 130, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFileDialog", "line_number": 134, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 134, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFileDialog", "line_number": 135, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 135, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 140, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QFileDialog", "line_number": 140, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 140, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFileDialog", "line_number": 143, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 143, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QFileDialog", "line_number": 144, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 144, "usage_type": "name"}]}
{"seq_id": "6960473071", "text": "from django.contrib import messages\nfrom django.http import HttpResponse\nfrom django.shortcuts import render, redirect\nfrom django.conf import settings\nimport os\nfrom .models import Product, Complained\nfrom django.core.mail import send_mail\nfrom django.template.loader import render_to_string\n\n\n# Create your views here.\n\ndef error_404(request, exception):\n    return render(request, 'nothing.html')\n\n\ndef home(request):\n    prod = Product.objects.all()\n    params = {\n        'prod': prod\n    }\n    return render(request, 'home.html', params)\n\n\ndef contact(request):\n    return render(request, 'contact-us.html')\n\n\ndef show(request, pk):\n    prod = Product.objects.get(id=pk)\n    params = {\n        'prod': prod\n    }\n    return render(request, 'show.html', params)\n\n\ndef search(request):\n    if request.method == 'GET':\n        prod_name = request.GET['query']\n        product = Product.objects.filter(prod_name__icontains=prod_name)\n        context = {\n            'product': product,\n            'prod_name': prod_name,\n        }\n\n        return render(request, 'search.html', context)\n    return render(request, 'home.html')\n\n\ndef customer_service(request):\n    return render(request, 'customer-service.html')\n\n\ndef store_locator(request):\n    return render(request, 'store-locator.html')\n\n\ndef led_bulb(request, slug):\n    prod = Product.objects.filter(slug=slug)\n    params = {\n        'prod': prod\n    }\n\n    return render(request, 'led-bulb.html', params)\n\n\ndef sunlight_fans(request, slug):\n    prod = Product.objects.filter(slug=slug)\n    params = {\n        'prod': prod\n    }\n    return render(request, 'fans.html', params)\n\n\ndef sunlight_inverter(request, slug):\n    prod = Product.objects.filter(slug=slug)\n    params = {\n        'prod': prod\n    }\n    return render(request, 'sunlight_inverter.html', params)\n\n\ndef sunlight_cooler(request, slug):\n    prod = Product.objects.filter(slug=slug)\n    params = {\n        'prod': prod\n    }\n    return render(request, 'sunlight_cooler.html', params)\n\n\ndef query_form(request):\n    if request.method == 'POST':\n        name = request.POST['name'].capitalize()\n        email = request.POST['email']\n        address1 = request.POST['address1']\n        address2 = request.POST['address2']\n        city = request.POST['city']\n        product = request.POST['product']\n        mobile = request.POST['mobile']\n        phone = request.POST['phone']\n        complaint = request.POST['complaint']\n        comp = Complained(name=name, email=email, address1=address1, address2=address2, city=city, product=product,\n                          mobile=mobile, phone=phone, complaint=complaint)\n        comp.save()\n        html = render_to_string('contact-template.html',\n                                {\n                                    'name': name,\n                                    'email': email,\n                                    'address1': address1,\n                                    'address2': address2,\n                                    'product': product,\n                                    'mobile': mobile,\n                                    'complaint': complaint\n\n                                })\n        send_mail(\n            'message from' + \" \" + name,\n            complaint,\n            'cartoonart28@gmai.com',\n            ['dineshpaul28@gmail.com'],\n            fail_silently=False,\n            html_message=html\n        )\n        messages.success(request, 'Your request successfully submitted. We will get back to you soon.')\n        return redirect('query-form')\n\n    return render(request, 'query-form.html')\n\n\ndef products(request):\n    prod = Product.objects.all()\n\n    return render(request, 'products.html', {'prod': prod})\n\n\n", "repo_name": "dineshpaul28/Ecom_project", "sub_path": "ueat/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Product.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Product.objects.get", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Product.objects.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 40, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 51, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 55, "usage_type": "call"}, {"api_name": "models.Product.objects.filter", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 59, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Product.objects.filter", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 68, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 72, "usage_type": "call"}, {"api_name": "models.Product.objects.filter", "line_number": 76, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 76, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 80, "usage_type": "call"}, {"api_name": "models.Product.objects.filter", "line_number": 84, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 84, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 88, "usage_type": "call"}, {"api_name": "models.Complained", "line_number": 102, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 105, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 116, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 124, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 124, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 125, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 127, "usage_type": "call"}, {"api_name": "models.Product.objects.all", "line_number": 131, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 131, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 131, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 133, "usage_type": "call"}]}
{"seq_id": "21515178057", "text": "\"\"\"revision10\n\nRevision ID: 3b8be48286e9\nRevises: 6c85f155e308\nCreate Date: 2023-06-06 21:53:40.662610\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '3b8be48286e9'\ndown_revision = '6c85f155e308'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('salarydata', sa.Column('user_id', sa.Integer(), nullable=True))\n    op.create_foreign_key(None, 'salarydata', 'user', ['user_id'], ['id'])\n    op.add_column('user', sa.Column('salary_id', sa.Integer(), nullable=True))\n    op.create_foreign_key(None, 'user', 'salarydata', ['salary_id'], ['id'])\n    op.drop_column('user', 'live_time_token')\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('user', sa.Column('live_time_token', sa.VARCHAR(length=1024), autoincrement=False, nullable=True))\n    op.drop_constraint(None, 'user', type_='foreignkey')\n    op.drop_column('user', 'salary_id')\n    op.drop_constraint(None, 'salarydata', type_='foreignkey')\n    op.drop_column('salarydata', 'user_id')\n    # ### end Alembic commands ###\n", "repo_name": "Artemio007/Testtask_fastapi", "sub_path": "salary_check/salary_check/backend/alembic/versions/3b8be48286e9_revision10.py", "file_name": "3b8be48286e9_revision10.py", "file_ext": "py", "file_size_in_byte": 1229, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 25, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op.drop_constraint", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 34, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 34, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}]}
{"seq_id": "40597408511", "text": "# Main equation solver of PYDYON\n\n\nimport numpy as np\nimport sys\nfrom scipy.integrate import solve_ivp\nfrom . IonHandler import IonHandler\nfrom . PlasmaVolume import PlasmaVolume\nfrom . SimulationResult import SimulationResult\nfrom . UnknownQuantityHandler import UnknownQuantityHandler\n\nfrom . Equations import *\n\n\nclass Simulation:\n    \n\n    unknowns = None\n    _terms = []\n    \n    # Default settings\n    settings = {\n        # Tokamak parameters\n        'a': 0.5,           # Minor radius (m)\n        'ta': None,         # Time for minor radius array (s)\n        'R': 3,             # Major radius (m)\n        'V_vessel': 100,    # Vessel volume (m^3)\n        'Bphi': 2.3,        # Toroidal magnetic field (T)\n        'Bv': 1e-3,         # Vertical magnetic field (T)\n        'l_MK2': 1,         # Distance between plasma centre and passive structure (m)\n\n        # Electrical parameters\n        'Vloop': 20,        # External loop voltage (V)\n        'Lp': 5.4e-6,       # Plasma inductance (H)\n        'LMK2': 9.1e-6,     # Inductance of passive structure (H)\n        'M': 2.49e-6,       # Mutual inductance of plasma and passive structure (H)\n        'RMK2': 7.5e-4      # Resistance of passive structure (Ohm)\n    }\n\n\n    def __init__(self, **settings):\n        \"\"\"\n        Constructor.\n\n        :param settings: Dictionary containing simulation parameters.\n        \"\"\"\n        self.ions = IonHandler()\n\n        s = self.updateSettings(self.settings, settings)\n\n\n    def addIon(self, name, Z, m=None):\n        \"\"\"\n        Add ions to the simulation.\n\n        :param name: Name of ion.\n        :param Z:    Atomic charge number.\n        :param m:    Atomic mass (if ``None``, calculated as Z*(m_p + m_n)).\n        \"\"\"\n        self.ions.addIon(name=name, Z=Z, m=m)\n\n\n    def _constructEquationSystem(self):\n        \"\"\"\n        Constructs the equation system.\n        \"\"\"\n        def i(name): return self.unknowns.map[name]\n        eqsys = [None] * len(self.unknowns)\n\n        s = self.settings\n\n        #######\n        # Power balance equations\n        tausettings = {'Bphi': s['Bphi'], 'Bv': s['Bv'], 'l_MK2': s['l_MK2']}\n        Poh    = OhmicPowerTerm(self.unknowns, self.ions)\n        Prad   = RadiatedPowerTerm(self.unknowns, self.ions)\n        Pequi  = EquilibrationPowerTerm(self.unknowns, self.ions)\n        Pconve = ElectronConvectivePowerTerm(self.unknowns, self.ions, **tausettings)\n        Pconvi = IonConvectivePowerTerm(self.unknowns, self.ions, **tausettings)\n        Pcx    = ChargeExchangePowerTerm(self.unknowns, self.ions)\n\n        # Ensure that terms are not deleted by the garbage collector\n        self._terms.extend((Poh, Prad, Pequi, Pconve, Pconvi, Pcx))\n\n        eqsys[i('We')] = lambda t, x : Poh(t, x) - Prad(t, x) - Pequi(t, x) - Pconve(t, x)\n        eqsys[i('Wi')] = lambda t, x : Pequi(t, x) - Pcx(t, x) - Pconvi(t, x)\n\n        #######\n        # Circuit equation\n        circuitsettings = {\n            'Vloop': s['Vloop'],\n            'Lp':    s['Lp'],\n            'LMK2':  s['LMK2'],\n            'M':     s['M'],\n            'RMK2':  s['RMK2']\n        }\n        circuit = CircuitEquation(self.unknowns, self.ions, **circuitsettings)\n        self._terms.append(circuit)\n\n        eqsys[i('Ip')]   = lambda t, x : circuit.dIp_dt(t, x)\n        eqsys[i('IMK2')] = lambda t, x : circuit.dIMK2_dt(t, x)\n\n        #######\n        # Ion equations\n        Dizcx0  = DeuteriumAtomBalance(self.unknowns, self.ions)\n        Dizcx1  = DeuteriumIonBalance(self.unknowns, self.ions)\n        Din     = DeuteriumInflux(self.unknowns, self.ions, simple=False, **tausettings)\n        Itransp = IonTransport(self.unknowns, self.ions, **tausettings)\n        Iizcx   = IonParticleBalance(self.unknowns, self.ions)\n        Iin     = IonInflux(self.unknowns, self.ions, **tausettings)\n\n        self._terms.extend((Dizcx0, Dizcx1, Din, Itransp, Iizcx, Iin))\n        mainIon = self.ions.getMainIonName()\n        for ion in self.ions:\n            A = ion['name']\n            Z = ion['Z']\n\n            if A == mainIon:\n                eqsys[i(f'ni{mainIon}_0')] = lambda t, x : Dizcx0(t, x) + Din(t, x)\n                eqsys[i(f'ni{mainIon}_1')] = lambda t, x : Dizcx1(t, x) - Itransp(t, x, mainIon, Z0=1)\n            else:\n                # Add neutral equations\n                for Z0 in range(0, Z+1):\n                    if Z0 == 0:\n                        eqsys[i(f'ni{A}_{Z0}')] = lambda t, x, A=A, Z0=Z0 : Iizcx(t, x, A, Z0) + Iin(t, x, A)\n                    else:\n                        eqsys[i(f'ni{A}_{Z0}')] = lambda t, x, A=A, Z0=Z0 : Iizcx(t, x, A, Z0) - Itransp(t, x, A, Z0=Z0)\n\n        atol = [0.0] * len(eqsys)\n\n        atol[i('Ip')] = 1e-1\n        atol[i('IMK2')] = 1\n\n        return eqsys, atol\n\n\n    def initialize(self, **values):\n        \"\"\"\n        Set the initial values of all quantities.\n        \"\"\"\n        if len(self.ions) == 0:\n            raise Exception(\"Ions must be added before calling 'initialize()'.\")\n\n        s = self.settings\n\n        pv = PlasmaVolume(a=s['a'], R=s['R'], V_vessel=s['V_vessel'], ions=self.ions, ta=s['ta'])\n        self.unknowns = UnknownQuantityHandler(self.ions, pv)\n        self.unknowns.setvector(values)\n\n\n    def solve(self, tMax):\n        \"\"\"\n        Solve the system of equations using the given settings.\n\n        :param tMax: Duration of simulation.\n        \"\"\"\n        equations, atol = self._constructEquationSystem()\n\n        def dydt(t, x):\n            print(f't = {t} s')\n            self.unknowns.update(t, x)\n            var = np.array([f(t,x) for f in equations])\n            return var.flatten()\n\n        #sol = solve_ivp(dydt, t_span=(0, tMax), y0=self.unknowns.x)\n        sol = solve_ivp(dydt, t_span=(0, tMax), y0=self.unknowns.x, method='Radau')\n        \n        return SimulationResult(sol.t, self.unknowns.getdict(x=sol.y), simulation=self)\n\n\n    def updateSettings(self, set1, set2):\n        \"\"\"\n        Update the settings defined in the dict 'set1' with values given\n        in the dict 'set2'. Entries which are only defined in 'set1' are\n        kept as is, while entries which only exist in 'set2' raise an\n        exception.\n        \"\"\"\n        for k, v in set2.items():\n            if k not in set1:\n                raise KeyError(f\"No setting '{k}' provided by PYDYON.\")\n            else:\n                set1[k] = v\n\n        return set1\n\n", "repo_name": "chalmersplasmatheory/STREAM", "sub_path": "py/PYDYON/Simulation.py", "file_name": "Simulation.py", "file_ext": "py", "file_size_in_byte": 6341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "46", "api": [{"api_name": "IonHandler.IonHandler", "line_number": 47, "usage_type": "call"}, {"api_name": "PlasmaVolume.PlasmaVolume", "line_number": 146, "usage_type": "call"}, {"api_name": "UnknownQuantityHandler.UnknownQuantityHandler", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "scipy.integrate.solve_ivp", "line_number": 166, "usage_type": "call"}, {"api_name": "SimulationResult.SimulationResult", "line_number": 168, "usage_type": "call"}]}
{"seq_id": "31010813347", "text": "from django.conf import settings as django_settings\nfrom django.contrib import admin, messages\nfrom django.contrib.auth import REDIRECT_FIELD_NAME, get_user_model\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.shortcuts import redirect, render, resolve_url\nfrom django.template.loader import render_to_string\nfrom django.urls import path, reverse\nfrom django.utils.decorators import method_decorator\nfrom django.utils.http import urlencode, url_has_allowed_host_and_scheme\nfrom django.utils.translation import gettext as _\nfrom django.views.decorators.cache import never_cache\n\nfrom .decorators import no_impersonation\nfrom .settings import app_settings\nfrom .signals import impersonation_started, impersonation_ended\n\n\nclass AdminSite(admin.AdminSite):\n    enable_nav_sidebar = False\n\n    @never_cache\n    @method_decorator(no_impersonation)\n    def login(self, request, extra_context = None):\n        \"\"\"\n        Overrides login view to redirect to the standard login view.\n\n        If the authenticated user is not authorised to view the admin then it will show a\n        permission denied page.\n        \"\"\"\n        # Work out where we will be redirecting users on successful login\n        if REDIRECT_FIELD_NAME in request.GET:\n            next_url = request.GET[REDIRECT_FIELD_NAME]\n        else:\n            next_url = reverse('admin:index', current_app = self.name)\n        # If the user is already authenticated, redirect them where they want to go\n        if request.method == 'GET' and self.has_permission(request):\n            return redirect(next_url)\n        # If the user is authenticated but doesn't have permission to access the admin, show an error\n        if request.user.is_authenticated and not self.has_permission(request):\n            context = {\n                **self.each_context(request),\n                'title': _('Permission denied'),\n                'app_path': request.get_full_path(),\n                'username': request.user.get_username(),\n            }\n            context.update(extra_context or {})\n            return render(request, 'admin/permission_denied.html', context)\n        # Redirect the user to the configured login url with the next URL as a query param\n        login_url = resolve_url(django_settings.LOGIN_URL)\n        params = urlencode({ REDIRECT_FIELD_NAME: next_url })\n        return redirect(f'{login_url}?{params}')\n\n    def get_urls(self):\n        \"\"\"\n        Override get_urls to register our additional urls.\n        \"\"\"\n        def wrap(view, cacheable = False):\n            wrapped = self.admin_view(view, cacheable)\n            wrapped.admin_site = self\n            return wrapped\n\n        impersonate_urls = [\n            path('impersonate/<path:pk>/', wrap(self.impersonate), name = 'impersonate'),\n            path('impersonate_end/', wrap(self.impersonate_end), name = 'impersonate_end'),\n        ]\n        return impersonate_urls + super().get_urls()\n\n    def admin_view(self, view, cacheable = False):\n        \"\"\"\n        Override the admin_view decorator to disable impersonation for admin views.\n\n        This doesn't currently work due to https://code.djangoproject.com/ticket/32477.\n        A PR has been submitted with a fix - https://github.com/django/django/pull/14038/.\n        For now, we rely on URL pattern exclusions in the middleware.\n        \"\"\"\n        return no_impersonation(super().admin_view(view, cacheable))\n\n    def redirect_to_referer(self, request):\n        \"\"\"\n        Redirects the user back to where they come from, unless it is unsafe.\n        \"\"\"\n        referer = request.META.get('HTTP_REFERER', '')\n        referer_is_safe = url_has_allowed_host_and_scheme(\n            url = referer,\n            allowed_hosts = { request.get_host() },\n            require_https = request.is_secure()\n        )\n        if referer_is_safe:\n            next_url = resolve_url(referer)\n        else:\n            next_url = reverse('admin:index', current_app = self.name)\n        return redirect(next_url)\n\n    @never_cache\n    def impersonate(self, request, pk, extra_context = None):\n        \"\"\"\n        View to start impersonating a user.\n        \"\"\"\n        # Try to find the user given by the pk\n        User = get_user_model()\n        try:\n            impersonatee = User.objects.get(pk = pk)\n        except ObjectDoesNotExist:\n            # If the user does not exist, set a message\n            messages.add_message(\n                request,\n                messages.WARNING,\n                'User with ID \"{}\" doesn\\'t exist.'.format(pk)\n            )\n        else:\n            # Next, test if the authenticated user is allowed to impersonate the given user\n            if app_settings.IMPERSONATE_IS_PERMITTED_USER(request.user, impersonatee):\n                # If the impersonation is allowed, set the session key\n                # Get the previous setting of the key first as we only want to fire the\n                # signal when the key changes\n                previous_pk = request.session.get(app_settings.IMPERSONATE_SESSION_KEY)\n                request.session[app_settings.IMPERSONATE_SESSION_KEY] = pk\n                # Dispatch the signal to indicate that an impersonation has started\n                if previous_pk != pk:\n                    messages.add_message(\n                        request,\n                        messages.SUCCESS,\n                        'Started impersonating user \"{}\".'.format(impersonatee)\n                    )\n                    impersonation_started.send(\n                        impersonatee.__class__,\n                        impersonator = request.user,\n                        impersonatee = impersonatee\n                    )\n            else:\n                # If the impersonation is not allowed, set a message\n                # Use a nicer message if the user is trying to impersonate themselves\n                if request.user.pk == impersonatee.pk:\n                    message = 'No need to impersonate yourself!'\n                    level = messages.WARNING\n                else:\n                    message = 'You are not allowed to impersonate user \"{}\".'.format(impersonatee)\n                    level = messages.ERROR\n                messages.add_message(request, level, message)\n        # Redirect the user back where they came from\n        return self.redirect_to_referer(request)\n\n    @never_cache\n    def impersonate_end(self, request, extra_context = None):\n        \"\"\"\n        View to end impersonating a user.\n        \"\"\"\n        # Remove the key from the session\n        if app_settings.IMPERSONATE_SESSION_KEY in request.session:\n            del request.session[app_settings.IMPERSONATE_SESSION_KEY]\n        # If there is an impersonation active on this request, dispatch the signal\n        if request.impersonatee:\n            messages.add_message(\n                request,\n                messages.SUCCESS,\n                'Stopped impersonating user \"{}\".'.format(request.impersonatee)\n            )\n            impersonation_ended.send(\n                request.user.__class__,\n                impersonator = request.user,\n                impersonatee = request.impersonatee\n            )\n        return self.redirect_to_referer(request)\n", "repo_name": "cedadev/jasmin-auth-django", "sub_path": "jasmin_auth/admin_site.py", "file_name": "admin_site.py", "file_ext": "py", "file_size_in_byte": 7219, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.contrib.admin.AdminSite", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 18, "usage_type": "name"}, {"api_name": "django.contrib.auth.REDIRECT_FIELD_NAME", "line_number": 31, "usage_type": "name"}, {"api_name": "django.contrib.auth.REDIRECT_FIELD_NAME", "line_number": 32, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 37, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.resolve_url", "line_number": 49, "usage_type": "call"}, {"api_name": "django.conf.settings.LOGIN_URL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 49, "usage_type": "name"}, {"api_name": "django.utils.http.urlencode", "line_number": 50, "usage_type": "call"}, {"api_name": "django.contrib.auth.REDIRECT_FIELD_NAME", "line_number": 50, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 21, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 22, "usage_type": "call"}, {"api_name": "decorators.no_impersonation", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 63, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 64, "usage_type": "call"}, {"api_name": "decorators.no_impersonation", "line_number": 76, "usage_type": "call"}, {"api_name": "django.utils.http.url_has_allowed_host_and_scheme", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.resolve_url", "line_number": 89, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 91, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 92, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 100, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 103, "usage_type": "name"}, {"api_name": "django.contrib.messages.add_message", "line_number": 105, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 105, "usage_type": "name"}, {"api_name": "django.contrib.messages.WARNING", "line_number": 107, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 107, "usage_type": "name"}, {"api_name": "settings.app_settings.IMPERSONATE_IS_PERMITTED_USER", "line_number": 112, "usage_type": "call"}, {"api_name": "settings.app_settings", "line_number": 112, "usage_type": "name"}, {"api_name": "settings.app_settings.IMPERSONATE_SESSION_KEY", "line_number": 116, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 116, "usage_type": "name"}, {"api_name": "settings.app_settings.IMPERSONATE_SESSION_KEY", "line_number": 117, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 117, "usage_type": "name"}, {"api_name": "django.contrib.messages.add_message", "line_number": 120, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 120, "usage_type": "name"}, {"api_name": "django.contrib.messages.SUCCESS", "line_number": 122, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 122, "usage_type": "name"}, {"api_name": "signals.impersonation_started.send", "line_number": 125, "usage_type": "call"}, {"api_name": "signals.impersonation_started", "line_number": 125, "usage_type": "name"}, {"api_name": "django.contrib.messages.WARNING", "line_number": 135, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 135, "usage_type": "name"}, {"api_name": "django.contrib.messages.ERROR", "line_number": 138, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 138, "usage_type": "name"}, {"api_name": "django.contrib.messages.add_message", "line_number": 139, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 139, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 94, "usage_type": "name"}, {"api_name": "settings.app_settings.IMPERSONATE_SESSION_KEY", "line_number": 149, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 149, "usage_type": "name"}, {"api_name": "settings.app_settings.IMPERSONATE_SESSION_KEY", "line_number": 150, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 150, "usage_type": "name"}, {"api_name": "django.contrib.messages.add_message", "line_number": 153, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 153, "usage_type": "name"}, {"api_name": "django.contrib.messages.SUCCESS", "line_number": 155, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 155, "usage_type": "name"}, {"api_name": "signals.impersonation_ended.send", "line_number": 158, "usage_type": "call"}, {"api_name": "signals.impersonation_ended", "line_number": 158, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.never_cache", "line_number": 143, "usage_type": "name"}]}
{"seq_id": "5559354287", "text": "import torch\nimport utils\nimport numpy as np\n\n\nclass BeamSearch(object):\n    def __init__(self, model, max_len: int, eos_idx: int, beam_size: int):\n        self.model = model\n        self.max_len = max_len\n        self.eos_idx = eos_idx\n        self.beam_size = beam_size\n        self.b_s = None\n        self.device = None\n        self.seq_mask = None\n        self.seq_logprob = None\n        self.outputs = None\n        self.log_probs = None\n        self.selected_words = None\n        self.all_log_probs = None\n\n    def _expand_state(self, selected_beam, cur_beam_size):\n        print(\"\\n\\n _expand_state\")\n\n        def fn(s):\n            shape = [int(sh) for sh in s.shape]\n            print(\"shape\", shape)\n            beam = selected_beam\n            print(\"beam\", beam)\n\n            for _ in shape[1:]:\n                beam = beam.unsqueeze(-1)\n            print(\"beam again\", beam)\n            print(\"self.beam_size\",self.beam_size)\n            print(\"self.b_s\",self.b_s)\n            print(\"cur_beam_size\",cur_beam_size)\n            print(\"shape[1:])\", shape[1:])\n            print(\"index 0\", [self.b_s, cur_beam_size] + shape[1:])\n            print(\"index0\", ([self.b_s, self.beam_size] + shape[1:]))\n            print(\"s.view(*([self.b_s, cur_beam_size] + shape[1:]))\", s.view(*([self.b_s, cur_beam_size] + shape[1:])))\n            print(\"other\", beam.expand(*([self.b_s, self.beam_size] + shape[1:])))\n\n            s = torch.gather(s.view(*([self.b_s, cur_beam_size] + shape[1:])), 1,\n                             beam.expand(*([self.b_s, self.beam_size] + shape[1:])).long())\n            print(\"this is s\", s)\n            print(\"this is s\", s.size())\n\n            s = s.view(*([-1, ] + shape[1:]))\n            print(\"this is s.size(\", s.size())\n\n            return s\n\n        return fn\n\n    def _expand_visual(self, visual: utils.TensorOrSequence, cur_beam_size: int, selected_beam: torch.Tensor):\n        if isinstance(visual, torch.Tensor):\n            visual_shape = visual.shape\n            visual_exp_shape = (self.b_s, cur_beam_size) + visual_shape[1:]\n            visual_red_shape = (self.b_s * self.beam_size,) + visual_shape[1:]\n            selected_beam_red_size = (self.b_s, self.beam_size) + tuple(1 for _ in range(len(visual_exp_shape) - 2))\n            selected_beam_exp_size = (self.b_s, self.beam_size) + visual_exp_shape[2:]\n            visual_exp = visual.view(visual_exp_shape)\n            selected_beam_exp = selected_beam.view(selected_beam_red_size).expand(selected_beam_exp_size)\n            visual = torch.gather(visual_exp, 1, selected_beam_exp).view(visual_red_shape)\n        else:\n            new_visual = []\n            for im in visual:\n                visual_shape = im.shape\n                visual_exp_shape = (self.b_s, cur_beam_size) + visual_shape[1:]\n                visual_red_shape = (self.b_s * self.beam_size,) + visual_shape[1:]\n                selected_beam_red_size = (self.b_s, self.beam_size) + tuple(1 for _ in range(len(visual_exp_shape) - 2))\n                selected_beam_exp_size = (self.b_s, self.beam_size) + visual_exp_shape[2:]\n                visual_exp = im.view(visual_exp_shape)\n                selected_beam_exp = selected_beam.view(selected_beam_red_size).expand(selected_beam_exp_size)\n                new_im = torch.gather(visual_exp, 1, selected_beam_exp).view(visual_red_shape)\n                new_visual.append(new_im)\n            visual = tuple(new_visual)\n        return visual\n\n    def apply(self, visual: utils.TensorOrSequence, out_size=1, return_probs=False, **kwargs):\n        print(\"\\n\\n function apply\")\n        self.b_s = utils.get_batch_size(visual)\n        print(\"self.b_s batch size\", self.b_s)\n        self.device = utils.get_device(visual)\n        self.seq_mask = torch.ones((self.b_s, self.beam_size, 1), device=self.device)\n        print(\"self.seq_mask\",  self.seq_mask)\n        self.seq_logprob = torch.zeros((self.b_s, 1, 1), device=self.device)\n        print(\"self.seq_logprob\", self.seq_logprob)\n        self.log_probs = []\n        self.selected_words = None\n        if return_probs:\n            self.all_log_probs = []\n\n        outputs = []\n        with self.model.statefulness(self.b_s):\n            print(\"entrei no statefulness\")\n            for t in range(self.max_len):\n                visual, outputs = self.iter(t, visual, outputs, return_probs, **kwargs)\n                print(\"visual\", visual)\n                print(\"outputs\", outputs)\n        # Sort result\n        seq_logprob, sort_idxs = torch.sort(self.seq_logprob, 1, descending=True)\n        print(\"seq_logprob\", seq_logprob)\n        print(\"sort_idxs\", sort_idxs)\n        outputs = torch.cat(outputs, -1)\n        outputs = torch.gather(outputs, 1, sort_idxs.expand(self.b_s, self.beam_size, self.max_len))\n        log_probs = torch.cat(self.log_probs, -1)\n        log_probs = torch.gather(log_probs, 1, sort_idxs.expand(self.b_s, self.beam_size, self.max_len))\n        if return_probs:\n            all_log_probs = torch.cat(self.all_log_probs, 2)\n            all_log_probs = torch.gather(all_log_probs, 1, sort_idxs.unsqueeze(-1).expand(self.b_s, self.beam_size,\n                                                                                          self.max_len,\n                                                                                          all_log_probs.shape[-1]))\n\n        outputs = outputs.contiguous()[:, :out_size]\n        log_probs = log_probs.contiguous()[:, :out_size]\n        if out_size == 1:\n            outputs = outputs.squeeze(1)\n            log_probs = log_probs.squeeze(1)\n\n        if return_probs:\n            return outputs, log_probs, all_log_probs\n        else:\n            return outputs, log_probs\n\n    def select(self, t, candidate_logprob, **kwargs):\n        print(\"\\n\\nfunction select\")\n        \n        selected_logprob, selected_idx = torch.sort(candidate_logprob.view(self.b_s, -1), -1, descending=True)\n        print(\"selected_logprob\", selected_logprob)\n        print(\"selected_idx\", selected_idx)\n        selected_logprob, selected_idx = selected_logprob[:, :self.beam_size], selected_idx[:, :self.beam_size]\n        print(\"selected_idx\", selected_idx)\n        print(\"selected_logprob\", selected_logprob)\n        return selected_idx, selected_logprob\n\n    def iter(self, t: int, visual: utils.TensorOrSequence, outputs, return_probs, **kwargs):\n        print(\"\\n\\nfunction iter\")\n        cur_beam_size = 1 if t == 0 else self.beam_size\n\n        word_logprob = self.model.step(t, self.selected_words, visual, None, mode='feedback', **kwargs)\n        print(\"\\nword_logprob\", word_logprob)\n        print(\"word_logprob extender\", word_logprob[0,:100])\n        print(\"\\nword_logprob size(\", word_logprob.size())\n\n        word_logprob = word_logprob.view(self.b_s, cur_beam_size, -1)\n        print(\"word_logprob after view\", word_logprob.size())\n        print(\"word_logprob after view\", word_logprob)\n\n        candidate_logprob = self.seq_logprob + word_logprob\n        print(\"candidate_logprob\", candidate_logprob)\n        # Mask sequence if it reaches EOS\n        if t > 0:\n            mask = (self.selected_words.view(self.b_s, cur_beam_size) != self.eos_idx).float().unsqueeze(-1)\n            print(\"mask\", mask)\n            self.seq_mask = self.seq_mask * mask\n            print(\"self.seq_mask\", self.seq_mask)\n            word_logprob = word_logprob * self.seq_mask.expand_as(word_logprob)\n            print(\"word_logprob\", word_logprob)\n            old_seq_logprob = self.seq_logprob.expand_as(candidate_logprob).contiguous()\n            print(\"old_seq_logprob\", old_seq_logprob)\n            old_seq_logprob[:, :, 1:] = -999\n            print(\"old_seq_logprob\", old_seq_logprob)\n            candidate_logprob = self.seq_mask * candidate_logprob + old_seq_logprob * (1 - self.seq_mask)\n            print(\"candidate_logprob\", candidate_logprob)\n        selected_idx, selected_logprob = self.select(t, candidate_logprob, **kwargs)\n        print(\"selected_idx\", selected_idx)\n        print(\"selected_logprob\", selected_logprob)\n        selected_beam = selected_idx / candidate_logprob.shape[-1]\n        selected_words = selected_idx - selected_beam * candidate_logprob.shape[-1]\n        print(\"selected_beam\", selected_beam)\n        print(\"selected_words\", selected_words)\n\n        self.model.apply_to_states(self._expand_state(selected_beam, cur_beam_size))\n        visual = self._expand_visual(visual, cur_beam_size, selected_beam)\n        print(\"this is visual\", visual)\n        print(\"this is visual size\", visual.size())\n        self.seq_logprob = selected_logprob.unsqueeze(-1)\n        print(\"seq log prog\", self.seq_logprob)\n        print(\"self.seq_mask before\", self.seq_mask)\n        self.seq_mask = torch.gather(self.seq_mask, 1, selected_beam.unsqueeze(-1))\n        print(\"self seq mask after gather\", self.seq_mask)\n        outputs = list(torch.gather(o, 1, selected_beam.unsqueeze(-1)) for o in outputs)\n        print(\"outputs lists\", outputs)\n        \n        outputs.append(selected_words.unsqueeze(-1))\n        print(\"outputs\", outputs)\n\n        if return_probs:\n            if t == 0:\n                self.all_log_probs.append(word_logprob.expand((self.b_s, self.beam_size, -1)).unsqueeze(2))\n            else:\n                self.all_log_probs.append(word_logprob.unsqueeze(2))\n\n        this_word_logprob = torch.gather(word_logprob, 1,\n                                         selected_beam.unsqueeze(-1).expand(self.b_s, self.beam_size,\n                                                                            word_logprob.shape[-1]))\n        print(\"this is word log prob\", this_word_logprob)\n        print(\"this is word log prob size\", this_word_logprob.size())\n\n        \n        this_word_logprob = torch.gather(this_word_logprob, 2, selected_words.unsqueeze(-1))\n        \n        print(\"this is word log prob aftter\", this_word_logprob)\n        print(\"this is word log prob aftter size\", this_word_logprob.size())\n\n        self.log_probs = list(\n            torch.gather(o, 1, selected_beam.unsqueeze(-1).expand(self.b_s, self.beam_size, 1)) for o in self.log_probs)\n        \n        print(\"self.log_probs\", self.log_prob)\n        print(\"self.log_probs\", self.log_prob.size())\n\n        self.log_probs.append(this_word_logprob)\n        \n        print(\"self.log_probs with word log prob\", self.log_prob)\n        print(\"self.log_probs with word log prob\", self.log_prob.size())\n\n        self.selected_words = selected_words.view(-1, 1)\n        print(\"selected words\",self.selected_words)\n        return visual, outputs\n", "repo_name": "RitaRamo/rerank", "sub_path": "src/meshed-memory-transformer/models/beam_search/beam_search.py", "file_name": "beam_search.py", "file_ext": "py", "file_size_in_byte": 10548, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "46", "api": [{"api_name": "torch.gather", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.TensorOrSequence", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.gather", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 74, "usage_type": "call"}, {"api_name": "utils.TensorOrSequence", "line_number": 79, "usage_type": "attribute"}, {"api_name": "utils.get_batch_size", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.get_device", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.sort", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.sort", "line_number": 128, "usage_type": "call"}, {"api_name": "utils.TensorOrSequence", "line_number": 136, "usage_type": "attribute"}, {"api_name": "torch.gather", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 207, "usage_type": "call"}]}
{"seq_id": "20841924855", "text": "import matplotlib.patches as patches\nimport matplotlib.pyplot as plt\n\nfrom SSDModel import SSDModel\nfrom Utils.DefaultBox import *\n\n\ndef ProposedMethod():\n    ssdmodel = SSDModel()\n    ssdmodel.LoadCaffeModel('../Models/SSD_300x300/VOC0712/deploy.prototxt', '../Models/SSD_300x300/VOC0712/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel')\n    TestModel(ssdmodel)\n\nimport io\nimport skimage.transform\nimport urllib\ndef prep_image(url):\n    ext = url.split('.')[-1]\n    im = plt.imread(io.BytesIO(urllib.urlopen(url).read()), ext)\n    # Resize so smallest dim = 256, preserving aspect ratio\n    if im.shape.__len__() == 3:\n        h, w, _ = im.shape\n    else:\n        if im.shape.__len__() == 4:\n            h, w, _, _ = im.shape\n        else:\n            if im.shape.__len__() == 2:\n                h, w = im.shape\n    if h < w:\n        im = skimage.transform.resize(im, (300, 300), preserve_range=True)\n    else:\n        im = skimage.transform.resize(im, (300, 300), preserve_range=True)\n\n    # Central crop to 224x224\n    if im.shape.__len__() == 3:\n        h, w, _ = im.shape\n    else:\n        if im.shape.__len__() == 4:\n            h, w, _, _ = im.shape\n        else:\n            if im.shape.__len__() == 2:\n                h, w = im.shape\n    return im\n\ndef TestModel(ssdmodel):\n    image_urls = [ '/media/badapple/Data/PROJECTS/Machine Learning/Dataset/VOC/VOC2012/Train/JPEGImages/2007_001960.jpg',\n                   '/media/badapple/Data/PROJECTS/Machine Learning/Dataset/VOC/VOC2007/Test/JPEGImages/003304.jpg',\n                   '/media/badapple/Data/PROJECTS/Machine Learning/Dataset/VOC/VOC2007/Test/JPEGImages/004932.jpg',\n                   '/media/badapple/Data/PROJECTS/Machine Learning/Dataset/VOC/VOC2007/Test/JPEGImages/004934.jpg',\n                   '/media/badapple/Data/PROJECTS/Machine Learning/Dataset/VOC/VOC2007/Test/JPEGImages/004835.jpg',\n                   '/media/badapple/Data/PROJECTS/Machine Learning/Dataset/VOC/VOC2007/Test/JPEGImages/004936.jpg',\n                   '/media/badapple/Data/PROJECTS/Machine Learning/Dataset/VOC/VOC2007/Test/JPEGImages/004937.jpg',\n                   '/media/badapple/Data/PROJECTS/Machine Learning/Dataset/VOC/VOC2007/Test/JPEGImages/004938.jpg',\n                   '/media/badapple/Data/PROJECTS/Machine Learning/Dataset/VOC/VOC2007/Test/JPEGImages/004939.jpg']\n\n    VGG_MEAN = numpy.asarray([103.939, 116.779, 123.68])\n    VGG_MEAN = numpy.reshape(VGG_MEAN, (1, 3, 1, 1))\n\n    defaultBox = DefaultBox(300,\n                            [[38, 38, 4],\n                             [19, 19, 6],\n                             [10, 10, 6],\n                             [5, 5, 6],\n                             [3, 3, 4],\n                             [1, 1, 4]],\n                            0.5)\n\n    for url in image_urls:\n        try:\n            im = prep_image(url)\n            if im.shape.__len__() != 3:\n                continue\n            rawIm = numpy.copy(im).astype('uint8')\n\n            # Convert RGB to BGR\n            im = im[:, :, [2, 1, 0]]\n            im = numpy.transpose(im, (2, 0, 1))\n            im = numpy.reshape(im, (1, 3, 300, 300))\n            im = numpy.asarray(im - VGG_MEAN, dtype=numpy.float32)\n\n            im2 = numpy.zeros((2, 3, 300, 300), dtype=numpy.float32)\n            im2[0, :, :, :] = im\n            im2[1, :, :, :] = im\n\n            output = ssdmodel.TestNetwork(im2)\n\n            bestBox = defaultBox.Bbox(output[0][1], output[1][1])\n\n            fig, ax = plt.subplots(1)\n            ax.imshow(rawIm)\n\n            for box in bestBox:\n                rect = patches.Rectangle((box[0] * 300, box[1] * 300), (box[2] - box[0]) * 300, (box[3] - box[1]) * 300, linewidth=1, edgecolor='r', facecolor='none')\n                # Add the patch to the Axes\n                ax.add_patch(rect)\n\n            plt.show()\n            plt.axis('off')\n\n        except IOError:\n            print('bad url: ' + url)\n            continue\n\n\nif __name__ == '__main__':\n    ProposedMethod()\n", "repo_name": "CaoDuyThanh/Object_Tracking", "sub_path": "Src/MainSSD.py", "file_name": "MainSSD.py", "file_ext": "py", "file_size_in_byte": 3969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "SSDModel.SSDModel", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 18, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 18, "usage_type": "call"}, {"api_name": "skimage.transform.transform.resize", "line_number": 29, "usage_type": "call"}, {"api_name": "skimage.transform.transform", "line_number": 29, "usage_type": "attribute"}, {"api_name": "skimage.transform", "line_number": 29, "usage_type": "name"}, {"api_name": "skimage.transform.transform.resize", "line_number": 31, "usage_type": "call"}, {"api_name": "skimage.transform.transform", "line_number": 31, "usage_type": "attribute"}, {"api_name": "skimage.transform", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}]}
{"seq_id": "74220205899", "text": "import cv2\n\nfaceCascade = cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")\n\nimg = cv2.imread(\"images/car-man.png\")\n\ngray_img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\nfaces = faceCascade.detectMultiScale(gray_img, scaleFactor=1.2, minNeighbors=5, minSize=(50, 50))\n\nfor (x,y,w,h) in faces:\n    cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0),2)\n\ncv2.imshow(\"identified Faces\", img)\ncv2.waitKey(0) #processed image will close as soon as the program is run. Wait key will add a delay to prevent this\ncv2.destroyAllWindows() # close all existing windows\n\n\n\n\n", "repo_name": "DelwinMathewJoseph/facial_identification", "sub_path": "identify.py", "file_name": "identify.py", "file_ext": "py", "file_size_in_byte": 564, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "9715753903", "text": "from __future__ import absolute_import, with_statement\n\n__all__ = ['ProjectHandler', 'TaskStatusHandler', 'MilestoneHandler',\n           'TaskHandler', 'StartTaskTimeHandler', 'StopTaskTimeHandler',\n           'TaskTimeHandler']\n\nfrom datetime import datetime\nfrom treeio.core.api.utils import rc\nfrom piston3.handler import BaseHandler\nfrom treeio.core.api.handlers import ObjectHandler\nfrom treeio.projects.models import Task, Project, Milestone, TaskStatus, TaskTimeSlot\nfrom treeio.projects.forms import ProjectForm, TaskForm, MilestoneForm, TaskStatusForm, TaskTimeSlotForm\n\n\ndef check_parent_perm(request, model, pk, mode):\n    try:\n        parent_obj = model.objects.get(pk=pk)\n        return request.user.profile.has_permission(parent_obj, mode=mode)\n    except model.DoesNotExist:\n        pass\n    return True\n\n\nclass ProjectHandler(ObjectHandler):\n    \"Entrypoint for Project model.\"\n\n    model = Project\n    form = ProjectForm\n\n    def flatten_dict(self, dct):\n        dct = super(ProjectHandler, self).flatten_dict(dct)\n        dct['project_id'] = None\n        return dct\n\n    def check_create_permission(self, request, mode):\n        if 'parent' in request.data:\n            return check_parent_perm(request, Project, request.data['parent'], mode)\n        return True\n\n    @classmethod\n    def resource_uri(cls, obj=None):\n        object_id = \"id\"\n        if obj is not None:\n            object_id = obj.id\n        return ('api_projects', [object_id])\n\n\nclass TaskStatusHandler(ObjectHandler):\n    \"Entrypoint for TaskStatus model.\"\n\n    model = TaskStatus\n    form = TaskStatusForm\n\n    def check_create_permission(self, request, mode):\n        return request.user.profile.is_admin('treeio.projects')\n\n    @classmethod\n    def resource_uri(cls, obj=None):\n        object_id = \"id\"\n        if obj is not None:\n            object_id = obj.id\n        return ('api_projects_status', [object_id])\n\n\nclass MilestoneHandler(ObjectHandler):\n    \"Entrypoint for Milestone model.\"\n\n    model = Milestone\n    form = MilestoneForm\n\n    def flatten_dict(self, dct):\n        dct = super(MilestoneHandler, self).flatten_dict(dct)\n        dct['project_id'] = None\n        return dct\n\n    def check_create_permission(self, request, mode):\n        if 'project' in request.data:\n            return check_parent_perm(request, Project, request.data['project'], mode)\n        return True\n\n    @classmethod\n    def resource_uri(cls, obj=None):\n        object_id = \"id\"\n        if obj is not None:\n            object_id = obj.id\n        return ('api_projects_milestones', [object_id])\n\n\nclass TaskHandler(ObjectHandler):\n    \"Entrypoint for Task model.\"\n\n    model = Task\n    form = TaskForm\n\n    def flatten_dict(self, dct):\n        dct = super(TaskHandler, self).flatten_dict(dct)\n        dct['parent'] = None\n        dct['project_id'] = None\n        dct['milestone_id'] = None\n        return dct\n\n    def check_create_permission(self, request, mode):\n        if 'project' in request.data and not check_parent_perm(request, Project, request.data['project'], mode):\n            return False\n        if 'milestone' in request.data and not check_parent_perm(request, Milestone, request.data['milestone'], mode):\n            return False\n        if 'parent' in request.data and not check_parent_perm(request, Task, request.data['parent'], mode):\n            return False\n        else:\n            request.data.setdefault('parent', None)\n        return True\n\n    @classmethod\n    def resource_uri(cls, obj=None):\n        object_id = \"id\"\n        if obj is not None:\n            object_id = obj.id\n        return ('api_projects_tasks', [object_id])\n\n\nclass StartTaskTimeHandler(BaseHandler):\n    \"Start TaskTimeSlot for preselected Task\"\n\n    # model = True  # for auto documentation\n    allowed_methods = ('GET',)\n\n    def read(self, request, task_id, *args, **kwargs):\n        try:\n            task = Task.objects.get(pk=task_id)\n        except Task.DoesNotExist:\n            return rc.NOT_FOUND\n\n        if not request.user.profile.has_permission(task, mode='x'):\n            return rc.FORBIDDEN\n\n        if not task.is_being_done_by(request.user.profile):\n            task_time_slot = TaskTimeSlot(\n                task=task, time_from=datetime.now(), user=request.user.profile)\n            task_time_slot.save()\n            task_time_slot.set_user_from_request(request)\n            return task_time_slot\n        return (\"The task isn't in progress\", 401)\n\n    @classmethod\n    def resource_uri(cls, obj=None):\n        object_id = \"task_id\"\n        if obj is not None:\n            object_id = obj.id\n        return ('api_projects_tasktime_start', [object_id])\n\n\nclass StopTaskTimeHandler(BaseHandler):\n    \"Stop TaskTimeSlot for preselected Task\"\n\n    # model = True  # for auto documentation\n    allowed_methods = ('POST',)\n\n    def create(self, request, slot_id, *args, **kwargs):\n        try:\n            slot = TaskTimeSlot.objects.get(pk=slot_id)\n        except Task.DoesNotExist:\n            return rc.NOT_FOUND\n\n        if not request.user.profile.has_permission(slot, mode='x'):\n            return rc.FORBIDDEN\n\n        slot.time_to = datetime.now()\n        slot.details = request.data.get('details', '')\n        slot.save()\n\n        return rc.ALL_OK\n\n    @classmethod\n    def resource_uri(cls, obj=None):\n        object_id = \"slot_id\"\n        if obj is not None:\n            object_id = obj.id\n        return ('api_projects_tasktime_stop', [object_id])\n\n\nclass TaskTimeHandler(ObjectHandler):\n    \"Entrypoint for TaskTime model.\"\n\n    model = TaskTimeSlot\n    form = TaskTimeSlotForm\n\n    def flatten_dict(self, dct):\n        dct = super(TaskTimeHandler, self).flatten_dict(dct)\n        dct['task_id'] = None\n        return dct\n\n    def create_instance(self, request, *args, **kwargs):\n        return TaskTimeSlot(task=request.task, time_to=datetime.now(), user=request.user.profile)\n\n    def check_create_permission(self, request, mode):\n        if \"task\" in request.data:\n            try:\n                parent_obj = Task.objects.get(pk=request.data[\"task\"])\n                request.task = parent_obj\n                return request.user.profile.has_permission(parent_obj, mode=mode)\n            except Task.DoesNotExist:\n                pass\n        return False\n\n    def check_instance_permission(self, request, task_time_slot, mode):\n        if not request.user.profile.has_permission(task_time_slot, mode=mode) \\\n                and not request.user.profile.has_permission(task_time_slot.task, mode=mode):\n            return False\n        return True\n\n    @classmethod\n    def resource_uri(cls, obj=None):\n        object_id = \"id\"\n        if obj is not None:\n            object_id = obj.id\n        return ('api_projects_tasktimes', [object_id])\n", "repo_name": "treeio/treeio", "sub_path": "treeio/projects/api/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 6750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1500, "dataset": "github-code", "pt": "46", "api": [{"api_name": "treeio.core.api.handlers.ObjectHandler", "line_number": 24, "usage_type": "name"}, {"api_name": "treeio.projects.models.Project", "line_number": 27, "usage_type": "name"}, {"api_name": "treeio.projects.forms.ProjectForm", "line_number": 28, "usage_type": "name"}, {"api_name": "treeio.projects.models.Project", "line_number": 37, "usage_type": "argument"}, {"api_name": "treeio.core.api.handlers.ObjectHandler", "line_number": 48, "usage_type": "name"}, {"api_name": "treeio.projects.models.TaskStatus", "line_number": 51, "usage_type": "name"}, {"api_name": "treeio.projects.forms.TaskStatusForm", "line_number": 52, "usage_type": "name"}, {"api_name": "treeio.core.api.handlers.ObjectHandler", "line_number": 65, "usage_type": "name"}, {"api_name": "treeio.projects.models.Milestone", "line_number": 68, "usage_type": "name"}, {"api_name": "treeio.projects.forms.MilestoneForm", "line_number": 69, "usage_type": "name"}, {"api_name": "treeio.projects.models.Project", "line_number": 78, "usage_type": "argument"}, {"api_name": "treeio.core.api.handlers.ObjectHandler", "line_number": 89, "usage_type": "name"}, {"api_name": "treeio.projects.models.Task", "line_number": 92, "usage_type": "name"}, {"api_name": "treeio.projects.forms.TaskForm", "line_number": 93, "usage_type": "name"}, {"api_name": "treeio.projects.models.Project", "line_number": 103, "usage_type": "argument"}, {"api_name": "treeio.projects.models.Milestone", "line_number": 105, "usage_type": "argument"}, {"api_name": "treeio.projects.models.Task", "line_number": 107, "usage_type": "argument"}, {"api_name": "piston3.handler.BaseHandler", "line_number": 121, "usage_type": "name"}, {"api_name": "treeio.projects.models.Task.objects.get", "line_number": 129, "usage_type": "call"}, {"api_name": "treeio.projects.models.Task.objects", "line_number": 129, "usage_type": "attribute"}, {"api_name": "treeio.projects.models.Task", "line_number": 129, "usage_type": "name"}, {"api_name": "treeio.projects.models.Task.DoesNotExist", "line_number": 130, "usage_type": "attribute"}, {"api_name": "treeio.projects.models.Task", "line_number": 130, "usage_type": "name"}, {"api_name": "treeio.core.api.utils.rc.NOT_FOUND", "line_number": 131, "usage_type": "attribute"}, {"api_name": "treeio.core.api.utils.rc", "line_number": 131, "usage_type": "name"}, {"api_name": "treeio.core.api.utils.rc.FORBIDDEN", "line_number": 134, "usage_type": "attribute"}, {"api_name": "treeio.core.api.utils.rc", "line_number": 134, "usage_type": "name"}, {"api_name": "treeio.projects.models.TaskTimeSlot", "line_number": 137, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 138, "usage_type": "name"}, {"api_name": "piston3.handler.BaseHandler", "line_number": 152, "usage_type": "name"}, {"api_name": "treeio.projects.models.TaskTimeSlot.objects.get", "line_number": 160, "usage_type": "call"}, {"api_name": "treeio.projects.models.TaskTimeSlot.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "treeio.projects.models.TaskTimeSlot", "line_number": 160, "usage_type": "name"}, {"api_name": "treeio.projects.models.Task.DoesNotExist", "line_number": 161, "usage_type": "attribute"}, {"api_name": "treeio.projects.models.Task", "line_number": 161, "usage_type": "name"}, {"api_name": "treeio.core.api.utils.rc.NOT_FOUND", "line_number": 162, "usage_type": "attribute"}, {"api_name": "treeio.core.api.utils.rc", "line_number": 162, "usage_type": "name"}, {"api_name": "treeio.core.api.utils.rc.FORBIDDEN", "line_number": 165, "usage_type": "attribute"}, {"api_name": "treeio.core.api.utils.rc", "line_number": 165, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 167, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 167, "usage_type": "name"}, {"api_name": "treeio.core.api.utils.rc.ALL_OK", "line_number": 171, "usage_type": "attribute"}, {"api_name": "treeio.core.api.utils.rc", "line_number": 171, "usage_type": "name"}, {"api_name": "treeio.core.api.handlers.ObjectHandler", "line_number": 181, "usage_type": "name"}, {"api_name": "treeio.projects.models.TaskTimeSlot", "line_number": 184, "usage_type": "name"}, {"api_name": "treeio.projects.forms.TaskTimeSlotForm", "line_number": 185, "usage_type": "name"}, {"api_name": "treeio.projects.models.TaskTimeSlot", "line_number": 193, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 193, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 193, "usage_type": "name"}, {"api_name": "treeio.projects.models.Task.objects.get", "line_number": 198, "usage_type": "call"}, {"api_name": "treeio.projects.models.Task.objects", "line_number": 198, "usage_type": "attribute"}, {"api_name": "treeio.projects.models.Task", "line_number": 198, "usage_type": "name"}, {"api_name": "treeio.projects.models.Task.DoesNotExist", "line_number": 201, "usage_type": "attribute"}, {"api_name": "treeio.projects.models.Task", "line_number": 201, "usage_type": "name"}]}
{"seq_id": "34996473146", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth import logout\n# from django.db import IntegrityError\nfrom .forms import TaskForm\nfrom .models import Task\nfrom django.utils import timezone\nfrom django.contrib.auth.decorators import login_required\n\n# Create your views here.\n\n\n@login_required\ndef tasks(request):\n    tasks = Task.objects.filter(user=request.user, datecompleted__isnull=True)\n    return render(request, \"tasks.html\", {\n        \"tasks\": tasks,\n        \"condition\": \"not completed\"\n    })\n\n@login_required\ndef tasks_completed(request):\n    tasks = Task.objects.filter(\n        user=request.user, datecompleted__isnull=False).order_by('-datecompleted')\n    return render(request, \"tasks.html\", {\n        \"tasks\": tasks,\n        \"condition\": \"completed\"\n    })\n\n@login_required\ndef create_task(request):\n    if request.method == \"GET\":\n        return render(request, \"create_task.html\", {\n            \"form\": TaskForm\n        })\n    else:\n        try:\n            form = TaskForm(request.POST)\n            new_task = form.save(commit=False)\n            new_task.user = request.user\n            new_task.save()\n            return redirect(\"tasks\")\n        except ValueError:\n            return render(request, \"create_task.html\", {\n                \"form\": TaskForm,\n                \"error\": \"Por favor proporcione datos válidos.\"\n            })\n\n@login_required\ndef task_detail(request, task_id):\n    if request.method == \"GET\":\n        task = get_object_or_404(Task, pk=task_id, user=request.user)\n        form = TaskForm(instance=task)\n        return render(request, \"task_detail.html\", {\n            \"task\": task,\n            \"form\": form,\n        })\n    else:\n        try:\n            task = get_object_or_404(Task, pk=task_id, user=request.user)\n            form = TaskForm(request.POST, instance=task)\n            form.save()\n            return redirect(\"tasks\")\n        except ValueError:\n            return render(request, \"task_detail.html\", {\n                \"task\": task,\n                \"form\": form,\n                \"error\": \"Error al actualizar la\"\n            })\n\n@login_required\ndef complete_task(request, task_id):\n    task = get_object_or_404(Task, pk=task_id, user=request.user)\n    if request.method == \"POST\":\n        task.datecompleted = timezone.now()\n        task.save()\n        return redirect(\"tasks\")\n\n@login_required\ndef delete_task(request, task_id):\n    task = get_object_or_404(Task, pk=task_id, user=request.user)\n    if request.method == \"POST\":\n        task.delete()\n        return redirect(\"tasks\")\n\n@login_required\ndef signout(request):\n    logout(request)\n    return redirect(\"home\")\n", "repo_name": "secchi7/OrganizadorTareas", "sub_path": "tasks/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "models.Task.objects.filter", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Task.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.Task", "line_number": 14, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 12, "usage_type": "name"}, {"api_name": "models.Task.objects.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "models.Task.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.Task", "line_number": 22, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "forms.TaskForm", "line_number": 33, "usage_type": "name"}, {"api_name": "forms.TaskForm", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 41, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "forms.TaskForm", "line_number": 44, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 29, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Task", "line_number": 51, "usage_type": "argument"}, {"api_name": "forms.TaskForm", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Task", "line_number": 59, "usage_type": "argument"}, {"api_name": "forms.TaskForm", "line_number": 60, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 64, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 72, "usage_type": "call"}, {"api_name": "models.Task", "line_number": 72, "usage_type": "argument"}, {"api_name": "django.utils.timezone.now", "line_number": 74, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 74, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 76, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 70, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 80, "usage_type": "call"}, {"api_name": "models.Task", "line_number": 80, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 83, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 78, "usage_type": "name"}, {"api_name": "django.contrib.auth.logout", "line_number": 87, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 85, "usage_type": "name"}]}
{"seq_id": "14433526796", "text": "import itertools as it\n\nimport numpy as np\n\nfrom ..utilities import normalize_ids\n\n\ndef test_get_atomic_positions(case_data, get_atom_ids):\n    \"\"\"\n    Test :meth:`.Molecule.get_atomic_positions`.\n\n    Parameters\n    ----------\n    case_data : :class:`.CaseData`.\n        A test case. Holds the molecule to test and the correct\n        atomic positions.\n\n    get_atom_ids : :class:`callable`\n        Takes a single parameter, `molecule` and returns a valid\n        `atom_ids` parameter for\n        :meth:`.Molecule.get_atomic_positions`. This allows the testing\n        of different values of this parameter.\n\n    Returns\n    -------\n    None : :class:`NoneType`\n\n    \"\"\"\n\n    _test_get_atomic_positions(\n        molecule=case_data.molecule,\n        position_matrix=case_data.position_matrix,\n        get_atom_ids=get_atom_ids,\n    )\n\n\ndef _test_get_atomic_positions(\n    molecule,\n    position_matrix,\n    get_atom_ids,\n):\n    \"\"\"\n    Test :meth:`.Molecule.get_atomic_positions`.\n\n    Parameters\n    ----------\n    molecule : :class:`.Molecule`\n        The molecule to test.\n\n    position_matrix : :class:`numpy.ndarray`\n        The correct atomic positions.\n\n    get_atom_ids : :class:`callable`\n        Takes a single parameter, `molecule` and returns a valid\n        `atom_ids` parameter for\n        :meth:`.Molecule.get_atomic_positions`. This allows the testing\n        of different values of this parameter.\n\n    Returns\n    -------\n    None : :class:`NoneType`\n\n    \"\"\"\n\n    for atom_id, position in it.zip_longest(\n        normalize_ids(molecule, get_atom_ids(molecule)),\n        molecule.get_atomic_positions(get_atom_ids(molecule)),\n    ):\n        assert np.allclose(\n            a=position,\n            b=position_matrix[atom_id],\n            atol=1e-6,\n        )\n", "repo_name": "lukasturcani/stk", "sub_path": "tests/molecular/molecules/molecule/test_get_atomic_positions.py", "file_name": "test_get_atomic_positions.py", "file_ext": "py", "file_size_in_byte": 1776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 212, "dataset": "github-code", "pt": "40", "api": [{"api_name": "itertools.zip_longest", "line_number": 65, "usage_type": "call"}, {"api_name": "utilities.normalize_ids", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 69, "usage_type": "call"}]}
{"seq_id": "69848435641", "text": "from __future__ import annotations\n\nimport pickle\nfrom pathlib import Path\nfrom typing import List\n\nimport cachetools\nimport numpy as np\nimport pandas as pd\nimport qlib\nfrom qlib.constant import REG_CN\nfrom qlib.contrib.ops.high_freq import BFillNan, Cut, Date, DayCumsum, DayLast, FFillNan, IsInf, IsNull, Select\nfrom qlib.data.dataset import DatasetH\n\ndataset = None\n\n\nclass DataWrapper:\n    def __init__(\n        self,\n        feature_dataset: DatasetH,\n        backtest_dataset: DatasetH,\n        columns_today: List[str],\n        columns_yesterday: List[str],\n        _internal: bool = False,\n    ):\n        assert _internal, \"Init function of data wrapper is for internal use only.\"\n\n        self.feature_dataset = feature_dataset\n        self.backtest_dataset = backtest_dataset\n        self.columns_today = columns_today\n        self.columns_yesterday = columns_yesterday\n\n    @cachetools.cached(  # type: ignore\n        cache=cachetools.LRUCache(100),\n        key=lambda _, stock_id, date, backtest: (stock_id, date.replace(hour=0, minute=0, second=0), backtest),\n    )\n    def get(self, stock_id: str, date: pd.Timestamp, backtest: bool = False) -> pd.DataFrame:\n        start_time, end_time = date.replace(hour=0, minute=0, second=0), date.replace(hour=23, minute=59, second=59)\n        dataset = self.backtest_dataset if backtest else self.feature_dataset\n        return dataset.handler.fetch(pd.IndexSlice[stock_id, start_time:end_time], level=None)\n\n\ndef init_qlib(qlib_config: dict, part: str = None) -> None:\n    \"\"\"Initialize necessary resource to launch the workflow, including data direction, feature columns, etc..\n\n    Parameters\n    ----------\n    qlib_config:\n        Qlib configuration.\n\n        Example::\n\n            {\n                \"provider_uri_day\": DATA_ROOT_DIR / \"qlib_1d\",\n                \"provider_uri_1min\": DATA_ROOT_DIR / \"qlib_1min\",\n                \"feature_root_dir\": DATA_ROOT_DIR / \"qlib_handler_stock\",\n                \"feature_columns_today\": [\n                    \"$open\", \"$high\", \"$low\", \"$close\", \"$vwap\", \"$bid\", \"$ask\", \"$volume\",\n                    \"$bidV\", \"$bidV1\", \"$bidV3\", \"$bidV5\", \"$askV\", \"$askV1\", \"$askV3\", \"$askV5\",\n                ],\n                \"feature_columns_yesterday\": [\n                    \"$open_1\", \"$high_1\", \"$low_1\", \"$close_1\", \"$vwap_1\", \"$bid_1\", \"$ask_1\", \"$volume_1\",\n                    \"$bidV_1\", \"$bidV1_1\", \"$bidV3_1\", \"$bidV5_1\", \"$askV_1\", \"$askV1_1\", \"$askV3_1\", \"$askV5_1\",\n                ],\n            }\n    part\n        Identifying which part (stock / date) to load.\n    \"\"\"\n\n    global dataset  # pylint: disable=W0603\n\n    def _convert_to_path(path: str | Path) -> Path:\n        return path if isinstance(path, Path) else Path(path)\n\n    provider_uri_map = {}\n    if \"provider_uri_day\" in qlib_config:\n        provider_uri_map[\"day\"] = _convert_to_path(qlib_config[\"provider_uri_day\"]).as_posix()\n    if \"provider_uri_1min\" in qlib_config:\n        provider_uri_map[\"1min\"] = _convert_to_path(qlib_config[\"provider_uri_1min\"]).as_posix()\n\n    qlib.init(\n        region=REG_CN,\n        auto_mount=False,\n        custom_ops=[DayLast, FFillNan, BFillNan, Date, Select, IsNull, IsInf, Cut, DayCumsum],\n        expression_cache=None,\n        calendar_provider={\n            \"class\": \"LocalCalendarProvider\",\n            \"module_path\": \"qlib.data.data\",\n            \"kwargs\": {\n                \"backend\": {\n                    \"class\": \"FileCalendarStorage\",\n                    \"module_path\": \"qlib.data.storage.file_storage\",\n                    \"kwargs\": {\"provider_uri_map\": provider_uri_map},\n                },\n            },\n        },\n        feature_provider={\n            \"class\": \"LocalFeatureProvider\",\n            \"module_path\": \"qlib.data.data\",\n            \"kwargs\": {\n                \"backend\": {\n                    \"class\": \"FileFeatureStorage\",\n                    \"module_path\": \"qlib.data.storage.file_storage\",\n                    \"kwargs\": {\"provider_uri_map\": provider_uri_map},\n                },\n            },\n        },\n        provider_uri=provider_uri_map,\n        kernels=1,\n        redis_port=-1,\n        clear_mem_cache=False,  # init_qlib will be called for multiple times. Keep the cache for improving performance\n    )\n\n    if part == \"skip\":\n        return\n\n    # this won't work if it's put outside in case of multiprocessing\n    from qlib.data import D  # noqa pylint: disable=C0415,W0611\n\n    if part is None:\n        feature_path = Path(qlib_config[\"feature_root_dir\"]) / \"feature.pkl\"\n        backtest_path = Path(qlib_config[\"feature_root_dir\"]) / \"backtest.pkl\"\n    else:\n        feature_path = Path(qlib_config[\"feature_root_dir\"]) / \"feature\" / (part + \".pkl\")\n        backtest_path = Path(qlib_config[\"feature_root_dir\"]) / \"backtest\" / (part + \".pkl\")\n\n    with feature_path.open(\"rb\") as f:\n        feature_dataset = pickle.load(f)\n    with backtest_path.open(\"rb\") as f:\n        backtest_dataset = pickle.load(f)\n\n    dataset = DataWrapper(\n        feature_dataset,\n        backtest_dataset,\n        qlib_config[\"feature_columns_today\"],\n        qlib_config[\"feature_columns_yesterday\"],\n        _internal=True,\n    )\n\n\ndef fetch_features(stock_id: str, date: pd.Timestamp, yesterday: bool = False, backtest: bool = False) -> pd.DataFrame:\n    assert dataset is not None, \"You must call init_qlib() before doing this.\"\n\n    if backtest:\n        fields = [\"$close\", \"$volume\"]\n    else:\n        fields = dataset.columns_yesterday if yesterday else dataset.columns_today\n\n    data = dataset.get(stock_id, date, backtest)\n    if data is None or len(data) == 0:\n        # create a fake index, but RL doesn't care about index\n        data = pd.DataFrame(0.0, index=np.arange(240), columns=fields, dtype=np.float32)  # FIXME: hardcode here\n    else:\n        data = data.rename(columns={c: c.rstrip(\"0\") for c in data.columns})\n        data = data[fields]\n    return data\n", "repo_name": "hausuresh/qlib_playground", "sub_path": "qlib/rl/data/integration.py", "file_name": "integration.py", "file_ext": "py", "file_size_in_byte": 5909, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "qlib.data.dataset.DatasetH", "line_number": 21, "usage_type": "name"}, {"api_name": "qlib.data.dataset.DatasetH", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 24, "usage_type": "name"}, {"api_name": "pandas.Timestamp", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pandas.IndexSlice", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cachetools.cached", "line_number": 34, "usage_type": "call"}, {"api_name": "cachetools.LRUCache", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 73, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 74, "usage_type": "argument"}, {"api_name": "qlib.init", "line_number": 82, "usage_type": "call"}, {"api_name": "qlib.constant.REG_CN", "line_number": 83, "usage_type": "name"}, {"api_name": "qlib.contrib.ops.high_freq.DayLast", "line_number": 85, "usage_type": "name"}, {"api_name": "qlib.contrib.ops.high_freq.FFillNan", "line_number": 85, "usage_type": "name"}, {"api_name": "qlib.contrib.ops.high_freq.BFillNan", "line_number": 85, "usage_type": "name"}, {"api_name": "qlib.contrib.ops.high_freq.Date", "line_number": 85, "usage_type": "name"}, {"api_name": "qlib.contrib.ops.high_freq.Select", "line_number": 85, "usage_type": "name"}, {"api_name": "qlib.contrib.ops.high_freq.IsNull", "line_number": 85, "usage_type": "name"}, {"api_name": "qlib.contrib.ops.high_freq.IsInf", "line_number": 85, "usage_type": "name"}, {"api_name": "qlib.contrib.ops.high_freq.Cut", "line_number": 85, "usage_type": "name"}, {"api_name": "qlib.contrib.ops.high_freq.DayCumsum", "line_number": 85, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 122, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 123, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 125, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 126, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 129, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 142, "usage_type": "attribute"}]}
{"seq_id": "22015735866", "text": "## Calculate SPEC B field (and related quantities) purely in python\n## attempt to remove pyoculus dependency (easier installation and more transparent calculation)\n## TESTED against existing pyoculus-based implementation\n## not tested for nonstell-symmetric cases\n\nimport numpy as np\nfrom numba import njit\n\n## parameters ---------------------------------------------------------\n\n## numba signficantly speeds up calculations, can be installed with pip install ... numba\nrun_with_numba = True\n\n##---------------------------------------------------------------------\n\nif(run_with_numba):\n    run_decorator = njit(fastmath=True, cache=True, parallel=False)\nelse:\n    run_decorator = lambda x: x\n\ndef get_field_mod(self, lvol, sarr, tarr, zarr, Bcontrav=None, g=None):\n\n    if(Bcontrav is None):\n        Bcontrav = self.get_field_contrav(lvol, sarr, tarr, zarr)\n\n    if(g is None):\n        g = self.get_metric(sarr, tarr, zarr, lvol)\n\n    Bmod = np.sqrt(np.einsum(\"i...,ij...,j...->...\", Bcontrav, g, Bcontrav, optimize=True))\n\n    return Bmod\n\n\ndef get_field_cov(self, lvol, sarr, tarr, zarr):\n\n    Bcontrav = get_field_contrav(self, lvol, sarr, tarr, zarr)\n    metric = self.get_metric(sarr, tarr, zarr, lvol)\n\n    Bcov = np.einsum('i...,ij...->j...', Bcontrav, metric, optimize=True)\n\n    return Bcov\n\ndef get_field_cart(self, lvol, sarr, tarr, zarr):\n    \"\"\"Generates the cartesian magnetic field in (R, Z, phi) coordinates)\"\"\"\n    \n    # geometry = self.input.physics.Igeometry\n    # rpol = self.input.physics.rpol\n    # rtor = self.input.physics.rtor\n    # Rac, Rbc = self.output.Rbc[lvol : lvol + 2]\n    # Zas, Zbs = self.output.Zbs[lvol : lvol + 2]\n    # # Ras, Rbs = self.output.Rbs[lvol : lvol + 2]\n    # # Zac, Zbc = self.output.Zbc[lvol : lvol + 2]\n    # im = np.array(self.output.im, dtype=int)\n    # in_ = np.array(self.output.in_, dtype=int)\n\n    Rarr, Zarr = self.get_coord_transform(lvol, sarr, tarr, zarr)\n\n    Bcontrav = self.get_field_contrav(lvol, sarr, tarr, zarr)\n\n    Bcart = np.empty_like(Bcontrav)\n\n    Bcart[0] = Bcontrav[0] * Rarr[1] + Bcontrav[1] * Rarr[2] + Bcontrav[2] * Rarr[3]\n    Bcart[1] = Bcontrav[0] * Zarr[1] + Bcontrav[1] * Zarr[2] + Bcontrav[2] * Zarr[3] \n    Bcart[2] = Bcontrav[2] * Rarr[0]\n\n    return Bcart\n\n\ndef get_field_contrav(self, lvol, sarr, tarr, zarr):\n    \n    Aze = np.atleast_2d(self.vector_potential.Aze[lvol])\n    Ate = np.atleast_2d(self.vector_potential.Ate[lvol])\n    # Azo = np.atleast_2d(self.vector_potential.Azo[lvol])\n    # Ato = np.atleast_2d(self.vector_potential.Ato[lvol])\n    \n    lrad = self.input.physics.Lrad[lvol]\n    mpol = self.input.physics.Mpol\n    im = self.output.im\n    _in = self.output.in_\n    coord_sing = self.input.physics.Igeometry > 1 and lvol == 0\n\n    jac = self.get_jacobian(sarr, tarr, zarr, lvol)\n\n    if(coord_sing):\n        sbar = np.where(0.5*(sarr+1) > 0.0, 0.5*(sarr+1), 0)\n        basis = get_zernike_basis(sbar, lrad, mpol)\n    \n        Bcontrav = get_field_sing(sarr, tarr, zarr, lrad, im, _in, basis, Aze, Ate, jac)\n    \n    else:\n        basis = get_cheby_basis(sarr, lrad)\n\n        Bcontrav = get_field_nonsing(sarr, tarr, zarr, lrad, im, _in, basis, Aze, Ate, jac)\n\n    return Bcontrav\n\n\n@run_decorator\ndef get_field_nonsing(sarr, tarr, zarr, lrad, im, _in, basis, Aze, Ate, jac):\n    \"\"\"Generates the contravariant magnetic field (volume without coordinate singularity)\n    \"\"\"    \n\n    Bcontrav = np.zeros((3, len(sarr), len(tarr), len(zarr)))\n\n    for t in range(len(tarr)):\n        for z in range(len(zarr)):\n            for ii in range(len(im)):\n                \n                mi = im[ii]\n                ni = _in[ii]\n                arg = mi * tarr[t] - ni * zarr[z]\n                carg = np.cos(arg)\n                sarg = np.sin(arg)\n                \n                for s in range(len(sarr)):\n                    for l in range(lrad+1):\n                        Bcontrav[0, s, t, z] += (- mi * Aze[ii, l] - ni * Ate[ii, l]) * basis[l, 0, s] * sarg\n                        Bcontrav[1, s, t, z] += (- Aze[ii, l] * basis[l, 1, s]) * carg\n                        Bcontrav[2, s, t, z] += (Ate[ii, l] * basis[l, 1, s]) * carg\n\n                    # if(not stell_sym): # needs Ato, Azo\n                    #     Bcontrav[0, s, t, z] += (- mi * Azo[ii, l+1] - ni * Ato[ii, l+1]) * basis[l, 0, s] * carg\n                    #     Bcontrav[1, s, t, z] += (- Azo[ii, l+1] * basis[l, 1, s]) * sarg\n                    #     Bcontrav[2, s, t, z] += (Ato[ii, l+1] * basis[l, 1, s]) * sarg\n\n    Bcontrav /= jac\n    \n    return Bcontrav\n\n\n@run_decorator\ndef get_field_sing(sarr, tarr, zarr, lrad, im, _in, basis, Aze, Ate, jac):\n    \"\"\"Generates the contravariant magnetic field (volume with coordinate singularity)\n    \"\"\"    \n\n    Bcontrav = np.zeros((3, len(sarr), len(tarr), len(zarr)))\n\n    for t in range(len(tarr)):\n        for z in range(len(zarr)):\n            for ii in range(len(im)):\n                \n                mi = im[ii]\n                ni = _in[ii]\n                arg = mi * tarr[t] - ni * zarr[z]\n                carg = np.cos(arg)\n                sarg = np.sin(arg)\n                \n                for s in range(len(sarr)):\n                    for l in range(lrad+1):\n                        Bcontrav[0, s, t, z] += (- mi * Aze[ii, l] - ni * Ate[ii, l]) * basis[l, mi, 0, s] * sarg\n                        Bcontrav[1, s, t, z] += (- Aze[ii, l] * basis[l, mi, 1, s]) * 0.5 * carg\n                        Bcontrav[2, s, t, z] += (Ate[ii, l] * basis[l, mi, 1, s]) * 0.5 * carg \n\n                    # if(not stell_sym): # needs Ato, Azo\n                    #     Bcontrav[0, s, t, z] += (- mi * Azo[ii, l+1] - ni * Ato[ii, l+1]) * basis[l, mi, 0, s] * carg\n                    #     Bcontrav[1, s, t, z] += (- Azo[ii, l+1] * basis[l, mi, 1, s]) * 0.5 * sarg\n                    #     Bcontrav[2, s, t, z] += (Ato[ii, l+1] * basis[l, mi, 1, s]) * 0.5 * sarg\n\n    Bcontrav /= jac\n\n    return Bcontrav\n\n\n@run_decorator\ndef get_cheby_basis(sarr, lrad):\n\n    # [radial mode (0->lrad), func/derivative (0/1), radial coord (sarr)]\n    basis = np.zeros((lrad+1, 2, sarr.size)) \n\n    for s in range(len(sarr)):\n        \n        basis[0, 0, s] = 1.0\n        basis[1, 0, s] = sarr[s]\n        basis[1, 1, s] = 1.0\n\n        for l in range(2, lrad+1):\n            basis[l, 0, s] = 2.0 * sarr[s] * basis[l-1, 0, s] - basis[l-2, 0, s]\n            basis[l, 1, s] = 2.0 * basis[l-1, 0, s] + 2.0 * sarr[s] * basis[l-1, 1, s] - basis[l-2, 1, s]\n\n        # basis recombination\n        basis[:, 0, s] -= (-1)**np.arange(lrad+1)\n\n    # rescale for conditioning\n    for l in range(0, lrad+1):\n        basis[l] /= (l + 1.0)\n    \n    return basis\n\n\n@run_decorator\ndef get_zernike_basis(sbararr, lrad, mpol):\n\n    # [radial mode (0->lrad), poloidal mode (0->mpol), func/derivative (0/1), radial coord (sarr)]\n    basis = np.zeros((lrad+1, mpol+1, 2, sbararr.size)) \n\n    for s in range(len(sbararr)):\n        r = sbararr[s]\n        rm = 1.0 # sbar^m\n        rm1 = 0.0 # sbar^(m-1)\n        for m in range(mpol+1):\n\n            if(m <= lrad):\n                basis[m, m, 0, s] = rm\n                basis[m, m, 1, s] = m * rm1\n\n            if(m+2 <= lrad):\n                basis[m+2, m, 0, s] = (m+2) * rm * r**2.0 - (m+1) * rm\n                basis[m+2, m, 1, s] = (m+2)**2.0 * rm * r - (m+1) * m * rm1 \n            \n            for l in range(m+4, lrad+1, 2):\n                fac1 = l / (l**2.0 - m**2.0)\n                fac2 = 4.0 * (l - 1)\n                fac3 = (l - 2 + m) **2.0 / (l - 2) + (l - m)**2.0 / l\n                fac4 = ((l - 2)**2.0 - m**2.0) / (l - 2.0)\n\n                basis[l, m, 0, s] = fac1 * ( (fac2 * r**2.0 - fac3) * basis[l-2, m, 0, s] - fac4 * basis[l-4, m, 0, s])\n                basis[l, m, 1, s] = fac1 * ( 2.0 * fac2 * r * basis[l-2, m, 0, s] \n                                            + (fac2 * r**2.0 - fac3) * basis[l-2, m, 1, s] - fac4 * basis[l-4, m, 1, s])\n                \n            rm1 = rm\n            rm = rm * r\n    \n    # basis recombination\n    for l in range(2, lrad+1, 2):\n        basis[l, 0, 0] -= (-1)**(l/2.0)\n\n    if(mpol >= 1):\n        for s in range(len(sbararr)):\n            for l in range(3, lrad+1, 2):\n                basis[l, 1, 0, s] -= (-1)**((l-1)*0.5) * (l+1) * 0.5 * sbararr[s]\n                basis[l, 1, 1, s] -= (-1)**((l-1)*0.5) * (l+1) * 0.5\n    \n    # rescaling\n    for m in range(0, mpol+1):\n        for l in range(m, lrad+1, 2):\n            basis[l, m, :] /= (l + 1.0)\n\n    return basis\n", "repo_name": "ErolBa/pyspec", "sub_path": "py_spec/output/_get_field.py", "file_name": "_get_field.py", "file_ext": "py", "file_size_in_byte": 8455, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "numba.njit", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.einsum", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.einsum", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.atleast_2d", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.atleast_2d", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 194, "usage_type": "call"}]}
{"seq_id": "22581477609", "text": "import sys\nimport os\nfrom random import randint\nfrom PySide6.QtWidgets import (\n    QMainWindow, QApplication, QWidget,\n    QLabel, QCheckBox, QComboBox, QListWidget, QLineEdit, QPushButton,\n    QLineEdit, QSpinBox, QDoubleSpinBox, QSlider,\n    QLayout, QHBoxLayout\n)\n\nfrom PySide6.QtCore import Qt, QSize, QUrl\n\nfrom PySide6.QtMultimedia import (QAudio, QAudioOutput, QMediaFormat, QMediaPlayer, QSoundEffect)\n\nfrom PySide6.QtGui import QPixmap,QIcon\n\nclass MainWindow(QMainWindow):\n\n    def __init__(self):\n        super(MainWindow, self).__init__()\n\n        self.cat_number = 0\n        self.windows = []\n\n        self.setWindowTitle(\"My App\")\n        \n\n        label = QLabel(\"select number of cats\")\n\n        spin = QSpinBox()\n        spin.setMinimum(0)\n        spin.valueChanged.connect(self.setcatnumber)\n\n        button = QPushButton(QIcon(\"cat.png\"), \"press to generate kittens\")\n        button.clicked.connect(self.showcats)\n\n        layout = QHBoxLayout()\n        layout.addWidget(label)\n        layout.addWidget(spin)\n        layout.addWidget(button)\n\n        dummy = QWidget()\n        dummy.setLayout(layout)\n\n        self.setCentralWidget(dummy)\n\n        \"\"\" AUDIO \"\"\"\n        self._audio_output = QAudioOutput()\n        self._player = QMediaPlayer()\n            # NOTE: to play music: self._player.play()\n            # NOTE: to pause music: self._player.pause()\n            # NOTE: to stop: self._player.stop()\n            # NOTE: to setup source:  self._player.setSource(self._playlist[self._playlist_index]) \n        self._player.setAudioOutput(self._audio_output)\n\n        \"\"\"using a low latency sound effect\n        read: https://doc.qt.io/qtforpython/PySide6/QtMultimedia/QSoundEffect.html\n        \"\"\"\n        miew_sound = QSoundEffect()\n        miew_sound.setSource(QUrl.fromLocalFile(\"Kitten_Meow.wav\"))\n        miew_sound.setLoopCount(QSoundEffect.Infinite)\n        miew_sound.setVolume(100)\n        miew_sound.playingChanged.connect(print(\"playing changed!\"))\n        miew_sound.play()\n        print(\"initialised main window\")\n\n    def setcatnumber(self, number):\n        self.cat_number = number\n        print(\"current cat number: \",int(number))\n\n    def showcats(self, checked):\n        print(\"generating {} cats\".format(self.cat_number))\n        for i in range(self.cat_number):\n            # create a new window and show ignorantly\n            cat = QLabel()\n            cat.setPixmap(QPixmap(\"cat.png\"))\n            cat.setGeometry(randint(1,10)*100,randint(1,5)*50,800,800)\n            cat.show()\n            self.windows.append(cat)\n\n\"\"\"app = QApplication(sys.argv)\nw = MainWindow()\nw.show()\nsys.exit(app.exec_())\n\"\"\"\n\nif __name__ == \"__main__\":\n\n    app = QApplication(sys.argv)\n    window = MainWindow()\n    window.show()\n    sys.exit(app.exec_())\n", "repo_name": "silver-general/BCI_LSL_GUI", "sub_path": "research_and_experiments/QT_GUI/pythonguis_examples/cats.py", "file_name": "cats.py", "file_ext": "py", "file_size_in_byte": 2780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "PySide6.QtWidgets.QMainWindow", "line_number": 17, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 28, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QSpinBox", "line_number": 30, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QPushButton", "line_number": 34, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QIcon", "line_number": 34, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QHBoxLayout", "line_number": 37, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QWidget", "line_number": 42, "usage_type": "call"}, {"api_name": "PySide6.QtMultimedia.QAudioOutput", "line_number": 48, "usage_type": "call"}, {"api_name": "PySide6.QtMultimedia.QMediaPlayer", "line_number": 49, "usage_type": "call"}, {"api_name": "PySide6.QtMultimedia.QSoundEffect", "line_number": 59, "usage_type": "call"}, {"api_name": "PySide6.QtCore.QUrl.fromLocalFile", "line_number": 60, "usage_type": "call"}, {"api_name": "PySide6.QtCore.QUrl", "line_number": 60, "usage_type": "name"}, {"api_name": "PySide6.QtMultimedia.QSoundEffect.Infinite", "line_number": 61, "usage_type": "attribute"}, {"api_name": "PySide6.QtMultimedia.QSoundEffect", "line_number": 61, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 75, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QPixmap", "line_number": 76, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 77, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QApplication", "line_number": 89, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 92, "usage_type": "call"}]}
{"seq_id": "34768423238", "text": "#!/usr/bin/env python3\nimport glob\nimport importlib\nimport json\nimport os\nimport selectors\nimport socket\nimport threading\nfrom console.logger import logger\nfrom mutex.rwlock import ReadWriteLock\nfrom platform.game import GameBase\nfrom typing import Any, NewType\n\nGameConstructor = NewType(\"GameConstructor\", Any)\nlogger.level = 3\n\nHOST = os.getenv('HOST', 'localhost')\nPORT = int(os.getenv('PORT', '18880'))\n\ndef is_alive(sock) -> bool:\n    \"\"\"Check if a socket is alive\n    \"\"\"\n    try:\n        sock.settimeout(3)\n        data = sock.recv(1, socket.MSG_PEEK)\n        return True\n    except BlockingIOError:\n        return False\n    except ConnectionResetError:\n        return False\n    except socket.timeout:\n        return False\n\ndef recvline(sock) -> bytes:\n    \"\"\"Receive a line of data\n\n    Receive data from a socket until it reads '\\n'\n    \"\"\"\n    data = b''\n    while True:\n        c = sock.recv(1)\n        if c == b'':\n            raise ConnectionRefusedError(\"Connection closed from client\")\n        if c == b'\\n':\n            break\n        data += c\n    return data\n\ndef sendline(sock, raw):\n    assert b'\\n' not in raw, \"Do not include newline\"\n    sock.sendall(raw + b'\\n')\n\ndef recv_data(sock):\n    \"\"\"Receive a JSON data\n    \"\"\"\n    return json.loads(recvline(sock).decode())\n\ndef send_data(sock, data, type):\n    \"\"\"Send data to client\n    \"\"\"\n    payload = {'result': 'success', 'type': type, 'data': data}\n    sendline(sock, json.dumps(payload).encode())\n\ndef send_error(sock, msg):\n    \"\"\"Send error message to client\n    \"\"\"\n    data = {'result': 'error', 'reason': msg}\n    sock.sendall(json.dumps(data).encode())\n    sock.close()\n    raise Exception(msg)\n\nknown_games: dict[str, GameConstructor] = {}\nknown_games_lock = ReadWriteLock()\n\nconnections = {}\nconnections_lock = ReadWriteLock()\n\ndef start_game(game_class: GameConstructor, players):\n    \"\"\"Game manager\n\n    Start a new game\n    \"\"\"\n    num_players = len(players)\n    name_by_id = {pid: player[0] for pid, player in enumerate(players)}\n    sock_by_id = {pid: player[1] for pid, player in enumerate(players)}\n\n    # Initialize game\n    game = game_class(num_players, name_by_id)\n    game.initialize()\n\n    # Game loop\n    logger.info(f\"New game started: {game_class.name()}\")\n    logger.info(f\"Players:\")\n    for pid in range(num_players):\n        logger.info(f\"- {name_by_id[pid]}\")\n\n    selector = selectors.DefaultSelector()\n    for pid in range(num_players):\n        selector.register(sock_by_id[pid], selectors.EVENT_READ, pid)\n\n    no_game = False\n    while True:\n        # Check if game is over\n        is_over = game.is_over()\n        for pid in range(num_players):\n            send_data(sock_by_id[pid], is_over, type='gameover')\n        if is_over: break\n\n        # Who to ask?\n        pid_list = game.next_players()\n        if not isinstance(pid_list, list):\n            raise ValueError(\"Invalid game implementation: \"\n                             \"Player ID list not given by 'next_players'\")\n        if not all([0 <= pid < num_players for pid in pid_list]):\n            raise ValueError(\"Invalid game implementation: \"\n                             \"Invalid player ID given by 'next_players'\")\n\n        # Share current state\n        for pid in range(num_players):\n            state = game.share_state(pid)\n            logger.debug(f\"Sharing state with {name_by_id[pid]}: {state}\")\n            try:\n                send_data(sock_by_id[pid], state, type='state')\n                send_data(sock_by_id[pid], pid in pid_list, type='turn')\n            except Exception as _e:\n                # Abort game by connection error\n                no_game = True\n                break\n            # TODO: ここで時間計測開始\n\n        # Abort game if a client died\n        if no_game: break\n\n        inputs_by_id = {}\n        # Get inputs\n        while len(inputs_by_id) < len(pid_list):\n            events = selector.select()\n\n            for key, _ in events:\n                sock, pid = key.fileobj, key.data\n\n                # Check if it's this player's turn now\n                print(pid, pid_list)\n                if pid not in pid_list:\n                    logger.warning(f\"{name_by_id[pid]} sent input on invalid turn\")\n                    no_game = True\n                    break\n\n                # Check if not yet received\n                if pid in inputs_by_id:\n                    logger.warning(f\"{name_by_id[pid]} sent inputs twice\")\n\n                try:\n                    inputs_by_id[pid] = recv_data(sock)\n                except Exception as _e:\n                    logger.warning(f\"Could not receive input from\"\n                                   f\"{name_by_id[pid]}\")\n                    no_game = True\n                    break\n\n                # TODO: タイムアウト処理\n\n            if no_game: break\n\n        # Abort game if a client died\n        if no_game: break\n\n        # Update if everyone sent the next moves\n        for pid in pid_list:\n            try:\n                game.update_state(pid, inputs_by_id[pid])\n            except Exception as e:\n                # TODO: 失格リストを作る\n                logger.warning(f\"Invalid input from {name_by_id[pid]}: {e}\")\n                no_game = True\n                break\n\n        if no_game: break\n\n    if no_game:\n        # Game interrupted by connection error\n        payload = {\"result\": \"abort\", \"reason\": \"Game aborted by connection error\"}\n        score_by_id = {}\n        for pid in range(num_players):\n            try:\n                if not is_alive(sock_by_id[pid]):\n                    # FIXME: Not working\n                    raise ConnectionRefusedError(\"Client is not alive\")\n\n                sendline(sock_by_id[pid], json.dumps(payload).encode())\n                score_by_id[pid] = 0\n            except Exception as _e:\n                score_by_id[pid] = -9999 # TODO: configuable penalty\n    else:\n        # Game over\n        score_by_id = game.is_over()\n        print(score_by_id)\n        if not isinstance(score_by_id, dict):\n            raise ValueError(\"Invalid game implementation: \"\n                             \"'is_over' must return score in dictionary\")\n\n    # Show result\n    logger.info(\"Game result:\")\n    rank_by_id = {}\n    rank = 1\n    for (pid, score) in sorted(score_by_id.items(), key=lambda x:-x[1]):\n        logger.info(f\"{rank}. {name_by_id[pid]} (score: {score})\")\n        rank_by_id[pid] = rank\n        rank += 1\n\n    # Notify result\n    for pid in range(num_players):\n        payload = {\n            \"result\": \"success\",\n            \"data\": {\"score\": score_by_id[pid], \"rank\": rank_by_id[pid]}\n        }\n        try:\n            sendline(sock_by_id[pid], json.dumps(payload).encode())\n        except Exception as _e:\n            logger.warning(f\"Could not send game result to {name_by_id[pid]}\")\n\ndef handle_connection(sock, client_host: str):\n    \"\"\"Handle new connection from client\n    \"\"\"\n    try:\n        # Receive client data\n        data = json.loads(recvline(sock).decode())\n        if 'game' not in data:\n            send_error(sock, \"Game not specified\")\n        if 'name' not in data:\n            send_error(sock, \"Your name not given\")\n        if not isinstance(data['game'], str):\n            send_error(sock, \"Game name must be string\")\n        if not isinstance(data['name'], str):\n            send_error(sock, \"Client name must be string\")\n\n        game = data[\"game\"]\n        name = data[\"name\"]\n\n        # Lookup game\n        known_games_lock.acquire_read()\n        is_known_game = game in known_games\n        known_games_lock.release_read()\n        if not is_known_game:\n            send_error(sock, f\"Game '{data['game']}' not found\")\n\n        # If everything is valid, add to client list\n        connections_lock.acquire_write()\n        if game not in connections:\n            connections[game] = []\n        connections[game].append((name, sock))\n\n        # Notify the bot that it's registered\n        send_data(sock, \"Registered\", type='meta')\n\n        # Check if we can start the game\n        cnt = known_games[game].min_players()\n        if len(connections[game]) >= cnt:\n            # Pass to game manager\n            threading.Thread(\n                target=start_game,\n                args=(known_games[game], connections[game][:cnt])\n            ).start()\n            connections[game] = connections[game][cnt:]\n        connections_lock.release_write()\n\n    except Exception as e:\n        logger.error(f\"Error: {e} (Connection closed: {client_host})\")\n        sock.close()\n        return\n\ndef load_game(game_dir: str) -> GameConstructor:\n    \"\"\"Load a single game\n\n    Args:\n      game_dir (str): Path to the game folder to import\n    \"\"\"\n    if not os.path.isfile(f\"{game_dir}/game.py\"):\n        raise FileNotFoundError(f\"'game.py' not found in {game_dir}\")\n\n    # Import game\n    module_name = f\"{game_dir.replace('/', '.')}.game\"\n    game_class = importlib.import_module(module_name).Game\n\n    return game_class\n\ndef load_all(root_dir: str) -> dict[str, GameConstructor]:\n    \"\"\"Load every game from game root directory\n    \"\"\"\n    known_games = {}\n\n    # Strip the leading './'\n    if root_dir.startswith(\"./\"):\n        root_dir = root_dir[2:]\n\n    for game_dir in glob.iglob(f\"{root_dir}/*\"):\n        if not os.path.isdir(game_dir): continue\n        try:\n            game = load_game(game_dir)\n            known_games[game.name()] = game\n        except Exception as e:\n            logger.error(f\"Could not load a game: {e}\")\n\n    return known_games\n\ndef main():\n    global known_games\n    known_games = load_all(\"./game/\")\n\n    # Setup socket\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n    s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)\n    s.bind(('0.0.0.0', PORT))\n    s.setblocking(False)\n    s.listen(128)\n\n    selector = selectors.DefaultSelector()\n    selector.register(s, selectors.EVENT_READ)\n\n    while True:\n        events = selector.select()\n        for key, _ in events:\n            sock, addr = key.fileobj.accept()\n            logger.debug(f\"New connection from {addr[0]}:{addr[1]}\")\n\n            threading.Thread(target=handle_connection, args=(sock, addr)).start()\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "ptr-yudai/hitsuji-wars", "sub_path": "src/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 10264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "typing.NewType", "line_number": 14, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 14, "usage_type": "argument"}, {"api_name": "console.logger.logger.level", "line_number": 15, "usage_type": "attribute"}, {"api_name": "console.logger.logger", "line_number": 15, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 17, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 18, "usage_type": "call"}, {"api_name": "socket.MSG_PEEK", "line_number": 25, "usage_type": "attribute"}, {"api_name": "socket.timeout", "line_number": 31, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 68, "usage_type": "call"}, {"api_name": "mutex.rwlock.ReadWriteLock", "line_number": 73, "usage_type": "call"}, {"api_name": "mutex.rwlock.ReadWriteLock", "line_number": 76, "usage_type": "call"}, {"api_name": "console.logger.logger.info", "line_number": 92, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 92, "usage_type": "name"}, {"api_name": "console.logger.logger.info", "line_number": 93, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 93, "usage_type": "name"}, {"api_name": "console.logger.logger.info", "line_number": 95, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 95, "usage_type": "name"}, {"api_name": "selectors.DefaultSelector", "line_number": 97, "usage_type": "call"}, {"api_name": "selectors.EVENT_READ", "line_number": 99, "usage_type": "attribute"}, {"api_name": "console.logger.logger.debug", "line_number": 121, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 121, "usage_type": "name"}, {"api_name": "console.logger.logger.warning", "line_number": 145, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 145, "usage_type": "name"}, {"api_name": "console.logger.logger.warning", "line_number": 151, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 151, "usage_type": "name"}, {"api_name": "console.logger.logger.warning", "line_number": 156, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 156, "usage_type": "name"}, {"api_name": "console.logger.logger.warning", "line_number": 174, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 174, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 190, "usage_type": "call"}, {"api_name": "console.logger.logger.info", "line_number": 203, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 203, "usage_type": "name"}, {"api_name": "console.logger.logger.info", "line_number": 207, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 207, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 218, "usage_type": "call"}, {"api_name": "console.logger.logger.warning", "line_number": 220, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 220, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 227, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 260, "usage_type": "call"}, {"api_name": "console.logger.logger.error", "line_number": 268, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 268, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path", "line_number": 278, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 283, "usage_type": "call"}, {"api_name": "glob.iglob", "line_number": 296, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path", "line_number": 297, "usage_type": "attribute"}, {"api_name": "console.logger.logger.error", "line_number": 302, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 302, "usage_type": "name"}, {"api_name": "socket.socket", "line_number": 311, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 311, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 311, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 312, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 312, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 313, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEPORT", "line_number": 313, "usage_type": "attribute"}, {"api_name": "selectors.DefaultSelector", "line_number": 318, "usage_type": "call"}, {"api_name": "selectors.EVENT_READ", "line_number": 319, "usage_type": "attribute"}, {"api_name": "console.logger.logger.debug", "line_number": 325, "usage_type": "call"}, {"api_name": "console.logger.logger", "line_number": 325, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 327, "usage_type": "call"}]}
{"seq_id": "24230821526", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar  1 14:31:45 2018\n\n@author: vuik/dupuits\n\"\"\"\n\nfrom collections import namedtuple\nimport numpy as np\n\n\ndef overtopping(Hm0, Tm, slope, Rc, **kwargs):\n    \"\"\"\n    required input:\n        Hm0   : significant wave height (m)\n        Tm    : spectral mean wave period Tm-1,0 (s)\n        slope : characteristic outer slope angle of dike slope=tan(alpha)\n        Rc    : relative freeboard (with respect to MWL) (m)\n    optional input:\n        yb    : reduction factor for presence of berm\n        yf    : reduction factor for roughness\n        betaw : angle of oblique incidence (degrees)\n        yv    : reduction factor for effect of wave wall\n        ctype : probabilic ('prob') or deterministic ('det')\n        ed    : edition of EurOtop: 2007 or 2016\n        C**   : parameters for overtopping formulas, depending on ctype and ed.\n                A default value is used if C**=0 or not set at all.\n    output:\n        q     : average overtopping discharge (m3/s/m)\n    \"\"\"\n\n    if Hm0.shape != Tm.shape or Hm0.shape != slope.shape or Hm0.shape != Rc.shape:\n        raise ValueError('Hm0, Tm, slope and/or Rc do not have the same array shape')\n\n    # Default values\n    yb    = kwargs.get(\"yb\",    np.ones(Hm0.shape))\n    yf    = kwargs.get(\"yf\",    np.ones(Hm0.shape))\n    betaw = kwargs.get(\"betaw\", np.zeros(Hm0.shape))\n    yv    = kwargs.get(\"yv\",    np.ones(Hm0.shape))\n    ctype = kwargs.get(\"ctype\", \"prob\")\n    ed    = kwargs.get(\"ed\",    2016)\n    g     = kwargs.get(\"g\",     9.81)\n    debug = kwargs.get(\"debug\", False)\n\n    if ed not in [2007, 2016] or ctype not in [\"prob\", \"det\"]:\n        raise ValueError(\"Unrecognized value for 'ed'.\")\n\n    # Default values for overtopping constants.\n    parnames = [\"C1a\", \"C1b\", \"C1c\", \"C2a\", \"C2b\", \"C2c\", \"C3a\", \"C3b\", \"C3c\"]\n    if ed == 2007 and ctype == \"prob\":\n        par = dict(zip(parnames, [0.067, 4.75, 1.0, 0.20,   2.60, 1.0, -0.92, 0.33, 0.022]))\n    if ed == 2007 and ctype == \"det\":\n        par = dict(zip(parnames, [0.067, 4.30, 1.0, 0.20,   2.30, 1.0, -0.68, 0.33, 0.022]))\n    if ed == 2016 and ctype == \"prob\":\n        par = dict(zip(parnames, [0.023, 2.70, 1.3, 0.09,   1.50, 1.3, -0.79, 0.33, 0.022]))\n    if ed == 2016 and ctype == \"det\":\n        par = dict(zip(parnames, [0.026, 2.50, 1.3, 0.1035, 1.35, 1.3,  0.50, 0.33, 0.022]))\n\n    # Check if any of the overtopping constants needs to be overwritten.\n    for key, value in kwargs.items():\n        if key in parnames and value != 0.0:\n            par[key] = value\n\n    # Convert to a namedtuple because:\n    #  - Makes it less painful to look at long formulas (use of dot operator).\n    #  - Raises an error if code tries to alter a value of the namedtuple.\n    OvertoppingConstants = namedtuple(\"OvertoppingConstants\", parnames)\n    par = OvertoppingConstants(**par)\n\n    # Create copies of the input\n    Hm0 = Hm0.copy()\n    Tm = Tm.copy()\n    slope = slope.copy()\n    Rc = Rc.copy()\n\n    # Create empty arrays to fill later\n    q1 = np.empty(Hm0.shape)\n    q2 = np.empty(Hm0.shape)\n    q3 = np.empty(Hm0.shape)\n    zeta = np.empty(Hm0.shape)\n    q = np.empty(Hm0.shape)\n\n    # Special case for negative freeboard\n    rc_neg = Rc < 0\n    rc_pos = Rc >= 0\n    if ed == 2007:\n        q[rc_neg] = 0.60 * np.sqrt(g * np.abs(Rc[rc_neg])**3)\n    elif ed == 2016:\n        q[rc_neg] = 0.54 * np.sqrt(g * np.abs(Rc[rc_neg])**3)\n\n    # Negative or NaN results in NaN\n    hm0_neg = (Hm0 < 0) | (Tm < 0) | np.isnan(Hm0) | np.isnan(Tm)\n    q[rc_pos & hm0_neg] = np.nan\n\n    # Zero wave height or period is zero discharge\n    hm0_zer = ((Hm0 == 0) & (Tm >= 0)) | ((Tm == 0) & (Hm0 >= 0))\n    q[rc_pos & hm0_zer] = 0.0\n\n    # Valid indices for non-negative freeboards and positive waveheight/periods\n    valid_ind = rc_pos & (Hm0 > 0) & (Tm > 0)\n\n    # Fix betaw to be between 0 and 180, and derive gamma beta\n    if betaw > 180:\n        betaw = 360-betaw\n\n    ybeta = np.ones(Hm0.shape) * 0.736\n    \n    if betaw <= 80:\n        ybeta = 1.0 - 0.0033 * betaw # still valid in 2016, Eq. 5.29\n\n    # Zero overtopping discharge for waves not attacking the profile\n    q[valid_ind & (betaw > 110)] = 0.0\n    valid_ind = valid_ind & (betaw <= 110)\n\n    # Reduce wave height and periods for very oblique waves\n    if valid_ind & (betaw > 80):\n        Hm0 = Hm0 * (110.0 - betaw) / 30.0\n        Tm  = Tm * np.sqrt((110.0 - betaw) / 30.0)\n\n    # Reduce variables to the valid indices, makes the calculations for\n    # q1, q2 and q3 more readable\n    Hm0 = Hm0[valid_ind]\n    Tm = Tm[valid_ind]\n    Rc = Rc[valid_ind]\n    slope = slope[valid_ind]\n    ybeta = ybeta\n    yb = yb\n    yf = yf\n    yv = yv\n    # Calculate L0, sm and zeta\n    L0    = g * Tm**2 / 2.0 / np.pi\n    sm    = Hm0 / L0\n    zeta[valid_ind]  = slope / np.sqrt(sm)\n    q1[valid_ind] = par.C1a / np.sqrt(slope) * np.sqrt(g * Hm0**3) * yb * zeta[valid_ind]\n    q1[valid_ind] *= np.exp(-(par.C1b * Rc / (zeta[valid_ind] * Hm0 * yb * yf * ybeta * yv))**par.C1c)\n\n    q2[valid_ind] = par.C2a * np.sqrt(g * Hm0**3)\n    q2[valid_ind] *= np.exp(-(par.C2b * Rc / (Hm0 * yf * ybeta))**par.C2c)\n\n    q3[valid_ind] = 10.0**par.C3a * np.sqrt(g * Hm0**3)\n    q3[valid_ind] *= np.exp(-Rc / (yf * ybeta * Hm0 * (par.C3b + par.C3c * zeta[valid_ind])))\n\n    # Zeta conditionals\n    zeta_smal = valid_ind & (zeta < 5)\n    q[zeta_smal] = np.minimum(q1[zeta_smal], q2[zeta_smal])\n\n    zeta_larg = valid_ind & (zeta > 7)\n    q[zeta_larg] = q3[zeta_larg]\n\n    zeta_between = valid_ind & (zeta <= 7) & (zeta >= 5)\n    lb = np.minimum(q1[zeta_between], q2[zeta_between])\n    q[zeta_between] = (zeta[zeta_between] - 5.0) / (7.0 - 5.0) * (q3[zeta_between] - lb) + lb\n\n    return q\n\nif __name__ == '__main__':  \n    # fill in function which corresponds to results of JORL2\n    Hm0 = 4.07 # m\n    Tm = 8.11 # s Tm-1,0 (s)\n    slope = 0.25 # mTAW\n    Rc = 1.09\n    betaw = 0.0\n    \n    yb = 0.8\n    yf = 0.9\n    yv = 0.7\n    \n    q = overtopping(np.array(Hm0),np.array(Tm),np.array(slope),np.array(Rc), \n                    betaw = betaw, yf = yf, yb = yb, yv = yv, \n                    ed = 2007, ctype = 'det')\n\n    print(q*1000) # result JORL2 sheet = 561.2 l/s/m", "repo_name": "witteveenbos/KPZSS", "sub_path": "overslag/overtopping.py", "file_name": "overtopping.py", "file_ext": "py", "file_size_in_byte": 6153, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "40", "api": [{"api_name": "numpy.ones", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 39, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 168, "usage_type": "call"}]}
{"seq_id": "20074650476", "text": "import unittest2\n\nimport openerp\n\n\nclass test_domain_normalization(unittest2.TestCase):\n    def test_normalize_domain(self):\n        expression = openerp.osv.expression\n        norm_domain = domain = ['&', (1, '=', 1), ('a', '=', 'b')]\n        assert norm_domain == expression.normalize_domain(domain), \"Normalized domains should be left untouched\"\n        domain = [('x', 'in', ['y', 'z']), ('a.v', '=', 'e'), '|', '|', ('a', '=', 'b'), '!', ('c', '>', 'd'), ('e', '!=', 'f'), ('g', '=', 'h')]\n        norm_domain = ['&', '&', '&'] + domain\n        assert norm_domain == expression.normalize_domain(domain), \"Non-normalized domains should be properly normalized\"\n", "repo_name": "Johnzero/OE7", "sub_path": "openerp/tests/test_expression.py", "file_name": "test_expression.py", "file_ext": "py", "file_size_in_byte": 664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "40", "api": [{"api_name": "unittest2.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "openerp.osv", "line_number": 8, "usage_type": "attribute"}]}
{"seq_id": "10789125485", "text": "# Time:  O(n)\n# Space: O(n)\n\n# hash table\nclass Solution(object):\n    def arithmeticTriplets(self, nums, diff):\n        \"\"\"\n        :type nums: List[int]\n        :type diff: int\n        :rtype: int\n        \"\"\"\n        lookup = set(nums)\n        return sum((x-diff in lookup) and (x-2*diff in lookup) for x in nums)\n\n    \n# Time:  O(n)\n# Space: O(n)\nimport collections\n\n\n# dp\nclass Solution2(object):\n    def arithmeticTriplets(self, nums, diff):\n        \"\"\"\n        :type nums: List[int]\n        :type diff: int\n        :rtype: int\n        \"\"\"\n        result = 0\n        cnt1 = collections.Counter()\n        cnt2 = collections.Counter()\n        for x in nums:\n            result += cnt2[x-diff]\n            cnt2[x] += cnt1[x-diff]\n            cnt1[x] += 1\n        return result\n", "repo_name": "kamyu104/LeetCode-Solutions", "sub_path": "Python/number-of-arithmetic-triplets.py", "file_name": "number-of-arithmetic-triplets.py", "file_ext": "py", "file_size_in_byte": 778, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4314, "dataset": "github-code", "pt": "40", "api": [{"api_name": "collections.Counter", "line_number": 30, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 31, "usage_type": "call"}]}
{"seq_id": "9551400152", "text": "from distutils.log import debug\nfrom tarfile import PAX_FIELDS\nimport pandas as pd\nimport plotly.express as px\nfrom dash import Dash, html, dcc\n\napp = Dash(__name__)\n\ndf = pd.read_csv('materiais.csv')\ndf = df.sort_values(by='Grupo')\n\nfig = px.bar(df, x='Grupo', y='Classe', color='PDM', barmode='group')\n\napp.layout = html.Div(children=[\n    html.H1(children='Olá Dashboard'),\n\n    html.Div(children='''\n        Dash: Uma aplicação web framework para seus dados.    \n    '''),\n\n    dcc.Graph(\n        id='exemplo de gráfico',\n        figure=fig\n    )\n])\n\nif __name__ == '__main__':\n    app.run_server(debug=True)", "repo_name": "guilhermebrumatti/DashPy", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 616, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "dash.Dash", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "plotly.express.bar", "line_number": 12, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 12, "usage_type": "name"}, {"api_name": "dash.html.Div", "line_number": 14, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 14, "usage_type": "name"}, {"api_name": "dash.html.H1", "line_number": 15, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 15, "usage_type": "name"}, {"api_name": "dash.html.Div", "line_number": 17, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 17, "usage_type": "name"}, {"api_name": "dash.dcc.Graph", "line_number": 21, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "5792902470", "text": "import time\n\nimport pandas as pd\nfrom sqlalchemy.orm import sessionmaker\n\nfrom models import *\nfrom settings import PROVIDER\n\nengine = create_engine(PROVIDER['postgres'], echo=False)\nSession = sessionmaker(bind=engine)\nsession = Session()\n\ntestcase = 'K'\norm = 'SQLAlchemy'\n\n\ndef main():\n    result = pd.DataFrame()\n    objs = list(session.query(Customer).all())\n    count = len(objs)\n\n    start = time.time()\n\n    for obj in objs:\n        session.delete(obj)\n    session.commit()\n\n    now = time.time()\n\n    query_res = {'Testcase': testcase, 'Table': 'Item', 'Rows': count, 'Total time': f\"{(now - start):10.2f}\",\n                 'RowsPerSec': f\"{count / (now - start): 10.2f}\", \"ORM\": orm}\n    result = result.append(query_res, ignore_index=True)\n    result.to_csv('sqlalchemy_outputs/test_k.csv')\n    print(f\"SQLAlchemy Testcase K, Rows deleted: {count}, time taken: {now-start}\")\n    print(f\"SQLAlchemy ORM, K: Rows/sec: {count / (now - start): 10.2f}\")\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "dollyagarwal/orm-benchmarks", "sub_path": "SQL Alchemy/test_k.py", "file_name": "test_k.py", "file_ext": "py", "file_size_in_byte": 1000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "settings.PROVIDER", "line_number": 9, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "33020576840", "text": "#!/usr/bin/python\n\nimport os\nimport pathlib\nimport datetime\nimport requests\nimport logging\nfrom dateutil import tz\nimport configparser\nimport argparse\nimport json\nimport requests\n\ndef count_images_between_string_time_infilename(\n    source_directory, start_time, end_time, local_tz\n):\n    \"\"\"group images together by string time in their file name and copy them to a target directory\"\"\"\n    count = 0\n    for file in os.listdir(source_directory):\n        filename = os.fsdecode(file)\n        filepath = source_directory + filename\n        fname = pathlib.Path(filepath)\n        if filepath.endswith(\".jpg\"):\n            filetime = filename[:14]\n            filetime = datetime.datetime.strptime(filetime, \"%Y%m%d_%H-%M\")\n            filetime = filetime.replace(tzinfo=local_tz)\n            if filetime >= start_time and filetime <= end_time:\n                count += 1\n            else:\n                continue\n    return count\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--config\",\n        required=True,\n        help=\"filename - dhconfig.txt or bhconfig.txt\",\n    )\n    args = parser.parse_args()\n    config = configparser.ConfigParser()\n    config_file = os.path.join(os.path.dirname(__file__), args.config)\n    config.read(config_file)\n    image_storage_root_directory = config[\"directories\"][\"image_storage_root_directory\"]\n    image_check_location = config[\"logging\"][\"image_check_location\"]\n    slackhook = config[\"slack\"][\"slackhook\"]\n    slackheaders = config[\"slack\"][\"slackheaders\"]\n\n    # convert srting config inputs to dicts so they are read correctly\n    slackheaders = json.loads(slackheaders)\n\n    logging.basicConfig(\n        filename=image_check_location,\n        format=\"%(asctime)s - %(message)s\",\n        datefmt=\"%d-%b-%y %H:%M:%S\",\n        level=logging.INFO,\n    )\n\n\n    local_tz = tz.gettz(\"America/New_York\")\n    today = datetime.datetime.today()\n    start_time = today.replace(hour=00, minute=00, second=00).astimezone(local_tz)\n    end_time = today.replace(hour=23, minute=59, second=59).astimezone(local_tz)\n\n    directions = [\"north\", \"east\", \"south\", \"west\"]\n    stats_dict = {\"north\": {\"#images\": 0, \"#missing\": 0}, \"east\": {\"#images\": 0, \"#missing\": 0}, \"south\": {\"#images\": 0, \"#missing\": 0}, \"west\": {\"#images\": 0, \"#missing\": 0}}\n\n    for i in directions:\n        source_directory = image_storage_root_directory + i\n        count = count_images_between_string_time_infilename(source_directory, start_time, end_time, local_tz)\n        expected_count = (today.hour * 60) + today.minute\n        difference = expected_count - count\n        stats_dict[i]['#images'] = count\n        stats_dict[i]['#missing'] = difference\n\n    logging.info(stats_dict)\n\n    slackjson = {\n        \"text\": args.config + \" \" + str(stats_dict)\n    }\n    r = requests.post(slackhook, json=slackjson, headers=slackheaders)\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "BostonTimelapse/timelapse", "sub_path": "image_check.py", "file_name": "image_check.py", "file_ext": "py", "file_size_in_byte": 2914, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "os.listdir", "line_number": 19, "usage_type": "call"}, {"api_name": "os.fsdecode", "line_number": 20, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 35, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 43, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 53, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 57, "usage_type": "attribute"}, {"api_name": "dateutil.tz.gettz", "line_number": 61, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 61, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 77, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 82, "usage_type": "call"}]}
{"seq_id": "11365062710", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport sys,os,time\nimport subprocess\nimport scipy.io as sio\nimport tensorflow as tf\nfrom six.moves import urllib\nimport numpy as np\nimport shutil  \n\nsys.path.append(\"utils/\")\nimport cifar10\nimport cifar10_input\nimport modules\nfrom candidate import Candidate\nimport cifar_DetaNet\nimport evolution_algo\nimport train\n\nFLAGS = None\n\ndef main(_):\n  # clear save files\n  shutil.rmtree(FLAGS.save_dir)  \n  os.mkdir(FLAGS.save_dir)\n  shutil.rmtree(\"./tmp/\")  \n  os.mkdir(\"./tmp/\")\n  #os.remove(\"/dataset/svhn/svhn_train.tfrecords\")\n  \n  seed_network = Candidate()\n  seed_network.set_empty_prefix()\n  structure1 = task(data_set = 'cifar20', prefix_structure = seed_network)\n  structure2 = task(data_set = 'cifar10', prefix_structure = structure1)\n  structure3 = task(data_set = 'svhn', prefix_structure = structure2)\n\n\ndef task(data_set, prefix_structure = None):\n  # evolution\n  best_candidate = evolution_algo.evolution_algo(\n    FLAGS = FLAGS,\n    data_set = data_set,\n    prefix_structure = prefix_structure\n  )\n  # combine\n  if prefix_structure != None:\n    best_structure = prefix_structure.extend(best_candidate)\n  else:\n    best_structure = best_candidate\n  # train\n  best_structure.display_structure()\n  '''\n  train structure should be desperate to restore previous fixed weights\n  '''\n  _, final_avg_acc = train.train(\n    FLAGS = FLAGS,\n    prefix_structure = prefix_structure, \n    candidate = best_candidate, \n    data_set = data_set, \n    max_steps = FLAGS.max_step\n  )\n\n  # display\n  print(\"------------------transfer results------------------\")\n  print(\"best structure training_avg_acc \"+ str(final_avg_acc))\n  best_structure.display_structure()\n  compurtation_of_network = best_structure.compurtation_of_network()\n  print(\"compurtation_of_network :  \" + str( )+ str(compurtation_of_network)) \n  print(\"\")\n\n  return best_structure\n\n\nif __name__ == '__main__':\n  parser = argparse.ArgumentParser()\n  \n  \n  parser.add_argument('--save_dir', type=str, default='./save/transfer/',\n                      help='save trained argues')\n  parser.add_argument('--learning_rate', type=float, default=0.01,\n                      help='Initial learning rate')\n  parser.add_argument('--T', type=int, default=3000,\n                      help='The Number of steps per geopath')\n  parser.add_argument('--batch_size', type=int, default=256,\n                      help='The Number of batches per each geopath')\n  parser.add_argument('--candi', type=int, default=6,\n                      help='The Number of Candidates of geopath, should greater than 4')\n  parser.add_argument('--max_generations', type = int,default = 6,\n                      help='The Generation Number of Evolution')\n  parser.add_argument('--max_step', type = int,default = 15000,#200000,\n                      help='The max training step of final structure')\n  parser.add_argument('--dropout', type=float, default=1,\n                      help='Keep probability for training dropout.')\n  parser.add_argument('--use_fp16', type=bool, default=False,\n                      help='Use float16 in netwrok or not.')\n  parser.add_argument('--use_moving_avg', type=bool, default=False,#True,\n                      help='Use shadow variables to replace origal variables for evaluation or not when restorage variables')\n  # cifar\n  parser.add_argument('--cifar_data_dir', type=str, default='/dataset/cifar_data/cifar-10-batches-bin/',\n                      help='Directory for storing input data')\n  parser.add_argument('--cifar100_data_dir', type=str, default='/dataset/cifar100/cifar-100-binary/',\n                      help='Directory for storing input data')\n  # svhn\n  parser.add_argument('--svhn_data_dir', type=str, default='/dataset/svhn/',\n                      help='Directory for storing input data')\n  # mnist argus\n  parser.add_argument('--data_dir', type=str, default='/dataset/mnist/',\n                      help='Directory for storing input data')\n  parser.add_argument('--a1', type=int, default=1,\n                      help='The first class of task1')\n  parser.add_argument('--a2', type=int, default=3,\n                      help='The second class of task1')\n  parser.add_argument('--b1', type=int, default=1,\n                      help='The first class of task2')\n  parser.add_argument('--b2', type=int, default=2,\n                      help='The second class of task2')\n  parser.add_argument('--c1', type=int, default=3,\n                      help='The first class of task3')\n  parser.add_argument('--c2', type=int, default=4,\n                      help='The second class of task3')\n  parser.add_argument('--d1', type=int, default=4,\n                      help='The first class of task4')\n  parser.add_argument('--d2', type=int, default=5,\n                      help='The second class of task4')\n              \n\n  FLAGS, unparsed = parser.parse_known_args()\n  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)", "repo_name": "Wind-Wing/SMART", "sub_path": "transfer.py", "file_name": "transfer.py", "file_ext": "py", "file_size_in_byte": 5004, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.path.append", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 27, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 28, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 29, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 30, "usage_type": "call"}, {"api_name": "candidate.Candidate", "line_number": 33, "usage_type": "call"}, {"api_name": "evolution_algo.evolution_algo", "line_number": 42, "usage_type": "call"}, {"api_name": "train.train", "line_number": 57, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.app.run", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 130, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 130, "usage_type": "attribute"}]}
{"seq_id": "5706160817", "text": "import pygame\nfrom pygame.locals import *\n\n\n__all__ = [\"SquarePlayer\", \"Ghost\", \"Goal\", \"Portal\", \"Level\"]\n\n\nclass SquarePlayer:\n    def __init__(self, display, color, width, height, x, y):\n        self.display = display\n        self.color = color\n        self.x, self.y = x, y\n        self.width, self.height = width, height\n        self.object = pygame.rect.Rect(x, y, width, height)\n\n    def draw(self):\n        pygame.draw.rect(self.display, self.color, self.object, 5)\n\n\nclass Ghost:\n    def __init__(self, display, ghost_x, ghost_y):\n        self.display = display\n        self.color = (0, 255, 0)\n        self.width, self.height = 64, 64\n        self.x, self.y = ghost_x, ghost_y\n        self.object = pygame.rect.Rect(self.x, self.y, self.width, self.height)\n\n    def draw(self):\n        pygame.draw.rect(self.display, self.color, self.object, 5)\n\n    def kill(self,):\n        #self.x, self.y = player_Coords\n        #self.object = pygame.rect.Rect(self.x, self.y, self.width, self.height)\n        pygame.draw.rect(self.display, self.color, self.object, 5)\n\n\nclass Goal:\n    def __init__(self, display, x, y):\n        self.display = display\n        self.color = (255, 0, 0)\n        self.x, self.y = x, y\n        self.coords = (self.x, self.y)\n        self.width, self.height = (64, 64)\n        self.object = pygame.rect.Rect(self.x, self.y, self.width, self.height)\n        self.goal_completed = False\n\n    def draw(self):\n        pygame.draw.rect(self.display, self.color, self.object)\n\n\nclass Portal:\n    def __init__(self, display, x, y):\n        self.display = display\n        self.color = (255, 0, 0)\n        self.x, self.y = x, y\n        self.width, self.height = (64, 64)\n        self.object = pygame.rect.Rect(self.x, self.y, self.width, self.height)\n        self.goal_completed = False\n\n    def draw(self, color):\n        self.color = color\n        pygame.draw.rect(self.display, self.color, self.object)\n\n\nclass Level:\n    def __init__(self, display, grid, name):\n        x = 0\n        y = 0\n        self.name = name\n\n        self.display = display\n        self.grid = grid\n        self.surface = pygame.Surface((512, 512))\n        self.surface.set_colorkey((0, 0, 0))\n\n        self.wall_points = []\n\n        self.goal = 0\n\n        self.safe_zone = []\n        for rows in grid:\n            for square_code in rows:\n                if x == 512:\n                    x = 0\n                if square_code == 1:\n                    self.square_x = x\n                    self.square_y = y\n                    self.player_spawn = pygame.rect.Rect(\n                        self.square_x, self.square_y, 64, 64\n                    )\n                    self.safe_zone.append(self.player_spawn)\n\n                if square_code == 2:\n                    self.goal = Goal(self.display, x, y)\n                    pygame.draw.rect(self.surface, self.goal.color, self.goal.object)\n\n                if square_code == 3:\n                    wall_object = pygame.rect.Rect(x, y, 32, 32)\n                    self.wall_points.append(wall_object)\n                    pygame.draw.rect(self.surface, (128, 128, 128), wall_object)\n\n                if square_code == 4:\n                    safe_square = pygame.rect.Rect(x, y, 32, 32)\n                    self.safe_zone.append(safe_square)\n                    pygame.draw.rect(self.surface, (128, 255, 255), safe_square)\n\n                x += 32\n            y += 32\n\n    def blit_level(self):\n        self.display.blit(self.surface, (0, 0))\n\n    def level_reset(self):\n        self.goal.goal_completed = False", "repo_name": "tristanML/square-game", "sub_path": "src/square_game/players.py", "file_name": "players.py", "file_ext": "py", "file_size_in_byte": 3552, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pygame.rect.Rect", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.rect", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.rect.Rect", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.rect", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.rect.Rect", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.rect", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.rect.Rect", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.rect", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.rect.Rect", "line_number": 88, "usage_type": "call"}, {"api_name": "pygame.rect", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.rect.Rect", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.rect", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 100, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pygame.rect.Rect", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.rect", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 105, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 105, "usage_type": "attribute"}]}
{"seq_id": "38690679798", "text": "import tkinter as tk\nfrom window_manager import get_tibia_windows, get_window_id\nfrom utils import send_key\nfrom time import sleep\nWINDOW_ID = int(get_window_id(), 16)\nAPP_TITLE = \"EyePatch BOT\"\n\nclass App:\n    def __init__(self, master):\n        self.master = master\n        self.master.title(f\"{APP_TITLE}\")\n\n        # Create the menu bar\n        menubar = tk.Menu(master)\n        filemenu = tk.Menu(menubar, tearoff=0)\n        filemenu.add_command(label=\"Open\", command=self.open_file)\n        filemenu.add_command(label=\"Save\", command=self.save_file)\n        filemenu.add_separator()\n        filemenu.add_command(label=\"Exit\", command=master.quit)\n        menubar.add_cascade(label=\"File\", menu=filemenu)\n\n        # Create a dropdown list\n        self.var = tk.StringVar(master)\n        self.var.set(\"Pick a Client\")\n        clientmenu = tk.Menu(menubar, tearoff=0)\n        for client in get_tibia_windows():\n            clientmenu.add_command(label=client, command=lambda c=client: self.select_client(c))\n        menubar.add_cascade(label=\"Pick Client\", menu=clientmenu)\n\n        # Add the menu bar to the master window\n        master.config(menu=menubar)\n\n\n        # Create a button\n        self.button = tk.Button(master, text=\"Start EyePatch\", command=self.startBot)\n        self.button.pack()\n\n        self.buttonKey = tk.Button(master, text=\"Send Key\", command=self.sendKey)\n        self.buttonKey.pack()\n\n    def select_client(self, client):\n        self.var.set(client)\n        self.master.title(f\"{client} - {APP_TITLE}\")\n\n    def open_file(self):\n        print(\"Opening file...\")\n\n    def save_file(self):\n        print(\"Saving file...\")\n\n    def startBot(self):\n        tibia_window = self.var.get()\n        if tibia_window != \"Pick a Client\":\n            print(f\"Proceeding with Tibia window '{tibia_window}'\")\n\n    def sendKey(self):\n        key = \"F7\"\n        window_id = self.var.get()\n        send_key(key, window_id)\n\n\n\n\nif __name__ == '__main__':\n    root = tk.Tk()\n    app = App(root)\n    root.minsize(200, 50)\n    print(f\"{WINDOW_ID}\")\n    root.mainloop()\n", "repo_name": "pawelrogowski/TibiaBot", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2081, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "window_manager.get_window_id", "line_number": 5, "usage_type": "call"}, {"api_name": "tkinter.Menu", "line_number": 14, "usage_type": "call"}, {"api_name": "tkinter.Menu", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 23, "usage_type": "call"}, {"api_name": "tkinter.Menu", "line_number": 25, "usage_type": "call"}, {"api_name": "window_manager.get_tibia_windows", "line_number": 26, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 38, "usage_type": "call"}, {"api_name": "utils.send_key", "line_number": 59, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 65, "usage_type": "call"}]}
{"seq_id": "4670104539", "text": "from django.http.response import HttpResponse\nfrom django.utils import timezone\nfrom rest_framework.exceptions import ParseError\n\n\ndef delete_or_400(model, text, **kwargs):\n    \"\"\"\"Удаляем объект или возвращаем код 400.\"\"\"\n    if not model.objects.filter(**kwargs).exists():\n        raise ParseError(text)\n    model.objects.filter(**kwargs).delete()\n\n\ndef make_txt_response(ingredients):\n    \"\"\"Отдаём список покупок в txt формате.\"\"\"\n    shopping_list = 'Купить в магазине:\\n'\n    for ingredient in ingredients:\n        shopping_list += (\n            f'{ingredient[\"ingredient__name\"]}: '\n            f'({ingredient[\"ingredient__measurement_unit\"]})'\n            f' - {ingredient[\"amount\"]} \\n'\n        )\n    now = timezone.now()\n    file_name = f'ingredients list{now:%Y-%m-%d}'\n    response = HttpResponse(shopping_list, content_type='text/plain')\n    response['Content-Disposition'] = (\n        f'attachment; filename=\"{file_name}.txt\"')\n    return response\n", "repo_name": "IlianL/foodgram-project-react", "sub_path": "backend/core/utils_api_view.py", "file_name": "utils_api_view.py", "file_ext": "py", "file_size_in_byte": 1033, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "rest_framework.exceptions.ParseError", "line_number": 9, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 22, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponse", "line_number": 24, "usage_type": "call"}]}
{"seq_id": "13003417881", "text": "import math\nfrom typing import List\n\n\ndef maxSubArray(nums):\n    max_sum = None\n    sum_table = set()\n\n    if len(nums) == 1:\n        return nums[0]\n\n    for start in range(len(nums)):\n        for i in range(len(nums) + 1):\n            slice_arr = nums[start:i]\n\n            if not slice_arr:\n                continue\n\n            curr_sum = sum(slice_arr)\n\n            if curr_sum in sum_table:\n                continue\n\n            if max_sum is None:\n                max_sum = curr_sum\n            else:\n                max_sum = max(curr_sum, max_sum)\n\n            sum_table.add(curr_sum)\n\n    return max_sum\n\n\ndef lengthOfLongestSubstring(s):\n    window_start = 0\n    max_length = 0\n    char_index_map = {}\n\n    for window_end in range(len(s)):\n        curr_char = s[window_end]\n\n        if curr_char in char_index_map:\n            window_start = max(window_start, char_index_map[curr_char] + 1)\n\n        char_index_map[curr_char] = window_end\n        max_length = max(max_length, window_end - window_start + 1)\n\n    return max_length\n\n\ndef minSubArrayLen(target, nums):\n    start = 0\n    min_len = math.inf\n    curr_sum = 0\n\n    for end, num in enumerate(nums):\n        curr_sum += num\n\n        while curr_sum >= target:\n            min_len = min(min_len, end - start + 1)\n            curr_sum = curr_sum - nums[start]\n            start += 1\n\n    if min_len != math.inf:\n        return min_len\n\n    return 0\n\n\ndef findMaxLenSubArray(nums1, nums2):\n    max_len = 0\n    len1 = len(nums1)\n    len2 = len(nums2)\n\n    for i in range(len1 + len2 - 1):\n        start1 = max(0, len1 - i - 1)\n        start2 = max(0, i - (len1 - 1))\n        curr_cons = 0\n\n        while start1 < len1 and start2 < len2:\n            if nums1[start1] == nums2[start2]:\n                curr_cons += 1\n                max_len = max(max_len, curr_cons)\n            else:\n                curr_cons = 0\n\n            start1 += 1\n            start2 += 1\n\n    return max_len\n\n\ndef getLenOfLongestSubstringWithRepeatedChars(str):\n    if len(str) == 1:\n        return 1\n\n    max_sub_len = 1\n    prev_char = str[0]\n    cons_chars = 1\n\n    for curr_char in str[1:]:\n        if prev_char == curr_char:\n            cons_chars += 1\n        else:\n            cons_chars = 1\n\n        max_sub_len = max(max_sub_len, cons_chars)\n        prev_char = curr_char\n\n    return max_sub_len\n\n\ndef getLenOfLongestSubstringWithAtLeastKRepeatedChars(s, k):\n    if k == 1:\n        return len(s)\n    elif len(s) == 1 and k > 1:\n        return 0\n    elif len(s) == 2 and 0 < k <= 2:\n        return 2\n\n    sub_map = set()\n    max_sub_len = 0\n\n    for start in range(len(s)):\n        count_map = {k: 0 for k in 'abcdefghijklmnopqrstuvwxyz'}\n\n        for end in range(start, len(s)):\n            count_map[s[end]] = count_map[s[end]] + 1\n            sub_str = s[start:end + 1]\n            if sub_str not in sub_map:\n                if doesSubStrMeetKCriteria(k, count_map):\n                    max_sub_len = max(max_sub_len, len(sub_str))\n                sub_map.add(sub_str)\n\n    return max_sub_len\n\n\ndef doesSubStrMeetKCriteria(k, count_map):\n    meets_k_count = 0\n    letter_count = 0\n\n    for freq in count_map.values():\n        if freq > 0:\n            letter_count += 1\n\n        if freq >= k:\n            meets_k_count += 1\n\n        if meets_k_count != letter_count:\n            return False\n\n    return True\n\n\n# input is a sorted array that removes duplicates in-place and returns new array length\ndef removeDuplicates(nums):\n    if len(nums) == 0:\n        return 0\n    elif len(nums) == 1:\n        return 1\n\n    start_index = 0  # also acts as a unique found counter\n\n    for i in range(1, len(nums)):\n        if nums[start_index] != nums[i]:\n            start_index += 1\n            nums[start_index] = nums[i]\n\n    return start_index + 1\n\n\n# input is a sorted array that removes duplicates in-place that appeared more than two times and returns new array length\n# [1,1,1,2,2,3]\ndef removeDuplicates2(nums):\n    if len(nums) == 0:\n        return 0\n    elif len(nums) == 1:\n        return 1\n\n    start_index = 0  # also acts as a unique found counter\n    num_dups_found = 0\n    i = 1\n\n    for j in range(2, len(nums)):\n        if nums[i - 1] != nums[j]:\n            nums[i + 1] = nums[j]\n            i += 1\n\n    return i + 1\n\n\ndef mergeSortedArray(nums1, m, nums2, n):\n    while m > 0 and n > 0:\n        if nums1[m - 1] >= nums2[n - 1]:\n            nums1[m + n - 1] = nums1[m - 1]\n            m -= 1\n        else:\n            nums1[m + n - 1] = nums2[n - 1]\n            n -= 1\n    if n > 0:\n        nums1[:n] = nums2[:n]\n\n\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\n\ndef mergeTwoSortedLinkedLists(l1, l2):\n    newNodeStart = None\n    newNodeCurr = None\n\n    while l1 and l2:\n        if l1.val <= l2.val:\n            if newNodeStart is None:\n                newNodeStart = ListNode(l1.val)\n                newNodeCurr = newNodeStart\n            else:\n                newNodeCurr.next = ListNode(l1.val)\n                newNodeCurr = newNodeCurr.next\n\n            l1 = l1.next\n        else:\n            if newNodeStart is None:\n                newNodeStart = ListNode(l2.val)\n                newNodeCurr = newNodeStart\n            else:\n                newNodeCurr.next = ListNode(l2.val)\n                newNodeCurr = newNodeCurr.next\n\n            l2 = l2.next\n\n    if newNodeStart:\n        if l1 is None and l2:\n            newNodeCurr.next = l2\n        elif l1 and l2 is None:\n            newNodeCurr.next = l1\n    elif l1:\n        newNodeStart = l1\n    else:\n        newNodeStart = l2\n\n    return newNodeStart\n\n\ndef shiftingLetters(s: str, shifts: List[int]):\n    char_arr = list(s)\n    sum_shifts = 0\n\n    for char_index in reversed(range(len(char_arr))):\n        sum_shifts += shifts[char_index]\n        char_pos_in_alphabet = ord(s[char_index]) - ord('a')\n        num_shifts = sum_shifts % 26\n\n        new_pos = char_pos_in_alphabet + num_shifts\n\n        if new_pos > 25:\n            new_pos = new_pos - 26\n\n        new_char = chr(ord('a') + new_pos)\n        char_arr[char_index] = new_char\n\n    s = ''.join(char_arr)\n    return s\n\n\n\n\n\n\n\n\n# l1 = ListNode(1, ListNode(2, ListNode(4, None)))\n# l2 = ListNode(1, ListNode(3, ListNode(4, None)))\n# mergeTwoSortedLinkedLists(l1, l2)\n", "repo_name": "davelasquez12/PythonPractice", "sub_path": "algos/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6443, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "math.inf", "line_number": 53, "usage_type": "attribute"}, {"api_name": "math.inf", "line_number": 64, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 254, "usage_type": "name"}]}
{"seq_id": "34806107382", "text": "\"\"\"\nAuthor: Hayle\nDescription: Preprocess some stuff idk\nI put EFFORT in but guess we will j b sheeple who use tutorials\n\"\"\"\nimport nltk\nimport heapq\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as c\nimport os\nimport math\nfrom nltk.stem import WordNetLemmatizer\nimport re\nfrom nltk.corpus import stopwords\nfrom wordcloud import WordCloud\nimport json\nimport zlib\nimport base64\nimport pickle\n\nZIPJSON_KEY = 'base64(zip(o))'\n\n\ndef json_zip(j, fp):\n    j = {\n        ZIPJSON_KEY: base64.b64encode(\n            zlib.compress(\n                json.dump((j).encode('utf-8'), fp)\n            )\n        ).decode('ascii')\n    }\n\n    return j\n\n\nNUMBER_OF_WORDS = 200\nPATH_TO_NEWS_GROUP = 'C:\\\\Users\\\\Zacha\\\\Desktop\\\\20_newsgroups'\n\n\nclass TotalCorp(object):\n    def __init__(self, path_to_news_group=PATH_TO_NEWS_GROUP, folder_name='alt.atheism', b='51060', lemma=False):\n        self.p = path_to_news_group\n        self.folder = folder_name\n        self.corpus = []\n        self.word_freq_dict = {}\n        self.lemmatizer = WordNetLemmatizer()\n        self.lemma = lemma\n        self.tokenizer = nltk.tokenize.RegexpTokenizer(r'\\w+')  # drops everything except alphanumeric characters\n        self.total_words = 0\n        self.break_at = b\n        self.word_dict = {}\n\n    def create_corp(self):\n        total_documents = len(os.listdir(os.path.join(self.p, self.folder)))\n        for idx, f in enumerate(os.listdir(os.path.join(self.p, self.folder))):\n            print(\"{}_{}\".format(idx, total_documents))\n            a = open(os.path.join(self.p, self.folder, f), 'r')\n            self.corpus = self.corpus + (nltk.sent_tokenize(a.read()))\n            self.word_freq_dict[f] = {}\n            self.word_dict[f] = []\n            self.word_freq_dict[f]['frequencies'], self.word_freq_dict[f]['total_words'], self.word_freq_dict[\n                f]['comma_seperated_words'], self.word_freq_dict[f]['sentence_vectors'] = self.find_frequency()\n            # _, _, _, self.word_freq_dict[f]['sentence)vectors'] = self.find_frequency()\n            if str(f) == self.break_at:  # it takes a really long time to run so I j did this\n                break\n\n    def save(self):\n        print(\"SAVING CORP\")\n        if self.lemma:\n            pickle.dump(self.word_freq_dict, open(\"{}_{}.p\".format(\"NO_LEMMA\", self.folder), \"wb\"))\n        else:\n            pickle.dump(self.word_freq_dict, open(\"{}_{}.p\".format(\"NO_LEMMA\", self.folder), \"wb\"))\n\n    def find_frequency(self):\n        freq_dict = {}\n        word_dict = []\n        total_words = 0\n        sentence_vecs = []\n        for idx, cor in enumerate(self.corpus):\n            self.corpus[idx] = cor.lower().replace(\"_\", \"\")\n            tokens = self.tokenizer.tokenize(self.corpus[idx])\n            tokens = [re.sub('\\S*@\\S*\\s?', '', sent) for sent in tokens]\n            tokens = [re.sub('\\s+', ' ', sent) for sent in tokens]\n            tokens = [re.sub(\"\\'\", \"\", sent) for sent in tokens]\n\n            if self.lemma:\n                stop_words = set(stopwords.words('english'))\n                tokens = [w for w in tokens if not w in stop_words]\n            tokens_ = []\n            for token in tokens:\n                if self.lemma:\n                    token = self.lemmatizer.lemmatize(token)\n\n                if token not in freq_dict.keys():\n                    freq_dict[token] = 1\n                else:\n                    freq_dict[token] += 1\n                total_words += 1\n                word_dict.append(token)\n                tokens_.append(token)\n            sentence_vecs.append(tokens_)\n\n        # print(\"example of processed token:{}\".format(tokens))  # prints example of word tokens can be taken out later\n        return freq_dict, total_words, word_dict, sentence_vecs\n\n\nclass BagOfWords(object):\n    def __init__(self, corpus, word_freq_dict, words=NUMBER_OF_WORDS):\n        self.words = words\n        self.corpus = corpus\n        self.wordfreq = word_freq_dict\n        self.sentence_vecs = []\n        self.tokenizer = nltk.tokenize.RegexpTokenizer(r'\\w+')  # drops everything except alphanumeric characters\n\n    def create_sentence_vectors(self):\n        if self.wordfreq == {}:\n            raise ValueError(\"Create the frequency dictionary in the corpus class pls\")\n        most_freq = heapq.nlargest(NUMBER_OF_WORDS, self.wordfreq, key=self.wordfreq.get)\n\n        for sentence in self.corpus:\n            sentence_tokens = self.tokenizer.tokenize(sentence)\n            sent_vec = []\n            for token in most_freq:\n                if token in sentence_tokens:\n                    sent_vec.append(1)\n                else:\n                    sent_vec.append(0)\n            self.sentence_vecs.append(sent_vec)\n\n    def plot_vectors(self, c_map='jet'):\n        \"\"\"this is hella ugly i will try to make it better eventually lol \"\"\"\n        cmap = getattr(c, c_map)\n        plt.imshow(self.sentence_vecs, cmap=cmap)\n        plt.show()\n\n\nclass TFIDF(object):\n    def __init__(self, corpus, wordfreq):\n        self.corpus = corpus\n        self.wordfreq = wordfreq\n        self.tf_scores = {}\n        self.tfidf_scores = {}\n        self.word_instances = {}\n\n    def compute_tf(self):\n        \"\"\"Frequency of term in doc/ total number of terms in the doc\"\"\"\n        for k in self.wordfreq.keys():\n            self.tf_scores[k] = {}\n            for k2 in self.wordfreq[k]['frequencies'].keys():  # k2 = word name\n\n                word_count = self.wordfreq[k]['frequencies'][k2]\n                self.tf_scores[k][k2] = word_count / self.wordfreq[k]['total_words']\n\n    def compute_tfidf(self):\n        \"\"\"ln(total number of docs/ number of docs with term in it) \"\"\"\n        self.compute_tf()\n        total_docs = len(self.wordfreq.keys())\n        for k in self.wordfreq.keys():\n            self.tfidf_scores[k] = {}\n            for k2 in self.wordfreq[k]['frequencies'].keys():\n                if k2 in self.word_instances.keys():\n                    self.tfidf_scores[k][k2] = self.tf_scores[k][k2] * math.log(total_docs / self.word_instances[k2])\n                else:\n                    self.tfidf_scores[k][k2] = self.tf_scores[k][k2] * math.log(\n                        total_docs / self.find_total_occurrences(k2))\n\n    def find_total_occurrences(self, word):\n        instance = 0\n        for k in self.wordfreq.keys():\n            if word in self.wordfreq[k]['frequencies'].keys():\n                instance += 1\n\n        self.word_instances[word] = instance\n        return instance\n\n\naethism = TotalCorp()\naethism.create_corp()\n\n# bag_aethism = BagOfWords(aethism.corpus, aethism.word_freq_dict['comma_seperated_words'])\n# bag_aethism.create_sentence_vectors()\n\"\"\"\nfor c in os.listdir(PATH_TO_NEWS_GROUP):\n    print(c)\n    corp = TotalCorp(lemma=False, folder_name=c, b='')\n    corp.create_corp()\n    corp.save()\n\n\nwordcloud = WordCloud()\nwordcloud.generate_from_frequencies(aethism.word_freq_dict['51060']['frequencies'])\nplt.imshow(wordcloud)\nplt.axis(\"off\")\nplt.show()\nbag_aethism = BagOfWords(aethism.corpus, aethism.word_freq_dict['51060']['frequencies'])\nbag_aethism.create_sentence_vectors()\n\nTFIDF_aethism = TFIDF(aethism.corpus, aethism.word_freq_dict)\nTFIDF_aethism.compute_tfidf()\n\"\"\"\n", "repo_name": "haley-weinstein/dv_midterm", "sub_path": "pre_process.py", "file_name": "pre_process.py", "file_ext": "py", "file_size_in_byte": 7172, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "base64.b64encode", "line_number": 26, "usage_type": "call"}, {"api_name": "zlib.compress", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 28, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 46, "usage_type": "call"}, {"api_name": "nltk.tokenize.RegexpTokenizer", "line_number": 48, "usage_type": "call"}, {"api_name": "nltk.tokenize", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "nltk.sent_tokenize", "line_number": 58, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 70, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 72, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 82, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 83, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 84, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 87, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 87, "usage_type": "name"}, {"api_name": "nltk.tokenize.RegexpTokenizer", "line_number": 113, "usage_type": "call"}, {"api_name": "nltk.tokenize", "line_number": 113, "usage_type": "attribute"}, {"api_name": "heapq.nlargest", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 132, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "math.log", "line_number": 162, "usage_type": "call"}, {"api_name": "math.log", "line_number": 164, "usage_type": "call"}]}
{"seq_id": "17068325237", "text": "from utils import console, get_input, submit\n\ncnt = \" abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n\n\ndef part1(inp):\n    res = 0\n    for i in inp:\n        ln = len(i) // 2\n        c = set(i[:ln]).intersection(set(i[ln:])).pop()\n        res += cnt.index(c)\n    return res\n\n\ndef part2(inp):\n    res = 0\n    for i in range(0, len(inp), 3):\n        c = (\n            set(inp[i])\n            .intersection(set(inp[i + 1]))\n            .intersection(set(inp[i + 2]))\n            .pop()\n        )\n        res += cnt.index(c)\n    return res\n\n\nassert (\n    part1(\n        \"\"\"vJrwpWtwJgWrhcsFMMfFFhFp\njqHRNqRjqzjGDLGLrsFMfFZSrLrFZsSL\nPmmdzqPrVvPwwTWBwg\nwMqvLMZHhHMvwLHjbvcjnnSBnvTQFn\nttgJtRGJQctTZtZT\nCrZsJsPPZsGzwwsLwLmpwMDw\"\"\".splitlines()\n    )\n    == 157\n)\nconsole.log(part1(get_input(2022, 3)))\nsubmit(year=2022, day=3, level=1, answer=part1(get_input(2022, 3)))\n\nassert (\n    part2(\n        \"\"\"vJrwpWtwJgWrhcsFMMfFFhFp\njqHRNqRjqzjGDLGLrsFMfFZSrLrFZsSL\nPmmdzqPrVvPwwTWBwg\nwMqvLMZHhHMvwLHjbvcjnnSBnvTQFn\nttgJtRGJQctTZtZT\nCrZsJsPPZsGzwwsLwLmpwMDw\"\"\".splitlines()\n    )\n    == 70\n)\nconsole.log(part2(get_input(2022, 3)))\nsubmit(year=2022, day=3, level=2, answer=part2(get_input(2022, 3)))\n", "repo_name": "anze3db/adventofcode2022", "sub_path": "03.py", "file_name": "03.py", "file_ext": "py", "file_size_in_byte": 1190, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "utils.console.log", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.console", "line_number": 39, "usage_type": "name"}, {"api_name": "utils.get_input", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.submit", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.get_input", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.console.log", "line_number": 53, "usage_type": "call"}, {"api_name": "utils.console", "line_number": 53, "usage_type": "name"}, {"api_name": "utils.get_input", "line_number": 53, "usage_type": "call"}, {"api_name": "utils.submit", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.get_input", "line_number": 54, "usage_type": "call"}]}
{"seq_id": "28066613657", "text": "from itertools import (\n    permutations,\n    starmap,\n)\n\nfrom inttools.special_numbers import is_polygonal_number\n\ndef is_polygonal_representative_set(int_set, poly_reps):\n    \"\"\"\n        If P is a set of polygonal side lengths n, a set S is called a\n        P-polygonal representative set, or simply P-polygonal, if S\n        consists of a different n-gonal number for every n in P, e.g.\n        the set {2882, 8128, 8281} is {3,4,5}-polygonal because it\n        contains the triangular number 8128, the square number 8281 and\n        the pentagonal number 2882.\n    \"\"\"\n    return any(\n        (\n            False not in starmap(is_polygonal_number, zip(int_set, poly_rep_perm))\n            for poly_rep_perm in permutations(poly_reps)\n        )\n    )\n", "repo_name": "sr-murthy/inttools", "sub_path": "special_sets/polygonal.py", "file_name": "polygonal.py", "file_ext": "py", "file_size_in_byte": 755, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "itertools.starmap", "line_number": 19, "usage_type": "call"}, {"api_name": "inttools.special_numbers.is_polygonal_number", "line_number": 19, "usage_type": "argument"}, {"api_name": "itertools.permutations", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "73903159799", "text": "# Convenience python script for generating correctly formatted\n# lines of the two key tables in the main static file\n# Dashed off by Dylan Morris in July 2016\n\n# Feel free to use, modify, and improve!\n\nimport datetime as dt\nimport pytz as tz\n\ndef suffix(natural_number):\n        if (natural_number % 100) in [11, 12, 13]:\n            suffix = \"th\"\n        elif (natural_number % 10) == 1:\n            suffix = \"st\"\n        elif (natural_number % 10) == 2:\n            suffix = \"nd\"\n        elif (natural_number % 10) == 3:\n            suffix = \"rd\"\n        else:\n            suffix = \"th\"\n        return suffix\n\n\nthe_timezone = tz.timezone(\"US/Eastern\")\nstart_date = dt.datetime(2016, 9, 7, 12, 30, 0)\n\nthe_date = the_timezone.localize(start_date)\n\nschedule_string = \"\"\"<tr><td><a href=\"#{0}\"><time datetime=\"{1}\">{2}<sup>{3}</sup> – 12:30pm</time></a></td><td><a href=\"#{0}\">Available</a></td></tr>\"\"\"\n\nfor k in range(18):\n    print(schedule_string.format(\n        the_date.strftime(\"%Y-%m-%d\"),\n        the_date.isoformat(),\n        the_date.strftime(\"%a. %B %-d\"),\n        suffix(the_date.day)))\n    the_date += dt.timedelta(7)\n\ntitle_abs_string = \"\"\"<dt id=\"{0}\"><time datetime=\"{1}\">{2}<sup>{3}</sup> – 12:30pm</time></dt><dd><header><strong>Speaking slot available</strong><EM></em></header><p></p><a href=\"#schedule\">Back to schedule</a></dd>\"\"\"\n\nthe_date = the_timezone.localize(start_date)\nfor k in range(18):\n    print(title_abs_string.format(\n              the_date.strftime(\"%Y-%m-%d\"),\n              the_date.isoformat(),\n              the_date.strftime(\"%a. %B %-d\"),\n              suffix(the_date.day)))\n    the_date += dt.timedelta(7)\n", "repo_name": "dylanhmorris/labtea", "sub_path": "generate-static-html.py", "file_name": "generate-static-html.py", "file_ext": "py", "file_size_in_byte": 1655, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pytz.timezone", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 48, "usage_type": "call"}]}
{"seq_id": "70734454200", "text": "#-*- encoding: utf-8 -*-. -\n\nimport cv2\n\n\ndef make_thumbnail(file_name):\n    video_dir = \"/home/taehyeon/capstone-2021-23/mysite/notification/video/\"\n    thumbnail_dir = \"/home/taehyeon/capstone-2021-23/mysite/notification/thumbnail/\"\n\n    cap = cv2.VideoCapture(video_dir + file_name)\n    count = 0\n\n    while True:\n        ret,frame = cap.read()\n\n        if not ret:\n            print(\"can not read video\")\n\n        if count > 50:\n            cv2.imwrite(f\"{thumbnail_dir + file_name.split('.')[0]}.jpg\", frame)\n            break\n\n        count += 1\n\n    return f\"{file_name.split('.')[0]}.jpg\"\n\n# if __name__ == \"__main__\":\n#     make_thumbnail(\"stop.mp4\")\n", "repo_name": "kookmin-sw/capstone-2021-23", "sub_path": "mysite/notification/gmail/thumbnail.py", "file_name": "thumbnail.py", "file_ext": "py", "file_size_in_byte": 660, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "40", "api": [{"api_name": "cv2.VideoCapture", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "35376567526", "text": "import sqlite3\nfrom django.shortcuts import render\nfrom raterprojectapi.models import Review\nfrom raterprojectreports.views import Connection\n\n\ndef topreviewers_count(request):\n    if request.method == 'GET':\n        # Connect to project database\n        with sqlite3.connect(Connection.db_path) as conn:\n            conn.row_factory = sqlite3.Row\n            db_cursor = conn.cursor()\n\n            # Query for all games, with related rating info.\n            db_cursor.execute(\"\"\"\n                  SELECT COUNT(u.username) as count, u.username\n                  FROM raterprojectapi_review r\n                  JOIN raterprojectapi_game g ON r.game_id = g.id\n                  JOIN raterprojectapi_player p ON r.player_id = p.id\n                  JOIN auth_user u ON p.user_id = u.id\n                  GROUP BY player_id\n                  ORDER BY COUNT(u.username) DESC\n                  LIMIT 3\n            \"\"\")\n\n            dataset = db_cursor.fetchall()\n\n            top_reviewers = []\n\n            for row in dataset:\n                review = Review()\n                review.username = row[\"username\"]\n                review.count = row[\"count\"]\n\n                top_reviewers.append(review)\n\n        # Specify the Django template and provide data context\n        template = 'reviews/top_reviewers.html'\n        context = {\n            'topreviewers_count': top_reviewers\n        }\n\n        return render(request, template, context)\n", "repo_name": "jeaninebeckle/raterproject-server", "sub_path": "raterprojectreports/views/reviews/topreviewers.py", "file_name": "topreviewers.py", "file_ext": "py", "file_size_in_byte": 1439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sqlite3.connect", "line_number": 10, "usage_type": "call"}, {"api_name": "raterprojectreports.views.Connection.db_path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "raterprojectreports.views.Connection", "line_number": 10, "usage_type": "name"}, {"api_name": "sqlite3.Row", "line_number": 11, "usage_type": "attribute"}, {"api_name": "raterprojectapi.models.Review", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}]}
{"seq_id": "37018104986", "text": "import streamlit as st\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\ndf = pd.read_csv(\"startup_cleaned.csv\")\r\n\r\ndf['date'] = pd.to_datetime(df['date'],errors='coerce')\r\ndf['year'] = df['date'].dt.year\r\n\r\n# extracting month from date column\r\ndf['month'] = df['date'].dt.month\r\n\r\nst.set_page_config(layout='wide')\r\n\r\ndef load_overall_analysis():\r\n\r\n    st.title(\"Overall Analysis\")\r\n    col1,col2,col3,col4 = st.columns(4)\r\n    \r\n\r\n    with col1:\r\n        # Total Investment\r\n        total = round(df['amount'].sum())\r\n        st.metric('Total (in Cr)', total )\r\n    with col2:\r\n        # Highest Investment\r\n        max = df.groupby('startup')['amount'].max().sort_values(ascending = False).head(1)\r\n        st.metric('Max (in Cr)', max.values[0] , max.index[0])\r\n    with col3:\r\n        # Avg funding\r\n        avg_funding = round(df.groupby('startup')['amount'].sum().mean())\r\n        st.metric(\"Avg Funding(in Cr)\",avg_funding)\r\n    with col4:\r\n        # No. of startups funded\r\n        n_startups = df['startup'].nunique()\r\n        st.metric(\"Total Startups\",n_startups)\r\n\r\n    # month on month graphs\r\n    st.subheader('Month on Month Graph')\r\n    mom_bnt = st.selectbox('Select Type',['Total','Count'])\r\n    \r\n    #Total no.of startups by month on month\r\n    if mom_bnt == 'Total':\r\n        temp_df = df.groupby(['year','month'])['amount'].sum().reset_index()\r\n    else:\r\n        temp_df = df.groupby(['year','month'])['amount'].count().reset_index()\r\n\r\n    temp_df['x_axis'] = temp_df['month'].astype('str') + '-' + temp_df['year'].astype('str')\r\n\r\n    fig , ax = plt.subplots(figsize=(10,5))\r\n    ax.plot(temp_df['x_axis'],temp_df['amount'])\r\n    ax.set_xlabel('Year')\r\n    #ax.set_ylabel('Total No. of Startups')\r\n    st.pyplot(fig)\r\n    \r\n\r\n\r\ndef load_inverstor_details(investor):\r\n    st.title(investor)\r\n    # loading the Top 5 recent investments\r\n    st.subheader('Most Recent Investments')\r\n    top5_df = df[df['Investors'].str.contains(investor)].head()[['date','startup','vertical','City  Location','round','amount']]\r\n    st.dataframe(top5_df)\r\n\r\n\r\n    col1 , col2 = st.columns(2)\r\n    with col1:\r\n        # loading the Biggest investments\r\n        st.subheader('Biggest Investments')\r\n        big_df = df[df['Investors'].str.contains(investor)].groupby('startup')['amount'].sum().sort_values(ascending = False).head()\r\n\r\n\r\n        fig , ax = plt.subplots(figsize=(5,4))\r\n        plot = ax.bar(big_df.index,big_df.values)\r\n        ax.set_xlabel('StartUps')\r\n        ax.set_ylabel('Amount (Cr)')\r\n        ax.bar_label(plot,big_df.values)\r\n        st.pyplot(fig)\r\n\r\n    with col2:\r\n        st.subheader('Sectors Invested in')\r\n        vertical_series = df[df['Investors'].str.contains(investor)].groupby('vertical')['amount'].sum()\r\n\r\n        fig1 , ax1 = plt.subplots(figsize=(3,5))\r\n        plot = ax1.pie(vertical_series,labels=vertical_series.index,autopct='%0.01f%%')\r\n        st.pyplot(fig1)\r\n\r\n    col1 , col2 = st.columns(2)\r\n    with col1:\r\n        \r\n        st.subheader('City')\r\n        city_series = df[df['Investors'].str.contains(investor)].groupby('City  Location')['amount'].sum()\r\n\r\n        fig2 , ax2 = plt.subplots(figsize=(3,5))\r\n        plot = ax2.pie(city_series,labels=city_series.index,autopct='%0.01f%%')\r\n        st.pyplot(fig2)\r\n\r\n    with col2:\r\n        st.subheader('Stage')\r\n        stage_series = df[df['Investors'].str.contains(investor)].groupby('round')['amount'].sum()\r\n\r\n        fig3 , ax3 = plt.subplots(figsize=(3,5))\r\n        plot = ax3.pie(stage_series,labels=stage_series.index,autopct='%0.01f%%')\r\n        st.pyplot(fig3)\r\n\r\n    # Year on Year line Plot\r\n    \r\n\r\n    st.subheader('Year on Year Investments')\r\n    yoy_df = df[df['Investors'].str.contains(investor)].groupby('year')['amount'].sum()\r\n\r\n\r\n    fig4 , ax4 = plt.subplots(figsize=(10,5))\r\n    plot = ax4.plot(yoy_df.index,yoy_df.values)\r\n    ax4.set_xlabel('Year')\r\n    ax4.set_ylabel('Amount (Cr)')\r\n    \r\n    st.pyplot(fig4)\r\n\r\n\r\nst.sidebar.title('Startup Funding Analysis')\r\n\r\noption = st.sidebar.selectbox('Select One',['Overall Analysis','StartUp','Investor'])\r\n\r\nif option == 'Overall Analysis':\r\n    load_overall_analysis()\r\n\r\nelif option == 'StartUp':\r\n    st.sidebar.selectbox('Select StartUp',sorted(df[\"startup\"].unique().tolist()))\r\n    st.title(\"StartUp Analysis\")\r\n    btn1 = st.sidebar.button(\"Find StartUp Details\")\r\n\r\nelse:\r\n    seleted_investor = st.sidebar.selectbox('Select Investor',sorted(set(df[\"Investors\"].str.split(',').sum())))\r\n    st.title(\"Inveator Analysis\")\r\n    btn2 = st.sidebar.button(\"Find Investor Details\")\r\n    if btn2:\r\n        load_inverstor_details(seleted_investor)", "repo_name": "akashnalla7/Indian-Startup-Funding-analysis", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4632, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pandas.read_csv", "line_number": 4, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 6, "usage_type": "call"}, {"api_name": "streamlit.set_page_config", "line_number": 12, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 17, "usage_type": "call"}, {"api_name": "streamlit.metric", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit.metric", "line_number": 27, "usage_type": "call"}, {"api_name": "streamlit.metric", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.metric", "line_number": 35, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 53, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 58, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 60, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 65, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 77, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 85, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 87, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 95, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 103, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 117, "usage_type": "call"}, {"api_name": "streamlit.sidebar.title", "line_number": 120, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 120, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 122, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 122, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 128, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 128, "usage_type": "attribute"}, {"api_name": "streamlit.title", "line_number": 129, "usage_type": "call"}, {"api_name": "streamlit.sidebar.button", "line_number": 130, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 130, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 133, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 133, "usage_type": "attribute"}, {"api_name": "streamlit.title", "line_number": 134, "usage_type": "call"}, {"api_name": "streamlit.sidebar.button", "line_number": 135, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 135, "usage_type": "attribute"}]}
{"seq_id": "22586308787", "text": "import json\nfrom compensation_info import CompensationInfo\n\nsurvey_2_field_mapper = {\n    'salary': 'Total Base Salary in 2018 (in USD)',\n    'employment_type': 'Employment Type',\n    'vacation_weeks': 'Annual Vacation (in Weeks)',\n    'company': 'Company Name',\n    'job_title': 'Job Title In Company'\n}\n\ndef get_data(file_path):\n    \"\"\"Reads JSON file from path and returns Python object\"\"\"\n    \n    with open(file_path) as f:\n        data = json.load(f)\n\n    return data\n\n\ndef get_normalized_records(file_path = 'salary_survey-2.json'):\n    \"\"\"Normalizes field names from list of dicts into friendly/consistent field names\"\"\"\n    survey_2_data = get_data(file_path)\n\n    records = [\n        { k : x[survey_2_field_mapper[k]] for k in survey_2_field_mapper } \n        for x in survey_2_data\n    ]\n\n    return records\n\n\ndef get_records(request_args):\n    \"\"\"Gets records that match the request arguments.\n\n    Args:\n        request_args (ImmutableMultiDict): Request query params for specifying data to return.\n        data_file_path (str): Optional; The relative path to the JSON data file.\n    \n    Returns:\n        list: List of records as dicts.\n    \"\"\"\n\n    records = get_normalized_records()\n\n    sort_field = request_args.get('sort')\n    limit = request_args.get('limit')\n\n    records = filter_by(request_args, records)\n    if sort_field is not None:\n        records = sort_by(sort_field, records)\n    if limit is not None:\n        records = limit_by(limit, records)\n\n    return records\n\n\ndef get_record(id):\n    \"\"\"Returns dict from list by its index\"\"\"\n    records = get_normalized_records()\n    try:\n        return records[id]\n    except IndexError:\n        return None\n\n\ndef get_gte(records, field, requested_value):\n    \"\"\"Returns list of records with field value greater than or equal to field and value requested\"\"\"\n\n    return [x for x in records if float(x[field]) >= int(requested_value)]\n\n\ndef get_match(records, field, requested_value):\n    \"\"\"Returns list of records that match the field and value requested\"\"\"\n\n    return [x for x in records if x[field].lower() == requested_value.lower()]\n\n\ndef filter_by(filter_args, records):\n    \"\"\"Returns list of records matching requested filter arguments\"\"\"\n\n    for k, v in filter_args.items():\n        if k[-5:] == '[gte]':\n            records = get_gte(records, k[:-5], v)\n        elif k in survey_2_field_mapper.keys():\n            records = get_match(records, k, v)\n    \n    return records\n\n\ndef sort_by(field, records):\n    return sorted(records, key=lambda k: k[field])\n\n\ndef limit_by(limit, records):\n    return records[:int(limit)]\n\n\ndef validate_request_args(request_args):\n    \"\"\"Validates field and value of request arguments.\n\n    Args:\n        request_args (ImmutableMultiDict): Request query params.\n    \n    Returns:\n        None\n    \n    Raises:\n        ValueError: Unknown field, or invalid value, or invalid value type was provided.\n    \"\"\"\n\n    sort_field = request_args.get('sort')\n    valid_fields = survey_2_field_mapper.keys()\n    if sort_field is not None and sort_field not in valid_fields:\n        raise ValueError(f'Invalid sort argument: \"{sort_field}\". Must be one of: {list(valid_fields)}')\n    \n    integer_type_fields = ['limit', 'salary', 'salary[gte]', 'vacation_weeks']\n    for arg in integer_type_fields:\n        val = request_args.get(arg)\n        if val is not None:\n            try:\n                int(val)\n            except:\n                raise ValueError(f'Invalid value passed for \"{arg}\": \"{val}\" must be an integer.')\n", "repo_name": "emilywatkins/flask-rest-api", "sub_path": "data_retriever.py", "file_name": "data_retriever.py", "file_ext": "py", "file_size_in_byte": 3530, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "json.load", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "28873777658", "text": "import os\nimport sys\nimport json\nimport pickle\nimport numpy as np\n\nfrom src.attributers.attributer import Attributer\nfrom src import exptutil\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\nnominal_gamma = 1e-2\nseed = 1234\n\n\ndataname, num_anom, modeltype, scoretype, attrtype = \\\n    sys.argv[1], int(sys.argv[2]), sys.argv[3], sys.argv[4], sys.argv[5]\n\nif len(sys.argv) > 6:\n    if attrtype not in ['comp', 'ASH', 'ASHexact']:\n        raise ValueError('setting gamma for this attrtype does not make sense')\n    gamma = float(sys.argv[6])\nelse:\n    gamma = nominal_gamma\n\nif (modeltype=='dagmm' or (modeltype=='vae' and scoretype=='negelbo')) \\\n    and (attrtype=='marg' or attrtype=='SFE'):\n    raise ValueError('incompatible pair of detector and attributor')\n\nif gamma==nominal_gamma:\n    print('start %s na%d_%s_%s_%s' % (dataname, num_anom, modeltype, scoretype, attrtype))\nelse:\n    print('start %s na%d_%s_%s_%s_%0.1e' % (dataname, num_anom, modeltype, scoretype, attrtype, gamma))\n\ndatadir = os.path.join('data', 'processed', dataname)\n\n# load data\ndata_train, _, data_test = exptutil.load_data(datadir)\ndim_x = data_test.shape[1]\n\n# use only normal part (first half) of test data\ndata_test = data_test[:int(data_test.shape[0]/2)]\n\n# load best model\nwith open(os.path.join('models', dataname, modeltype, 'detection', 'best_model.json'), 'r') as fp:\n    best_model = json.load(fp)\nmodel = exptutil.load_model(best_model[scoretype], modeltype)\n\n# some settings\nif dataname=='lympho':\n    num_cases = 6\n    num_tries = 20\nelse:\n    num_cases = 60\n    num_tries = 2\nub = 2.0\nlb = 1.0\n\n# create perturbed data\nnp.random.seed(seed)\ncase_indices = np.random.permutation(data_test.shape[0])\ncase_all = []\nfeat_all = []\ntarget_all = []\nfor i in range(num_cases):\n    for j in range(num_tries):\n        case_all.append(case_indices[i])\n\n        # target features\n        feat_indices = np.random.permutation(data_test.shape[1])\n        target_feat = feat_indices[:num_anom]\n\n        # perturb values\n        target = np.copy(data_test[case_indices[i]])\n        for idx in target_feat:\n            if dataname=='lympho':\n                target[idx] = 1-target[idx]\n            else:\n                sign=[-1,1]; sign = sign[np.random.choice(2)]\n                perturbation = sign*(np.random.rand()*(ub-lb)+lb)\n                target[idx] += perturbation\n\n        feat_all.append(target_feat)\n        target_all.append(target)\n\n# set attributer\nif modeltype=='gmm':\n    detector_type = 'GMM'\nelif modeltype=='vae' and scoretype=='recerr':\n    detector_type='VAE-r'\nelif modeltype=='vae' and scoretype=='negelbo':\n    detector_type='VAE-e'\nelif modeltype=='dagmm':\n    detector_type='DAGMM'\nelse:\n    raise ValueError('unknown model/score type')\nAT = Attributer(detector_type, model, data_train, False if dataname=='lympho' else True)\n\n# do attribution\nnp.random.seed(seed+1)\nattr_all = []\ninfo_all = []\nfor i in range(len(target_all)):\n    attr, info = AT.attribute(target_all[i], attrtype, gamma=gamma)\n    attr_all.append(attr)\n    info_all.append(info)\n    # print('\\r  attributed #%03d/%d' % (i+1, len(target_all)), end='')\n\n# save\noutdir = os.path.join('out', 'expt_synth', dataname)\nos.makedirs(outdir, exist_ok=True)\nif gamma==nominal_gamma:\n    outname = os.path.join(outdir, 'na%d_%s_%s_%s.pickle' % (num_anom, modeltype, scoretype, attrtype))\nelse:\n    outname = os.path.join(outdir, 'na%d_%s_%s_%s_%0.1e.pickle' % (num_anom, modeltype, scoretype, attrtype, gamma))\nwith open(outname, 'wb') as fp:\n    pickle.dump([attr_all, info_all, case_all, feat_all], fp)\n\nif gamma==nominal_gamma:\n    print('finish %s na%d_%s_%s_%s' % (dataname, num_anom, modeltype, scoretype, attrtype))\nelse:\n    print('finish %s na%d_%s_%s_%s_%0.1e' % (dataname, num_anom, modeltype, scoretype, attrtype, gamma))\n", "repo_name": "n-takeishi/anomaly-attribution", "sub_path": "expt_synth.py", "file_name": "expt_synth.py", "file_ext": "py", "file_size_in_byte": 3800, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "warnings.filterwarnings", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "src.exptutil.load_data", "line_number": 40, "usage_type": "call"}, {"api_name": "src.exptutil", "line_number": 40, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 48, "usage_type": "call"}, {"api_name": "src.exptutil.load_model", "line_number": 49, "usage_type": "call"}, {"api_name": "src.exptutil", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 82, "usage_type": "attribute"}, {"api_name": "src.attributers.attributer.Attributer", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 119, "usage_type": "call"}]}
{"seq_id": "74700239479", "text": "import time\nimport cv2\nimport imutils\nfrom imutils.video import VideoStream\nfrom img_aux import filter_colour, track_ball, draw_frame\nimport paho.mqtt.client as mqtt\n\nMQTT_BROKER = \"192.168.0.117\"\nMQTT_PORT = 1883\nMQTT_KEEPALIVE_INTERVAL = 6000\n\ncommand_motor_direction = \"objectFinderBot/motor/command/direction\"\ncommand_motor_speed = \"objectFinderBot/motor/command/speed\"\n\n\ndef get_broadrcaster():\n    global broadrcaster\n    broadrcaster = mqtt.Client()\n    print(\"connecting with the server\")\n    broadrcaster.connect(MQTT_BROKER, MQTT_PORT, MQTT_KEEPALIVE_INTERVAL)\n    print(\"Server connected\")\n    return broadrcaster\n\n\ndef module_img_ai(title, camera, ifImg):\n    videoStream = cv2.VideoCapture('http://192.168.0.117:8081')\n    print(\"PLEASE WAIT WHILE MODULE INITIALIZE THE CAMERA MODILE\")\n    time.sleep(2.0)\n    global broadrcaster, command_motor_direction, command_motor_speed\n    broadrcaster = get_broadrcaster()\n    while True:\n        _, frame = videoStream.read()\n        blurred = cv2.GaussianBlur(frame, (11, 11), 0)\n        hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)\n\n        mask = filter_colour(frame)\n        location, _ = track_ball(mask)\n        frame = draw_frame(mask, frame)\n        x, y, w, h = location\n        if(x >= 1):\n            if(x <= 200):\n                print(\"left movement x\")\n                broadrcaster.publish(command_motor_direction, 1)\n        if(x >= 401):\n            if(x <= 600):\n                print(\"right movement x\")\n                broadrcaster.publish(command_motor_direction, 3)\n        if(x >= 201):\n            if(x <= 400):\n                print(\"forworad movment\")\n                broadrcaster.publish(command_motor_direction, 0)\n        if(x == 0):\n            print(\"object not found\")\n            broadrcaster.publish(command_motor_direction, 4)\n        if ifImg:\n            cv2.imshow(title, frame)\n            # cv2.imshow(\"maskFrame\", hsv)\n            key = cv2.waitKey(1) & 0xFF\n            if key == ord(\"q\"):\n                break\n    videoStream.stop()\n    cv2.destroyAllWindows()\n    return\n\n\nmodule_img_ai(\"module_img_ai\", 0, True)\n", "repo_name": "extinctCoder/objectFinderBot", "sub_path": "img.py", "file_name": "img.py", "file_ext": "py", "file_size_in_byte": 2114, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "paho.mqtt.client.Client", "line_number": 18, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 18, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 34, "usage_type": "attribute"}, {"api_name": "img_aux.filter_colour", "line_number": 36, "usage_type": "call"}, {"api_name": "img_aux.track_ball", "line_number": 37, "usage_type": "call"}, {"api_name": "img_aux.draw_frame", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 62, "usage_type": "call"}]}
{"seq_id": "20214605797", "text": "import torch\nimport torchvision.transforms as transforms\nfrom torchvision.datasets import ImageFolder,\\\n    CIFAR10, \\\n    CIFAR100, \\\n    MNIST\nfrom torch.utils.data import DataLoader, random_split, Subset, ConcatDataset\nimport numpy as np\nfrom sklearn.manifold import TSNE\nimport matplotlib.pyplot as plt\nfrom custom_archs import wfmodels_lora as wfmodels\n\nunl_class = 0\ntotal_images_number = 5000\ndataset = 'cifar10'\nmodel_name = 'vit_tiny'\n\nif 'cifar10' in dataset:\n    if 'tiny' in model_name:\n        ours_ckp = f\"/mnt/beegfs/work/dnai_explainability/unlearning/icml2023/alpha_matrices/checkpoints_acm/cifar10-vit_tiny_16224-1-1-0/2023-04-28/0.1-100/best_CLASS-{unl_class}.pt\"\n        loss_diff_ckp = f\"/mnt/beegfs/work/dnai_explainability/unlearning/icml2023/alpha_matrices/checkpoints_acm/cifar10-vit_tiny_16224-1-1-0/2023-04-28/0.1-100/best_CLASS-{unl_class}.pt\"\n        gold_ckp = f\"/mnt/beegfs/work/dnai_explainability/ssarto/checkpoints_gold/CIFAR10/vit_tiny/cifar10_vit_tiny-4-ckpt_original_{unl_class}.t7\"\n    else:\n        ours_ckp = f\"/mnt/beegfs/work/dnai_explainability/unlearning/icml2023/alpha_matrices/checkpoints_acm/cifar10-vit_small_16224-1-1-0/2023-04-28/0.1-100/best_CLASS-{unl_class}.pt\"\n        loss_diff_ckp = f\"/mnt/beegfs/work/dnai_explainability/lbaraldi/alpha_matrices/vit_small_neggrad_64_0.0001_1.0_0.01/2023-04-30/cifar10/difference/1.0-0.01/best_CLASS-{unl_class}.pt\"\n        # gold_ckp = f\"/mnt/beegfs/work/dnai_explainability/ssarto/checkpoints_gold/CIFAR10/vit_tiny/cifar10_vit_tiny-4-ckpt_original_{unl_class}.t7\"\n    c_number = 10\nelse:\n    if 'tiny' in model_name:\n        ours_ckp = f\"/mnt/beegfs/work/dnai_explainability/lbaraldi/unlearning/icml2023/alpha_matrices/checkpoints_acm/vit_tiny_16224_lora_zero_3way_fixed_l1_AB_256_0.01_0.001_1.0/2023-05-03/0.001-1.0/best_CLASS-{unl_class}.pt\"\n        loss_diff_ckp = f\"/mnt/beegfs/work/dnai_explainability/lbaraldi/unlearning/icml2023/alpha_matrices/checkpoints_acm/vit_tiny_16224_neggrad_256_0.0001_1.0_0.005/2023-05-03/1.0-0.005/best_CLASS-{unl_class}.pt\"\n        gold_ckp = f\"/mnt/beegfs/work/dnai_explainability/ssarto/checkpoints_gold/CIFAR10/vit_tiny/cifar10_vit_tiny-4-ckpt_original_{unl_class}.t7\"\n    else:\n        ...\n    c_number = 20\n\n# Define the PyTorch model\n\nif model_name == 'vit_tiny':\n    kind = wfmodels.vit_tiny_16224\nelse:\n    kind = wfmodels.vit_small_16224\n\nmodel = wfmodels.WFTransformer(\n    kind=kind, pretrained=True,\n    resume=ours_ckp,\n    dataset=dataset.lower(), alpha=False\n)\nmodel = model.arch\n\nif 'cifar10' in dataset and 'tiny' in model_name:\n    loss_diff_ckp = \\\n        f\"/mnt/beegfs/work/dnai_explainability/unlearning/icml2023/alpha_matrices/checkpoints_acm/debug_instance_prod_64_5e-05_2.5e-05_1.0/2023-05-02/2.5e-05-1.0/best_CLASS-0.pt\"\nldiff = wfmodels.WFTransformer(\n    kind=kind, pretrained=True,\n    resume=loss_diff_ckp,\n    dataset=dataset.lower(), alpha=False\n)\nldiff = ldiff.arch\n\ngeneral = wfmodels.WFTransformer(\n    kind=kind, pretrained=False,\n    resume=None,\n    dataset=dataset.lower(), alpha=False\n)\nstandard = general.arch\n\n# gold = wfmodels.WFTransformer(\n#     kind=wfmodels.vit_tiny_16224, pretrained=True,\n#     resume=gold_ckp,\n#     dataset=dataset.lower(), alpha=False\n# )\n# gold = gold.arch\n\n\nif 'cifar10' in dataset:\n    size = 32\n    T = transforms.Compose([\n        transforms.RandomCrop(32, padding=4),\n        transforms.Resize(size),\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n    ])\n    inverse_transform = transforms.Compose([\n        transforms.Normalize((-0.4914/0.2023, -0.4822/0.1994, -0.4465/0.2010), (1/0.2023, 1/0.1994, 1/0.2010)),\n    ])\n\n    transform_test = transforms.Compose([\n        transforms.Resize(size),\n        transforms.ToTensor(),\n        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n    ])\n\n    _train = CIFAR10(\n        root='/work/dnai_explainability/unlearning/datasets/cifar10_classification/train',\n        transform=T, download=True, train=True\n    )\n\n    _val = CIFAR10(\n        root='/work/dnai_explainability/unlearning/datasets/cifar10_classification/val',\n        transform=transform_test, download=True, train=False\n    )\n\n\n    unlearning_ids = np.where(np.isin(np.array(\n        _val.targets\n        ), unl_class))[0]\n    unlearning_set = Subset(_val, unlearning_ids)\n    unlearning_set.targets = torch.Tensor(_val.targets).long()[unlearning_ids].tolist()\n\n    retaining_ids = np.where(~np.isin(np.array(\n        _val.targets\n        ), unl_class))[0]\n    retaining_set = Subset(_val, retaining_ids)\n    retaining_set.targets = torch.Tensor(_val.targets).long()[retaining_ids].tolist()\n\n    \n    # Define the data loader\n    unlearning_loader = DataLoader(unlearning_set, batch_size=256, shuffle=True)\n    unlearning_loader_iter = iter(unlearning_loader)\n    c_number = 10\n\nelif 'cifar20' in dataset.lower():\n\n        class_mapping_dict = \\\n                {0: 4,\n                1: 1,\n                2: 14,\n                3: 8,\n                4: 0,\n                5: 6,\n                6: 7,\n                7: 7,\n                8: 18,\n                9: 3,\n                10: 3,\n                11: 14,\n                12: 9,\n                13: 18,\n                14: 7,\n                15: 11,\n                16: 3,\n                17: 9,\n                18: 7,\n                19: 11,\n                20: 6,\n                21: 11,\n                22: 5,\n                23: 10,\n                24: 7,\n                25: 6,\n                26: 13,\n                27: 15,\n                28: 3,\n                29: 15,\n                30: 0,\n                31: 11,\n                32: 1,\n                33: 10,\n                34: 12,\n                35: 14,\n                36: 16,\n                37: 9,\n                38: 11,\n                39: 5,\n                40: 5,\n                41: 19,\n                42: 8,\n                43: 8,\n                44: 15,\n                45: 13,\n                46: 14,\n                47: 17,\n                48: 18,\n                49: 10,\n                50: 16,\n                51: 4,\n                52: 17,\n                53: 4,\n                54: 2,\n                55: 0,\n                56: 17,\n                57: 4,\n                58: 18,\n                59: 17,\n                60: 10,\n                61: 3,\n                62: 2,\n                63: 12,\n                64: 12,\n                65: 16,\n                66: 12,\n                67: 1,\n                68: 9,\n                69: 19,\n                70: 2,\n                71: 10,\n                72: 0,\n                73: 1,\n                74: 16,\n                75: 12,\n                76: 9,\n                77: 13,\n                78: 15,\n                79: 13,\n                80: 16,\n                81: 19,\n                82: 2,\n                83: 4,\n                84: 6,\n                85: 19,\n                86: 5,\n                87: 5,\n                88: 8,\n                89: 19,\n                90: 18,\n                91: 1,\n                92: 2,\n                93: 15,\n                94: 6,\n                95: 0,\n                96: 17,\n                97: 8,\n                98: 14,\n                99: 13\n                }\n\n        class CIFAR20(CIFAR100):\n\n            def __init__(self, root, c_to_del=[], train=True, transform=None, target_transform=None, download=False):\n                super().__init__(root, train, transform, _cifar100_to_cifar20, download)\n\n        def _cifar100_to_cifar20(target):\n            mapping = class_mapping_dict[target]\n            return mapping\n        \n        def _cifar20_from_cifar100(target):\n            mapping = _cifar100_to_cifar20(target)\n            demapping = [x for x,v in mapping if v==target[0]]\n\n            return demapping\n\n\n        means = (0.4914, 0.4822, 0.4465)\n        stds = (0.2023, 0.1994, 0.2010)\n        size = 32\n        T = transforms.Compose([\n            transforms.RandomCrop(32, padding=4),\n            transforms.Resize(size),\n            transforms.RandomHorizontalFlip(),\n            transforms.ToTensor(),\n            transforms.Normalize(means, stds),\n        ])\n\n        transform_test = transforms.Compose([\n            transforms.Resize(size),\n            transforms.ToTensor(),\n            transforms.Normalize(means, stds),\n        ])\n\n        # _train = CIFAR20(\n        #     root='/work/dnai_explainability/unlearning/datasets/cifar20_classification/train',\n        #     transform=T, download=True, train=True\n        # )\n\n        _val = CIFAR20(\n            root='/work/dnai_explainability/unlearning/datasets/cifar20_classification/val',\n            transform=transform_test, download=True, train=False\n        )\n        c_number = 20\n\n\n\n\n# whole_loader = DataLoader(Subset(_val, tuple(range(len(_val)))), batch_size=256, shuffle=True)\nboth = ConcatDataset((_train,_val))\nboth.datasets[0].targets = torch.ones(len(_train))\nboth.datasets[1].targets = torch.zeros(len(_val))\nwhole_loader = DataLoader(both, batch_size=16, shuffle=True)\n# whole_loader = DataLoader(Subset(_val, tuple(range(total_images_number))), batch_size=256, shuffle=True)\n# total_lenght = len(whole_loader.dataset)\n\n\n# Set the model to evaluation mode\nmodel.eval()\n\n# Define the selected layer from which to extract activations\n\n\n# Extract the activations from the selected layer\nours_act, ldiff_act, std_act, gold_act = [], [], [], []\n\nlabels = []\nprint('forwards')\n\nmodel=model.cuda()\nstandard = standard.cuda()\nldiff = ldiff.cuda()\n\nfrom tqdm import tqdm\n# with torch.no_grad():\nfor inputs, targets in tqdm(whole_loader):\n    inputs, targets = inputs.cuda(), targets.cuda()\n    inputs.requires_grad_(True)\n\n    outputs = model(inputs)\n    ours_act.append(outputs)\n\n    outputs = standard(inputs)\n    std_act.append(outputs)\n    \n    outputs = ldiff(inputs)\n    ldiff_act.append(outputs)\n\n    # outputs = gold(inputs)\n    # gold_act.append(outputs)\n\n    labels.append(targets.detach().cpu().numpy())\n\nours_act = np.concatenate(ours_act, axis=0)\nldiff_act = np.concatenate(ldiff_act, axis=0)\nstd_act = np.concatenate(std_act, axis=0)\n# gold_act = np.concatenate(gold_act, axis=0)\nlabels = np.concatenate(labels, axis=0)\n\n# Apply t-SNE to the activations to obtain a lower-dimensional representation\ntsne = TSNE(n_components=2, random_state=0)\n\nours_act = ours_act.reshape(total_lenght,-1)\nldiff_act = ldiff_act.reshape(total_lenght,-1)\nstd_act = std_act.reshape(total_lenght,-1)\n# gold_act = gold_act.reshape(total_lenght,-1)\n\nprint('tsne')\nours_emb = tsne.fit_transform(ours_act)\nldiff_emb = tsne.fit_transform(ldiff_act)\nstd_emb = tsne.fit_transform(std_act)\n# gold_emb = tsne.fit_transform(gold_act)\n\n# Visualize the embedding with the labels\n\nidx = labels == unl_class\n\nplt.figure()\nplt.scatter(ours_emb[~idx, 0], ours_emb[~idx, 1], c=labels[~idx], s=10)\nplt.scatter(ours_emb[idx, 0], ours_emb[idx, 1], c='crimson', marker='X', s=10)\n# plt.axis('off')\nplt.xticks([])\nplt.yticks([])\nplt.savefig(f'tsne_ours-{unl_class}_{dataset}_{model_name}.pdf', bbox_inches='tight')\n\nplt.figure()\nplt.scatter(ldiff_emb[~idx, 0], ldiff_emb[~idx, 1], c=labels[~idx], s=10)\nplt.scatter(ldiff_emb[idx, 0], ldiff_emb[idx, 1], c='crimson', marker='X', s=10)\n# plt.axis('off')\nplt.xticks([])\nplt.yticks([])\nplt.savefig(f'tsne_ldiff-{unl_class}_{dataset}_{model_name}.pdf', bbox_inches='tight')\n\nplt.figure()\nplt.scatter(std_emb[~idx, 0], std_emb[~idx, 1], c=labels[~idx], s=10)\nplt.scatter(std_emb[idx, 0], std_emb[idx, 1], c='crimson', marker='X', s=10)\n# plt.axis('off')\nplt.xticks([])\nplt.yticks([])\nplt.savefig(f'tsne_std-{unl_class}_{dataset}_{model_name}.pdf', bbox_inches='tight')\n\n# plt.figure()\n# plt.scatter(gold_emb[~idx, 0], gold_emb[~idx, 1], c=labels[~idx], s=10)\n# plt.scatter(gold_emb[idx, 0], gold_emb[idx, 1], c='crimson', marker='X', s=10)\n# # plt.axis('off')\n# plt.xticks([])\n# plt.yticks([])\n# plt.savefig(f'tsne_gold-{unl_class}_{dataset}.pdf', bbox_inches='tight')\n\n", "repo_name": "seppia978/acm_mm_2023", "sub_path": "tsne.py", "file_name": "tsne.py", "file_ext": "py", "file_size_in_byte": 12055, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "custom_archs.wfmodels_lora.vit_tiny_16224", "line_number": 40, "usage_type": "attribute"}, {"api_name": "custom_archs.wfmodels_lora", "line_number": 40, "usage_type": "name"}, {"api_name": "custom_archs.wfmodels_lora.vit_small_16224", "line_number": 42, "usage_type": "attribute"}, {"api_name": "custom_archs.wfmodels_lora", "line_number": 42, "usage_type": "name"}, {"api_name": "custom_archs.wfmodels_lora.WFTransformer", "line_number": 44, "usage_type": "call"}, {"api_name": "custom_archs.wfmodels_lora", "line_number": 44, "usage_type": "name"}, {"api_name": "custom_archs.wfmodels_lora.WFTransformer", "line_number": 54, "usage_type": "call"}, {"api_name": "custom_archs.wfmodels_lora", "line_number": 54, "usage_type": "name"}, {"api_name": "custom_archs.wfmodels_lora.WFTransformer", "line_number": 61, "usage_type": "call"}, {"api_name": "custom_archs.wfmodels_lora", "line_number": 61, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 78, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 78, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomCrop", "line_number": 79, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 79, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 80, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 80, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 81, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 81, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 82, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 82, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 83, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 83, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 85, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 85, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 86, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 86, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 89, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 89, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 90, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 90, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 91, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 91, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 92, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 92, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 95, "usage_type": "call"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 120, "usage_type": "call"}, {"api_name": "torchvision.datasets.CIFAR100", "line_number": 229, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 248, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 248, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomCrop", "line_number": 249, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 249, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 250, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 250, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 251, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 251, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 252, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 252, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 253, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 253, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 256, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 256, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 257, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 257, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 258, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 258, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 259, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 259, "usage_type": "name"}, {"api_name": "torch.utils.data.ConcatDataset", "line_number": 277, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 278, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 279, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 280, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 325, "usage_type": "call"}, {"api_name": "sklearn.manifold.TSNE", "line_number": 328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 345, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 345, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 346, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 346, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 347, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 347, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 349, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 349, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 350, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 350, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 351, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 351, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 353, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 353, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 354, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 354, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 355, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 355, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 357, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 358, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 359, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 359, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 361, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 361, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 362, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 362, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 363, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 363, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 365, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 365, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 366, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 366, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 367, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 367, "usage_type": "name"}]}
{"seq_id": "11780178133", "text": "import numpy as np\nimport scipy.io.wavfile as wio\nimport matplotlib.pyplot as plt\nimport pitch_extraction as pitch\n\ndef power_spectol(data, Fs):\n    # fundamental frequency\n    spectol = np.fft.fft(data, Fs)\n    power_spec = np.log(np.abs(spectol))\n    return power_spec\n\ndef cepstrum(data, Fs):\n    power_spec = power_spectol(data, Fs)\n    cepstrum = np.fft.ifft(power_spec, Fs)\n    return cepstrum\n    \ndef envelope(data, Fs):\n    c = cepstrum(data, Fs)\n    c[45:] = 0\n    env = np.fft.fft(c)\n    return env\n\n# +6 dB/oct\ndef high_enphasis(data):\n    ret = np.copy(data)\n    for k in range(data.shape[0] - 1):\n        ret[k+1] = data[k+1] - 0.97 * data[k]\n    return ret\n\nif __name__ == '__main__':\n    def draw_envelope(filename, **kwarg):\n        Fs, data = wio.read(filename)\n        env = envelope(data, Fs)\n        plt.plot(np.abs(env[:Fs/2]), **kwarg)\n        plt.xlabel('Frequency[Hz]')\n    draw_envelope('../data/wav/A_a.wav', label='A')\n    draw_envelope('../data/wav/B_a.wav', label='B')\n    draw_envelope('../data/wav/C_a.wav', label='C')\n    plt.legend()\n    plt.savefig('../data/img/envelopes.png')\n    plt.show()\n", "repo_name": "machine105/audioprocess", "sub_path": "py/spectrum.py", "file_name": "spectrum.py", "file_ext": "py", "file_size_in_byte": 1128, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "numpy.fft.fft", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.fft.ifft", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.fft.fft", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.read", "line_number": 32, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}]}
{"seq_id": "22597709011", "text": "import vectormath as vmath\nimport math\nimport numpy as np\nfrom pyquaternion import Quaternion\nimport logging\n\nON_STRING = 1\n# Off the string and below at least one stick\nOFF_STRING_LOOSE = 2\n# Off the string and above the sticks\nFLYING = 3\n# Outside the border of the ellipse. Requires position correction\nOUTSIDE_STRING = 4\n\nclass DiaboloSimConfig():\n    '''\n    geometry_msgs/PoseArray initial_poses # Initial positions of diabolo [0], stick_left [1] and stick_right[2]\n    float32 rot_velocity # Rotational velocity of the diabolo\n    geometry_msgs/Point trans_velocity # Translation velocity of the diabolo (world frame)\n    float32 pv_pre_cap_scale # pv = pull velocity\n    float32 pv_post_cap_scale\n    float32 pv_cap_scale\n    float32 velocity_diffusion_factor # The factor by which the velocity reduces every time step\n    float32 time_step # The length of a time step\n    bool caps_valid # Set to true if want to change the pv scaling factors\n    float32 mass # Mass of the diabolo. Ignored if 0\n    float32 axle_radius # Radius of the diabolo axle\n    float32 string_length # Ignored if 0 or less than initial distance between strings\n    string file_name # This is the name of the experiment file. \n    '''\n    def __init__(self,\n            initial_poses,\n            rot_velocity,\n            trans_velocity,\n            pv_pre_cap_scale,\n            pv_post_cap_scale,\n            pv_cap_scale,\n            velocity_diffusion_factor,\n            time_step,\n            caps_valid,\n            mass,\n            axle_radius,\n            string_length,\n            file_name\n        ):\n        self.initial_poses = initial_poses\n        self.rot_velocity = rot_velocity\n        self.trans_velocity = trans_velocity\n        self.pv_pre_cap_scale = pv_pre_cap_scale\n        self.pv_post_cap_scale = pv_post_cap_scale\n        self.pv_cap_scale = pv_cap_scale\n        self.velocity_diffusion_factor = velocity_diffusion_factor\n        self.time_step = time_step\n        self.caps_valid = caps_valid\n        self.mass = mass\n        self.axle_radius = axle_radius\n        self.string_length = string_length\n        self.file_name = file_name\n\n    def __str__(self):\n        output = \"\"\n        output += str(self.initial_poses)\n        output += \" \"+str(self.rot_velocity)\n        output += \" \"+str(self.trans_velocity)\n        output += \" \"+str(self.pv_pre_cap_scale)\n        output += \" \"+str(self.pv_post_cap_scale)\n        output += \" \"+str(self.pv_cap_scale)\n        output += \" \"+str(self.velocity_diffusion_factor)\n        output += \" \"+str(self.time_step)\n        output += \" \"+str(self.caps_valid)\n        output += \" \"+str(self.mass)\n        output += \" \"+str(self.axle_radius )\n        output += \" \"+str(self.string_length)\n        output += \" \"+str(self.file_name)\n        return output\n\n# PoseArray -> variable poses that is an array of Pose's\n# Pose -> variable position is a point (x,y,z) and orientation is a quaternion (x,y,z,w)\n\nclass PoseArray():\n    # poses is a list of Pose objects\n    def __init__(self, poses):\n        self.poses = poses\n    \n    def __str__(self):\n        return str([str(pose) for pose in self.poses])\n\nclass Pose():\n    # position is a Vector3 object\n    # quaternion is a Quaternion object\n    def __init__(self, position, quaternion):\n        self.position = position\n        self.quaternion = quaternion\n\n    def __str__(self):\n        return f\"{self.quaternion} {self.position}\"\n\nclass MyQuaternion(Quaternion):\n    @property\n    def w(self):\n        return self.q[0]\n    @w.setter\n    def w(self, value):\n        self.q[0] = value\n    @property\n    def x(self):\n        return self.q[1]\n    @x.setter\n    def x(self, value):\n        self.q[1] = value\n    @property\n    def y(self):\n        return self.q[2]\n    @y.setter\n    def y(self, value):\n        self.q[2] = value\n    @property\n    def z(self):\n        return self.q[3]\n    @z.setter\n    def z(self, value):\n        self.q[3] = value\n\n    def toEuler(self):\n        # roll (x-axis rotation)\n        sinr_cosp = 2 * (self.w * self.x + self.y * self.z)\n        cosr_cosp = 1 - 2 * (self.x * self.x + self.y * self.y)\n        roll = math.atan2(sinr_cosp, cosr_cosp)\n\n        # pitch (y-axis rotation)\n        sinp = 2 * (self.w * self.y - self.z * self.x)\n        if (abs(sinp) >= 1):\n            pitch = math.copysign(math.pi / 2, sinp) # use 90 degrees if out of range\n        else:\n            pitch = math.asin(sinp)\n\n        # yaw (z-axis rotation)\n        siny_cosp = 2 * (self.w * self.z + self.x * self.y)\n        cosy_cosp = 1 - 2 * (self.y * self.y + self.z * self.z)\n        yaw = math.atan2(siny_cosp, cosy_cosp)\n\n        return (roll, pitch, yaw)\n\nclass DiaboloState():\n    ON_STR=1\n    OFF_STR_LOOSE=2\n    FLY=3\n    OUTSIDE_STR=4\n    def __init__(self,\n        pose,\n        trans_velocity,\n        rot_velocity,\n        mass,\n        string_length,\n        string_state\n    ):\n        self.pose = pose\n        self.trans_velocity = trans_velocity\n        self.rot_velocity = rot_velocity\n        self.mass = mass\n        self.string_length = string_length\n        self.string_state = string_state\n        \n    def __str__(self):\n        output = \"\"\n        output += str(self.pose)\n        output += \" \"+str(self.trans_velocity)\n        output += \" \"+str(self.rot_velocity)\n        output += \" \"+str(self.mass)\n        output += \" \"+str(self.string_length)\n        output += \" \"+str(self.string_state)\n        return output\n\nclass Transform():\n    def __init__(self, m_basis=None, m_origin=None):\n        self.m_basis = m_basis\n        self.m_origin = m_origin\n    \n    # other is a vmath.Vector3 vector\n    def __mul__(self, other):\n        #self.logger.debug('__mul__')\n        # matrix multiplication of m_basis with other\n        dot_product = vmath.Vector3(np.dot(self.m_basis, other))\n        # Then add result ot m_origin\n        output = dot_product + self.m_origin\n        return output\n\n    def inverse(self):\n        inv = self.m_basis.T\n        return Transform(inv, np.dot(inv, -self.m_origin))\n    \n    def setOrigin(self, origin):\n        self.m_origin = origin\n\n    def setRotation(self, q):\n        self.m_basis = q.rotation_matrix\n\n    def getRotation(self):\n        return MyQuaternion(matrix = self.m_basis)\n\nclass DiaboloPredictor():\n    gravity_ = vmath.Vector3(0,0,-9.8)\n    logging.basicConfig(filename=\"console.log\",format='%(asctime)s %(message)s',filemode='w')\n    logger = logging.getLogger()\n    logger.setLevel(logging.DEBUG)\n\n    # initial_state is a DiaboloSimConfig object\n    def __init__(self, initial_state):\n        # intilize the variables that are in the h file but not in the init method\n        self.ellipse_transform_ = Transform()\n        self.diabolo_transform_ = Transform()\n\n        # initialize all the instance variables\n        self.initial_state_ = initial_state\n        \n        # stick positions\n        self.left_stick_last_position_ = initial_state.initial_poses.poses[1].position\n        self.right_stick_last_position_ = initial_state.initial_poses.poses[2].position\n        self.left_stick_current_position_ = self.left_stick_last_position_\n        self.right_stick_current_position_ = self.right_stick_last_position_\n\n        # diabolo data\n        # diabolo position\n        self.diabolo_last_position_ = initial_state.initial_poses.poses[0].position\n        self.diabolo_current_position_ = self.diabolo_last_position_\n        self.logger.debug(f\"Initial diabolo position = {self.diabolo_current_position_}\")\n        # diabolo attributes\n        self.diabolo_last_state_ = OFF_STRING_LOOSE\n        self.diabolo_state_ = 0\n        self.diabolo_current_velocity_ = initial_state.trans_velocity\n        self.diabolo_current_rot_velocity_ = initial_state.rot_velocity\n        \n        # factors\n        self.pv_pre_cap_scaling_factor_ = initial_state.pv_pre_cap_scale\n        self.pv_post_cap_scaling_factor_ = initial_state.pv_post_cap_scale\n        self.pv_cap_scaling_factor_ = initial_state.pv_cap_scale\n\n        self.velocity_diffusion_factor_ = initial_state.velocity_diffusion_factor\n\n        # constants\n        self.time_step_ = initial_state.time_step\n        self.string_length_ = initial_state.string_length\n        self.diabolo_axle_radius_ = initial_state.axle_radius\n        self.catching_string_taut_tolerance_ = 0.03\n        self.pv_string_taut_tolerance_ = 0.03\n        self.rot_friction_factor_ = 0.00015\n\n        # constrain 2D?\n        self.constrain_to_2D_flag = False\n        self.plane_point_2D = vmath.Vector3()\n        self.plane_normal_2D = vmath.Vector3()\n        # self.get_ros_parameters()\n\n        self.sleep_flag_ = False\n\n    def predict(self, left_poses, right_poses, stick_time_step):\n        predicted_states = []\n        left_times = [stick_time_step]\n        right_times = [stick_time_step]\n        return self.predict_(left_poses, right_poses, left_times, right_times, predicted_states, False)\n\n    def predict(self, left_poses, right_poses, left_times, right_times, predicted_states, store_states):\n        # Store first state in predicted states\n        ds_inital = DiaboloState(\n            Pose(self.diabolo_current_position_, MyQuaternion()),\n            self.diabolo_current_velocity_,\n            self.diabolo_current_rot_velocity_,\n            0,\n            self.string_length_,\n            self.diabolo_state_\n        )\n\n        if (store_states):\n            predicted_states.append(ds_inital)\n\n        self.logger.debug(f\"First diabolo state position = {ds_inital.pose}\")\n\n        self.logger.debug(\"Starting prediction trial\")\n        self.run_prediction_trial(left_poses, right_poses, left_times, right_times, predicted_states, store_states)\n        self.logger.debug(\"Finished prediction trial\")\n\n        # Resulting diabolo state from trial\n        ds = DiaboloState(\n            Pose(self.diabolo_current_position_, MyQuaternion()), \n            self.diabolo_current_velocity_,\n            self.diabolo_current_rot_velocity_,\n            0,\n            self.string_length_,\n            self.diabolo_state_\n        )\n\n        return ds\n\n    def get_current_state(self):\n        ds = DiaboloState(Pose(None, None),None,0,0,0,0)\n        # Position values\n        ds.pose.position = self.diabolo_current_position_\n\n        # Translational velocity\n        ds.trans_velocity = self.diabolo_current_velocity_\n        ds.string_length = self.string_length_\n\n        # Rotational velocity\n        ds.rot_velocity = self.diabolo_current_rot_velocity_\n\n        return ds\n\n    def get_current_state_full(self):\n        cs = DiaboloSimConfig(PoseArray(None), 0, None, 0, 0, 0, 0, 0, False, 0, 0, 0, '')\n        cs.initial_poses.poses = [None,None,None]\n\n        cs.initial_poses.poses[0].position = self.diabolo_current_position_\n        cs.initial_poses.poses[1].position = self.left_stick_current_position_\n        cs.initial_poses.poses[2].position = self.right_stick_current_position_\n\n        # Translational velocity\n        cs.trans_velocity = self.diabolo_current_velocity_\n\n        # Rotational velocity\n        cs.rot_velocity = self.diabolo_current_rot_velocity_\n\n        # Simulation parameters\n        cs.string_length = self.string_length_\n        cs.pv_pre_cap_scale = self.pv_pre_cap_scaling_factor_\n        cs.pv_post_cap_scale = self.pv_post_cap_scaling_factor_\n        cs.pv_cap_scale = self.pv_cap_scaling_factor_\n        cs.velocity_diffusion_factor = self.velocity_diffusion_factor_\n        cs.time_step = self.time_step_\n        cs.string_length = self.string_length_\n        cs.axle_radius = self.diabolo_axle_radius_\n\n        return cs\n\n    def run_prediction_trial(self, left_poses, right_poses, left_times, right_times, predicted_states, store_states):  \n        left_poses_counter = 0\n        right_poses_counter = 0\n        current_sim_time = 0\n        # diabolo_state_counter = 1  # The initial state is already put in the predicted states array\n        # The trajectories do not start with 0 time\n        # To get the interpolated stick positions between 0 and the first stick position, the following are used\n        # last_left_time = 0\n        # last_right_time = 0\n        # next_left_time = left_times[0]\n        # next_right_time = right_times[0]\n        while left_poses_counter < len(left_times) or right_poses_counter < len(right_times): # Run prediction trial for all poses\n            if current_sim_time > left_times[left_poses_counter] and left_poses_counter < len(left_times):\n            # Set left stick to the next pose point\n                # last_left_time = left_times[left_poses_counter]\n                self.left_stick_current_position_ = left_poses.poses[left_poses_counter].position\n                left_poses_counter+=1\n                # next_left_time = left_times[left_poses_counter]\n\n                left_stick_current_position_ = self.left_stick_current_position_\n                if left_stick_current_position_.x < 0.01:  # For debugging (sanity check)\n                    self.logger.debug(\"x value < 0.01, not normal for the robot. POSE IS LIKELY CORRUPT!\")\n                    self.logger.debug(f\"left_stick_current_position_ set to = {left_stick_current_position_}\")\n                    self.logger.debug(f\"left_poses_counter = {left_poses_counter}\")\n\n            if current_sim_time > right_times[right_poses_counter] and right_poses_counter < len(right_times):\n            # Set right stick to the next pose point\n                # right_left_time = right_times[right_poses_counter]\n                self.right_stick_current_position_ = right_poses.poses[right_poses_counter].position\n                right_poses_counter+=1\n                # next_right_time = right_times[right_poses_counter]\n\n                right_stick_current_position_ = self.right_stick_current_position_\n                if right_stick_current_position_.x < 0.01:  # For debugging (sanity check)\n                    self.logger.debug(\"x value < 0.01, not normal for the robot. POSE IS LIKELY CORRUPT!\")\n                    self.logger.debug(f\"right_stick_current_position_ set to = {right_stick_current_position_}\")\n                    self.logger.debug(f\"right_poses_counter = {right_poses_counter}\")\n\n            self.step()\n            current_sim_time += self.time_step_\n            # Store all diabolo states if store_states is true\n            if store_states:\n                pose = Pose(self.diabolo_current_position_, MyQuaternion())\n\n                velocity = self.diabolo_current_velocity_\n\n                rot_velocity = self.diabolo_current_rot_velocity_\n                string_length = self.string_length_\n                string_state = self.diabolo_state_\n\n                ds = DiaboloState(pose, velocity, rot_velocity, 0, string_length, string_state)\n                predicted_states.append(ds)\n                # diabolo_state_counter+=1\n\n            # TEMPORARY: Remove after debug\n            # What is the point of this section\n            self.marker_count_ = 0\n            if self.sleep_flag_:\n                self.publish_visualization_markers()\n                # ros::Duration(this->time_step_ / 2.0).sleep();\n            # End of temporary\n\n            self.right_stick_last_position_ = self.right_stick_current_position_\n            self.left_stick_last_position_ = self.left_stick_current_position_\n  \n        # Erase the empty elements in the predicted states vector\n        if store_states:\n            # predicted_states.erase(predicted_states.begin() + diabolo_state_counter, predicted_states.end());\n            pass\n        # ROS_WARN_STREAM(\"Predicted states vector size = \" << predicted_states.size());\n\n\n    def set_2D_constraint(self, plane_normal, plane_point):\n        self.constrain_to_2D_flag = True\n        self.plane_normal_2D = plane_normal\n        self.plane_normal_2D = self.plane_normal_2D.normalize()\n\n        self.plane_point_2D = plane_point\n\n    def remove_2D_constraint(self):\n        self.constrain_to_2D_flag = False\n\n    # What is this method supposed to do (I dont use this)\n    def get_ros_paramters(self):\n        if not self.n_.getParam(\"/constrain_diabolo_2D\", self.constrain_to_2D_flag):\n            self.constrain_to_2D_flag = False;  # False by default\n        else:\n            if self.constrain_to_2D_flag:\n                p_normal = None\n                p_point = None\n                self.logger.debug(\"Constraining diabolo motion to 2D\")\n                if not self.n_.getParam(\"/diabolo_2D_plane_normal\", p_normal):  # This is a double vector\n                    self.logger.debug(\"2D plane normal not set. Defaulting to world x-axis\")\n                    p_normal.clear()\n                    p_normal.push_back(1.0)\n                    p_normal.push_back(0.0)\n                    p_normal.push_back(0.0)\n                if not self.n_.getParam(\"/diabolo_2D_plane_point\", p_point):  # This is a double vector\n                    self.logger.debug(\"Point on 2D plane not set. Defaulting to (0.7,0,0)\")\n                    p_point.clear()\n                    p_point.push_back(0.7)\n                    p_point.push_back(0.0)\n                    p_point.push_back(0.0)\n\n                self.plane_normal_2D = vmath.Vector3((p_normal.at(0)), (p_normal.at(1)), (p_normal.at(2)))\n                self.plane_normal_2D = self.plane_normal_2D.normalize()\n                self.plane_point_2D = vmath.Vector3((p_point.at(0)), (p_point.at(1)), (p_point.at(2)))\n\n    def IsFinite(self, position):\n        return True\n\n    def step(self):\n        # This step replaces gazebo's internal velocity and acceleration calculation\n        # It treats the diabolo like a free body without constraints under gravity_\n        self.perform_freebody_calculation()\n        self.ellipse_current_velocity_ = self.get_ellipse_velocity()\n\n        if self.constrain_to_2D_flag:\n            self.diabolo_current_position_, self.diabolo_current_velocity_ = \\\n                self.constrain_to_2D(self.diabolo_current_position_, self.diabolo_current_velocity_)\n        # Store information about current ellipse\n        self.store_ellipse_axes_lengths()\n        # Store current ellipse transform\n        self.store_ellipse_transform()\n        pose = Pose(self.diabolo_current_position_, MyQuaternion())\n        self.store_diabolo_transform(pose)\n\n        # Get the diabolo state\n        self.diabolo_state_ = self.get_diabolo_state()\n\n        if self.diabolo_state_ == OUTSIDE_STRING:\n            diabolo_new_position_ = self.constrain_diabolo_position()\n            if not self.IsFinite(diabolo_new_position_): #create a IsFinite function\n                self.logger.debug(\"Constrained position is not finite. Not changing\")\n                diabolo_new_position_ = self.diabolo_current_position_\n\n            # Get the max pull velocity\n            pull_velocity_cap = self.get_pull_velocity_cap()\n            # Calculate the pull velocity using the new position and the old position\n            self.pull_velocity_ = self.get_pull_velocity(self.diabolo_current_position_, diabolo_new_position_)\n\n            self.diabolo_current_position_ = diabolo_new_position_\n            # Add the pull velocity to the current diabolo velocity\n\n            # Make pull velocity for this time step 0 if it is not directed towards the center of the ellipse\n            if not self.pull_velocity_is_directed_inward():\n                self.pull_velocity_ = vmath.Vector3(0,0,0)\n\n            if self.pull_velocity_.length > pull_velocity_cap.length:\n                self.pull_velocity_ = pull_velocity_cap\n            if self.pull_velocity_.length > 0.0:\n                ellipse_y_axis = self.left_stick_current_position_ - self.right_stick_current_position_\n                distance_between_sticks = (ellipse_y_axis).length\n                if self.string_length_ - self.pv_string_taut_tolerance_ <= distance_between_sticks:\n                    self.pull_velocity_ = self.apply_edge_case_pv_constraint(self.pull_velocity_)\n\n            self.pull_velocity_ = self.pull_velocity_ * self.pv_post_cap_scaling_factor_\n\n            # Remove the positive component of the diabolo's velocity along the\n            # positive direction of the surface normal of the ellipse\n\n            self.diabolo_current_velocity_ = self.constrain_diabolo_velocity(\n                self.diabolo_current_velocity_ * self.velocity_diffusion_factor_ + self.pull_velocity_)\n            if self.diabolo_last_state_ == ON_STRING or self.diabolo_last_state_ == OUTSIDE_STRING:\n                self.diabolo_current_rot_velocity_ += self.get_rot_velocity_change()\n\n        self.diabolo_last_position_ = self.diabolo_current_position_\n        self.diabolo_last_state_ = self.diabolo_state_\n\n    # only the publish methods and constrain_diabolo_position use the store transform values\n    def store_ellipse_transform(self):\n        ellipse_y_axis = self.left_stick_current_position_ - self.right_stick_current_position_\n        distance_between_focii = ellipse_y_axis.length\n        ellipse_y_axis = ellipse_y_axis.normalize()\n        y_axis = vmath.Vector3(0, 1, 0)\n        rotation_axis = y_axis.cross(ellipse_y_axis)\n        rotation_axis = rotation_axis.normalize()\n\n        # This is the cosine of the angle between the y axis (world frame) and the y\n        # axis of the ellipse\n        rotation_angle = y_axis.dot(ellipse_y_axis)\n\n        q = MyQuaternion(axis=rotation_axis, radians = math.acos(rotation_angle))\n\n        center = self.left_stick_current_position_ + self.right_stick_current_position_\n        center /= 2.0\n\n        self.ellipse_transform_.setOrigin(center) # self.ellipse_transform_: tf::Transform\n        self.ellipse_transform_.setRotation(q)\n\n    def store_ellipse_axes_lengths(self):\n        between_focii = self.left_stick_current_position_ - self.right_stick_current_position_\n        distance_between_focii = between_focii.length\n\n        self.ellipse_major_axis_length_ = self.string_length_ / 2.0 # From definition of ellipse\n        self.ellipse_minor_axis_length_ = math.sqrt(math.pow(self.ellipse_major_axis_length_, 2.0) - math.pow((distance_between_focii / 2.0), 2.0))\n\n    def get_ellipse_velocity(self):\n        last_origin = (self.left_stick_last_position_ + self.right_stick_last_position_) / 2.0\n        current_origin = (self.left_stick_current_position_ + self.right_stick_current_position_) / 2.0\n\n        velo = (current_origin - last_origin) / self.time_step_\n\n        return velo\n\n    def constrain_to_2D(self, pos, vel): # Fixed this method to overcome the python argument issue\n        # This function constrains the diabolo position and velocity to the desired 2D plane\n        pos = self.constrain_position_to_2D(pos)\n        vel = self.constrain_velocity_to_2D(vel)\n        return pos, vel\n\n    def constrain_position_to_2D(self, pos):\n        # This function projects the diabolo position onto the set 2D plane\n        # new_pos = old_pos - projection of (old_pos - plane_point) on the plane normal\n        # projection of (old_pos - plane_point) on the plane normal = p_c_normal\n        p_c_normal = pos - self.plane_point_2D # Intermediate step\n        \n        p_c_normal = p_c_normal.dot(self.plane_normal_2D) * self.plane_normal_2D\n\n        return pos - p_c_normal\n\n    def constrain_velocity_to_2D(self, vel):\n        # This function constrains the velocity vel to be parallel to the plane, by removing its component parallel to the\n        # plane normal\n\n        return vel - vel.dot(self.plane_normal_2D) * self.plane_normal_2D\n\n    def get_diabolo_state(self):\n        # Description:\n        # The diabolo has 4 possible states: ON_STRING, OUTSIDE_STRING, OFF_STRING_LOOSE, FLYING\n        # ON_STRING: The diabolo is exactly on the string. This state is rare\n        # OFF_STRING_LOOSE: The diabolo is off the string, but not so far off that it cannot be caught easily\n        #                  i.e. This is when the diabolo jumps, but does not pass the plane containing the y-axis of the\n        #                  ellipse and the cross product of the Z-axis in the world frame and the y-axis of the ellipse,\n        #                  passing through the stick tip positions\n        # FLYING: The diabolo has left the string and crossed the plane described above\n        # OUTSIDE_STRING: This is when the diabolo needs to be constrained to the ellipse\n        # Transitions\n        # Allowed transitions: ON_STRING -> OFF_STRING_LOOSE, OFF_STRING_LOOSE -> ON_STRING, ON_STRING -> FLYING,\n        # OFF_STRING_LOOSE -> FLYING FLYING -> ON_STRING is allowed ONLY if the string is (close to) taut\n        distance_between_sticks = np.linalg.norm(self.left_stick_current_position_ - self.right_stick_current_position_)\n\n        string_taut_flag = False\n        if self.string_length_ - self.catching_string_taut_tolerance_ <= distance_between_sticks:\n            string_taut_flag = True\n            self.logger.debug(\"String is taut enough to catch diabolo\")\n\n        distance_to_stick_1 = np.linalg.norm(self.diabolo_current_position_ - self.left_stick_current_position_)\n        distance_to_stick_2 = np.linalg.norm(self.diabolo_current_position_ - self.right_stick_current_position_)\n        distance_to_sticks = distance_to_stick_1 + distance_to_stick_2\n        ########## Start check if diabolo is flying\n\n        # Get normal to the plane containing the ellipse y axis in the world frame and the world frame x axis\n        ellipse_y_axis = self.left_stick_current_position_ - self.right_stick_current_position_\n        plane_normal = None\n        if ellipse_y_axis.cross(vmath.Vector3(1.0, 0.0, 0.0)).length == 0.:\n            # If the ellipse is y-axis is parallel to the ground\n            # If ellipse y axis parallel to world x axis, plane normal is straight up\n            plane_normal = vmath.Vector3(0.0, 0.0, 1.0)\n        \n        # Otherwise,\n        # The normal to this plane is e_t x (e_z x z_world) where ey = ellipse y axis in world frame, z_world = world frame z\n        # axis The diabolo changes to state flying if it is \"above\" this plane\n        else:\n            plane_normal = vmath.Vector3(1.0, 0.0, 0.0).cross(ellipse_y_axis)\n\n        plane_normal = plane_normal.normalize()\n        # Get the vector from the ellipse center to the diabolo position in the world frame\n        # d_ellipse = d_world - e_world\n        ellipse_to_diabolo_vec = self.diabolo_current_position_ -\\\n            (self.left_stick_current_position_ + self.right_stick_current_position_) / 2.0\n        ellipse_to_diabolo_vec = ellipse_to_diabolo_vec.normalize()\n        # If the dot product of this vector and the plane_normal > 0.0, the diabolo is FLYING\n        check_val = ellipse_to_diabolo_vec.dot(plane_normal)\n        if check_val > 0.0 and self.diabolo_between_sticks() and \\\n            (self.diabolo_current_position_.z > self.left_stick_current_position_.z or \\\n            self.diabolo_current_position_.z > self.right_stick_current_position_.z):\n            return FLYING\n        # This is the edge case, when the ellipse is close to a line\n        if string_taut_flag:\n            if self.diabolo_last_state_ == FLYING: # Can transition to OFF_STRING_LOOSE if string is taut\n                # Check if the diabolo is between the stick tips\n                if self.diabolo_between_sticks():\n                    # Check if diabolo is close enough to the line between sticks\n                    # Get distance from diabolo to line between stick tips\n                    center_to_diabolo = \\\n                        self.diabolo_current_position_ - \\\n                        (self.left_stick_current_position_ + self.right_stick_current_position_) / 2.0\n                    axis_to_diabolo = \\\n                        center_to_diabolo - center_to_diabolo.dot(ellipse_y_axis.normalize()) * ellipse_y_axis.normalize()\n                    diabolo_distance_to_axis = axis_to_diabolo.length\n                    if diabolo_distance_to_axis < 0.1:  # Catch if distance to the axis is less than 10 cm\n                        return OFF_STRING_LOOSE\n                    else:\n                        self.logger.debug(f\"Unable to catch diabolo. Distance to string = {diabolo_distance_to_axis}\")\n        \n        if (self.string_length_ - (distance_to_sticks)) > 0.:  # The diabolo is not on the string, but it is not flying\n            if self.diabolo_last_state_ != FLYING:\n                return OFF_STRING_LOOSE;  # The diabolo has just \"hopped\" off the string. It can still get back on the string\n                                          # without the string being taut\n            else:\n                return FLYING\n\n        elif (distance_to_sticks - self.string_length_) > 0.:   # The string is taut and the diabolo needs position\n                                                                # correction\n            # First the case if the last state was OFF_STRING_LOOSE\n            if self.diabolo_last_state_ != FLYING:\n                # Can transition to OUTSIDE_STRING state in this case\n                return OUTSIDE_STRING\n            else:\n                return FLYING\n\n        return ON_STRING\n\n    def diabolo_between_sticks(self):\n        left_stick_to_diabolo = None\n        right_stick_to_diabolo = None\n        left_stick_to_diabolo = self.left_stick_current_position_ - self.diabolo_current_position_\n        right_stick_to_diabolo = self.right_stick_current_position_ - self.diabolo_current_position_\n\n        prod = left_stick_to_diabolo.dot(right_stick_to_diabolo)\n\n        if prod > 0:\n            return False\n\n        if prod <= 0:\n            return True\n\n    # This requires a Transform object\n    def constrain_diabolo_position(self):\n        # Use parametric equation of ellipse\n        # Calculate angles made with axes and solve for distance from center. Then\n        # find new coordinates\n        # Angle with z axis: phi\n        # Angle with x axis: theta\n\n        world_frame_pos = self.diabolo_current_position_\n\n        ellipse_frame_pos = self.ellipse_transform_.inverse() * world_frame_pos\n\n        ellipse_frame_pos_vector = ellipse_frame_pos\n\n        world_position = None\n\n        # Angle with xy plane\n        if np.all((ellipse_frame_pos_vector==0)):\n            new_ellipse_frame_pos = vmath.Vector3(0, 0, 0)\n            world_frame_pos = self.ellipse_transform_ * new_ellipse_frame_pos\n\n            world_position = world_frame_pos\n        else:\n            cos_phi = ellipse_frame_pos_vector.z / \\\n                    math.sqrt(math.pow(ellipse_frame_pos_vector.x, 2.0) + math.pow(ellipse_frame_pos_vector.y, 2.0) + \\\n                    math.pow(ellipse_frame_pos_vector.z, 2.0))\n            sin_phi = math.sqrt(math.pow(ellipse_frame_pos_vector.x, 2.0) + math.pow(ellipse_frame_pos_vector.y, 2.0)) / \\\n                    math.sqrt(math.pow(ellipse_frame_pos_vector.x, 2.0) + math.pow(ellipse_frame_pos_vector.y, 2.0) + \\\n                    math.pow(ellipse_frame_pos_vector.z, 2.0))\n\n            length = (math.sqrt(math.pow(ellipse_frame_pos_vector.x, 2.0) + math.pow(ellipse_frame_pos_vector.y, 2.0)))\n            new_x_ellipse_frame = None\n            new_y_ellipse_frame = None\n            new_z_ellipse_frame = None\n            r = None\n            if not length == 0.0:\n                cos_theta = ellipse_frame_pos_vector.x / \\\n                             (math.sqrt(math.pow(ellipse_frame_pos_vector.x, 2.0) + math.pow(ellipse_frame_pos_vector.y, 2.0)))\n                sin_theta = ellipse_frame_pos_vector.y / \\\n                                    (math.sqrt(math.pow(ellipse_frame_pos_vector.x, 2.0) + math.pow(ellipse_frame_pos_vector.y, 2.0)))\n\n                r = math.sqrt(1.0 / ((math.pow(cos_theta, 2.0) * math.pow(sin_phi, 2.0)) / math.pow(self.ellipse_minor_axis_length_, 2.0) + \\\n                                (math.pow(sin_theta, 2.0) * math.pow(sin_phi, 2.0)) / math.pow(self.ellipse_major_axis_length_, 2.0) + \\\n                                (math.pow(cos_phi, 2.0)) / math.pow(self.ellipse_minor_axis_length_, 2.0)))\n\n                # New position of diabolo\n                new_x_ellipse_frame = r * cos_theta * sin_phi\n                new_y_ellipse_frame = r * sin_theta * sin_phi\n                new_z_ellipse_frame = r * cos_phi\n            else:\n                r = self.ellipse_minor_axis_length_\n                new_x_ellipse_frame = 0.\n                new_y_ellipse_frame = 0.\n                new_z_ellipse_frame = r * cos_phi\n            \n            new_ellipse_frame_pos = vmath.Vector3(new_x_ellipse_frame, new_y_ellipse_frame, new_z_ellipse_frame)\n            world_frame_pos = self.ellipse_transform_ * new_ellipse_frame_pos\n\n            world_position = world_frame_pos\n\n        return world_position\n\n    def get_pull_velocity_cap(self):\n        # This cap is 0 in some cases, so this function determines\n        # if the pull velocity is applied at all.\n        # The cap is 0 e.g. when the sticks are moving in the same direction as the diabolo\n\n        origin_velocity_cap = None\n        minor_axis_cap = None\n        # Initially get the vector along which the pull velocity will be directed.\n        # It is from the current diabolo position to the center of the ellipse\n        current_center = \\\n            (self.left_stick_current_position_ + self.right_stick_current_position_) / 2.0\n        last_center = (self.left_stick_last_position_ + self.right_stick_last_position_) / 2.0\n        direction_vector = self.get_ellipse_normal_in_world_frame(self.diabolo_current_position_)\n        direction_vector = direction_vector.normalize()\n        direction_vector = -direction_vector\n        ellipse_origin_change = current_center - last_center\n\n        if direction_vector.dot(ellipse_origin_change) > 0.0:   # If the change in origin velocity has a positive component\n                                                                # along the direction_vector direction\n            origin_velocity_cap = direction_vector * (ellipse_origin_change.length / self.time_step_)\n        else:\n            origin_velocity_cap = vmath.Vector3(0, 0, 0)\n\n        # Now get the velocity due to the change of length of the minor axis\n        last_minor_axis = \\\n            self.get_ellipse_minor_axis_length(self.left_stick_last_position_, self.right_stick_last_position_)\n        current_minor_axis = \\\n            self.get_ellipse_minor_axis_length(self.left_stick_current_position_, self.right_stick_current_position_)\n\n        if current_minor_axis <= last_minor_axis: # If the minor axis length has decreased\n            minor_axis_cap = direction_vector * ((last_minor_axis - current_minor_axis) / self.time_step_)\n        else:\n            minor_axis_cap = vmath.Vector3(0, 0, 0)\n\n        return (origin_velocity_cap + minor_axis_cap) * self.pv_cap_scaling_factor_\n\n    def get_ellipse_minor_axis_length(self, foci_1, foci_2):\n        between_focii = foci_1 - foci_2\n        distance_between_focii = between_focii.length\n\n        major_axis_length = self.string_length_ / 2.0;  # From definition of ellipse\n        minor_axis_length_ = math.sqrt(math.pow(major_axis_length, 2.0) - math.pow((distance_between_focii / 2.0), 2.0))\n\n        return minor_axis_length_\n\n    def get_ellipse_normal_in_world_frame(self, world_frame_pos_vec):\n        world_frame_pos = world_frame_pos_vec\n\n        ellipse_frame_pos = self.ellipse_transform_.inverse() * world_frame_pos\n\n        # Get normal to ellipse at diabolo's location\n\n        ellipse_frame_normal = vmath.Vector3(\n            2.0 * (ellipse_frame_pos.x / self.ellipse_minor_axis_length_), \n            2.0 * (ellipse_frame_pos.y / self.ellipse_major_axis_length_),  # major axis is aligned along y direction\n            2.0 * (ellipse_frame_pos.z / self.ellipse_minor_axis_length_)\n        )\n\n        ellipse_frame_normal = ellipse_frame_normal.normalize()\n\n        # Want only the rotation (not the position) transform of the normal vector\n        # Using quaternion rotation\n        # v_new_as_quat = Q * v_old_as_quat * Q_inverse\n        # does not work: need to implement quaternion methods\n        ellipse_to_world_rot = self.ellipse_transform_.getRotation() # Rotation to rotate from ellipse frame to world frame\n        ellipse_frame_normal_as_quat = MyQuaternion(ellipse_frame_normal.x, ellipse_frame_normal.y,\n                                                    ellipse_frame_normal.z, 0.0)\n\n        # Using quaternion rotataion identity v' = qvq*\n        world_frame_normal_as_quat = ellipse_to_world_rot * ellipse_frame_normal_as_quat\n        world_frame_normal_as_quat = world_frame_normal_as_quat * ellipse_to_world_rot.inverse\n\n        # Ignoring the w term gives us the new vector\n        world_frame_normal_vector = vmath.Vector3(\n            world_frame_normal_as_quat.x, world_frame_normal_as_quat.y, world_frame_normal_as_quat.z)\n        return world_frame_normal_vector.normalize()\n\n    def get_pull_velocity(self, old_position, new_position):\n        v_pull = new_position - old_position\n        v_pull = v_pull / self.time_step_\n        normal_dir = self.get_ellipse_normal_in_world_frame(new_position)\n        normal_dir = -normal_dir\n        v_pull = v_pull.dot(normal_dir) * normal_dir * self.pv_pre_cap_scaling_factor_\n        return v_pull\n\n    def pull_velocity_is_directed_inward(self):\n        # Get position of ellipse center in world frame\n        # Get vector from diabolo to ellipse center\n        # Find dot product of calculated pull velocity and the above vector.\n        # Return false if dot product is negative, and true if positive\n\n        center = (self.left_stick_current_position_ + self.right_stick_current_position_) / 2.0\n        direction_vector = center - self.diabolo_current_position_\n\n        dot_product = direction_vector.dot(self.pull_velocity_)\n        if dot_product >= 0:\n            return True\n        else:\n            return False\n\n    # Fix issues relating to python argumenting passing\n    def apply_edge_case_pv_constraint(self, v_pull):\n        # This is used when the sticks are close to taut, and the ellipse is very thin.\n        # Throwing the diabolo in 3D can cause the diabolo to be pulled forward/backward relative to the player,\n        # as the curvature changes \n\n        ellipse_y_axis = self.left_stick_current_position_ - self.right_stick_current_position_\n        # Get normal to the plane containing ellipse y axis and world z axis\n        normal = ellipse_y_axis.cross(vmath.Vector3(0.0, 0.0, 1.0))\n        normal = normal.normalize()\n        v_pull_mag = v_pull.length\n        # Get component of the pull velocity in the direction of the normal\n        v_pull_in_normal_direction = v_pull.dot(normal) * normal\n        return (v_pull - v_pull_in_normal_direction).normalize() * v_pull_mag; # keep the magnitude of the pull velocity\n\n    def constrain_diabolo_velocity(self, diabolo_velocity):\n        world_frame_normal_vector = self.get_ellipse_normal_in_world_frame(self.diabolo_current_position_)\n\n        new_velocity = None\n        velocity_in_normal_dir = None\n        speed_in_normal_dir = diabolo_velocity.dot(world_frame_normal_vector)\n        if speed_in_normal_dir > 0.0:   # If the diabolo velocity has a positive\n                                        # component in the direction of the surface\n                                        # normal\n            # Project the velocity along the tangent to the ellipse\n            velocity_in_normal_dir = world_frame_normal_vector * speed_in_normal_dir\n            new_velocity = diabolo_velocity - velocity_in_normal_dir\n        else:\n            # If the component of diabolo velocity in the direction of the ellipse\n            # normal is negative, no change\n            new_velocity = diabolo_velocity\n        return new_velocity\n\n    def get_rot_velocity_change(self):\n        # The length of string that moved past the diabolo in the last time step\n        old_dist_to_right_stick = np.linalg.norm(self.right_stick_last_position_-self.diabolo_last_position_)\n        new_dist_to_right_stick = np.linalg.norm(self.right_stick_current_position_ - self.diabolo_current_position_)\n\n        string_velo = (new_dist_to_right_stick - old_dist_to_right_stick) / (self.time_step_)\n        # This is the speed of the diabolo axle at axle_radius distance from the center\n        axle_velo = self.diabolo_current_rot_velocity_ * self.diabolo_axle_radius_\n\n        return (((string_velo - axle_velo) * self.rot_friction_factor_) / self.diabolo_axle_radius_)\n\n    def perform_freebody_calculation(self):\n        self.diabolo_current_position_ = self.diabolo_current_position_ +\\\n                                         self.diabolo_current_velocity_ * self.time_step_ +\\\n                                         self.gravity_ * self.time_step_ * self.time_step_ * 0.5\n\n        self.diabolo_current_velocity_ = self.diabolo_current_velocity_ + self.gravity_ * self.time_step_\n\n    # only the publish methods use the store transform values\n    def store_diabolo_transform(self, pose_):\n        self.diabolo_transform_.setOrigin(pose_.position) # diabolo_transform_: tf::Transform\n        self.diabolo_transform_.setRotation(pose_.quaternion)", "repo_name": "jebc314/Motion-Capture-Analysis-of-Diabolo-Juggling", "sub_path": "Code/jebcui-diabolo/diabolo_motion_classes.py", "file_name": "diabolo_motion_classes.py", "file_ext": "py", "file_size_in_byte": 41050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pyquaternion.Quaternion", "line_number": 98, "usage_type": "name"}, {"api_name": "math.atan2", "line_number": 128, "usage_type": "call"}, {"api_name": "math.copysign", "line_number": 133, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 133, "usage_type": "attribute"}, {"api_name": "math.asin", "line_number": 135, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 140, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 190, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 202, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 203, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 204, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 205, "usage_type": "attribute"}, {"api_name": "vectormath.Vector3", "line_number": 250, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 251, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 438, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 440, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 480, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 508, "usage_type": "call"}, {"api_name": "math.acos", "line_number": 516, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 529, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 529, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 574, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 574, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 581, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 581, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 582, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 582, "usage_type": "attribute"}, {"api_name": "vectormath.Vector3", "line_number": 589, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 592, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 598, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 679, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 680, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 686, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 686, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 687, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 688, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 688, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 689, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 689, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 690, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 692, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 692, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 699, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 699, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 701, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 701, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 703, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 703, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 704, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 705, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 717, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 745, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 756, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 765, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 765, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 776, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 797, "usage_type": "call"}, {"api_name": "vectormath.Vector3", "line_number": 832, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 859, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 859, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 860, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 860, "usage_type": "attribute"}]}
{"seq_id": "20865341223", "text": "import json\nimport torchvision\nimport random\nimport os\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport cv2\nfrom torch.utils.data import Dataset\nfrom torch.autograd import Variable\n \nfrom timesformer.models.transforms import *\n\n \nclass VideoClassificationDataset(Dataset):\n    def __init__(self, opt, mode):\n        # python 3\n        # super().__init__()\n        super(VideoClassificationDataset, self).__init__()\n        self.mode = mode  # to load train/val/test data\n        self.feats_dir = opt['feats_dir']\n        if self.mode == 'val':\n            self.n = 10000         #提取的视频数量\n        if self.mode != 'inference':\n            print(f'load feats from {self.feats_dir}')\n \n            with open(self.feats_dir) as f:\n                feat_class_list = f.readlines()\n            self.feat_class_list = feat_class_list\n    \n            mean =[0.485, 0.456, 0.406]\n            std = [0.229, 0.224, 0.225]\n   \n            model_transform_params  = {\n                \"side_size\": 256,\n                \"crop_size\": 224,\n                \"num_segments\": 8,\n                \"sampling_rate\": 5\n            }\n \n            # Get transform parameters based on model\n            transform_params = model_transform_params\n            \n            transform_train = torchvision.transforms.Compose([\n                       GroupMultiScaleCrop(transform_params[\"crop_size\"], [1, .875, .75, .66]),\n                       GroupRandomHorizontalFlip(is_flow=False),\n                       Stack(roll=False),\n                       ToTorchFormatTensor(div=True),\n                       GroupNormalize(mean, std),\n                   ])\n            \n            transform_val = torchvision.transforms.Compose([\n                       GroupScale(int(transform_params[\"side_size\"])),\n                       GroupCenterCrop(transform_params[\"crop_size\"]),\n                       Stack(roll=False),\n                       ToTorchFormatTensor(div=True),\n                       GroupNormalize(mean, std),\n                   ])\n        \n            self.transform_params = transform_params\n            self.transform_train = transform_train\n            self.transform_val = transform_val\n \n        print(\"Finished initializing dataloader.\")\n \n    def __getitem__(self, ix):\n        \"\"\"This function returns a tuple that is further passed to collate_fn\n        \"\"\"\n        ix = ix % self.n\n        fc_feat = self._load_video(ix)\n \n        data = {\n            'fc_feats': Variable(fc_feat),\n            'video_id': ix,\n        }\n \n        return data\n \n    def __len__(self):\n        return self.n\n \n    def _load_video(self, idx):\n        prefix = '{:05d}.jpg'\n        feat_path_list = []\n        for i in range(len(self.feat_class_list)):\n            video_name = self.feat_class_list[i].rstrip('\\n').split('\\t')[0]+'-'\n            feat_path = self.feat_class_list[i].rstrip('\\n').split('\\t')[1]\n            feat_path_list.append(feat_path)\n \n        video_data = {}\n        if self.mode == 'val':\n            images = []\n            #print('######################feat_path_list:',feat_path_list[0])\n            frame_list =os.listdir(feat_path_list[idx])\n            #print('$$$$$$$$$$$$$$fram_list 长度：',len(frame_list))\n            average_duration = len(frame_list) // self.transform_params[\"num_segments\"]\n            #print(\"&&&&&&&&&&&&&&&&&&average_duration\",average_duration)\n            # offests为采样坐标\n            offsets = np.array([int(average_duration / 2.0 + average_duration * x) for x in range(self.transform_params[\"num_segments\"])])\n            offsets = offsets + 1\n            for seg_ind in offsets:\n                p = int(seg_ind)\n                seg_imgs = Image.open(os.path.join(feat_path_list[idx], prefix.format(p))).convert('RGB')\n                images.append(seg_imgs)\n            video_data = self.transform_val(images)\n            video_data = video_data.view((-1, self.transform_params[\"num_segments\"]) + video_data.size()[1:])\n \n        return video_data", "repo_name": "likeyouli/TimeSformer", "sub_path": "timesformer/absFT/dataloader.py", "file_name": "dataloader.py", "file_ext": "py", "file_size_in_byte": 4018, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 15, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 52, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 73, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}]}
{"seq_id": "36754708252", "text": "import tkinter as tk\nimport pymysql as ps\nimport pandas as pd\nnum = 1\ndef select_from(place):\n\tsql = \"select * from studentinfo1 where bornplace='{}'\".format(place)\n\tcur.execute(sql)\n\tdata = cur.fetchall()\n\treturn data\ndef count_bornplace():\n\tdata = select_from(var_bornplace_input.get())\n\tinfo.delete('1.0','end')\n\tinfo.insert('end',len(data))\ndef print_bornplace():\n\tdata = select_from(var_bornplace_input.get())\n\tinfo.delete('1.0','end')\n\tinfo.insert('end',pd.DataFrame(list(data),columns=['学号','姓名','性别','籍贯','成绩']))\ndef delete_database():\n\tsql1 = \"create table studentinfo{} like studentinfo\".format(num)\n\tsql2 = \"drop table studentinfo\"\n\tsql3 = \"select * from studentinfo1 where bornplace='{}'\".format(var_bornplace_input.get())\n\tcur.execute(sql1)\n\tcur.execute(sql2)\n\tcur.execute(sql3)\n\tdata = cur.fetchall()\n\tinfo.delete('1.0','end')\n\tinfo.insert('end',pd.DataFrame(list(data),columns=['学号','姓名','性别','籍贯','成绩']))\ndef muti_table():\n\tsql_3 = \"select teac.教师 from stud,relation,teac where (stud.姓名='{}') and (stud.学号=relation.学号) and (relation.课程=teac.课程名)\".format(var_bornplace_input.get())\n\tcur.execute(sql_3)\n\tdata = cur.fetchall()\n\tinfo.delete('1.0','end')\n\tinfo.insert('end',pd.DataFrame(list(data)))\n\t\nconn = ps.connect(host='127.0.0.1',user='root',passwd='zhaojunnan213315',port=3306,db='dbmcbase',charset='utf8')\ncur = conn.cursor()\n\nwindow = tk.Tk()\nwindow.title('XingYou_MySql')\nwindow.geometry('400x300')\n\nvar_bornplace_input = tk.StringVar()\nbornplace_input = tk.Entry(window,textvariable=var_bornplace_input,font=('Arial',28))\nbornplace_input.pack()\nbornplace_input.place(x=0,y=0)\ncount_button = tk.Button(window,text='count',font=('Arial',20),width=4,height=1,command=count_bornplace)\nprint_button = tk.Button(window,text='print',font=('Arial',20),width=4,height=1,command=print_bornplace)\ndelete_creat_button = tk.Button(window,text='delete',font=('Arial',20),width=4,height=1,command=delete_database)\nsearch_button = tk.Button(window,text='search',font=('Arial',20),width=4,height=1,command=muti_table)\n#insert_button = tk.Button(window,text='insert',font=('Arial',20),width=4,height=1,command=insert_info)\ncount_button.pack()\ncount_button.place(x=1,y=80)\nprint_button.pack()\nprint_button.place(x=100,y=80)\ndelete_creat_button.pack()\ndelete_creat_button.place(x=200,y=80)\nsearch_button.pack()\nsearch_button.place(x=300,y=80)\ninfo = tk.Text(window,height=11)\ninfo.pack()\ninfo.place(x=1,y=200)\nwindow.mainloop()\n\ncur.close()", "repo_name": "Xing-You-Doto/CCF-Python-new", "sub_path": "others/tkinter_study.py", "file_name": "tkinter_study.py", "file_ext": "py", "file_size_in_byte": 2509, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 38, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 42, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 43, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 46, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 47, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 48, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 49, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 59, "usage_type": "call"}]}
{"seq_id": "29410758090", "text": "import numpy as np\n#import h5py\nimport sys\nsys.path.insert(0, '/project/GAMNSCM2/funcs') \nfrom plotter_funcs import *\nfrom error_func import *\nimport pickle\nimport scipy.optimize as op\nimport emcee\nfrom emcee.utils import MPIPool\nfrom decimal import Decimal\n\nmass_bins=int(sys.argv[1])-1  #Mass bin\nsim_type=sys.argv[2]          #'dm_only' 'DTFE'\ncosmology=sys.argv[3]          #DMONLY:'lcdm'  'cde0'  'wdm2'DMGAS: 'lcdm' 'cde000' 'cde050' 'cde099'\nsnapshot=sys.argv[4]      #'12  '11'...\nden_type=sys.argv[5]           #'DTFE' 'my_den'\nsmooth_scl=Decimal(sys.argv[6])#Smoothing scale in physical units Mpc/h. 2  3.5  5\ntot_mass_bins=int(sys.argv[7])      #Number of Halo mass bins\nparticles_filt=int(sys.argv[8])   #Halos to filter out based on number of particles, ONLY for Dot Product Spin-LSS(SECTION 5.)\n#MCMC initial cond.\nndim, nwalkers=1,int(sys.argv[9])         #ndim-num. of dimentions of paramter space. nwalkers- amount of walkers\nburn_in=int(sys.argv[10])                   #how many steps are disposed of from the walker chain\nsteps_wlk=int(sys.argv[11])                #number of steps for each walker taken\ninitial_c=Decimal(sys.argv[12])               #initial value of what c-correlation coeffiient could be\n\nsim_sz=500                    #Size of simulation in physical units Mpc/h cubed\ngrid_nodes=1250               #Density Field grid resolution\nlss_type=2                    #Cluster-3 Filament-2 Sheet-1 Void-0\nbins=500                      #Plotting: the posterior histogram bins\nsigma_area=0.3413             #sigma percentile area as decimal\n\nfilehandler = open('/scratch/GAMNSCM2/%s/%s/snapshot_0%s/correl/%s/files/dotproduct/spin_lss/%s_snapshot_0%s_dp_LSS%s_spin_sim%sMpc_grid%s_smth%sMpc_%sbins_partclfilt%s.pkl'%(sim_type,cosmology,snapshot,den_type,cosmology,snapshot,lss_type,sim_sz,grid_nodes,smooth_scl,tot_mass_bins,particles_filt),\"rb\")\ndiction=pickle.load(filehandler)\nfilehandler.close()\n\nc_results={}\nresults=np.zeros(5)\nresults[0:2]=diction['results'][mass_bins,0:2]\n\ndot_val=np.asarray(diction[mass_bins])\n    \ndef lnlike(c,dot_val):\n    loglike=np.zeros((1))\n    loglike[0]=sum(np.log((1-c)*np.sqrt(1+(c/2))*(1-c*(1-3*(dot_val*dot_val/2)))**(-1.5)))#log-likelihood \n\n    return loglike\n    \ndef lnprior(c):\n    \n    if (-1.5 < c < 0.99):#Assumes a flat prior, uninformative prior\n        return 0.0\n    return -np.inf\n    \ndef lnprob(c,dot_val):\n    lp = lnprior(c)\n    if not np.isfinite(lp):\n        return -np.inf\n    return lp + lnlike(c,dot_val)\n#Parallel MCMC - initiallizes pool object; if process isn't running as master, wait for instr. and exit\npool=MPIPool()\nif not pool.is_master():\n    pool.wait()\n    sys.exit(0)\n\npos = [initial_c+1e-2*np.random.randn(ndim) for i in range(nwalkers)]#initial positions for walkers \"Gaussian ball\"\n \n#MCMC Running\nsampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob,args=[dot_val],pool=pool)\n\npos, _, _=sampler.run_mcmc(pos,burn_in)#running of emcee burn-in period\nsampler.reset()\n\nsampler.run_mcmc(pos, steps_wlk)#running of emcee for steps specified, using pos as initial walker positions\npool.close()\nchain=sampler.flatchain[:,0]  \nc_results[mass_bins]=chain   \n \n#Redefine posterior into scatter plot of posterior\na=np.histogram(chain,density=True,bins=bins)\nx=a[1]\ndx=(np.max(x)-np.min(x))/bins\nx=np.delete(x,len(x)-1,0)+dx/2\ny=a[0]\n \nperc_lo,results[4],perc_hi,results[3],results[2],area=error(x,y,dx,sigma_area)\nc_results['results']=results\nfilehandler = open('/scratch/GAMNSCM2/%s/%s/snapshot_0%s/correl/%s/files/mod_fit/mcmc_mthd/myden_mcmcresults_LSS%s_spin_sim%sMpc_grid%s_smth%sMpc_%sbins_partclfilt%s.pkl'%(sim_type,cosmology,snapshot,den_type,lss_type,sim_sz,grid_nodes,smooth_scl,mass_bins,particles_filt),\"wb\")\npickle.dump(c_results,filehandler)\nfilehandler.close() \n", "repo_name": "ajib0457/test_codes", "sub_path": "my_code_prep/new_pipeline/mod_fit/mcmc.py", "file_name": "mcmc.py", "file_ext": "py", "file_size_in_byte": 3776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.path.insert", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.isfinite", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 58, "usage_type": "attribute"}, {"api_name": "emcee.utils.MPIPool", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 66, "usage_type": "attribute"}, {"api_name": "emcee.EnsembleSampler", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 83, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 89, "usage_type": "call"}]}
{"seq_id": "34301576398", "text": "\"\"\"\nStore decoded packets for processing later.\n\"\"\"\n\nimport asyncio\nfrom datetime import datetime\nfrom io import TextIOBase\nfrom os import rename,system\n\nHOST = \"127.0.0.1\"  # The server's hostname or IP address\nPORT = 30003  # The port used by the server\n\nasync def test():\n    reader, _ = await asyncio.open_connection(HOST, PORT)\n    prev_filename = None\n    file: TextIOBase = None\n    while True:\n        message = (await reader.readline()).decode().strip()\n        filename = \"data/bst/\"+datetime.now().strftime(\"%Y%m%d-%H\")+\".bst.temp\"\n        if prev_filename != filename:\n            if file: \n                file.flush()\n                file.close()\n                file.__exit__()\n                del(file)\n                rename(prev_filename, \".\".join(prev_filename.split(\".\")[:-1]))\n                system(f\"gzip -9 {'.'.join(prev_filename.split('.')[:-1])}\")\n            file = open(filename, \"w\")\n            prev_filename = filename\n        file.write(f\"{message}\\n\")\n                \nasyncio.run(test())\n", "repo_name": "melazarus/adsb-tools", "sub_path": "dump_data.py", "file_name": "dump_data.py", "file_ext": "py", "file_size_in_byte": 1023, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "asyncio.open_connection", "line_number": 14, "usage_type": "call"}, {"api_name": "io.TextIOBase", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "name"}, {"api_name": "os.rename", "line_number": 26, "usage_type": "call"}, {"api_name": "os.system", "line_number": 27, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 32, "usage_type": "call"}]}
{"seq_id": "72975062201", "text": "import matplotlib.pyplot as pl\nimport numpy as np\nimport scipy as sp\nimport scipy.spatial\nimport sys\n\neps = sys.float_info.epsilon\n\n\n\nn_towers = int(sys.argv[1])\ntowers = np.random.rand(n_towers, 2)\nbounding_box = np.array([0., 1., 0., 1.]) # [x_min, x_max, y_min, y_max]\n\ndef in_box(towers, bounding_box):\n    return np.logical_and(np.logical_and(bounding_box[0] <= towers[:, 0],\n                                         towers[:, 0] <= bounding_box[1]),\n                          np.logical_and(bounding_box[2] <= towers[:, 1],\n                                         towers[:, 1] <= bounding_box[3]))\n\n\ndef row_to_str(r):\n    return \" \".join(list(map(lambda x: str(x), list(r))))+\"\\n\"\n\n\ndef voronoi(towers, bounding_box):\n\n    # Select towers inside the bounding box\n    i = in_box(towers, bounding_box)\n    # Mirror points\n    points_center = towers[i, :]\n    points_left = np.copy(points_center)\n    points_left[:, 0] = bounding_box[0] - (points_left[:, 0] - bounding_box[0])\n    points_right = np.copy(points_center)\n    points_right[:, 0] = bounding_box[1] + (bounding_box[1] - points_right[:, 0])\n    points_down = np.copy(points_center)\n    points_down[:, 1] = bounding_box[2] - (points_down[:, 1] - bounding_box[2])\n    points_up = np.copy(points_center)\n    points_up[:, 1] = bounding_box[3] + (bounding_box[3] - points_up[:, 1])\n    points = np.append(points_center,\n                       np.append(np.append(points_left,\n                                           points_right,\n                                           axis=0),\n                                 np.append(points_down,\n                                           points_up,\n                                           axis=0),\n                                 axis=0),\n                       axis=0)\n    # Compute Voronoi\n    vor = sp.spatial.Voronoi(points)\n    # Filter regions\n    regions = []\n    for region in vor.regions:\n        flag = True\n        for index in region:\n            if index == -1:\n                flag = False\n                break\n            else:\n                x = vor.vertices[index, 0]\n                y = vor.vertices[index, 1]\n                if not(bounding_box[0] - eps <= x and x <= bounding_box[1] + eps and\n                       bounding_box[2] - eps <= y and y <= bounding_box[3] + eps):\n                    flag = False\n                    break\n        if region != [] and flag:\n            regions.append(region)\n    vor.filtered_points = points_center\n    vor.filtered_regions = regions\n\n\n    fig = pl.figure()\n    ax = fig.gca()\n\n\n    # Plot initial points\n    ax.plot(vor.filtered_points[:, 0], vor.filtered_points[:, 1], 'b.')\n\n\n\n    # Plot ridges points\n    for region in vor.filtered_regions:\n        vertices = vor.vertices[region, :]\n        ax.plot(vertices[:, 0], vertices[:, 1], 'g.')\n\n    # Plot ridges\n    for region in vor.filtered_regions:\n        vertices = vor.vertices[region + [region[0]], :]\n        ax.plot(vertices[:, 0], vertices[:, 1], 'k-')\n\n\n    # print(\"Check points\")\n    # for i in range(len(vor.vertices)):\n    #     for j in range(len(vor.vertices)):\n    #         if i!=j:\n    #             if vor.vertices[i][0]==vor.vertices[j][0] and vor.vertices[i][1]==vor.vertices[j][1]:\n    #                 print(i, j)\n    # print(\"Done checking points\")\n\n    for i, p in enumerate(vor.vertices):\n        ax.annotate(str(i), (p[0], p[1]))\n        # print(p)\n\n\n\n    return vor\n\nprint(\"Generating voronoi graph\")\nvor = voronoi(towers, bounding_box)\n\nprint(\"Checking completeness\")\nwhile (len(vor.filtered_regions)!=n_towers):\n    towers = np.random.rand(n_towers, 2)\n    vor = voronoi(towers, bounding_box)\n\n\n\nfile = \"../src/voronoi_data\"\nf = open(file, 'w')\n\nprint(\"Writing data to \"+file)\n\n\nf.writelines(str(len(vor.vertices)) + \" \"+ str(len(vor.filtered_regions))+\"\\n\")\nfor i in range(len(vor.vertices)):\n    f.writelines(row_to_str(vor.vertices[i]))\n\nfor i in range(len(vor.filtered_regions)):\n    f.writelines(row_to_str(vor.filtered_regions[i]))\n\nf.close()\n\n\n\n# print(vor.filtered_points)\n# print(len(vor.filtered_points))\n# print(vor.vertices)\n# print(len(vor.vertices))\n\n# print(vor.filtered_regions)\n# print(len(vor.filtered_regions))\n# print(max(list(map(lambda x: max(x), vor.filtered_regions))))\n\n\nfig_path = \"../src/voronoi_graph.jpg\"\npl.savefig(fig_path)\nprint(\"Check the graph at \"+fig_path)\n\n", "repo_name": "txstc55/voronoi-minesweep", "sub_path": "src/voronoi.py", "file_name": "voronoi.py", "file_ext": "py", "file_size_in_byte": 4353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.float_info", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.spatial.Voronoi", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.spatial", "line_number": 50, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 113, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}]}
{"seq_id": "70388828601", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport pygame\nfrom pygame.locals import *\nimport array\n\n# Stwórz okno o wielkości 640x480 i 24 BPP.\nWINDOW_W = 640\nWINDOW_H = 480\npygame.display.init()\nwindow = pygame.display.set_mode([WINDOW_W, WINDOW_H], 0, 24)\n\n# Narysuj czarno-czerwony gradient w oddzielnym buforze.\nW = 256\nH = 256\ngradient = array.array('L', [0]) * W * H  # Array \nfor y in xrange(H):\n  for x in xrange(W):\n    gradient[x + y * W] = (x & 0xff)  # Red=x, Green=0, Blue=0\n\n# „Manualnie” skopiuj wiersze z powyższej tablicy do bufora klatki pygame.\npixels = pygame.PixelArray(window)\ncenter_x = (WINDOW_W - W) / 2\ncenter_y = (WINDOW_H - H) / 2\nfor y in xrange(H):\n  for x in xrange(W):\n    # PyGame używa formatu BGR, w buforze z gradientem celowo użyłem RGB.\n    # Poniżej dokonywana jest konwersja RGB do BGR (zakładam, że 'L' w \n    # array.array jest zapisane little-endian).\n    pixel = gradient[x + y * W]\n    pixel = (((pixel & 0x0000ff) << 16) |  # R jest przesuwane na górny bajt.\n             (pixel & 0x00ff00) |          # G pozostaje bez zmian.\n             ((pixel & 0xff0000) >> 16))   # B jest przesuwane na dolny bajt.\n    pixels[center_x + x, center_y + y] = pixel\n\n# Przerysuj ekran (tj. wyświetl bufor klatki po którym rysowaliśmy).\npygame.display.flip()\n\n# Poczekaj aż okno zostanie zamknięte lub zostanie naciśnięty przycisk ESC.\nwhile True:\n  event = pygame.event.wait()\n  if event.type == KEYDOWN and event.key == K_ESCAPE:\n    break\n  if event.type == QUIT:\n    break\npygame.quit()\n\n", "repo_name": "gynvael/zrozumiec-programowanie", "sub_path": "018-Czesc_IV-Rozdzial_12-Format_BMP_i_wstep_do_bitmap/show.py", "file_name": "show.py", "file_ext": "py", "file_size_in_byte": 1542, "program_lang": "python", "lang": "pl", "doc_type": "code", "stars": 78, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pygame.display.init", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 11, "usage_type": "attribute"}, {"api_name": "array.array", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.PixelArray", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.event.wait", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "35373576246", "text": "import sqlite3\nimport json\n\nfrom models.entry import Entry\nfrom models.mood import Mood\nfrom models.tag import Tag\n\ndef get_all_entries():\n    with sqlite3.connect(\"./dailyjournal.db\") as conn:\n\n        conn.row_factory = sqlite3.Row\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(\"\"\"\n        SELECT\n            j.id,\n            j.concept,\n            j.entry,\n            j.date,\n            j.moodId,\n            m.label mood_label\n        FROM journalentries j\n        JOIN moods m\n            ON m.id = j.moodId\n        \"\"\")\n\n        entries = []\n\n        dataset = db_cursor.fetchall()\n\n        for row in dataset:\n\n            entry = Entry(row['id'], row['concept'], row['entry'], row['date'], row['moodId'])\n\n            mood = Mood(row['moodId'], row['mood_label'])\n\n            entry.mood = mood.__dict__\n            \n            entries.append(entry.__dict__)\n\n            db_cursor.execute(\"\"\"\n            SELECT\n                t.id,\n                t.name,\n                e.tag_id\n            FROM tags t\n            JOIN entrytags e\n                ON t.id = e.tag_id\n            WHERE e.entry_id = ?\n            \"\"\", (row['id'], ))\n\n            tagset = db_cursor.fetchall()\n            tags = []\n            for tag in tagset:\n                each_tag = Tag(tag['id'], tag['name'])\n                tags.append(each_tag.__dict__)\n            entry.tags = tags\n\n    return json.dumps(entries)\n\ndef get_single_entry(id):\n    with sqlite3.connect(\"./dailyjournal.db\") as conn:\n        conn.row_factory = sqlite3.Row\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(\"\"\"\n        SELECT\n            a.id,\n            a.concept,\n            a.entry,\n            a.date,\n            a.moodId\n        FROM journalentries a\n        WHERE a.id = ?\n        \"\"\", ( id, ))\n\n        data = db_cursor.fetchone()\n\n        entry = Entry(data['id'], data['concept'], data['entry'], data['date'], data['moodId'])\n\n        return json.dumps(entry.__dict__)\n\ndef delete_entry(id):\n    with sqlite3.connect(\"./dailyjournal.db\") as conn:\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(\"\"\"\n        DELETE FROM journalentries\n        WHERE id = ?\n        \"\"\", (id, ))\n\ndef get_entries_by_search(term):\n    with sqlite3.connect(\"./dailyjournal.db\") as conn:\n        conn.row_factory = sqlite3.Row\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(f\"\"\"\n        select\n            c.id,\n            c.concept,\n            c.entry,\n            c.date,\n            c.moodId\n        FROM journalentries c\n        WHERE c.entry LIKE '%{term}%'\n        \"\"\")\n\n        entries = []\n        dataset = db_cursor.fetchall()\n\n        for row in dataset:\n            entry = Entry(row['id'], row['concept'], row['entry'], row['date'], row['moodId'])\n            entries.append(entry.__dict__)\n    \n    return json.dumps(entries)\n\ndef create_journal_entry(new_entry):\n    print(new_entry)\n    with sqlite3.connect(\"./dailyjournal.db\") as conn:\n        db_cursor = conn.cursor()\n        #a list is brackets, like an array in JS\n\n        db_cursor.execute(\"\"\"\n        INSERT INTO journalentries\n            ( concept, entry, date, moodId )\n        VALUES\n            ( ?, ?, ?, ?);\n        \"\"\", (new_entry['concept'],\n              new_entry['entry'], new_entry['date'], new_entry['moodId']))\n\n        id = db_cursor.lastrowid\n\n        new_entry['id'] = id\n\n        if new_entry['tags']:\n            for tag in new_entry['tags']:\n                db_cursor.execute(\"\"\"\n                INSERT INTO EntryTags\n                    ( entry_id, tag_id )\n                VALUES\n                    ( ?, ? );\n                \"\"\", (id, tag, ))\n        \n    return json.dumps(new_entry)\n\ndef update_entry(id, new_entry):\n    with sqlite3.connect(\"./dailyjournal.db\") as conn:\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(\"\"\"\n        UPDATE journalentries\n            SET\n                concept = ?,\n                entry = ?,\n                date = ?,\n                moodId = ?\n        WHERE id = ?\n        \"\"\", (new_entry['concept'], new_entry['entry'],\n              new_entry['date'], new_entry['moodId'], id, ))\n\n        rows_affected = db_cursor.rowcount\n\n    if rows_affected == 0:\n        return False\n    else:\n        return True\n", "repo_name": "jeaninebeckle/daily-journal-server", "sub_path": "entries/request.py", "file_name": "request.py", "file_ext": "py", "file_size_in_byte": 4271, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sqlite3.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.entry.Entry", "line_number": 33, "usage_type": "call"}, {"api_name": "models.mood.Mood", "line_number": 35, "usage_type": "call"}, {"api_name": "models.tag.Tag", "line_number": 55, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.entry.Entry", "line_number": 79, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 81, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 84, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 93, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 94, "usage_type": "attribute"}, {"api_name": "models.entry.Entry", "line_number": 112, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 115, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 119, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 144, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 147, "usage_type": "call"}]}
{"seq_id": "72953179641", "text": "import numpy as np\r\nimport re\r\nimport os\r\nimport copy\r\nimport pickle\r\nimport MeCab\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.models import load_model\r\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\r\n\r\n\r\n#---------------------------------------------\r\n# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\r\n# os.environ['TF_CPP_MIN_LOG_LEVEL']='2'\r\n#---------------------------------------------\r\n\r\n\r\n# 必要な関数群\r\n#--------------\r\n# 任意の確率和をしきい値にして正解が含まれるかの率\r\ndef predsum_acc(y_true, y_pred):\r\n    thres = 0.80\r\n    y_true = tf.cast(y_true, tf.int32)\r\n    pred_acc = tf.TensorArray(tf.float32, size=len(y_true), dynamic_size=True)\r\n    pred_kmax_values = tf.math.top_k(y_pred, k=len(y_pred[0,0])).values # 各文各単語の確率を降順\r\n    pred_kmax_index = tf.math.top_k(y_pred, k=len(y_pred[0,0])).indices # pred_kmax_valueのindex\r\n    label_pos = tf.cast(tf.reshape(tf.where(tf.equal(y_true, pred_kmax_index)), shape=(len(y_true), len(y_true[0]), -1))[:,:,2], tf.int32) # [文][単語][2] -->確率降順にしたときの正解ラベルのindex番号\r\n    n = 0\r\n    for i in tf.range(len(y_true)):\r\n        for j in tf.range(1,len(y_true[i])):\r\n            if y_true[i,j] == 0: break # 最初のpadding0以降、paddingの0が来たときbreakする(labelは 0,1,5,3,...,2,0,0,0,...,0 だから)\r\n            # paddingの0でないとき\r\n            k = label_pos[i,j] # 該当する正解単語のindex\r\n            pred_sum = tf.reduce_sum(pred_kmax_values[i,j,:k]) # 正解単語の1つ前までの確率和\r\n            if pred_sum < thres or k == 0: # 正解単語の1つ前までの和が閾値を超えていない or 正解単語が1位\r\n                pred_acc = pred_acc.write(n, 1.0)\r\n            else:\r\n                pred_acc = pred_acc.write(n, 0)\r\n            n += 1\r\n    acc = tf.reduce_mean(pred_acc.stack())\r\n    return acc\r\n\r\n\r\n# 誤字脱字検出メソッド\r\n#--------------\r\n# 文章の前処理関数\r\ndef regex_txt(text):\r\n    text_shaped = text.replace(' ', '')\r\n    text_shaped = text_shaped.translate(str.maketrans({chr(0xFF01 + i): chr(0x21 + i) for i in range(94)})) # 全角->半角\r\n    text_shaped = text_shaped.replace('\\n', '') # 改行文字の削除\r\n\r\n    text_shaped = re.sub('[「」]', '', text_shaped) # 「」の削除\r\n    text_shaped = re.sub('(\\(.+?\\))|(\\[.+?\\],*)', '', text_shaped) # ()[]内の文字を()[]ごと削除\r\n\r\n    text_shaped = re.sub('[a-zA-Z0-9]+', 'n', text_shaped) # 英数字を n に置換\r\n    text_shaped = re.sub('n[,.]', 'n', text_shaped) # n, や n. などを n に置換\r\n    text_shaped = text_shaped.replace('、', ',').replace('。', '.').replace(',', '読点').replace('.', '句点') # , . を一旦文字で置き換える\r\n    text_shaped = re.sub('\\W', '', text_shaped) # 非単語文字(半角記号など)を''に置換\r\n    text_shaped = re.sub('_', '', text_shaped)\r\n    text_shaped = re.sub('n{2,}', 'n', text_shaped) # nn, nnn, ... などの連続したnをnに置換\r\n    text_shaped = text_shaped.replace('読点', '、').replace('句点', '。')\r\n    text_shaped = re.sub('、{2,}', '、', text_shaped)\r\n    text_shaped = re.sub('(。{2,})|(、{1,}。)|(。{1,}、)', '。', text_shaped)\r\n    return text_shaped\r\n\r\n# 入力テキストをtoken化\r\ndef text_to_id(texts, vocab_dic):\r\n    mecab = MeCab.Tagger()\r\n    # r'-d \"/mecab-ipadic-neologd\"\r\n    terms = []\r\n    posList = []\r\n    texts = regex_txt(texts).split('。')\r\n    for text in texts:\r\n        words = []\r\n        poss = []\r\n        node = mecab.parseToNode(text)\r\n        while node:\r\n            term = node.surface\r\n            pos = node.feature.split(',')[0]\r\n            if pos not in '補助記号' or term == '、' or term == '。':\r\n                if term != '':\r\n                    words.append(term)\r\n                    poss.append(pos)\r\n            node = node.next\r\n        words.append('。')\r\n        terms.append(words)\r\n        posList.append(poss)\r\n    terms.pop()\r\n    posList.pop()\r\n  \r\n    text_id = []\r\n    for i in range(len(terms)):\r\n        term = terms[i]\r\n        word_id = []\r\n        for j in range(len(term)):\r\n            word = term[j]\r\n            try: # 語彙辞書に該当単語があったら\r\n                word_id.append(vocab_dic[word])\r\n            except: # 該当語が未知語の場合\r\n                pos_ = posList[i][j]\r\n                if pos_ == '名詞':\r\n                    word = 'norn'\r\n                elif pos_ == '動詞':\r\n                    word = 'verb'\r\n                elif pos_ == '形容詞':\r\n                    word = 'adj'\r\n                elif pos_ == '副詞':\r\n                    word = 'adv'\r\n                elif pos_ == '助詞':\r\n                    word = 'post'\r\n                else:\r\n                    word = 'any'\r\n                try:\r\n                    word_id.append(vocab_dic[word])\r\n                except:\r\n                    word_id.append(vocab_dic['<UNK>'])\r\n        text_id.append(word_id)\r\n    return terms, posList, text_id\r\n\r\n# modelに入力する用のデータを作成\r\ndef cre_InputData(texts, ids):\r\n    fwInputs = []\r\n    bwInputs = []\r\n    label = []\r\n    test_texts = []\r\n\r\n    for i in range(len(texts)):\r\n        input1 = copy.copy(ids[i])\r\n        input1[0:0] = [0, 0]\r\n        input2 = copy.copy(ids[i])\r\n        input2.append(0)\r\n        input2.append(0)\r\n        output = copy.copy(ids[i])\r\n        output[0:0] = [0]\r\n        output.append(0)\r\n        test_text = []\r\n\r\n        fwInputs.append(input1)\r\n        bwInputs.append(input2)\r\n        label.append(output)\r\n\r\n        for j in range(len(texts[i])):\r\n            text_ = copy.copy(texts[i])\r\n            text_[j] = '< >'\r\n            test_text.append(text_)\r\n        test_texts.append(test_text)\r\n\r\n    fwInputs = pad_sequences(fwInputs, padding='post')\r\n    bwInputs = pad_sequences(bwInputs, padding='post')\r\n    label = pad_sequences(label, padding='post')\r\n    fwInputs = np.reshape(fwInputs, (fwInputs.shape[0], fwInputs.shape[1], 1))\r\n    bwInputs = np.reshape(bwInputs, (bwInputs.shape[0], bwInputs.shape[1], 1))\r\n    label = np.reshape(label, (label.shape[0], label.shape[1], 1))\r\n    return fwInputs, bwInputs, label, test_texts\r\n\r\n\r\n# カスタム評価関数\r\n#--------------\r\n# selfのacc\r\ndef custom_acc_(y_true, y_pred): # 全文全単語の予測結果の平均を正解率とする\r\n    pred_acc = tf.TensorArray(tf.float32, size=1, dynamic_size=True)\r\n    false_indexes = [] # false(誤り判定)になった箇所のindexを格納(python配列にするのは妥協点)\r\n    y_true = tf.cast(y_true, tf.int32)\r\n    pred_argmax = tf.reshape(tf.cast(tf.math.argmax(y_pred, 2), tf.int32), shape=(len(y_true), len(y_true[0]), -1))# 予測確率が最も高いindex\r\n    match = tf.cast(tf.math.equal(y_true, pred_argmax), tf.float32) # 予測確率が最も高い語とlabelが一致しているか\r\n    n = 0\r\n    for i in tf.range(len(y_true)):\r\n        for j in tf.range(1, len(y_true[0])):\r\n            if y_true[i,j] == 0: break\r\n            pred_acc = pred_acc.write(n, match[i,j,0])\r\n            n += 1\r\n        false_index = tf.where(tf.equal(match[i,1:j], 0)).numpy()[:,0] # 誤り判定になった語のindexを取得\r\n        false_indexes.append(false_index)\r\n    # 正解率\r\n    acc = tf.reduce_mean(pred_acc.stack())\r\n    return acc, false_indexes\r\n\r\n# 任意の確率和をしきい値にして正解が含まれるかの率\r\ndef predsum_acc_(y_true, y_pred):\r\n    thres = 0.80\r\n    y_true = tf.cast(y_true, tf.int32)\r\n    false_indexes = []\r\n    pred_acc = tf.TensorArray(tf.float32, size=len(y_true), dynamic_size=True)\r\n    pred_kmax_values = tf.math.top_k(y_pred, k=len(y_pred[0][0])).values # 各文各単語の確率を降順\r\n    pred_kmax_index = tf.math.top_k(y_pred, k=len(y_pred[0][0])).indices # pred_kmax_valueのindex\r\n    label_pos = tf.reshape(tf.where(tf.equal(y_true, pred_kmax_index)), shape=(len(y_true), len(y_true[0]), -1))[:,:,2]\r\n    n = 0\r\n    for i in tf.range(len(y_true)):\r\n        pred_acc_j = tf.TensorArray(tf.float32, size=1, dynamic_size=True)\r\n        for j in tf.range(1,len(y_true[i])):\r\n            if y_true[i,j] == 0: break # 最初のpadding0以降、paddingの0が来たときbreakする(labelは 0,1,5,3,...,2,0,0,0,...,0 だから)\r\n            # paddingの0でないとき\r\n            k = label_pos[i][j] # 該当する正解単語のindex\r\n            pred_sum = tf.reduce_sum(pred_kmax_values[i][j][:k]) # 正解単語の1つ前までの確率和\r\n            if pred_sum < thres or k == 0: # 正解単語の1つ前までの和が閾値を超えていない or 正解単語が1位\r\n                pred_acc = pred_acc.write(n, 1.0)\r\n                pred_acc_j = pred_acc_j.write(j-1, 1.0)\r\n            else:\r\n                pred_acc = pred_acc.write(n, 0)\r\n                pred_acc_j = pred_acc_j.write(j-1, 0)\r\n            n += 1\r\n        false_index = tf.where(tf.equal(pred_acc_j.stack(), 0)).numpy()[:,0] # 誤り判定になった語のindexを取得\r\n        false_indexes.append(false_index)\r\n    acc = tf.reduce_mean(pred_acc.stack())\r\n    return acc, false_indexes\r\n\r\n\r\n\r\n\r\n#---------------------------------------------\r\n\r\n# 誤字検出システムの根幹\r\n#--------------\r\n# 入力文の予測結果を出力する\r\ndef texts_pred(pred, label, test_texts, vocab_dic, dic_vocab):\r\n    result = ''\r\n    for i in range(len(test_texts)):\r\n        for j in range(len(test_texts[i])):\r\n            # 予測\r\n            y_index = pred[i][j+1].argsort()[::-1][0:5]\r\n            y_prob = np.sort(pred[i][j+1])[::-1][0:5]\r\n\r\n            # 入力語\r\n            label_id = label[i][j+1][0]\r\n            word = dic_vocab[label_id]\r\n            prob = pred[i][j+1][label_id]\r\n\r\n\r\n            # print(test_texts[i][j])\r\n            result += str(test_texts[i][j]) + '\\n'\r\n\r\n            # print('----入力----')\r\n            # print(word, prob)\r\n            # print()\r\n            result += '----入力----\\n'\r\n            result += word + ' ' + str(prob) + '\\n'\r\n\r\n            # print('----予測----')\r\n            result += '----予測----\\n'\r\n            for k in range(3):\r\n                #print(dic_vocab[y_index[k]], y_prob[k])\r\n                result += dic_vocab[y_index[k]] + ' ' + str(y_prob[k]) + '\\n'\r\n            result += '\\n'\r\n    return result\r\n        \r\n\r\n# 評価関数を改良して、不正解判定になった箇所を返すようにする\r\n# 該当する文章と誤り箇所を出力 モデル予測による適切かも単語も出力する\r\ndef miss_check(method_acc, pred, label, test_texts, vocab_dic, dic_vocab):\r\n    result = ''\r\n    acc, falIndex = method_acc(label, pred)\r\n    # print('正解率: ', acc.numpy())\r\n    # print()\r\n    #result += '正解率: ' + str(acc.numpy()) + '\\n'\r\n\r\n    for i in range(len(falIndex)):\r\n        if falIndex[i].size == 0:\r\n            #print('誤字は検出されませんでした')\r\n            result += '誤りは検出されませんでした\\n'\r\n\r\n        else:\r\n            # print('---誤字を検出---')\r\n            # print()\r\n            result += '---誤字・誤用を検出---\\n'\r\n            try:\r\n                for j in range(len(falIndex[i])):\r\n                    fi = falIndex[i][j]\r\n                    label_id = test_texts[i][fi].index('< >')\r\n\r\n                    try:\r\n                        word = dic_vocab[label[i][fi+1][0]]\r\n                        prob = pred[i][fi+1][vocab_dic[word]]\r\n                    except:\r\n                        word = 'nan'\r\n                        prob = 0.0\r\n\r\n                    test_text = copy.copy(test_texts[i][fi])\r\n                    test_text[label_id] = '<<' + word + '>>'\r\n\r\n                    y_index = pred[i][fi+1].argsort()[::-1][0:5]\r\n                    y_prob = np.sort(pred[i][fi+1])[::-1][0:5]\r\n\r\n\r\n                    # print(test_text)\r\n                    # print('------入力------')\r\n                    # print(word, prob)\r\n                    # print()\r\n                    #print('----適切かもしれない語群----')\r\n                    result += str(test_text) + '\\n'\r\n                    result += '------入力------\\n'\r\n                    result += word + ' ' + str(prob) + '\\n'\r\n                    result += '----適切かもしれない語----\\n'\r\n                    for j in range(len(y_index)):\r\n                        # print(dic_vocab[y_index[j]], y_prob[j])\r\n                        result += dic_vocab[y_index[j]] + ' ' + str(y_prob[j]) + '\\n'\r\n                    # print()\r\n                    result += '\\n'\r\n            except:\r\n                continue\r\n    return result\r\n\r\n\r\n#-------------------------\r\n# メイン\r\n\r\ndef pred_word(text, mode='all'):\r\n\r\n    # 保存した辞書のload\r\n    with open('data/vocab_dic.pkl', 'rb') as f:\r\n        vocab_dic = pickle.load(f)\r\n    with open('data/dic_vocab.pkl', 'rb') as f:\r\n        dic_vocab = pickle.load(f)\r\n    with open('data/emb_matrix.pkl', 'rb') as f:\r\n        emb_matrix = pickle.load(f)\r\n\r\n\r\n    # model のロード\r\n    path = 'data/all_word_model.h5'\r\n    # model = load_model(path, custom_objects={'predsum_acc':predsum_acc})\r\n    model = cre_model(emb_matrix, dic_vocab)\r\n    model.load_weights(path)\r\n    # print(model.summary())\r\n\r\n    texts, pos, ids = text_to_id(text, vocab_dic)\r\n    fwInputs, bwInputs, label, test_texts = cre_InputData(texts, ids)\r\n    pred = model.predict([fwInputs, bwInputs])\r\n\r\n    if mode == 'all':\r\n        result = texts_pred(pred, label, test_texts, vocab_dic, dic_vocab)\r\n    elif mode == 'miss':\r\n        result = miss_check(predsum_acc_, pred, label, test_texts, vocab_dic, dic_vocab)\r\n    \r\n    # print(result)\r\n    return result\r\n\r\n\r\n\r\n\r\n# ---------------------------------------------------\r\n# 2023/1/18\r\n# load_modelでValueError: bad marshal data (unknown type code)が発生してしまう\r\n# 原因不明\r\n# モデルを新しく定義して、重みをloadする形で応急処置\r\nfrom tensorflow.keras.layers import LSTM, Dense, Embedding, Input, TimeDistributed, concatenate, Lambda, Dropout\r\nfrom tensorflow.keras.models import Model\r\n\r\ndef cre_model(emb_matrix, dic_vocab):\r\n    # model構築\r\n    # RNN model\r\n    #----------\r\n    # 順方向\r\n    input1 = Input(shape=(None,), name='forward')\r\n    # 逆方向\r\n    input2 = Input(shape=(None,), name='backward')\r\n\r\n    emb1 = Embedding(input_dim=emb_matrix.shape[0], output_dim=emb_matrix.shape[1],\r\n                    weights=[emb_matrix], mask_zero=True, name='emb1')(input1)\r\n    emb2 = Embedding(input_dim=emb_matrix.shape[0], output_dim=emb_matrix.shape[1],\r\n                    weights=[emb_matrix], mask_zero=True, name='emb2')(input2)\r\n\r\n    forlstm = LSTM(256, dropout=0, return_sequences=True, name='forlstm')(emb1)\r\n    backlstm = LSTM(256, dropout=0, return_sequences=True, go_backwards=True, name='backlstm')(emb2)\r\n    backlstm = Lambda(lambda x : x[:,::-1], name='rev_backlstm')(backlstm)\r\n\r\n    bilstm = concatenate([forlstm, backlstm], name='bilstm')\r\n\r\n    output = TimeDistributed(Dense(1024, activation='relu'), name='dense1')(bilstm)\r\n    output = Dropout(0.2)(output)\r\n    output = TimeDistributed(Dense(len(dic_vocab), activation='softmax'), name='softmax')(output)\r\n\r\n    model = Model(inputs=[input1, input2], outputs=output)\r\n    # model.summary()\r\n\r\n    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy', predsum_acc])\r\n\r\n    return model", "repo_name": "yamataro-46/proofreading_app", "sub_path": "all_wordpred.py", "file_name": "all_wordpred.py", "file_ext": "py", "file_size_in_byte": 15460, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "tensorflow.cast", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.TensorArray", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.math.top_k", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.math.top_k", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.where", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.range", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.range", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 40, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 52, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 53, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 55, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 56, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 58, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 59, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 60, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 62, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 63, "usage_type": "call"}, {"api_name": "MeCab.Tagger", "line_number": 68, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 128, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 130, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 133, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.TensorArray", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 163, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.math.argmax", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 164, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 164, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.math.equal", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 165, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 165, "usage_type": "attribute"}, {"api_name": "tensorflow.range", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.range", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.where", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 175, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 181, "usage_type": "attribute"}, {"api_name": "tensorflow.TensorArray", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 183, "usage_type": "attribute"}, {"api_name": "tensorflow.math.top_k", "line_number": 184, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 184, "usage_type": "attribute"}, {"api_name": "tensorflow.math.top_k", "line_number": 185, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 185, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.where", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.range", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.TensorArray", "line_number": 189, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 189, "usage_type": "attribute"}, {"api_name": "tensorflow.range", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 194, "usage_type": "call"}, {"api_name": "tensorflow.where", "line_number": 202, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 202, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 221, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 281, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 310, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 312, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 314, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 352, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 354, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Embedding", "line_number": 356, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Embedding", "line_number": 358, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 361, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 362, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Lambda", "line_number": 363, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.concatenate", "line_number": 365, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.TimeDistributed", "line_number": 367, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 367, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 368, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.TimeDistributed", "line_number": 369, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 369, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 371, "usage_type": "call"}]}
{"seq_id": "72041484919", "text": "#-*- coding: utf-8 -*-\n#Arquivo gerenciador de dados do banco de dados (NDB)\n\n#Google NDB\nfrom google.appengine.ext import ndb\n\nfrom random import shuffle\nimport datetime\n\n#Entidade que guarda os IDs dos chats\nclass Chats(ndb.Model):\n    chats = ndb.StringProperty(repeated = True)\n\n#Checa a exisencia do chat, se não existir cria todas as entidades necessárias\ndef checkChat(chat_id):\n    c = ndb.Key(Chats, 'chats').get()\n    if not c:\n        c = Chats(id = 'chats')\n        c.put()\n        checkChat(chat_id)\n    if not (chat_id in c.chats):\n        e = Enabled(id = chat_id)\n        s = Settings(id = chat_id)\n        r = Rank(id = chat_id)\n        d = Dados(id = chat_id)\n        c.chats.append(chat_id)\n        r.put()\n        d.put()\n        e.put()\n        s.put()\n        c.put()\n        return False\n    return True\n\n#Remove o chat e todas as suas dependências\ndef delChat(chat_id):\n    c = ndb.Key(Chats, 'chats').get()\n    e = ndb.Key(Enabled, chat_id).get()\n    s = ndb.Key(Settings, chat_id).get()\n    g = ndb.Key(Game, chat_id).get()\n    d = ndb.Key(Dados, chat_id).get()\n    r = ndb.Key(Rank, chat_id).get()\n    status = False\n    if chat_id in c.chats:\n        c.chats.remove(chat_id)\n        c.put()\n        status = True\n    if d:\n        d.key.delete()\n        status = True\n    if e:\n        e.key.delete()\n        status = True\n    if s:\n        s.key.delete()\n        status = True\n    if g:\n        g.key.delete()\n        status = True\n    if r:\n        r.key.delete()\n        status = True\n    return status\n\n#Retorna a lista de todos os chats ativos no momento\ndef getChats():\n    c = ndb.Key(Chats, 'chats').get()\n    return c.chats\n\ndef checkChatBd(chat_id):\n    c = ndb.Key(Chats, 'chats').get()\n    e = ndb.Key(Enabled, chat_id).get()\n    s = ndb.Key(Settings, chat_id).get()\n    d = ndb.Key(Dados, chat_id).get()\n    r = ndb.Key(Rank, chat_id).get()\n    status = True\n    if not e:\n        e = Enabled(id = chat_id)\n        e.put()\n        status = False\n    if not s:\n        s = Settings(id = chat_id)\n        s.put()\n        status = False\n    if not d:\n        d = Dados(id = chat_id)\n        d.put()\n        status = False\n    if not r:\n        r = Rank(id = chat_id)\n        r.put()\n        status = False\n    return status\n\n#Guarda o estado de \"ligado\" e \"deligado\" de cada chat\nclass Enabled(ndb.Model):\n    enabled = ndb.BooleanProperty(indexed = False, default = False)\n\n#Grava o dado recebido como parâmetro, todos os 'sets' serão assim\ndef setEnabled(chat_id, status):\n    e = ndb.Key(Enabled, chat_id).get()\n    e.enabled = status\n    e.put()\n    return\n\n#Retorna o estado atual do chat\ndef getEnabled(chat_id):\n    e = ndb.Key(Enabled, chat_id).get()\n    return e.enabled\n\nclass Shout(ndb.Model):\n    shout = ndb.StringProperty(indexed = False, default = '')\n    pos = ndb.IntegerProperty(indexed = False, default = 0)\n    enable = ndb.BooleanProperty(indexed = False, default = False)\n\ndef getShout():\n    s = ndb.Key(Shout, 'Shout').get()\n    if not s:\n        s = Shout(id = 'Shout')\n        s.put()\n        s = ndb.Key(Shout, 'Shout').get()\n    if s.enable:\n        chats = getChats()\n        s.pos = s.pos + 1 if (s.pos + 1) < len(chats) else 0\n        if s.pos == 0:\n            s.enable = False\n        s.put()\n        return [chats[s.pos], s.shout, (len(chats) - (s.pos + 1))]\n    return None\n\ndef setShout(shout):\n    s = ndb.Key(Shout, 'Shout').get()\n    s.shout = shout\n    s.enable = True\n    s.put()\n    return\n\ndef delShout():\n    s = ndb.Key(Shout, 'Shout').get()\n    s.enable = False\n    s.pos = 0\n    s.shout = ''\n    s.put()\n\ndef lessPos():\n    s = ndb.Key(Shout, 'Shout').get()\n    s.pos = s.pos - 1\n    s.put()\n\n#Guarda as configurações de cada chat\nclass Settings(ndb.Model):\n    language = ndb.StringProperty(indexed = False, default = 'enUS')\n    waiting = ndb.BooleanProperty(indexed = False, default = True)\n    first = ndb.BooleanProperty(indexed = False, default = True)\n    welcome = ndb.BooleanProperty(indexed = False, default = True)\n    categorias = ndb.BooleanProperty(indexed = False, default = False)\n    cats = ndb.IntegerProperty(repeated = True)\n\ndef setCats(chat_id,cats):\n    s = ndb.Key(Settings, chat_id).get()\n    s.cats = cats\n    s.categorias = False\n    s.put()\n    return\n\ndef getCats(chat_id):\n    s = ndb.Key(Settings, chat_id).get()\n    return s.cats\n\ndef getFirstWelcome(chat_id):\n    s = ndb.Key(Settings, chat_id).get()\n    return [s.first,s.welcome]\n\ndef setWelcome(chat_id):\n    s = ndb.Key(Settings, chat_id).get()\n    s.welcome = False\n    s.put()\n\ndef setFirst(chat_id):\n    s = ndb.Key(Settings, chat_id).get()\n    s.first = False\n    s.put()\n\n#Retorna a \"classe\" configurações do chat (caso não existir cria uma nova entidade)\ndef getSettings(chat_id):\n    s = ndb.Key(Settings, chat_id).get()\n    if s:\n        return s\n    if checkChat(chat_id):\n        checkChatBd(chat_id)\n    getSettings(chat_id)\n\ndef setLanguage(chat_id, language):\n    s = ndb.Key(Settings, chat_id).get()\n    s.language = language\n    s.put()\n    return\n\ndef setWaiting(chat_id, waiting):\n    s = ndb.Key(Settings, chat_id).get()\n    s.waiting = waiting\n    s.put()\n    return\n\ndef setCategorias(chat_id, state):\n    s = ndb.Key(Settings, chat_id).get()\n    s.categorias = state\n    s.put()\n    return\n\nclass User(ndb.Model):\n    u_id = ndb.StringProperty()\n    u_name = ndb.StringProperty()\n    u_score = ndb.IntegerProperty()\n\nclass Rank(ndb.Model):\n    players = ndb.StructuredProperty(User, repeated = True)\n\ndef addPlayerRank(chat_id, u_id, u_name):\n    r = ndb.Key(Rank, chat_id).get()\n    d = ndb.Key(Dados, chat_id).get()\n    for i in range(len(r.players)):\n        if u_id == r.players[i].u_id:\n            return False\n    user = User(u_id = u_id, u_name = u_name, u_score = 0)\n    d.players.append(user)\n    r.players.append(user)\n    r.put()\n    d.put()\n    return True\n\ndef getRank(chat_id):\n    r = ndb.Key(Rank, chat_id).get()\n    d = ndb.Key(Dados, chat_id).get()\n    if len(r.players) != 0:\n        rank = sorted(r.players, key = lambda players: players.u_score, reverse = True)\n        nomes = []\n        scores = []\n        for i in range(len(rank)):\n            nomes.append(rank[i].u_name.encode('utf-8'))\n            scores.append(rank[i].u_score)\n        r.players = rank\n        d.topPlayer = rank[0]\n        r.put()\n        d.put()\n        return [nomes, scores]\n    return []\n\ndef addScore(chat_id, u_id, score):\n    r = ndb.Key(Rank, chat_id).get()\n    for i in range(len(r.players)):\n        if r.players[i].u_id == u_id:\n            r.players[i].u_score += score\n    r.put()\n    return\n\nclass Dados(ndb.Model):\n    games = ndb.IntegerProperty(indexed = False, default = 0)\n    topPlayer = ndb.StructuredProperty(User, default = User(u_id = 'noID', u_name ='noPlayer', u_score = 0))\n    players = ndb.StructuredProperty(User, repeated = True)\n    last_att = ndb.IntegerProperty(indexed = False, default = 1)\n    jogos_dia = ndb.IntegerProperty(indexed = False, default = 0)\n\ndef getDadosChat(chat_id):\n    d = ndb.Key(Dados, chat_id).get()\n    if d:\n        return d\n    d = Dados(id = chat_id)\n    d.put()\n    return False\n\ndef getDadosGlobais(date):\n    c = ndb.Key(Chats, 'chats').get()\n    n_games = 0\n    n_players = 0\n    jogos_dia = 0\n    u_ids = []\n    for i in range (len(c.chats)):\n        d = ndb.Key(Dados, c.chats[i]).get()\n        if not d:\n            getDadosChat(c.chats[i])\n            d = ndb.Key(Dados, c.chats[i]).get()\n        jogos_dia = jogos_dia + getJogosDia(c.chats[i], date)\n        n_games += d.games\n        for j in range(len(d.players)):\n            if not (d.players[j].u_id in u_ids):\n                u_ids.append(d.players[j].u_id)\n    n_players = len(u_ids)\n    return [len(c.chats), n_players, jogos_dia, n_games]\n\ndef setJogosDia(chat_id, date):\n    d = ndb.Key(Dados, chat_id).get()\n    date = int(datetime.datetime.fromtimestamp(date).strftime('%d'))\n    if date != d.last_att:\n        d.last_att = date\n        d.jogos_dia = 0\n    d.jogos_dia += 1\n    d.put()\n\ndef getJogosDia(chat_id, date):\n    d = ndb.Key(Dados, chat_id).get()\n    date = int(datetime.datetime.fromtimestamp(date).strftime('%d'))\n    if d:\n        if date != d.last_att:\n            return 0\n        return d.jogos_dia\n    delChat(chat_id)\n    return 0\n\n#Contém todos os dados de cada jogo\nclass Game(ndb.Model):\n    pre_game = ndb.BooleanProperty(indexed = False, default = False)\n    in_game = ndb.BooleanProperty(indexed = False, default = False)\n    u_ids = ndb.StringProperty(repeated = True)\n    u_names = ndb.StringProperty(repeated = True)\n    message_ids = ndb.StringProperty(repeated = True)\n    adm = ndb.StringProperty(default = 'noAdm')\n    adm_name = ndb.StringProperty(default = 'noAdm')\n    adm_message = ndb.StringProperty(default = 'noAdm')\n    rnd = ndb.IntegerProperty(default = 0)\n    palavra = ndb.StringProperty(default = 'noPalavra')\n    categoria = ndb.StringProperty(default = 'noCategoria')\n    mascara = ndb.StringProperty(default = 'noMascara')\n    letras = ndb.StringProperty(repeated = True)\n    vidas = ndb.IntegerProperty(default = 6)\n    vidas_init = ndb.IntegerProperty(default = 6)\n    arriscarBlock = ndb.BooleanProperty(indexed = False, default = False)\n\ndef getPreGame(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    if g:\n        return g.pre_game\n    return False\n\ndef getRound(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    return g.rnd\n\ndef roundPlus(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    g.rnd = g.rnd+1 if g.rnd+1 < len(g.u_ids) else 0\n    g.put()\n    return\n\ndef checkRound(chat_id, u_id):\n    g = ndb.Key(Game, chat_id).get()\n    if g.u_ids[g.rnd] == u_id:\n        return True\n    return False\n\ndef setPreGame(chat_id, status, u_id = None, u_name = None, message_id = None):\n    g = ndb.Key(Game, chat_id).get()\n    if g:\n        g.pre_game = status\n        g.put()\n        return\n    g = Game(id = chat_id)\n    g.put()\n    g = ndb.Key(Game, chat_id).get()\n    g.pre_game = status\n    g.put()\n    addPlayer(chat_id, u_id, u_name, message_id)\n    setAdm(chat_id, u_id, u_name, message_id)\n    return\n\ndef setInGame(chat_id, status):\n    g = ndb.Key(Game, chat_id).get()\n    g.in_game = status\n    g.put()\n    return\n\ndef getInGame(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    if g:\n        return g.in_game\n    return False\n\ndef addPlayer(chat_id, u_id, u_name, message_id):\n    g = ndb.Key(Game, chat_id).get()\n    if not (u_id in g.u_ids):\n        addPlayerRank(chat_id, u_id, u_name)\n        g.u_ids.append(u_id)\n        g.u_names.append(u_name)\n        g.message_ids.append(message_id)\n        g.put()\n        return True\n    return False\n\ndef rmPlayer(chat_id, u_id, message_id):\n    g = ndb.Key(Game, chat_id).get()\n    if not g.pre_game:\n        g.rnd = g.rnd-1 if g.rnd != 0 else len(g.u_ids)-1\n    if u_id in g.u_ids:\n        ind = g.u_ids.index(u_id)\n        g.u_ids.pop(ind)\n        g.u_names.pop(ind)\n        g.message_ids.pop(ind)\n        g.put()\n        if len(g.u_ids) == 0:\n            delGame(chat_id)\n            return False\n        if checkAdm(chat_id, u_id):\n            setAdm(chat_id, g.u_ids[0], g.u_names[0], g.message_ids[0])\n            return 'setAdm'\n        return True\n    return 'semPlayer'\n\ndef getPlayers(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    u_ids = []\n    u_names = []\n    message_ids = []\n    for i in range(len(g.u_ids)):\n        u_ids.append(g.u_ids[i].encode('utf-8'))\n        u_names.append(g.u_names[i].encode('utf-8'))\n        message_ids.append(g.message_ids[i].encode('utf-8'))\n    return [u_ids, u_names, message_ids]\n\n#Randomizar a lista de participantes\ndef shufflePlayers(chat_id, date):\n    g = ndb.Key(Game, chat_id).get()\n    setJogosDia(chat_id, date)\n    u_names_shuf = []\n    u_ids_shuf = []\n    message_ids_shuf = []\n    index_shuf = range(len(g.u_ids))\n    shuffle(index_shuf)\n    for i in index_shuf:\n        u_ids_shuf.append(g.u_ids[i])\n        u_names_shuf.append(g.u_names[i])\n        message_ids_shuf.append(g.message_ids[i])\n    g.u_ids = u_ids_shuf\n    g.u_names = u_names_shuf\n    g.message_ids = message_ids_shuf\n    g.put()\n\ndef setAdm(chat_id, u_id, u_name, message_id):\n    g = ndb.Key(Game, chat_id).get()\n    g.adm = u_id\n    g.adm_name = u_name\n    g.adm_message = message_id\n    g.put()\n    return\n\ndef checkAdm(chat_id, u_id):\n    g = ndb.Key(Game, chat_id).get()\n    if g:\n        if g.adm == u_id:\n            return True\n    return False\n\ndef getAdm(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    if g:\n        return [g.adm.encode('utf-8'), g.adm_name.encode('utf-8') ,g.adm_message.encode('utf-8')]\n    return False\n\ndef setCP(chat_id, categoria, palavra):\n    g = ndb.Key(Game, chat_id).get()\n    d = ndb.Key(Dados, chat_id).get()\n    d.games += 1\n    g.categoria = categoria\n    g.palavra = palavra#palavra.decode('utf-8')\n    mascara = ''\n    for i in range(len(g.palavra)):\n        if palavra[i] == ' ':\n            mascara = mascara+' '\n        else:\n            mascara = mascara+'*'\n    g.mascara = mascara\n    g.put()\n    d.put()\n    return mascara\n\ndef getCategoria(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    return g.categoria.encode('utf-8')\n\n\ndef checkPalavra(chat_id, u_id, text):\n    g = ndb.Key(Game, chat_id).get()\n    if g:\n        if text == g.palavra.encode('utf-8').lower():\n            addScore(chat_id, u_id, (len(text)*2))\n            g.key.delete()\n            return True\n    return False\n\ndef getPalavra(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    return g.palavra\n\ndef getMascara(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    return g.mascara.encode('utf-8')\n\ndef checkUid(chat_id, u_id):\n    g = ndb.Key(Game, chat_id).get()\n    if u_id in g.u_ids:\n        if checkRound(chat_id, u_id):\n            return True\n        return 'rnd'\n    return 'out'\n\ndef setArriscarBlock(chat_id, opt):\n    g = ndb.Key(Game, chat_id).get()\n    if g:\n        g.arriscarBlock = opt\n        g.put()\n        return\n    return\n\ndef getArriscarBlock(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    if g:\n        return g.arriscarBlock\n    return False\n\ndef setVidas(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    modVida = len(g.palavra)/5 if len(g.palavra) > 5 else 0\n    modVida += len(g.u_ids)-3 if len(g.u_ids) > 4 else 0\n    modVida = 9 if modVida > 9 else modVida\n    vidas = g.vidas + modVida\n    g.vidas = vidas\n    g.vidas_init = g.vidas_init + modVida\n    g.put()\n    return vidas\n\ndef menosVida(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    g.vidas -= 1\n    if g.vidas <= 0:\n        delGame(chat_id)\n        return True\n    if g.vidas == 1:\n        g.put()\n        return 2\n    g.put()\n    return False\n\ndef getVidas(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    return g.vidas\n\ndef getVidasInit(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    return g.vidas_init\n\ndef getLetras(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    letras = [['Q', 'W', 'E', 'R', 'T', 'Y', 'U', 'I', 'O', 'P'],\n    ['A', 'S', 'D', 'F', 'G', 'H', 'J', 'K', 'L'],\n    ['Z', 'X', 'C', 'V', 'B', 'N', 'M']]\n    for i in range(len(g.letras)):\n        for j in range(len(letras)):\n            if g.letras[i].upper() in letras[j]:\n                letras[j].remove(g.letras[i].upper())\n    return letras\n\ndef checkLetra(chat_id, u_id, letra): #Ve claramente que é pura gambiarra!\n    g = ndb.Key(Game, chat_id).get()\n    chs = ['á','ã','â','é','ê','í','ó','õ','ô','ú','ç'] #Lista de caracteres especiais suportados no momento, NÃO ADICIONAR PALAVRAS COM CARACTERES NÃO SUPORTADOS!\n    ch = ['a','a','a','e','e','i','o','o','o','u','c'] #Acompanha o chs para funcionar. Gambiarra.\n    for i in range(len(chs)):\n        chs[i] = chs[i].decode('utf-8')\n        ch[i] = ch[i].decode('utf-8')\n\n    letra.decode('utf-8')\n    palavra = g.palavra.lower()\n    nPalavra = g.palavra.lower()\n    aux = [None] * len(nPalavra)\n    for i in range(len(nPalavra)):\n        if nPalavra[i] in chs:\n            idc = chs.index(palavra[i])\n            idx = palavra.index(chs[idc])\n            aux[i] = chs[idc]\n            nPalavra = nPalavra[:idx] + ch[idc] + nPalavra[idx+1:] #Reconstrói a palavra sem caracteres especiais\n    if not (letra in g.letras):\n        if letra.lower() in nPalavra.lower():\n            nMascara = ''\n            score = 0\n            for i in range(len(nPalavra)):\n                if nPalavra[i].lower() == letra:\n                    letraAnt = letra\n                    if aux[i]: #Se existem caracteres especiais\n                        letra = aux[i]\n                    nMascara = nMascara+letra #substitui a letra na posição\n                    letra = letraAnt\n                    score += 1\n                else:\n                    nMascara = nMascara+g.mascara[i]\n            g.mascara = nMascara\n            addScore(chat_id, u_id, (score*2))\n            g.letras.append(letra)\n            g.put()\n            if g.palavra.lower() == nMascara:\n                return nMascara.encode('utf-8')\n            return True\n        g.letras.append(letra)\n        g.put()\n        return False\n    g.put()\n    return 2\n\ndef delGame(chat_id):\n    g = ndb.Key(Game, chat_id).get()\n    g.key.delete()\n", "repo_name": "brunodalarosa/PlayHangmanBot", "sub_path": "bds.py", "file_name": "bds.py", "file_ext": "py", "file_size_in_byte": 17153, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "40", "api": [{"api_name": "google.appengine.ext.ndb.Model", "line_number": 11, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 11, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 12, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 12, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 16, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 16, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 37, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 37, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 38, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 38, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 39, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 39, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 40, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 40, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 41, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 41, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 42, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 42, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 67, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 67, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 71, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 71, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 72, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 72, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 73, "usage_type": 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"usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 186, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 194, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 194, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 200, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 200, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 206, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 206, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Model", "line_number": 211, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 211, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 212, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 212, "usage_type": "name"}, {"api_name": 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{"api_name": "google.appengine.ext.ndb", "line_number": 273, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 279, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 279, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 282, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 282, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 292, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 292, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 293, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 293, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 301, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 301, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 302, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 302, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb.Model", "line_number": 311, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 311, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.BooleanProperty", "line_number": 312, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 312, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.BooleanProperty", "line_number": 313, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 313, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 314, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 314, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 315, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 315, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 316, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 316, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 317, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 317, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 318, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 318, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 319, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 319, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.IntegerProperty", "line_number": 320, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 320, "usage_type": "name"}, {"api_name": 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"google.appengine.ext.ndb.Key", "line_number": 352, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 352, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 359, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 359, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 367, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 367, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 373, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 373, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 379, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 379, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 390, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 390, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 409, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 409, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 421, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 421, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 427, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 438, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 438, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 446, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 446, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 453, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 453, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 459, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 459, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 460, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 460, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 476, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 476, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 481, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 481, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 490, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 490, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 494, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 494, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 498, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 498, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 506, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 506, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 514, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 514, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 520, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 520, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 531, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 531, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 543, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 543, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 547, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 547, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 551, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 551, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 562, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 562, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 607, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 607, "usage_type": "name"}]}
{"seq_id": "41989932465", "text": "\"\"\"\n  Macht eine Maske von einem Mito Channel image\n\"\"\"\n\nimport os\nimport specpy as sp\nimport numpy as np\nfrom PIL import Image  # https://pillow.readthedocs.io/en/stable/handbook/index.html\nimport matplotlib.pyplot as plt\nimport scipy.ndimage as ndimage\nimport skimage.filters as filters\nimport cv2\n\ndef save_as_tif(output_path, data):\n    img = Image.fromarray(data.astype(np.uint8))\n    img.save(output_path, format='tiff')\n\n# alle Einstellungen\nroot_path = r'Q:\\00_Users\\Sarah Schweighofer (sschwei)\\Freiburg\\IF36_selected-for-analysis-with-Jan'\nfile_name = 'IF36_spl15_U2OS-DKO_pcDNA-Bax-wt_6hEx_14hAct_cytC-AF488_Tom20-AF594_Bax-SR_cl8_ringheaven.msr'\nfile_path = os.path.join(root_path, 'IF36_spl15_U2OS-DKO_pcDNA-Bax-wt_6hEx_14hAct_cytC-AF488_Tom20-AF594_Bax-SR_cl8_ringheaven.msr')\n\n# File lesen\nim_file = sp.File(file_path, sp.File.Read)\nnumber_stacks = im_file.number_of_stacks()\nprint('Messung {} hat {} Bilder.'.format(file_path, number_stacks))\n\n# nach dem mito Kanal suchen in allen Stacks\nfound_mito_channel = False\nfor i in range(number_stacks):\n    stack = im_file.read(i)\n    # print('Stack {} ist {}D mit der Größe {}'.format(stack.name(), stack.number_of_dimensions(), stack.sizes()))\n\n    # ist es der Mito Kanal?\n    if stack.name().startswith('Alexa 594_STED'):\n        # Ja\n        found_mito_channel = True\n        data = stack.data()\n\n        # Dimensionnen sind [1,1,Ny,Nx] wir wollen aber [Nx, Ny]\n\n        # reduce to [Ny, Nx]\n        size = data.shape\n        data = np.reshape(data, size[2:])\n\n        # transponieren [Nx, Ny]\n        data = np.transpose(data)\n\n        break\nif not found_mito_channel:\n    raise RuntimeError('Kein Mito Kanal in dieser Messung.')\n\n# display\nfig, (ax1, ax2) = plt.subplots(1, 2)\nim = ax1.imshow(data, cmap='Greens')\nax1.set_title('original mito image')\nplt.show()\n\n# Rauschen reduzieren\ndenoised_data = ndimage.gaussian_filter(data, sigma=2)\n\n# display\nim = ax2.imshow(denoised_data, cmap='Greens')\nax2.set_title('denoised mito image')\nplt.show()\n\nbinary_data = denoised_data.astype(np.uint8) #ich wandle mein denoised data mit astype in 8bit image um\n\n\nbinary_image = cv2.adaptiveThreshold(binary_data,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,401,-2)\n#So funktioniert der opencv adaptive threshold: neuesBild = cv.adaptiveThreshold(source, maxValue, adaptiveMethod,\n# thresholdType, blockSize, C[, dst]) die Zahlen am Ende muss man selber ausprobieren\n\notsu_image = cv2.threshold(binary_data, 0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n\n\n# adaptiver theshold für maske\n#fig, ax = filters.try_all_threshold(denoised_data)\n#plt.show()\n#maske_isodata = denoised_data > filters.threshold_isodata(denoised_data) #True vs false\n#maske_otsu = denoised_data > filters.threshold_multiotsu(denoised_data)\n# !!!The two lines were identical. after I changed filters.threshold_isodata to\n# filters.threshold_multiotsu, it doesn't work anymore\n\n# save everything\n#output_path = output_file_path = file_path[:-4] + '.denoised.tiff'\n#save_as_tif(output_path, denoised_data)\n\nmypath = os.path.join(root_path, 'thresholds') #macht neuen Ordner mit dem Namen, wenns den nicht schon gibt\nif not os.path.isdir(mypath):\n    os.makedirs(mypath)\n\no_p = os.path.join(mypath, file_name[:-4] + '.thresh.tiff')\nsave_as_tif(output_path=o_p, data=binary_image)\n#o_p = os.path.join(mypath, file_name[:-4] + '.otsu.tiff')\n#save_as_tif(output_path=o_p, data=otsu_image)\n\n\n\n\n#output_path = output_file_path = file_path[:-4] + '.threshold-isodata.tiff'\n#save_as_tif(output_path, maske_isodata)\n#output_path = output_file_path = file_path[:-4] + '.threshold-otsu.tiff'\n#save_as_tif(output_path, maske_otsu)\n\n\n", "repo_name": "AminoSarah16/Bax-analysis-code", "sub_path": "mito-threshold/mask_from_mito_channel_COPY.py", "file_name": "mask_from_mito_channel_COPY.py", "file_ext": "py", "file_size_in_byte": 3653, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "PIL.Image.fromarray", "line_number": 15, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "specpy.File", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "scipy.ndimage.gaussian_filter", "line_number": 60, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 67, "usage_type": "attribute"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_GAUSSIAN_C", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 74, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}]}
{"seq_id": "23102729620", "text": "from PIL import Image, ImageDraw, ImageFont\nimport random\nimport textwrap\nfrom better_profanity import profanity\nimport time\n\nwidth = 2000\nheight = 2000\nnumber_of_words = 2400\nnumber_of_characters = 300\nfont = ImageFont.truetype(r'cour.ttf', 48)\nmake_color = lambda : (random.randint(50, 255), random.randint(50, 255), random.randint(50,255))\n\nhow_many = int(input(\"How many? \"))\n\nwhile how_many > 0:\n\n    def list_of_words():\n        words = []\n        for i in range(number_of_words):\n            words.append(random.choice(open(\"pwd.txt\").read().split()))\n        return(\" \".join(words))\n\n    wrapper = textwrap.TextWrapper(width=number_of_characters)\n    value = str(list_of_words())\n    wrapped = wrapper.fill(text=value)\n\n    censored = profanity.censor(wrapped, \"\")\n\n    img = Image.new(\"RGB\", (width, height), (0,0,0)) # scrap image\n    draw = ImageDraw.Draw(img)\n    img2 = Image.new(\"RGB\", (width, height), (0,0,0)) # final image\n\n    fill = \" o \"\n    x = 0\n    w_fill, y = draw.textsize(fill)\n    x_draw, x_paste = 0, 0\n    for c in censored:\n        w_full = draw.textsize(fill+c)[0]\n        w = w_full - w_fill     # the width of the character on its own\n        draw.text((x_draw,0), fill+c, make_color())\n        iletter = img.crop((x_draw+w_fill, 0, x_draw+w_full, y))\n        img2.paste(iletter, (x_paste, 0))\n        x_draw += w_full\n        x_paste += w\n\n    date_string = time.strftime(\"%Y-%m-%d-%H_%M_%S\")\n    img2.save('output/background-' + str(how_many) + '_' + date_string + '.png')\n\n    how_many -=1\n\n", "repo_name": "nonfunjible/word-backgrounds", "sub_path": "2-generate-backgrounds.py", "file_name": "2-generate-backgrounds.py", "file_ext": "py", "file_size_in_byte": 1527, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "PIL.ImageFont.truetype", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 11, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 12, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 21, "usage_type": "call"}, {"api_name": "textwrap.TextWrapper", "line_number": 24, "usage_type": "call"}, {"api_name": "better_profanity.profanity.censor", "line_number": 28, "usage_type": "call"}, {"api_name": "better_profanity.profanity", "line_number": 28, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 31, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 32, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 47, "usage_type": "call"}]}
{"seq_id": "4494856457", "text": "import requests\nimport shutil\nimport tempfile\nimport subprocess\nimport os\nimport re\n\nclass VMProfTest(object):\n    def vmprof_exec(self, *args, cwd=None, jitlog=False, web=False, output=None):\n        script = args[0] \n        params = []\n        if web:\n            params += ['--web', '--web-url', self.vmprof_url]\n        if jitlog:\n            params += ['--jitlog']\n        if output:\n            params += ['-o', output]\n        return self.shell_exec(self.interp, \"testvmprof/test/examples/boot_env.py\" ,\n                               self.tmp + \"/\" + self.interpname + \"-env/bin/activate_this.py\",\n                               \"vmprof\", \"--\", *params, *args, cwd=cwd)\n\n    def shell_exec(self, *args, cwd=None, env={}):\n        if cwd == None:\n            cwd = os.getcwd()\n        print(\"shexe> %s (pwd: %s)\" % (' '.join(args), os.getcwd()))\n        proc = subprocess.Popen(args, cwd=cwd,\n                                stderr=subprocess.PIPE,\n                                stdout=subprocess.PIPE,\n                                env=env)\n        try:\n            outs, err = proc.communicate(timeout=60)\n        except subprocess.TimeoutExpired:\n            proc.kill()\n            raise AssertionError(\"command: %s. failed %d\" % \\\n                    (' '.join(args), ro.returncode))\n        assert proc.returncode == 0, \\\n            \"err\\n%s\\n\\nout\\n%s\\n\" % (err.decode(), outs.decode())\n        return outs, err, proc.returncode\n\n\nclass VMProfCPythonTest(VMProfTest):\n    def setup_class(self):\n        tmp, cpython = setup_local_cpython()\n        self.tmp = tmp\n        self.interp = os.path.join(tmp, cpython)\n        self.interpname = 'cpython'\n        self.vmprofargs = \"--web --web-url %s\" % self.vmprof_url\n\nclass VMProfPyPyTest(VMProfTest):\n    def setup_class(self):\n        tmp, pypy = setup_local_pypy()\n        self.tmp = tmp\n        self.interp = os.path.join(tmp, pypy)\n        self.interpname = 'pypy'\n        self.vmprofargs = \"--web --web-url %s\" % self.vmprof_url\n\n    def jitlog_exec(self, *args, cwd=None, web=False, upload=None, output=None, env={}):\n        script = args[0] \n        params = []\n        if web:\n            params += ['--web', '--web-url', self.vmprof_url]\n        if output:\n            params += ['-o', output]\n        if upload:\n            params += ['--upload', '--web-url', self.vmprof_url]\n        return self.shell_exec(self.interp, \"testvmprof/test/examples/boot_env.py\" ,\n                               self.tmp + \"/\" + self.interpname + \"-env/bin/activate_this.py\",\n                               \"jitlog\", \"--\", *params, *args, cwd=cwd, env=env)\n\ndef setup_local_cpython():\n    tmp = tempfile.mkdtemp()\n    absexe = \"/usr/bin/python\"\n    subprocess.run([\"virtualenv\", \"-p\", absexe, \"cpython-env\"], cwd=tmp)\n    subprocess.run([\"cpython-env/bin/pip\", \"install\", \"--no-cache-dir\", \"--pre\", \"vmprof\"], cwd=tmp)\n    return tmp, os.path.join(\"cpython-env\",\"bin\",\"python\")\n\n\ndef setup_local_pypy(branch='trunk', version='latest', dist='linux64'):\n    # uff, hardcoded paths & only for linux\n    filename = \"pypy.tar.bz2\"\n    tmp = tempfile.mkdtemp()\n    if \"TEST_PYPY_EXEC\" not in os.environ:\n        if \"TEST_PYPY_DOWNLOADED\" in os.environ:\n            shutil.copy(os.environ[\"TEST_PYPY_DOWNLOADED\"], os.path.join(tmp, filename))\n        else:\n            download_pypy(os.path.join(tmp, filename), branch, version, dist)\n        subprocess.run([\"tar\", \"xf\", os.path.join(tmp, filename)], cwd=tmp)\n        executable = None\n        found = False\n        for root, dirs, files in os.walk(tmp):\n            for dir in dirs:\n                if dir.startswith(\"pypy-\"):\n                    executable = os.path.join(dir, \"bin\", \"pypy\")\n                    found = True\n                    break\n        if not found:\n            raise AssertionError(\"could not setup local pypy\")\n        absexe = os.path.join(tmp, executable)\n    else:\n        absexe = executable = os.environ[\"TEST_PYPY_EXEC\"]\n    # poor man's dependency resolution\n    subprocess.run([\"virtualenv\", \"-p\", absexe, \"pypy-env\"], cwd=tmp)\n    subprocess.run([\"pypy-env/bin/pip\", \"install\", \"--no-cache-dir\",\n                    \"git+https://github.com/vmprof/vmprof-python.git\"], cwd=tmp)\n    return tmp, os.path.join(\"pypy-env\",\"bin\",\"python\")\n\ndef download_pypy(path, branch, version, dist):\n    link = \"http://buildbot.pypy.org/nightly/{}/pypy-c-jit-{}-{}.tar.bz2\".format(branch, version, dist)\n    print(\"downloading\", link)\n    r = requests.get(link, stream=True)\n    if r.status_code == 200:\n        with open(path, 'wb') as f:\n            r.raw.decode_content = True\n            shutil.copyfileobj(r.raw, f)\n            return\n\n    raise Exception(\"cannot download pypy!\")\n\ndef output_extract_urls(output):\n    found = {}\n    last_type = None\n    typematch = re.compile(\" => Uploading the ([a-z]+) \")\n    for line in output.splitlines():\n        match = typematch.search(line)\n        if match:\n            last_type = match.group(1)\n            continue\n        if last_type is not None:\n            found[last_type] = line.strip()\n            last_type = None\n    return found\n\n", "repo_name": "vmprof/vmprof-integration", "sub_path": "testvmprof/support.py", "file_name": "support.py", "file_ext": "py", "file_size_in_byte": 5114, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.getcwd", "line_number": 24, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 25, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 26, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "subprocess.TimeoutExpired", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 71, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 73, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 81, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 83, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 84, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 100, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 102, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 110, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 114, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 122, "usage_type": "call"}]}
{"seq_id": "38806680707", "text": "#\n# @lc app=leetcode.cn id=575 lang=python3\n#\n# [575] 分糖果\n#\n\nfrom typing import List\n\n# @lc code=start\nclass Solution:\n    def distributeCandies(self, candyType: List[int]) -> int:\n        candySet = set(candyType)\n        return min(len(candyType) // 2, len(candySet))\n# @lc code=end\n\n", "repo_name": "whuhenry/leetcode_solution", "sub_path": "575.分糖果.py", "file_name": "575.分糖果.py", "file_ext": "py", "file_size_in_byte": 292, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "typing.List", "line_number": 11, "usage_type": "name"}]}
{"seq_id": "3847715091", "text": "import os\nimport sys\nfrom pathlib import Path\nfrom typing import Dict, Callable\n\nimport numpy as np\nimport torch\n\nROOT_PROJECT = str(Path(os.path.realpath(__file__)).parent.parent.parent)\nsys.path[0] = ROOT_PROJECT\n\n\ndef input_constraint_maker(ind: int) -> Callable[[Dict], bool]:\n    def f(x: Dict) -> bool:\n        return x[f\"var_{ind}\"] < 0\n\n    return f\n\n\ndef test_rs_acq_opt_in_bobuilder() -> int:\n    acq_opt_id = \"rs\"\n    bo_builder = BoBuilder(model_id=\"gp_to\", acq_opt_id=acq_opt_id, acq_func_id=\"ei\", tr_id=\"basic\")\n    optimizer = bo_builder.build_bo(\n        search_space=search_space, n_init=10, input_constraints=input_constraints, dtype=dtype, device=device\n    )\n    print(optimizer.name, task.name)\n    for i in range(20):\n        x_next = optimizer.suggest(3)\n        y_next = task(x_next)\n        optimizer.observe(x_next, y_next)\n        print(f'Iteration {i + 1:>4d} - f(x) - {y_next[0][0]:.3f} - f(x*) - {optimizer.best_y:.3f}')\n\n    return 0\n\n\ndef test_rs_acq_opt() -> int:\n    x_train_pd = search_space.sample(100)\n    x_train = search_space.transform(x_train_pd)\n    y_train = torch.tensor(task(x_train_pd))\n\n    kernel = TransformedOverlap()\n    model = ExactGPModel(search_space, 1, kernel, dtype=dtype, device=device)\n    model.fit(x_train, y_train)\n\n    acq_opt = RandomSearchAcqOptimizer(search_space=search_space, input_constraints=input_constraints)\n\n    x = search_space.transform(search_space.sample(1))\n    acq_func = acq_factory(\"lcb\")\n    new_points = acq_opt.optimize(\n        x=x,\n        n_suggestions=3,\n        x_observed=x_train,\n        model=model,\n        acq_func=acq_func,\n        acq_evaluate_kwargs={},\n        tr_manager=None\n    )\n    new_points = search_space.inverse_transform(new_points)\n    assert np.all(\n        [[input_constraints[i](new_points.iloc[[j]]) for i in range(len(input_constraints))] for j in\n         range(len(new_points))])\n    print(new_points)\n    return 0\n\n\nif __name__ == '__main__':\n    from mcbo.task_factory import task_factory\n    from mcbo.optimizers import BoBuilder\n    from mcbo.acq_funcs import acq_factory\n    from mcbo.acq_optimizers.random_search_acq_optimizer import RandomSearchAcqOptimizer\n    from mcbo.models import ExactGPModel\n    from mcbo.models.gp.kernels import TransformedOverlap\n\n    dtype = torch.float64\n    device = torch.device(\"cuda\")\n    task = task_factory('levy', num_dims=[2, 3, 2], variable_type=['int', 'nominal', 'num'],\n                        num_categories=[None, 21, None])\n    search_space = task.get_search_space(dtype=dtype)\n\n    input_constraints = [input_constraint_maker(i) for i in range(1, 4)]\n\n    test_rs_acq_opt()\n", "repo_name": "huawei-noah/HEBO", "sub_path": "MCBO/tests/acq_optimizers/test_rs_acq_opt.py", "file_name": "test_rs_acq_opt.py", "file_ext": "py", "file_size_in_byte": 2644, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2286, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pathlib.Path", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 75, "usage_type": "call"}, {"api_name": "mcbo.task_factory.task_factory", "line_number": 76, "usage_type": "call"}]}
{"seq_id": "39854411394", "text": "import pymongo\r\nfrom bson.code import Code\r\n\t\r\nmyclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\r\nmydb = myclient[\"arth34\"]\r\nmycol = mydb[\"orders\"]\r\n\r\nmylist = [\r\n{ \"_id\": 1, \"cust_id\": \"Ant O. Knee\",  \"price\": 25, \"items\": [ { \"sku\": \"oranges\", \"qty\": 5, \"price\": 2.5 }, { \"sku\": \"apples\", \"qty\": 5, \"price\": 2.5 } ], \"status\": \"A\" },\r\n{ \"_id\": 2, \"cust_id\": \"Ant O. Knee\",  \"price\": 70, \"items\": [ { \"sku\": \"oranges\", \"qty\": 8, \"price\": 2.5 }, { \"sku\": \"chocolates\", \"qty\": 5, \"price\": 10 } ], \"status\": \"A\" },\r\n{ \"_id\": 3, \"cust_id\": \"Busby Bee\",  \"price\": 50, \"items\": [ { \"sku\": \"oranges\", \"qty\": 10, \"price\": 2.5 }, { \"sku\": \"pears\", \"qty\": 10, \"price\": 2.5 } ], \"status\": \"A\" },\r\n{ \"_id\": 4, \"cust_id\": \"Busby Bee\",  \"price\": 25, \"items\": [ { \"sku\": \"oranges\", \"qty\": 10, \"price\": 2.5 } ], \"status\": \"A\" },\r\n{ \"_id\": 5, \"cust_id\": \"Busby Bee\",  \"price\": 50, \"items\": [ { \"sku\": \"chocolates\", \"qty\": 5, \"price\": 10 } ], \"status\": \"A\"},\r\n{ \"_id\": 6, \"cust_id\": \"Cam Elot\",  \"price\": 35, \"items\": [ { \"sku\": \"carrots\", \"qty\": 10, \"price\": 1.0 }, { \"sku\": \"apples\", \"qty\": 10, \"price\": 2.5 } ], \"status\": \"A\" },\r\n{ \"_id\": 7, \"cust_id\": \"Cam Elot\",  \"price\": 25, \"items\": [ { \"sku\": \"oranges\", \"qty\": 10, \"price\": 2.5 } ], \"status\": \"A\" },\r\n{ \"_id\": 8, \"cust_id\": \"Don Quis\",  \"price\": 75, \"items\": [ { \"sku\": \"chocolates\", \"qty\": 5, \"price\": 10 }, { \"sku\": \"apples\", \"qty\": 10, \"price\": 2.5 } ], \"status\": \"A\" },\r\n{ \"_id\": 9, \"cust_id\": \"Don Quis\",  \"price\": 55, \"items\": [ { \"sku\": \"carrots\", \"qty\": 5, \"price\": 1.0 }, { \"sku\": \"apples\", \"qty\": 10, \"price\": 2.5 }, { \"sku\": \"oranges\", \"qty\": 10, \"price\": 2.5 } ], \"status\": \"A\" },\r\n{ \"_id\": 10, \"cust_id\": \"Don Quis\",  \"price\": 25, \"items\": [ { \"sku\": \"oranges\", \"qty\": 10, \"price\": 2.5 } ], \"status\": \"A\" }\r\n]\r\n\t\t\r\nx = mycol.insert_many(mylist)\r\n\r\nfor x in mycol.find():\r\n\tprint(x)\r\n\r\nmapFunction1 = Code(\"function() { emit(this.cust_id, this.price);}\")\r\nreduceFunction1 = Code(\"function(keyCustId, valuesPrices) {return Array.sum(valuesPrices)}\")\r\nresult = mycol.map_reduce(mapFunction1, reduceFunction1, \"result\")\r\n\r\nrescol = mydb[\"result\"]\r\nfor y in rescol.find():\r\n\tprint(y)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "umeshtyagi829/MongoDB-MapReduce-Python", "sub_path": "mp.py", "file_name": "mp.py", "file_ext": "py", "file_size_in_byte": 2210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pymongo.MongoClient", "line_number": 4, "usage_type": "call"}, {"api_name": "bson.code.Code", "line_number": 26, "usage_type": "call"}, {"api_name": "bson.code.Code", "line_number": 27, "usage_type": "call"}]}
{"seq_id": "18349739014", "text": "__author__ = 'jagzviruz'\n\nimport re\nimport os\nimport errno\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport sys, threading, time\nimport pycurl\n\nimport pymongo\nfrom pymongo import MongoClient\n\nclient = MongoClient()\ndb = client.medplus_data\n\n# Step 1 : Open the category listing page, and download the category pages, to extract the Product IDs\n\nclass DownLoadPageWithPyCurlInThread(threading.Thread):\n    def __init__(self, url, output_file):\n        threading.Thread.__init__(self)\n        self.curl = pycurl.Curl()\n        self.curl.setopt(pycurl.URL, url)\n        self.curl.setopt(pycurl.WRITEDATA, output_file)\n        self.curl.setopt(pycurl.FOLLOWLOCATION, 1)\n        self.curl.setopt(pycurl.MAXREDIRS, 5)\n        self.curl.setopt(pycurl.NOSIGNAL, 1)\n\n    def run(self):\n        self.curl.perform()\n        self.curl.close()\n        sys.stdout.write(\". \")\n        sys.stdout.flush()\n\n\nclass GetLinksFromFile:\n    def __init__(self, start_file):\n        text_file = open(start_file, \"r\")\n        self.start_urls = text_file.readlines()\n        text_file.close()\n\n\ndef processDownloadedCategoryFile(filepath):\n    print(\"\\r\\n** Processing \"+filepath + \" with BeautifulSoup\\n\\r\")\n    file_handle = open(filepath,encoding='ISO-8859-1')\n    content = file_handle.read()\n    beautiful_soup = BeautifulSoup(content, \"lxml\")\n\n    category_name = beautiful_soup.find('h1')\n\n    if category_name:\n        category_name = category_name.string.strip()\n    else:\n        category_name = ''\n\n    print(\"\\r\\n*** Category Name : \"+ category_name)\n    ind = filepath.rfind('/')\n    folder_path = filepath[0:ind + 1]\n    cat_modified_name = re.sub(r'\\W+', '',category_name)\n    new_file_path = folder_path + cat_modified_name + \".html\";\n    print(\"\\r\\n**** Renaming \" + filepath + ' to ' + new_file_path)\n    os.rename(filepath, new_file_path)\n\n    prodLinksFile = open('prodlinks.txt', 'a')\n\n    obj = {\n        'category_name': str(category_name),\n        'category_prod_links': []\n    }\n\n    replace_pattern = re.compile(r'[\\[\\]\\\"]')\n    inputStringWithAllProductIds = beautiful_soup.find('input', {'id': 'productIdString'})\n\n    if inputStringWithAllProductIds is None:\n        categoriesWithNoProducts = open('CategoriesWithNoProducts.txt', 'a')\n        categoriesWithNoProducts.write(category_name + '\\r\\n')\n        categoriesWithNoProducts.close()\n        return\n\n    inputStringWithAllProductIds = inputStringWithAllProductIds.get('value')\n    inputStringWithAllProductIds = replace_pattern.sub('', inputStringWithAllProductIds)\n    inputStringWithAllProductIds = inputStringWithAllProductIds.replace('\\u0026', '&')\n    inputStringWithAllProductIds = inputStringWithAllProductIds.split(\",\")\n\n    prodPath = 'http://www.medplusmart.com/product/'+ cat_modified_name +'/'\n\n    for prod_id in inputStringWithAllProductIds:\n        small_obj = {\n            \"prod_id\": prod_id,\n            \"prod_cat\": category_name,\n            \"prod_link\": prodPath + prod_id\n        }\n        prod_path = prodPath + prod_id\n\n        item = checkIfProductPresentInProducts(prod_id)\n        if item is None:\n            link_obj = {\n                \"prod_id\": prod_id,\n                \"prod_cat\": [category_name],\n                \"prod_link\": prod_path\n            }\n\n            storeDataInCollection('products', link_obj)\n            prodLinksFile.write(prod_id + ':' + prod_path + \"\\r\\n\")\n\n        else:\n            item['prod_cat'].append(category_name)\n            storeDataInCollection('products', item, True)\n        obj['category_prod_links'].append(small_obj)\n\n    storeDataInCollection('categories', obj)\n    prodLinksFile.close()\n\ndef checkIfProductPresentInProducts(prod_id):\n    prod = db.products.find_one({\n        'prod_id': prod_id\n    })\n\n    if prod:\n        return prod\n    else:\n        return None\n\ndef storeDataInCollection(collection_name, doc_to_store, updateMode=False):\n\n    if updateMode:\n        item = db[collection_name].find_one({\n         '_id' : doc_to_store['_id']\n        })\n\n        item.update(doc_to_store)\n\n        db[collection_name].update_one({\n            '_id': item['_id']\n        }, {\n            '$set': item\n        }, upsert=False)\n\n    else:\n        db[collection_name].insert(doc_to_store)\n\ndef chunks(l, n):\n    \"\"\"Yield successive n-sized chunks from l.\"\"\"\n    for i in range(0, len(l), n):\n        yield l[i:i+n]\n\ndef download (urls, output_folder):\n    fileno = 0\n    t1 = time.time()\n    for url in urls:\n        f = open(output_folder + str(fileno) + \".html\", \"wb\")\n        t = DownLoadPageWithPyCurlInThread(url.rstrip(), f)\n        t.start()\n        fileno = fileno + 1\n        t.join()\n        f.close()\n    t2 = time.time()\n    print( \"\\n** %d seconds elapsed for %d uris\" % (int(t2-t1), len(urls)))\n\ndef parallel_download (urls, output_folder):\n    threads = []\n    fileno = 0\n\n    # Start one thread per URI in parallel\n    t1 = time.time()\n    for url in urls:\n        f = open(output_folder + str(fileno) + \".html\", \"wb\")\n        t = DownLoadPageWithPyCurlInThread(url.rstrip(), f)\n        t.start()\n        threads.append((t, f))\n        fileno = fileno + 1\n\n    # # Wait for all threads to finish\n    for thread, file in threads:\n        thread.join()\n        file.close()\n        processDownloadedCategoryFile( file.name)\n\n\n    t2 = time.time()\n    print(\"\\n** Multithreading, %d seconds elapsed for %d uris\" % (int(t2-t1), len(urls) ) )\n\ndef make_sure_path_exists(path):\n    try:\n        os.makedirs(path)\n    except OSError as exception:\n        if exception.errno != errno.EEXIST:\n            raise\n\n\ndef startDownloadingCategoryPagesInParallelThreads(category_urls, path='somedir/', num_threads=1):\n\n    make_sure_path_exists(path)\n\n    if (num_threads > 30):\n        print (\"*** It is not advisable to run more than 30 threads in parallel.\\r\\n\")\n        exit()\n\n    elif (num_threads == 1):\n        download(category_urls, path)\n\n    else:\n        subs = chunks(category_urls, num_threads)\n        for sub in subs:\n            parallel_download(sub, path)\n\n\n# Step 1 : Hit the Master category page and build the mongo collections that will store the data.\n\n# startCrawl = GetLinksFromFile(\"start-urls.txt\")\n# ind = 0\n# print(\"* Crawler Started\")\n# print(\"*** Found \" + str(len(startCrawl.start_urls)) + \" urls to start crawling from\")\n\n# for start_url in startCrawl.start_urls:\n#     rootpath = str(start_url)[0:str(start_url).index('/pharmaHome')]\n#     ind += 1\n#     r = requests.get(start_url)\n#     beautiful_soup = BeautifulSoup(r.content, \"lxml\")\n#     divs = beautiful_soup.find_all('ul', {'class': 'brandlist-inline'})\n#     links = beautiful_soup.find_all('a', {'href': re.compile('^/drugsCategory/*')})\n\n#     print(\"***** Found \"+str(len(links)) + \" categories *** \\r\\n\")\n#     print(\"******** Storing all category links in one file : 'AllCategoryLinks.txt' ,\\r\\n\")\n#     print(\"********  which will be used to crawl to get product details\")\n\n#     fileToStoreLinksIn = open('AllCategoryLinks.txt', 'w')\n\n#     for link in links:\n#         w = rootpath\n#         w = w + str(link.get('href')) + '\\r\\n'\n#         fileToStoreLinksIn.write(w)\n#     fileToStoreLinksIn.close()\n\n\n# print(\"* Crawler will start crawling links in AllCategoryLinks.txt\")\n\n# startCrawl = GetLinksFromFile(\"AllCategoryLinks.txt\")\n\n\n# startDownloadingCategoryPagesInParallelThreads(startCrawl.start_urls, 'categories/',  30)\n\n\n# Step 2 :\nsource_filename=    \"prodlinks.txt\"\ncompleted_filename= \"completed_scraping.txt\"\nerror_filename=     \"error_scraping.txt\"\n\ndef fetchAllProdlinks() :\n    file = open(source_filename,\"r\")\n    links = file.readlines()\n    file.close()\n\n    return links\n\ndef updateProductMetaInMongo(prod_id, obj):\n    item = db.products.find_one({\n        'prod_id': prod_id\n    })\n    item.update(obj)\n    db.products.update_one({\n        '_id': item['_id']\n    }, {\n        '$set': obj\n    },upsert=False)\n\n    print(\"Updated : \"+ prod_id)\n\ndef getPageContentWithRequests(url, pid):\n    try:\n        r = requests.get(url)\n        beautiful_soup = BeautifulSoup(r.content, \"lxml\")\n        prod_name = str(beautiful_soup.find('h1').text)\n\n        dosage_form = beautiful_soup.find('strong',{ 'itemprop':\"dosageForm\" } )\n        if not dosage_form is None:\n            dosage_form = dosage_form.text.strip()\n        else:\n            dosage_form = 'NA'\n\n        comp = beautiful_soup.find('strong',{ 'itemprop':\"activeIngredient\" } )\n        if not comp is None:\n            comp = comp.text.strip()\n        else:\n            comp = 'NA'\n\n        pack_size = beautiful_soup.find('strong',{ 'itemprop':\"activeIngredient\" } )\n        if not pack_size is None:\n            pack_size = pack_size.text.strip()\n        else:\n            pack_size = 'NA'\n\n        manufacturer = beautiful_soup.find('strong',{ 'itemprop':\"name\" } )\n        if not manufacturer is None:\n            manufacturer = manufacturer.text.strip()\n        else:\n            manufacturer = 'NA'\n\n        mrp = beautiful_soup.find('span',{ 'itemprop':\"costPerUnit\" } )\n        if not mrp is None:\n            mrp = mrp.text.strip()\n        else:\n            mrp = 'NA'\n\n        prod_meta = {\n            'comp':  comp,\n            'pack_size': pack_size,\n            'manufacturer': manufacturer,\n            'mrp': mrp,\n            'form': dosage_form\n        }\n\n        alternates = beautiful_soup.find_all('tr',{'itemprop' : 'relatedDrug'})\n\n        alternatives = []\n\n        for alternate in alternates:\n            alternate = BeautifulSoup(str(alternate), \"lxml\")\n            link = alternate.find('a').get('href')\n            prod_id = link.split('/')[-1]\n\n            product_name = alternate.find('h5')\n            if not product_name is None:\n                product_name = product_name.text.strip()\n            else:\n                product_name = 'NA'\n\n            tmp = {\n                'prod_name' : product_name,\n                'prod_id' : prod_id,\n                'prod_link' : \"http://www.medplusmart.com\" + link,\n                'prod_mfg' : alternate.find('small', {'itemprop' : 'manufacturer'}).find('span').text.strip()\n            }\n            alternatives.append(tmp)\n\n        prod_information_blocks = beautiful_soup.find_all('div', {'class': 'tabbable'})\n\n        prod_information = []\n\n        for block in prod_information_blocks:\n            cont = BeautifulSoup(str(block), 'lxml')\n            cont = cont.find('div', {'class': 'container-fluid'})\n\n            if cont is None:\n                continue\n\n            titles = cont.find_all('h4')\n            descs = cont.find_all('p')\n            index = 0\n\n            for title in titles:\n                desc = descs[index]\n\n                if not desc is None:\n                    desc = desc.text.strip()\n                else:\n                    desc = 'NA'\n\n                tmp = {\n                    'title' : title.text.strip(),\n                    'description' : desc\n                }\n\n                prod_information.append(tmp)\n                index = index + 1\n\n\n        obj = {\n            'prod_name' : prod_name,\n            'prod_meta' : prod_meta,\n            'alternatives' : alternatives,\n            'prod_information' : prod_information\n        }\n\n        updateProductMetaInMongo(pid, obj)\n    \n    except Exception: \n        error_file = open(error_filename, 'a')\n        error_file.write(pid + ':' + url + '\\r')\n        error_file.close()\n        pass\n\n# ----------------------------------------------------------------------------------------------------------------------\n\nprod_links_lines = fetchAllProdlinks()\nlines_written = 0\n\nfor line in prod_links_lines:\n    if lines_written == 0 :\n        completed_file = open(completed_filename, \"a\")\n\n    parts = line.split(':', 1)\n    prod_id = parts[0]\n    prod_link = parts[1]\n    getPageContentWithRequests(prod_link, prod_id)\n    completed_file.write(line + '\\r')\n    lines_written = lines_written + 1\n    if lines_written == 100:\n        completed_file.close()\n        lines_written = 0\n\n", "repo_name": "jagzviruz/data-scraper", "sub_path": "medplus/Scraper.py", "file_name": "Scraper.py", "file_ext": "py", "file_size_in_byte": 11972, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pymongo.MongoClient", "line_number": 15, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 20, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 22, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pycurl.Curl", "line_number": 23, "usage_type": "call"}, {"api_name": "pycurl.URL", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pycurl.WRITEDATA", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pycurl.FOLLOWLOCATION", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pycurl.MAXREDIRS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pycurl.NOSIGNAL", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 34, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 48, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 60, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 63, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 72, "usage_type": "call"}, {"api_name": "time.time", "line_number": 150, "usage_type": "call"}, {"api_name": "time.time", "line_number": 158, "usage_type": "call"}, {"api_name": "time.time", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 181, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 186, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 188, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 272, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 273, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 319, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 342, "usage_type": "call"}]}
{"seq_id": "42026903925", "text": "from time import sleep\nimport sys\n\nimport gym\n\n\nmap_size = sys.argv[1]\n\nif map_size == '4':\n    map_name = '4x4'\nelif map_size == '8':\n    map_name = '8x8'\n\nenv = gym.make('FrozenLake-v1', desc=None, map_name=map_name, is_slippery=True)\n\nobservation = env.reset()\nfor _ in range(100):\n    sleep(0.2)\n    render = env.render(mode='human')\n    action = env.action_space.sample()  # uniformly random actions\n    observation, reward, done, info = env.step(action)\n\n    if done:\n        observation = env.reset()\nenv.close()\n", "repo_name": "eslopezag/reinforcement-learning-frozen-lake", "sub_path": "simulate_random_policy.py", "file_name": "simulate_random_policy.py", "file_ext": "py", "file_size_in_byte": 520, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 14, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "35168783886", "text": "import logging\n\nimport attr\n\nfrom subiquity.common.apidef import API\nfrom subiquity.common.types import WSLConfigurationBase\nfrom subiquity.server.controller import SubiquityController\nfrom subiquitycore.context import with_context\nfrom system_setup.common.wsl_conf import default_loader\nfrom system_setup.common.wsl_utils import convert_if_bool\n\nlog = logging.getLogger(\"system_setup.server.controllers.wslconfbase\")\n\n\nclass WSLConfigurationBaseController(SubiquityController):\n    endpoint = API.wslconfbase\n\n    autoinstall_key = model_name = \"wslconfbase\"\n    autoinstall_schema = {\n        \"type\": \"object\",\n        \"properties\": {\n            \"automount_root\": {\"type\": \"string\"},\n            \"automount_options\": {\"type\": \"string\"},\n            \"network_generatehosts\": {\"type\": \"boolean\"},\n            \"network_generateresolvconf\": {\"type\": \"boolean\"},\n        },\n        \"additionalProperties\": False,\n    }\n\n    def __init__(self, app):\n        super().__init__(app)\n\n        # load the config file\n        root_dir = self.app.base_model.root\n        data = default_loader(root_dir)\n\n        if data:\n            proc_data = {key: convert_if_bool(value) for (key, value) in data.items()}\n            conf_data = WSLConfigurationBase(**proc_data)\n            self.model.apply_settings(conf_data)\n\n    def load_autoinstall_data(self, data):\n        if data is not None:\n            identity_data = WSLConfigurationBase(**data)\n            self.model.apply_settings(identity_data)\n\n    @with_context()\n    async def apply_autoinstall_config(self, context=None):\n        pass\n\n    def make_autoinstall(self):\n        r = attr.asdict(self.model.wslconfbase)\n        return r\n\n    async def GET(self) -> WSLConfigurationBase:\n        data = WSLConfigurationBase()\n        if self.model.wslconfbase is not None:\n            data.automount_root = self.model.wslconfbase.automount_root\n            data.automount_options = self.model.wslconfbase.automount_options\n            data.network_generatehosts = self.model.wslconfbase.network_generatehosts\n            data.network_generateresolvconf = (\n                self.model.wslconfbase.network_generateresolvconf\n            )\n        return data\n\n    async def POST(self, data: WSLConfigurationBase):\n        self.model.apply_settings(data)\n        await self.configured()\n", "repo_name": "canonical/subiquity", "sub_path": "system_setup/server/controllers/wslconfbase.py", "file_name": "wslconfbase.py", "file_ext": "py", "file_size_in_byte": 2326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 420, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "subiquity.server.controller.SubiquityController", "line_number": 15, "usage_type": "name"}, {"api_name": "subiquity.common.apidef.API.wslconfbase", "line_number": 16, "usage_type": "attribute"}, {"api_name": "subiquity.common.apidef.API", "line_number": 16, "usage_type": "name"}, {"api_name": "system_setup.common.wsl_conf.default_loader", "line_number": 35, "usage_type": "call"}, {"api_name": "system_setup.common.wsl_utils.convert_if_bool", "line_number": 38, "usage_type": "call"}, {"api_name": "subiquity.common.types.WSLConfigurationBase", "line_number": 39, "usage_type": "call"}, {"api_name": "subiquity.common.types.WSLConfigurationBase", "line_number": 44, "usage_type": "call"}, {"api_name": "subiquitycore.context.with_context", "line_number": 47, "usage_type": "call"}, {"api_name": "attr.asdict", "line_number": 52, "usage_type": "call"}, {"api_name": "subiquity.common.types.WSLConfigurationBase", "line_number": 56, "usage_type": "call"}, {"api_name": "subiquity.common.types.WSLConfigurationBase", "line_number": 55, "usage_type": "name"}, {"api_name": "subiquity.common.types.WSLConfigurationBase", "line_number": 66, "usage_type": "name"}]}
{"seq_id": "38120187289", "text": "# -*- coding: utf-8 -*-\nimport sys\nimport json\nimport bisect\nimport hashlib\nimport logging\nsys.path.append(\"../../\")\n\nfrom hashring.common import exceptions as exc\nfrom hashring.common.config import Const\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(level=logging.INFO)\nhandler = logging.FileHandler(Const.LOG_FILE_NAME_RUN)\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s -%(lineno)d- %(message)s')\nhandler.setFormatter(formatter)\nlogger.addHandler(handler)\n\n\ndef get_hash(raw_str):\n    \"\"\"将字符串映射到2^32的数字中\"\"\"\n\n    md5_str = hashlib.md5(raw_str.encode('utf-8')).hexdigest()\n    return int(md5_str, 16) % (Const.MOD_NUM)\n\n\nclass DataManager(object):\n    \"\"\"Data manager.\"\"\"\n\n    def __init__(self, path):\n        # DataManager.filename=path\n        self.dev_inf = None  # 设备信息\n        self.filename = path\n\n    def load(self):\n        u\"\"\"加载文件.\n        :param filename: 文件名\n        \"\"\"\n\n        with open(self.filename) as f:\n            self.dev_inf = json.load(f)\n\n    def save(self):\n        u\"\"\"保存文件.\n        :param filename: 文件名\n        \"\"\"\n\n        with open(self.filename, \"w\") as f:\n            json.dump(self.dev_inf, f)\n\n\nclass RingBuilder(object):\n    \"\"\"ring builder.\"\"\"\n\n    def __init__(self, data_manager):\n        self.sorted_cache_list = []  # 排序好的hash值(虚拟节点)\n        self.cache_node = dict()  # 存储虚拟节点到物理设备的hash映射\n        self.data_manager = data_manager\n        logger.info(\"init device\")\n        for dev_id_key, item in self.data_manager.dev_inf.items():\n            part_num = item[Const.PART_NUM]\n            dev_weight = item[Const.DEV_WEIGHT]\n            self.rebalance(dev_id_key, Const.ADD, 0, part_num * dev_weight)\n        logger.info(\"init device success!\")\n\n    def add_dev(self, dev_id, dev_name=None, dev_weight=None, part_num=None):\n        u\"\"\"添加设备.\n        :param dev_id: 待添加的设备信息\n        \"\"\"\n\n        logger.info(\"Add device\")\n        # params type check            \n        if dev_id in self.data_manager.dev_inf.keys():\n            return \n        if dev_id and not isinstance(dev_id, str):\n            raise TypeError(\"dev_id must be str type\")\n        if dev_name and not isinstance(dev_name, str):\n            raise TypeError(\"dev_name must be str type\")\n        if dev_weight and  not isinstance(dev_weight, int):\n            raise TypeError(\"dev_weight must be int type\")\n        if part_num and not isinstance(part_num, int):\n            raise TypeError(\"part_num must be int type\")\n\n        # 更新设备信息\n        added_dev = {dev_id: {Const.DEV_NAME:dev_name, Const.DEV_WEIGHT:dev_weight, Const.PART_NUM:part_num}}\n        self.data_manager.dev_inf.update(added_dev)\n\n        # rebalance\n        virtual_node_num = dev_weight * part_num\n        self.rebalance(dev_id, \n            add_or_remove=Const.ADD,\n            start_node_index=0,\n            end_node_index=virtual_node_num)\n\n        # save dev_inf\n        self.data_manager.save()\n        logger.info(\"Add device success!\")\n        return 0\n\n    def update_dev(self, dev_id, dev_weight=None):\n        u\"\"\"更新设备信息.\n\n        :param dev_id: 设备ID\n        :param weight: 权重\n        \"\"\"\n\n        logger.info(\"Update device\")\n        if dev_id not in self.data_manager.dev_inf:\n            return  \n        if dev_id and not isinstance(dev_id, str):\n            raise TypeError(\"dev_id must be str type\")\n        if dev_weight and not isinstance(dev_weight, int):\n            raise TypeError(\"dev_weight must be int type\")\n\n        # rebalance\n        info_of_dev_id = self.data_manager.dev_inf[dev_id]  # dev_id 的设备信息， 字典类型\n        virtual_num = info_of_dev_id[Const.PART_NUM]\n        old_dev_weight = info_of_dev_id[Const.DEV_WEIGHT]\n        old_virtual_node_num = old_dev_weight * virtual_num\n        new_virtual_node_num = dev_weight * virtual_num\n        logger.info(\"new_virtual_node_num:{}\".format(new_virtual_node_num))\n        logger.info(\"old_virtual_node_num:{}\".format(old_virtual_node_num))\n        if old_virtual_node_num < new_virtual_node_num:\n            self.rebalance(dev_id, \n                add_or_remove=Const.ADD, \n                start_node_index = old_virtual_node_num,\n                end_node_index = new_virtual_node_num)\n        elif old_virtual_node_num > new_virtual_node_num:\n            self.rebalance(dev_id,\n                add_or_remove = Const.REMOVE,\n                start_node_index = new_virtual_node_num,\n                end_node_index = old_virtual_node_num)\n        # save dev_inf                \n        self.data_manager.dev_inf[dev_id].update({Const.DEV_WEIGHT: dev_weight})\n        self.data_manager.save()\n        logger.info(\"Update device success! dev_id = {}, dev_weight = {}\".format(dev_id, dev_weight))\n        return 0\n        \n    def remove_dev(self, dev_id):\n        u\"\"\"删除设备.\n\n        :param dev_id: 待删除的设备ID\n        \"\"\"\n\n        logger.info(\"Remove device\")\n        item = self.data_manager.dev_inf[dev_id]  # dev_id : str\n        self.virtual_num = item[Const.PART_NUM]\n        del self.data_manager.dev_inf[dev_id]\n        virtual_node_num = self.virtual_num * item[Const.DEV_WEIGHT]\n\n        self.rebalance(dev_id,\n            add_or_remove=Const.REMOVE,\n            start_node_index=0, \n            end_node_index=virtual_node_num)\n        self.data_manager.save()\n        logger.info(\"Remove device success ! dev_id = {}\".format(dev_id))\n        return 0\n\n    # @TODO params check\n    def rebalance(self, dev_id, add_or_remove, start_node_index, end_node_index):\n        u\"\"\"重新平衡ring.\"\"\"\n\n        logger.info(\"Rebalance device!\")\n        logger.info(\"Rebalance: dev_id = {}, add_or_remove = {}\".format(dev_id, add_or_remove))\n        logger.info(\"Rebalance: start_node_index ={}, end_node_index = {}\".format(start_node_index,\n                                                                            end_node_index))\n        for index in range(start_node_index, end_node_index):\n            node_hash = get_hash(\"%s_%s\" % (dev_id, index))\n            if add_or_remove == Const.ADD:\n                bisect.insort(self.sorted_cache_list, node_hash)\n                self.cache_node[node_hash] = dev_id\n            else:\n                self.sorted_cache_list.remove(node_hash)\n                del self.cache_node[node_hash]\n        logger.info(\"Rebalance device success!\")\n        return 0\n\n    def hash_dev(self, key):\n        u\"\"\"获取指定key hash到的设备.\"\"\"\n        node_index = bisect.bisect_left(self.sorted_cache_list, key) # \n        node_index = node_index % len(self.sorted_cache_list)  # 若比最大的node hash还大，分发给第一个node\n        return self.cache_node[self.sorted_cache_list[node_index]]\n    \n    # 保存文件到本地\n    def save_hash_to_node(self, filename1, filename2):\n        with open(filename1,\"w\")as f:\n            json.dump(self.sorted_cache_list, f)  # 保存哈希列表到本地\n        with open(filename2, \"w\")as f:\n            json.dump(self.cache_node, f)  # 保存字典（\"哈希值-设备ID\"）到本地\n        return 0\n", "repo_name": "qyzhizi/consistent_hash_3", "sub_path": "hashring/hashring/app/ringbuilder.py", "file_name": "ringbuilder.py", "file_ext": "py", "file_size_in_byte": 7176, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 14, "usage_type": "call"}, {"api_name": "hashring.common.config.Const.LOG_FILE_NAME_RUN", "line_number": 14, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 14, "usage_type": "name"}, {"api_name": "logging.Formatter", "line_number": 15, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 23, "usage_type": "call"}, {"api_name": "hashring.common.config.Const.MOD_NUM", "line_number": 24, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 24, "usage_type": "name"}, {"api_name": "json.load", "line_number": 41, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 49, "usage_type": "call"}, {"api_name": "hashring.common.config.Const.PART_NUM", "line_number": 61, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 61, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.DEV_WEIGHT", "line_number": 62, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 62, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.ADD", "line_number": 63, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 63, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.DEV_NAME", "line_number": 85, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 85, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.DEV_WEIGHT", "line_number": 85, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const.PART_NUM", "line_number": 85, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const.ADD", "line_number": 91, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 91, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.PART_NUM", "line_number": 117, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 117, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.DEV_WEIGHT", "line_number": 118, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 118, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.ADD", "line_number": 125, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 125, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.REMOVE", "line_number": 130, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 130, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.DEV_WEIGHT", "line_number": 134, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 134, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.PART_NUM", "line_number": 147, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 147, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.DEV_WEIGHT", "line_number": 149, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 149, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.REMOVE", "line_number": 152, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 152, "usage_type": "name"}, {"api_name": "hashring.common.config.Const.ADD", "line_number": 169, "usage_type": "attribute"}, {"api_name": "hashring.common.config.Const", "line_number": 169, "usage_type": "name"}, {"api_name": "bisect.insort", "line_number": 170, "usage_type": "call"}, {"api_name": "bisect.bisect_left", "line_number": 180, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 187, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 189, "usage_type": "call"}]}
{"seq_id": "17315017174", "text": "from collections import deque\nimport threading\nimport randomgen\nimport numpy as np\n\nfrom utils.sampling.perlin import create_perlin_noise, calc_fade\n\n\nclass SamplingProvider:\n    \"\"\"\n    Multi-threaded util that runs in the background and precalculates noise samples. To be used in with statement!\n    Even with the GIL, this is helpful because the GIL is released while we're waiting for model predictions.\n    - If we're attacking a local model, TensorFlow releases GIL while blocking on sess.run\n    - If we're attacking a remote model, blocking calls like HTTPRequest also release GIL\n    - The RNG we use also releases GIL\n\n    We can use that time to fill the queues with the next batch of samples.\n\n    This implementation may be a bit over-engineered. The background is that we used to run this on a AWS instance that would sometimes\n    crash if running >2 threads. For that reason, we wrote this util so we can fill (and block on) multiple deques with a single thread.\n    \"\"\"\n    def __init__(self, shape, n_threads=1, queue_lengths=40):\n        self.shape = shape\n        self.n_threads = n_threads\n        self.queue_lengths = queue_lengths\n        self.queue_normal = deque()\n        self.queue_perlin = deque()\n\n        self.perlin_fade = calc_fade(shape[0], freq=20)             # Tiny ImageNet: 5 works well. ImageNet: 20 is better.\n        self.perlin_color = True\n\n        # Manually reimplemented Queue locking to cover 2 deques instead of 1.\n        self.lock = threading.Lock()\n        self.cv_not_full = threading.Condition(self.lock)           # Producer condition\n        self.cv_not_empty = threading.Condition(self.lock)          # Consumer condition\n        self.is_running = True\n        self.threads = []\n        for thread_id in range(n_threads):\n            thread = threading.Thread(target=self._thread_fun, args=(thread_id,))\n            thread.start()\n            self.threads.append(thread)\n\n    def __enter__(self):\n        # Threads already started at __init__\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        # Stop all the threads.\n        for thread in self.threads:\n            thread.do_run = False\n\n        self.is_running = False\n\n        # Stop producers.\n        with self.cv_not_full:\n            self.cv_not_full.notify_all()\n\n        # Stop consumers, if any.\n        # Usually, Consumers are from the same thread that creates and __exit__s this object. So there will be none waiting at this point.\n        # If, however, there is a consumer from a background thread, they could be still waiting, and then they will receive an\n        #  InterruptedError here.\n        with self.cv_not_empty:\n            self.cv_not_empty.notify_all()\n\n        for thread in self.threads:\n            thread.join()\n        print(\"SamplingProvider: all threads stopped.\")\n\n    def _thread_fun(self, thread_id):\n        # create a thread-specifc RNG\n        rng = randomgen.RandomGenerator(randomgen.Xoroshiro128(seed=20 + thread_id))\n        rnd_normal = None\n        rnd_perlin = None\n\n        t = threading.currentThread()\n        while getattr(t, 'do_run', True):\n\n            # Prepare one of each sampling patterns\n            if rnd_normal is None:\n                rnd_normal = rng.standard_normal(size=self.shape, dtype='float32')\n                rnd_normal /= np.linalg.norm(rnd_normal)\n            if rnd_perlin is None:\n                rnd_perlin = create_perlin_noise(px=self.shape[0], color=self.perlin_color, batch_size=1,\n                                                  normalize=True, precalc_fade=self.perlin_fade)[0]\n\n            # Lock and put them into the queues.\n            with self.cv_not_full:\n                if len(self.queue_normal) >= self.queue_lengths and len(self.queue_perlin) >= self.queue_lengths:\n                    self.cv_not_full.wait()\n\n                # Fill one or both queues.\n                if len(self.queue_normal) < self.queue_lengths:\n                    self.queue_normal.append(rnd_normal)\n                    rnd_normal = None\n                if len(self.queue_perlin) < self.queue_lengths:\n                    self.queue_perlin.append(rnd_perlin)\n                    rnd_perlin = None\n\n                self.cv_not_empty.notify_all()\n\n    def get_normal(self, return_stats=False):\n        \"\"\"\n        Returns a std-normal noise vector, normalized to L2=1\n        \"\"\"\n        with self.cv_not_empty:\n            while len(self.queue_normal) == 0:\n                self.cv_not_empty.wait()\n                if not self.is_running:\n                    raise InterruptedError(\"Trying to consume an item, but was already shut down!\")\n\n            sample = self.queue_normal.popleft()\n            self.cv_not_full.notify()\n\n        # Unused in this implementation.\n        # Use this field to record statistics of successful samples, see BlackboxWrapper.log_img\n        if return_stats:\n            return sample, {}\n        else:\n            return sample\n\n    def get_perlin(self, return_stats=False):\n        \"\"\"\n        Returns a perlin noise vector, normalized to L2=1\n        \"\"\"\n        with self.cv_not_empty:\n            while len(self.queue_perlin) == 0:\n                self.cv_not_empty.wait()\n                if not self.is_running:\n                    raise InterruptedError(\"Trying to consume an item, but was already shut down!\")\n\n            sample = self.queue_perlin.popleft()\n            self.cv_not_full.notify()\n\n        # Unused in this implementation.\n        # Use this field to record statistics of successful samples, see BlackboxWrapper.log_img\n        if return_stats:\n            return sample, {}\n        else:\n            return sample\n", "repo_name": "ttbrunner/biased_boundary_attack", "sub_path": "utils/sampling/sampling_provider.py", "file_name": "sampling_provider.py", "file_ext": "py", "file_size_in_byte": 5684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.deque", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 27, "usage_type": "call"}, {"api_name": "utils.sampling.perlin.calc_fade", "line_number": 29, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 33, "usage_type": "call"}, {"api_name": "threading.Condition", "line_number": 34, "usage_type": "call"}, {"api_name": "threading.Condition", "line_number": 35, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 39, "usage_type": "call"}, {"api_name": "randomgen.RandomGenerator", "line_number": 71, "usage_type": "call"}, {"api_name": "randomgen.Xoroshiro128", "line_number": 71, "usage_type": "call"}, {"api_name": "threading.currentThread", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 81, "usage_type": "attribute"}, {"api_name": "utils.sampling.perlin.create_perlin_noise", "line_number": 83, "usage_type": "call"}]}
{"seq_id": "30330092500", "text": "import json\nfrom googleapiclient.discovery import build, Resource\nfrom google.oauth2.credentials import Credentials\n\nfrom infrastructure.models import Environment\nfrom common.methods import set_progress\nfrom resourcehandlers.gcp.models import GCPHandler, GCPProject\n\n\nRESOURCE_IDENTIFIER = 'instance_name'\n\n\ndef discover_resources(**kwargs):\n    discovered_google_bigtables = []\n    \n    #create a set of all projects\n    gcp_envs = Environment.objects.filter(\n        resource_handler__resource_technology__name=\"Google Cloud Platform\")\n    projects = {env.gcp_project for env in gcp_envs}\n\n    for handler in GCPHandler.objects.all():\n        set_progress('Connecting to Google BigTable for \\\n                      handler: {}'.format(handler))\n        \n        #only get bigtables of projects in the set\n        for env in Environment.objects.filter(resource_handler_id=handler.id):\n            project = env.gcp_project\n            if project not in projects:\n                continue\n\n            wrapper = create_bigtable_api_wrapper(handler)\n            if not wrapper:\n                set_progress(\"Could not connect to Google Cloud.  Skipping this resource handler.\")\n                continue\n            gcp_project_name = GCPProject.objects.get(id=project).gcp_id\n            set_progress(\"Connection to Google Cloud established\")\n            instances = list_bigtables(wrapper, gcp_project_name).get(\"instances\", None)\n            if instances:\n                for bigtable in instances:\n                    if bigtable.get(\"displayName\"):\n                        discovered_google_bigtables.append(\n                            {\n                                'name': bigtable.get(\"displayName\", \"noName\"),\n                                'instance_name': bigtable.get(\"displayName\", \"noName\"),\n                                'google_rh_id': handler.id,\n                                'project_id': gcp_project_name,\n                            }\n                        )\n                #remove project from the set after getting its bigtables\n                projects.discard(gcp_project_name)\n\n    return discovered_google_bigtables\n\n\ndef list_bigtables(wrapper: Resource, project_id: str) -> dict:\n    \"\"\"\n    Get all bigtable instances in a given project\n    https://cloud.google.com/bigtable/docs/reference/admin/rest/v2/projects.instances/list\n    \"\"\"\n    proj_str = f\"projects/{project_id}\"\n    inst_list_request = wrapper.projects().instances().list(parent=proj_str)\n    return inst_list_request.execute()\n\n\ndef create_bigtable_api_wrapper(gcp_handler: GCPHandler) -> Resource:\n    \"\"\"\n    Using googleapiclient.discovery, build the api wrapper for the bigtableadmin api:\n    https://googleapis.github.io/google-api-python-client/docs/dyn/bigtableadmin_v2.projects.instances.html\n    \"\"\"\n    if not gcp_handler.gcp_api_credentials:\n        set_progress(\"Could not find Google Cloud credentials for this reource handler.\")\n        return None\n    credentials_dict = json.loads(gcp_handler.gcp_api_credentials)\n    credentials = Credentials(**credentials_dict)\n    bigtable_wrapper: Resource = build(\"bigtableadmin\", \"v2\", credentials=credentials, cache_discovery=False)\n    return bigtable_wrapper", "repo_name": "CloudBoltSoftware/cloudbolt-forge", "sub_path": "blueprints/google_bigtable/sync.py", "file_name": "sync.py", "file_ext": "py", "file_size_in_byte": 3222, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 38, "dataset": "github-code", "pt": "41", "api": [{"api_name": "infrastructure.models.Environment.objects.filter", "line_number": 17, "usage_type": "call"}, {"api_name": "infrastructure.models.Environment.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "infrastructure.models.Environment", "line_number": 17, "usage_type": "name"}, {"api_name": "resourcehandlers.gcp.models.GCPHandler.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "resourcehandlers.gcp.models.GCPHandler.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "resourcehandlers.gcp.models.GCPHandler", "line_number": 21, "usage_type": "name"}, {"api_name": "common.methods.set_progress", "line_number": 22, "usage_type": "call"}, {"api_name": "infrastructure.models.Environment.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "infrastructure.models.Environment.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "infrastructure.models.Environment", "line_number": 26, "usage_type": "name"}, {"api_name": "common.methods.set_progress", "line_number": 33, "usage_type": "call"}, {"api_name": "resourcehandlers.gcp.models.GCPProject.objects.get", "line_number": 35, "usage_type": "call"}, {"api_name": "resourcehandlers.gcp.models.GCPProject.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "resourcehandlers.gcp.models.GCPProject", "line_number": 35, "usage_type": "name"}, {"api_name": "common.methods.set_progress", "line_number": 36, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.Resource", "line_number": 55, "usage_type": "name"}, {"api_name": "resourcehandlers.gcp.models.GCPHandler", "line_number": 65, "usage_type": "name"}, {"api_name": "common.methods.set_progress", "line_number": 71, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 73, "usage_type": "call"}, {"api_name": "google.oauth2.credentials.Credentials", "line_number": 74, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.Resource", "line_number": 75, "usage_type": "name"}, {"api_name": "googleapiclient.discovery.build", "line_number": 75, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.Resource", "line_number": 65, "usage_type": "name"}]}
{"seq_id": "27052176998", "text": "import numpy as np\nimport nnfs\nfrom nnfs.datasets import spiral_data\n\nfrom lib.layers.dense import Dense\nfrom lib.layers.activation import Activation\nfrom lib.activation_functions import *\nfrom lib.loss.categorical_crossentropy import CategoricalCrossentropy\nnnfs.init()\n\nX, y = nnfs.datasets.spiral_data(samples=100, classes=3)\n\ndense1 = Dense(2, 3)\nactivation1 = Activation(activation_function=ReLU)\n\ndense2 = Dense(3, 3)\nactivation2 = Activation(activation_function=softmax)\n\nloss_function = CategoricalCrossentropy()\n\ndense1.forward(X)\nactivation1.forward(dense1.output)\ndense2.forward(activation1.output)\nactivation2.forward(dense2.output)\n\n\nprint(activation2.output[:5])\n\n# Perform a forward pass through loss function\n# it takes the output of second dense layer here and returns loss\nloss = loss_function.calculate(activation2.output, y)\n# Print loss value\nprint('loss:', loss)\n\npredictions = np.argmax(activation2.output, axis=1)\nif len(y.shape) == 2:\n y = np.argmax(y, axis=1)\naccuracy = np.mean(predictions==y)\n# Print accuracy\nprint('acc:', accuracy) ", "repo_name": "CarlosCalgaro/Neural-Network", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1062, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "nnfs.init", "line_number": 9, "usage_type": "call"}, {"api_name": "nnfs.datasets.spiral_data", "line_number": 11, "usage_type": "call"}, {"api_name": "nnfs.datasets", "line_number": 11, "usage_type": "attribute"}, {"api_name": "lib.layers.dense.Dense", "line_number": 13, "usage_type": "call"}, {"api_name": "lib.layers.activation.Activation", "line_number": 14, "usage_type": "call"}, {"api_name": "lib.layers.dense.Dense", "line_number": 16, "usage_type": "call"}, {"api_name": "lib.layers.activation.Activation", "line_number": 17, "usage_type": "call"}, {"api_name": "lib.loss.categorical_crossentropy.CategoricalCrossentropy", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 38, "usage_type": "call"}]}
{"seq_id": "74666816762", "text": "from os import path\nfrom math import log\n\nimport torch\nfrom torch import Tensor\nfrom torch.utils.data import Dataset, TensorDataset\nfrom torchvision.datasets import ImageFolder\nfrom torchvision.transforms import Compose, Resize, ToTensor\nimport numpy as np\nimport pandas as pd\n\n\ndef _format_celeba_dataset(\n    images: Tensor,\n    labels: Tensor,\n    start_sigma: float = 1.0,\n    end_sigma: float = 0.01,\n) -> Dataset:\n    idx = torch.randperm(images.shape[0])\n    images = images[idx]\n    labels = labels[idx]\n\n    sigmas = torch.linspace(log(start_sigma), log(end_sigma), idx.shape[0]).exp()\n    noise = torch.randn_like(images) * sigmas.view(-1, 1, 1, 1)\n    inputs = images + noise\n    targets = -noise\n\n    return TensorDataset(inputs, labels, targets)\n\n\ndef load_celeba_data() -> Dataset:\n    images = ImageFolder(\n        path.join(\"data\", \"celeba-dataset\", \"img_align_celeba\"),\n        transform=Compose([Resize((48, 64)), ToTensor()])\n    )\n    images = torch.stack([image[0] for image in images], dim=0)\n\n    labels = pd.read_csv(path.join(\"data\", \"celeba-dataset\", \"list_attr_celeba.csv\"))\n    labels = torch.from_numpy(labels.to_numpy()[:, 1:].astype(np.float32))\n\n    return _format_celeba_dataset(\n        images, labels, start_sigma=1.0, end_sigma=0.01\n    )\n\n\ndef load_celeba_small_data() -> Dataset:\n    images = ImageFolder(\n        path.join(\"data\", \"celeba-small-dataset\", \"img_align_celeba\"),\n        transform=Compose([Resize((48, 64)), ToTensor()])\n    )\n    images = torch.stack([image[0] for image in images], dim=0)\n\n    labels = pd.read_csv(path.join(\"data\", \"celeba-small-dataset\", \"list_attr_celeba.csv\"))\n    labels = torch.from_numpy(labels.to_numpy()[:, 1:].astype(np.float32))\n\n    return _format_celeba_dataset(\n        images, labels, start_sigma=1.0, end_sigma=0.01\n    )\n\n\n\n", "repo_name": "fkodom/ncsn", "sub_path": "ncsn/celeba/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1812, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "torch.Tensor", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.randperm", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 23, "usage_type": "call"}, {"api_name": "math.log", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.randn_like", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 18, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms.Resize", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms.Resize", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 46, "usage_type": "name"}]}
{"seq_id": "18810326205", "text": "import os\n\nimport kaggle\n\nfrom dask import dataframe as dd\n\n\nclass TxAnalysis:\n\n    def __init__(self):\n        return\n\n    @staticmethod\n    def load_dataset(dataset_ref: str):\n        dataset_path_parent = '../dataset/'\n        dataset_path = os.path.join(dataset_path_parent,\n                                    \"bank_transactions.csv\")\n        # Ensure the Kaggle API client is properly configured\n        kaggle.api.authenticate()\n        # Download the specified dataset\n        kaggle.api.dataset_download_files(dataset_ref,\n                                          path=dataset_path_parent,\n                                          unzip=True)\n\n        # Import a csv dataset from dask\n        dataset = dd.read_csv(dataset_path)\n\n        return dataset\n\n    @staticmethod\n    def prepare_dataset(dataset):\n        dataprep = dataset.groupby(\n            \"CustomerID\")[\"TransactionAmount (INR)\"].agg(['mean', 'count'])\n        dataprep = dataprep.reset_index()\n        dataprep = dataprep.rename(\n            columns={\n                \"TransactionAmount (INR)\": \"amount\",\n                \"mean\": \"avg_amount\",\n                \"count\": \"nb_tx\",\n            })\n        return dataprep\n\n    @staticmethod\n    def analyse(dataprep):\n        result = dataprep\n        return result\n\n    @staticmethod\n    def train_model(dataprep):\n        model = None\n        return model\n\n    def run(self):\n        ref_dataset = \"shivamb/bank-customer-segmentation\"\n        dataset = TxAnalysis.load_dataset(dataset_ref=ref_dataset)\n        # print(type(dataset))\n        # print(dataset.head(compute=True))\n        # print(len(dataset))\n        \n        dataprep = TxAnalysis.prepare_dataset(dataset)\n        dataprep = dataprep.sort_values(by=['nb_tx'], ascending=False)\n        print(dataprep.head(compute=True))\n\n        # analysis = TxAnalysis.analyse(dataprep)\n        # model = TxAnalysis.train_model(dataprep)\n\n        return\n", "repo_name": "FrankFacundo/ComputerScience-Data", "sub_path": "Data/DataScience/StudyCases-Challenge/Business/tx_classification/app/tx_analysis.py", "file_name": "tx_analysis.py", "file_ext": "py", "file_size_in_byte": 1926, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "kaggle.api.authenticate", "line_number": 19, "usage_type": "call"}, {"api_name": "kaggle.api", "line_number": 19, "usage_type": "attribute"}, {"api_name": "kaggle.api.dataset_download_files", "line_number": 21, "usage_type": "call"}, {"api_name": "kaggle.api", "line_number": 21, "usage_type": "attribute"}, {"api_name": "dask.dataframe.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 26, "usage_type": "name"}]}
{"seq_id": "9311099794", "text": "import pygame\nimport random\n\npygame.init()\naltura = 300\nlargura = 800\ntamanho = (largura, altura)\npygameDisplay = pygame.display\npygameDisplay.set_caption(\"SharkAttack\")\nbg = pygame.image.load(\"assets/fundo.png.jpeg\")\nbg_destroy = pygame.image.load(\"assets/bg-destroy.jpeg.jpeg\")\n\n\ngameDisplay = pygame.display.set_mode(tamanho)\ngameEvents = pygame.event\nclock = pygame.time.Clock()\n\ngameIcon = pygame.image.load(\"assets/tubarão.ico\")\npygameDisplay.set_icon(gameIcon)\n\npink = (255, 125, 198)\nblack = (0, 0, 0)\nwhite = (255, 255, 255)\n\n\nexplosaoSound = pygame.mixer.Sound(\"assets/explosao.wav.mpeg\")\nexplosaoSound.set_volume(0.5)\n\ndef dead(pontos):\n    gameDisplay.blit(bg_destroy, (0, 0))\n    pygame.mixer.music.stop()\n    pygame.mixer.Sound.play(explosaoSound)\n    fonte = pygame.font.Font(\"freesansbold.ttf\", 50)\n    texto = fonte.render(\"Você Perdeu com \"+str(pontos) +\n                         \" pontos!\", True, white)\n    gameDisplay.blit(texto, (50, 100))\n    fonteContinue = pygame.font.Font(\"freesansbold.ttf\", 25)\n    textoContinue = fonteContinue.render(\"press enter to restart\", True, white)\n    gameDisplay.blit(textoContinue, (50, 200))\n\n    pygameDisplay.update()\n\n\ndef jogo():\n    jogando = True\n    movimentoX = 0\n    movimentoY = random.randrange(0, altura)\n    velocidade = 1\n    direcao = True\n    posicaoXPeixe = 450\n    posicaoYPeixe = 100\n    movimentoXPeixe = 0\n    movimentoYPeixe = 0\n    pontos = 0\n    arpão = pygame.image.load(\"assets/arpão.png.jpeg\")\n    arpão = pygame.transform.flip(arpão, True, False)\n\n    peixe = pygame.image.load(\"assets/peixe.png.jpeg\")\n    pygame.mixer.music.load(\"assets/trilha.mp3.mpeg\")\n    pygame.mixer.music.play(-1)\n    pygame.mixer.music.set_volume(0.1)\n    larguraPeixe = 100\n    alturaPeixe = 100\n    larguraArpão = 150\n    alturaArpão = 52\n    limiar = 28\n    velocidadePeixe = 8\n\n    while True:\n    \n        for event in gameEvents.get():\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                quit()\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_LEFT:\n                    movimentoXPeixe = -velocidadePeixe\n                elif event.key == pygame.K_RIGHT:\n                    movimentoXPeixe = velocidadePeixe\n                elif event.key == pygame.K_UP:\n                    movimentoYPeixe = -velocidadePeixe\n                elif event.key == pygame.K_DOWN:\n                    movimentoYPeixe = velocidadePeixe\n                elif event.key == pygame.K_RETURN:\n                    jogo()\n\n            if event.type == pygame.KEYUP:\n                if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT or event.key == pygame.K_UP or event.key == pygame.K_DOWN:\n                    movimentoXPeixe = 0\n                    movimentoYPeixe = 0\n\n        if jogando == True:\n          \n            posicaoXPeixe = posicaoXPeixe + movimentoXPeixe\n            if posicaoXPeixe < 0:\n                posicaoXPeixe = 0\n            elif posicaoXPeixe > largura - larguraPeixe:\n                posicaoXPeixe = largura - larguraPeixe\n\n            posicaoYPeixe = posicaoYPeixe + movimentoYPeixe\n            if posicaoYPeixe < 0:\n                posicaoYPeixe = 0\n            elif posicaoYPeixe > altura - alturaPeixe:\n                posicaoYPeixe = altura - alturaPeixe\n\n            gameDisplay.fill(pink)\n            gameDisplay.blit(bg, (0, 0))\n            gameDisplay.blit(peixe, ( posicaoXPeixe, posicaoYPeixe ) )\n            gameDisplay.blit(arpão, (movimentoX, movimentoY))\n           \n\n            if direcao == True:\n                if movimentoX <= 800 - 150:\n                    movimentoX = movimentoX + velocidade\n                else:\n                    \n                    direcao = False\n                    pontos = pontos + 1\n                    movimentoY = random.randrange(0, altura)\n                    velocidade = velocidade + 1\n                    arpão = pygame.transform.flip(arpão, True, False)\n            else:\n                if movimentoX >= 0:\n                    movimentoX = movimentoX - velocidade\n                else:\n                    \n                    direcao = True\n                    pontos += 1\n                    movimentoY = random.randrange(0, altura)\n                    velocidade = velocidade + 1\n                    arpão = pygame.transform.flip(arpão, True, False)\n           \n            fonte = pygame.font.Font('freesansbold.ttf', 20)\n            texto = fonte.render(\"Pontos: \"+str(pontos), True, white)\n            gameDisplay.blit(texto, (20, 280))\n\n            pixelYArpão = list(range(movimentoY, movimentoY+alturaArpão + 1))\n            pixelXArpão = list(range(movimentoX, movimentoX+larguraArpão + 1))\n\n            pixelYPeixe = list(range(posicaoYPeixe, posicaoYPeixe + alturaPeixe +1 ))\n            pixelXPeixe= list(range(posicaoXPeixe, posicaoXPeixe + larguraPeixe +1 ))\n\n            colisaoY = list( set(pixelYArpão) & set(pixelYPeixe)  )\n            colisaoX = list( set(pixelXArpão) & set(pixelXPeixe)  )\n            if len(colisaoY) > limiar:\n                if len(colisaoX) > limiar:\n                    jogando =  False\n                    dead(pontos)\n\n            \n\n\n        pygameDisplay.update()\n        clock.tick(60)\n\n\njogo()\n", "repo_name": "xSergioLimax/SharkAttack", "sub_path": "tubarao.py", "file_name": "tubarao.py", "file_ext": "py", "file_size_in_byte": 5272, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.event", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.stop", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 37, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.set_volume", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.K_RETURN", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 88, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 119, "usage_type": "call"}, {"api_name": "pygame.transform.flip", "line_number": 121, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 121, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 129, "usage_type": "call"}, {"api_name": "pygame.transform.flip", "line_number": 131, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 133, "usage_type": "attribute"}]}
{"seq_id": "2783131449", "text": "import xlwings as xw\nimport pandas as pd\n\n#wb=xw.Book()\n#wb.save('test.xlsx')\n#wbtest=xw.Book('data.xlsx')\n#wb1=wbtest.sheets['Sheet1']\n#wb1.range('A1').value='hello'\nwb =xw.Book('Class.xlsx')\nsheet= wb.sheets(\"ClassA\")\n#rg=sheet.range('A').value\n#sheet.range('C2').value=' helloxlwings'\ndf =sheet.range('A1:C3').options(pd.DataFrame).value    #import pandas as pd\n#print(df)\ndf=df[:2]\nxw.view(df)\nwb.close\n\n", "repo_name": "Nellaiappan01/learn_python", "sub_path": "excl/ilwings.py", "file_name": "ilwings.py", "file_ext": "py", "file_size_in_byte": 408, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "xlwings.Book", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 13, "usage_type": "attribute"}, {"api_name": "xlwings.view", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "33773943464", "text": "# -*- coding: utf-8 -*-\n\nimport json\nfrom operator import itemgetter\nfrom itertools import groupby\nfrom datetime import datetime, date, timedelta\n\nfrom jinja2 import Environment\n\nfrom django.contrib.staticfiles.storage import staticfiles_storage\nfrom django.shortcuts import get_object_or_404, get_list_or_404\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponse, JsonResponse\nfrom django.shortcuts import render, redirect\nfrom django.db import models\nfrom django.db.models import Sum, Count\nfrom rest_framework import generics, filters\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nimport django_filters\n\nfrom .serializers import ClockItemSerializer\nfrom .models import ClockItem\nfrom tms.utils import Utils\nfrom myssg.items import Item\n\n\nCATEGORIES = ['工作', '学习', '生活', '其他']\nCATEGORIES_NAME_DICT = {u'工作': 'work', u'学习': 'study', u'生活': 'life', u'其他': 'other'}\nWEEK_DAY_STR = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']\nMONTHS_STR = ['一月', '二月', '三月', '四月', '五月', '六月', '七月', '八月', '九月', '十月', '十一月', '十二月']\n\n\nclass ClockItemFilter(filters.FilterSet):\n    min_st = django_filters.DateTimeFilter(name='start_time', lookup_expr='gte')\n    max_st = django_filters.DateTimeFilter(name='start_time', lookup_expr='lt')\n\n    class Meta:\n        model = ClockItem\n        fields = ['min_st', 'max_st', 'date', 'category', 'project', 'thing']\n\n\nclass ClockItemList(generics.ListAPIView):\n    queryset = ClockItem.objects.all().order_by('start_time')\n    serializer_class = ClockItemSerializer\n    filter_class = ClockItemFilter\n\n\ndef index(request):\n    cur_dt = datetime.now()\n    tms_begin_date = TMS_BEGIN_DATE\n    tms_end_date = cur_dt.date()\n\n    # 各年简报\n    years_stats_dict = dict()\n    tms_data_group_by_year_category = ClockItem.objects.values('year', 'category').\\\n        annotate(Count('time_cost_min'), tc_sum=Sum('time_cost_min'))\n    for item in tms_data_group_by_year_category:\n        year = item['year']\n        category = item['category']\n        tc_sum = item['tc_sum']\n        if year not in years_stats_dict:\n            years_stats_dict[year] = {\n                'year': year,\n                'work_time': 0,\n                'study_time': 0,\n                'all_time': 0,\n                'valid_time': 0,\n            }\n        year_stats = years_stats_dict[year]\n        if category == '工作':\n            year_stats['work_time'] += tc_sum\n            year_stats['valid_time'] += tc_sum\n        elif category == '学习':\n            year_stats['study_time'] += tc_sum\n            year_stats['valid_time'] += tc_sum\n        year_stats['all_time'] += tc_sum\n    years_stats = list(year_stats for (year, year_stats) in years_stats_dict.items())\n    years_stats.sort(key=itemgetter('year'), reverse=True)\n    for year_stats in years_stats:\n        year = year_stats['year']\n\n        # 计算各年总天数\n        begin_date = datetime(year=year, month=1, day=1).date()\n        end_date = datetime(year=year, month=12, day=31).date()\n        if begin_date < tms_begin_date:\n            begin_date = tms_begin_date\n        if end_date > tms_end_date:\n            end_date = tms_end_date\n        days_num = (end_date - begin_date).days + 1\n        year_stats['days_num'] = days_num\n        year_stats['avg_valid_time'] = Utils.min_to_hour(float(year_stats['valid_time']) / float(days_num), 2)\n        year_stats['all_time'] = Utils.min_to_hour(year_stats['all_time'])\n        year_stats['work_time'] = Utils.min_to_hour(year_stats['work_time'])\n        year_stats['study_time'] = Utils.min_to_hour(year_stats['study_time'])\n        year_stats['valid_time'] = Utils.min_to_hour(year_stats['valid_time'])\n\n    # 本年各月简报\n    cur_year = cur_dt.year\n    months_stats_dict = dict()\n    for month in range(1, 13):\n        months_stats_dict[month] = {\n            'month': month,\n            'month_str': MONTHS_STR[month - 1],\n            'work_time': 0,\n            'study_time': 0,\n            'all_time': 0,\n            'valid_time': 0,\n        }\n    cur_year_data_group_by_month_category = ClockItem.objects.filter(year=cur_year).values('month', 'category'). \\\n        annotate(Count('time_cost_min'), tc_sum=Sum('time_cost_min'))\n    for item in cur_year_data_group_by_month_category:\n        month = item['month']\n        category = item['category']\n        tc_sum = item['tc_sum']\n\n        month_stats = months_stats_dict[month]\n        if category == '工作':\n            month_stats['work_time'] += tc_sum\n            month_stats['valid_time'] += tc_sum\n        elif category == '学习':\n            month_stats['study_time'] += tc_sum\n            month_stats['valid_time'] += tc_sum\n        month_stats['all_time'] += tc_sum\n    months_stats = list(month_stats for (month, month_stats) in months_stats_dict.items())\n    months_stats.sort(key=itemgetter('month'))\n    for month_stats in months_stats:\n        month = month_stats['month']\n\n        # 计算本年各月总天数\n        begin_date = datetime(year=cur_year, month=month, day=1).date()\n        if month == 12:\n            end_date = datetime(year=cur_year, month=12, day=31).date()\n        else:\n            end_date = datetime(year=cur_year, month=month + 1, day=1).date() - timedelta(days=1)\n        if begin_date < tms_begin_date:\n            begin_date = tms_begin_date\n        if end_date > tms_end_date:\n            end_date = tms_end_date\n        days_num = (end_date - begin_date).days + 1\n        if days_num < 0:\n            days_num = 0\n            month_stats['avg_valid_time'] = 0\n            month_stats['valid_time'] = 0\n        else:\n            month_stats['avg_valid_time'] = Utils.min_to_hour(float(month_stats['valid_time']) / float(days_num), 2)\n            month_stats['valid_time'] = Utils.min_to_hour(month_stats['valid_time'])\n        month_stats['days_num'] = days_num\n        month_stats['all_time'] = Utils.min_to_hour(month_stats['all_time'])\n        month_stats['work_time'] = Utils.min_to_hour(month_stats['work_time'])\n        month_stats['study_time'] = Utils.min_to_hour(month_stats['study_time'])\n\n    # 本日的统计\n    today_work_time = 0\n    today_study_time = 0\n    today_data_group_by_month_category = ClockItem.objects.filter(date=cur_dt).values('category'). \\\n        annotate(Count('time_cost_min'), tc_sum=Sum('time_cost_min'))\n    for item in today_data_group_by_month_category:\n        category = item['category']\n        tc_sum = item['tc_sum']\n        if category == '工作':\n            today_work_time += tc_sum\n        elif category == '学习':\n            today_study_time += tc_sum\n    today_work_time = Utils.min_to_hour(today_work_time)\n    today_study_time = Utils.min_to_hour(today_study_time)\n\n    clock_items = ClockItem.objects.filter(date=cur_dt).order_by('-start_time')\n    latest_clock_item = None\n    if len(clock_items) >= 1:\n        latest_clock_item = clock_items[0]\n\n    return render(request, 'tms/index.html', {\n        'years_stats': years_stats,\n        'months_stats': months_stats,\n        'cur_dt': cur_dt,\n        'cur_year': cur_dt.year,\n        'cur_month': cur_dt.month,\n        'today_work_time': today_work_time,\n        'today_study_time': today_study_time,\n        'latest_clock_item': latest_clock_item\n\n    })\n\n\n@api_view(['GET'])\ndef api_day_stats(request):\n    date_str = request.GET['date']\n    if 'days_num' in request.GET:\n        days_num = int(request.GET['days_num'])\n    else:\n        days_num = 1\n    min_date = datetime.strptime(date_str, '%Y-%m-%d').date()\n    max_date = min_date + timedelta(days=days_num)\n    clock_items = ClockItem.objects.filter(start_time__gte=min_date, end_time__lt=max_date).order_by('start_time')\n    days_stats = generate_days_stats(clock_items, min_date, days_num)\n    report = generate_report(clock_items, days_num)\n    return Response({\n        'days_stats': days_stats,\n        'report': report,\n        'clock_items': ClockItemSerializer(clock_items, many=True).data,\n    })\n\n\n\nDEFAULT_MIN_DT = datetime(year=1970, month=1, day=1)\nDEFAULT_MAX_DT = datetime(year=9999, month=12, day=31)\nTMS_BEGIN_DATE = date(year=2015, month=2, day=20)\n\n\n@api_view(['GET'])\ndef api_project_stats(request):\n    if 'min_dt' in request.GET:\n        min_dt = Utils.parse_dt_str(request.GET['min_dt'])\n    else:\n        min_dt = DEFAULT_MIN_DT\n    if 'max_dt' in request.GET:\n        max_dt = Utils.parse_dt_str(request.GET['max_dt'])\n    else:\n        max_dt = DEFAULT_MAX_DT\n\n    category = request.GET['c']\n    project = request.GET['p']\n    thing = request.GET.get('t', None)\n    clock_items = ClockItem.objects.filter(start_time__gte=min_dt, end_time__lt=max_dt).order_by('-start_time')\n    if category is not None:\n        clock_items = clock_items.filter(category=category)\n    if project is not None:\n        clock_items = clock_items.filter(project=project)\n    if thing is not None:\n        clock_items = clock_items.filter(thing=thing)\n    clock_items = clock_items.order_by('start_time')\n    if min_dt == DEFAULT_MIN_DT:\n        min_dt = clock_items[0].start_time\n    if max_dt == DEFAULT_MAX_DT:\n        last_clock_item = clock_items[len(clock_items) - 1]\n        print(last_clock_item.end_time, last_clock_item.thing)\n        max_dt = last_clock_item.end_time\n\n    days_num = (max_dt.date() - min_dt.date()).days + 1\n    print(min_dt, max_dt, days_num)\n    # if days_num > 500:\n    if days_num > 200:\n        # Months stats\n        interval = 'month'\n        stats = generate_months_stats(clock_items, min_dt.date(), max_dt.date())\n    elif days_num > 100:\n        # Weeks stats\n        interval = 'week'\n        stats = generate_weeks_stats(clock_items, min_dt.date(), max_dt.date())\n    else:\n        # Days stats\n        interval = 'day'\n        stats = generate_days_stats(clock_items, min_dt.date(), days_num, with_hours_stats=False)\n\n    return Response({\n        'min_dt': min_dt,\n        'max_dt': max_dt,\n        'category': category,\n        'interval': interval,\n        'stats': stats,\n    })\n\n\ndef generate_months_stats(clock_items, min_date, max_date):\n    first_first_date = date(year=min_date.year, month=min_date.month, day=1)\n    days_stats_dict = dict()\n    first_date = first_first_date\n    while True:\n        interval_id = Utils.get_month_id(first_date)\n        days_stats_dict[interval_id] = {\n            'id': interval_id,\n            'date': first_date,\n            'all_time': 0,\n            'valid_time': 0,\n            'work_time': 0,\n            'study_time': 0,\n            'items_num': 0,\n            }\n        one_day_next_month = first_date + timedelta(days=32)\n        first_date = date(year=one_day_next_month.year, month=one_day_next_month.month, day=1)\n        if first_date > max_date:\n            break\n\n    # Fill days_stats_dict\n    for clock_item in clock_items:\n        dt = clock_item.date\n        interval_id = Utils.get_month_id(dt)\n        time_cost_min = clock_item.time_cost_min\n        days_stats_dict[interval_id]['all_time'] += time_cost_min\n        days_stats_dict[interval_id]['items_num'] += 1\n        category = clock_item.category\n        if category == '工作':\n            days_stats_dict[interval_id]['work_time'] += time_cost_min\n            days_stats_dict[interval_id]['valid_time'] += time_cost_min\n        elif category == '学习':\n            days_stats_dict[interval_id]['study_time'] += time_cost_min\n            days_stats_dict[interval_id]['valid_time'] += time_cost_min\n\n    weeks_stats = list(days_stats_dict.values())\n    weeks_stats.sort(key=itemgetter('date'))\n\n    return weeks_stats\n\n\ndef generate_weeks_stats(clock_items, min_date, max_date):\n    first_monday_date = min_date - timedelta(days=min_date.weekday())\n    days_stats_dict = dict()\n    monday_date = first_monday_date\n    while True:\n        week_id = Utils.get_week_id(monday_date)\n        days_stats_dict[week_id] = {\n            'id': week_id,\n            'date': monday_date,\n            'all_time': 0,\n            'valid_time': 0,\n            'work_time': 0,\n            'study_time': 0,\n            'items_num': 0,\n            }\n        monday_date += timedelta(days=7)\n        if monday_date > max_date:\n            break\n\n    # Fill days_stats_dict\n    for clock_item in clock_items:\n        dt = clock_item.date\n        week_id = Utils.get_week_id(dt)\n        # print(dt)\n        time_cost_min = clock_item.time_cost_min\n        days_stats_dict[week_id]['all_time'] += time_cost_min\n        days_stats_dict[week_id]['items_num'] += 1\n        category = clock_item.category\n        if category == '工作':\n            days_stats_dict[week_id]['work_time'] += time_cost_min\n            days_stats_dict[week_id]['valid_time'] += time_cost_min\n        elif category == '学习':\n            days_stats_dict[week_id]['study_time'] += time_cost_min\n            days_stats_dict[week_id]['valid_time'] += time_cost_min\n\n    weeks_stats = list(days_stats_dict.values())\n    weeks_stats.sort(key=itemgetter('date'))\n\n    return weeks_stats\n\n\ndef generate_days_stats(clock_items, min_date, days_num,\n                        with_hours_stats=True, with_sparkline_data=False):\n    days_stats_dict = dict()\n    for i in range(days_num):\n        dt = min_date + timedelta(days=i)\n        days_stats_dict[dt] = {\n            'date': dt,\n            'date_str': dt.strftime('%Y-%m-%d'),\n            'week_day': WEEK_DAY_STR[dt.weekday()],\n            'all_time': 0,\n            'valid_time': 0,\n            'work_time': 0,\n            'study_time': 0,\n            'items_num': 0,\n        }\n        if with_hours_stats:\n            days_stats_dict[dt]['hours_stats'] = [{'work': 0, 'study': 0, 'life': 0, 'other': 0} for i in range(24)]\n\n    # Fill days_stats_dict\n    for clock_item in clock_items:\n        dt = clock_item.date\n        # print(dt)\n        time_cost_min = clock_item.time_cost_min\n        days_stats_dict[dt]['all_time'] += time_cost_min\n        days_stats_dict[dt]['items_num'] += 1\n        category = clock_item.category\n        if category == '工作':\n            days_stats_dict[dt]['work_time'] += time_cost_min\n            days_stats_dict[dt]['valid_time'] += time_cost_min\n        elif category == '学习':\n            days_stats_dict[dt]['study_time'] += time_cost_min\n            days_stats_dict[dt]['valid_time'] += time_cost_min\n\n        if with_hours_stats:\n            # Hour stats\n            hours_stats = days_stats_dict[dt]['hours_stats']\n            start_time = clock_item.start_time\n            end_time = clock_item.end_time\n            start_hour = start_time.hour\n            end_hour = end_time.hour\n            category_name = CATEGORIES_NAME_DICT[clock_item.category]\n            if start_hour == end_hour:\n                hours_stats[start_hour][category_name] += end_time.minute - start_time.minute\n            else:\n                if end_hour < start_hour:\n                    end_hour += 24\n                for hour in range(start_hour, end_hour + 1):\n                    if hour >= 24:\n                        next_dt = dt + timedelta(days=1)\n                        if next_dt not in days_stats_dict:\n                            continue\n                        hours_stats = days_stats_dict[next_dt]['hours_stats']\n                        hour -= 24\n                    if hour == start_hour:\n                        hours_stats[hour][category_name] += 60 - start_time.minute\n                    elif hour == end_hour or hour == end_hour - 24:\n                        hours_stats[hour][category_name] += end_time.minute\n                    else:\n                        hours_stats[hour][category_name] += 60\n\n    days_stats = list(days_stats_dict.values())\n    days_stats.sort(key=itemgetter('date'), reverse=True)\n\n    # 额外信息\n    for day_stats in days_stats:\n        day_stats['work_time_str'] = Utils.min_to_hour2(day_stats['work_time'])\n        day_stats['study_time_str'] = Utils.min_to_hour2(day_stats['study_time'])\n        day_stats['valid_time_str'] = Utils.min_to_hour2(day_stats['valid_time'])\n        day_stats['all_time_str'] = Utils.min_to_hour2(day_stats['all_time'])\n\n    # Sparkline data\n    if with_sparkline_data:\n        # Generate sparkline data, see http://omnipotent.net/jquery.sparkline/\n        for day_stats in days_stats:\n            sparkline_data = '['\n            for hour_stats in day_stats['hours_stats']:\n                sparkline_data += '[%d, %d, %d, %d], ' % (hour_stats['work'], hour_stats['study'], hour_stats['life'], hour_stats['other'])\n            sparkline_data += ']'\n            day_stats['sparkline_data'] = sparkline_data\n\n    return days_stats\n\n\ndef generate_report(clock_items, days_num):\n    \"\"\"\n    Sample data\n        report_data = {\n            'categories': [\n                {\n                    'name': '工作',\n                    'projects': [\n                        {\n                            'name': 'XX项目',\n                            'things': [\n                                {\n                                    'name': 'XXX模块开发',\n                                    'time_cost': 30,\n                                    },\n\n                                ]\n                        }\n                    ]\n                }\n            ]\n        }\n    \"\"\"\n    categories_data = list()\n    project_id = 1\n    total_cost = 0\n    for clock_item in clock_items:\n        category = clock_item.category\n        project = clock_item.project\n        thing = clock_item.thing\n        time_cost_min = clock_item.time_cost_min\n        total_cost += time_cost_min\n\n        # Find category\n        category_data = None\n        for it in categories_data:\n            if it['name'] == category:\n                category_data = it\n                break\n        if category_data is None:\n            category_data = {\n                'name': category,\n                'cost': time_cost_min\n            }\n            categories_data.append(category_data)\n        else:\n            category_data['cost'] += time_cost_min\n\n        if 'projects' not in category_data:\n            category_data['projects'] = list()\n        projects_data = category_data['projects']\n\n        # Find project\n        project_data = None\n        for it in projects_data:\n            if it['name'] == project:\n                project_data = it\n                break\n        if project_data is None:\n            project_data = {\n                'name': project,\n                'id': project_id,\n                'cost': time_cost_min\n            }\n            project_id += 1\n            projects_data.append(project_data)\n        else:\n            project_data['cost'] += time_cost_min\n\n        if 'things' not in project_data:\n            project_data['things'] = list()\n        things_data = project_data['things']\n\n        # Find thing\n        thing_data = None\n        for it in things_data:\n            if it['name'] == thing:\n                thing_data = it\n                break\n        if thing_data is None:\n            thing_data = {\n                'name': thing,\n                'cost': time_cost_min\n            }\n            things_data.append(thing_data)\n        else:\n            thing_data['cost'] += time_cost_min\n\n    # Calculate percentage\n    minutes_of_days = days_num * 24 * 60\n    for category_data in categories_data:\n        category_data['pct'] = \"{0:.1f}%\".format(float(category_data['cost'])/minutes_of_days * 100)\n        category_time_cost = category_data['cost']\n        for project_data in category_data['projects']:\n            print(u'' + category_data['name'], category_time_cost)\n            project_data['pct'] = \"{0:.1f}%\".format(float(project_data['cost'])/category_time_cost * 100)\n\n    # Sort\n    categories_data.sort(key=lambda x: CATEGORIES.index(x['name']))\n    for category_data in categories_data:\n        projects_data = category_data['projects']\n        projects_data.sort(key=itemgetter('cost'), reverse=True)\n        for project_data in projects_data:\n            thing_data = project_data['things']\n            thing_data.sort(key=itemgetter('cost'), reverse=True)\n\n    # Change form of time cost\n    for category_data in categories_data:\n        category_data['cost'] = Utils.min_to_hour2(category_data['cost'])\n        for project_data in category_data['projects']:\n            project_data['cost'] = Utils.min_to_hour2(project_data['cost'])\n\n    report_data = {\n        'categories': categories_data,\n        'total_cost': total_cost\n    }\n\n    return report_data\n\n\n\n@api_view(['GET'])\ndef api_week_stats(request):\n    iso_year = int(request.GET['year'])\n    week = int(request.GET['week'])\n    week_data_group_by_category = ClockItem.objects.filter(iso_year=iso_year, week=week). \\\n        values('category').annotate(Count('id'), tc_sum=Sum('time_cost_min')). \\\n        order_by('-tc_sum')\n    week_data_group_by_category = list((i['category'], i['id__count'], i['tc_sum'])\n                                       for i in week_data_group_by_category)\n    week_data_group_by_project = ClockItem.objects.filter(iso_year=iso_year, week=week). \\\n        values('project', 'category').annotate(Count('id'), tc_sum=Sum('time_cost_min')). \\\n        order_by('-tc_sum')\n\n    week_data_group_by_project = list((i['category'], i['project'], i['id__count'], i['tc_sum'])\n                                      for i in week_data_group_by_project)\n\n    legend_data = list(t[0] for t in week_data_group_by_category)\n    legend_data.sort(key=lambda x: CATEGORIES.index(x))\n    week_data_group_by_category.sort(key=lambda x: CATEGORIES.index(x[0]))\n    week_data_group_by_project.sort(key=lambda x: legend_data.index(x[0]))\n    inner_data = list({'name': t[0], 'value': t[2]} for t in week_data_group_by_category)\n    outer_data = list({'name': t[1], 'value': t[3]} for t in week_data_group_by_project)\n    return Response({\n        'legend': legend_data,\n        'inner': inner_data,\n        'outer': outer_data,\n        })\n\n\ndef get_project(request):\n    category = request.GET['c']\n    project = request.GET['p']\n    clock_items = ClockItem.objects.filter(category=category, project=project).order_by('start_time')\n    cis_num = len(clock_items)\n    start_time = clock_items[0].start_time\n    end_time = clock_items[cis_num - 1].end_time\n    days_num = (end_time.date() - start_time.date()).days + 1\n    return render(request, 'tms/project.html', {\n        'category': category,\n        'project': project,\n        'clock_items': clock_items,\n        'start_time': start_time,\n        'end_time': end_time,\n        'days_num': days_num\n    })\n\n\ndef all_projects(request):\n    year = None\n    all_projects_stats = ClockItem.objects\n    if year is not None:\n        all_projects_stats = all_projects_stats.filter(year=year)\n    all_projects_stats = all_projects_stats.values('category', 'project'). \\\n        annotate(tc_count=models.Count('id'), tc_sum=models.Sum('time_cost_min'),\n                 start_time_min=models.Min('start_time'), end_time_max=models.Max('end_time')). \\\n        order_by('-tc_sum')\n    for project_stats in all_projects_stats:\n            project_stats['days_num'] = \\\n            (project_stats['end_time_max'].date() - project_stats['start_time_min'].date()).days + 1\n    return render(request, 'tms/all_projects.html', {\n        'all_projects_stats': all_projects_stats,\n        })\n\n\ndef time_analyzer(request):\n    category = request.GET['c']\n    project = request.GET['p']\n    clock_items = ClockItem.objects.filter(category=category, project=project).order_by('start_time')\n    cis_num = len(clock_items)\n    start_time = clock_items[0].start_time\n    end_time = clock_items[cis_num - 1].end_time\n    return render(request, 'tms/time_analyzer.html', {\n        'category': category,\n        'project': project,\n        'clock_items': clock_items,\n        'start_time': start_time,\n        'end_time': end_time,\n    })\n\n\ndef calendar(request):\n    return render(request, 'tms/calendar.html')\n\n\ndef get_report(request):\n    dt = datetime.now()\n    iso_year, week, weekday = dt.isocalendar()\n    year = dt.year\n    month = dt.month\n\n    clock_items = ClockItem.objects.filter(iso_year=iso_year).all()\n    return render(request, 'tms/report.html', {\n        'clock_items': clock_items,\n    })\n\n\ndef get_monday_date_of_iso_week(iso_year, week):\n    ret = datetime.strptime('%04d-%02d-1' % (iso_year, week), '%Y-%W-%w')\n    if date(iso_year, 1, 4).isoweekday() > 4:\n        ret -= timedelta(days=7)\n    return ret\n\n\ndef day_report(request):\n    if 'date' in request.GET:\n        return day_report_detail(request, request.GET['date'])\n\n    if 'num' in request.GET:\n        days_num = int(request.GET['num'])\n    else:\n        days_num = 31\n    cur_date = datetime.now().date()\n    min_date = cur_date - timedelta(days=days_num - 1)\n    clock_items = ClockItem.objects.filter(date__gte=min_date)\n    days_stats = generate_days_stats(clock_items, min_date, days_num, with_sparkline_data=True)\n\n    return render(request, 'tms/day_report.html', {\n        'days_stats': days_stats\n        })\n\n\ndef day_report_detail(request, date_str):\n    cur_date_str = datetime.now().strftime('%Y-%m-%d')\n    if date_str == cur_date_str:\n        is_cur_day = True\n    else:\n        is_cur_day = False\n    dt = datetime.strptime(date_str, '%Y-%m-%d')\n    prev_date_str = (dt - timedelta(days=1)).strftime('%Y-%m-%d')\n    next_date_str = (dt + timedelta(days=1)).strftime('%Y-%m-%d')\n\n    clock_items = ClockItem.objects.filter(date=date_str).order_by('start_time')\n    report_data = generate_report(clock_items, 1)\n    return render(request, 'tms/day_report_detail.html', {\n        'is_cur_day': is_cur_day,\n        'date_str': date_str,\n        'date': dt,\n        'clock_items': clock_items,\n        'prev_date_str': prev_date_str,\n        'next_date_str': next_date_str,\n        'report_data': report_data,\n        })\n\n\ndef week_report(request):\n    cur_iso_year, cur_week, weekday = datetime.now().isocalendar()\n    if 'year' not in request.GET and 'week' not in request.GET:\n        iso_year = cur_iso_year\n        week = cur_week\n    else:\n        iso_year = int(request.GET['year'])\n        week = int(request.GET['week'])\n\n    if iso_year == cur_iso_year and week == cur_week:\n        is_cur_week = True\n    else:\n        is_cur_week = False\n\n    dt_of_this_monday = get_monday_date_of_iso_week(iso_year, week)\n    dt_of_prev_monday = dt_of_this_monday - timedelta(days=7)\n    dt_of_next_monday = dt_of_this_monday + timedelta(days=7)\n    iso_year_of_prev_week, prev_week, weekday = dt_of_prev_monday.isocalendar()\n    iso_year_of_next_week, next_week, weekday = dt_of_next_monday.isocalendar()\n\n    clock_items = ClockItem.objects.filter(iso_year=iso_year, week=week).order_by('start_time')\n    clock_items_list = list()\n    for dt, clock_items_of_day in groupby(clock_items, itemgetter('date')):\n        clock_items_list.append({\n            'date': dt,\n            'clock_items': list(clock_items_of_day),\n        })\n\n\n    print('This monday: ', dt_of_this_monday)\n    report_data = generate_report(clock_items, 7)\n    days_stats = generate_days_stats(clock_items, min_date=dt_of_this_monday.date(),\n                                     days_num=7, with_sparkline_data=True)\n    days_stats.sort(key=itemgetter('date'))\n\n    return render(request, 'tms/week_report.html', {\n        'is_cur_week': is_cur_week,\n        'iso_year': iso_year,\n        'week': week,\n        'clock_items': clock_items,\n        'clock_items_list': clock_items_list,\n        'prev_week': prev_week,\n        'iso_year_of_prev_week': iso_year_of_prev_week,\n        'next_week': next_week,\n        'iso_year_of_next_week': iso_year_of_next_week,\n        'report_data': report_data,\n        'days_stats': days_stats,\n        # 'daily_overview': daily_overview,\n    })\n\n\ndef custom_report(request):\n    # 默认取上周五下午六点到本周五下午六点作为start_time和end_time\n    today = datetime.now().date()\n    # 获取本周五18点的datetime\n    end_dt_of_work_week = datetime(year=today.year, month=today.month, day=today.day,\n                                   hour=18, minute=0, second=0)\n    end_dt_of_work_week += timedelta(4 - today.weekday())\n    # 获取上周四14点的datetime\n    begin_dt_of_work_week = end_dt_of_work_week - timedelta(weeks=1)\n\n    start_time_str = request.GET.get('start', None)\n    if start_time_str is None:\n        start_time = begin_dt_of_work_week\n    else:\n        start_time = datetime.strptime(start_time_str, '%Y%m%d')\n    start_time_str = start_time.strftime('%Y-%m-%d %H:%M')\n\n    end_time_str = request.GET.get('end', None)\n    if end_time_str is None:\n        end_time = end_dt_of_work_week\n    else:\n        end_time = datetime.strptime(end_time_str, '%Y%m%d') + timedelta(days=1)\n    end_time_str = end_time.strftime('%Y-%m-%d %H:%M')\n\n    clock_items = ClockItem.objects.filter(start_time__gte=start_time, end_time__lt=end_time).order_by('start_time')\n    days_num = (end_time - start_time).days\n    report_data = generate_report(clock_items, days_num)\n\n    return render(request, 'tms/custom_report.html', {\n        'start_time_str': start_time_str,\n        'end_time_str': end_time_str,\n        'days_num': days_num,\n        'clock_items': clock_items,\n        'report_data': report_data,\n        })\n\n\ndef year_report(request):\n    tms_begin_date = TMS_BEGIN_DATE\n    tms_end_date = datetime.now().date()\n    cur_year = datetime.now().year\n    if 'year' not in request.GET:\n        year = cur_year\n    else:\n        year = int(request.GET['year'])\n    clock_items = ClockItem.objects.filter(year=year).order_by('start_time')\n\n    # 总体统计\n    total_stats = dict()\n    # 计算总天数\n    begin_date = datetime(year=year, month=1, day=1).date()\n    end_date = datetime(year=year, month=12, day=31).date()\n    if begin_date < tms_begin_date:\n        begin_date = tms_begin_date\n    if end_date > tms_end_date:\n        end_date = tms_end_date\n    days_num = (end_date - begin_date).days + 1\n    total_stats['days_num'] = days_num\n\n    # 计算各项总时间\n    all_time = 0\n    work_time = 0\n    study_time = 0\n    valid_time = 0\n    for clock_item in clock_items:\n        time_cost_min = clock_item.time_cost_min\n        category = clock_item.category\n        all_time += time_cost_min\n        if category in ['工作', '学习']:\n            valid_time += time_cost_min\n        if category in ['工作']:\n            work_time += time_cost_min\n        if category in ['学习']:\n            study_time += time_cost_min\n    total_stats['all_time'] = Utils.min_to_hour(all_time)\n    total_stats['work_time'] = Utils.min_to_hour(work_time)\n    total_stats['study_time'] = Utils.min_to_hour(study_time)\n    total_stats['valid_time'] = Utils.min_to_hour(valid_time)\n    total_stats['avg_valid_time'] = Utils.min_to_hour(float(valid_time) / float(days_num), 2)\n\n    # 生成报表\n    report_data = generate_report(clock_items, days_num)\n\n    return render(request, 'tms/year_report.html', {\n        'year': year,\n        'begin_date': begin_date,\n        'end_date': end_date,\n        'clock_items': clock_items,\n        'report_data': report_data,\n        'total_stats': total_stats,\n    })\n\n\ndef week_stats(request):\n    iso_year = int(request.GET['year'])\n    week = int(request.GET['week'])\n    week_data_group_by_category = ClockItem.objects.filter(iso_year=iso_year, week=week).\\\n        values('category').annotate(Count('id'), tc_sum=Sum('time_cost_min')).\\\n        order_by('-tc_sum')\n    week_data_group_by_category = list((i['category'], i['id__count'], i['tc_sum'])\n                                       for i in week_data_group_by_category)\n    week_data_group_by_project = ClockItem.objects.filter(iso_year=iso_year, week=week). \\\n        values('project', 'category').annotate(Count('id'), tc_sum=Sum('time_cost_min')).\\\n        order_by('-tc_sum')\n\n    week_data_group_by_project = list((i['category'], i['project'], i['id__count'], i['tc_sum'])\n                                      for i in week_data_group_by_project)\n\n\n    legend_data = list(t[0] for t in week_data_group_by_category)\n    legend_data.sort(key=lambda x: CATEGORIES.index(x))\n    week_data_group_by_category.sort(key=lambda x: CATEGORIES.index(x[0]))\n    week_data_group_by_project.sort(key=lambda x: legend_data.index(x[0]))\n    inner_data = list({'name': t[0], 'value': t[2]} for t in week_data_group_by_category)\n    outer_data = list({'name': t[1], 'value': t[3]} for t in week_data_group_by_project)\n    return JsonResponse({\n        'legend': legend_data,\n        'inner': inner_data,\n        'outer': outer_data,\n    })\n\n\ndef year_stats_step_by_month_and_week(request):\n    \"\"\"\n    data = {\n        'labels': ['一月', '二月', '三月', '...'],\n        'valid_time': [15, 50, 33, ...],\n        'work_time': [1.7, 25, 22, ...],\n        'study_time': [13, 25, 11, ...],\n    }\n    \"\"\"\n    year = int(request.GET['year'])\n    time_cost_group_by_month_category = ClockItem.objects.filter(year=year). \\\n        values('month', 'category').annotate(Sum('time_cost_min'), Count('time_cost_min'))\n\n    valid_time_of_month = [0] * 12\n    work_time_of_month = [0] * 12\n    study_time_of_month = [0] * 12\n    for item in time_cost_group_by_month_category:\n        category = item['category']\n        month = item['month']\n        time_cost_min = item['time_cost_min__sum']\n        if category == '工作':\n            work_time_of_month[month - 1] += time_cost_min\n            valid_time_of_month[month - 1] += time_cost_min\n        elif category == '学习':\n            study_time_of_month[month - 1] += time_cost_min\n            valid_time_of_month[month - 1] += time_cost_min\n    month_labels = ['一月', '二月', '三月', '四月', '五月', '六月', '七月', '八月', '九月', '十月', '十一月', '十二月']\n    valid_time_of_month = ['%.1f' % (float(i) / 60.0) for i in valid_time_of_month]\n    work_time_of_month = ['%.1f' % (float(i) / 60.0) for i in work_time_of_month]\n    study_time_of_month = ['%.1f' % (float(i) / 60.0) for i in study_time_of_month]\n\n    # Get last week of year\n    time_cost_group_by_week_category = ClockItem.objects.filter(iso_year=year). \\\n        values('week', 'category').annotate(Sum('time_cost_min'), Count('time_cost_min'))\n    last_day_of_year = datetime(year=year, month=12, day=31)\n    while True:\n        last_week = last_day_of_year.isocalendar()[1]\n        if last_week != 1:\n            break\n        else:\n            last_day_of_year -= timedelta(days=1)\n\n    valid_time_of_week = [0] * last_week\n    work_time_of_week = [0] * last_week\n    study_time_of_week = [0] * last_week\n    for item in time_cost_group_by_week_category:\n        category = item['category']\n        week = item['week']\n        time_cost_min = item['time_cost_min__sum']\n        if category == '工作':\n            work_time_of_week[week - 1] += time_cost_min\n            valid_time_of_week[week - 1] += time_cost_min\n        elif category == '学习':\n            study_time_of_week[week - 1] += time_cost_min\n            valid_time_of_week[week - 1] += time_cost_min\n    week_labels = ['W%d' % w for w in range(1, last_week + 1)]\n    valid_time_of_week = ['%.1f' % (float(i) / 60.0) for i in valid_time_of_week]\n    work_time_of_week = ['%.1f' % (float(i) / 60.0) for i in work_time_of_week]\n    study_time_of_week = ['%.1f' % (float(i) / 60.0) for i in study_time_of_week]\n\n    data = {\n        'month': {\n            'labels': month_labels,\n            'valid_time': valid_time_of_month,\n            'work_time': work_time_of_month,\n            'study_time': study_time_of_month,\n            },\n        'week': {\n            'labels': week_labels,\n            'valid_time': valid_time_of_week,\n            'work_time': work_time_of_week,\n            'study_time': study_time_of_week,\n        }\n    }\n\n    return JsonResponse(data)\n\n\n\n", "repo_name": "oldzhong/mysite", "sub_path": "tms/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 35905, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "rest_framework.filters.FilterSet", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rest_framework.filters", "line_number": 34, "usage_type": "name"}, {"api_name": "django_filters.DateTimeFilter", "line_number": 35, "usage_type": "call"}, {"api_name": "django_filters.DateTimeFilter", "line_number": 36, "usage_type": "call"}, {"api_name": "models.ClockItem", "line_number": 39, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 43, "usage_type": "name"}, {"api_name": "models.ClockItem.objects.all", "line_number": 44, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 44, "usage_type": "name"}, {"api_name": "serializers.ClockItemSerializer", "line_number": 45, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "name"}, {"api_name": "models.ClockItem.objects.values", "line_number": 56, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 56, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 57, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "call"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 92, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 92, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 93, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 93, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 94, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 94, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 95, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 95, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 96, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 96, "usage_type": "name"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 110, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 110, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 111, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 111, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 131, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 133, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 135, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 135, "usage_type": "call"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 146, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 146, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 147, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 147, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 149, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 149, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 150, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 150, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 151, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 151, "usage_type": "name"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 156, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 156, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 156, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 157, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 157, "usage_type": "call"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 165, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 165, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 166, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 166, "usage_type": "name"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 168, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 168, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 168, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 173, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 193, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 193, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 194, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 195, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 195, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 195, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 198, "usage_type": "call"}, {"api_name": "serializers.ClockItemSerializer", "line_number": 201, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 186, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 206, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 207, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 208, "usage_type": "call"}, {"api_name": "tms.utils.Utils.parse_dt_str", "line_number": 214, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 214, "usage_type": "name"}, {"api_name": "tms.utils.Utils.parse_dt_str", "line_number": 218, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 218, "usage_type": "name"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 225, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 225, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 225, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 256, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 211, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 266, "usage_type": "call"}, {"api_name": "tms.utils.Utils.get_month_id", "line_number": 270, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 270, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 280, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 281, "usage_type": "call"}, {"api_name": "tms.utils.Utils.get_month_id", "line_number": 288, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 288, "usage_type": "name"}, {"api_name": "operator.itemgetter", "line_number": 301, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 307, "usage_type": "call"}, {"api_name": "tms.utils.Utils.get_week_id", "line_number": 311, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 311, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 321, "usage_type": "call"}, {"api_name": "tms.utils.Utils.get_week_id", "line_number": 328, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 328, "usage_type": "name"}, {"api_name": "operator.itemgetter", "line_number": 342, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 351, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 395, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 408, "usage_type": "call"}, {"api_name": "tms.utils.Utils.min_to_hour2", "line_number": 412, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 412, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour2", "line_number": 413, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 413, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour2", "line_number": 414, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 414, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour2", "line_number": 415, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 415, "usage_type": "name"}, {"api_name": "operator.itemgetter", "line_number": 531, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 534, "usage_type": "call"}, {"api_name": "tms.utils.Utils.min_to_hour2", "line_number": 538, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 538, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour2", "line_number": 540, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 540, "usage_type": "name"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 555, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 555, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 555, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 556, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 556, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 560, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 560, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 560, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 561, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 561, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 573, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 551, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 583, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 583, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 583, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 588, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 600, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 600, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 604, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 604, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 604, "usage_type": "call"}, {"api_name": "django.db.models.Min", "line_number": 605, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 605, "usage_type": "name"}, {"api_name": "django.db.models.Max", "line_number": 605, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 610, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 618, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 618, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 618, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 622, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 632, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 636, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 636, "usage_type": "name"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 641, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 641, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 641, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 642, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 648, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 648, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 649, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 650, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 662, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 662, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 663, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 664, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 664, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 664, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 667, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 673, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 673, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 678, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 678, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 679, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 680, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 682, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 682, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 682, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 684, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 696, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 696, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 710, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 711, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 715, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 715, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 715, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 717, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 717, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 728, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 730, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 748, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 748, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 750, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 752, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 754, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 760, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 760, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 767, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 767, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 767, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 770, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 770, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 770, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 774, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 785, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 785, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 786, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 786, "usage_type": "name"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 791, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 791, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 791, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 796, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 797, "usage_type": "call"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 820, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 820, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 821, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 821, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 822, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 822, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 823, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 823, "usage_type": "name"}, {"api_name": "tms.utils.Utils.min_to_hour", "line_number": 824, "usage_type": "call"}, {"api_name": "tms.utils.Utils", "line_number": 824, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 829, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 842, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 842, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 842, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 843, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 843, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 847, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 847, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 847, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 848, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 848, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 861, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 878, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 878, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 878, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 879, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 879, "usage_type": "call"}, {"api_name": "models.ClockItem.objects.filter", "line_number": 900, "usage_type": "call"}, {"api_name": "models.ClockItem.objects", "line_number": 900, "usage_type": "attribute"}, {"api_name": "models.ClockItem", "line_number": 900, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 901, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 901, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 902, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 908, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 943, "usage_type": "call"}]}
{"seq_id": "70210809083", "text": "#coding:utf-8\nimport torch\nimport torch.utils.data as data\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nimport os,re,jieba\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom config import Config\nfrom multi_proc_utils import parallelize\nfrom sklearn.preprocessing import MultiLabelBinarizer as MLB\nfrom tqdm import tqdm\nimport pickle\n\nconfig = Config() \n\n\"\"\" 一：对样本和标签进行数值化，建立词表 \"\"\"\ndef build_dataset(config):\n    \n    \"\"\" 1：加载数据 \"\"\"\n    print(\"\\nLoading the dataset ... \\n\")\n    point_df = pd.read_csv(config.point_path,header=None)\n    point_df.dropna(inplace=True)\n    point_df = point_df.rename(columns={0:\"label\",1:\"content\"})\n    print(f\"\\nThe shape of the dataset : {point_df.shape}\\n\")\n    \n    \"\"\" 2：开多进程进行数据清洗和分词 \"\"\"\n    print(\"\\nCleaning text and segmenting ... \\n\")\n    point_df = parallelize(point_df,proc)\n    \n    \"\"\" 3：对样本进行 zero pad，并转化为id \"\"\"\n    print(\"\\nZero padding and transfering id ...\\n\")\n    text_tokenizer = Tokenizer(oov_token=\"<unk>\")\n    text_tokenizer.fit_on_texts(point_df[\"content\"])\n    corpus_x = text_tokenizer.texts_to_sequences(point_df[\"content\"])\n    config.max_len = calcu_max_len(point_df)\n    corpus_x = pad_sequences(corpus_x,maxlen=config.max_len,padding=\"post\",truncating=\"post\")\n    \n    \"\"\" 4: 对多标签分类的标签进行数值化 \"\"\" \n    print(\"\\nNumeralizing the multiclass labels ... \\n\")\n    point_df[\"label\"] = point_df[\"label\"].apply(lambda x:x.split())\n    mlb = MLB()\n    corpus_y = mlb.fit_transform(point_df[\"label\"])\n    config.num_classes = corpus_y.shape[1]\n    \n    \"\"\" 5: 用样本构建词表，加入 <pad> 的id \"\"\" \n    print(\"\\nBuilding the vocab ...\\n\")\n    word_index = text_tokenizer.word_index\n    vocab = dict({\"<pad>\":0}, **word_index)\n    config.vocab_size = len(vocab)\n    \n    \"\"\" 保存好标签转化器，在模型预测时用 \"\"\"\n    save_pickle(mlb, config.mlb_path)    \n    \n    \"\"\" 保存好词表，模型预测时用 \"\"\"\n    save_pickle(vocab,config.vocab_path)\n    \n    return corpus_x,corpus_y,vocab\n\n\"\"\" 加载停用词 \"\"\" \ndef load_stop_words(stop_word_path):\n    file = open(stop_word_path, 'r', encoding='utf-8')\n    stop_words = file.readlines()\n    stop_words = [stop_word.strip() for stop_word in stop_words]\n    return stop_words\n\nstop_words = load_stop_words(config.stopwords_path) \n\n\"\"\" 清洗文本 \"\"\" \ndef clean_sentence(line):\n    line = re.sub(\n            \"[a-zA-Z0-9]|[\\s+\\-\\|\\!\\/\\[\\]\\{\\}_,.$%^*(+\\\"\\')]+|[:：+——()?【】《》“”！，。？、~@#￥%……&*（）]+|题目\", '',line)\n    words = jieba.lcut(line, cut_all=False)\n    return words\n\n\"\"\" 进行分词 \"\"\" \ndef sentence_proc(sentence):\n    words = clean_sentence(sentence)\n    words = [word for word in words if word not in stop_words]\n    return ' '.join(words)\n\ndef proc(df):\n    df[\"content\"] = df[\"content\"].apply(sentence_proc)\n    return df\n\n\"\"\" 计算输入长度，使其能涵盖95%样本 \"\"\" \ndef calcu_max_len(df):\n    \n    df[\"lengths\"] = df[\"content\"].apply(lambda x:x.count(' ')+1)\n    max_lengths = max(df[\"lengths\"])\n    for len_ in range(50,max_lengths,50):\n        bool_ = df[\"lengths\"] < len_\n        cover_rate = sum(bool_.apply(int)) / len(bool_)\n        if cover_rate >= 0.95:\n            return len_\n\n\n\"\"\" 四：计算类别权重，缓解类别不平衡问题 \"\"\"\ndef calcu_class_weights(labels,config):\n    labels = torch.FloatTensor(labels)\n    \n    freqs = torch.zeros_like(labels[0])\n    for y in labels:\n        freqs += y\n    \n    weights = freqs / len(labels)\n    weights = 1 / torch.log(1.01 + weights)\n    \n    weights = weights.to(config.device)\n    return weights   \n\n    \n\"\"\" 二: 加载预训练词向量，并与词表相对应 \"\"\" \ndef load_embed_matrix(vocab,config):\n    \n    \"\"\" 1: 加载百度百科词向量 \"\"\" \n    print(\"\\nLoading baidu baike word2vec ...\\n\")\n    embed_index = load_w2v(config.w2v_path)\n    \n    \"\"\" 2: 词向量矩阵与词表相对应 \"\"\" \n    vocab_size = len(vocab)\n    embed_matrix = np.zeros((vocab_size,config.embed_dim))\n    for word,index in vocab.items():\n        vector = embed_index.get(word)\n        if vector is not None:\n            embed_matrix[index] = vector\n            \n    embed_matrix = torch.FloatTensor(embed_matrix)\n            \n    return embed_matrix\n        \n\"\"\" 加载百度百科词向量 \"\"\"\ndef load_w2v(path):\n    \n    file = open(path,encoding=\"utf-8\")\n    \n    embed_index = {}\n    for i,line in tqdm(enumerate(file)):\n        if i == 0:\n            continue\n        value = line.split()\n        word = value[0]\n        emb = np.asarray(value[1:], dtype=\"float32\")\n        embed_index[word] = emb\n        \n    return embed_index\n\n\n\"\"\" 三: 划分数据集，并生成 batch 迭代器 \"\"\" \ndef batch_generator(x,y,size,config):\n    \n    \"\"\" 1: 划分数据集 \"\"\" \n    print(\"\\nSpliting the dataset ... \\n\")\n    train_data, valid_data, test_data = split_dataset(x, y, size, config.device)\n    \n    \"\"\" 2: 生成 batch 迭代器 \"\"\"\n    train_iter = batch_iterator(train_data, config.batch_size)\n    valid_iter = batch_iterator(valid_data, config.batch_size)\n    test_iter = batch_iterator(test_data, config.batch_size)\n    \n    return train_iter,valid_iter,test_iter  \n\n\"\"\" 划分数据集 \"\"\" \ndef split_dataset(x,y,size,device):\n    \n    train_x, valid_x, train_y, valid_y = train_test_split(x,y,test_size=size,random_state=10)\n    train_x, test_x, train_y, test_y = train_test_split(train_x, train_y, test_size=size,random_state=10) \n    \n    train_x = torch.LongTensor(train_x).to(device)\n    valid_x = torch.LongTensor(valid_x).to(device)\n    test_x = torch.LongTensor(test_x).to(device)\n    \n    train_y = torch.FloatTensor(train_y).to(device)\n    valid_y = torch.FloatTensor(valid_y).to(device)\n    test_y = torch.FloatTensor(test_y).to(device)    \n    \n    return (train_x,train_y) , (valid_x,valid_y), (test_x, test_y)\n    \n\"\"\" 重写 dataset 类\"\"\"\nclass MyDataset(data.Dataset):\n    def __init__(self, dataset):\n        self.X, self.Y = dataset\n\n    def __getitem__(self, index):\n        x, y = self.X[index], self.Y[index]\n        return x, y\n\n    def __len__(self):\n        return len(self.X)\n\n\"\"\" 生成 迭代器 \"\"\"     \ndef batch_iterator(dataset,batch_size):\n    dataset = MyDataset(dataset)\n    batcher = data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True)\n    return batcher\n \n\"\"\" 保存为pickle对象 \"\"\" \ndef save_pickle(s,file_path):\n    with open(file_path,'wb') as f:\n        pickle.dump(s,f,protocol=2)\n        \n\"\"\" 加载pickle对象 \"\"\" \ndef load_pickle(file_path):\n    with open(file_path,'rb') as f:\n        s = pickle.load(f)\n    return s\n\n     \nif __name__ == \"__main__\":\n    \n    corpus_x, corpus_y, vocab = build_dataset(config)\n    \n    class_weights = calcu_class_weights(corpus_y,config)\n    \n    embed_matrix = load_embed_matrix(vocab,config)\n    \n    train_iter, valid_iter, test_iter = batch_generator(corpus_x, corpus_y, 0.15, config)\n    for x_batch, y_batch in train_iter:\n        print(f\"The shape of content batch is {x_batch.shape}\")\n        print(f\"The shape of label batch is {y_batch.shape}\")\n        break\n", "repo_name": "DengYangyong/exam_annotation", "sub_path": "textcnn/data_loader.py", "file_name": "data_loader.py", "file_ext": "py", "file_size_in_byte": 7263, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 35, "dataset": "github-code", "pt": "41", "api": [{"api_name": "config.Config", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "config.point_path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "multi_proc_utils.parallelize", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.preprocessing.text.Tokenizer", "line_number": 34, "usage_type": "call"}, {"api_name": "config.max_len", "line_number": 37, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 38, "usage_type": "call"}, {"api_name": "config.max_len", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.MultiLabelBinarizer", "line_number": 43, "usage_type": "call"}, {"api_name": "config.num_classes", "line_number": 45, "usage_type": "attribute"}, {"api_name": "config.vocab_size", "line_number": 51, "usage_type": "attribute"}, {"api_name": "config.mlb_path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "config.vocab_path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "config.stopwords_path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 72, "usage_type": "call"}, {"api_name": "jieba.lcut", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 108, "usage_type": "call"}, {"api_name": "config.device", "line_number": 110, "usage_type": "attribute"}, {"api_name": "config.w2v_path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "config.embed_dim", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 129, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 144, "usage_type": "call"}, {"api_name": "config.device", "line_number": 155, "usage_type": "attribute"}, {"api_name": "config.batch_size", "line_number": 158, "usage_type": "attribute"}, {"api_name": "config.batch_size", "line_number": 159, "usage_type": "attribute"}, {"api_name": "config.batch_size", "line_number": 160, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 167, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 181, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 181, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 195, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 201, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 206, "usage_type": "call"}]}
{"seq_id": "13449898823", "text": "import httplib\nimport json\nimport argparse\n\nimport vmware.common.constants as constants\nimport vmware.common.global_config as global_config\n\npylogger = global_config.pylogger\nCAT_API_SERVER = 'cat-api.eng.vmware.com'\n\n\ndef retry_testrun(workunit_id=None, deliverable_id=None, api_key=None,\n                  username=None):\n    # Use this method only when absolutely necessary.\n    # CAT team is not willing to allow more frequent reruns until the new UI\n    # is ready. Also, this is not recommended due to limited resources on cloud\n    connection = httplib.HTTPSConnection(CAT_API_SERVER)\n    headers = {'content-type': 'application/json'}\n    url = 'http://%s/api/v2.0/testrun/?api_key=%s&username=%s' % (\n        CAT_API_SERVER, api_key, username)\n    deliverable_endpoint = '/api/v2.0/deliverable'\n    deliverables = []\n    for id_ in deliverable_id:\n        deliverables.append('%s/%s/' % (deliverable_endpoint, id_))\n    pylogger.info('deliverable list %s' % deliverables)\n    body = {\"deliverables\": deliverables,\n            \"workunit\": \"/api/v2.0/workunit/%s/\" % workunit_id}\n    body = json.dumps(body)\n    connection.request(constants.HTTPVerb.POST, url, body, headers)\n    response = connection.getresponse()\n\n    status = response.status\n    raw_data = response.read()\n    if status != httplib.CREATED:\n        pylogger.error(\"Retry testrun for workunit %s failed: %s\" % (\n            workunit_id, status))\n        raise RuntimeError(raw_data)\n    data = json.loads(raw_data)\n    testrun_url = 'https://%s/testrun/%s' % (\n        CAT_API_SERVER,  data['id'])\n    pylogger.info(\"Testrun started %s\" % testrun_url)\n\nif __name__ == '__main__':\n    # Parse commandline arguments.\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--deliverable\", dest=\"deliverable\", action=\"append\",\n                        help=\"Deliverable ID\")\n    parser.add_argument(\"--workunit\", dest=\"workunit\", action=\"store\",\n                        help=\"Workunit ID\")\n    # To get apikey, click on the username on cat.eng.vmware.com and\n    # follow instructions given there\n    parser.add_argument(\"--apikey\", dest=\"api_key\", action=\"store\",\n                        help=\"API key for write access\")\n    parser.add_argument(\"--username\", dest=\"username\", action=\"store\",\n                        help=\"CAT user name\")\n    options = parser.parse_args()\n\n    retry_testrun(\n        workunit_id=options.workunit, deliverable_id=options.deliverable,\n        api_key=options.api_key, username=options.username)\n", "repo_name": "Cloudxtreme/MyProject", "sub_path": "SystemTesting/pylib/vmware/utilities/cat_helper.py", "file_name": "cat_helper.py", "file_ext": "py", "file_size_in_byte": 2507, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "vmware.common.global_config.pylogger", "line_number": 8, "usage_type": "attribute"}, {"api_name": "vmware.common.global_config", "line_number": 8, "usage_type": "name"}, {"api_name": "httplib.HTTPSConnection", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}, {"api_name": "vmware.common.constants.HTTPVerb", "line_number": 29, "usage_type": "attribute"}, {"api_name": "vmware.common.constants", "line_number": 29, "usage_type": "name"}, {"api_name": "httplib.CREATED", "line_number": 34, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 45, "usage_type": "call"}]}
{"seq_id": "29080574049", "text": "#! /usr/bin/env python\n#coding=utf-8\nfrom __future__ import unicode_literals\nimport sys,os\nreload(sys)\nsys.setdefaultencoding('utf-8')\nsys.path.append(os.path.split(os.path.realpath(__file__))[0])\nsys.path.append(\"../\")\nfrom Whoosh_Module.Whoosh_Module import Whoosh_Module\nfrom flask import Flask,url_for,render_template,request\n\nWhoosh = None\nflask_app = Flask(__name__)\n@flask_app.route('/',methods=['GET', 'POST'])\ndef index():\n    global Whoosh\n    if request.method == 'POST':\n        keyword=request.form['keyword']\n        results = Whoosh.search(keyword)\n        return render_template('result.html',results=results,keyword=keyword)\n    else :\n        return render_template('index.html',results=None)\n\n\nclass Flask_Module(object):\n    def __init__(self,WhooshName):\n        global Whoosh\n        Whoosh = WhooshName\n    def run(self):\n        #flask_app.debug=True\n        flask_app.run(host='0.0.0.0')\n\nif __name__ == '__main__':\n    Whoosh=Whoosh_Module()\n    #flask_app.debug=True\n    flask_app.run(host='0.0.0.0')\n\n", "repo_name": "sxphealer/Search-Engine-System-For-NPU", "sub_path": "sdk/Search-Engine-System-For-NPU/Flask_Module/Flask_Module.py", "file_name": "Flask_Module.py", "file_ext": "py", "file_size_in_byte": 1029, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}, {"api_name": "Whoosh_Module.Whoosh_Module.Whoosh_Module", "line_number": 34, "usage_type": "call"}]}
{"seq_id": "72429401402", "text": "import re\nimport numpy as np\n\nfrom ..adapters import Adapter\nfrom ..config import ConfigValidator, StringField, NumberField, BoolField, ListField\nfrom ..representation import (\n    DetectionPrediction,\n    ActionDetectionPrediction,\n    ContainerPrediction,\n    ClassificationPrediction\n)\nfrom ..utils import contains_all\n\n\nclass ActionDetection(Adapter):\n    __provider__ = 'action_detection'\n    prediction_types = (ActionDetectionPrediction, DetectionPrediction, )\n\n    @classmethod\n    def parameters(cls):\n        parameters = super().parameters()\n        parameters.update({\n            'loc_out': StringField(description=\"Name of layer containing box coordinates in SSD format.\"),\n            'main_conf_out': StringField(description=\"Name of layer containing detection confidences.\"),\n            'multihead_net': BoolField(\n                optional=True, default=False,\n                description=\"Whether to configure for the multi head network architecture.\"\n            ),\n            'priorbox_out': StringField(\n                optional=True, description=\"Name of layer containing prior boxes in SSD format.\"\n            ),\n            'add_conf_out_prefix': StringField(\n                description=\"Prefix for generation name of layers containing action confidences \"\n                            \"if topology has several following layers or layer name.\"\n            ),\n            'add_conf_out_suffix': StringField(\n                optional=True,\n                description=\"Suffix for generation name of layers containing action confidences \"\n                            \"if topology has several following layers or layer name.\"\n            ),\n            'head_sizes': ListField(\n                optional=True, value_type=NumberField(value_type=int, min_value=1, description=\"Head size\"),\n                description=\"Number of anchors for each SSD head.\"\n            ),\n            'head_scales': ListField(\n                optional=True, value_type=NumberField(value_type=int, min_value=0, description=\"Network head scales\"),\n                description=\"Variances to decode SSD prediction.\"\n            ),\n            'anchors': ListField(\n                optional=True, value_type=ListField(value_type=ListField(value_type=NumberField(\n                    value_type=float, min_value=0.0))),\n                description=\"SSD anchors.\"\n            ),\n            'variance': ListField(\n                optional=True, value_type=NumberField(value_type=float, min_value=0.0, description=\"SSD variance\"),\n                description=\"Variances to decode SSD prediction.\"\n            ),\n            'in_sizes': ListField(\n                optional=True, value_type=NumberField(value_type=int, min_value=0, description=\"Network input sizes\"),\n                description=\"Variances to decode SSD prediction.\"\n            ),\n            'add_conf_out_count': NumberField(\n                optional=True, min_value=1, value_type=int,\n                description=\"Number of layers with action confidences (optional, you can not provide this argument \"\n                            \"if action confidences contained in one layer).\"\n            ),\n            'num_action_classes': NumberField(description=\"Number classes for action recognition.\", value_type=int),\n            'detection_threshold': NumberField(\n                optional=True, value_type=float, min_value=0, max_value=1, default=0,\n                description=\"Minimal detection confidences level for valid detections.\"),\n            'action_scale': NumberField(\n                optional=True, value_type=float, default=3, description=\"Scale for correct action score calculation.\"\n            ),\n            'action_confidence_threshold': NumberField(\n                optional=True, value_type=float, min_value=0, max_value=1, default=0,\n                description=\"Action confidence threshold.\"\n            )\n        })\n\n        return parameters\n\n    @classmethod\n    def validate_config(cls, config, fetch_only=False, **kwargs):\n        return super().validate_config(\n            config, fetch_only=fetch_only, on_extra_argument=ConfigValidator.WARN_ON_EXTRA_ARGUMENT\n        )\n\n    def configure(self):\n        self.multihead = self.get_value_from_config('multihead_net')\n        self.loc_out = self.get_value_from_config('loc_out')\n        self.main_conf_out = self.get_value_from_config('main_conf_out')\n        self.num_action_classes = self.get_value_from_config('num_action_classes')\n        self.detection_threshold = self.get_value_from_config('detection_threshold')\n        self.action_threshold = self.get_value_from_config('action_confidence_threshold')\n        self.action_scale = self.get_value_from_config('action_scale')\n        add_conf_out_prefix = self.get_value_from_config('add_conf_out_prefix')\n        self.outputs_verified = False\n\n        if self.multihead:\n            self.in_sizes = self.get_value_from_config('in_sizes')\n            self.variance = self.get_value_from_config('variance')\n            self.head_sizes = self.get_value_from_config('head_sizes')\n            self.head_scales = self.get_value_from_config('head_scales')\n            self.anchors = self.get_value_from_config('anchors')\n            add_conf_out_suffix = self.get_value_from_config('add_conf_out_suffix')\n\n            self.add_conf_outs = []\n            self.glob_layer_id_map = []\n            for head_id, head_size in enumerate(self.head_sizes):\n                glob_layer_ids = []\n                for anchor_id in np.arange(start=1, stop=head_size + 1):\n                    self.add_conf_outs.append(\n                        '{}{}{}{}'.format(add_conf_out_prefix, head_id + 1, add_conf_out_suffix, anchor_id)\n                    )\n                    glob_layer_ids.append(len(self.add_conf_outs) - 1)\n\n                self.glob_layer_id_map.append(glob_layer_ids)\n        else:\n            self.priorbox_out = self.get_value_from_config('priorbox_out')\n            add_conf_out_count = self.get_value_from_config('add_conf_out_count')\n            if add_conf_out_count is None:\n                self.add_conf_outs = [add_conf_out_prefix]\n                self.head_sizes = [1]\n                self.glob_layer_id_map = [[0]]\n            else:\n                self.add_conf_outs = []\n                for num in np.arange(start=1, stop=add_conf_out_count + 1):\n                    self.add_conf_outs.append('{}{}'.format(add_conf_out_prefix, num))\n                self.head_sizes = [add_conf_out_count]\n                self.glob_layer_id_map = [list(range(add_conf_out_count))]\n\n    def process(self, raw, identifiers, frame_meta):\n        result = []\n        raw_outputs = self._extract_predictions(raw, frame_meta)\n        if not self.outputs_verified:\n            self._get_output_names(raw_outputs)\n        input_shape = list(frame_meta[0].get('input_shape', {'data': (1, 3, 416, 416)}).values())[0]\n        nchw_layout = input_shape[1] == 3\n        prior_boxes = raw_outputs[self.priorbox_out][0][0].reshape(-1, 4) if not self.multihead else None\n        prior_variances = raw_outputs[self.priorbox_out][0][1].reshape(-1, 4) if not self.multihead else None\n\n        head_shifts = self.estimate_head_shifts(\n            raw_outputs, self. head_sizes, self.add_conf_outs, self.multihead, nchw_layout)\n\n        for batch_id, identifier in enumerate(identifiers):\n            labels, class_scores, x_mins, y_mins, x_maxs, y_maxs, main_scores = self.prepare_detection_for_id(\n                batch_id, raw_outputs, prior_boxes, prior_variances, head_shifts, nchw=nchw_layout\n            )\n            action_prediction = ActionDetectionPrediction(\n                identifier, labels, class_scores, main_scores, x_mins, y_mins, x_maxs, y_maxs\n            )\n            person_prediction = DetectionPrediction(\n                identifier, [1] * len(labels), main_scores, x_mins, y_mins, x_maxs, y_maxs\n            )\n            result.append(ContainerPrediction({\n                'action_prediction': action_prediction, 'class_agnostic_prediction': person_prediction\n            }))\n\n        return result\n\n    def prepare_detection_for_id(self, batch_id, raw_outputs, prior_boxes, prior_variances, head_shifts,\n                                 default_label=0, nchw=True):\n        num_detections = raw_outputs[self.loc_out][batch_id].size // 4\n        locs = raw_outputs[self.loc_out][batch_id].reshape(-1, 4)\n        main_conf = raw_outputs[self.main_conf_out][batch_id].reshape(num_detections, -1)\n\n        add_confs = [raw_outputs[layer][batch_id] for layer in self.add_conf_outs]\n        if not nchw:\n            add_confs = [np.transpose(conf_l, (2, 0, 1)) for conf_l in add_confs]\n\n        if self.multihead:\n            spatial_sizes = [layer.shape[1:] for layer in add_confs]\n            add_confs = [layer.reshape(self.num_action_classes, -1) for layer in add_confs]\n        else:\n            add_confs = [layer.reshape(-1, self.num_action_classes) for layer in add_confs]\n\n        labels, class_scores, x_mins, y_mins, x_maxs, y_maxs, main_scores = [], [], [], [], [], [], []\n        for index in range(num_detections):\n            if main_conf[index, 1] < self.detection_threshold:\n                continue\n\n            head_id = self.get_head_id(index, head_shifts)\n            head_anchors_num = self.head_sizes[head_id]\n            head_index = index - head_shifts[head_id]\n            head_anchor_id = head_index % head_anchors_num\n            glob_anchor_id = self.glob_layer_id_map[head_id][head_anchor_id]\n            head_spatial_pos = head_index // head_anchors_num\n\n            prior_box_data = self.generate_prior_box(\n                head_spatial_pos, self.head_scales[head_id], self.anchors[head_id][head_anchor_id],\n                self.in_sizes, spatial_sizes[glob_anchor_id]) if self.multihead else prior_boxes[index]\n            prior_variance_data = self.variance if self.multihead else prior_variances[index]\n            bbox_loc_data = locs[index][[1, 0, 3, 2]] if self.multihead else locs[index]\n            x_min, y_min, x_max, y_max = self.decode_box(prior_box_data, prior_variance_data, bbox_loc_data)\n\n            add_confs_data = add_confs[glob_anchor_id]\n            action_confs = add_confs_data[:, head_spatial_pos] if self.multihead else add_confs_data[head_spatial_pos]\n            exp_action_confs = np.exp(self.action_scale * action_confs)\n            sum_exp_conf = np.sum(exp_action_confs)\n            action_label = np.argmax(action_confs)\n            action_score = exp_action_confs[action_label] / sum_exp_conf\n\n            if action_score < self.action_threshold:\n                action_label = default_label\n                action_score = 0\n            labels.append(action_label)\n            class_scores.append(action_score)\n            x_mins.append(x_min)\n            y_mins.append(y_min)\n            x_maxs.append(x_max)\n            y_maxs.append(y_max)\n            main_scores.append(main_conf[index, 1])\n\n        return labels, class_scores, x_mins, y_mins, x_maxs, y_maxs, main_scores\n\n    @staticmethod\n    def decode_box(prior, var, deltas):\n        prior_width = prior[2] - prior[0]\n        prior_height = prior[3] - prior[1]\n        prior_center_x = 0.5 * (prior[0] + prior[2])\n        prior_center_y = 0.5 * (prior[1] + prior[3])\n\n        decoded_box_center_x = var[0] * deltas[0] * prior_width + prior_center_x\n        decoded_box_center_y = var[1] * deltas[1] * prior_height + prior_center_y\n        decoded_box_width = np.exp(var[2] * deltas[2]) * prior_width\n        decoded_box_height = np.exp(var[3] * deltas[3]) * prior_height\n\n        decoded_xmin = decoded_box_center_x - 0.5 * decoded_box_width\n        decoded_ymin = decoded_box_center_y - 0.5 * decoded_box_height\n        decoded_xmax = decoded_box_center_x + 0.5 * decoded_box_width\n        decoded_ymax = decoded_box_center_y + 0.5 * decoded_box_height\n\n        return decoded_xmin, decoded_ymin, decoded_xmax, decoded_ymax\n\n    @staticmethod\n    def estimate_head_shifts(raw_outputs, head_sizes, add_conf_outs, multihead_net, nchw=True):\n        layer_id = 0\n        head_shift = 0\n        head_shifts = [0]\n        for head_size in head_sizes:\n            for _ in range(head_size):\n                layer = add_conf_outs[layer_id]\n                layer_shape = raw_outputs[layer][0].shape\n                if len(layer_shape) == 3 and not nchw:\n                    layer_shape = layer_shape[::-1]\n                layer_size = np.prod(layer_shape[1:]) if multihead_net else np.prod(layer_shape[:2])\n                head_shift += layer_size\n                layer_id += 1\n\n            head_shifts.append(head_shift)\n\n        return head_shifts\n\n    @staticmethod\n    def get_head_id(index, head_shifts):\n        head_id = 0\n        while index >= head_shifts[head_id + 1]:\n            head_id += 1\n        return head_id\n\n    @staticmethod\n    def generate_prior_box(pos, step, anchor, image_size, blob_size):\n        image_height, image_width = image_size\n        anchor_height, anchor_width = anchor\n\n        row = pos // blob_size[1]\n        col = pos % blob_size[1]\n\n        center_x = (col + 0.5) * step\n        center_y = (row + 0.5) * step\n\n        normalized_bbox = [\n            (center_x - 0.5 * anchor_width) / float(image_width),\n            (center_y - 0.5 * anchor_height) / float(image_height),\n            (center_x + 0.5 * anchor_width) / float(image_width),\n            (center_y + 0.5 * anchor_height) / float(image_height)\n        ]\n\n        return normalized_bbox\n\n    def _get_output_names(self, raw_outputs):\n        loc_out_regex = re.compile(self.loc_out)\n        main_conf_out_regex = re.compile(self.main_conf_out)\n\n        def find_layer(regex, output_name, all_outputs):\n            suitable_layers = [layer_name for layer_name in all_outputs if regex.match(layer_name)]\n            if not suitable_layers:\n                raise ValueError('suitable layer for {} output is not found'.format(output_name))\n\n            if len(suitable_layers) > 1:\n                raise ValueError('more than 1 layers matched to regular expression, please specify more detailed regex')\n\n            return suitable_layers[0]\n\n        self.loc_out = find_layer(loc_out_regex, 'loc', raw_outputs)\n        self.main_conf_out = find_layer(main_conf_out_regex, 'main confidence', raw_outputs)\n        if hasattr(self, 'priorbox_out'):\n            self.priorbox_out = self.check_output_name(self.priorbox_out, raw_outputs)\n        self.outputs_verified = True\n        add_conf_outs = [self.check_output_name(layer, raw_outputs) for layer in self.add_conf_outs]\n        if contains_all(raw_outputs, add_conf_outs):\n            self.add_conf_outs = add_conf_outs\n            return\n\n        add_conf_with_bias = [self.check_output_name(layer_name + '/add_', raw_outputs)\n                              for layer_name in self.add_conf_outs]\n        if contains_all(raw_outputs, add_conf_with_bias):\n            self.add_conf_outs = add_conf_with_bias\n            return\n        return\n\nclass ActionRecognitionWithNoAction(Adapter):\n    __provider__ = 'action_recognition_with_condition'\n\n    @classmethod\n    def parameters(cls):\n        params = super().parameters()\n        params.update({\n            'no_action_id': NumberField(\n                optional=True, default=0, min_value=0, value_type=int, description='no_action label id'),\n            'no_action_threshold': NumberField(\n                optional=True, default=0.5, min_value=0, max_value=1, value_type=float,\n                description='threshold for selection no action'),\n            'action_output': StringField(optional=True, description='output layer name with action scores')\n        })\n        return params\n\n    def configure(self):\n        self.no_action_id = self.get_value_from_config('no_action_id')\n        self.no_action_threshold = self.get_value_from_config('no_action_threshold')\n        self.action_output = self.get_value_from_config('action_output')\n        self.output_verified = False\n\n    def process(self, raw, identifiers, frame_meta):\n        predictions = []\n        if not self.output_verified:\n            self.select_output_blob(raw)\n        outputs = self._extract_predictions(raw, frame_meta)\n        for identifier, action_out in zip(identifiers, outputs[self.action_output]):\n            no_action_score = action_out[self.no_action_id]\n            if no_action_score >= self.no_action_threshold:\n                mask = np.ones_like(action_out, dtype=bool)\n                mask[self.no_action_id] = False\n                scores = action_out[mask] = 0.\n            else:\n                scores = action_out\n                scores[self.no_action_id] = 0.\n            predictions.append(ClassificationPrediction(identifier, scores))\n        return predictions\n\n    def select_output_blob(self, outputs):\n        self.output_verified = True\n        if self.action_output:\n            self.action_output = self.check_output_name(self.action_out, outputs)\n            return\n        super().select_output_blob(outputs)\n        self.action_output = self.output_blob\n        return\n", "repo_name": "openvinotoolkit/open_model_zoo", "sub_path": "tools/accuracy_checker/openvino/tools/accuracy_checker/adapters/action_recognition.py", "file_name": "action_recognition.py", "file_ext": "py", "file_size_in_byte": 17085, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3804, "dataset": "github-code", "pt": "41", "api": [{"api_name": "adapters.Adapter", "line_number": 15, "usage_type": "name"}, {"api_name": "representation.ActionDetectionPrediction", "line_number": 17, "usage_type": "name"}, {"api_name": "representation.DetectionPrediction", "line_number": 17, "usage_type": "name"}, {"api_name": "config.StringField", "line_number": 23, "usage_type": "call"}, {"api_name": "config.StringField", "line_number": 24, "usage_type": "call"}, {"api_name": "config.BoolField", "line_number": 25, "usage_type": "call"}, {"api_name": "config.StringField", "line_number": 29, "usage_type": "call"}, {"api_name": "config.StringField", "line_number": 32, "usage_type": "call"}, {"api_name": "config.StringField", "line_number": 36, "usage_type": "call"}, {"api_name": "config.ListField", "line_number": 41, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 42, "usage_type": "call"}, {"api_name": "config.ListField", "line_number": 45, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 46, "usage_type": "call"}, {"api_name": "config.ListField", "line_number": 49, "usage_type": "call"}, {"api_name": "config.ListField", "line_number": 50, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 50, "usage_type": "call"}, {"api_name": "config.ListField", "line_number": 54, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 55, "usage_type": "call"}, {"api_name": "config.ListField", "line_number": 58, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 59, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 62, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 67, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 68, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 71, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 74, "usage_type": "call"}, {"api_name": "config.ConfigValidator.WARN_ON_EXTRA_ARGUMENT", "line_number": 85, "usage_type": "attribute"}, {"api_name": "config.ConfigValidator", "line_number": 85, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 127, "usage_type": "call"}, {"api_name": "representation.ActionDetectionPrediction", "line_number": 149, "usage_type": "call"}, {"api_name": "representation.DetectionPrediction", "line_number": 152, "usage_type": "call"}, {"api_name": "representation.ContainerPrediction", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 246, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 282, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 283, "usage_type": "call"}, {"api_name": "utils.contains_all", "line_number": 301, "usage_type": "call"}, {"api_name": "utils.contains_all", "line_number": 307, "usage_type": "call"}, {"api_name": "adapters.Adapter", "line_number": 312, "usage_type": "name"}, {"api_name": "config.NumberField", "line_number": 319, "usage_type": "call"}, {"api_name": "config.NumberField", "line_number": 321, "usage_type": "call"}, {"api_name": "config.StringField", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 342, "usage_type": "call"}, {"api_name": "representation.ClassificationPrediction", "line_number": 348, "usage_type": "call"}]}
{"seq_id": "33465411232", "text": "import os\nimport joblib\nimport numpy as np\nfrom tqdm import trange\nfrom sklearn import linear_model\n\nfrom ndbox.utils import get_root_logger, files_form_folder\nfrom ndbox.utils.registry import MODEL_REGISTRY\nfrom .ml_base_model import MLBaseModel\n\n\n@MODEL_REGISTRY.register()\nclass WienerCascadeRegression(MLBaseModel):\n    \"\"\"\n    Class for the Wiener Cascade Decoder.\n    \"\"\"\n\n    _default_params = {'degree': 3}\n\n    def __init__(self, opt):\n        \"\"\"\n        Initializes model.\n\n        :param opt: dict. Configuration. It could contain:\n            degree - integer, optional, default 3.\n                The degree of the polynomial used for the static nonlinear\n        \"\"\"\n\n        super().__init__(opt, self._default_params)\n        self.degree = self.opt['degree']\n\n    def fit(self, X, y):\n        \"\"\"\n        Train Wiener Cascade Decoder.\n\n        :param X: numpy 2d array of shape [n_samples, n_features]\n            This is the neural data.\n        :param y: numpy 2d array of shape [n_samples, n_outputs]\n            This is the outputs that are being predicted\n        \"\"\"\n\n        num_outputs = y.shape[1]\n        models = {}\n        with trange(num_outputs) as t:\n            for i in t:\n                t.set_description(f\"Train Wiener Cascade Regression\")\n                reg = linear_model.LinearRegression()\n                reg.fit(X, y[:, i])\n                y_pred = reg.predict(X)\n                poly = np.polyfit(y_pred, y[:, i], self.degree)\n                models['r_' + str(i)] = reg\n                models['p_' + str(i)] = poly\n        self.model = models\n\n    def predict(self, X):\n        \"\"\"\n        Predict outcomes using trained Wiener Cascade Decoder.\n\n        :param X: numpy 2d array of shape [n_samples, n_features]\n            This is the neural data being used to predict outputs.\n        :return: numpy 2d array of shape [n_samples, n_outputs]\n            The predicted outputs.\n        \"\"\"\n\n        num_outputs = len(self.model) // 2\n        y_pred = np.empty([X.shape[0], num_outputs])\n        with trange(num_outputs) as t:\n            for i in t:\n                t.set_description(f\"Test Wiener Cascade Regression\")\n                reg, poly = self.model['r_' + str(i)], self.model['p_' + str(i)]\n                y_pred_linear = reg.predict(X)\n                y_pred[:, i] = np.polyval(poly, y_pred_linear)\n        return y_pred\n\n    def load(self, path):\n        \"\"\"\n        Load model.\n\n        :param path: str. The path of models to be loaded.\n        \"\"\"\n\n        logger = get_root_logger()\n        reg_files = files_form_folder(path, 'r_*.pkl')\n        poly_files = files_form_folder(path, 'p_*.pkl')\n        if len(reg_files) != len(poly_files):\n            raise ValueError(f\"The number of linear regression model files is not \"\n                             f\"equal to that of polynomial regression model files!\"\n                             f\" The former is [{len(reg_files)}] and the latter is\"\n                             f\" [{len(poly_files)}].\")\n        num_outputs = len(reg_files)\n        models = {}\n        with trange(num_outputs) as t:\n            for i in t:\n                t.set_description(f\"Load Model\")\n                models['r_' + str(i)] = joblib.load(\n                    os.path.join(path, 'r_' + str(i) + '.pkl')\n                )\n                models['p_' + str(i)] = joblib.load(\n                    os.path.join(path, 'p_' + str(i) + '.pkl')\n                )\n        self.model = models\n        logger.info(f\"Loading {self.__class__.__name__} model from '{path}'\")\n\n    def save(self, path):\n        \"\"\"\n        Save model.\n\n        :param path: str. The path of models to be saved.\n        \"\"\"\n\n        if not os.path.exists(path):\n            os.makedirs(path)\n        for filename, model in self.model.items():\n            joblib.dump(model, os.path.join(path, filename + '.pkl'))\n", "repo_name": "JasonNerd/SpikePlotter", "sub_path": "ndbox/models/wiener_cascade.py", "file_name": "wiener_cascade.py", "file_ext": "py", "file_size_in_byte": 3870, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "ml_base_model.MLBaseModel", "line_number": 13, "usage_type": "name"}, {"api_name": "tqdm.trange", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 66, "usage_type": "call"}, {"api_name": "tqdm.trange", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.polyval", "line_number": 72, "usage_type": "call"}, {"api_name": "ndbox.utils.get_root_logger", "line_number": 82, "usage_type": "call"}, {"api_name": "ndbox.utils.files_form_folder", "line_number": 83, "usage_type": "call"}, {"api_name": "ndbox.utils.files_form_folder", "line_number": 84, "usage_type": "call"}, {"api_name": "tqdm.trange", "line_number": 92, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 112, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "ndbox.utils.registry.MODEL_REGISTRY.register", "line_number": 12, "usage_type": "call"}, {"api_name": "ndbox.utils.registry.MODEL_REGISTRY", "line_number": 12, "usage_type": "name"}]}
{"seq_id": "73010380284", "text": "import scipy.sparse as sp\n\nimport cvxpy.settings as s\nfrom cvxpy.constraints import PSD, SOC, ExpCone, PowCone3D\nfrom cvxpy.reductions.solution import Solution, failure_solution\nfrom cvxpy.reductions.solvers import utilities\nfrom cvxpy.reductions.solvers.conic_solvers.conic_solver import ConicSolver\n\n\ndef dims_to_solver_cones(cone_dims):\n\n    import clarabel\n    cones = []\n\n    # assume that constraints are presented\n    # in the preferred ordering of SCS.\n\n    if cone_dims.zero > 0:\n        cones.append(clarabel.ZeroConeT(cone_dims.zero))\n\n    if cone_dims.nonneg > 0:\n        cones.append(clarabel.NonnegativeConeT(cone_dims.nonneg))\n\n    for dim in cone_dims.soc:\n        cones.append(clarabel.SecondOrderConeT(dim))\n\n    # for dim in cone_dims.psd :\n    #    PJG  : Placeholder for future PSD support\n\n    for _ in range(cone_dims.exp):\n        cones.append(clarabel.ExponentialConeT())\n\n    for pow in cone_dims.p3d:\n        cones.append(clarabel.PowerConeT(pow))\n    return cones\n\n\nclass CLARABEL(ConicSolver):\n    \"\"\"An interface for the Clarabel solver.\n    \"\"\"\n\n    # Solver capabilities.\n    MIP_CAPABLE = False\n    SUPPORTED_CONSTRAINTS = ConicSolver.SUPPORTED_CONSTRAINTS \\\n        + [SOC, ExpCone, PowCone3D]\n    REQUIRES_CONSTR = True\n\n    STATUS_MAP = {\n                    \"Solved\": s.OPTIMAL,\n                    \"PrimalInfeasible\": s.INFEASIBLE,\n                    \"DualInfeasible\": s.UNBOUNDED,\n                    \"AlmostSolved\": s.OPTIMAL_INACCURATE,\n                    \"AlmostPrimalInfeasible\": s.INFEASIBLE_INACCURATE,\n                    \"AlmostDualInfeasible\": s.UNBOUNDED_INACCURATE,\n                    \"MaxIterations\": s.USER_LIMIT,\n                    \"MaxTime\": s.USER_LIMIT,\n                    \"NumericalError\": s.SOLVER_ERROR,\n                    \"InsufficientProgress\": s.SOLVER_ERROR\n                }\n\n    # Order of exponential cone arguments for solver.\n    EXP_CONE_ORDER = [0, 1, 2]\n\n    def name(self):\n        \"\"\"The name of the solver.\n        \"\"\"\n        return 'CLARABEL'\n\n    def import_solver(self) -> None:\n        \"\"\"Imports the solver.\n        \"\"\"\n        import clarabel  # noqa F401\n\n    def supports_quad_obj(self) -> bool:\n        \"\"\"Clarabel supports quadratic objective with any combination of conic constraints.\n        \"\"\"\n        return True\n\n    @staticmethod\n    def extract_dual_value(result_vec, offset, constraint):\n        \"\"\"Extracts the dual value for constraint starting at offset.\n        \"\"\"\n\n        # PJG : I will leave PSD handling from SCS here\n        # as a placeholder to remind me to implement something\n        # appropriate once PSD cones are supported\n\n        if isinstance(constraint, PSD):\n            raise RuntimeError(\"PSD cones not yet supported. This line should be unreachable.\")\n\n        else:\n            return utilities.extract_dual_value(result_vec, offset,\n                                                constraint)\n\n    def invert(self, solution, inverse_data):\n        \"\"\"Returns the solution to the original problem given the inverse_data.\n        \"\"\"\n\n        attr = {}\n        status = self.STATUS_MAP[str(solution.status)]\n        attr[s.SOLVE_TIME] = solution.solve_time\n        attr[s.NUM_ITERS] = solution.iterations\n        # attr[s.EXTRA_STATS] = solution.extra.FOO #more detailed statistics here when available\n\n        if status in s.SOLUTION_PRESENT:\n            primal_val = solution.obj_val\n            opt_val = primal_val + inverse_data[s.OFFSET]\n            primal_vars = {\n                inverse_data[CLARABEL.VAR_ID]: solution.x\n            }\n            eq_dual_vars = utilities.get_dual_values(\n                solution.z[:inverse_data[ConicSolver.DIMS].zero],\n                self.extract_dual_value,\n                inverse_data[CLARABEL.EQ_CONSTR]\n            )\n            ineq_dual_vars = utilities.get_dual_values(\n                solution.z[inverse_data[ConicSolver.DIMS].zero:],\n                self.extract_dual_value,\n                inverse_data[CLARABEL.NEQ_CONSTR]\n            )\n            dual_vars = {}\n            dual_vars.update(eq_dual_vars)\n            dual_vars.update(ineq_dual_vars)\n            return Solution(status, opt_val, primal_vars, dual_vars, attr)\n        else:\n            return failure_solution(status, attr)\n\n    @staticmethod\n    def parse_solver_opts(verbose, opts):\n        import clarabel\n\n        settings = clarabel.DefaultSettings()\n        settings.verbose = verbose\n\n        # use_quad_obj is only for canonicalization.\n        if \"use_quad_obj\" in opts:\n            del opts[\"use_quad_obj\"]\n\n        for opt in opts.keys():\n            try:\n                settings.__setattr__(opt, opts[opt])\n            except TypeError as e:\n                raise TypeError(f\"Clarabel: Incorrect type for setting '{opt}'.\") from e\n            except AttributeError as e:\n                raise TypeError(f\"Clarabel: unrecognized solver setting '{opt}'.\") from e\n\n        return settings\n\n    def solve_via_data(self, data, warm_start: bool, verbose: bool, solver_opts, solver_cache=None):\n        \"\"\"Returns the result of the call to the solver.\n\n        Parameters\n        ----------\n        data : dict\n            Data generated via an apply call.\n        warm_start : Bool\n            Whether to warm_start Clarabel.\n            PJG: From SCS.   We don't support this, not sure if relevant\n        verbose : Bool\n            Control the verbosity.\n        solver_opts : dict\n            Clarabel-specific solver options.\n\n        Returns\n        -------\n        The result returned by a call to clarabel.solve().\n        \"\"\"\n        import clarabel\n\n        A = data[s.A]\n        b = data[s.B]\n        c = data[s.C]\n\n        if s.P in data:\n            P = data[s.P]\n        else:\n            nvars = c.size\n            P = sp.csc_matrix((nvars, nvars))\n\n        cones = dims_to_solver_cones(data[ConicSolver.DIMS])\n\n        def solve(_solver_opts):\n\n            _settings = CLARABEL.parse_solver_opts(verbose, _solver_opts)\n            _solver = clarabel.DefaultSolver(P, c, A, b, cones, _settings)\n            _results = _solver.solve()\n\n            return _results, _results.status\n\n        results, status = solve(solver_opts)\n\n        if solver_cache is not None and self.STATUS_MAP[str(status)]:\n            solver_cache[self.name()] = results\n\n        return results\n", "repo_name": "Derngineer/stockportfoliobuilder", "sub_path": "lib/python3.10/site-packages/cvxpy/reductions/solvers/conic_solvers/clarabel_conif.py", "file_name": "clarabel_conif.py", "file_ext": "py", "file_size_in_byte": 6341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "clarabel.ZeroConeT", "line_number": 19, "usage_type": "call"}, {"api_name": "clarabel.NonnegativeConeT", "line_number": 22, "usage_type": "call"}, {"api_name": "clarabel.SecondOrderConeT", "line_number": 25, "usage_type": "call"}, {"api_name": "clarabel.ExponentialConeT", "line_number": 31, "usage_type": "call"}, {"api_name": "clarabel.PowerConeT", "line_number": 34, "usage_type": "call"}, {"api_name": "cvxpy.reductions.solvers.conic_solvers.conic_solver.ConicSolver", "line_number": 38, "usage_type": "name"}, {"api_name": "cvxpy.reductions.solvers.conic_solvers.conic_solver.ConicSolver.SUPPORTED_CONSTRAINTS", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cvxpy.reductions.solvers.conic_solvers.conic_solver.ConicSolver", "line_number": 44, "usage_type": "name"}, {"api_name": "cvxpy.constraints.SOC", "line_number": 45, "usage_type": "name"}, {"api_name": "cvxpy.constraints.ExpCone", "line_number": 45, "usage_type": "name"}, {"api_name": "cvxpy.constraints.PowCone3D", "line_number": 45, "usage_type": "name"}, {"api_name": "cvxpy.settings.OPTIMAL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 49, "usage_type": "name"}, {"api_name": "cvxpy.settings.INFEASIBLE", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 50, "usage_type": "name"}, {"api_name": "cvxpy.settings.UNBOUNDED", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 51, "usage_type": "name"}, {"api_name": "cvxpy.settings.OPTIMAL_INACCURATE", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 52, "usage_type": "name"}, {"api_name": "cvxpy.settings.INFEASIBLE_INACCURATE", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 53, "usage_type": "name"}, {"api_name": "cvxpy.settings.UNBOUNDED_INACCURATE", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 54, "usage_type": "name"}, {"api_name": "cvxpy.settings.USER_LIMIT", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 55, "usage_type": "name"}, {"api_name": "cvxpy.settings.USER_LIMIT", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 56, "usage_type": "name"}, {"api_name": "cvxpy.settings.SOLVER_ERROR", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 57, "usage_type": "name"}, {"api_name": "cvxpy.settings.SOLVER_ERROR", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 58, "usage_type": "name"}, {"api_name": "cvxpy.constraints.PSD", "line_number": 88, "usage_type": "argument"}, {"api_name": "cvxpy.reductions.solvers.utilities.extract_dual_value", "line_number": 92, "usage_type": "call"}, {"api_name": "cvxpy.reductions.solvers.utilities", "line_number": 92, "usage_type": "name"}, {"api_name": "cvxpy.settings.SOLVE_TIME", "line_number": 101, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 101, "usage_type": "name"}, {"api_name": "cvxpy.settings.NUM_ITERS", "line_number": 102, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 102, "usage_type": "name"}, {"api_name": "cvxpy.settings.SOLUTION_PRESENT", "line_number": 105, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 105, "usage_type": "name"}, {"api_name": "cvxpy.settings.OFFSET", "line_number": 107, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 107, "usage_type": "name"}, {"api_name": "{'clarabel': 'clarabel'}.VAR_ID", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cvxpy.reductions.solvers.utilities.get_dual_values", "line_number": 111, "usage_type": "call"}, {"api_name": "cvxpy.reductions.solvers.utilities", "line_number": 111, "usage_type": "name"}, {"api_name": "cvxpy.reductions.solvers.conic_solvers.conic_solver.ConicSolver.DIMS", "line_number": 112, "usage_type": "attribute"}, {"api_name": "cvxpy.reductions.solvers.conic_solvers.conic_solver.ConicSolver", "line_number": 112, "usage_type": "name"}, {"api_name": "{'clarabel': 'clarabel'}.EQ_CONSTR", "line_number": 114, "usage_type": "attribute"}, {"api_name": "cvxpy.reductions.solvers.utilities.get_dual_values", "line_number": 116, "usage_type": "call"}, {"api_name": "cvxpy.reductions.solvers.utilities", "line_number": 116, "usage_type": "name"}, {"api_name": "cvxpy.reductions.solvers.conic_solvers.conic_solver.ConicSolver.DIMS", "line_number": 117, "usage_type": "attribute"}, {"api_name": "cvxpy.reductions.solvers.conic_solvers.conic_solver.ConicSolver", "line_number": 117, "usage_type": "name"}, {"api_name": "{'clarabel': 'clarabel'}.NEQ_CONSTR", "line_number": 119, "usage_type": "attribute"}, {"api_name": "cvxpy.reductions.solution.Solution", "line_number": 124, "usage_type": "call"}, {"api_name": "cvxpy.reductions.solution.failure_solution", "line_number": 126, "usage_type": "call"}, {"api_name": "clarabel.DefaultSettings", "line_number": 132, "usage_type": "call"}, {"api_name": "cvxpy.settings.A", "line_number": 170, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 170, "usage_type": "name"}, {"api_name": "cvxpy.settings.B", "line_number": 171, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 171, "usage_type": "name"}, {"api_name": "cvxpy.settings.C", "line_number": 172, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 172, "usage_type": "name"}, {"api_name": "cvxpy.settings.P", "line_number": 174, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 174, "usage_type": "name"}, {"api_name": "cvxpy.settings.P", "line_number": 175, "usage_type": "attribute"}, {"api_name": "cvxpy.settings", "line_number": 175, "usage_type": "name"}, {"api_name": "scipy.sparse.csc_matrix", "line_number": 178, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 178, "usage_type": "name"}, {"api_name": "cvxpy.reductions.solvers.conic_solvers.conic_solver.ConicSolver.DIMS", "line_number": 180, "usage_type": "attribute"}, {"api_name": "cvxpy.reductions.solvers.conic_solvers.conic_solver.ConicSolver", "line_number": 180, "usage_type": "name"}, {"api_name": "{'clarabel': 'clarabel'}.parse_solver_opts", "line_number": 184, "usage_type": "call"}, {"api_name": "clarabel.DefaultSolver", "line_number": 185, "usage_type": "call"}]}
{"seq_id": "69829484921", "text": "\nimport time\nimport threading\n\nfrom web3 import Web3\n\n\ndef binary_to_hex_string(binary_contract_code) :\n\thex_string = ''\n\n\tfor index in binary_contract_code :\n\t\tbyte_hex_number = str(hex(index))\n\t\thex_string += byte_hex_number[ 2 : ]\n\n\treturn hex_string\n\ndef get_contract_code(contract_address) :\n\teth_connector = Web3(Web3.HTTPProvider('http://node1.web3api.com'))\n\n\treturn binary_to_hex_string(eth_connector.eth.getCode(contract_address))\n\ndef get_current_blocknumber() :\n\teth_connector = Web3(Web3.HTTPProvider('http://node1.web3api.com'))\n\n\treturn eth_connector.eth.blockNumber\n\ndef get_block(block_height) :\n\teth_connector = Web3(Web3.HTTPProvider('http://node1.web3api.com'))\n\n\treturn eth_connector.eth.getBlock(block_height)\n\ndef get_transation(transation_hash) :\n\teth_connector = Web3(Web3.HTTPProvider('http://node1.web3api.com'))\n\n\treturn eth_connector.eth.getTransaction(transation_hash)\n\ndef get_contract_create_from_blockdata(block_height) :\n\tblock_data = get_block(block_height)\n\tcontract_result = {}\n\n\tfor transation_id in block_data['transactions'] :\n\t\ttransaction_data = get_transation(transation_id)\n\n\t\tif None == transaction_data['creates'] :\n\t\t\tcontinue\n\n\t\tcontract_result[transaction_data['creates']] = transaction_data['input'][ 2 : ]\n\n\treturn contract_result\n\n\nTIME_INTERVAL = 5.0\n\nif __name__ == '__main__' :\n\tlast_block_height = 0\n\n\twhile True :\n\t\tcurrent_block_height = get_current_blocknumber()\n\t\ttime_tick = time.time()\n\n\t\tif current_block_height > last_block_height :\n\t\t\tprint('new blockheight:',current_block_height)\n\t\t\tprint(get_contract_create_from_blockdata(current_block_height))\n\n\t\t\tcurrent_block_height = last_block_height\n\t\telse :\n\t\t\tprint('no found new block ..')\n\n\t\toutdate_time = time.time() - time_tick\n\n\t\tif outdate_time > 0 :\n\t\t\ttime.sleep(outdate_time)\n\n\n\t#block_height = get_current_blocknumber()\n\t#block_data = get_block(block_height)\n\n\t#print(block_height)\n\t#print(block_data)\n\n\t#for transation_id in block_data['transactions'] :\n\t#\tprint('>>>>' ,transation_id)\n\n\n\t#print(get_contract_code('0xce0b7fc318A2b29193c536388d8BD8802c33deE7'))\n\n\n\n\n", "repo_name": "lcatro/Ethereum_Similar_Contract_Classify", "sub_path": "spider_contract.py", "file_name": "spider_contract.py", "file_ext": "py", "file_size_in_byte": 2088, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "40", "api": [{"api_name": "web3.Web3", "line_number": 18, "usage_type": "call"}, {"api_name": "web3.Web3.HTTPProvider", "line_number": 18, "usage_type": "call"}, {"api_name": "web3.Web3", "line_number": 23, "usage_type": "call"}, {"api_name": "web3.Web3.HTTPProvider", "line_number": 23, "usage_type": "call"}, {"api_name": "web3.Web3", "line_number": 28, "usage_type": "call"}, {"api_name": "web3.Web3.HTTPProvider", "line_number": 28, "usage_type": "call"}, {"api_name": "web3.Web3", "line_number": 33, "usage_type": "call"}, {"api_name": "web3.Web3.HTTPProvider", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 59, "usage_type": "call"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}]}
{"seq_id": "6819885236", "text": "import os\nimport threading\nimport time\nimport traceback\n\nimport numpy as np\nimport tensorflow as tf\nfrom infolog import log\nfrom sklearn.model_selection import train_test_split\nfrom tacotron.utils.text import text_to_sequence\n\n_batches_per_group = 64\n\nclass Feeder:\n\t\"\"\"\n\t\tFeeds batches of data into queue on a background thread.\n\t\"\"\"\n\n\tdef __init__(self, coordinator, metadata_filename, hparams):\n\t\tsuper(Feeder, self).__init__()\n\t\tself._coord = coordinator\n\t\tself._hparams = hparams\n\t\tself._cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]\n\t\tself._train_offset = 0\n\t\tself._test_offset = 0\n\n\t\t# Load metadata\n\t\tself._mel_dir = os.path.join(os.path.dirname(metadata_filename), 'mels')\n\t\tself._linear_dir = os.path.join(os.path.dirname(metadata_filename), 'linear')\n\t\twith open(metadata_filename, encoding='utf-8') as f:\n\t\t\tself._metadata = [line.strip().split('|') for line in f]\n\t\t\tframe_shift_ms = hparams.hop_size / hparams.sample_rate\n\t\t\thours = sum([int(x[4]) for x in self._metadata]) * frame_shift_ms / (3600)\n\t\t\tlog('Loaded metadata for {} examples ({:.2f} hours)'.format(len(self._metadata), hours))\n\n\t\t#Train test split\n\t\tif hparams.tacotron_test_size is None:\n\t\t\tassert hparams.tacotron_test_batches is not None\n\n\t\ttest_size = (hparams.tacotron_test_size if hparams.tacotron_test_size is not None\n\t\t\telse hparams.tacotron_test_batches * hparams.tacotron_batch_size)\n\t\tindices = np.arange(len(self._metadata))\n\t\ttrain_indices, test_indices = train_test_split(indices,\n\t\t\ttest_size=test_size, random_state=hparams.tacotron_data_random_state)\n\n\t\t#Make sure test_indices is a multiple of batch_size else round down\n\t\tlen_test_indices = self._round_down(len(test_indices), hparams.tacotron_batch_size)\n\t\textra_test = test_indices[len_test_indices:]\n\t\ttest_indices = test_indices[:len_test_indices]\n\t\ttrain_indices = np.concatenate([train_indices, extra_test])\n\n\t\tself._train_meta = list(np.array(self._metadata)[train_indices])\n\t\tself._test_meta = list(np.array(self._metadata)[test_indices])\n\n\t\tself.test_steps = len(self._test_meta) // hparams.tacotron_batch_size\n\n\t\tif hparams.tacotron_test_size is None:\n\t\t\tassert hparams.tacotron_test_batches == self.test_steps\n\n\t\t#pad input sequences with the <pad_token> 0 ( _ )\n\t\tself._pad = 0\n\t\t#explicitely setting the padding to a value that doesn't originally exist in the spectogram\n\t\t#to avoid any possible conflicts, without affecting the output range of the model too much\n\t\tif hparams.symmetric_mels:\n\t\t\tself._target_pad = -hparams.max_abs_value\n\t\telse:\n\t\t\tself._target_pad = 0.\n\t\t#Mark finished sequences with 1s\n\t\tself._token_pad = 1.\n\n\t\twith tf.device('/cpu:0'):\n\t\t\t# Create placeholders for inputs and targets. Don't specify batch size because we want\n\t\t\t# to be able to feed different batch sizes at eval time.\n\t\t\tself._placeholders = [\n\t\t\ttf.placeholder(tf.int32, shape=(None, None), name='inputs'),\n\t\t\ttf.placeholder(tf.int32, shape=(None, ), name='input_lengths'),\n\t\t\ttf.placeholder(tf.float32, shape=(None, None, hparams.num_mels), name='mel_targets'),\n\t\t\ttf.placeholder(tf.float32, shape=(None, None), name='token_targets'),\n\t\t\ttf.placeholder(tf.float32, shape=(None, None, hparams.num_freq), name='linear_targets'),\n\t\t\ttf.placeholder(tf.int32, shape=(None, ), name='targets_lengths'),\n\t\t\ttf.placeholder(tf.int32, shape=(hparams.tacotron_num_gpus, None), name='split_infos'),\n\t\t\t]\n\n\t\t\t# Create queue for buffering data\n\t\t\tqueue = tf.FIFOQueue(8, [tf.int32, tf.int32, tf.float32, tf.float32, tf.float32, tf.int32, tf.int32], name='input_queue')\n\t\t\tself._enqueue_op = queue.enqueue(self._placeholders)\n\t\t\tself.inputs, self.input_lengths, self.mel_targets, self.token_targets, self.linear_targets, self.targets_lengths, self.split_infos = queue.dequeue()\n\n\t\t\tself.inputs.set_shape(self._placeholders[0].shape)\n\t\t\tself.input_lengths.set_shape(self._placeholders[1].shape)\n\t\t\tself.mel_targets.set_shape(self._placeholders[2].shape)\n\t\t\tself.token_targets.set_shape(self._placeholders[3].shape)\n\t\t\tself.linear_targets.set_shape(self._placeholders[4].shape)\n\t\t\tself.targets_lengths.set_shape(self._placeholders[5].shape)\n\t\t\tself.split_infos.set_shape(self._placeholders[6].shape)\n\n\t\t\t# Create eval queue for buffering eval data\n\t\t\teval_queue = tf.FIFOQueue(1, [tf.int32, tf.int32, tf.float32, tf.float32, tf.float32, tf.int32, tf.int32], name='eval_queue')\n\t\t\tself._eval_enqueue_op = eval_queue.enqueue(self._placeholders)\n\t\t\tself.eval_inputs, self.eval_input_lengths, self.eval_mel_targets, self.eval_token_targets, \\\n\t\t\t\tself.eval_linear_targets, self.eval_targets_lengths, self.eval_split_infos = eval_queue.dequeue()\n\n\t\t\tself.eval_inputs.set_shape(self._placeholders[0].shape)\n\t\t\tself.eval_input_lengths.set_shape(self._placeholders[1].shape)\n\t\t\tself.eval_mel_targets.set_shape(self._placeholders[2].shape)\n\t\t\tself.eval_token_targets.set_shape(self._placeholders[3].shape)\n\t\t\tself.eval_linear_targets.set_shape(self._placeholders[4].shape)\n\t\t\tself.eval_targets_lengths.set_shape(self._placeholders[5].shape)\n\t\t\tself.eval_split_infos.set_shape(self._placeholders[6].shape)\n\n\tdef start_threads(self, session):\n\t\tself._session = session\n\t\tthread = threading.Thread(name='background', target=self._enqueue_next_train_group)\n\t\tthread.daemon = True #Thread will close when parent quits\n\t\tthread.start()\n\n\t\tthread = threading.Thread(name='background', target=self._enqueue_next_test_group)\n\t\tthread.daemon = True #Thread will close when parent quits\n\t\tthread.start()\n\n\tdef _get_test_groups(self):\n\t\tmeta = self._test_meta[self._test_offset]\n\t\tself._test_offset += 1\n\n\t\ttext = meta[5]\n\n\t\tinput_data = np.asarray(text_to_sequence(text, self._cleaner_names), dtype=np.int32)\n\t\tmel_target = np.load(os.path.join(self._mel_dir, meta[1]))\n\t\t#Create parallel sequences containing zeros to represent a non finished sequence\n\t\ttoken_target = np.asarray([0.] * (len(mel_target) - 1))\n\t\tlinear_target = np.load(os.path.join(self._linear_dir, meta[2]))\n\t\treturn (input_data, mel_target, token_target, linear_target, len(mel_target))\n\n\tdef make_test_batches(self):\n\t\tstart = time.time()\n\n\t\t# Read a group of examples\n\t\tn = self._hparams.tacotron_batch_size\n\t\tr = self._hparams.outputs_per_step\n\n\t\t#Test on entire test set\n\t\texamples = [self._get_test_groups() for i in range(len(self._test_meta))]\n\n\t\t# Bucket examples based on similar output sequence length for efficiency\n\t\texamples.sort(key=lambda x: x[-1])\n\t\tbatches = [examples[i: i+n] for i in range(0, len(examples), n)]\n\t\tnp.random.shuffle(batches)\n\n\t\tlog('\\nGenerated {} test batches of size {} in {:.3f} sec'.format(len(batches), n, time.time() - start))\n\t\treturn batches, r\n\n\tdef _enqueue_next_train_group(self):\n\t\twhile not self._coord.should_stop():\n\t\t\tstart = time.time()\n\n\t\t\t# Read a group of examples\n\t\t\tn = self._hparams.tacotron_batch_size\n\t\t\tr = self._hparams.outputs_per_step\n\t\t\texamples = [self._get_next_example() for i in range(n * _batches_per_group)]\n\n\t\t\t# Bucket examples based on similar output sequence length for efficiency\n\t\t\texamples.sort(key=lambda x: x[-1])\n\t\t\tbatches = [examples[i: i+n] for i in range(0, len(examples), n)]\n\t\t\tnp.random.shuffle(batches)\n\n\t\t\tlog('\\nGenerated {} train batches of size {} in {:.3f} sec'.format(len(batches), n, time.time() - start))\n\t\t\tfor batch in batches:\n\t\t\t\tfeed_dict = dict(zip(self._placeholders, self._prepare_batch(batch, r)))\n\t\t\t\tself._session.run(self._enqueue_op, feed_dict=feed_dict)\n\n\tdef _enqueue_next_test_group(self):\n\t\t#Create test batches once and evaluate on them for all test steps\n\t\ttest_batches, r = self.make_test_batches()\n\t\twhile not self._coord.should_stop():\n\t\t\tfor batch in test_batches:\n\t\t\t\tfeed_dict = dict(zip(self._placeholders, self._prepare_batch(batch, r)))\n\t\t\t\tself._session.run(self._eval_enqueue_op, feed_dict=feed_dict)\n\n\tdef _get_next_example(self):\n\t\t\"\"\"Gets a single example (input, mel_target, token_target, linear_target, mel_length) from_ disk\n\t\t\"\"\"\n\t\tif self._train_offset >= len(self._train_meta):\n\t\t\tself._train_offset = 0\n\t\t\tnp.random.shuffle(self._train_meta)\n\n\t\tmeta = self._train_meta[self._train_offset]\n\t\tself._train_offset += 1\n\n\t\ttext = meta[5]\n\n\t\tinput_data = np.asarray(text_to_sequence(text, self._cleaner_names), dtype=np.int32)\n\t\tmel_target = np.load(os.path.join(self._mel_dir, meta[1]))\n\t\t#Create parallel sequences containing zeros to represent a non finished sequence\n\t\ttoken_target = np.asarray([0.] * (len(mel_target) - 1))\n\t\tlinear_target = np.load(os.path.join(self._linear_dir, meta[2]))\n\t\treturn (input_data, mel_target, token_target, linear_target, len(mel_target))\n\n\tdef _prepare_batch(self, batches, outputs_per_step):\n\t\tassert 0 == len(batches) % self._hparams.tacotron_num_gpus\n\t\tsize_per_device = int(len(batches) / self._hparams.tacotron_num_gpus)\n\t\tnp.random.shuffle(batches)\n\n\t\tinputs = None\n\t\tmel_targets = None\n\t\ttoken_targets = None\n\t\tlinear_targets = None\n\t\ttargets_lengths = None\n\t\tsplit_infos = []\n\n\t\ttargets_lengths = np.asarray([x[-1] for x in batches], dtype=np.int32) #Used to mask loss\n\t\tinput_lengths = np.asarray([len(x[0]) for x in batches], dtype=np.int32)\n\n\t\t#Produce inputs/targets of variables lengths for different GPUs\n\t\tfor i in range(self._hparams.tacotron_num_gpus):\n\t\t\tbatch = batches[size_per_device * i: size_per_device * (i + 1)]\n\t\t\tinput_cur_device, input_max_len = self._prepare_inputs([x[0] for x in batch])\n\t\t\tinputs = np.concatenate((inputs, input_cur_device), axis=1) if inputs is not None else input_cur_device\n\t\t\tmel_target_cur_device, mel_target_max_len = self._prepare_targets([x[1] for x in batch], outputs_per_step)\n\t\t\tmel_targets = np.concatenate(( mel_targets, mel_target_cur_device), axis=1) if mel_targets is not None else mel_target_cur_device\n\n\t\t\t#Pad sequences with 1 to infer that the sequence is done\n\t\t\ttoken_target_cur_device, token_target_max_len = self._prepare_token_targets([x[2] for x in batch], outputs_per_step)\n\t\t\ttoken_targets = np.concatenate((token_targets, token_target_cur_device),axis=1) if token_targets is not None else token_target_cur_device\n\t\t\tlinear_targets_cur_device, linear_target_max_len = self._prepare_targets([x[3] for x in batch], outputs_per_step)\n\t\t\tlinear_targets = np.concatenate((linear_targets, linear_targets_cur_device), axis=1) if linear_targets is not None else linear_targets_cur_device\n\t\t\tsplit_infos.append([input_max_len, mel_target_max_len, token_target_max_len, linear_target_max_len])\n\n\t\tsplit_infos = np.asarray(split_infos, dtype=np.int32)\n\t\treturn (inputs, input_lengths, mel_targets, token_targets, linear_targets, targets_lengths, split_infos)\n\n\tdef _prepare_inputs(self, inputs):\n\t\tmax_len = max([len(x) for x in inputs])\n\t\treturn np.stack([self._pad_input(x, max_len) for x in inputs]), max_len\n\n\tdef _prepare_targets(self, targets, alignment):\n\t\tmax_len = max([len(t) for t in targets])\n\t\tdata_len = self._round_up(max_len, alignment)\n\t\treturn np.stack([self._pad_target(t, data_len) for t in targets]), data_len\n\n\tdef _prepare_token_targets(self, targets, alignment):\n\t\tmax_len = max([len(t) for t in targets]) + 1\n\t\tdata_len = self._round_up(max_len, alignment)\n\t\treturn np.stack([self._pad_token_target(t, data_len) for t in targets]), data_len\n\n\tdef _pad_input(self, x, length):\n\t\treturn np.pad(x, (0, length - x.shape[0]), mode='constant', constant_values=self._pad)\n\n\tdef _pad_target(self, t, length):\n\t\treturn np.pad(t, [(0, length - t.shape[0]), (0, 0)], mode='constant', constant_values=self._target_pad)\n\n\tdef _pad_token_target(self, t, length):\n\t\treturn np.pad(t, (0, length - t.shape[0]), mode='constant', constant_values=self._token_pad)\n\n\tdef _round_up(self, x, multiple):\n\t\tremainder = x % multiple\n\t\treturn x if remainder == 0 else x + multiple - remainder\n\n\tdef _round_down(self, x, multiple):\n\t\tremainder = x % multiple\n\t\treturn x if remainder == 0 else x - remainder\n", "repo_name": "Rayhane-mamah/Tacotron-2", "sub_path": "tacotron/feeder.py", "file_name": "feeder.py", "file_ext": "py", "file_size_in_byte": 11744, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2207, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 29, "usage_type": "call"}, {"api_name": "infolog.log", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 81, "usage_type": "attribute"}, {"api_name": "tensorflow.FIFOQueue", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tensorflow.FIFOQueue", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 113, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 127, "usage_type": "call"}, {"api_name": "tacotron.utils.text.text_to_sequence", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 147, "usage_type": "attribute"}, {"api_name": "infolog.log", "line_number": 149, "usage_type": "call"}, {"api_name": "time.time", "line_number": 149, "usage_type": "call"}, {"api_name": "time.time", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 164, "usage_type": "attribute"}, {"api_name": "infolog.log", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 191, "usage_type": "call"}, {"api_name": "tacotron.utils.text.text_to_sequence", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 191, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 210, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 211, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 228, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 252, "usage_type": "call"}]}
{"seq_id": "74151741884", "text": "import pygame\r\nimport math \r\nfrom queue import PriorityQueue\r\n\r\n\r\nWIDTH = 800\r\nWINDOW = pygame.display.set_mode((WIDTH,WIDTH))\r\npygame.display.set_caption(\"HMPS PROJECT:PATHFINDER\")\r\n\r\n\r\nRED = (255, 0, 0)\r\nGREEN = (0, 255, 0)\r\nBLUE = (0, 255, 0)\r\nYELLOW = (255, 255, 0)\r\nWHITE = (255, 255, 255)\r\nBLACK = (0, 0, 0)\r\nPURPLE = (128, 0, 128)\r\nORANGE = (255, 165 ,0)\r\nGREY = (128, 128, 128)\r\nTURQUOISE = (64, 224, 208)\r\n\r\n\r\nclass Spot:\r\n    def __init__(self, row, col, width, total_rows): #we inititalise the init fn\r\n        self.row = row\r\n        self.col = col\r\n        self.x = row*width  \r\n        self.y = col*width\r\n        self.color= WHITE # a block with white color wld mean that the node is yet to be looked at by the algo\r\n        self.neighbors = []\r\n        self.width= width\r\n        self.total_rows= total_rows\r\n\r\n    def get_pos(self):\r\n        return self.row, self.col #this fn will give us the position of the node we are looking for\r\n    \r\n    def is_closed(self):\r\n        return self.color==RED #this wld mean that the block has been searched for\r\n    \r\n    def is_open(self):\r\n        return self.color== GREEN #the node is currently in open set and being looked at\r\n    \r\n    def is_barrier(self):\r\n        return self.color==BLACK#the node is an obstacle and cant be used as a path\r\n    \r\n    def is_start(self):\r\n        return self.color== ORANGE#the node is the start point\r\n    \r\n    def is_end(self):\r\n        return self.color== TURQUOISE #the node is the end point\r\n\r\n    def reset(self):\r\n        self.color = WHITE #will change it back to a normal node\r\n    \r\n    def make_closed(self):\r\n        self.color = RED #changes the color of the node to red and hence marking it as a closed node\r\n    \r\n    def make_open(self):\r\n        self.color = GREEN #changes the color of the node to green making it an open node\r\n    \r\n    def make_barrier(self):\r\n        self.color = BLACK #changes the color to black making it an obstacle\r\n    \r\n    def make_start(self):\r\n        self.color = ORANGE\r\n\r\n    def make_end(self):\r\n        self.color = TURQUOISE #changes color to turquoise making it an end point\r\n\r\n    def make_path(self):\r\n        self.color= PURPLE\r\n\r\n    def draw(self,window):\r\n        pygame.draw.rect(window, self.color, (self.x, self.y,self.width,self.width))#here self.x and self.y define the cordinates of the node that is going to be drawn\r\n    #self. color represents thte color we give to it which will represent its fn eg black means obstacle\r\n\r\n    def update_neighbors(self,grid):#we are using this class so that the start or end point dont end up in a circle of barrier and hence being unreachable\r\n        self.neighbors=[]\r\n        if self.row < self.total_rows-1 and not grid[self.row+1][self.col].is_barrier():\r\n            self.neighbors.append(grid[self.row+1][self.col])\r\n        # self.row < self.total_rows-1 is checking if the current row has a row below it\r\n        #grid[self.row+1][self.col].is_barrier() is checking if the node *below* our current node is a barrier or not\r\n        #if it passes both test then it is a suitable neighbor and can be added to the neighbors list we made at start\r\n\r\n        if self.row>0 and not grid[self.row-1][self.col].is_barrier():#checks if node above our current node is a suitable neighbor\r\n            self.neighbors.append(grid[self.row-1][self.col])\r\n        \r\n        if self.col < self.total_rows-1 and not grid[self.row][self.col+1].is_barrier():#checks if node on the right is a suitable neighbor\r\n            self.neighbors.append(grid[self.row][self.col+1])\r\n\r\n        if self.col > 0 and not grid[self.row][self.col-1].is_barrier():#checks on its left\r\n            self.neighbors.append(grid[self.row][self.col-1])\r\n\r\n    def __lt__(self, other): #lt stands for less. we wld invoke this fn when comapring two nodes/spots as named here\r\n        return False\r\n\r\n\r\ndef h(p1,p2): #this is the hysteric fn or the h score which will give us teh shortest dist between 2 points\r\n    x1,y1= p1 #eg p1 is (8,10) then x1 and y1 will directly take value from it coz of python\r\n    x2,y2,=p2\r\n    return abs(x1-x2)+abs(y1-y2) #abs() is a default python fn it gives absolute value.\r\n\r\ndef reconstruct_path(came_from,current,draw):\r\n    while current in came_from:\r\n        current=came_from[current]\r\n        current.make_path()#it simply run a for loop until it hits the start node which isnt in the list and keeps making a path until then\r\n        draw()\r\n\r\ndef algorithm(draw,grid,start,end):\r\n    count= 0\r\n    open_set=PriorityQueue()\r\n    open_set.put((0,count,start))#its the same as push but for the priorityqueue library.it basically adds the elemnt to priority queue\r\n    came_from={}#creats an empty list/dictionary\r\n    g_score={spot: float(\"inf\") for row in grid for spot in row } #float(\"inf\") initializes inifinity in python\r\n    #for row in grid for spot in row is shorter way for nested for loops\r\n    #we are basically just giving infinity f score to all nodes\r\n    g_score[start]=0\r\n    f_score={spot:float(\"inf\")for row in grid for spot in row}\r\n    f_score[start]=h(start.get_pos(),end.get_pos())\r\n    \r\n    open_set_hash={start}#this helps us check the nodes that are present in the queue and the ones that arent coz we cant check them in priority queue directly\r\n\r\n    while not open_set.empty():#this while will run when the algo has checked all the possible paths.and if we still havent found a path then it donesnt exist\r\n        for event in pygame.event.get():\r\n            if event.type== pygame.QUIT:\r\n                pygame.quit()\r\n        \r\n        current = open_set.get()[2]#this will get us the node we are currently looking at.\r\n        open_set_hash.remove(current)#this is to synchronise our hash list with our open set list\r\n\r\n        if current == end:#then we have found our shortest path\r\n            reconstruct_path(came_from,end,draw)\r\n            end.make_end()\r\n            return True\r\n        \r\n        for neighbor in current.neighbors:#we are checking all the valid neighbors of current node\r\n            temp_g_score=g_score[current] + 1#coz its adjacent to it so it will take only 1 to reach  the neighbor\r\n\r\n            if temp_g_score < g_score[neighbor]:#if the temp score is lower than the actual g score of the neighbor then we actually found a better path so we will upadte the score\r\n                came_from[neighbor]= current\r\n                g_score[neighbor]= temp_g_score\r\n                f_score[neighbor]=temp_g_score+h(neighbor.get_pos(),end.get_pos())\r\n                if neighbor not in open_set_hash:#this is why we are using hash list so that we can actually check\r\n                    count+=1#we increase the count of nodes in the original list\r\n                    open_set.put((f_score[neighbor],count,neighbor))\r\n                    open_set_hash.add(neighbor)#we add the same node to hash list so that both list remain same\r\n                    neighbor.make_open()#indicates to algo that this node needs to be considered too\r\n                \r\n        \r\n        draw()\r\n\r\n        if current  != start:\r\n            current.make_closed()#we have looked into its neighbor in above loop so now we close the current node and if we need to open this we will be able when it becomes one of the neighbor with the above loop\r\n\r\n    return False        \r\n        \r\n        \r\n        \r\n\r\n\r\n\r\ndef make_grid(rows,width):#will hold all the nodes\r\n    grid=[]\r\n    gap = width // rows#width / rows will give the size or dimensions of the node\r\n    for i in range(rows):# this is for loop for row.\r\n        grid.append([])#makes an empty list for the data of node to sit in\r\n        for j in range(rows):#for loop for col. we are assigning nodes their place in grid\r\n            spot = Spot(i,j,gap,rows)#giving the nodes its values\r\n            grid[i].append(spot)#assigning them to grid\r\n        \r\n    return grid#if ukuk XD\r\n\r\n\r\ndef draw_grid(window,rows,width):#it draws the grid lines to separate each spot/node\r\n    gap=width//rows\r\n    for i in range(rows):\r\n        pygame.draw.line(window,GREY,(0,i*gap),(width,i*gap))#for horizontal lines\r\n        #(0,i*gap) and (width,i*gap) gives us the x and y position to draw the horizontal lines in between them\r\n        #for eg (0,3*gap) ,(800,3*gap) this wld draw a horizontal line between those points\r\n        for j in range(rows):\r\n            pygame.draw.line(window,GREY,(j*gap,0),(j*gap,width))#for vertical lines\r\n\r\n\r\ndef draw(window,grid,rows,width):#this function draws the everything\r\n    window.fill(WHITE)#this fills the whole window with one color os our choice at start of every frame\r\n    # i.e essentially clearing the whole frame and giving us a blank frame to work with\r\n    for row in grid:\r\n        for spot in row:\r\n            spot.draw(window)#we are using the draw function to draw each node using the two for loops.\r\n            #ie black white red etc.\r\n    draw_grid(window,rows,width)\r\n    pygame.display.update()#it tells pygame to refresh the screen with new(this) data\r\n\r\n\r\ndef get_clicked_pos(pos,rows,width):\r\n    gap= width//rows\r\n    y,x=pos#directly assigns x,y of pos\r\n\r\n    row = y//gap#this will tell us exactly which row and col and hence the actual node being clicked\r\n    col = x//gap\r\n\r\n    return row, col\r\n\r\n\r\ndef main(window,width):\r\n    ROWS = 50 #defines total rows in our window\r\n    grid=make_grid(ROWS,width)#makes an empty grid of nodes with dimensions rows and width\r\n\r\n    start=None\r\n    end=None\r\n\r\n    run=True#these are our flags\r\n    \r\n\r\n    while run:\r\n        draw(window,grid,ROWS,width)\r\n        for event in pygame.event.get():\r\n            if event.type==pygame.QUIT:\r\n                run=False\r\n            if pygame.mouse.get_pressed()[0]:#left click\r\n                pos=pygame.mouse.get_pos()#we get the pos whre mouse was clicked\r\n                row, col=get_clicked_pos(pos,ROWS,width)\r\n                spot=grid[row][col]\r\n                if not start and spot != end: #assigning start node\r\n                    start = spot\r\n                    start.make_start()\r\n                \r\n                elif not end and spot != start:#assigning end node\r\n                    end =spot\r\n                    end.make_end()\r\n\r\n                elif spot != start and spot != end :#assigning barriers\r\n                    spot.make_barrier() \r\n            \r\n            elif pygame.mouse.get_pressed()[2]:#right click. [1] means middle button\r\n                pos = pygame.mouse.get_pos()\r\n                row,col=get_clicked_pos(pos,ROWS,width)\r\n                spot=grid[row][col]\r\n                spot.reset()#erasing the clicked node\r\n                if spot == start:#if start node is clicked then its flag also needs to be reset.\r\n                    start=None\r\n                elif spot == end:#same for end node\r\n                    end=None\r\n\r\n            if event.type == pygame.KEYDOWN:#KEYDOWN=key pressed    \r\n                if event.key == pygame.K_SPACE and start and end:\r\n                    for row in grid:\r\n                        for spot in row:\r\n                            spot.update_neighbors(grid)\r\n\r\n                    algorithm(lambda: draw(window,grid,ROWS,width),grid,start,end)  #lambda is an anonymous function. \r\n                    #it acts a name for a function. it helps u to pass fucntions as an argument to another funciton.\r\n                    # for eg x=lambda:print(\"hello\") and then u call x(), will print hello.\r\n                if event.key== pygame.K_r:#for resetting\r\n                    start=None\r\n                    end=None\r\n                    grid = make_grid(ROWS,width)  \r\n\r\n\r\n    pygame.quit()#when close button is clicked\r\n\r\n\r\nmain(WINDOW,WIDTH)#calling the main function\r\n", "repo_name": "tvt15/Pathfinder", "sub_path": "HMPS_PROJECT.py", "file_name": "HMPS_PROJECT.py", "file_ext": "py", "file_size_in_byte": 11716, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pygame.display.set_mode", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 74, "usage_type": "attribute"}, {"api_name": "queue.PriorityQueue", "line_number": 111, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 124, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 177, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 177, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 181, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 181, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 192, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 192, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 217, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 218, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 220, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 220, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 221, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 221, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 235, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 235, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 236, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 236, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 245, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 246, "usage_type": "attribute"}, {"api_name": "pygame.K_r", "line_number": 254, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 260, "usage_type": "call"}]}
{"seq_id": "14585038392", "text": "'''\nSECTION 0 - DATASETS\n(1) Observations: Download from ftp://ftp.cdc.noaa.gov/Datasets/gistemp/combsavetxt(\"times.csv\",times,delimiter=\",\")ined/1200km/air.2x2.1200.mon.anom.comb.nc\n(2) Models: Use the command aws s3 cp s3://nasanex/... ./\n(3) Preprocessing:\n'''\nimport tqdm\nimport os\nimport glob\nimport xarray as xr\n\nos.chdir(\"/home/ubuntu/mean_folder\")\nfor file in tqdm.tqdm(glob.glob(\"*.nc\")):\n    name = file.split('.')[0]\n    xr.open_dataset(file).to_zarr(name,'w')\n    os.remove(file)\n\nos.chdir(\"/home/ubuntu/anomaly_folder\")\nfor file in tqdm.tqdm(glob.glob(\"*.nc\")):\n    name = file.split('.')[0]\n    xr.open_dataset(file).to_zarr(name,'w')\n    os.remove(file)\n\n######################################################################################################\n\nimport dask.array as da\nimport numpy as np\nimport dask, requests, csv\nimport netCDF4 as nc4\nimport time\nfrom multiprocessing.pool import ThreadPool\nfrom numpy import linalg as LA\nfrom sklearn.preprocessing import Imputer\nfrom scipy import stats\nfrom numba import jit, prange\nfrom dask.diagnostics import ProgressBar\nfrom eofs.xarray import Eof\n\ndask.set_options(pool=ThreadPool(8))\n#dask.set_options(scheduler='threads')\ndask.set_options(scheduler='processes')\n\nnum_workers = 8\n\ndef my_linregress(y):\n    x = np.linspace(1,y.shape[0],y.shape[0])\n    xd = x.astype(np.float32)\n    sum_x = np.sum(xd)\n    n = xd.size\n    sum_xx = np.sum(xd**2)\n    slopes = np.empty([y.shape[1],y.shape[2]])\n    for i in range(y.shape[1]):\n        for j in range(y.shape[2]):\n            yd = y[:,i,j].astype(np.float32)\n            sum_y = np.sum(yd)\n            sum_xy = np.sum(xd*yd)\n            sum_yy = np.sum(yd**2)\n            slopes[i,j] = (sum_xy - (sum_x*sum_y)/n)/(sum_xx - (sum_x*sum_x)/n)\n    return slopes\n\n'''\nSECTION 1 - OBSERVATIONAL DATA\n(1) Access observational data.\n(2) Perform linear regression on each data point, describing the temperature change of the data with respect to time. The slope of each linear regression is stored in the corresponding cell.\n'''\n\nstart1 = time.time()\n\nprint('Setting up observational data...')\t\n\nos.chdir(\"/home/ubuntu/observations\")\nnew = xr.open_dataset(\"observations.nc\")\nanomalies_obs = new.air[1320:1632,:,:]\nanomalies_obs = anomalies_obs.reindex(lat=new['lat'],lon=new['lon'],method='nearest')\nobs = anomalies_obs\n\nanomaly_obs_slopes = np.zeros([anomalies_obs.lat.shape[0],anomalies_obs.lon.shape[0]])\n\nanomalies_obs_da = da.from_array(anomalies_obs.data, chunks=[312,45,45])\n\nanomaly_obs_slopes = da.map_blocks(my_linregress, anomalies_obs_da, dtype=np.ndarray,drop_axis=[0])\n\nanomaly_obs_slopes = anomaly_obs_slopes.compute(num_workers = num_workers)\n\nprint('Linear regression slopes:')\nprint(anomaly_obs_slopes)\nprint('Units are temperature change for each data point in degrees/year.')\n\nend1 = time.time()\nsection1 = end1 - start1\nprint(section1)\n\n######################################################################################################\n\n'''\nSECTION 2 - COMPUTING MODEL MEAN (1950-1980)\n(1) Access a folder with model temperature max and mins from 1950-1980.\n(2) Compute the monthly mean over the period.\n'''\n\nstart2 = time.time()\n\ndef monthly_mean_LEAP(X):\n    time = [0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 366]\n    means = np.empty([12,X.shape[1],X.shape[2]])\n    for t in range(12):\n        means[t,:,:] = X[time[t]:time[t+1],:,:].mean(axis=0)\n    return means\n\ndef monthly_mean_NOLEAP(X):\n    time = [0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365]\n    means = np.empty([12,X.shape[1],X.shape[2]])\n    for t in range(12):\n        means[t,:,:] = X[time[t]:time[t+1],:,:].mean(axis=0)\n    return means\n\ndef monthly_std_LEAP(X):\n    time = [0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 366]\n    stds = np.empty([12,X.shape[1],X.shape[2]])\n    for t in range(12):\n        stds[t,:,:] = X[time[t]:time[t+1],:,:].std(axis=0)\n    return stds\n\ndef monthly_std_NOLEAP(X):\n    time = [0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365]\n    stds = np.empty([12,X.shape[1],X.shape[2]])\n    for t in range(12):\n        stds[t,:,:] = X[time[t]:time[t+1],:,:].std(axis=0)\n    return stds\n\nos.chdir(\"../mean_folder\")\nn = 0\nfor file in tqdm.tqdm(glob.glob(\"*\")):\n    ds = xr.open_zarr(file)\n    ds.load()\n    ds = ds.reindex(lat=obs['lat'], lon=obs['lon'], method='nearest')\n    year = float(file.split(\"_\")[6].split(\".\")[0])\n    print(ds.values)\n    if file.split(\"_\")[0] == 'tasmax':\n        if n == 0:\n            average_temp = np.zeros([12, ds.lat.shape[0],ds.lon.shape[0]])    \n        if np.mod(year, 4) == 0:\n            temp_da = da.from_array(ds.tasmax.data, chunks=[366,45,45])\n            temp = da.map_blocks(monthly_mean_LEAP, temp_da, dtype=np.ndarray)\n        else:\n            temp_da = da.from_array(ds.tasmax.data, chunks=[365,45,45])\n            temp = da.map_blocks(monthly_mean_NOLEAP, temp_da, dtype=np.ndarray)\n    else:\n        if n == 0:\n            average_temp = np.zeros([12, ds.lat.shape[0],ds.lon.shape[0]])\n        if np.mod(year, 4) == 0:\n            temp_da = da.from_array(ds.tasmin.data, chunks=[366,45,45])\n            temp = da.map_blocks(monthly_mean_LEAP, temp_da, dtype=np.ndarray)\n        else:\n            temp_da = da.from_array(ds.tasmin.data, chunks=[365,45,45])\n            temp = da.map_blocks(monthly_mean_NOLEAP, temp_da, dtype=np.ndarray)\n    n += 1\n    average_temp += temp.compute(num_workers = num_workers)\n\naverage_temp = average_temp/n\n\nprint(average_temp)\nprint(n)\nprint(average_temp.shape)\n\nend2 = time.time()\nsection2 = end2 - start2\nprint(section2)\n######################################################################################################\n\n'''\nSECTION 3 - COMPUTING MODEL ANOMALIES (1990-2015)\n(1) Access a folder with model temperature max and mins from 1990-2015.\n(2) Compute the monthly anomalies by subtracting the mean monthly temperatures over 1950-1980 from the monthly temepratures over 1990-2015.\n'''\nstart3 = time.time()\n\nos.chdir(\"/home/ubuntu/anomaly_folder\")\ni_max = 0\ni_min = 0\n\nfor file in tqdm.tqdm(glob.glob(\"*\")):\n    ds = xr.open_zarr(file)\n    ds.load()\n    ds = ds.reindex(lat=obs['lat'],lon=obs['lon'],method='nearest')\n    month_times = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n    year = float(file.split(\"_\")[6].split(\".\")[0])\n    if file.split(\"_\")[0] == 'tasmax':\n        print(ds.tasmax.data)      \n        if np.mod(year, 4) == 0:\n            temp_da = da.from_array(ds.tasmax.data, chunks=[366,45,45])\n            temp = da.map_blocks(monthly_mean_LEAP, temp_da, dtype=np.ndarray)\n            temp_sd = da.map_blocks(monthly_std_LEAP, temp_da, dtype=np.ndarray)\n        else:\n            temp_da = da.from_array(ds.tasmax.data, chunks=[365,45,45])\n            temp = da.map_blocks(monthly_mean_NOLEAP, temp_da, dtype=np.ndarray)\n            temp_sd = da.map_blocks(monthly_std_NOLEAP, temp_da, dtype=np.ndarray)\n        if i_max == 0:\n            anomalies_max = xr.DataArray(temp.compute(num_workers = num_workers) - average_temp,coords=[month_times,ds.lat.data,ds.lon.data],dims  =['time','lat','lon'])\n            anomalies_max_sd = xr.DataArray(temp_sd.compute(num_workers = num_workers),coords=[month_times,ds.lat.data,ds.lon.data],dims  =['time','lat','lon'])\n            i_max += 1\n        else:            \n            anoms = xr.DataArray(temp.compute(num_workers = num_workers) - average_temp,coords=[month_times,ds.lat.data,ds.lon.data],dims  =['time','lat','lon'])\n            anomalies_max = xr.concat([anomalies_max,anoms],'time')\n            anoms_sd = xr.DataArray(temp_sd.compute(num_workers = num_workers),coords=[month_times,ds.lat.data,ds.lon.data],dims  =['time','lat','lon'])\n            anomalies_max_sd = xr.concat([anomalies_max_sd,anoms_sd],'time')\n    else:\n        if np.mod(year, 4) == 0:\n            temp_da = da.from_array(ds.tasmin.data, chunks=[366,45,45])\n            temp = da.map_blocks(monthly_mean_LEAP, temp_da, dtype=np.ndarray)\n            temp_sd = da.map_blocks(monthly_std_LEAP, temp_da, dtype=np.ndarray)\n        else:\n            temp_da = da.from_array(ds.tasmin.data, chunks=[365,45,45])\n            temp = da.map_blocks(monthly_mean_NOLEAP, temp_da, dtype=np.ndarray)\n            temp_sd = da.map_blocks(monthly_std_NOLEAP, temp_da, dtype=np.ndarray)\n        if i_min == 0:\n            anomalies_min = xr.DataArray(temp.compute(num_workers = num_workers) - average_temp,coords=[month_times,ds.lat.data,ds.lon.data],dims  =['time','lat','lon'])\n            anomalies_min_sd = xr.DataArray(temp_sd.compute(num_workers = num_workers),coords=[month_times,ds.lat.data,ds.lon.data],dims  =['time','lat','lon'])\n            i_min += 1\n        else:\n            anoms = xr.DataArray(temp.compute(num_workers = num_workers) - average_temp,coords=[month_times,ds.lat.data,ds.lon.data],dims  =['time','lat','lon'])\n            anomalies_min = xr.concat([anomalies_min,anoms],'time')\n            anoms_sd = xr.DataArray(temp_sd.compute(num_workers = num_workers), coords=[month_times,ds.lat.data,ds.lon.data],dims  =['time','lat','lon'])\n            anomalies_min_sd = xr.concat([anomalies_min_sd,anoms_sd],'time')\n\nanomalies = (anomalies_max.sortby('time') + anomalies_min.sortby('time'))/2\ntime_months = np.linspace(1,len(anomalies['time']),len(anomalies['time']))\nanomalies_sd = (anomalies_max_sd.sortby('time') + anomalies_min_sd.sortby('time'))/2\n\nimp = Imputer(missing_values='NaN', strategy='mean', axis = 0)\nfor i in range(len(anomalies.time)):\n    anomalies[i,:,:] = imp.fit_transform(anomalies[i,:,:])\n    anomalies_obs[i,:,:] = imp.fit_transform(anomalies_obs[i,:,:])\nprint(anomalies)\nprint(anomalies_obs)\nx = np.resize(anomalies.data[~np.isnan(anomalies_obs.data)],[-1,1])\ny = np.resize(anomalies_obs.data[~np.isnan(anomalies.data)],[-1,1])\nx1 = x[~np.isnan(x)]\ny1 = y[~np.isnan(y)]\n\nanomalies_corr = stats.pearsonr(x1,y1)\nanomalies_ste = np.sum(np.square(x1 - y1))/(len(obs.time)*len(obs.lat)*len(obs.lon))\nanomalies_sd_avg = np.mean(anomalies_sd)\n\nprint(anomalies)\nprint(anomalies_sd_avg)\nprint(anomalies_corr)\nprint(anomalies_ste)\n\nend3 = time.time()\nsection3 = end3 - start3\nprint(section3)\n\n######################################################################################################\n\n'''\nSECTION 4 - LINEAR REGRESSION ON THE ANOMALIES (1990-2015)\n(1) Perform linear regression on the anomalies, describing the temperature change of the data with respect to time. The slope of each linear regression is stored in the corresponding cell.\n'''\n\nstart4 = time.time()\n\nanomalies_da = da.from_array(anomalies.data, chunks=[312,180,90])\n\nanomaly_slopes = da.map_blocks(my_linregress, anomalies_da, dtype=np.ndarray,drop_axis=[0])\n\nanomaly_slopes = anomaly_slopes.compute(num_workers = num_workers)\n\nprint(anomaly_slopes)\n\nend4 = time.time()\nsection4 = end4 - start4\nprint(section4)\n\n######################################################################################################\n\n'''\nSECTION 5 - PRINCIPAL COMPONENT ANALYSIS (1990-2015)\n(1) Perform PCA on the observations and models.\n(2) Compare to see differences\n'''\n\nstart5 = time.time()\n\nos.chdir(\"/home/ubuntu\")\nlon= 180\nlat= 90\ndim= lon * lat\nmonths = 24\n\ndata = np.resize(x1,[dim,months])\n\nsolver = Eof(xr.DataArray(anomalies.data,dims=['time','lat','lon']))\n\npcs = solver.pcs(npcs=3, pcscaling=1)\neofs = solver.eofs(neofs=5, eofscaling=1)\n\nvariance_fractions = solver.varianceFraction()\nvariance_fractions = solver.varianceFraction(neigs=3)\nprint(variance_fractions)\n\nmyFile1 = open('PC1.csv', 'w')\nwith myFile1:\n    writer = csv.writer(myFile1)\n    writer.writerows(eofs[0,:,:].data)\n\nmyFile2 = open('PC2.csv', 'w')\nwith myFile2:\n    writer = csv.writer(myFile2)\n    writer.writerows(eofs[1,:,:].data)\n\nmyFile3 = open('PC3.csv', 'w')\nwith myFile3:\n    writer = csv.writer(myFile3)\n    writer.writerows(eofs[2,:,:].data)\n\nmyFile4 = open('anomalies.csv', 'w')\nwith myFile4:\n    writer = csv.writer(myFile4)\n    writer.writerows(anomalies[0,:,:].data)\n\nend5 = time.time()\n\nsection5 = end5 - start5\nprint(section5)\n\n######################################################################################################\n\n'''\nSECTION 6 - COMPUTING ANOMALY DIFFERENCE (1990-2015)\n(1) Compute the difference between the observed and modelled anomalies.\n(2) Save as a csv file.\n'''\nstart6 = time.time()\n\nanomalies_dif = anomaly_obs_slopes - anomaly_slopes\n\nmyFile = open('anomalies_dif.csv', 'w')\nwith myFile:\n    writer = csv.writer(myFile)\n    writer.writerows(anomalies_dif)\n\nend6 = time.time()\nsection6 = end6 - start6\nprint(section6)\n\ntimes = [section1, section2, section3, section4, section5, section6]\nanomalies_sd_avg = np.float32(anomalies_sd_avg)\nanomalies_corr = np.float32(anomalies_corr)[0]\ntaylor_diagram = [anomalies_sd_avg, anomalies_corr,  anomalies_ste]\n\nnp.savetxt(\"times.csv\",times,delimiter=\",\")\nnp.savetxt(\"taylor_diagram.csv\",taylor_diagram,delimiter=\",\", fmt='%s')\nnp.savetxt(\"fractions.csv\",times,delimiter=\",\")\n", "repo_name": "ebonil01/cs205project", "sub_path": "ProjectCode.py", "file_name": "ProjectCode.py", "file_ext": "py", "file_size_in_byte": 12966, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.chdir", "line_number": 12, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 13, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 13, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 15, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 16, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 18, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 19, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 19, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 21, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 22, "usage_type": "call"}, {"api_name": "dask.set_options", "line_number": 39, "usage_type": "call"}, {"api_name": "multiprocessing.pool.ThreadPool", "line_number": 39, "usage_type": "call"}, {"api_name": "dask.set_options", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 57, "usage_type": "call"}, {"api_name": "time.time", "line_number": 67, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 71, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "dask.array.from_array", "line_number": 79, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 79, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 81, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 81, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 126, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 131, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 133, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 133, "usage_type": "call"}, {"api_name": "xarray.open_zarr", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 142, "usage_type": "call"}, {"api_name": "dask.array.from_array", "line_number": 143, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 143, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 144, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 144, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 144, "usage_type": "attribute"}, {"api_name": "dask.array.from_array", "line_number": 146, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 146, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 147, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 147, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 151, "usage_type": "call"}, {"api_name": "dask.array.from_array", "line_number": 152, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 152, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 153, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 153, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 153, "usage_type": "attribute"}, {"api_name": "dask.array.from_array", "line_number": 155, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 155, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 156, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 156, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 156, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 176, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 178, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 182, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 182, "usage_type": "call"}, {"api_name": "xarray.open_zarr", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 190, "usage_type": "call"}, {"api_name": "dask.array.from_array", "line_number": 191, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 191, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 192, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 192, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 192, "usage_type": "attribute"}, {"api_name": "dask.array.map_blocks", "line_number": 193, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 193, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 193, "usage_type": "attribute"}, {"api_name": "dask.array.from_array", "line_number": 195, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 195, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 196, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 196, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 196, "usage_type": "attribute"}, {"api_name": "dask.array.map_blocks", "line_number": 197, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 197, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 197, "usage_type": "attribute"}, {"api_name": "xarray.DataArray", "line_number": 199, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 200, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 203, "usage_type": "call"}, {"api_name": "xarray.concat", "line_number": 204, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 205, "usage_type": "call"}, {"api_name": "xarray.concat", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 208, "usage_type": "call"}, {"api_name": "dask.array.from_array", "line_number": 209, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 209, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 210, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 210, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 210, "usage_type": "attribute"}, {"api_name": "dask.array.map_blocks", "line_number": 211, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 211, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 211, "usage_type": "attribute"}, {"api_name": "dask.array.from_array", "line_number": 213, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 213, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 214, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 214, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 214, "usage_type": "attribute"}, {"api_name": "dask.array.map_blocks", "line_number": 215, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 215, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 215, "usage_type": "attribute"}, {"api_name": "xarray.DataArray", "line_number": 217, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 218, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 221, "usage_type": "call"}, {"api_name": "xarray.concat", "line_number": 222, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 223, "usage_type": "call"}, {"api_name": "xarray.concat", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 227, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 239, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 241, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 241, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 243, "usage_type": "call"}, {"api_name": "time.time", "line_number": 250, "usage_type": "call"}, {"api_name": "time.time", "line_number": 261, "usage_type": "call"}, {"api_name": "dask.array.from_array", "line_number": 263, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 263, "usage_type": "name"}, {"api_name": "dask.array.map_blocks", "line_number": 265, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 265, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 265, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 271, "usage_type": "call"}, {"api_name": "time.time", "line_number": 283, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 291, "usage_type": "call"}, {"api_name": "eofs.xarray.Eof", "line_number": 293, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 293, "usage_type": "call"}, {"api_name": "eofs.xarray", "line_number": 296, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 304, "usage_type": "call"}, {"api_name": "eofs.xarray", "line_number": 305, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 309, "usage_type": "call"}, {"api_name": "eofs.xarray", "line_number": 310, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 314, "usage_type": "call"}, {"api_name": "eofs.xarray", "line_number": 315, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 319, "usage_type": "call"}, {"api_name": "time.time", "line_number": 322, "usage_type": "call"}, {"api_name": "time.time", "line_number": 334, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 340, "usage_type": "call"}, {"api_name": "time.time", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 354, "usage_type": "call"}]}
{"seq_id": "7751093441", "text": "# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\"\"\"\nThese plugins modify the behavior of py.test and are meant to be imported\ninto conftest.py in the root directory.\n\"\"\"\nfrom __future__ import (absolute_import, division, print_function,\n                        unicode_literals)\n\nimport __future__\n\nfrom ..extern import six\n\nimport ast\nimport datetime\nimport io\nimport locale\nimport math\nimport os\nimport re\nimport sys\nimport types\nfrom pkgutil import find_loader\nfrom collections import OrderedDict\n\nimport pytest\n\nfrom ..config.paths import set_temp_config, set_temp_cache\nfrom .helper import treat_deprecations_as_exceptions, ignore_warnings\nfrom .helper import enable_deprecations_as_exceptions  # pylint: disable=W0611\nfrom ..utils.argparse import writeable_directory\nfrom ..utils.introspection import resolve_name\n\ntry:\n    import importlib.machinery as importlib_machinery\nexcept ImportError:  # Python 2.7\n    importlib_machinery = None\n\npytest_plugins = ['astropy.tests.pytest_repeat']\n\n_PLUGINS_PREFIX = 'astropy.extern.plugins'\nfor plugin in ['pytest_doctestplus', 'pytest_openfiles', 'pytest_remotedata']:\n    if find_loader(plugin) is None:\n        pytest_plugins.append('{}.{}.plugin'.format(_PLUGINS_PREFIX, plugin))\n\n# these pytest hooks allow us to mark tests and run the marked tests with\n# specific command line options.\n\n\ndef pytest_addoption(parser):\n\n    parser.addoption(\"--config-dir\", nargs='?', type=writeable_directory,\n                     help=\"specify directory for storing and retrieving the \"\n                          \"Astropy configuration during tests (default is \"\n                          \"to use a temporary directory created by the test \"\n                          \"runner); be aware that using an Astropy config \"\n                          \"file other than the default can cause some tests \"\n                          \"to fail unexpectedly\")\n\n    parser.addoption(\"--cache-dir\", nargs='?', type=writeable_directory,\n                     help=\"specify directory for storing and retrieving the \"\n                          \"Astropy cache during tests (default is \"\n                          \"to use a temporary directory created by the test \"\n                          \"runner)\")\n    parser.addini(\"config_dir\",\n                  \"specify directory for storing and retrieving the \"\n                  \"Astropy configuration during tests (default is \"\n                  \"to use a temporary directory created by the test \"\n                  \"runner); be aware that using an Astropy config \"\n                  \"file other than the default can cause some tests \"\n                  \"to fail unexpectedly\", default=None)\n\n    parser.addini(\"cache_dir\",\n                  \"specify directory for storing and retrieving the \"\n                  \"Astropy cache during tests (default is \"\n                  \"to use a temporary directory created by the test \"\n                  \"runner)\", default=None)\n\n\ndef pytest_configure(config):\n    treat_deprecations_as_exceptions()\n\ndef pytest_runtest_setup(item):\n    config_dir = item.config.getini('config_dir')\n    cache_dir = item.config.getini('cache_dir')\n\n    # Command-line options can override, however\n    config_dir = item.config.getoption('config_dir') or config_dir\n    cache_dir = item.config.getoption('cache_dir') or cache_dir\n\n    # We can't really use context managers directly in py.test (although\n    # py.test 2.7 adds the capability), so this may look a bit hacky\n    if config_dir:\n        item.set_temp_config = set_temp_config(config_dir)\n        item.set_temp_config.__enter__()\n    if cache_dir:\n        item.set_temp_cache = set_temp_cache(cache_dir)\n        item.set_temp_cache.__enter__()\n\n\n\ndef pytest_runtest_teardown(item, nextitem):\n    if hasattr(item, 'set_temp_cache'):\n        item.set_temp_cache.__exit__()\n    if hasattr(item, 'set_temp_config'):\n        item.set_temp_config.__exit__()\n\n\nPYTEST_HEADER_MODULES = OrderedDict([('Numpy', 'numpy'),\n                                     ('Scipy', 'scipy'),\n                                     ('Matplotlib', 'matplotlib'),\n                                     ('h5py', 'h5py'),\n                                     ('Pandas', 'pandas')])\n\n# This always returns with Astropy's version\nfrom .. import __version__\n\nTESTED_VERSIONS = OrderedDict([('Astropy', __version__)])\n\n\ndef pytest_report_header(config):\n\n    try:\n        stdoutencoding = sys.stdout.encoding or 'ascii'\n    except AttributeError:\n        stdoutencoding = 'ascii'\n\n    if six.PY2:\n        args = [x.decode('utf-8') for x in config.args]\n    else:\n        args = config.args\n\n    # TESTED_VERSIONS can contain the affiliated package version, too\n    if len(TESTED_VERSIONS) > 1:\n        for pkg, version in TESTED_VERSIONS.items():\n            if pkg != 'Astropy':\n                s = \"\\nRunning tests with {0} version {1}.\\n\".format(\n                    pkg, version)\n    else:\n        s = \"\\nRunning tests with Astropy version {0}.\\n\".format(\n            TESTED_VERSIONS['Astropy'])\n\n    # Per https://github.com/astropy/astropy/pull/4204, strip the rootdir from\n    # each directory argument\n    if hasattr(config, 'rootdir'):\n        rootdir = str(config.rootdir)\n        if not rootdir.endswith(os.sep):\n            rootdir += os.sep\n\n        dirs = [arg[len(rootdir):] if arg.startswith(rootdir) else arg\n                for arg in args]\n    else:\n        dirs = args\n\n    s += \"Running tests in {0}.\\n\\n\".format(\" \".join(dirs))\n\n    s += \"Date: {0}\\n\\n\".format(datetime.datetime.now().isoformat()[:19])\n\n    from platform import platform\n    plat = platform()\n    if isinstance(plat, bytes):\n        plat = plat.decode(stdoutencoding, 'replace')\n    s += \"Platform: {0}\\n\\n\".format(plat)\n    s += \"Executable: {0}\\n\\n\".format(sys.executable)\n    s += \"Full Python Version: \\n{0}\\n\\n\".format(sys.version)\n\n    s += \"encodings: sys: {0}, locale: {1}, filesystem: {2}\".format(\n        sys.getdefaultencoding(),\n        locale.getpreferredencoding(),\n        sys.getfilesystemencoding())\n    if sys.version_info < (3, 3, 0):\n        s += \", unicode bits: {0}\".format(\n            int(math.log(sys.maxunicode, 2)))\n    s += '\\n'\n\n    s += \"byteorder: {0}\\n\".format(sys.byteorder)\n    s += \"float info: dig: {0.dig}, mant_dig: {0.dig}\\n\\n\".format(\n        sys.float_info)\n\n    for module_display, module_name in six.iteritems(PYTEST_HEADER_MODULES):\n        try:\n            with ignore_warnings(DeprecationWarning):\n                module = resolve_name(module_name)\n        except ImportError:\n            s += \"{0}: not available\\n\".format(module_display)\n        else:\n            try:\n                version = module.__version__\n            except AttributeError:\n                version = 'unknown (no __version__ attribute)'\n            s += \"{0}: {1}\\n\".format(module_display, version)\n\n    special_opts = [\"remote_data\", \"pep8\"]\n    opts = []\n    for op in special_opts:\n        op_value = getattr(config.option, op, None)\n        if op_value:\n            if isinstance(op_value, six.string_types):\n                op = ': '.join((op, op_value))\n            opts.append(op)\n    if opts:\n        s += \"Using Astropy options: {0}.\\n\".format(\", \".join(opts))\n\n    if six.PY2:\n        s = s.encode(stdoutencoding, 'replace')\n\n    return s\n\n\ndef pytest_pycollect_makemodule(path, parent):\n    # This is where we set up testing both with and without\n    # from __future__ import unicode_literals\n\n    # On Python 3, just do the regular thing that py.test does\n    if six.PY2:\n        return Pair(path, parent)\n    else:\n        return pytest.Module(path, parent)\n\n\nclass Pair(pytest.File):\n    \"\"\"\n    This class treats a given test .py file as a pair of .py files\n    where one has __future__ unicode_literals and the other does not.\n    \"\"\"\n\n    def collect(self):\n        # First, just do the regular import of the module to make\n        # sure it's sane and valid.  This block is copied directly\n        # from py.test\n        try:\n            mod = self.fspath.pyimport(ensuresyspath=True)\n        except SyntaxError:\n            import py\n            excinfo = py.code.ExceptionInfo()\n            raise self.CollectError(excinfo.getrepr(style=\"short\"))\n        except self.fspath.ImportMismatchError:\n            e = sys.exc_info()[1]\n            raise self.CollectError(\n                \"import file mismatch:\\n\"\n                \"imported module {!r} has this __file__ attribute:\\n\"\n                \"  {}\\n\"\n                \"which is not the same as the test file we want to collect:\\n\"\n                \"  {}\\n\"\n                \"HINT: remove __pycache__ / .pyc files and/or use a \"\n                \"unique basename for your test file modules\".format(e.args))\n\n        # Now get the file's content.\n        with io.open(six.text_type(self.fspath), 'rb') as fd:\n            content = fd.read()\n\n        # If the file contains the special marker, only test it both ways.\n        if b'TEST_UNICODE_LITERALS' in content:\n            # Return the file in both unicode_literal-enabled and disabled forms\n            return [\n                UnicodeLiteralsModule(mod.__name__, content, self.fspath, self),\n                NoUnicodeLiteralsModule(mod.__name__, content, self.fspath, self)\n            ]\n        else:\n            return [pytest.Module(self.fspath, self)]\n\n\n_RE_FUTURE_IMPORTS = re.compile(br'from __future__ import ((\\(.*?\\))|([^\\n]+))',\n                                flags=re.DOTALL)\n\n\nclass ModifiedModule(pytest.Module):\n    def __init__(self, mod_name, content, path, parent):\n        self.mod_name = mod_name\n        self.content = content\n        super(ModifiedModule, self).__init__(path, parent)\n\n    def _importtestmodule(self):\n        # We have to remove the __future__ statements *before* parsing\n        # with compile, otherwise the flags are ignored.\n        content = re.sub(_RE_FUTURE_IMPORTS, b'\\n', self.content)\n\n        new_mod = types.ModuleType(self.mod_name)\n        new_mod.__file__ = six.text_type(self.fspath)\n\n        if hasattr(self, '_transform_ast'):\n            # ast.parse doesn't let us hand-select the __future__\n            # statements, but built-in compile, with the PyCF_ONLY_AST\n            # flag does.\n            tree = compile(\n                content, six.text_type(self.fspath), 'exec',\n                self.flags | ast.PyCF_ONLY_AST, True)\n            tree = self._transform_ast(tree)\n            # Now that we've transformed the tree, recompile it\n            code = compile(\n                tree, six.text_type(self.fspath), 'exec')\n        else:\n            # If we don't need to transform the AST, we can skip\n            # parsing/compiling in two steps\n            code = compile(\n                content, six.text_type(self.fspath), 'exec',\n                self.flags, True)\n\n        pwd = os.getcwd()\n        try:\n            os.chdir(os.path.dirname(six.text_type(self.fspath)))\n            six.exec_(code, new_mod.__dict__)\n        finally:\n            os.chdir(pwd)\n        self.config.pluginmanager.consider_module(new_mod)\n        return new_mod\n\n\nclass UnicodeLiteralsModule(ModifiedModule):\n    flags = (\n        __future__.absolute_import.compiler_flag |\n        __future__.division.compiler_flag |\n        __future__.print_function.compiler_flag |\n        __future__.unicode_literals.compiler_flag\n    )\n\n\nclass NoUnicodeLiteralsModule(ModifiedModule):\n    flags = (\n        __future__.absolute_import.compiler_flag |\n        __future__.division.compiler_flag |\n        __future__.print_function.compiler_flag\n    )\n\n    def _transform_ast(self, tree):\n        # When unicode_literals is disabled, we still need to convert any\n        # byte string containing non-ascii characters into a Unicode string.\n        # If it doesn't decode as utf-8, we assume it's some other kind\n        # of byte string and just ultimately leave it alone.\n\n        # Note that once we drop support for Python 3.2, we should be\n        # able to remove this transformation and just put explicit u''\n        # prefixes in the test source code.\n\n        class NonAsciiLiteral(ast.NodeTransformer):\n            def visit_Str(self, node):\n                s = node.s\n                if isinstance(s, bytes):\n                    try:\n                        s.decode('ascii')\n                    except UnicodeDecodeError:\n                        try:\n                            s = s.decode('utf-8')\n                        except UnicodeDecodeError:\n                            pass\n                        else:\n                            return ast.copy_location(ast.Str(s=s), node)\n                return node\n        return NonAsciiLiteral().visit(tree)\n\n\ndef pytest_terminal_summary(terminalreporter):\n    \"\"\"Output a warning to IPython users in case any tests failed.\"\"\"\n\n    try:\n        get_ipython()\n    except NameError:\n        return\n\n    if not terminalreporter.stats.get('failed'):\n        # Only issue the warning when there are actually failures\n        return\n\n    terminalreporter.ensure_newline()\n    terminalreporter.write_line(\n        'Some tests are known to fail when run from the IPython prompt; '\n        'especially, but not limited to tests involving logging and warning '\n        'handling.  Unless you are certain as to the cause of the failure, '\n        'please check that the failure occurs outside IPython as well.  See '\n        'http://docs.astropy.org/en/stable/known_issues.html#failing-logging-'\n        'tests-when-running-the-tests-in-ipython for more information.',\n        yellow=True, bold=True)\n", "repo_name": "waynebhayes/SpArcFiRe", "sub_path": "scripts/SpArcFiRe-pyvenv/lib/python2.7/site-packages/astropy/tests/pytest_plugins.py", "file_name": "pytest_plugins.py", "file_ext": "py", "file_size_in_byte": 13541, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "40", "api": [{"api_name": "importlib.machinery", "line_number": 36, "usage_type": "name"}, {"api_name": "pkgutil.find_loader", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.argparse.writeable_directory", "line_number": 51, "usage_type": "name"}, {"api_name": "utils.argparse.writeable_directory", "line_number": 59, "usage_type": "name"}, {"api_name": "helper.treat_deprecations_as_exceptions", "line_number": 80, "usage_type": "call"}, {"api_name": "config.paths.set_temp_config", "line_number": 93, "usage_type": "call"}, {"api_name": "config.paths.set_temp_cache", "line_number": 96, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 108, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 117, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 123, "usage_type": "attribute"}, {"api_name": "extern.six.PY2", "line_number": 127, "usage_type": "attribute"}, {"api_name": "extern.six", "line_number": 127, "usage_type": "name"}, {"api_name": "config.paths.args", "line_number": 128, "usage_type": "attribute"}, {"api_name": "config.paths", "line_number": 128, "usage_type": "name"}, {"api_name": "config.paths.args", "line_number": 130, "usage_type": "attribute"}, {"api_name": "config.paths", "line_number": 130, "usage_type": "name"}, {"api_name": "config.paths", "line_number": 144, "usage_type": "argument"}, {"api_name": "config.paths.rootdir", "line_number": 145, "usage_type": "attribute"}, {"api_name": "config.paths", "line_number": 145, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 147, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 156, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 156, "usage_type": "attribute"}, {"api_name": "platform.platform", "line_number": 159, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 163, "usage_type": "attribute"}, {"api_name": "sys.version", "line_number": 164, "usage_type": "attribute"}, {"api_name": "sys.getdefaultencoding", "line_number": 167, "usage_type": "call"}, {"api_name": "locale.getpreferredencoding", "line_number": 168, "usage_type": "call"}, {"api_name": "sys.getfilesystemencoding", "line_number": 169, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 170, "usage_type": "attribute"}, {"api_name": "math.log", "line_number": 172, "usage_type": "call"}, {"api_name": "sys.maxunicode", "line_number": 172, "usage_type": "attribute"}, {"api_name": "sys.byteorder", "line_number": 175, "usage_type": "attribute"}, {"api_name": "sys.float_info", "line_number": 177, "usage_type": "attribute"}, {"api_name": "extern.six.iteritems", "line_number": 179, "usage_type": "call"}, {"api_name": "extern.six", "line_number": 179, "usage_type": "name"}, {"api_name": "helper.ignore_warnings", "line_number": 181, "usage_type": "call"}, {"api_name": "utils.introspection.resolve_name", "line_number": 182, "usage_type": "call"}, {"api_name": "config.paths.option", "line_number": 195, "usage_type": "attribute"}, {"api_name": "config.paths", "line_number": 195, "usage_type": "name"}, {"api_name": "extern.six.string_types", "line_number": 197, "usage_type": "attribute"}, {"api_name": "extern.six", "line_number": 197, "usage_type": "name"}, {"api_name": "extern.six.PY2", "line_number": 203, "usage_type": "attribute"}, {"api_name": "extern.six", "line_number": 203, "usage_type": "name"}, {"api_name": "extern.six.PY2", "line_number": 214, "usage_type": "attribute"}, {"api_name": "extern.six", "line_number": 214, "usage_type": "name"}, {"api_name": "pytest.Module", "line_number": 217, "usage_type": "call"}, {"api_name": "pytest.File", "line_number": 220, "usage_type": "attribute"}, {"api_name": "py.code.ExceptionInfo", "line_number": 234, "usage_type": "call"}, {"api_name": "py.code", "line_number": 234, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 237, "usage_type": "call"}, {"api_name": "io.open", "line_number": 248, "usage_type": "call"}, {"api_name": "extern.six.text_type", "line_number": 248, "usage_type": "call"}, {"api_name": "extern.six", "line_number": 248, "usage_type": "name"}, {"api_name": "pytest.Module", "line_number": 259, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 262, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 263, "usage_type": "attribute"}, {"api_name": "pytest.Module", "line_number": 266, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 275, "usage_type": "call"}, {"api_name": "types.ModuleType", "line_number": 277, "usage_type": "call"}, {"api_name": "extern.six.text_type", "line_number": 278, "usage_type": "call"}, {"api_name": "extern.six", "line_number": 278, "usage_type": "name"}, {"api_name": "extern.six.text_type", "line_number": 285, "usage_type": "call"}, {"api_name": "extern.six", "line_number": 285, "usage_type": "name"}, {"api_name": "ast.PyCF_ONLY_AST", "line_number": 286, "usage_type": "attribute"}, {"api_name": "extern.six.text_type", "line_number": 290, "usage_type": "call"}, {"api_name": "extern.six", "line_number": 290, "usage_type": "name"}, {"api_name": "extern.six.text_type", "line_number": 295, "usage_type": "call"}, {"api_name": "extern.six", "line_number": 295, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 298, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path", "line_number": 300, "usage_type": "attribute"}, {"api_name": "extern.six.text_type", "line_number": 300, "usage_type": "call"}, {"api_name": "extern.six", "line_number": 300, "usage_type": "name"}, {"api_name": "extern.six.exec_", "line_number": 301, "usage_type": "call"}, {"api_name": "extern.six", "line_number": 301, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 303, "usage_type": "call"}, {"api_name": "__future__.absolute_import", "line_number": 310, "usage_type": "attribute"}, {"api_name": "__future__.division", "line_number": 311, "usage_type": "attribute"}, {"api_name": "__future__.print_function", "line_number": 312, "usage_type": "attribute"}, {"api_name": "__future__.unicode_literals", "line_number": 313, "usage_type": "attribute"}, {"api_name": "__future__.absolute_import", "line_number": 319, "usage_type": "attribute"}, {"api_name": "__future__.division", "line_number": 320, "usage_type": "attribute"}, {"api_name": "__future__.print_function", "line_number": 321, "usage_type": "attribute"}, {"api_name": "ast.NodeTransformer", "line_number": 334, "usage_type": "attribute"}, {"api_name": "ast.copy_location", "line_number": 346, "usage_type": "call"}, {"api_name": "ast.Str", "line_number": 346, "usage_type": "call"}]}
{"seq_id": "70162597241", "text": "import setuptools\nimport site\nimport sys\n\nsite.ENABLE_USER_SITE = \"--user\" in sys.argv[1:]\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"idse\",\n    version=\"0.1.0\",\n    author=\"Kris\",\n    author_email=\"31852063+krisfris@users.noreply.github.com\",\n    description=\"Interactive data structure editing\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/krisfris/idse\",\n    packages=setuptools.find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.8',\n    install_requires=[\n        'Click',\n        'pyqt5',\n    ],\n    entry_points='''\n        [console_scripts]\n        idse=idse.cli:cli\n    ''',\n)\n", "repo_name": "krisfris/idse", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 873, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "site.ENABLE_USER_SITE", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 5, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 10, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "23950281418", "text": "import bisect\nfrom typing import List\n\n\nclass Solution:\n    def searchRange(self, nums: List[int], target: int) -> List[int]:\n        left = bisect.bisect_left(nums, target)\n        right = bisect.bisect_right(nums, target)\n\n        if left == right:\n            return [-1, -1]\n        else:\n            return [left, right - 1]\n\n\nif __name__ == '__main__':\n    nums = [5, 7, 7, 8, 8, 10]\n    target = 8\n    print(f\"Input: number = {nums}, target = {target}\")\n    print(f\"Output: {Solution().searchRange(nums, target)}\")\n", "repo_name": "rajitbanerjee/leetcode", "sub_path": "src/firstLastPos/search.py", "file_name": "search.py", "file_ext": "py", "file_size_in_byte": 522, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "bisect.bisect_left", "line_number": 7, "usage_type": "call"}, {"api_name": "bisect.bisect_right", "line_number": 8, "usage_type": "call"}]}
{"seq_id": "2248001640", "text": "import os\nfrom datetime import date, datetime\nfrom pathlib import Path\nclass LogSystem:\n    '''\n    Help on logging system modul:\n\n    NAME:\n        LogSystem\n\n    DESCRIPTION:\n        This module is designed to deal with logging files to track events status.\n\n    FUNCTIONS:\n        write_into_log() -> None\n            Function to write into log file to add an event to logs\n    '''\n\n    def __init__(self) -> None:\n        base_dir = Path(__file__).resolve().parent.parent\n        self.__loging_dir = base_dir / \"logging_api\" / \"logs\"\n        # Getting the today date from the local datetime\n        self.__logging_date = str(date.today())\n        self.__time_now = str(datetime.now())\n        self.__log_file_name = os.path.join(self.__loging_dir, f\"Log-{self.__logging_date}.txt\")\n        self.__current_logs = os.listdir()\n        self.__create_log_file()\n\n    def __get_time_now(self):\n        time_now = self.__time_now.split(\".\")[0]\n        return time_now\n    \n    def __create_log_file(self: \"LogSystem\") -> None:\n        ''' Function to create a log file if it doesn't exist. '''\n        if self.__log_file_name in self.__current_logs:\n            pass\n        else:\n            self.write_into_log(\"+\", \"Log file is created\")\n\n    def write_into_log(self: \"LogSystem\", status: str, message: str) -> None:\n        '''\n        Function to write into Log.txt file to add an event to logs\n\n        Args:\n            self (LogSystem): [LogSystem object]\n            status (str): [(!) -> Warning,\n                           (-) -> Error,\n                           (+) -> Info]\n            message (str): [event message]\n        '''\n        try:\n            with open(self.__log_file_name, 'a') as log_file_obj:\n                # write the statement to the log file\n                log_file_obj.write(f\"[{status}] | {self.__get_time_now()} | {message}\\n\")\n        except FileNotFoundError as error:\n            print(error)\n\n\nif __name__ == '__main__':\n    log_obj = LogSystem()\n    log_obj.write_into_log(\"+\", \"Front-end application is running :)\")\n", "repo_name": "kaleem-project/kaleem-backend", "sub_path": "logging_api/logs_api.py", "file_name": "logs_api.py", "file_ext": "py", "file_size_in_byte": 2058, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pathlib.Path", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 23, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 26, "usage_type": "call"}]}
{"seq_id": "35794822172", "text": "import pandas as pd\nimport argparse\nimport json\nfrom transformers import pipeline\nfrom datasets import Dataset\n\ndef normalize(text: str) -> str:\n  text = text.lower()\n  text = text.replace(\"_\", \" \")\n  return text\n\ndef get_pipeline(checkpoint_path: str):\n    classifier = pipeline(\"ner\", checkpoint_path, \n                         device=0, aggregation_strategy='simple')\n    return classifier\n\ndef get_dataset(dataset_path: str):\n    df = pd.read_csv(dataset_path)\n    df['text'] = df['text'].apply(normalize)\n    return Dataset.from_pandas(df)\n\ndef inference(checkpoint_path: str, dataset_path: str):\n   classifier = get_pipeline(checkpoint_path)\n   dataset = get_dataset(dataset_path)\n   return classifier(dataset['text'])\n\ndef count_num_type_entities(result: list):\n    entities_list = []\n    count = 0\n    for entity in result:\n        if entity['entity_group'] not in entities_list:\n            count += 1\n            entities_list.append(entity['entity_group'])\n    return count\n\ndef get_result_by_num_type_entites(dataset_path: str, result: list):\n    result_by_num_entities = {\"index\":[], \"text\": [], \"intent\": [], \"num_entities\":[]}\n    df = pd.read_csv(dataset_path)\n    df['text'] = df['text'].apply(normalize)\n    for i in range(len(result)):\n        result_by_num_entities[\"index\"].append(i)\n        result_by_num_entities[\"text\"].append(df[\"text\"].values[i])\n        result_by_num_entities[\"intent\"].append(df[\"label\"].values[i])\n        result_by_num_entities[\"num_entities\"].append(count_num_type_entities(result[i]))\n    \n    return pd.DataFrame(result_by_num_entities)\n\ndef create_pseudo_label(df:pd.DataFrame, result:list):\n    result_object = {\"rasa_nlu_data\":{\"common_examples\":[]}}\n    original_df = df\n    df = df.drop(df[df['intent'] == 'chitchat_ask_math'].index)\n    df = df.sort_values('num_entities', ascending=False)\n    df = df.iloc[:7000]\n    for index in df['index']:\n        entities_list = []\n        for entity in result[index]:\n            if entity['entity_group'] == \"ROLE\":\n                entity['entity_group'] = \"POSITION\"\n            entities_list.append({\n                \"end\":entity['end'],\n                \"entity\": entity['entity_group'],\n                \"start\": entity['start'],\n                \"value\": entity[\"word\"],\n            })\n        added_object = {}\n        added_object['entities'] = entities_list\n        added_object['intent'] = original_df.iloc[index][\"intent\"]\n        added_object['text'] = original_df.iloc[index][\"text\"]\n        added_object['source'] = \"train\"\n        result_object['rasa_nlu_data']['common_examples'].append(added_object)\n    return result_object\n\ndef write_json_file(result_object: dict):\n    new_json_object = json.dumps(result_object,ensure_ascii=False).encode('utf8')\n    with open(\"sample.json\", \"w\", encoding='utf8') as outfile:\n        outfile.write(new_json_object.decode())\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--checkpoint-path\", type=str, required=True)\n    parser.add_argument(\"--data-directory\", type=str, required=True)\n    args = parser.parse_args()\n\n    result = inference(checkpoint_path=args.checkpoint_path, dataset_path=args.data_directory)\n    result_by_num_entities = get_result_by_num_type_entites(dataset_path=args.data_directory, result=result)\n\n    result_object = create_pseudo_label(result_by_num_entities, result)\n    write_json_file(result_object=result_object)", "repo_name": "hungsvdut2k2/NER", "sub_path": "src/inference.py", "file_name": "inference.py", "file_ext": "py", "file_size_in_byte": 3434, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "transformers.pipeline", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "datasets.Dataset.from_pandas", "line_number": 20, "usage_type": "call"}, {"api_name": "datasets.Dataset", "line_number": 20, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 48, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 74, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 79, "usage_type": "call"}]}
{"seq_id": "72867333241", "text": "r\"\"\"@package motsfinder.exprs.series\n\nBase class for general truncated series expressions.\n\"\"\"\n\nfrom __future__ import print_function\nfrom six import add_metaclass, iteritems\n\nfrom abc import abstractmethod\nfrom scipy.interpolate import interp1d\nimport numpy as np\nfrom mpmath import mp\n\nfrom ..utils import isiterable\nfrom .numexpr import NumericExpression\n\n\n__all__ = [\n    \"SeriesExpression\",\n]\n\n\ndef resize_coeffs(coeffs, old_shape, new_shape):\n    r\"\"\"Resize 2D coefficient matrix to a new shape.\n\n    For orthogonal bases, this is equivalent to *resampling* the series\n    expansion to a different resolution. Increasing the resolution and\n    shrinking it afterwards is lossless.\n\n    This function can be used to resize 1D coefficients by specifying the\n    second dimension to have one element, i.e.:\n\n        coeffs = resize_coeffs(coeffs, (old_num, 1), (new_num, 1))\n\n    @param coeffs\n        Flattened list of current coefficients, which should be interpreted\n        via `old_shape`.\n    @param old_shape\n        2-tuple/list of the coefficient matrix size.\n    @param new_shape\n        2-tuple/list of the desired new matrix size.\n\n    @return Flattened coefficient list corresponding to the new shape.\n    \"\"\"\n    arr = np.array(coeffs, dtype=object)\n    mat = arr.reshape(*old_shape)\n    N1, N2 = new_shape\n    mat = mat[:N1, :N2]\n    mat = np.pad(mat, ((0, max(0, N1-mat.shape[0])), (0, max(0, N2-mat.shape[1]))),\n                 mode='constant', constant_values=0)\n    return list(mat.flatten())\n\n\ndef transform_domains(points, from_domain, to_domain, use_mp=False, dps=None):\n    r\"\"\"Linearly transform a set of points between two intervals.\n\n    The points are transformed bijectively, linearly, and orientation\n    preserving. The values are guaranteed to lie in the target domain, i.e.\n    any numerical noise will not lead to points lying slightly outside after\n    the transformation.\n\n    @param points\n        Iterable of all points to transform. Points must be plain values, i.e.\n        no vectors.\n    @param from_domain\n        2-tuple/list indicating the interval the points are currently living\n        in.\n    @param to_domain\n        2-tuple/list indicating the target interval the returned points should\n        live in.\n    @param use_mp\n        Whether to use arbitrary precision math operations (`True`) or faster\n        floating point precision operations (`False`, default).\n    @param dps\n        Number of decimal places to use in case of `use_mp==True`.\n\n    @return A `list` of the transformed points.\n    \"\"\"\n    with mp.workdps(dps or mp.dps):\n        fl = mp.mpf if use_mp else float\n        a, b = map(fl, to_domain)\n        c, d = map(fl, from_domain)\n        scale = (b-a)/(d-c)\n        trans = a - (b-a)*c/(d-c)\n        return [min(b, max(a, scale*p + trans)) for p in points]\n\n\nclass SeriesExpression(NumericExpression):\n    r\"\"\"Base class for truncated series expressions.\n\n    This base class is responsible for managing the coefficient list, domain\n    transformations and many of the sampling and resampling tasks. To perform\n    its task, sub-classes need to implement the following functions:\n\n        _from_physical_space()      # compute coefficients from function values\n        internal_domain()           # the domain of the basis\n        create_collocation_points() # collocation points of the basis\n\n    ... plus the following for numexpr.NumericExpression:\n\n        _expr_str()  # represent the expression\n        _evaluator() # create an evaluator object\n    \"\"\"\n\n    def __init__(self, a_n, domain, **kw):\n        r\"\"\"Parent init for series expressions.\n\n        @param a_n\n            Iterable of coefficient values. May also be empty.\n        @param domain\n            Domain on which this expansion should be defined. This is\n            independent of the domain the basis is defined on.\n\n        Further keyword arguments are passed to the parent init, that is to\n        numexpr.NumericExpression.__init__().\n        \"\"\"\n        super(SeriesExpression, self).__init__(domain=domain, **kw)\n        self._check_a_n(a_n)\n        self.a_n = a_n\n        self.a = domain[0]\n        self.b = domain[1]\n\n    def _check_a_n(self, a_n):\n        r\"\"\"Make sure the coefficients are iterable.\"\"\"\n        if not isiterable(a_n):\n            raise ValueError('Coefficient list must be an iterable.')\n\n    def get_dof(self):\n        r\"\"\"Return the degrees of freedom, \\ie the current number of coefficients.\"\"\"\n        return len(self.a_n)\n\n    @property\n    def N(self):\n        r\"\"\"Degrees of freedom, \\ie current number of coefficients.\"\"\"\n        return self.get_dof()\n\n    def copy(self):\n        r\"\"\"Create a copy of the series expansion expression.\n\n        The coefficients will be copied, i.e. the new expression can be\n        modified independently.\n\n        Sub classes are responsible for transferring additional state over to\n        the copy and should override this method in case they need to.\n        \"\"\"\n        obj = type(self)(self.a_n[:], domain=self.domain, name=self.name)\n        if isinstance(obj.a_n, np.ndarray):\n            obj.a_n = obj.a_n.copy()\n        return obj\n\n    def resample(self, num, use_mp=False, dps=None):\n        r\"\"\"Change resolution of the expansion.\n\n        For orthogonal bases, upsampling is lossless. Also in that case,\n        increasing and then reducing the resolution to its original value is\n        lossless and introduces no numerical noise.\n\n        For non-orthogonal bases, the function currently represented by the\n        expansion will be sampled at the new requested number of collocation\n        points to perform a lossy resampling.\n        \"\"\"\n        if self.get_dof() == num:\n            return\n        if self.is_orthogonal():\n            self.a_n = resize_coeffs(self.a_n, (self.N, 1), (num, 1))\n        else:\n            with self.context(use_mp, dps):\n                self.approximate(self.evaluator(use_mp), num=num,\n                                 use_mp=use_mp)\n\n    def interpolate(self, y, x=None, kind='cubic', num=None, lobatto=True,\n                    use_mp=False, dps=None):\n        r\"\"\"Interpolate data and model the result as a series.\n\n        This is a convenience function that interpolates the given data using\n        `scipy.interpolate.interp1d` via cubic splines (by default) and then\n        approximates the result as a series with `num` terms.\n\n        If no x-values are present, the values `y` are assumed to lie\n        equidistant in the domain `[a,b]` (borders included).\n\n        Since the resulting interpolation function is approximated as a\n        series, there is no guarantee that \\f[\n            f(x_i) = y_i\n        \\f]\n        holds. In general, the higher the value of `num`, the better the above\n        equation is satisfied.\n\n        @param y\n            Data y-values.\n        @param x\n            Positions of the data values on the x-axis. By default, an\n            equidistant grid on the domain `[a,b]` is used.\n        @param kind (string or int, optional)\n            `kind` parameter for the `interp1d` call. Default is `'cubic'`.\n        @param num\n            Number of series terms to use for approximation. Default is to use\n            the current length of the coefficient list.\n        @param use_mp\n            Whether to use `mpmath` computations. Default is `False`. Only\n            affects the approximation step as the interpolation is done by\n            SciPy.\n        @param dps\n            Number of decimal places to use when `use_mp==True`. Default is to\n            use the current global setting.\n\n        \"\"\"\n        if x is None:\n            x = np.linspace(float(self.a), float(self.b), len(y))\n        f = interp1d(x, y, kind=kind)\n        self.approximate(lambda x: float(f(float(x))),\n                         num=num, lobatto=lobatto, use_mp=use_mp, dps=dps)\n\n    def approximate(self, f, num=None, lobatto=True, use_mp=False, dps=None):\n        r\"\"\"Approximate a function.\n\n        This samples the function `f` at `num` points in the current interval\n        `[a,b]` to create a series approximation using `num` terms.\n\n        After calling this, the series represented by this object is\n        guaranteed to have `num` elements, corresponding to `N = num-1` in\n        most textbooks for most bases.\n\n        @param f\n            The function to approximate. May also be a constant, in which case\n            we still create `num` elements (most of which will be zero for\n            most bases).\n        @param num\n            Number of terms to use for approximation. This is also the number\n            of times `f` is evaluated. Default is to use the current length of\n            the coefficient list.\n        @param lobatto\n            Whether to use the Gauss-Lobatto (default) points for sampling or\n            just the Gauss points. May not be used by all child classes.\n        @param use_mp\n            Whether to use `mpmath` computations. Default is `False`.\n        @param dps\n            Number of decimal places to use when `use_mp==True`. Default is to\n            use the current global setting.\n        \"\"\"\n        with self.context(use_mp, dps):\n            if num is None:\n                num = len(self.a_n)\n            if num == 0:\n                raise ValueError(\"Cannot sample function with zero points.\")\n            if not callable(f):\n                try:\n                    f = f.evaluator(use_mp)\n                except AttributeError:\n                    pass\n            if not callable(f):\n                self._approximate_constant(f, num, lobatto, use_mp, dps)\n            else:\n                self._approximate(f, num, lobatto, use_mp, dps)\n\n    def _approximate(self, f, num, lobatto, use_mp, dps):\n        r\"\"\"Perform the approximation of a function.\n\n        This samples the function at the desired number of collocation points\n        and then triggers these samples to be converted to coefficients. The\n        actual conversion will be handled by set_coefficients().\n        \"\"\"\n        with self.context(use_mp, dps):\n            pts = self.collocation_points(num, lobatto=lobatto, use_mp=use_mp, dps=dps)\n            self.set_coefficients(\n                [f(x) for x in pts],\n                physical_space=True, lobatto=lobatto, use_mp=use_mp, dps=dps\n            )\n\n    def _approximate_constant(self, value, num, lobatto, use_mp, dps):\n        r\"\"\"Approximate a constant value.\n\n        Subclasses may implement a more efficient algorithm here. This default\n        implementation simply creates a constant function and approximates\n        this function.\n        \"\"\"\n        self._approximate(lambda x: value, num, lobatto, use_mp, dps)\n\n    def set_coefficients(self, a_n, physical_space=False, lobatto=True,\n                         use_mp=False, dps=None):\n        r\"\"\"Store a new list of coefficients after optional transformation.\n\n        Takes a new coefficient list and either stores it directly. If the\n        list actually represents function values at the collocation points\n        (i.e. in physical space), they are transformed to spectral space\n        before being stored.\n\n        The actual transformation must be implemented by sub classes in the\n        _from_physical_space() method.\n        \"\"\"\n        self._check_a_n(a_n)\n        if physical_space:\n            self.a_n = self._from_physical_space(a_n, lobatto=lobatto,\n                                                 use_mp=use_mp, dps=dps)\n        else:\n            self.a_n = a_n\n\n    @abstractmethod\n    def _from_physical_space(self, a_n, lobatto, use_mp, dps):\n        r\"\"\"Convert values in physical space to spectral space.\"\"\"\n        pass\n\n    def collocation_points(self, num=None, lobatto=True, internal_domain=False,\n                           use_mp=False, dps=None, **kw):\n        r\"\"\"Return the Gauss-Lobatto or Gauss points for this series.\n\n        The returned list will have `num` elements. If you know the value of\n        `N`, you can find the corresponding value of `num` by checking the\n        textbooks for the number of degrees of freedom. The exact use of `N`\n        seems to be inconsistent across different books for many bases.\n\n        The ``**kw`` argument is passed to the subclass method for creating\n        the collocation points. These may be used to further specify options\n        such as symmetry, etc.\n\n        @param num (int, optional)\n            Number of collocation points to create. Note that in most printed\n            formulas, the points have indices `0, 1, ..., N`, i.e. there are\n            `N+1` points. This function, however, returns `num` points. To get\n            the points that belong to a certain value of `N` in these\n            formulas, call this function with `num=N+1`.\n            The default is the current number of coefficients.\n        @param lobatto (boolean, optional)\n            If `True` (default), return the Gauss-Lobatto points, which\n            include the endpoints of the interval. Otherwise, return the\n            interior (Gauss) points.\n        @param internal_domain (boolean, optional)\n            If `False` (the default), return the collocation points mapped to\n            the range `[a,b]`. If `True`, use the internal domain (depends on\n            subclass).\n        @param use_mp (boolean, optional)\n            Whether to compute the points using `mpmath` arbitrary precision\n            calculations. The result will then be a list of `mpmath.mpf`\n            floats. Default is `False`.\n        @param dps (int, optional)\n            Number of decimal places to use when `use_mp==True`.\n\n        @return List of collocation points either in the domain of the basis (if\n            `internal_domain==True`) or the physical domain (if\n            `internal_domain==False`).\n        \"\"\"\n        if num is None:\n            num = len(self.a_n)\n        x = self.create_collocation_points(num, lobatto=lobatto,\n                                           use_mp=use_mp, dps=dps, **kw)\n        if not internal_domain:\n            x = transform_domains(x, self.internal_domain(), self.domain,\n                                  use_mp=use_mp, dps=dps)\n        return x\n\n    @abstractmethod\n    def internal_domain(self):\n        pass\n\n    @abstractmethod\n    def create_collocation_points(cls, num, lobatto=True, use_mp=False, dps=None, **kw):\n        \"\"\"Subclasses should implement this as @classmethod.\"\"\"\n        # pylint: disable=no-self-argument\n        pass\n\n    @abstractmethod\n    def is_orthogonal(self):\n        r\"\"\"Whether this series consists of orthogonal functions.\"\"\"\n        pass\n\n    @classmethod\n    def validate_and_fix_resolution(cls, proposed_resolution):\n        return proposed_resolution\n\n    @classmethod\n    def from_function(cls, f, num, domain=None, lobatto=True, use_mp=False,\n                      dps=None, **kw):\n        if domain is None:\n            domain = f.domain\n        expr = cls([], domain=domain, **kw)\n        expr.approximate(f, num=num, lobatto=lobatto, use_mp=use_mp, dps=dps)\n        return expr\n", "repo_name": "daniel-dpk/distorted-motsfinder-public", "sub_path": "motsfinder/exprs/series.py", "file_name": "series.py", "file_ext": "py", "file_size_in_byte": 15088, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "40", "api": [{"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 49, "usage_type": "call"}, {"api_name": "mpmath.mp.workdps", "line_number": 79, "usage_type": "call"}, {"api_name": "mpmath.mp", "line_number": 79, "usage_type": "name"}, {"api_name": "mpmath.mp.dps", "line_number": 79, "usage_type": "attribute"}, {"api_name": "mpmath.mp.mpf", "line_number": 80, "usage_type": "attribute"}, {"api_name": "mpmath.mp", "line_number": 80, "usage_type": "name"}, {"api_name": "numexpr.NumericExpression", "line_number": 88, "usage_type": "name"}, {"api_name": "utils.isiterable", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 209, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 210, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 298, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 351, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 355, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 361, "usage_type": "name"}]}
{"seq_id": "71931500281", "text": "\"\"\"\nValue function that uses frame stacking.\n\nAuthor: Ian Char\nDate: December 12, 2022\n\"\"\"\nimport torch\n\nfrom rlkit.torch.networks.mlp import Mlp\nfrom rlkit.torch.core import PyTorchModule\n\n\nclass FrameStackedQNet(PyTorchModule):\n    def __init__(\n        self,\n        obs_dim: int,\n        act_dim: int,\n        hidden_depth: int,\n        hidden_width: int,\n        lookback_len: int,\n        encode_action_seq: bool = False,\n    ):\n        \"\"\"Constructor.\n\n        Args:\n            obs_dim: Size of the observation dim.\n            act_dim: Size of the action dim.\n            encode_size: Size of the statistic.\n            hidden_depth: Depth of the network.\n            hidden_width: Width of the network.\n            lookback_len: The lookback to consider for the integral.\n            encode_action_seq: Whether to encode past action sequence.\n        \"\"\"\n        super().__init__()\n        self.lookback_len = lookback_len\n        self.encode_action_seq = encode_action_seq\n        in_dim = obs_dim * lookback_len + act_dim\n        if encode_action_seq:\n            in_dim += act_dim * lookback_len\n        self.net = Mlp(\n            input_size=in_dim,\n            output_size=1,\n            hidden_sizes=[hidden_width for _ in range(hidden_depth)],\n        )\n\n    def forward(self, obs_seq, prev_act_seq, act, **kwargs):\n        \"\"\"Forward pass.\n\n        Args:\n            obs_seq: Observation sequence (batch_size, L, obs_dim)\n            prev_act_seq: Previous action sequence (batch_size, L, act_dim)\n            act: The current action (batch_size, act_dim)\n\n        Returns: Value for last observation + action (batch_size, 1)\n        \"\"\"\n        net_in = obs_seq[:, -self.lookback_len:].view(obs_seq.shape[0], -1)\n        if self.encode_action_seq:\n            net_in = torch.cat([\n                net_in,\n                prev_act_seq[:, -self.lookback_len].view(obs_seq.shape[0], -1),\n            ], dim=-1)\n        net_in = torch.cat([\n            net_in,\n            act,\n        ], dim=-1)\n        return self.net(net_in)\n", "repo_name": "IanChar/rlkit2", "sub_path": "rlkit/torch/networks/seq2val/frame_stacked_val.py", "file_name": "frame_stacked_val.py", "file_ext": "py", "file_size_in_byte": 2044, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "rlkit.torch.core.PyTorchModule", "line_number": 13, "usage_type": "name"}, {"api_name": "rlkit.torch.networks.mlp.Mlp", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 62, "usage_type": "call"}]}
{"seq_id": "36006283011", "text": "import os\nimport argparse\nimport numpy as np\nfrom src.create_dataset import user_item_rating_matrix_from_dataset\n\n\ndef evaluate_total_variation(original_matrix, result_matrix):\n    different = sum(sum(original_matrix != result_matrix))\n    size = original_matrix.size\n    return different / size * 100\n\n\ndef evaluate_ones_variation(original_matrix, result_matrix):\n    equal = sum(sum(np.logical_and(original_matrix, result_matrix)))\n    original_ones_number = sum(sum(original_matrix))\n    result_ones_number = sum(sum(result_matrix))\n    ones_variation = (1 - equal / original_ones_number) * 100\n\n    return original_ones_number, result_ones_number, ones_variation\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--original_dataset', required=True)\nparser.add_argument('--result_root_directory', required=False, default='results')\n\nargs = parser.parse_args()\ndataset_path = args.original_dataset\ndataset_name = os.path.split(dataset_path)[-1].split('.')[0]\nresult_directory = args.result_root_directory\n\nrating_matrix = user_item_rating_matrix_from_dataset(dataset_path)\n\nprint(f'dataset: {dataset_name}')\n\nfor root, directory, files in os.walk(result_directory):\n    for filename in sorted(files):\n        file_path = os.path.join(root, filename)\n        eps = filename.split(\"_\")[2]\n        eps = \".\".join([eps[0], eps[1:]]) if eps[0] == '0' else eps\n        result = user_item_rating_matrix_from_dataset(file_path)\n        variation_percentage = evaluate_total_variation(rating_matrix, result)\n        original_ones_number, result_ones_number, ones_variation = evaluate_ones_variation(rating_matrix, result)\n\n        print(f'epsilon: {eps} '\n              f'\\nnumero di 1 originale: {original_ones_number}'\n              f'\\nnumero di 1 risultato: {original_ones_number}'\n              f'\\nvariazione sugli 1: {ones_variation}%'\n              f'\\nvariazione totale: {variation_percentage}%')\n", "repo_name": "sisinflab/LHider", "sub_path": "analysis/evaluate_variation.py", "file_name": "evaluate_variation.py", "file_ext": "py", "file_size_in_byte": 1907, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.logical_and", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "src.create_dataset.user_item_rating_matrix_from_dataset", "line_number": 31, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "src.create_dataset.user_item_rating_matrix_from_dataset", "line_number": 40, "usage_type": "call"}]}
{"seq_id": "20728207576", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport os\nimport json\nimport datetime\nimport time\nimport copy\nimport pymongo\nimport logging\nimport gridfs\n\n\ndef get_doc(browser,id,name):\n    url = 'https://jnportal.ujn.gov.rs/tender-eo/' + str(id)\n    browser.get(url)\n    time.sleep(3)\n    elem = browser.find_element_by_css_selector('html body.skin-blue.sidebar-mini form#form1 div.wrapper div#uiContentWrapper.content-wrapper section#uiContent.content div#uiMainContentPH_uiTenderWrapper.notsubtracted div#documentControl_uiMainContentPH_uiMainContentPH_uiDocumentControl.dx-accordion.dx-widget.dx-visibility-change-handler.dx-collection div.dx-accordion-wrapper div.dx-item.dx-accordion-item.dx-item-selected.dx-accordion-item-opened div.dx-item-content.dx-accordion-item-title a')\n    elem.click()\n    time.sleep(1)\n    if(len(name)>100):\n        namex = str(name)[:100]\n    else:\n        namex = str(name)\n    try:\n        os.rename(\"Konkursna dokumentacija za ponuđača.zip\", namex + \".zip\")\n    except Exception as e:\n        print(\"ovde greska\")\n        print(e)\n    place = browser.find_element_by_id(\"uiMainContentPH_uiMainContentPH_ucBasicDetails_uiNuts\")\n    mesto = str(place.text)\n    word = mesto.split(',')\n    return word[1].strip()\n\n\n\ncurr_date = datetime.date.today()\nusername = input(\"Enter db username: \")\npassword = input(\"Enter db password: \")\n\nclient = pymongo.MongoClient(\"mongodb+srv://\"+ username + \":\" + password + \"@cluster0.dstyf.mongodb.net/test?retryWrites=true&w=majority\",27017)\ndb = client.bonafides\nnabavke = db.nabavke\nkonkursne = db.konkursne\nfs = gridfs.GridFSBucket(db)\n\nprofile = webdriver.FirefoxProfile()\nprofile.set_preference(\"browser.download.dir\",'/home/duhizjame/Desktop/python')\nprofile.set_preference(\"browser.download.folderList\",2)\nprofile.set_preference(\"browser.helperApps.neverAsk.saveToDisk\", \"text/plain,text/x-csv,text/csv,application/vnd.ms-excel,application/csv,application/x-csv,text/csv,text/comma-separated-values,text/x-comma-separated-values,text/tab-separated-values,application/pdf,application/zip\")\nprofile.set_preference(\"browser.download.manager.showWhenStarting\",False)\nprofile.set_preference(\"browser.helperApps.neverAsk.openFile\",\"text/plain,text/x-csv,text/csv,application/vnd.ms-excel,application/csv,application/x-csv,text/csv,text/comma-separated-values,text/x-comma-separated-values,text/tab-separated-values,application/pdf,application/zip\")\nprofile.set_preference(\"browser.helperApps.alwaysAsk.force\", False)\nprofile.set_preference(\"browser.download.manager.useWindow\", False)\nprofile.set_preference(\"browser.download.manager.focusWhenStarting\", False)\nprofile.set_preference(\"browser.helperApps.neverAsk.openFile\", \"\")\nprofile.set_preference(\"browser.download.manager.alertOnEXEOpen\", False)\nprofile.set_preference(\"browser.download.manager.showAlertOnComplete\", False)\nprofile.set_preference(\"browser.download.manager.closeWhenDone\", True)\nprofile.set_preference(\"pdfjs.disabled\", True)\n\nurl = 'https://jnportal.ujn.gov.rs/'\nbrowser = webdriver.Firefox(firefox_profile=profile)\nbrowser.get(url + 'prijava')\nusername = browser.find_element_by_id('uiUsername')\nusername.send_keys('amilosevic')\npass_field = browser.find_element_by_id('uiPassword')\npassword = input('Unesite password:')\npass_field.send_keys(password)\n\nlog_button = browser.find_element_by_id('uiLogin')\nlog_button.click()\n\ndani = input(\"unesite broj dana: \")\n\ntimed = datetime.timedelta(days=int(dani))\ncurr_date = curr_date - timed\n\n# params = f'oglasi-svi?initFilter=[\"PublishDate\",\">=\",\"{curr_date}\"]'\n# browser = webdriver.Firefox(firefox_profile=profile)\n# url = 'https://jnportal.ujn.gov.rs/'\n# browser.get(url + params)\n# json_dl = browser.find_element_by_css_selector('html body.skin-blue.sidebar-mini form#form1 div.wrapper div#uiContentWrapper.content-wrapper section#uiContent.content div#searchGridContainer.dx-widget.searchGridContainer.dx-visibility-change-handler div.dx-datagrid.dx-gridbase-container div.dx-datagrid-header-panel div.dx-toolbar.dx-widget.dx-visibility-change-handler.dx-collection div.dx-toolbar-items-container div.dx-toolbar-after div.dx-item.dx-toolbar-item.dx-toolbar-button div.dx-item-content.dx-toolbar-item-content div.dx-button.dx-button-normal.dx-button-mode-contained.dx-widget.dx-button-has-icon div.dx-button-content i.dx-icon.fa.fa-file-o') \n# curr_time = datetime.datetime.now()\n# json_dl.click()\n# times = str(curr_time.year) + '-' + str(curr_time.month-1) + '-' + str(curr_time.day) + '-' + str(curr_time.hour) + '-' + str(curr_time.minute) + '-' + str(curr_time.second)\n# times2 = str(curr_time.year) + '-' + str(curr_time.month-1) + '-' + str(curr_time.day) + '-' + str(curr_time.hour) + '-' + str(curr_time.minute) + '-' + str(curr_time.second+1)\n# filename = 'Огласи о јавној набавци_' + times + '.json'\n# filename2 = 'Огласи о јавној набавци_' + times2 + '.json'\n\nparams = f'postupci-svi?initFilter=[\"NoticePublishDate\",\">=\",\"{curr_date}\"]'\nbrowser.get(url + params)\njson_dl = browser.find_element_by_css_selector('html body.skin-blue.sidebar-mini form#form1 div.wrapper div#uiContentWrapper.content-wrapper section#uiContent.content div#searchGridContainer.dx-widget.searchGridContainer.dx-visibility-change-handler div.dx-datagrid.dx-gridbase-container div.dx-datagrid-header-panel div.dx-toolbar.dx-widget.dx-visibility-change-handler.dx-collection div.dx-toolbar-items-container div.dx-toolbar-after div.dx-item.dx-toolbar-item.dx-toolbar-button div.dx-item-content.dx-toolbar-item-content div.dx-button.dx-button-normal.dx-button-mode-contained.dx-widget.dx-button-has-icon div.dx-button-content i.dx-icon.fa.fa-file-o') \ncurr_time = datetime.datetime.now()\njson_dl.click()\ntimes = str(curr_time.year) + '-' + str(curr_time.month-1) + '-' + str(curr_time.day) + '-' + str(curr_time.hour) + '-' + str(curr_time.minute) + '-' + str(curr_time.second)\ntimes2 = str(curr_time.year) + '-' + str(curr_time.month-1) + '-' + str(curr_time.day) + '-' + str(curr_time.hour) + '-' + str(curr_time.minute) + '-' + str(curr_time.second+1)\nfilename = 'Поступци јавних набавки_' + times + '.json'\nfilename2 = 'Поступци јавних набавки_' + times2 + '.json'\n\ntime.sleep(1)\n\ntry:\n    f = open(filename,'r')\nexcept:\n    f = open(filename2,'r')\ndata = json.load(f)\ntry:\n    os.remove(filename)\nexcept:\n    os.remove(filename2)\n\n\n# mesta = open('mesta.json','r')\n# mjson = json.load(mesta)\n\nfor i in data:\n    id = i['Id']\n    name = i['Name']\n    value = i['EstimatedValue']\n    tip = i['TypeContract']\n    procId = i['ProcedureType']\n    cpv = i['CPVExtended'][:8]\n    rok = i['SubmissionDeadline']\n    if rok is not None:\n        rok = i['SubmissionDeadline'][:10]\n    narucilac = i['BusinessEntityName']\n    planId = i['PlanItemId']\n    mKod = i['Nuts']\n    # for m in mjson:\n    #     if m['Code'] == mKod:\n    #         mesto = m['Region']\n    if tip == \"Добра\":\n        continue\n    nabavka = {\n        \"name\" : name,\n        \"value\" : value,\n        \"type\" : tip,\n        \"procedure\" : procId,\n        \"cpv\" : cpv,\n        \"deadline\" : rok,\n        \"narucilac\" : narucilac,\n        \"planId\" : planId,\n        # \"region\" : mesto,\n        \"id\" : id\n    }\n    try:\n        mesto = get_doc(browser,id,name)\n        nabavka.update({'mesto':mesto})\n        f = open(str(name)+\".zip\",'rb')\n        content = f.read()\n        fs.upload_from_stream_with_id(id,name,content)\n        nabavke.insert_one(nabavka)\n    except Exception as e:\n        print(e)\n    \n\n\n\n    \n\n\n\n\n", "repo_name": "duhizjame/pjn_scrapper", "sub_path": "nba + ostalo/scrapper.py", "file_name": "scrapper.py", "file_ext": "py", "file_size_in_byte": 7572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 19, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 40, "usage_type": "call"}, {"api_name": "gridfs.GridFSBucket", "line_number": 44, "usage_type": "call"}, {"api_name": "selenium.webdriver.FirefoxProfile", "line_number": 46, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 46, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 62, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 62, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 100, "usage_type": "call"}, {"api_name": "json.load", "line_number": 106, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 108, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 110, "usage_type": "call"}]}
{"seq_id": "29242748910", "text": "#\n\n#!Celery\nfrom celery import shared_task\n\n\n#!Django\nfrom django.template.loader import render_to_string\nfrom django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode\nfrom django.utils.encoding import force_bytes\nfrom django.contrib.auth.tokens import default_token_generator\nfrom django.core.mail import EmailMessage\n\n\n#!Apps\nimport apps\n\n\n# ?activateAccount\n@shared_task(queue=\"tasks\")\ndef activateAccount(**kwargs):\n    pk = kwargs.get(\"pk\")\n    domain = kwargs.get(\"domain\")\n    account = apps.account.models.Account.objects.get(pk=pk)\n\n    mail_subject = \"Please activate your account!\"\n    message = render_to_string(\n        \"account/account_verification.html\",\n        {\n            \"account\": account,\n            \"domain\": domain,\n            \"uid\": urlsafe_base64_encode(force_bytes(account.pk)),\n            \"token\": default_token_generator.make_token(account),\n        },\n    )\n    to_email = account.email\n    send_email = EmailMessage(mail_subject, message, to=[to_email])\n    result = send_email.send()\n    return True\n", "repo_name": "riadelimemmedov/RentBook-System", "sub_path": "tasks/ex_activate_account.py", "file_name": "ex_activate_account.py", "file_ext": "py", "file_size_in_byte": 1049, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "apps.account.models.Account.objects.get", "line_number": 24, "usage_type": "call"}, {"api_name": "apps.account", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.template.loader.render_to_string", "line_number": 27, "usage_type": "call"}, {"api_name": "django.utils.http.urlsafe_base64_encode", "line_number": 32, "usage_type": "call"}, {"api_name": "django.utils.encoding.force_bytes", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.tokens.default_token_generator.make_token", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.auth.tokens.default_token_generator", "line_number": 33, "usage_type": "name"}, {"api_name": "django.core.mail.EmailMessage", "line_number": 37, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "7453361466", "text": "import math\r\nimport random\r\nimport numpy as np\r\nfrom collections import defaultdict\r\n\r\nimport uuid\r\nimport mesa\r\nimport numpy as np\r\nimport pandas\r\nfrom mesa import space\r\nfrom mesa.batchrunner import BatchRunner\r\nfrom mesa.datacollection import DataCollector\r\nfrom mesa.time import RandomActivation\r\nfrom mesa.visualization.ModularVisualization import ModularServer, VisualizationElement\r\nfrom mesa.visualization.modules import ChartModule\r\n\r\nclass ContinuousCanvas(VisualizationElement):\r\n    local_includes = [\r\n        \"./js/simple_continuous_canvas.js\",\r\n    ]\r\n\r\n    def __init__(self, canvas_height=500,\r\n                 canvas_width=500, instantiate=True):\r\n        self.canvas_height = canvas_height\r\n        self.canvas_width = canvas_width\r\n        self.identifier = \"space-canvas\"\r\n        if (instantiate):\r\n            new_element = (\"new Simple_Continuous_Module({}, {},'{}')\".\r\n                           format(self.canvas_width, self.canvas_height, self.identifier))\r\n            self.js_code = \"elements.push(\" + new_element + \");\"\r\n\r\n    def portrayal_method(self, obj):\r\n        return obj.portrayal_method()\r\n\r\n    def render(self, model):\r\n        representation = defaultdict(list)\r\n        for obj in model.schedule.agents:\r\n            portrayal = self.portrayal_method(obj)\r\n            if portrayal:\r\n                portrayal[\"x\"] = ((obj.pos[0] - model.space.x_min) /\r\n                                  (model.space.x_max - model.space.x_min))\r\n                portrayal[\"y\"] = ((obj.pos[1] - model.space.y_min) /\r\n                                  (model.space.y_max - model.space.y_min))\r\n            representation[portrayal[\"Layer\"]].append(portrayal)\r\n        return representation\r\n\r\ndef wander(x, y, speed, model):\r\n    r = random.random() * math.pi * 2\r\n    new_x = max(min(x + math.cos(r) * speed, model.space.x_max), model.space.x_min)\r\n    new_y = max(min(y + math.sin(r) * speed, model.space.y_max), model.space.y_min)\r\n\r\n    return new_x, new_y\r\n\r\n\r\ndef num_population(m):\r\n    i = 0\r\n    for agents in m.schedule.agents:\r\n        if agents.agent_class == None:\r\n            i += 1\r\n    return i\r\n\r\ndef num_persons(m):\r\n    i = 0\r\n    for agents in m.schedule.agents:\r\n        if agents.agent_class == None:\r\n            if not agents.loup:\r\n                i += 1\r\n    return i\r\n\r\ndef num_loups(m):\r\n    i = 0\r\n    for agents in m.schedule.agents:\r\n        if agents.agent_class == None:\r\n            if agents.loup:\r\n                i += 1\r\n    return i\r\n\r\ndef num_loups_transform(m):\r\n    i = 0\r\n    for agents in m.schedule.agents:\r\n        if agents.agent_class == None:\r\n            if agents.loup:\r\n                if agents.transform:\r\n                    i += 1\r\n    return i\r\n\r\n\r\n\r\nclass  Village(mesa.Model):\r\n    def  __init__(self,  n_villagers, n_loup, n_cleric, n_hunter):\r\n        mesa.Model.__init__(self)\r\n        self.space = mesa.space.ContinuousSpace(600, 600, False)\r\n        self.schedule = RandomActivation(self)\r\n\r\n        self.dc = DataCollector(model_reporters={\"num_population\": lambda m: num_population(m),\r\n                                                 \"num_persons\": lambda m: num_persons(m),\r\n                                                 \"num_loups\": lambda m: num_loups(m),\r\n                                                 \"num_loups_transform\": lambda m: num_loups_transform(m)})\r\n\r\n        for  _  in  range(n_villagers):\r\n            self.schedule.add(Villager(random.random()  *  600,  random.random()  *  600,  10, uuid.uuid1(), self))\r\n        for  _  in  range(n_loup):\r\n            self.schedule.add(Villager(random.random()  *  600,  random.random()  *  600,  10, uuid.uuid1(), self, loup=True ))\r\n        for  _  in  range(n_cleric):\r\n            self.schedule.add(Villager(random.random()  *  600,  random.random()  *  600,  10, uuid.uuid1(), self, agent_class=\"Cleric\"))\r\n        for  _  in  range(n_hunter):\r\n            self.schedule.add(Villager(random.random()  *  600,  random.random()  *  600,  10, uuid.uuid1(), self, agent_class=\"Hunter\"))\r\n\r\n\r\n\r\n    def step(self):\r\n        self.schedule.step()\r\n        self.dc.collect(self)\r\n        if self.schedule.steps >= 1000:\r\n            self.running = False\r\n\r\nclass Villager(mesa.Agent):\r\n    def __init__(self, x, y, speed, unique_id: int, model: Village, distance_attack=40, p_attack=0.6, loup = False, transform = False, agent_class = None, distance_cleric=30, distance_hunt = 40):\r\n        super().__init__(unique_id, model)\r\n        self.pos = (x, y)\r\n        self.speed = speed\r\n        self.model = model\r\n        self.distance_attack = distance_attack\r\n        self.p_attack = p_attack\r\n        self.loup = loup\r\n        self.transform = transform\r\n        self.agent_class = agent_class\r\n        self.distance_cleric = distance_cleric\r\n        self.distance_hunt = distance_hunt\r\n\r\n\r\n    def portrayal_method(self):\r\n\r\n        if self.agent_class == \"Cleric\":\r\n            color = \"green\"\r\n            r = 3\r\n\r\n        elif self.agent_class == \"Hunter\":\r\n            color = \"black\"\r\n            r = 3\r\n\r\n        else:\r\n            if self.loup:\r\n                color = \"red\"\r\n                if self.transform:\r\n                    r = 6\r\n                else:\r\n                    r = 3\r\n\r\n            else:\r\n                color = \"blue\"\r\n                r = 3\r\n\r\n\r\n\r\n        portrayal = {\"Shape\": \"circle\",\r\n                     \"Filled\": \"true\",\r\n                     \"Layer\": 1,\r\n                     \"Color\": color,\r\n                     \"r\": r}\r\n        return portrayal\r\n\r\n    def list_near_agents(self, agents, distance):\r\n\r\n        list_near_agents = []\r\n\r\n        for obj in agents:\r\n            if np.square(obj.pos[0] - self.pos[0]) + np.square(obj.pos[1] - self.pos[1]) < np.square(distance):\r\n                list_near_agents.append(obj)\r\n\r\n        return list_near_agents\r\n\r\n\r\n    def step(self):\r\n\r\n        self.pos = wander(self.pos[0], self.pos[1], self.speed, self.model)\r\n\r\n        if self.agent_class == \"Cleric\":\r\n\r\n            list_near_agents = self.list_near_agents(self.model.schedule.agents, self.distance_cleric)\r\n\r\n            for obj in list_near_agents:\r\n                if obj.loup and not obj.transform:\r\n                    obj.loup = False\r\n\r\n        if self.agent_class == \"Hunter\":\r\n\r\n            list_near_agents = self.list_near_agents(self.model.schedule.agents, self.distance_hunt)\r\n\r\n            for obj in list_near_agents:\r\n                if obj.transform:\r\n                    if np.random.random() < self.p_attack:\r\n                        self.model.schedule.remove(obj)\r\n\r\n        else:\r\n\r\n            if self.loup and np.random.random() < 0.1:\r\n                self.transform = True\r\n\r\n            if self.loup:\r\n                if self.transform:\r\n                    list_near_agents = self.list_near_agents(self.model.schedule.agents, self.distance_attack)\r\n\r\n                    for obj in list_near_agents:\r\n                        if np.random.random():\r\n                            obj.loup = True\r\n\r\n\r\n\r\n\r\ndef run_single_server():\r\n\r\n    slider_villagers = mesa.visualization.ModularVisualization.UserSettableParameter('slider', \"slider_villagers\", 50,\r\n                                                                                     0, 50, 1)\r\n\r\n    slider_clerics = mesa.visualization.ModularVisualization.UserSettableParameter('slider', \"slider_clerics\", 1, 0,\r\n                                                                                   20, 1)\r\n\r\n    slider_hunters = mesa.visualization.ModularVisualization.UserSettableParameter('slider', \"slider_hunters\", 2, 0,\r\n                                                                                   20, 1)\r\n\r\n    slider_loups = mesa.visualization.ModularVisualization.UserSettableParameter('slider', \"slider_loups\", 5, 0, 10, 1)\r\n\r\n    server = ModularServer(Village, [ContinuousCanvas(), ChartModule([{\"Label\": \"num_population\", \"Color\": \"Green\"},\r\n                                                                      {\"Label\": \"num_persons\", \"Color\": \"Blue\"},\r\n                                                                      {\"Label\": \"num_loups\", \"Color\": \"Orange\"},\r\n                                                                      {\"Label\": \"num_loups_transform\", \"Color\": \"Red\"}],\r\n                                                                     data_collector_name='dc')],\r\n                           \"Village\",\r\n                           {\"n_villagers\": slider_villagers, \"n_loup\": slider_loups, \"n_cleric\": slider_clerics,\r\n                            \"n_hunter\": slider_hunters})\r\n    server.port = 8521\r\n    server.launch()\r\n\r\n\r\ndef run_batch():\r\n\r\n    model_reporters = {\"num_population\": lambda m: num_population(m),\r\n                       \"num_persons\": lambda m: num_persons(m),\r\n                       \"num_loups\": lambda m: num_loups(m),\r\n                       \"num_loups_transform\": lambda m: num_loups_transform(m)}\r\n\r\n    params_dict = {\"n_villagers\": [50], \"n_loup\": [5], \"n_cleric\": range(0, 6),\r\n                            \"n_hunter\": [1]}\r\n\r\n\r\n    batchrunner = BatchRunner(Village, params_dict, model_reporters=model_reporters)\r\n\r\n    batchrunner.run_all()\r\n\r\n\r\n\r\n\r\nif  __name__  ==  \"__main__\":\r\n    #run_batch()\r\n    run_single_server()", "repo_name": "yangqingchen98/Multi-agent", "sub_path": "TP1/village.py", "file_name": "village.py", "file_ext": "py", "file_size_in_byte": 9271, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "mesa.visualization.ModularVisualization.VisualizationElement", "line_number": 17, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 36, "usage_type": "call"}, {"api_name": "random.random", "line_number": 48, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 48, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 49, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 50, "usage_type": "call"}, {"api_name": "mesa.Model", "line_number": 89, "usage_type": "attribute"}, {"api_name": "mesa.Model.__init__", "line_number": 91, "usage_type": "call"}, {"api_name": "mesa.Model", "line_number": 91, "usage_type": "attribute"}, {"api_name": "mesa.space.ContinuousSpace", "line_number": 92, "usage_type": "call"}, {"api_name": "mesa.space", "line_number": 92, "usage_type": "attribute"}, {"api_name": "mesa.time.RandomActivation", "line_number": 93, "usage_type": "call"}, {"api_name": "mesa.datacollection.DataCollector", "line_number": 95, "usage_type": "call"}, {"api_name": "random.random", "line_number": 101, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 101, "usage_type": "call"}, {"api_name": "random.random", "line_number": 103, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 103, "usage_type": "call"}, {"api_name": "random.random", "line_number": 105, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 105, "usage_type": "call"}, {"api_name": "random.random", "line_number": 107, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 107, "usage_type": "call"}, {"api_name": "mesa.Agent", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.square", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 192, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 197, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 205, "usage_type": "attribute"}, {"api_name": "mesa.visualization.ModularVisualization.UserSettableParameter", "line_number": 213, "usage_type": "call"}, {"api_name": "mesa.visualization", "line_number": 213, "usage_type": "attribute"}, {"api_name": "mesa.visualization.ModularVisualization.UserSettableParameter", "line_number": 216, "usage_type": "call"}, {"api_name": "mesa.visualization", "line_number": 216, "usage_type": "attribute"}, {"api_name": "mesa.visualization.ModularVisualization.UserSettableParameter", "line_number": 219, "usage_type": "call"}, {"api_name": "mesa.visualization", "line_number": 219, "usage_type": "attribute"}, {"api_name": "mesa.visualization.ModularVisualization.UserSettableParameter", "line_number": 222, "usage_type": "call"}, {"api_name": "mesa.visualization", "line_number": 222, "usage_type": "attribute"}, {"api_name": "mesa.visualization.ModularVisualization.ModularServer", "line_number": 224, "usage_type": "call"}, {"api_name": "mesa.visualization.modules.ChartModule", "line_number": 224, "usage_type": "call"}, {"api_name": "mesa.batchrunner.BatchRunner", "line_number": 247, "usage_type": "call"}]}
{"seq_id": "24915883907", "text": "from flamejam import app, db, cache\nfrom flamejam.models import Jam, JamStatusCode, Game, GamePackage\nfrom flamejam.forms import ParticipateForm, CancelParticipationForm, TeamFinderFilter\nfrom flask import render_template, redirect, flash, request\nfrom flask_login import login_required, current_user\nfrom sqlalchemy import desc\n\n\n@app.route('/jams/')\n@cache.cached(timeout=50)\ndef jams():\n    return render_template(\"jams.html\", jams=Jam.query.order_by(desc(Jam.start_time)).all())\n\n\n@app.route('/jams/<jam_slug>/', methods=(\"GET\", \"POST\"))\n@cache.cached(timeout=50)\ndef jam_info(jam_slug):\n    jam = Jam.query.filter_by(slug=jam_slug).first_or_404()\n    return render_template('jam/info.html', jam=jam)\n\n\n@app.route('/jams/<jam_slug>/games/<page>')\ndef jam_games_lis(jam_slug, page):\n    jam = Jam.query.filter_by(slug=jam_slug).first_or_404()\n    if page.isdigit():\n        page = int(page)\n    else:\n        page = 1\n\n    if page < 1:\n        page = 1\n    games = Game.query.filter_by(is_deleted=False, jam_id=jam.id).paginate(page, 6, False)\n    jams = Jam.query.all()\n\n    return render_template(\"game/list.html\", games=games, jams=jams, jam=jam)\n\n\n@app.route('/jams/<jam_slug>/countdown', methods=(\"GET\", \"POST\"))\ndef countdown(jam_slug):\n    jam = Jam.query.filter_by(slug=jam_slug).first_or_404()\n    return render_template('misc/countdown.html', jam=jam)\n\n\n@app.route('/jams/<jam_slug>/participate/', methods=[\"POST\", \"GET\"])\n@login_required\ndef jam_participate(jam_slug):\n    jam = Jam.query.filter_by(slug=jam_slug).first_or_404()\n    user = current_user\n\n    if jam.getStatus().code > JamStatusCode.PACKAGING:\n        flash(\"You cannot register for participation in a jam after it has finished or is in rating phase.\", \"error\")\n        return redirect(jam.url())\n\n    if jam.getStatus().code < JamStatusCode.REGISTRATION:\n        flash(\"You cannot register for participation before the registration started.\", \"error\")\n        return redirect(jam.url())\n\n    if user.getParticipation(jam):\n        flash(\"You already participate in this jam.\", \"warning\")\n        return redirect(jam.url())\n\n    form = ParticipateForm()\n\n    if form.validate_on_submit():\n        user.joinJam(jam)\n        user.getParticipation(jam).show_in_finder = form.show_in_finder.data\n        db.session.commit()\n        flash(\"You are now registered for this jam.\", \"success\")\n        return redirect(jam.url())\n\n    return render_template('jam/participate.html', jam=jam, form=form)\n\n\n@app.route('/jams/<jam_slug>/cancel-participation/', methods=[\"POST\", \"GET\"])\n@login_required\ndef jam_cancel_participation(jam_slug):\n    jam = Jam.query.filter_by(slug=jam_slug).first_or_404()\n\n    if jam.getStatus().code > JamStatusCode.PACKAGING:\n        flash(\"You cannot unregister from a jam after it has finished or is in rating phase.\", \"error\")\n        return redirect(jam.url())\n\n    form = CancelParticipationForm()\n\n    if form.validate_on_submit():\n        current_user.leaveJam(jam)\n        db.session.commit()\n        flash(\"You are now unregistered from this jam.\", \"success\")\n        return redirect(jam.url())\n\n    return render_template('jam/cancel_participation.html', jam=jam, form=form)\n\n\n@app.route('/jams/<jam_slug>/games/')\ndef jam_games(jam_slug):\n    jam = Jam.query.filter_by(slug=jam_slug).first_or_404()\n    filters = set(request.args['filter'].split(' ')) if 'filter' in request.args else set()\n    games = jam.gamesByScore(filters) if jam.showRatings else jam.gamesByTotalRatings(filters)\n    return render_template('jam/games.html', jam=jam, games=games, filters=filters,\n                           package_types=GamePackage.packageTypes(), typeStringShort=GamePackage.typeStringShort)\n\n\n@app.route('/jams/<jam_slug>/participants/')\ndef jam_participants(jam_slug):\n    jam = Jam.query.filter_by(slug=jam_slug).first_or_404()\n    return render_template('jam/participants.html', jam=jam)\n\n\n@app.route('/jams/<jam_slug>/team_finder/toggle/')\ndef jam_toggle_show_in_finder(jam_slug):\n    jam = Jam.query.filter_by(slug=jam_slug).first_or_404()\n    r = current_user.getParticipation(jam)\n    if not r: abort(404)\n    r.show_in_finder = not r.show_in_finder\n    db.session.commit()\n    flash(\"You are now %s in the team finder for the jam \\\"%s\\\".\" % (\n        \"shown\" if r.show_in_finder else \"hidden\", jam.title), \"success\")\n    return redirect(jam.url())\n\n\n@app.route('/jams/<jam_slug>/team_finder/', methods=(\"GET\", \"POST\"))\ndef jam_team_finder(jam_slug):\n    jam = Jam.query.filter_by(slug=jam_slug).first_or_404()\n    form = TeamFinderFilter()\n    l = []\n    for r in jam.participations:\n        u = r.user\n        if (not form.show_teamed.data) and r.team and (not r.team.isSingleTeam):\n            continue  # don't show teamed people\n\n        if not r.show_in_finder:\n            continue\n\n        matches = 0\n\n        if form.need_programmer.data and u.ability_programmer: matches += 1\n        if form.need_gamedesigner.data and u.ability_gamedesigner: matches += 1\n        if form.need_2dartist.data and u.ability_2dartist: matches += 1\n        if form.need_3dartist.data and u.ability_3dartist: matches += 1\n        if form.need_composer.data and u.ability_composer: matches += 1\n        if form.need_sounddesigner.data and u.ability_sounddesigner: matches += 1\n\n        if matches == 0 and not form.show_empty.data: continue\n\n        l.append((r, matches))\n\n    if form.order.data == \"abilities\":\n        l.sort(key=lambda pair: pair[1], reverse=True)\n    elif form.order.data == \"location\":\n        l.sort(key=lambda pair: pair[0].user.location)\n    else:  # username\n        l.sort(key=lambda pair: pair[0].user.username)\n\n    return render_template('jam/team_finder.html', jam=jam, form=form, results=l)\n", "repo_name": "innogames/gamejam", "sub_path": "flamejam/views/jams.py", "file_name": "jams.py", "file_ext": "py", "file_size_in_byte": 5719, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query.order_by", "line_number": 12, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 12, "usage_type": "name"}, {"api_name": "sqlalchemy.desc", "line_number": 12, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.start_time", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flamejam.app.route", "line_number": 9, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 9, "usage_type": "name"}, {"api_name": "flamejam.cache.cached", "line_number": 10, "usage_type": "call"}, {"api_name": "flamejam.cache", "line_number": 10, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.filter_by", "line_number": 18, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flamejam.app.route", "line_number": 15, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 15, "usage_type": "name"}, {"api_name": "flamejam.cache.cached", "line_number": 16, "usage_type": "call"}, {"api_name": "flamejam.cache", "line_number": 16, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.filter_by", "line_number": 24, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 24, "usage_type": "name"}, {"api_name": "flamejam.models.Game.query.filter_by", "line_number": 32, "usage_type": "call"}, {"api_name": "flamejam.models.Game.query", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flamejam.models.Game", "line_number": 32, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.all", "line_number": 33, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flamejam.app.route", "line_number": 22, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 22, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.filter_by", "line_number": 40, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "flamejam.app.route", "line_number": 38, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 38, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.filter_by", "line_number": 47, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 47, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 48, "usage_type": "name"}, {"api_name": "flamejam.models.JamStatusCode.PACKAGING", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flamejam.models.JamStatusCode", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 52, "usage_type": "call"}, {"api_name": "flamejam.models.JamStatusCode.REGISTRATION", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flamejam.models.JamStatusCode", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 60, "usage_type": "call"}, {"api_name": "flamejam.forms.ParticipateForm", "line_number": 62, "usage_type": "call"}, {"api_name": "flamejam.db.session.commit", "line_number": 67, "usage_type": "call"}, {"api_name": "flamejam.db.session", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flamejam.db", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 71, "usage_type": "call"}, {"api_name": "flamejam.app.route", "line_number": 44, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 44, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 45, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.filter_by", "line_number": 77, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 77, "usage_type": "name"}, {"api_name": "flamejam.models.JamStatusCode.PACKAGING", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flamejam.models.JamStatusCode", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 81, "usage_type": "call"}, {"api_name": "flamejam.forms.CancelParticipationForm", "line_number": 83, "usage_type": "call"}, {"api_name": "flask_login.current_user.leaveJam", "line_number": 86, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 86, "usage_type": "name"}, {"api_name": "flamejam.db.session.commit", "line_number": 87, "usage_type": "call"}, {"api_name": "flamejam.db.session", "line_number": 87, "usage_type": "attribute"}, {"api_name": "flamejam.db", "line_number": 87, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 91, "usage_type": "call"}, {"api_name": "flamejam.app.route", "line_number": 74, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 74, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 75, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.filter_by", "line_number": 96, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 96, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 96, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 99, "usage_type": "call"}, {"api_name": "flamejam.models.GamePackage.packageTypes", "line_number": 100, "usage_type": "call"}, {"api_name": "flamejam.models.GamePackage", "line_number": 100, "usage_type": "name"}, {"api_name": "flamejam.models.GamePackage.typeStringShort", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flamejam.app.route", "line_number": 94, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 94, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.filter_by", "line_number": 105, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 105, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 106, "usage_type": "call"}, {"api_name": "flamejam.app.route", "line_number": 103, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 103, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.filter_by", "line_number": 111, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 111, "usage_type": "name"}, {"api_name": "flask_login.current_user.getParticipation", "line_number": 112, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 112, "usage_type": "name"}, {"api_name": "flamejam.db.session.commit", "line_number": 115, "usage_type": "call"}, {"api_name": "flamejam.db.session", "line_number": 115, "usage_type": "attribute"}, {"api_name": "flamejam.db", "line_number": 115, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 118, "usage_type": "call"}, {"api_name": "flamejam.app.route", "line_number": 109, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 109, "usage_type": "name"}, {"api_name": "flamejam.models.Jam.query.filter_by", "line_number": 123, "usage_type": "call"}, {"api_name": "flamejam.models.Jam.query", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flamejam.models.Jam", "line_number": 123, "usage_type": "name"}, {"api_name": "flamejam.forms.TeamFinderFilter", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 154, "usage_type": "call"}, {"api_name": "flamejam.app.route", "line_number": 121, "usage_type": "call"}, {"api_name": "flamejam.app", "line_number": 121, "usage_type": "name"}]}
{"seq_id": "37557724372", "text": "import os\nimport pandas as pd\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom trainer import Trainer\nfrom time import time\nimport numpy as np \nfrom defragTrees import DefragModel\n\ndef generate_data():\n    os.system(\"python3 data_prep/data_prep_adult.py\")\n    os.system(\"python3 data_prep/data_prep_bank.py\")\n    os.system(\"python3 data_prep/data_prep_compas.py\")\n    os.system(\"python3 data_prep/data_prep_breast.py\")\n\ndef train_xgb_model(dataset_name, n_estimators, max_depth):\n    training_data_loc = \"./data/{}/train.csv\".format(dataset_name)\n    testing_data_loc = \"./data/{}/test.csv\".format(dataset_name)\n        \n    model_bundle = Trainer(\n        training_data_loc=training_data_loc,\n        testing_data_loc=testing_data_loc,\n        scikit_model=GradientBoostingClassifier(\n            n_estimators=n_estimators, max_depth=max_depth, random_state=1\n        ),\n    )\n    if sum(model_bundle.y_train_pred) == 0:\n        print(\"Model doen't learn any positive\")\n    accuracy = model_bundle.get_accuracy()\n    auc = model_bundle.get_auc()\n    accuracy = [ float('%.3f' % x) for x in accuracy]\n    auc = [ float('%.3f' % x) for x in auc]\n    return model_bundle, accuracy, auc\n\n\ndef explain_with_rules(model_bundle, Kmax, file_name, sampling):\n    # parse sklearn tree ensembles into the array of (feature index, threshold)\n    model_skl = DefragModel.parseSLtrees(model_bundle.model) \n    defrag_model = DefragModel(modeltype='classification', restart=3)\n    y_train_pred = model_bundle.model.predict(model_bundle.x_train)\n\n    num_train = int(sampling*len(model_bundle.x_train))\n        \n    start_time = time()\n    np.random.seed(123)\n    defrag_model.fit(model_bundle.x_train[:num_train], y_train_pred[:num_train], model_skl, Kmax, fittype='FAB', featurename=model_bundle.feature_names)\n        \n    rules = defrag_model.rule_\n    print(\"Number of (defrag) rules:\", len(rules))\n    labels = defrag_model.pred_\n    default_label = defrag_model.pred_default_\n    for i in range(len(rules)):\n        r = rules[i]\n        l = labels[i]\n        r_str = []\n        for t in r:\n            if(t[1] == 0):\n                r_str.append(str(model_bundle.feature_names[t[0] - 1]) + \" <= \" + str(t[2]))\n            if(t[1] == 1):\n                r_str.append(str(model_bundle.feature_names[t[0] - 1]) + \" > \" + str(t[2]))\n        r_str = \" & \".join(r_str)\n        #print(\"rule\", i + 1, \":\", r_str, l)\n    #print(\"rule\", len(rules) + 1, \":\", \"otherwise\", default_label)\n    \n    # Rules can be read using\n    # print(str(defrag_model))\n    with open('./results/global_result/defrag/' + file_name + '.txt', 'w') as f:\n        f.write(str(defrag_model))\n\n    run_time = time() - start_time\n\n    train_fidelities = get_fidelity(\n        defrag_model,\n        X=model_bundle.x_train, \n        y=model_bundle.y_train_pred)\n    train_fidelities = [ float('%.6f' % fid) for fid in train_fidelities]\n\n    test_fidelities = get_fidelity(\n        defrag_model,\n        X=model_bundle.x_test, \n        y=model_bundle.y_test_pred)\n    test_fidelities = [ float('%.6f' % fid) for fid in test_fidelities]\n\n    return rules, run_time, train_fidelities, test_fidelities\n\ndef get_fidelity(explainer, X, y):\n    y_rules = explainer.predict(X)\n    fidelity_positives = 0.0\n    fidelity_negatives = 0.0\n    positives = 0.0 + 1e-6\n    negatives = 0.0 + 1e-6\n    for i in range(len(y)):\n        if y[i] == 1:\n            positives = positives + 1\n            if y[i] == y_rules[i]:\n                fidelity_positives = fidelity_positives + 1\n        if y[i] == 0:\n            negatives = negatives + 1\n            if y[i] == y_rules[i]:\n                fidelity_negatives = fidelity_negatives + 1\n\n    fidelity = (fidelity_positives + fidelity_negatives) / (\n        positives + negatives\n    )\n    fidelity_positives = fidelity_positives / positives\n    fidelity_negatives = fidelity_negatives / negatives\n    return (fidelity, fidelity_positives, fidelity_negatives)\n\n#generate_data()\n\nresult = {}\nsummary = []\nte2rules = pd.read_csv('./results/global_result/te2rules/experiment.csv')\n\nfor dataset in [\"compas\", \"bank\", \"adult\"]:\n    result['dataset']=dataset\n    for num_trees in [100, 200, 500]:\n        result['num_trees']=num_trees\n        for depth in [3, 5]:\n            result['depth']=depth\n            sampling = 0.1\n            result['sampling'] = sampling\n            model_bundle, accuracy, auc = train_xgb_model(\n                dataset_name=dataset,\n                n_estimators=num_trees,\n                max_depth=depth)\n            print(\"Model Trained\", dataset, num_trees, depth)\n\n\n            result['train_accuracy'] = accuracy[0]\n            result['test_accuracy'] = accuracy[1]\n            result['train_auc'] = auc[0]\n            result['test_auc'] = auc[1]\n\n            df = te2rules[te2rules.dataset == dataset]\n            df = df[df.num_trees == num_trees]\n            df = df[df.depth == depth]\n            df = df[df.num_stages == 3]\n            df = df[df.jaccard_threshold == 0.2]            \n            assert(len(df) == 1)\n            df = df['num_rules']\n            num_te2rules = df.iloc[0]\n\n            for Kmax in [num_te2rules, 5*num_te2rules, 10*num_te2rules]:\n                print(\"TERules got \", num_te2rules, \" rules\")\n                print(\"Running defrag with Kmax =\", Kmax)\n                rules, run_time, train_fidelities, test_fidelities = explain_with_rules(\n                    model_bundle, Kmax=Kmax, \n                    file_name = \"rules_num_trees_\" + str(num_trees) + \"_depth_\" + str(depth) + \"_Kmax_\" + str(Kmax),\n                    sampling=sampling)\n                print(\"Model Explained\", dataset, num_trees, depth)\n\n                result['num_rules']= len(rules)\n                result['time']=run_time\n                result['train_fidelity_total']=train_fidelities[0]\n                result['train_fidelity_positive']=train_fidelities[1]\n                result['train_fidelity_negative']=train_fidelities[2]\n                result['test_fidelity_total']=test_fidelities[0]\n                result['test_fidelity_positive']=test_fidelities[1]\n                result['test_fidelity_negative']=test_fidelities[2]\n\n                df = pd.DataFrame(result, index=[0])\n                summary.append(df)\n                summary_df = pd.concat(summary, ignore_index=True)\n                summary_df.to_csv(\"./results/global_result/defrag/experiment.csv\",index=False)\n                '''\n                pd.set_option('display.max_columns', None)  \n                print(summary_df)\n                '''\n", "repo_name": "linkedin/TE2Rules", "sub_path": "demo/run_defrag.py", "file_name": "run_defrag.py", "file_ext": "py", "file_size_in_byte": 6557, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.system", "line_number": 10, "usage_type": "call"}, {"api_name": "os.system", "line_number": 11, "usage_type": "call"}, {"api_name": "os.system", "line_number": 12, "usage_type": "call"}, {"api_name": "os.system", "line_number": 13, "usage_type": "call"}, {"api_name": "trainer.Trainer", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingClassifier", "line_number": 22, "usage_type": "call"}, {"api_name": "defragTrees.DefragModel.parseSLtrees", "line_number": 37, "usage_type": "call"}, {"api_name": "defragTrees.DefragModel", "line_number": 37, "usage_type": "name"}, {"api_name": "defragTrees.DefragModel", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 112, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 161, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 163, "usage_type": "call"}]}
{"seq_id": "20510933501", "text": "import requests                 # for making HTTP requests\nimport json                     # library for handling JSON data\nimport time                     # module for sleep operation\n\nfrom boltiot import Bolt        # importing Bolt from boltiot module\nimport conf                     # config file\n\nmybolt = Bolt(conf.bolt_api_key, conf.device_id)\n\ndef get_sensor_value_from_pin(pin):\n    \"\"\"Returns the sensor value. Returns -999 if request fails\"\"\"\n    try:\n        response = mybolt.analogRead(pin)\n        data = json.loads(response)\n        if data[\"success\"] != 1:\n            print(\"Request not successfull\")\n            print(\"This is the response->\", data)\n            return -999\n        sensor_value = int(data[\"value\"])\n        return sensor_value\n    except Exception as e:\n        print(\"Something went wrong when returning the sensor value\")\n        print(e)\n        return -999\n\ndef send_telegram_message(message):\n    \"\"\"Sends message via Telegram\"\"\"\n    url = \"https://api.telegram.org/\" + conf.telegram_bot_id + \"/sendMessage\"\n    data = {\n        \"chat_id\": conf.telegram_chat_id,\n        \"text\": message\n    }\n    try:\n        response = requests.request(\n            \"GET\",\n            url,\n            params=data\n        )\n        print(\"This is the Telegram response\")\n        print(response.text)\n        telegram_data = json.loads(response.text)\n        return telegram_data[\"ok\"]\n    except Exception as e:\n        print(\"An error occurred in sending the alert message via Telegram\")\n        print(e)\n        return False\n\n\nwhile True:\n    # Step 1\n    sensor_value = get_sensor_value_from_pin(\"A0\")    \n    print(\"The current sensor value is:\", sensor_value)\n    \n    # Step 2\n    if sensor_value == -999:\n        print(\"Request was unsuccessfull. Skipping.\")\n        time.sleep(10)\n        continue\n    \n    # Step 3\n    if sensor_value >= conf.threshold:\n        print(\"Sensor value has exceeded threshold\")\n        message = \"Alert! Sensor value has exceeded \" + str(conf.threshold) + \". The current value is \" + str(sensor_value)\n        telegram_status = send_telegram_message(message)\n        print(\"This is the Telegram status:\", telegram_status)\n\n    # Step 4\n    time.sleep(10)", "repo_name": "ajay-prabhakar/awesome-IOT", "sub_path": "bolt/TelegramBot/telegram_alert.py", "file_name": "telegram_alert.py", "file_ext": "py", "file_size_in_byte": 2218, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "40", "api": [{"api_name": "boltiot.Bolt", "line_number": 8, "usage_type": "call"}, {"api_name": "conf.bolt_api_key", "line_number": 8, "usage_type": "attribute"}, {"api_name": "conf.device_id", "line_number": 8, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "conf.telegram_bot_id", "line_number": 28, "usage_type": "attribute"}, {"api_name": "conf.telegram_chat_id", "line_number": 30, "usage_type": "attribute"}, {"api_name": "requests.request", "line_number": 34, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 41, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "conf.threshold", "line_number": 61, "usage_type": "attribute"}, {"api_name": "conf.threshold", "line_number": 63, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 68, "usage_type": "call"}]}
{"seq_id": "70930319481", "text": "import sys\r\nimport json\r\nimport requests\r\n\r\ndef parse_guids(activity_response):\r\n    '''Parse response response from Activity endpoint\r\n       return a unique set of GUIDs\r\n    '''\r\n    return {entry['connector_guid'] for entry in activity_response['data']}\r\n\r\ndef main():\r\n    '''The main logic of the script\r\n    '''\r\n    client_id = 'a1b2c3d4e5f6g7h8i9j0'\r\n    api_key = 'a1b2c3d4-e5f6-g7h8-i9j0-k1l2m3n4o5p6'\r\n\r\n    try:\r\n        sha256 = sys.argv[1]\r\n    except IndexError:\r\n        sha256 = input('Enter a SHA256: ')\r\n\r\n    # Instantiate requestions session object\r\n    amp_session = requests.session()\r\n    amp_session.auth = (client_id, api_key)\r\n\r\n    # Containers for data\r\n    output_json = {'executed':False, 'sha256':sha256, 'hosts':[]}\r\n    guids = set()\r\n\r\n    # Activity URL\r\n    activity_url = 'https://api.amp.cisco.com/v1/computers/activity?q={}'.format(sha256)\r\n\r\n    # Query the Activity API endpoint\r\n    response_json = amp_session.get(activity_url).json()\r\n\r\n    # Print the number of computers that have seen the file\r\n    total_guids = response_json['metadata']['results']['total']\r\n    print('\\nComputers that have seen the file: {}'.format(total_guids))\r\n\r\n    # Extract GUIDs from JSON\r\n    guids = guids.union(parse_guids(response_json))\r\n\r\n    # Check if there are more pages and repeat\r\n    while 'next' in response_json['metadata']['links']:\r\n        next_url = response_json['metadata']['links']['next']\r\n        response_json = amp_session.get(next_url).json()\r\n        index = response_json['metadata']['results']['index']\r\n        print('Processing index: {}'.format(index))\r\n        guids = guids.union(parse_guids(response_json))\r\n\r\n    # Get GUID trajectory\r\n    for guid in guids:\r\n        trajectory_url = 'https://api.amp.cisco.com/v1/computers/{}/trajectory?q={}'.format(guid, sha256)\r\n        trajectory_response_json = amp_session.get(trajectory_url).json()\r\n        data = trajectory_response_json.get('data')\r\n        computer = data.get('computer', {'hostname':'Hostname not found'})\r\n        hostname = computer.get('hostname')\r\n        events = data.get('events')\r\n\r\n        guid_json = {'guid':guid, 'hostname':hostname, 'seen':True, 'executed':False, 'executed_file':None}\r\n\r\n        if events is not None:\r\n            for event in events:\r\n                event_type = event.get('event_type')\r\n                # Verify the event type and the executed SHA256 is not the parent\r\n                if event_type == 'Executed by' and sha256 == event['file']['identity']['sha256']:\r\n                    file_name = event['file']['file_name']\r\n                    file_path = event['file']['file_path']\r\n                    if guid_json['executed_file'] is None:\r\n                        guid_json['executed_file'] = set()\r\n                    guid_json['executed_file'].add((file_name, file_path))\r\n                    guid_json['executed'] = True\r\n                    output_json['executed'] = True\r\n\r\n        # Convert set to list so it is serializable for JSON output\r\n        if guid_json['executed_file'] is not None:\r\n            guid_json['executed_file'] = list(guid_json['executed_file'])\r\n\r\n        # Append to hosts list\r\n        output_json['hosts'].append(guid_json)\r\n\r\n    # Parse output_json and print computers that executed the SHA256\r\n    print('\\nHosts observed executing the file:')\r\n    for host in output_json['hosts']:\r\n        if host['executed']:\r\n            print('{} - {}'.format(host['guid'], host['hostname']))\r\n            for file in host['executed_file']:\r\n                print('  File: {}\\n  Path: {}\\n'.format(file[0], file[1]))\r\n\r\n    # Write output to a file\r\n    with open('output.json', 'w') as file:\r\n        file.write(json.dumps(output_json))\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n", "repo_name": "CiscoSecurity/amp-04-check-sha256-execution", "sub_path": "check_for_execution.py", "file_name": "check_for_execution.py", "file_ext": "py", "file_size_in_byte": 3777, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "requests.session", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 92, "usage_type": "call"}]}
{"seq_id": "19669307822", "text": "import bisect\n\nimport attrs\n\nfrom issue_wrapper import IssueWrapper\n\nimport logging\n\n\n@attrs.frozen\nclass RedmineIssueSorter:\n    _sorted_list: list[IssueWrapper] = attrs.field(factory=list)\n\n    def add(self, issue_wrapped: IssueWrapper):\n        if 2845 == issue_wrapped.get_id():\n            logging.info(\"\")\n        show_in_report = issue_wrapped.is_shown_in_report()\n        if not show_in_report:\n            return\n        bisect.insort(self._sorted_list, issue_wrapped)\n\n    def get_items(self):\n        return self._sorted_list\n", "repo_name": "txemi/redmine_sorted_tree", "sub_path": "redmine_issue_sorter.py", "file_name": "redmine_issue_sorter.py", "file_ext": "py", "file_size_in_byte": 537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "issue_wrapper.IssueWrapper", "line_number": 12, "usage_type": "name"}, {"api_name": "attrs.field", "line_number": 12, "usage_type": "call"}, {"api_name": "issue_wrapper.IssueWrapper", "line_number": 14, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 16, "usage_type": "call"}, {"api_name": "bisect.insort", "line_number": 20, "usage_type": "call"}, {"api_name": "attrs.frozen", "line_number": 10, "usage_type": "attribute"}]}
{"seq_id": "22795666118", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Nov 29 16:56:54 2021\r\n\r\n@author: Microsoft\r\n\"\"\"\r\n\r\n# Projeto para automatização da busca de leads no LinkedIn.\r\n\r\n# Import dos módulos\r\n\r\n# Módulos para tratamento do navegador\r\nfrom selenium import webdriver\r\nfrom pynput.keyboard import Key , Controller\r\n\r\n# Módulos gerais\r\nimport pandas as pd\r\nimport time\r\nimport os\r\n\r\n\r\n\r\n\r\n## ETAPAS DO PROCESSO\r\n# 1 - Ler o arquivo ICP\r\n# 2 - Passar por cada categoria do ICP, Item por item\r\n# 3 - Para cada item de cada categoria, identificar qual campo de busca ele está associado\r\n# 4 - Preencher o campo de busca com o item correspondente \r\n# 5 - Salvar Pesquisa com a nomenclatura adequada\r\n\r\n\r\n# Mostrando o conteúdo da pasta arquivosPloomes\r\npasta = os.listdir(\"Arquivos\")\r\nICP = pd.read_excel(\"Arquivos/\" + pasta[0])\r\n\r\n#Retirando NaN e Duplicatas de Segmento\r\n\r\ncleanedSegmento = [i for i in ICP[\"SEGMENTO\"] if str(i) != 'nan']\r\nA=len(ICP) - len(cleanedSegmento)\r\nICPsegmento = pd.read_excel(\"Arquivos/\" + pasta[0],usecols=(\"C\"),keep_default_na = False,skipfooter= A )\r\nICPsegmento = ICPsegmento.drop_duplicates()\r\nICPsegmento = ICPsegmento.reset_index(drop=True)\r\nprint((ICPsegmento[\"SEGMENTO\"]))\r\n\r\n\r\n#Retirando NaN e Duplicatas de Estados\r\n\r\ncleanedEstados = [i for i in ICP[\"ESTADO\"] if str(i) != 'nan']\r\nA=len(ICP) - len(cleanedEstados)\r\nICPestado = pd.read_excel(\"Arquivos/\" + pasta[0],usecols=(\"B\"),keep_default_na = False,skipfooter= A )\r\nICPestado = ICPestado.drop_duplicates()\r\nICPestado = ICPestado.reset_index(drop=True)\r\nprint((ICPestado[\"ESTADO\"]))\r\n\r\n#Retirando NaN e Duplicatas de Porte\r\n\r\ncleanedPorte = [i for i in ICP[\"PORTE\"] if str(i) != 'nan']\r\nA=len(ICP) - len(cleanedPorte)\r\nICPporte = pd.read_excel(\"Arquivos/\" + pasta[0],usecols=(\"D\"),keep_default_na = False,skipfooter= A )\r\nICPporte = ICPporte.drop_duplicates()\r\nICPporte = ICPporte.reset_index(drop=True)\r\nprint((ICPporte[\"PORTE\"]))\r\n\r\n#Retirando NaN e Duplicatas de Departamento\r\n\r\ncleanedDepartamento = [i for i in ICP[\"DEPARTAMENTO\"] if str(i) != 'nan']\r\nA=len(ICP) - len(cleanedDepartamento)\r\nICPdepartamento = pd.read_excel(\"Arquivos/\" + pasta[0],usecols=(\"F\"),keep_default_na = False,skipfooter= A )\r\nICPdepartamento = ICPdepartamento.drop_duplicates()\r\nICPdepartamento = ICPdepartamento.reset_index(drop=True)\r\nprint((ICPdepartamento[\"DEPARTAMENTO\"]))\r\n\r\n#Retirando NaN e Duplicatas de Cargo\r\n\r\ncleanedCargo = [i for i in ICP[\"CARGO\"] if str(i) != 'nan']\r\nA=len(ICP) - len(cleanedCargo)\r\nICPcargo = pd.read_excel(\"Arquivos/\" + pasta[0],usecols=(\"E\"),keep_default_na = False,skipfooter= A )\r\nICPcargo = ICPcargo.drop_duplicates()\r\nICPcargo = ICPcargo.reset_index(drop=True)\r\nprint((ICPcargo[\"CARGO\"]))\r\n\r\n\r\n# Mostra mensagem ao usuário\r\nprint(\"Aguarde enquanto o filtro no LinkedIn é feito...\")\r\n\r\n\r\n# Variáveis para alocar as informações\r\ndf = pd.DataFrame()\r\ndf[\"Segmento\"] = ICPsegmento[\"SEGMENTO\"]\r\ndf[\"Estado\"] = ICPestado[\"ESTADO\"]\r\ndf[\"Porte\"] = ICPporte[\"PORTE\"]\r\ndf[\"Cargo\"] = ICPcargo[\"CARGO\"]\r\ndf[\"Departamento\"] = ICPdepartamento[\"DEPARTAMENTO\"]\r\n\r\n\r\n\r\n\r\nICPestado[\"ESTADO\"] = ICPestado[\"ESTADO\"].replace(\" - SP\", \", SÃO PAULO\", regex=True)\r\nICPestado[\"ESTADO\"] = ICPestado[\"ESTADO\"].replace(\" - MG\", \", MINAS GERAIS\", regex=True)\r\n\r\n# correção ortográfica dos segmentos\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ADMINISTRACAO AGRICOLA'],['Administração agrícola'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ADMINISTRACAO DE SERVICOS'],['Administração de serviços']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ADMINISTRACAO GOVERNAMENTAL'],['Administração governamental']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ANIMACAO'],['Animação']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ARRECADACAO DE RECURSOS'],['Arrecadação de Recursos']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ARTES CENICAS'],['Artes Cênicas'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ATENDIMENTO MEDICO E HOSPITALAR'],['Atendimento médico e hospitalar']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ATENDIMENTO MEDICO PSIQUIATRICO'],['Atendimento médico psiquiátrico'])           \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['AUTOMACAO INDUSTRIAL'],['automação industrial'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['AVIACAO E AEROESPACIAL'],['Aviação e Aeroespacial']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['CAPITAL DE RISCO E PARTICIPACOES PRIVADAS'],['Capital de risco e participações privadas'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['COMPOSICAO E REVISAO DE TEXTOS'],['Composição e Revisão de Textos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['COMERCIO E DESENVOLVIMENTO INTERNACIONAL'],['Comércio e desenvolvimento internacional'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['CONSTRUCAO'],['Construção']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['CONSTRUCAO DE FERROVIA'],['Construção de Ferrovia']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['CONSTRUCAO NAVAL'],['Construção Naval'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['COSMETICA'],['Cosmética'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['DEFESA E ESPACO'],['Defesa e espaço'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['DESIGN GRAFICO'],['Design Gráfico']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['DISPOSITIVOS MEDICOS'],['Dispositivos Médicos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['EDITORACAO'],['Editoração']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['EDUCACAO A DISTANCIA'],['Educação à distância']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ENGENHARIA MECANICA OU INDUSTRIAL'],['Engenharia mecânica ou industrial']) \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ENSINO FUNDAMENTAL/MEDIO'],['Ensino fundamental/médio'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['EXECUCAO DA LEI'],['Execução da Lei'])     \r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['GESTAO DE INVESTIMENTOS'],['gestão de investimentos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['GESTAO DE ORGANIZACAO SEM FINS LUCRATIVOS'],['Gestão de Organização sem fins Lucrativos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['GESTAO EDUCACIONAL'],['Gestão Educacional'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['IMOBILIARIO'],['Imobiliário'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['IMPORTACAO E EXPORTACAO'],['Importação e Exportação'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['IMPRESSAO'],['Impressão'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['IMOVEIS COMERCIAIS'],['Imóveis Comerciais'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['INDUSTRIA AUTOMOTIVA'],['Indústria Automotiva'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['INDUSTRIA FARMACEUTICA'],['Indústria Farmacêutica'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['INDUSTRIA QUIMICA'],['Indústria química'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['INDUSTRIA TEXTIL'],['Indústria têxtil'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['INSTALACOES E SERVICOS RECREATIVOS'],['Instalações e Serviços De Recreação'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['INSTITUICOES RELIGIOSAS'],['Instituições Religiosas'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['JUDICIARIO'],['Judiciário'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['LATICINIOS'],['Laticínios'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['LINHAS AEREAS/AVIACAO'],['Linhas Aéreas/Aviação'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['LOGISTICA E CADEIA DE SUPRIMENTOS'],['Logística e Cadeia de Suprimentos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['MANUFATURA DE ELETROELETRONICOS'],['Manufatura de eletroeletrônicos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['MAQUINARIO'],['Maquinário'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['MATERIAIS DE CONSTRUCAO'],['Materiais de Construção'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['MIDIA DE DIFUSAO'],['Mídia de difusão'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['MIDIA ONLINE'],['Mídia Online'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['MINERACAO E METAIS'],['Mineração e Metais'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['MUSICA'],['Música'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['MUSEUS E INSTITUICOES'],['Museu e Instituições'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['MOVEIS'],['Móveis'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ORGANIZACOES DE PESQUISA E ORIENTACAO'],['Organizações de pesquisa e orientação'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ORGANIZACAO CIVICA E SOCIAL'],['Organização cívica e social'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['ORGANIZACAO POLITICA'],['Organização Política'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['PETROLEO E ENERGIA'],['Petróleo e energia'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['PLASTICO'],['Plástico'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['POLITICA PUBLICA'],['Política Pública'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['PRODUCAO DE MIDIA'],['Produção de mídia'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['PRODUTOS ALIMENTICIOS'],['Produtos Alimentícios'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['PRODUTOS ELETRONICOS'],['Produtos Eletrônicos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['PRATICA JURIDICA'],['Prática Jurídica'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['PRATICA MEDICA'],['Prática Médica'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['RECRUTAMENTO E SELECAO'],['Recrutamento e seleção'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['REDES SEM FIO'],['Rede sem fio'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['RECURSOS RENOVAVEIS E MEIO AMBIENTE'],['Recursos Renováveis e meio ambiente'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['RELACOES GOVERNAMENTAIS'],['Relações Governamentais'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['RELACOES INTERNACIONAIS'],['Relações Internacionais'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['RELACOES PUBLICAS E COMUNICACOES'],['Relações Públicas e Comunicações'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['RESOLUCAO ALTERNATIVA DE LITIGIOS'],['Resolução alternativa de Litígios'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SAUDE, BEM-ESTAR E EDUCACAO FISICA'],['Saúde, bem-estar e educação física'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SEGURANCA DE REDES E COMPUTADORES'],['Segurança de Redes e Computadores'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SEGURANCA E INVESTIGACOES'],['Segurança e Investigações'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SEGURANCA PUBLICA'],['Segurança Pública'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SERVICOS AMBIENTAIS'],['Serviços Ambientais'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SERVICOS DA INFORMACAO'],['Serviços da Informação'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SERVICOS FINANCEIROS'],['Serviços Financeiros'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SERVICOS INDIVIDUAIS E FAMILIARES'],['Serviços Individuais e Familiares'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SERVICOS JURIDICOS'],['Serviços Jurídicos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SERVICOS PARA EVENTOS'],['Serviços para Eventos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['SERVICOS PUBLICOS'],['Serviços Públicos'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['TECNOLOGIA DA INFORMACAO E SERVICOS'],['Tecnologia da Informação e Serviços'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['TELECOMUNICACOES'],['Telecomunicações'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['TERCEIRIZACAO E OFFSHORING'],['Terceirização e Offshoring'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['TRADUCAO E LOCALIZACAO'],['Tradução e Localização'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['TRANSPORTE/CAMINHOES/TRENS'],['Transporte/Caminhões/Trens'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['TRANSPORTE MARITIMO'],['Transporte Marítimo'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['TREINAMENTO E ORIENTACAO PROFISSIONAL'],['Treinamento e Orientação Profissional'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['VETERINARIA'],['Veterinária'])\r\nICPsegmento[\"SEGMENTO\"] = ICPsegmento[\"SEGMENTO\"].replace(['VIDRO, CERAMICA E CONCRETO'],['Vidro, Cerâmica e Concreto'])\r\n\r\n\r\n\r\n\r\n\r\n# INTERAÇÕES COM O NAVEGADOR\r\n# Abrindo o navegador e a conexão com o Chrome\r\n# Observe que é necessário passar o caminho de diretório do Chromedriver como parâmetro\r\ndriver = webdriver.Chrome(\"bin/chromedriver.exe\")\r\n\r\n# Acessando o google no navegador \r\ndriver.get(\"http://www.google.com\")\r\n\r\n # Acessando o linkedin no navegador \r\ndriver.get(\"https://www.linkedin.com/login/pt\")\r\n    # Acessando a conta do Rhavi\r\n    # elemento HTML de ID do Linkedin\r\nelem = driver.find_element_by_xpath('//*[@id=\"username\"]')\r\n\r\n    # Escrevendo na caixa de usuário do LinkedIn\r\nelem.send_keys(str(\"inteligencia@grupoescalar.com.br\"))  #hugojoseamaral@hotmail.com #rhavimoreira@yahoo.com.br\r\n \r\n    # elemento HTML de senha do Linkedin\r\nelem1 = driver.find_element_by_xpath('//*[@id=\"password\"]')\r\n    \r\n    # Escrevendo na caixa de senha do LinkedIn\r\nelem1.send_keys(str(\"Escalar@2021!\")) #hugo7466 #Escalar@2021!\r\n    \r\n    # Clicando no elemento para fazer o Log IN\r\nelem1.submit()\r\n\r\n\r\n# Acessando o SALES NAVIGATOR no navegador \r\ndriver.get(\"https://www.linkedin.com/sales/search/people?viewAllFilters=true\")\r\n\r\ntime.sleep(3) \r\n\r\n# elemento HTML de “Localidade”\r\nelem4 = driver.find_element_by_xpath('//*[@id=\"ember155\"]/div/button')\r\n\r\n# Clicando no elemento para Localidade\r\nelem4.click()\r\n\r\n#Elemento de texto (para permitir a escrita na caixa de texto - repete)\r\nelem5 = driver.find_element_by_xpath('//*[@id=\"ember154-typeahead-region\"]')\r\n\r\n# Clicando na caixa de texto\r\nelem5.click()\r\ntime.sleep(3)\r\ni = 0\r\n\r\nwhile i < len(ICPestado[\"ESTADO\"]):\r\n# escrevendo o nome do estado no campo de estado\r\n\r\n    elem5.send_keys(str(ICPestado[\"ESTADO\"][i])) \r\n    time.sleep(2)\r\n# achando o nome do estado pesquisado \r\n    elem6 = driver.find_element_by_xpath('//*[@id=\"ember154\"]/div[3]/ol/li[1]/button')\r\n# clicando no nome encontrado\r\n    elem6.click()\r\n# Somando ao contador\r\n    i+=1\r\n\r\n\r\n        \r\n# elemento HTML de “Segmento”\r\nelem7 = driver.find_element_by_xpath('//*[@id=\"ember166\"]/div/button')\r\n\r\n# Clicando no elemento para Segmento\r\nelem7.click()\r\n\r\n#Elemento de texto (para permitir a escrita na caixa de texto - repete)\r\n\r\nelem8 = driver.find_element_by_xpath('//*[@id=\"ember165-typeahead\"]')\r\n# Clicando na caixa de texto\r\nelem8.click()\r\ntime.sleep(3)\r\ni = 0\r\nprint(ICPsegmento[\"SEGMENTO\"])\r\nwhile i < len(ICPsegmento[\"SEGMENTO\"]):\r\n# escrevendo o nome do SEGMENTO no campo de Setor\r\n    elem8.send_keys(str(ICPsegmento[\"SEGMENTO\"][i]).lower())\r\n    time.sleep(2)\r\n# achando o SEGMENTO pesquisado\r\n    elem9 = driver.find_element_by_xpath('//*[@id=\"ember165\"]/div[3]/ol/li[1]/button') #font-weight-400\r\n    time.sleep(1)\r\n# clicando no nome encontrado\r\n    elem9.click()\r\n    time.sleep(1)\r\n# Somando ao contador\r\n    i+=1\r\n\r\n# elemento HTML de “Porte”\r\nelem10 = driver.find_element_by_xpath('//*[@id=\"ember226\"]/div')\r\n\r\n# Clicando no elemento para Segmento\r\nelem10.click()  \r\ntime.sleep(3)\r\ni=0\r\nICPporte[\"PORTE\"].iloc[::-1]\r\n\r\nj=len(ICPporte[\"PORTE\"])-1\r\nprint(len(ICPporte[\"PORTE\"]))\r\nwhile j > -1:\r\n    \r\n    if ICPporte[\"PORTE\"][j] == \"10001+\": # 1o Porte\r\n        elem11 = driver.find_element_by_xpath('//*[@id=\"ember225\"]/div[3]/ol/li[9]/button')\r\n        elem11.click() \r\n        print(j)\r\n        print(ICPporte[\"PORTE\"][j])\r\n        j-=1\r\n        time.sleep(3)   \r\n        \r\n    elif ICPporte[\"PORTE\"][j] == \"5001-10000\": # 2o Porte\r\n        elem11 = driver.find_element_by_xpath('//*[@id=\"ember225\"]/div[3]/ol/li[8]/button')\r\n        elem11.click() \r\n        print(j)\r\n        print(ICPporte[\"PORTE\"][j])\r\n        j-=1\r\n        time.sleep(3)  \r\n\r\n    elif ICPporte[\"PORTE\"][j] == \"1001-5000\": # 3o Porte\r\n        elem11 = driver.find_element_by_xpath('//*[@id=\"ember225\"]/div[3]/ol/li[7]/button')\r\n        elem11.click() \r\n        print(j)\r\n        print(ICPporte[\"PORTE\"][j])\r\n        j-=1\r\n        time.sleep(3)\r\n        \r\n    elif ICPporte[\"PORTE\"][j] == \"501-1000\": # 4o Porte\r\n        elem11 = driver.find_element_by_xpath('//*[@id=\"ember225\"]/div[3]/ol/li[6]/button')\r\n        elem11.click() \r\n        print(j)\r\n        print(ICPporte[\"PORTE\"][j])\r\n        j-=1\r\n        time.sleep(3)\r\n    elif ICPporte[\"PORTE\"][j] == \"201-500\": # 5o Porte\r\n        elem11 = driver.find_element_by_xpath('//*[@id=\"ember225\"]/div[3]/ol/li[5]/button')\r\n        elem11.click() \r\n        print(j)\r\n        print(ICPporte[\"PORTE\"][j])\r\n        j-=1\r\n        time.sleep(3)\r\n    elif ICPporte[\"PORTE\"][j] == \"51-200\": # 6o Porte\r\n        elem11 = driver.find_element_by_xpath('//*[@id=\"ember225\"]/div[3]/ol/li[4]/button')\r\n        elem11.click() \r\n        print(j)\r\n        print(ICPporte[\"PORTE\"][j])\r\n        j-=1\r\n        time.sleep(3)\r\n    elif ICPporte[\"PORTE\"][j] == \"11-50\": # 7o Porte\r\n        elem11 = driver.find_element_by_xpath('//*[@id=\"ember225\"]/div[3]/ol/li[3]/button')\r\n        elem11.click() \r\n        print(j)\r\n        print(ICPporte[\"PORTE\"][j])\r\n        j-=1\r\n        time.sleep(3)\r\n    elif ICPporte[\"PORTE\"][j] == \"1-10\": # 8o Porte\r\n        elem11 = driver.find_element_by_xpath('//*[@id=\"ember225\"]/div[3]/ol/li[2]/button')\r\n        elem11.click() \r\n        print(j)\r\n        print(ICPporte[\"PORTE\"][j])\r\n        j-=1\r\n        time.sleep(3)\r\n\r\n\r\n\r\n\r\n\r\n# elemento HTML de “Cargo”\r\nelem13 = driver.find_element_by_xpath('//*[@id=\"ember209\"]/div/button')\r\n\r\n# Clicando no elemento para Cargo\r\nelem13.click()\r\n\r\n\r\n#Elemento de texto (para permitir a escrita na caixa de texto - repete)\r\nelem14 = driver.find_element_by_xpath('//*[@id=\"ember208-typeahead\"]')\r\n\r\n# Clicando na caixa de texto\r\nelem14.click()\r\ni=0\r\nj=0\r\nprint(ICPdepartamento[\"DEPARTAMENTO\"])\r\nprint(ICPcargo[\"CARGO\"])\r\nprint(len(ICPcargo[\"CARGO\"]))\r\nprint(len(ICPdepartamento[\"DEPARTAMENTO\"]))\r\nwhile i < len(ICPcargo[\"CARGO\"]):\r\n    j=0\r\n    while j < len(ICPdepartamento[\"DEPARTAMENTO\"]):\r\n    # Escrevendo o nome cargo + departamento de forma concatenada\r\n        #time.sleep(3)\r\n        elem14.send_keys(str(ICPcargo[\"CARGO\"][i].lower()) + ' ' + str(ICPdepartamento[\"DEPARTAMENTO\"][j]).lower())\r\n        time.sleep(3)\r\n    # apertando enter\r\n        \r\n        keyboard = Controller()\r\n        \r\n        keyboard.press(Key.enter)\r\n        time.sleep(3)\r\n\r\n        keyboard.release(Key.enter)\r\n        \r\n        print(ICPcargo[\"CARGO\"][i] +\" \"+ ICPdepartamento[\"DEPARTAMENTO\"][j])\r\n        print(j)\r\n        print(i)\r\n        j+=1\r\n    i-=1\r\n    i+=2\r\n\r\n#tradução dos termos de cargo que estão no ICP\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['ADVOGADO'],['ATTORNEY'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['ANALISTA'],['ANALYST'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['ARQUITETO'],['ARCHITECT'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['ATENDENTE'],['CLERK'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['AUDITOR'],['CONTROLLER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['COMPRADOR'],['BUYER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['CONSELHEIRO'],['ADVISOR'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['CONSULTOR'],['CONSULTANT'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['CONTROLADOR'],['CONTROLLER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['COORDENADOR'],['COORDINATOR'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['CORRETOR'],['BROKER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['DIRETOR'],['DIRECTOR'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['ENCARREGADO'],['IN CHARGE'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['ENFERMEIRO'],['NURSE'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['ENGENHEIRO'],['ENGINEER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['ESPECIALISTA'],['SPECIALIST'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['FARMACEUTICO'],['PHARMACEUTICAL'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['FUNDADOR'],['FOUNDER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['GEOLOGO'],['GEOLOGIST'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['GERENTE'],['MANAGER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['INSPETOR'],['INSPECTOR'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['LABORATORISTA'],['LABORATORIST'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['MEDICO'],['DOCTOR'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['PERITO'],['EXPERT'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['PRESIDENTE'],['PRESIDENT'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['PROFESSOR'],['TEACHER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['PROGRAMADOR'],['PROGRAMMER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['PROJETISTA'],['DESIGNER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['PROPRIETARIO'],['OWNER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['PSICOLOGO'],['PSYCHOLOGIST'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['SOCIO'],['PARTNER'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['SUPERINTENDENTE'],['SUPERINTENDENT'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['SUPERVISOR TECNICO'],['SUPERVISOR TECHNICIAN'])\r\nICPcargo[\"CARGO\"] = ICPcargo[\"CARGO\"].replace(['VENDEDOR'],['SELLER'])\r\n\r\n\r\n\r\n#tradução dos termos de departamento que estão no ICP\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['ADMINISTRATIVO'],['ADMINISTRATIVE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['AGROPECUARIA'],['AGRICULTURE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['ALMOXARIFADO'],['WAREHOUSE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['AMBIENTAL'],['ENVIRONMENTAL'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['ARQUITETURA'],['ARCHITECTURE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['ASSISTANT'],['ASSISTANT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['ATENDIMENTO'],['ATTENDANCE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['BENEFICIO'],['BENEFIT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['COMERCIAL'],['COMMERCIAL'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['COMUNICACOES'],['COMMUNICATIONS'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['COMPRAS'],['PURCHASES'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['COMUNICACAO'],['COMMUNICATION'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['CONFORMIDADE'],['CONFORMITY'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['CONTABIL'],['ACCOUNTING'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['CONTRATOS'],['CONTRACTS'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['CONTROLADORIA'],['CONTROLLERSHIP'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['CONSELHO'],['COUNCIL'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['DADOS'],['DATA'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['DESENVOLVIMENTO'],['DEVELOPMENT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['DOCUMENTOS'],['DOCUMENTS'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['EDUCACAO'],['EDUCATION'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['ENERGIA'],['ENERGY'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['ENGENHARIA'],['ENGINEERING'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['ESTOQUE'],['STOCK'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['EVENTOS'],['EVENTS'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['FABRICACAO'],['MANUFACTURING'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['FARMACEUTICO'],['PHARMACEUTICAL'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['FINANCEIRO'],['FINANCIAL'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['GERAL'],['GENERAL'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['IMOBILIARIO'],['REAL ESTATE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['IMPORTACAO'],['IMPORT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['IMPRENSA'],['PRESS'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['INFRAESTRUTURA'],['INFRASTRUCTURE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['INOVACAO'],['INNOVATION'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['INTELIGENCIA DE MERCADO'],['MARKET INTELLIGENCE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['INVESTIMENTO'],['INVESTMENT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['JURIDICO'],['LEGAL'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['LABORATORIO'],['LABORATORY'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['LOGISTICA'],['LOGISTICS'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['MANUFATURA'],['MANUFACTURE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['MANUTENCAO'],['MAINTENANCE'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['MEIO AMBIENTE'],['ENVIRONMENT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['NEGOCIOS'],['BUSINESS'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['OBRAS'],['CONSTRUCTION'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['OPERACAO'],['OPERATION'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['ORCAMENTO'],['BUDGET'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['PESQUISA'],['SEARCH'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['PLANEJAMENTO'],['PLANNING'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['PROCESSO'],['PROCESS'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['PRODUCAO'],['PRODUCTION'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['PROJETO'],['PROJECT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['QUALIDADE'],['QUALITY'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['RH'],['HR'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['REDES'],['NETWORKS'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['RELACIONAMENTO'],['RELATIONSHIP'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['REMUNERACAO'],['REMUNERATION'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['RISCO'],['RISK'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['SAUDE'],['HEALTH'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['SEGURANCA'],['SAFETY'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['SEGURANCA DA INFORMACAO'],['INFORMATION SECURITY'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['SEGURANCA DO TRABALHO'],['WORKPLACE SAFETY'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['SERVICOS'],['SERVICES'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['SUPORTE'],['SUPPORT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['SUPRIMENTOS'],['SUPPLIES'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['TELECOMUNICACAO'],['TELECOMMUNICATION'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['TI'],['IT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['TOPOGRAFIA'],['TOPOGRAPHY'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['TRANSPORTE'],['TRANSPORT'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['TREINAMENTO'],['TRAINING'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['TRIBUTARIO'],['TAX'])\r\nICPdepartamento[\"DEPARTAMENTO\"] = ICPdepartamento[\"DEPARTAMENTO\"].replace(['VENDAS'],['SALES'])\r\n\r\n\r\n#Elemento de texto (para permitir a escrita na caixa de texto - repete)\r\nelem14 = driver.find_element_by_xpath('//*[@id=\"ember208-typeahead\"]')\r\n\r\n# Clicando na caixa de texto\r\nelem14.click()\r\ni=0\r\nj=0\r\nprint(ICPdepartamento[\"DEPARTAMENTO\"])\r\nprint(ICPcargo[\"CARGO\"])\r\nprint(len(ICPcargo[\"CARGO\"]))\r\nprint(len(ICPdepartamento[\"DEPARTAMENTO\"]))\r\nwhile i < len(ICPdepartamento[\"DEPARTAMENTO\"]):\r\n    j=0\r\n    while j < len(ICPcargo[\"CARGO\"]):\r\n    # Escrevendo o nome cargo + departamento traduzidos de forma concatenada\r\n        elem14.send_keys(str(ICPdepartamento[\"DEPARTAMENTO\"][i].lower()) + ' ' + str(ICPcargo[\"CARGO\"][j]).lower())\r\n        time.sleep(3)\r\n    # apertando enter\r\n        keyboard = Controller()\r\n        keyboard.press(Key.enter)\r\n        time.sleep(3)\r\n\r\n        keyboard.release(Key.enter)\r\n        print(ICPdepartamento[\"DEPARTAMENTO\"][i] +\" \"+ ICPcargo[\"CARGO\"][j])\r\n        print(j)\r\n        print(i)\r\n        j+=1\r\n    i-=1\r\n    i+=2\r\n\r\n\r\n# elemento HTML tipo de empresa\r\n\r\nelem15 = driver.find_element_by_xpath('//*[@id=\"ember231\"]/div')\r\n\r\n# clicando em tipo de empresa\r\n\r\nelem15.click()\r\n\r\n# elemento HTML Empresa de Capital aberto\r\nelem16 = driver.find_element_by_xpath('//*[@id=\"ember230\"]/div[3]/ol/li[1]/button')\r\n\r\n# clicando em Empresa de Capital aberto\r\n\r\nelem16.click()\r\n\r\n# elemento HTML Empresa Privada\r\nelem17 = driver.find_element_by_xpath('//*[@id=\"ember230\"]/div[3]/ol/li[1]/button')\r\n\r\n# clicando em Empresa Privada\r\n\r\nelem17.click()\r\n\r\n# elemento HTML Sociedade\r\nelem18 = driver.find_element_by_xpath('//*[@id=\"ember230\"]/div[3]/ol/li[3]/button')\r\n\r\n# clicando em Sociedade\r\n\r\nelem18.click()\r\n\r\n# elemento HTML Proprietário Único\r\nelem19 = driver.find_element_by_xpath('//*[@id=\"ember230\"]/div[3]/ol/li[4]/button')\r\n\r\n# clicando em Proprietário Único\r\n\r\nelem19.click()\r\n\r\nprint(\"Filtro de ICP pronto. Abra o navegador para visualizar.\") #Não Esqueca de inserir o tipo de Empresa.\r\n#print(\"Empresa de capital aberto\")\r\n#print(\"Empresa privada\")\r\n#print(\"Sociedade\")\r\n#print(\"Proprietário único\")", "repo_name": "MatheusMalva/AutomacaoFiltroLN", "sub_path": "AutomacaoICP-LN/AutomacaoICP-LN.py", "file_name": "AutomacaoICP-LN.py", "file_ext": "py", "file_size_in_byte": 32110, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 88, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 199, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 199, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 226, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 239, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 246, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 267, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 273, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 276, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 279, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 288, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 302, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 310, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 318, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 326, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 333, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 340, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 347, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 354, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 384, "usage_type": "call"}, {"api_name": "pynput.keyboard.Controller", "line_number": 387, "usage_type": "call"}, {"api_name": "pynput.keyboard.Key.enter", "line_number": 389, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 389, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 390, "usage_type": "call"}, {"api_name": "pynput.keyboard.Key.enter", "line_number": 392, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 392, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 529, "usage_type": "call"}, {"api_name": "pynput.keyboard.Controller", "line_number": 531, "usage_type": "call"}, {"api_name": "pynput.keyboard.Key.enter", "line_number": 532, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 532, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 533, "usage_type": "call"}, {"api_name": "pynput.keyboard.Key.enter", "line_number": 535, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 535, "usage_type": "name"}]}
{"seq_id": "74848415801", "text": "from collections import namedtuple\n\nfrom aocd_tools import load_input_data\n\n\nEXAMPLE = \"\"\"\nshiny gold bags contain 2 dark red bags.\ndark red bags contain 2 dark orange bags.\ndark orange bags contain 2 dark yellow bags.\ndark yellow bags contain 2 dark green bags.\ndark green bags contain 2 dark blue bags.\ndark blue bags contain 2 dark violet bags.\ndark violet bags contain no other bags.\"\"\".strip()\n\nBagRule = namedtuple(\"bag_rule\", \"colour count\")\n\n\ndef run():\n    input_data = load_input_data(2020, 7)\n    # input_data = EXAMPLE\n    print(f\"loaded input data ({len(input_data)} bytes)\")\n    print(\"solution2 = \", solution2(input_data.split(\"\\n\")))\n\n\ndef parse_rules(line):\n    key, _, contains = line.partition(\" contain \")\n    key, _, _ = key.partition(\" bag\")\n    values = []\n    if \"no other bags\" not in contains:\n        for field in contains.split(\",\"):\n            field = field.strip()\n            count, _, field = field.partition(\" \")\n            count = int(count)\n            colour, _, _ = field.partition(\" bag\")\n            values.append(BagRule(colour, count))\n\n    return key.strip(), values\n\n\ndef count_containing(colour, rules):\n    queue = [(1, colour)]\n    count = 0\n\n    while queue:\n        n, colour = queue.pop(0)\n        contains = rules[colour]\n        count += n\n        for bag in contains:\n            queue.append((bag.count * n, bag.colour))\n    return count - 1\n\n\ndef solution2(lines):\n    bag_rules = {}\n    for line in lines:\n        k, v = parse_rules(line)\n        bag_rules[k] = v\n\n    return count_containing(\"shiny gold\", bag_rules)\n\n\nif __name__ == \"__main__\":\n    run()\n", "repo_name": "heroworkshop/advent_of_code", "sub_path": "y2020/day_07_part2.py", "file_name": "day_07_part2.py", "file_ext": "py", "file_size_in_byte": 1614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "collections.namedtuple", "line_number": 15, "usage_type": "call"}, {"api_name": "aocd_tools.load_input_data", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "42441040074", "text": "import sys\nimport traceback\nimport logging\nimport DCHub\n\nclass DCClientLogHandler(logging.Handler):\n    '''Logging handler that sends log messages to a Direct Connect client'''\n    def __init__(self, user, bot):\n        logging.Handler.__init__(self)\n        self.escapetranstable = (('\\n', '\\r\\n'), ('|', '&#124;'), ('$', '&#36;'))\n        self.user = user\n        self.bot = bot\n        \n    def emit(self, record):\n        '''Send user the log message via a private message from the bot\n        \n        Note that log messages for data sent that include private messages from\n        the bot are not sent to the user, as this would allow for an infinite\n        loop.  If multiple commands were sent to the user, and even one is a \n        private message from the bot, the user will not get a related log \n        message.\n        \n        Also note that the user will not be sent tracebacks if the the message\n        is logged using exception.\n        '''\n        message = self.format(record)\n        # Note that if the log level for data sent messages is changed from\n        # the default of 1, you could get an infinite loop unless you change\n        # the value here\n        if record.levelno == 1 and message.find('From: %s $<%s>' % (self.bot.nick, self.bot.nick)) != -1:\n            # Ignore data sent messages that include private messages from the\n            # bot\n            return\n        # Escape illegal characters\n        for changethis, tothis in self.escapetranstable:\n            message = message.replace(changethis, tothis)\n        self.user.sendmessage(\"$To: %s From: %s $<%s> %s: %s|\" % (self.user.nick, \n          self.bot.nick, self.bot.nick, record.levelname, message))\n\nclass LogBot(DCHub.DCHubBot):\n    '''Remote logging bot, allowing ops to register and receive log messages\n    \n    To register to receive log messages, an op should private message the bot\n    with the command start.  To stop receiving messages, an op should private\n    message the bot with the command stop. An op can change the logging level \n    (verbosity) by private messaging the bot the command level X, where X is\n    the numeric verbosity level (10 - Debug, 20 - Info, etc.).  An op can set\n    the initial level by adding a level to the start command (e.g. start 20).\n    \n    The bot closes all log handlers it has opened when it is reloaded, so after\n    reloading the bots, ops that want to continue to receive logging messages\n    should resend the start command.\n    '''\n    \n    def __init__(self, hub, nick = 'LogBot'):\n        DCHub.DCHubBot.__init__(self, hub, nick)\n        self.handlers = {}\n        self.execbefore['removeuser'] = self.removeloghandler\n                \n    def close(self):\n        '''Close all handlers that the bot has opened'''\n        for handler in self.handlers.values():\n            self.hub.log.removeHandler(handler)\n        \n    def processcommand(self, user, command):\n        '''Add/Remove/Change verbosity of log messages sent to users'''\n        if not user.op:\n            return\n        if command.startswith('start'):\n            if user.nick not in self.handlers:\n                self.handlers[user.nick] = DCClientLogHandler(user, self)\n                self.hub.log.addHandler(self.handlers[user.nick])\n            try:\n                level = int(command[6:])\n            except ValueError:\n                pass\n            else:\n                self.setlevel(user, level)\n                \n        elif command == 'stop':\n            self.removeloghandler(user)\n        elif command.startswith('level'):\n            try:\n                level = int(command[6:])\n            except ValueError:\n                pass\n            else:\n                self.setlevel(user, level)\n                \n    def removeloghandler(self, user):\n        '''Remove logging handlers for ops that have left the hub'''\n        if user.nick in self.handlers and user.loggedin:\n            self.hub.log.removeHandler(self.handlers[user.nick])\n            del self.handlers[user.nick]\n            \n    def setlevel(self, user, level):\n        '''Change the verbosity of the log messages sent to a user'''\n        if user.nick in self.handlers and user.loggedin:\n            self.handlers[user.nick].setLevel(level)\n", "repo_name": "NareshPS/pyDCHub", "sub_path": "examples/bots/LogBot.py", "file_name": "LogBot.py", "file_ext": "py", "file_size_in_byte": 4256, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "logging.Handler", "line_number": 6, "usage_type": "attribute"}, {"api_name": "logging.Handler.__init__", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.Handler", "line_number": 9, "usage_type": "attribute"}, {"api_name": "DCHub.DCHubBot", "line_number": 40, "usage_type": "attribute"}, {"api_name": "DCHub.DCHubBot.__init__", "line_number": 56, "usage_type": "call"}, {"api_name": "DCHub.DCHubBot", "line_number": 56, "usage_type": "attribute"}]}
{"seq_id": "16658027669", "text": "from numpy.fft import fft\nfrom scipy.interpolate import interp1d\nimport numpy as np\nimport pandas as pd\nimport argparse\n\ndef interpolate_signal(x_pos, y_grayval, oversampling=10):\n    f = interp1d(x_pos, y_grayval, kind='cubic')\n    x = np.linspace(x_pos[0], x_pos[-1], len(x_pos)*oversampling) #replace 1000 with oversampling*len(x_pos)\n    y = f(x)\n    return x, y\n\ndef center_peak(psf, crop_factor):\n    peak_idx = psf.argmax()\n    start_idx = max(0, peak_idx - len(psf)//crop_factor)\n    end_idx =  min(len(psf), peak_idx + len(psf)//crop_factor)\n    \n    return psf[start_idx:end_idx] # center the psf\n\ndef retrieve_MTF(x_pos, y_grayval, oversampling=10, crop_factor=4, unit='mm'):\n    x, y = interpolate_signal(x_pos, y_grayval, oversampling=oversampling)\n\n    psf = np.diff(y)\n    x = x[1:]\n\n    psf = psf/psf.sum()\n    psf = center_peak(psf, crop_factor=crop_factor)\n    psf *= np.hanning(len(psf))\n\n    # compute MTF\n    # determining frequencies (because I always forget): <https://www.mathworks.com/matlabcentral/answers/88685-how-to-obtain-the-frequencies-from-the-fft-function>\n    N = len(psf)\n    dx = np.diff(x)[0] # mm\n    fmax = 1/(2*dx) # 1/mm\n    df = fmax / (N // 2)\n    freq = np.arange(0, fmax, df)\n\n    mtfs = np.abs(fft(psf)/np.sum(psf))[:len(psf)//2]\n\n    mtf = pd.DataFrame({f'frequency [1/{unit}]' : freq, 'MTF': mtfs})\n    mtf = mtf.applymap(lambda x: np.round(x, decimals=3))\n    return mtf\n\ndef from_csv(fname):\n    '''\n    csv expected to be in ImageJ output format produced by Radial Profile plugin: <https://imagej.nih.gov/ij/plugins/radial-profile.html>\n    Radius_[units], Normalized_Integrated_intensity\n    '''\n    df = pd.read_csv(fname, encoding = 'latin1').dropna()\n    unit = df.columns[0].split('_')[-1][1:-1]\n    data = df.to_numpy()\n    x_pos = data[:, 0]\n    y_grayval = data[:, 1]\n    return retrieve_MTF(x_pos, y_grayval, unit=unit, oversampling=10, crop_factor=4)\n\n\ndef cli():\n    parser = argparse.ArgumentParser('Calculate MTF from an ImageJ radial line profile')\n    parser.add_argument('csv_filename', help='path to csv file output from ImageJ radial line profile')\n    parser.add_argument('-output_file', default=None)\n\n    args = parser.parse_args()\n\n    mtf = from_csv(args.csv_filename)\n\n    if args.output_file:\n        mtf.to_csv(args.output_file)\n    else:\n        print(mtf[(mtf['MTF']<0.01).cumsum()<1].to_string()) #only print MTFs > 1% the rest is useless noise\n\n\nfrom gooey import Gooey, GooeyParser\n\n# @Gooey\ndef main():\n    parser = GooeyParser(description='Calculate MTF from an ImageJ radial line profile')\n    parser.add_argument('csv_filename',\n                         help='path to csv file output from ImageJ radial line profile',\n                         widget=\"FileChooser\")\n    parser.add_argument('-output_file', default=None)\n\n    args = parser.parse_args()\n\n    mtf = from_csv(args.csv_filename)\n\n    if args.output_file:\n        mtf.to_csv(args.output_file)\n    else:\n        print(mtf[(mtf['MTF']<0.01).cumsum()<1].to_string()) #only print MTFs > 1% the rest is useless noise", "repo_name": "bnel1201/mtf-tool", "sub_path": "src/xri_mtf/mtf.py", "file_name": "mtf.py", "file_ext": "py", "file_size_in_byte": 3058, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "scipy.interpolate.interp1d", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.hanning", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.fft.fft", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 58, "usage_type": "call"}, {"api_name": "gooey.GooeyParser", "line_number": 76, "usage_type": "call"}]}
{"seq_id": "5743237090", "text": "import torch\nimport torch.nn as nn\n\n\nclass Unet(nn.Module):\n    \"\"\"\n    this is the unet model implementation base on paper https://arxiv.org/pdf/1505.04597.pdf\n\n    args:\n        n_class: number of classes --> in our case 22\n        in_channels: number of input channels --> in our case 3\n        filters: list of filters for each layer --> in our case [64, 128, 256, 512, 1024] --> you can change this according to\n        your need but make sure the number of filters are in the power of 2 and change encoder and decoder accordingly.\n\n    return:\n        x42: output of the model\n\n    \"\"\"\n\n    def __init__(self, n_class=22, in_channels=3, filters=[64, 128, 256, 512, 1024]):\n        super(Unet, self).__init__()\n\n        # Encoder\n\n        self.conv1 = nn.Conv2d(in_channels, filters[0], kernel_size=3, padding=1)\n        self.relu1 = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(filters[0], filters[0], kernel_size=3, padding=1)\n        self.relu2 = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)\n        self.conv3 = nn.Conv2d(filters[0], filters[1], kernel_size=3, padding=1)\n        self.relu3 = nn.ReLU(inplace=True)\n        self.conv4 = nn.Conv2d(filters[1], filters[1], kernel_size=3, padding=1)\n        self.relu4 = nn.ReLU(inplace=True)\n        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n        self.conv5 = nn.Conv2d(filters[1], filters[2], kernel_size=3, padding=1)\n        self.relu5 = nn.ReLU(inplace=True)\n        self.conv6 = nn.Conv2d(filters[2], filters[2], kernel_size=3, padding=1)\n        self.relu6 = nn.ReLU(inplace=True)\n        self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n        self.conv7 = nn.Conv2d(filters[2], filters[3], kernel_size=3, padding=1)\n        self.relu7 = nn.ReLU(inplace=True)\n        self.conv8 = nn.Conv2d(filters[3], filters[3], kernel_size=3, padding=1)\n        self.relu8 = nn.ReLU(inplace=True)\n        # reshape to [1, 512, 70, 70]\n        self.maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2)\n        self.conv9 = nn.Conv2d(filters[3], filters[4], kernel_size=3, padding=1)\n        self.relu9 = nn.ReLU(inplace=True)\n        self.conv10 = nn.Conv2d(filters[4], filters[4], kernel_size=3, padding=1)\n        self.relu10 = nn.ReLU(inplace=True)\n\n        # Decoder\n\n        self.trans1 = nn.ConvTranspose2d(\n            filters[4], filters[3], kernel_size=2, stride=2\n        )\n        self.conv11 = nn.Conv2d(filters[4], filters[3], kernel_size=3, padding=1)\n        self.relu11 = nn.ReLU(inplace=True)\n        self.conv12 = nn.Conv2d(filters[3], filters[3], kernel_size=3, padding=1)\n        self.relu12 = nn.ReLU(inplace=True)\n\n        self.trans2 = nn.ConvTranspose2d(\n            filters[3], filters[2], kernel_size=2, stride=2\n        )\n        self.conv13 = nn.Conv2d(filters[3], filters[2], kernel_size=3, padding=1)\n        self.relu13 = nn.ReLU(inplace=True)\n        self.conv14 = nn.Conv2d(filters[2], filters[2], kernel_size=3, padding=1)\n\n        self.trans3 = nn.ConvTranspose2d(\n            filters[2], filters[1], kernel_size=2, stride=2\n        )\n        self.conv15 = nn.Conv2d(filters[2], filters[1], kernel_size=3, padding=1)\n        self.relu15 = nn.ReLU(inplace=True)\n        self.conv16 = nn.Conv2d(filters[1], filters[1], kernel_size=3, padding=1)\n\n        self.trans4 = nn.ConvTranspose2d(\n            filters[1], filters[0], kernel_size=2, stride=2\n        )\n        self.conv17 = nn.Conv2d(filters[1], filters[0], kernel_size=3, padding=1)\n        self.relu17 = nn.ReLU(inplace=True)\n        self.conv18 = nn.Conv2d(filters[0], filters[0], kernel_size=3, padding=1)\n\n        self.conv19 = nn.Conv2d(filters[0], n_class, kernel_size=1)\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x):\n        # Encoder\n        x1 = self.conv1(x)\n        x2 = self.relu1(x1)\n        x3 = self.conv2(x2)\n        x4 = self.relu2(x3)\n        x5 = self.maxpool(x4)\n\n        x6 = self.conv3(x5)\n        x7 = self.relu3(x6)\n        x8 = self.conv4(x7)\n        x9 = self.relu4(x8)\n        x10 = self.maxpool2(x9)\n\n        x11 = self.conv5(x10)\n        x12 = self.relu5(x11)\n        x13 = self.conv6(x12)\n        x14 = self.relu6(x13)\n        x15 = self.maxpool3(x14)\n\n        x16 = self.conv7(x15)\n        x17 = self.relu7(x16)\n        x18 = self.conv8(x17)\n        x19 = self.relu8(x18)\n        # reshape to [1, 512, 70, 70]\n\n        x20 = self.maxpool4(x19)\n        print(x19.shape)\n\n        x21 = self.conv9(x20)\n        x22 = self.relu9(x21)\n        x23 = self.conv10(x22)\n        x24 = self.relu10(x23)\n\n        # Decoder\n\n        x25 = self.trans1(x24)\n        # concatenate x19 and x25\n        cat = torch.cat([x19, x25], dim=1)\n        x26 = self.conv11(cat)\n        x27 = self.relu11(x26)\n        x28 = self.conv12(x27)\n        x29 = self.relu12(x28)\n\n        x30 = self.trans2(x29)\n        cat2 = torch.cat([x14, x30], dim=1)\n        x31 = self.conv13(cat2)\n        x32 = self.relu13(x31)\n        x33 = self.conv14(x32)\n        x34 = self.trans3(x33)\n        cat3 = torch.cat([x9, x34], dim=1)\n        x35 = self.conv15(cat3)\n        x36 = self.relu15(x35)\n        x37 = self.conv16(x36)\n        x38 = self.trans4(x37)\n        cat4 = torch.cat([x4, x38], dim=1)\n        x39 = self.conv17(cat4)\n        x40 = self.relu17(x39)\n        x41 = self.conv18(x40)\n        x42 = self.conv19(x41)\n        x42 = self.sigmoid(x42)\n\n        return x42\n\n\nfrom torch.nn.functional import relu\n\n\nclass UNet1(nn.Module):\n    \"\"\"\n    unet model\n\n\n\n\n    \"\"\"\n\n    def __init__(self, n_class):\n        super().__init__()\n\n        # Encoder\n        # In the encoder, convolutional layers with the Conv2d function are used to extract features from the input image.\n        # Each block in the encoder consists of two convolutional layers followed by a max-pooling layer, with the exception of the last block which does not include a max-pooling layer.\n        # -------\n        # input: 572x572x3\n        self.e11 = nn.Conv2d(3, 64, kernel_size=3, padding=1)  # output: 570x570x64\n        self.e12 = nn.Conv2d(64, 64, kernel_size=3, padding=1)  # output: 568x568x64\n        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)  # output: 284x284x64\n\n        # input: 284x284x64\n        self.e21 = nn.Conv2d(64, 128, kernel_size=3, padding=1)  # output: 282x282x128\n        self.e22 = nn.Conv2d(128, 128, kernel_size=3, padding=1)  # output: 280x280x128\n        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)  # output: 140x140x128\n\n        # input: 140x140x128\n        self.e31 = nn.Conv2d(128, 256, kernel_size=3, padding=1)  # output: 138x138x256\n        self.e32 = nn.Conv2d(256, 256, kernel_size=3, padding=1)  # output: 136x136x256\n        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)  # output: 68x68x256\n\n        # input: 68x68x256\n        self.e41 = nn.Conv2d(256, 512, kernel_size=3, padding=1)  # output: 66x66x512\n        self.e42 = nn.Conv2d(512, 512, kernel_size=3, padding=1)  # output: 64x64x512\n        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)  # output: 32x32x512\n\n        # input: 32x32x512\n        self.e51 = nn.Conv2d(512, 1024, kernel_size=3, padding=1)  # output: 30x30x1024\n        self.e52 = nn.Conv2d(1024, 1024, kernel_size=3, padding=1)  # output: 28x28x1024\n\n        # Decoder\n        self.upconv1 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)\n        self.d11 = nn.Conv2d(1024, 512, kernel_size=3, padding=1)\n        self.d12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)\n\n        self.upconv2 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)\n        self.d21 = nn.Conv2d(512, 256, kernel_size=3, padding=1)\n        self.d22 = nn.Conv2d(256, 256, kernel_size=3, padding=1)\n\n        self.upconv3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)\n        self.d31 = nn.Conv2d(256, 128, kernel_size=3, padding=1)\n        self.d32 = nn.Conv2d(128, 128, kernel_size=3, padding=1)\n\n        self.upconv4 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)\n        self.d41 = nn.Conv2d(128, 64, kernel_size=3, padding=1)\n        self.d42 = nn.Conv2d(64, 64, kernel_size=3, padding=1)\n\n        # Output layer\n        self.outconv = nn.Conv2d(64, n_class, kernel_size=1)\n\n    def forward(self, x):\n        # Encoder\n        xe11 = relu(self.e11(x))\n        xe12 = relu(self.e12(xe11))\n        xp1 = self.pool1(xe12)\n\n        xe21 = relu(self.e21(xp1))\n        xe22 = relu(self.e22(xe21))\n        xp2 = self.pool2(xe22)\n\n        xe31 = relu(self.e31(xp2))\n        xe32 = relu(self.e32(xe31))\n        xp3 = self.pool3(xe32)\n\n        xe41 = relu(self.e41(xp3))\n        xe42 = relu(self.e42(xe41))\n        xp4 = self.pool4(xe42)\n\n        xe51 = relu(self.e51(xp4))\n        xe52 = relu(self.e52(xe51))\n\n        # Decoder\n        xu1 = self.upconv1(xe52)\n        xu11 = torch.cat([xu1, xe42], dim=1)\n        xd11 = relu(self.d11(xu11))\n        xd12 = relu(self.d12(xd11))\n\n        xu2 = self.upconv2(xd12)\n        xu22 = torch.cat([xu2, xe32], dim=1)\n        xd21 = relu(self.d21(xu22))\n        xd22 = relu(self.d22(xd21))\n\n        xu3 = self.upconv3(xd22)\n        xu33 = torch.cat([xu3, xe22], dim=1)\n        xd31 = relu(self.d31(xu33))\n        xd32 = relu(self.d32(xd31))\n\n        xu4 = self.upconv4(xd32)\n        xu44 = torch.cat([xu4, xe12], dim=1)\n        xd41 = relu(self.d41(xu44))\n        xd42 = relu(self.d42(xd41))\n\n        # Output layer\n        out = self.outconv(xd42)\n\n        return out\n\n\nif __name__ == \"__main__\":\n    model = Unet()\n    x = torch.randn(1, 3, 256, 256)\n    print(model)\n", "repo_name": "AmzadHossainrafis/DroneImage-segmentation", "sub_path": "src/DronVid/components/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 9484, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "40", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 153, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 170, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 171, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 181, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 182, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 185, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 186, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 187, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 190, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 191, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 194, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 195, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 196, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 198, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 200, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 202, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 202, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 203, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 204, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 207, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 208, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 211, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 263, "usage_type": "call"}]}
{"seq_id": "20922831614", "text": "# NFA ShotGrid Project Creator tool by Gilles Vink\nimport re\nfrom shotgun_api3 import shotgun\nimport os\nimport datetime\nimport urllib3\nimport requests\nimport webbrowser\n\n\nclass ProjectCreation:\n    def __init__(self):\n        print(\"NFA ShotGrid Project Creator\")\n        print(\"\\n\")\n        print(\"Tool is starting... Please wait.\")\n\n        # Set here your credentials\n        self.sg = shotgun.Shotgun(\n            \"https://nfa.shotgunstudio.com\",\n            script_name=\"****\",\n            api_key=\"****\",\n        )\n\n        print(\"Successfully connected to ShotGrid servers.\")\n        print(\"\\n\")\n\n        # If user is validated, start project creation\n        if self.__get_user():\n            self.create_project()\n\n        input(\n            \"Press 'enter' to continue... Or just look at this beautiful command prompt window :)\"\n        )\n\n    def create_project(self):\n        # Project name\n        project_name = self.project_name()\n\n        # Project code\n        project_code = self.project_code()\n\n        # Supervisors\n        supervisors = self.supervisors()\n        supervisor_ids = supervisors[0]\n        supervisor_names = supervisors[1]\n\n        # Render engine\n        render_engine = self.render_engines()\n\n        # Project type\n        project_type = self.project_type()\n\n        # FPS\n        fps = self.fps()\n\n        # Validate settings for project creation\n        if self.project_settings(\n                project_name=project_name,\n                project_code=project_code,\n                supervisors=supervisor_names,\n                render_engine=render_engine,\n                fps=fps,\n        ):\n            print(\"Starting project creation for '%s'.\" % project_name)\n\n            # Starting submission with all set variables\n            try:\n                project = self.__setup_project(\n                    project_name=project_name,\n                    project_code=project_code,\n                    render_engine=render_engine,\n                    project_type=project_type,\n                    fps=fps,\n                    supervisor_ids=supervisor_ids,\n                    student_year=self.student_year,\n                    graduation_year=self.student_graduation_year,\n                )\n\n                # Set correct permission groups\n                try:\n                    for supervisor in supervisor_ids:\n                        self.__update_user_permissions(supervisor)\n\n                except Exception as e:\n                    print(\n                        \"Something went wrong while updating the user settings. \"\n                        \"Please contact a pipeline administrator.\"\n                    )\n\n                print(\"Project '%s' successfully created\" % project_name)\n\n                # Launch project in webbrowser\n                self.__open_project(project)\n\n                print(\n                    \"You can now open and edit the project on https://nfa.shotgunstudio.com/ (which is also opened for you currently) \"\n                    \"or open software via ShotGrid desktop (please remember to refresh).\"\n                )\n\n            except Exception as e:\n                print(\"Something went wrong. Please contact the pipline administrator.\")\n                print(str(e))\n\n        else:\n            print(\n                \"Project creation cancelled. You can restart this program to create a project.\"\n            )\n\n    # Ask user for project name, and validate name\n    def project_name(self):\n        print(\"How would you like to name the project?\")\n        validated = False\n\n        project_name = \"\"\n\n        while validated is False:\n            project_name = input()\n            validated = self.__validate_projectname(project_name)\n\n            if not validated:\n                print(\n                    \"Project name is incorrect or already existing. Please only use lowercase and underscores.\"\n                )\n\n        print(\"Project name: %s\" % str(project_name))\n        print(\"\\n\")\n\n        return project_name\n\n    # Validates name for correct naming convention\n    def __validate_projectname(self, project_name):\n        validated = False\n\n        regex = \"^[a-z_]*$\"\n        match = re.match(regex, project_name)\n\n        if match:\n            # Search for project name\n            project_search = [[\"name\", \"is\", project_name]]\n            project = self.sg.find_one(\"Project\", project_search)\n\n            if project is None:\n                validated = True\n\n        return validated\n\n    # Ask user for project code, and validate code\n    def project_code(self):\n        print(\"What would be the project code? (3 letters)\")\n        validated = False\n\n        project_code = \"\"\n\n        while validated is False:\n            project_code = input()\n            validated = self.__validate_projectcode(project_code)\n\n            if not validated:\n                print(\n                    \"Project code is incorrect or already existing. Only use 3 letters in lowercase.\"\n                )\n\n        print(\"Project code: %s\" % str(project_code))\n        print(\"\\n\")\n\n        return project_code\n\n    # Validates name for correct naming convention\n    def __validate_projectcode(self, project_code):\n        validated = False\n\n        if len(project_code) == 3:\n            regex = \"^[a-z_]*$\"\n            match = re.match(regex, project_code)\n\n            if match:\n                projectcode_search = [[\"sg_projectcode\", \"is\", project_code]]\n                projectcode = self.sg.find_one(\"Project\", projectcode_search)\n\n                if projectcode is None:\n                    validated = True\n\n        return validated\n\n    # Search for other supervisors on the ShotGrid user database\n    def supervisors(self):\n        print(\n            \"Are there, besides you, other supervisors on this project? Type [Y]es or [N]o\"\n        )\n        validated = False\n\n        supervisor_ids = []\n        supervisor_names = []\n        supervisor_ids.append(self.sg_user_id)\n        supervisor_names.append(self.sg_username)\n\n        answer = \"\"\n\n        while validated is False:\n            answer = input()\n            validated = self.__validate_boolean(answer)\n\n            if not validated:\n                print(\n                    \"Please choose [Y]es or [N]o. (Just the letter 'Y' for yes or 'N' for no)\"\n                )\n\n        # If user says yes, then go through all the options\n        if answer.lower() == \"y\":\n            more_supervisors = True\n            while more_supervisors is True:\n                found_supervisor = self.__find_supervisor()\n                found_supervisor_id = found_supervisor[0]\n                found_supervisor_name = found_supervisor[1]\n\n                # If the user is not already in the supervisor id's, add the supervisor to the supervisor list\n                if not found_supervisor_id in supervisor_ids:\n                    supervisor_ids.append(found_supervisor_id)\n                    supervisor_names.append(found_supervisor_name)\n                    print(\n                        \"Added %s to the list of supervisors.\" % found_supervisor_name\n                    )\n                    print(\"\\n\")\n\n                else:\n                    print(\n                        \"%s is already in the list of supervisors for this project. Please choose someone else \"\n                        \"or proceed.\" % found_supervisor_name\n                    )\n                    print(\"\\n\")\n\n                print(\"Do you want to add another supervisor? Type [Y]es or [N]o\")\n\n                validated = False\n\n                while validated is False:\n                    answer = input()\n                    validated = self.__validate_boolean(answer)\n\n                    if not validated:\n                        print(\n                            \"Please choose [Y]es or [N]o. (Just the letter 'Y' for yes or 'N' for no)\"\n                        )\n\n                if answer.lower() == \"n\":\n                    print(\"Supervisors: %s\" % supervisor_names)\n                    return supervisor_ids, supervisor_names\n\n        else:\n            print(\"No other supervisors beside you on this project, roger that!\")\n            print(\"\\n\")\n            return supervisor_ids, supervisor_names\n\n    # Find other users on the ShotGrid database\n    def __find_supervisor(self):\n        while True:\n            print(\"Please type the name (just a part of the name to find him/her).\")\n            name = input()\n\n            found_user = self.__find_user(name)\n            if found_user:\n                found_user_name = found_user.get(\"name\")\n                found_user = found_user.get(\"id\")\n\n                print(\"Do you mean %s? Type [Y]es or [N]o\" % found_user_name)\n                validated = False\n                while validated is False:\n                    answer = input()\n                    print(\"\\n\")\n                    validated = self.__validate_boolean(answer)\n\n                    if not validated:\n                        print(\n                            \"Please choose [Y]es or [N]o. (Just the letter 'Y' for yes or 'N' for no)\"\n                        )\n\n                if answer.lower() == \"y\":\n                    return found_user, found_user_name\n\n            else:\n                print(\"Did not find %s, please try again.\" % name)\n\n    # Ask the user the render engine to use, this will later set the environment for launching software.\n    def render_engines(self):\n        print(\n            \"What is the render engine to be used? \\n 1. All \\n 2. Arnold \\n 3. RenderMan\"\n        )\n        validated = False\n\n        render_engine = \"\"\n\n        while validated is False:\n            render_engine = input()\n            validated = self.__validate_renderengine(render_engine)\n\n            if not validated:\n                print(\"Render engine is incorrect, choose 1, 2 or 3.\")\n\n        if render_engine == \"1\":\n            render_engine_name = \"All\"\n\n        elif render_engine == \"2\":\n            render_engine_name = \"Arnold\"\n\n        else:\n            render_engine_name = \"RenderMan\"\n\n        print(\"Render engine: %s\" % str(render_engine_name))\n        print(\"\\n\")\n\n        return render_engine_name\n\n    # Validates name for correct naming convention\n    @staticmethod\n    def __validate_renderengine(render_engine):\n        validated = False\n        try:\n            render_engine = int(render_engine)\n        except:\n            return False\n        if render_engine > 0 and render_engine < 4:\n            validated = True\n\n        return validated\n\n    # Ask the user what type the project will be\n    def project_type(self):\n        print(\"What is the project type? \\n 1. Fiction \\n 2. Documentary\")\n        validated = False\n\n        project_type = \"\"\n\n        while validated is False:\n            project_type = input()\n            validated = self.__validate_projecttype(project_type)\n\n            if not validated:\n                print(\"Project type is incorrect, choose 1 or 2.\")\n\n        if project_type == \"1\":\n            project_type_name = \"Fiction\"\n\n        else:\n            project_type_name = \"Documentary\"\n\n        print(\"Project type: %s\" % str(project_type_name))\n        print(\"\\n\")\n\n        return project_type_name\n\n    # Validates name for correct naming convention\n    @staticmethod\n    def __validate_projecttype(project_type):\n        validated = False\n        try:\n            project_type = int(project_type)\n        except:\n            return False\n        if project_type > 0 and project_type < 3:\n            validated = True\n\n        return validated\n\n    # Ask the user what the FPS for the project will be\n    def fps(self):\n        print(\"What is the FPS for the project? (Only digits, like 25 for 25fps)\")\n        validated = False\n\n        while validated is False:\n            fps = input()\n            validated = self.__validate_fps(fps)\n\n            if not validated:\n                print(\"FPS is incorrect, only use digits, like 25\")\n\n        print(\"FPS: %s fps\" % str(fps))\n        print(\"\\n\")\n\n        return fps\n\n    # Validates name for correct naming convention\n    @staticmethod\n    def __validate_fps(fps):\n        validated = False\n        if len(fps) > 1:\n            try:\n                fps = int(fps)\n            except Exception as e:\n                return False\n\n            validated = True\n\n        return validated\n\n    # Return all project settings to use, and if returned \"no\", then stop the project creation\n    def project_settings(\n            self, project_name, project_code, supervisors, render_engine, fps\n    ):\n        print(\"You choose the following settings for the project:\")\n        print(\"Project name: %s\" % project_name)\n        print(\"Project code: %s\" % project_code)\n        print(\"Supervisors: %s\" % supervisors)\n        print(\"Render engine: %s\" % render_engine)\n        print(\"FPS: %s\" % fps)\n        print(\n            \"Are you sure you want to create the project with these settings? Type [Y]es or [N]o\"\n        )\n        validated = False\n\n        while validated is False:\n            answer = input()\n            validated = self.__validate_boolean(answer)\n\n            if not validated:\n                print(\n                    \"Please choose [Y]es or [N]o. (Just the letter 'Y' for yes or 'N' for no)\"\n                )\n\n        proceed_creation = False\n\n        if answer.lower() == \"y\":\n            proceed_creation = True\n\n        print(\"\\n\")\n\n        return proceed_creation\n\n    # Validates name for correct naming convention\n    def __validate_boolean(self, answer):\n        validated = False\n\n        if len(answer) == 1:\n            if answer.lower() == \"y\" or answer.lower() == \"n\":\n                validated = True\n\n        return validated\n\n    # Get the current user, and check if the user exists on ShotGrid\n    def __get_user(self):\n        username = os.getlogin()\n\n        # Simple bypass to allow use on non user pc's\n        if username == \"VFXIM\":\n            username = \"Gilles.Vink\"\n\n        # Build filters\n        columns = [\"name\"]\n        search_user = [[\"login\", \"is\", username]]\n\n        # Find the user\n        sg_user = self.sg.find_one(\"HumanUser\", search_user, columns)\n\n        # If no user is found, return a error\n        if sg_user is None:\n            print(\n                \"Could not find user %s on ShotGrid. Please contact a pipeline administrator.\"\n                % username\n            )\n            return False\n\n        self.sg_username = sg_user.get(\"name\")\n\n        self.sg_user_id = sg_user.get(\"id\")\n\n        # Get the student year from the current student according to ShotGrid database\n        student_data = self.__get_student_year(self.sg_user_id)\n\n        self.student_year = student_data[0]\n        self.student_graduation_year = student_data[1]\n\n        # Found user! Now let's welcome the user :)\n        print(\"Welcome %s :) \" % self.sg_username)\n\n        return True\n\n    # Look into the database to collect the student year, and\n    # automatically adjust it after summer to match year transition\n    def __get_student_year(self, id):\n        # Get user data\n        filters = [[\"id\", \"is\", id]]\n        columns = [\"sg_lichting\"]\n\n        # Get current year\n        sg_user_data = self.sg.find_one(\"HumanUser\", filters, columns)\n        graduation_year = sg_user_data.get(\"sg_lichting\")\n        sg_lichting = graduation_year[1:]\n        sg_lichting = int(sg_lichting)\n\n        current_time = datetime.datetime.now()\n        corrected_time = current_time + datetime.timedelta(days=120)\n\n        current_year = int(corrected_time.strftime(\"%Y\"))\n\n        to_graduation_year = sg_lichting - current_year\n\n        if to_graduation_year == 0:\n            student_year = 4\n\n        elif to_graduation_year == 1:\n            student_year = 3\n\n        else:\n            student_year = 2\n\n        return student_year, graduation_year\n\n    # Here we will create the project with all specified variables\n    def __setup_project(\n            self,\n            project_name,\n            project_code,\n            render_engine,\n            project_type,\n            fps,\n            supervisor_ids,\n            student_year,\n            graduation_year,\n    ):\n        supervisors = []\n        for supervisor in supervisor_ids:\n            supervisors.append({\"id\": supervisor, \"type\": \"HumanUser\"})\n\n        project_data = {\n            \"name\": project_name,\n            \"tank_name\": project_name,\n            \"sg_projectcode\": project_code,\n            \"users\": supervisors,\n            \"sg_supervisors\": supervisors,\n            \"sg_render_engine\": render_engine,\n            \"sg_type\": project_type,\n            \"sg_lichting\": graduation_year,\n            \"sg_fps\": int(fps),\n            \"sg_status\": \"Active\",\n        }\n        created_project = self.sg.create(\"Project\", project_data)\n\n        print(\"Created ShotGrid project, now adding pipeline configuration.\")\n        print(\"\\n\")\n\n        project_id = created_project.get(\"id\")\n\n        descriptor = self.__get_descriptor(student_year)\n\n        descriptor_lichting = \"s\" + str(student_year)\n\n        pipeline_configuration_data = {\n            \"code\": \"Primary\",\n            \"descriptor\": descriptor,\n            \"plugin_ids\": \"basic.*\",\n            \"project\": {\"id\": project_id, \"type\": \"Project\"},\n            \"sg_lichting\": descriptor_lichting,\n        }\n\n        self.sg.create(\"PipelineConfiguration\", pipeline_configuration_data)\n\n        print(\"Created pipeline configuration.\")\n        print(\"\\n\")\n\n        return project_id\n\n    # Request the latest published pipeline configuration from ShotGrid to use according to student year\n    @staticmethod\n    def __get_descriptor(student_year):\n        latest_release = requests.get(\n            \"https://api.github.com/repos/nfa-vfxim/nfa-shotgun-configuration/releases/latest\"\n        )\n        latest_release = latest_release.json()[\"name\"]\n\n        release_length = len(latest_release)\n        if latest_release.endswith(\"s4\"):\n            latest_release = latest_release[: release_length - 2]\n\n        elif latest_release.endswith(\"s3\"):\n            latest_release = latest_release[: release_length - 2]\n\n        elif latest_release.endswith(\"s2\"):\n            latest_release = latest_release[: release_length - 2]\n\n        print(\"Latest pipeline release is %s.\" % latest_release)\n        print(\"\\n\")\n\n        descriptor = (\n                \"sgtk:descriptor:git?path=https://github.com/nfa-vfxim/nfa-shotgun-configuration.git&version=\"\n                + latest_release\n                + \"s\"\n                + str(student_year)\n        )\n\n        return descriptor\n\n    # Update the user with the supervision permissions\n    def __update_user_permissions(self, id):\n        filters = [[\"id\", \"is\", id]]\n        columns = [\"permission_rule_set\", \"name\"]\n\n        # Get current year\n        current_permission = self.sg.find_one(\"HumanUser\", filters, columns)\n        user_name = current_permission.get(\"name\")\n        current_permission = current_permission.get(\"permission_rule_set\")\n        current_permission = current_permission.get(\"name\")\n\n        if current_permission == \"Admin\":\n            print(\"%s is an admin, skipping user settings.\" % user_name)\n            print(\"\\n\")\n\n        elif current_permission == \"Artist\":\n            user_data = {\n                \"permission_rule_set\": {\n                    \"id\": 190,\n                    \"name\": \"Supervisor\",\n                    \"type\": \"PermissionRuleSet\",\n                }\n            }\n            self.sg.update(\"HumanUser\", id, user_data)\n\n            print(\"Updated user settings for %s.\" % user_name)\n            print(\"\\n\")\n\n        else:\n            print(\n                \"%s already has different settings, skipping updating user permissions.\"\n                % user_name\n            )\n            print(\"\\n\")\n\n    # Find user according to name\n    def __find_user(self, name):\n        filters = [[\"name\", \"contains\", name]]\n        columns = [\"name\"]\n\n        sg_user_data = self.sg.find_one(\"HumanUser\", filters, columns)\n\n        return sg_user_data\n\n    # Open the project in webbrowser according to id\n    def __open_project(self, id):\n        url = \"https://nfa.shotgunstudio.com/page/project_overview?project_id=\" + str(\n            id\n        )\n        webbrowser.open(url, new=2)\n\n\n# When this script is run, run the main class\nProjectCreation()\n", "repo_name": "nfa-vfxim/nfa-shotgrid-project-creation", "sub_path": "ShotGridProjectCreation.py", "file_name": "ShotGridProjectCreation.py", "file_ext": "py", "file_size_in_byte": 20370, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "shotgun_api3.shotgun.Shotgun", "line_number": 18, "usage_type": "call"}, {"api_name": "shotgun_api3.shotgun", "line_number": 18, "usage_type": "name"}, {"api_name": "re.match", "line_number": 135, "usage_type": "call"}, {"api_name": "re.match", "line_number": 174, "usage_type": "call"}, {"api_name": "os.getlogin", "line_number": 440, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 489, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 489, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 490, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 564, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 640, "usage_type": "call"}]}
{"seq_id": "33319930833", "text": "from tkinter import Variable\nfrom xml.dom import minidom\nimport xml.etree.ElementTree as xml\nimport os.path\nfrom canvas import Canvas\nfrom global_instance import *\n\nclass merge_xmls():\n    def __init__(self, canvas, *args, **kwargs):\n        self.file_path = my_path\n        self.canvas = canvas\n        self.cards = self.canvas.get_cards()\n    \n    def read_xmls(self):\n        root = xml.Element('root')\n        for card in self.cards:\n            path = self.file_path +'files\\\\'+ card + '.xml'\n            if(os.path.isfile(path)):\n                mydoc = minidom.parse(path)\n                item = mydoc.getElementsByTagName('action')\n                if(item[0].firstChild.data == \"openurl\"):\n                    url_data = mydoc.getElementsByTagName('link')\n                    browser_data = mydoc.getElementsByTagName('browser')\n                    session_data = mydoc.getElementsByTagName('session')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    link = xml.Element('url')\n                    browser = xml.Element('browser')\n                    action.set('type', 'openurl')\n                    session.text = session_data[0].firstChild.data\n                    link.text = url_data[0].firstChild.data\n                    browser.text = browser_data[0].firstChild.data\n                    action.append(session)\n                    action.append(link)\n                    action.append(browser)\n                    root.append(action)\n                if(item[0].firstChild.data == \"close_browser\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    action.set('type', 'close_browser')\n                    session.text = session_data[0].firstChild.data\n                    action.append(session)\n                    root.append(action)\n                if(item[0].firstChild.data == \"navigation\"):\n                    navigation_data = mydoc.getElementsByTagName('navigation')\n                    action = xml.Element('Action')\n                    navigation = xml.Element('navigation')\n                    action.set('type', 'navigation')\n                    navigation.text = navigation_data[0].firstChild.data\n                    action.append(navigation)\n                    root.append(action)\n                if(item[0].firstChild.data == \"click\"):\n                    action = xml.Element('Action')\n                    action.set('type', 'click')\n                    locator_name_data = mydoc.getElementsByTagName('locator_name')\n                    locator_value_data = mydoc.getElementsByTagName('locator_value')\n                    locator_name = xml.Element('locator_name')\n                    locator_value = xml.Element('locator_value')\n                    locator_name.text = locator_name_data[0].firstChild.data\n                    locator_value.text = locator_value_data[0].firstChild.data\n                    action.append(locator_name)\n                    action.append(locator_value)\n                    root.append(action)\n                if(item[0].firstChild.data == \"clear\"):\n                    action = xml.Element('Action')\n                    action.set('type', 'clear')\n                    locator_name_data = mydoc.getElementsByTagName('locator_name')\n                    locator_value_data = mydoc.getElementsByTagName('locator_value')\n                    locator_name = xml.Element('locator_name')\n                    locator_value = xml.Element('locator_value')\n                    locator_name.text = locator_name_data[0].firstChild.data\n                    locator_value.text = locator_value_data[0].firstChild.data\n                    action.append(locator_name)\n                    action.append(locator_value)\n                    root.append(action)\n                if(item[0].firstChild.data == \"readtext\" or item[0].firstChild.data == \"get_element\" or item[0].firstChild.data == \"get_elements\"):\n                    variable_data = mydoc.getElementsByTagName('variable')\n                    action = xml.Element('Action')\n                    variable_name = xml.Element('variable')\n                    variable_name.text = variable_data[0].firstChild.data\n                    if(item[0].firstChild.data == \"readtext\"):\n                        action.set('type', 'readtext')\n                    elif(item[0].firstChild.data == \"get_element\"):\n                        action.set('type', 'get_element')\n                    elif(item[0].firstChild.data == \"get_elements\"):\n                        action.set('type', 'get_elements')\n                    locator_name_data = mydoc.getElementsByTagName('locator_name')\n                    locator_value_data = mydoc.getElementsByTagName('locator_value')\n                    locator_name = xml.Element('locator_name')\n                    locator_value = xml.Element('locator_value')\n                    locator_name.text = locator_name_data[0].firstChild.data\n                    locator_value.text = locator_value_data[0].firstChild.data\n                    action.append(locator_name)\n                    action.append(locator_value)\n                    action.append(variable_name)\n                    root.append(action)\n                if(item[0].firstChild.data == \"inputtext\"):\n                    value_data = mydoc.getElementsByTagName('value')\n                    action = xml.Element('Action')\n                    value = xml.Element('value')\n                    value.text = value_data[0].firstChild.data\n                    action.set('type', 'inputtext')\n                    locator_name_data = mydoc.getElementsByTagName('locator_name')\n                    locator_value_data = mydoc.getElementsByTagName('locator_value')\n                    locator_name = xml.Element('locator_name')\n                    locator_value = xml.Element('locator_value')\n                    locator_name.text = locator_name_data[0].firstChild.data\n                    locator_value.text = locator_value_data[0].firstChild.data\n                    action.append(locator_name)\n                    action.append(locator_value)\n                    action.append(value)\n                    root.append(action)\n                if(item[0].firstChild.data == \"select_option\"):\n                    action = xml.Element('Action')\n                    action.set('type', 'select_option')\n                    locator_name_data = mydoc.getElementsByTagName('locator_name')\n                    locator_value_data = mydoc.getElementsByTagName('locator_value')\n                    select_by_name_data = mydoc.getElementsByTagName('select_by_name')\n                    select_by_value_data = mydoc.getElementsByTagName('select_by_value')\n                    locator_name = xml.Element('locator_name')\n                    locator_value = xml.Element('locator_value')\n                    select_by_name = xml.Element('select_by_name')\n                    select_by_value = xml.Element('select_by_value')\n                    locator_name.text = locator_name_data[0].firstChild.data\n                    locator_value.text = locator_value_data[0].firstChild.data\n                    select_by_name.text = select_by_name_data[0].firstChild.data\n                    select_by_value.text = select_by_value_data[0].firstChild.data\n                    action.append(locator_name)\n                    action.append(locator_value)\n                    action.append(select_by_name)\n                    action.append(select_by_value)\n                    root.append(action)\n                if(item[0].firstChild.data == \"drag_and_drop\"):\n                    action = xml.Element('Action')\n                    action.set('type', 'drag_and_drop')\n                    locator_name_data = mydoc.getElementsByTagName('locator_name')\n                    locator_value_data = mydoc.getElementsByTagName('locator_value')\n                    locator_name_target_data = mydoc.getElementsByTagName('locator_name_target')\n                    locator_value_target_data = mydoc.getElementsByTagName('locator_value_target')\n                    locator_name = xml.Element('locator_name')\n                    locator_value = xml.Element('locator_value')\n                    locator_name_target = xml.Element('locator_name_target')\n                    locator_value_target = xml.Element('locator_value_target')\n                    locator_name.text = locator_name_data[0].firstChild.data\n                    locator_value.text = locator_value_data[0].firstChild.data\n                    locator_name_target.text = locator_name_target_data[0].firstChild.data\n                    locator_value_target.text = locator_value_target_data[0].firstChild.data\n                    action.append(locator_name)\n                    action.append(locator_value)\n                    action.append(locator_name_target)\n                    action.append(locator_value_target)\n                    root.append(action)\n                if(item[0].firstChild.data == \"messagebox\"):\n                    variable_data = mydoc.getElementsByTagName('variable')\n                    print(variable_data[0].firstChild.data)\n                    action = xml.Element('Action')\n                    variable_name = xml.Element('variable')\n                    variable_name.text = variable_data[0].firstChild.data\n                    action.set('type', 'messagebox')\n                    action.append(variable_name)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_open\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    location_data = mydoc.getElementsByTagName('location')\n                    filename_data = mydoc.getElementsByTagName('filename')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    location = xml.Element('location')\n                    filename = xml.Element('filename')\n                    session.text = session_data[0].firstChild.data\n                    location.text = location_data[0].firstChild.data\n                    filename.text = filename_data[0].firstChild.data\n                    action.set('type', 'excel_open')\n                    action.append(session)\n                    action.append(location)\n                    action.append(filename)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_create\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    location_data = mydoc.getElementsByTagName('location')\n                    filename_data = mydoc.getElementsByTagName('filename')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    location = xml.Element('location')\n                    filename = xml.Element('filename')\n                    session.text = session_data[0].firstChild.data\n                    location.text = location_data[0].firstChild.data\n                    filename.text = filename_data[0].firstChild.data\n                    action.set('type', 'excel_create')\n                    action.append(session)\n                    action.append(location)\n                    action.append(filename)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_save_workbook\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    location_data = mydoc.getElementsByTagName('location')\n                    filename_data = mydoc.getElementsByTagName('filename')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    location = xml.Element('location')\n                    filename = xml.Element('filename')\n                    session.text = session_data[0].firstChild.data\n                    location.text = location_data[0].firstChild.data\n                    filename.text = filename_data[0].firstChild.data\n                    action.set('type', 'excel_save_workbook')\n                    action.append(session)\n                    action.append(location)\n                    action.append(filename)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_delete_file\"):\n                    location_data = mydoc.getElementsByTagName('location')\n                    filename_data = mydoc.getElementsByTagName('filename')\n                    action = xml.Element('Action')\n                    location = xml.Element('location')\n                    filename = xml.Element('filename')\n                    location.text = location_data[0].firstChild.data\n                    filename.text = filename_data[0].firstChild.data\n                    action.set('type', 'excel_delete_file')\n                    action.append(location)\n                    action.append(filename)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_delete_worksheet\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    sheetname_data = mydoc.getElementsByTagName('sheetname')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    sheetname = xml.Element('sheetname')\n                    session.text = session_data[0].firstChild.data\n                    sheetname.text = sheetname_data[0].firstChild.data\n                    action.set('type', 'excel_delete_worksheet')\n                    action.append(session)\n                    action.append(sheetname)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_create_worksheet\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    sheetname_data = mydoc.getElementsByTagName('sheetname')\n                    position_data = mydoc.getElementsByTagName('position')\n                    value_data = mydoc.getElementsByTagName('value')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    sheetname = xml.Element('sheetname')\n                    position = xml.Element('position')\n                    value = xml.Element('value')\n                    session.text = session_data[0].firstChild.data\n                    sheetname.text = sheetname_data[0].firstChild.data\n                    position.text = position_data[0].firstChild.data\n                    value.text = value_data[0].firstChild.data\n                    action.set('type', 'excel_create_worksheet')\n                    action.append(session)\n                    action.append(sheetname)\n                    action.append(position)\n                    action.append(value)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_set_value\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    sheetname_data = mydoc.getElementsByTagName('sheetname')\n                    cell_data = mydoc.getElementsByTagName('cell')\n                    value_data = mydoc.getElementsByTagName('value')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    sheetname = xml.Element('sheetname')\n                    cell = xml.Element('cell')\n                    value = xml.Element('value')\n                    session.text = session_data[0].firstChild.data\n                    sheetname.text = sheetname_data[0].firstChild.data\n                    cell.text = cell_data[0].firstChild.data\n                    value.text = value_data[0].firstChild.data\n                    action.set('type', 'excel_set_value')\n                    action.append(session)\n                    action.append(sheetname)\n                    action.append(cell)\n                    action.append(value)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_get_value\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    sheetname_data = mydoc.getElementsByTagName('sheetname')\n                    cell_data = mydoc.getElementsByTagName('cell')\n                    save_to_data = mydoc.getElementsByTagName('variable')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    sheetname = xml.Element('sheetname')\n                    cell = xml.Element('cell')\n                    save_to = xml.Element('variable')\n                    session.text = session_data[0].firstChild.data\n                    sheetname.text = sheetname_data[0].firstChild.data\n                    cell.text = cell_data[0].firstChild.data\n                    save_to.text = save_to_data[0].firstChild.data\n                    action.set('type', 'excel_get_value')\n                    action.append(session)\n                    action.append(sheetname)\n                    action.append(cell)\n                    action.append(save_to)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_set_formula\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    sheetname_data = mydoc.getElementsByTagName('sheetname')\n                    cell_data = mydoc.getElementsByTagName('cell')\n                    formula_data = mydoc.getElementsByTagName('formula')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    sheetname = xml.Element('sheetname')\n                    cell = xml.Element('cell')\n                    formula = xml.Element('formula')\n                    session.text = session_data[0].firstChild.data\n                    sheetname.text = sheetname_data[0].firstChild.data\n                    cell.text = cell_data[0].firstChild.data\n                    formula.text = formula_data[0].firstChild.data\n                    action.set('type', 'excel_set_formula')\n                    action.append(session)\n                    action.append(sheetname)\n                    action.append(cell)\n                    action.append(formula)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_merge_unmerge\"):\n                    session_data = mydoc.getElementsByTagName('session')\n                    sheetname_data = mydoc.getElementsByTagName('sheetname')\n                    merge_unmerge_data = mydoc.getElementsByTagName('merge_unmerge')\n                    from_cell_data = mydoc.getElementsByTagName('from_cell')\n                    to_cell_data = mydoc.getElementsByTagName('to_cell')\n                    action = xml.Element('Action')\n                    session = xml.Element('session')\n                    sheetname = xml.Element('sheetname')\n                    merge_unmerge = xml.Element('merge_unmerge')\n                    from_cell = xml.Element('from_cell')\n                    to_cell = xml.Element('to_cell')\n                    session.text = session_data[0].firstChild.data\n                    sheetname.text = sheetname_data[0].firstChild.data\n                    merge_unmerge.text = merge_unmerge_data[0].firstChild.data\n                    from_cell.text = from_cell_data[0].firstChild.data\n                    to_cell.text = to_cell_data[0].firstChild.data\n                    action.set('type', 'excel_merge_unmerge')\n                    action.append(session)\n                    action.append(sheetname)\n                    action.append(merge_unmerge)\n                    action.append(from_cell)\n                    action.append(to_cell)\n                    root.append(action)\n                if(item[0].firstChild.data == \"excel_copy_paste\"):\n                    copy_session_data = mydoc.getElementsByTagName('copy_session')\n                    copy_sheetname_data = mydoc.getElementsByTagName('copy_sheetname')\n                    copy_start_data = mydoc.getElementsByTagName('copy_start')\n                    copy_end_data = mydoc.getElementsByTagName('copy_end')\n                    paste_session_data = mydoc.getElementsByTagName('paste_session')\n                    paste_sheetname_data = mydoc.getElementsByTagName('paste_sheetname')\n                    paste_start_data = mydoc.getElementsByTagName('paste_start')\n                    paste_end_data = mydoc.getElementsByTagName('paste_end')\n                    action = xml.Element('Action')\n                    copy_session = xml.Element('copy_session')\n                    copy_sheetname = xml.Element('copy_sheetname')\n                    copy_start = xml.Element('copy_start')\n                    copy_end = xml.Element('copy_end')\n                    paste_session = xml.Element('paste_session')\n                    paste_sheetname = xml.Element('paste_sheetname')\n                    paste_start = xml.Element('paste_start')\n                    paste_end = xml.Element('paste_end')\n                    copy_session.text = copy_session_data[0].firstChild.data\n                    copy_sheetname.text = copy_sheetname_data[0].firstChild.data\n                    copy_start.text = copy_start_data[0].firstChild.data\n                    copy_end.text = copy_end_data[0].firstChild.data\n                    paste_session.text = paste_session_data[0].firstChild.data\n                    paste_sheetname.text = paste_sheetname_data[0].firstChild.data\n                    paste_start.text = paste_start_data[0].firstChild.data\n                    paste_end.text = paste_end_data[0].firstChild.data\n                    action.set('type', 'excel_copy_paste')\n                    action.append(copy_session)\n                    action.append(copy_sheetname)\n                    action.append(copy_start)\n                    action.append(copy_end)\n                    action.append(paste_session)\n                    action.append(paste_sheetname)\n                    action.append(paste_start)\n                    action.append(paste_end)\n                    root.append(action)\n                if(item[0].firstChild.data == \"create_variable\"):\n                    action = xml.Element('Action')\n                    action.set('type', 'create_variable')\n                    variable_name_data = mydoc.getElementsByTagName('variable_name')\n                    variable_value_data = mydoc.getElementsByTagName('variable_value')\n                    type_data = mydoc.getElementsByTagName('type')\n                    variable_name = xml.Element('variable_name')\n                    variable_value = xml.Element('variable_value')\n                    _type = xml.Element('type')\n                    variable_name.text = variable_name_data[0].firstChild.data\n                    variable_value.text = variable_value_data[0].firstChild.data\n                    _type.text = type_data[0].firstChild.data\n                    action.append(variable_name)\n                    action.append(variable_value)\n                    action.append(_type)\n                    root.append(action)\n                if(item[0].firstChild.data == \"for_loop\"):\n                    init_variable_data = mydoc.getElementsByTagName('init_variable')\n                    condition_for_data = mydoc.getElementsByTagName('condition_for')\n                    condition_data = mydoc.getElementsByTagName('condition')\n                    variable1_data = mydoc.getElementsByTagName('variable1')\n                    variable2_data = mydoc.getElementsByTagName('variable2')\n                    action = xml.Element('Action')\n                    action.set('type', 'inputtext')\n                    init_variable = xml.Element('init_variable')\n                    condition_for = xml.Element('condition_for')\n                    condition = xml.Element('condition')\n                    variable1 = xml.Element('variable1')\n                    variable2 = xml.Element('variable2')\n                    init_variable.text = init_variable_data[0].firstChild.data\n                    condition_for.text = condition_for_data[0].firstChild.data\n                    condition.text = condition_data[0].firstChild.data\n                    variable1.text = variable1_data[0].firstChild.data\n                    variable2.text = variable2_data[0].firstChild.data\n                    action.append(init_variable)\n                    action.append(condition_for)\n                    action.append(condition)\n                    action.append(variable1)\n                    action.append(variable2)\n                    root.append(action)\n                if(item[0].firstChild.data in ['split_string', 'list_get_value']):\n                    delimiter_data = mydoc.getElementsByTagName('delimiter')\n                    variable1_data = mydoc.getElementsByTagName('variable1')\n                    variable2_data = mydoc.getElementsByTagName('variable2')\n                    action = xml.Element('Action')\n                    action.set('type', item[0].firstChild.data)\n                    delimiter = xml.Element('delimiter')\n                    variable1 = xml.Element('variable1')\n                    variable2 = xml.Element('variable2')\n                    delimiter.text = delimiter_data[0].firstChild.data\n                    variable1.text = variable1_data[0].firstChild.data\n                    variable2.text = variable2_data[0].firstChild.data\n                    action.append(variable1)\n                    action.append(variable2)\n                    action.append(delimiter)\n                    root.append(action)\n                if(item[0].firstChild.data in ['count', 'index', 'find', 'strip', 'lstrip','rstrip']):\n                    variable1_data = mydoc.getElementsByTagName('variable1')\n                    variable2_data = mydoc.getElementsByTagName('variable2')\n                    action = xml.Element('Action')\n                    action.set('type', item[0].firstChild.data )\n                    variable1 = xml.Element('variable1')\n                    variable2 = xml.Element('variable2')\n                    variable1.text = variable1_data[0].firstChild.data\n                    variable2.text = variable2_data[0].firstChild.data\n                    action.append(variable1)\n                    action.append(variable2)\n                    root.append(action)\n                if(item[0].firstChild.data == \"concate_string\"):\n                    string1_data = mydoc.getElementsByTagName('string1')\n                    string2_data = mydoc.getElementsByTagName('string2')\n                    save_to_data = mydoc.getElementsByTagName('save_to')\n                    action = xml.Element('Action')\n                    action.set('type', item[0].firstChild.data )\n                    string1 = xml.Element('string1')\n                    string2 = xml.Element('string2')\n                    save_to = xml.Element('save_to')\n                    string1.text = string1_data[0].firstChild.data\n                    string2.text = string2_data[0].firstChild.data\n                    save_to.text = save_to_data[0].firstChild.data\n                    action.append(string1)\n                    action.append(string2)\n                    action.append(save_to)\n                    root.append(action)\n                if(item[0].firstChild.data in [\"increment\", 'decrement']):\n                    _variable_data = mydoc.getElementsByTagName('_variable')\n                    _variable_by_data = mydoc.getElementsByTagName('_variable_by')\n                    save_to_data = mydoc.getElementsByTagName('save_to')\n                    action = xml.Element('Action')\n                    action.set('type', item[0].firstChild.data )\n                    _variable = xml.Element('_variable')\n                    _variable_by = xml.Element('_variable_by')\n                    save_to = xml.Element('save_to')\n                    _variable.text = _variable_data[0].firstChild.data\n                    _variable_by.text = _variable_by_data[0].firstChild.data\n                    save_to.text = save_to_data[0].firstChild.data\n                    action.append(_variable)\n                    action.append(_variable_by)\n                    action.append(save_to)\n                    root.append(action)\n    \n        tree = xml.ElementTree(root)   \n        save_path_file = self.file_path + \"testtest\" + \".xml\"\n        xmlstr = minidom.parseString(xml.tostring(root)).toprettyxml(indent=\"   \")\n        with open(save_path_file, \"w\") as f:\n            f.write(xmlstr)\n            \n", "repo_name": "gofrankhan/TastAutomator", "sub_path": "merge_xmls.py", "file_name": "merge_xmls.py", "file_ext": "py", "file_size_in_byte": 28704, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "xml.etree.ElementTree.Element", "line_number": 15, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.path.isfile", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 18, "usage_type": "name"}, {"api_name": "xml.dom.minidom.parse", "line_number": 19, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 19, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 25, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 25, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 26, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 26, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 27, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 27, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 28, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 28, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 39, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 39, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 40, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 40, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 47, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 47, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 48, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 48, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 54, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 54, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 58, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 58, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 59, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 59, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 66, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 66, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 70, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 70, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 71, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 71, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 79, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 79, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 80, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 80, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 90, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 90, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 91, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 91, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 100, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 100, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 101, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 101, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 106, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 106, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 107, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 107, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 115, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 115, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 121, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 121, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 122, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 122, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 123, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 123, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 124, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 124, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 135, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 135, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 141, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 141, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 142, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 142, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 143, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 143, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 144, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 144, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 157, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 157, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 158, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 158, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 167, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 167, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 168, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 168, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 169, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 169, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 170, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 170, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 183, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 183, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 184, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 184, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 185, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 185, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 186, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 186, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 199, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 199, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 200, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 200, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 201, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 201, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 202, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 202, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 214, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 214, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 215, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 215, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 216, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 216, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 226, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 226, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 227, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 227, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 228, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 228, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 240, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 240, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 241, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 241, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 242, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 242, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 243, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 243, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 244, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 244, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 260, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 260, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 261, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 261, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 262, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 262, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 263, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 263, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 264, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 264, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 280, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 280, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 281, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 281, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 282, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 282, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 283, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 283, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 284, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 284, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 300, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 300, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 301, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 301, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 302, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 302, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 303, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 303, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 304, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 304, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 321, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 321, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 322, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 322, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 323, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 323, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 324, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 324, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 325, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 325, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 326, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 326, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 348, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 348, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 349, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 349, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 350, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 350, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 351, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 351, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 352, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 352, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 353, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 353, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 354, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 354, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 355, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 355, "usage_type": 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{"seq_id": "71986535160", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*\n# from time import time\n\nuse_real_robot = False\n\nimport time\nimport casadi as ca\nimport numpy as np\nfrom casadi import sin, cos, pi\nimport sys\nfrom utils import ur5_dh, sdf\nfrom utils import mr_casadi as mc\nif use_real_robot:\n    from utils import arm_control_client\nfrom utils.hmqr5_agl_map import hmqr5_agl_map\nimport rospy\nfrom scipy.spatial.transform import Rotation\nfrom trajectory_generator_ee import trajectory_generator_door\nfrom geometry_msgs.msg import Pose\n\nimport os\nfrom sensor_msgs.msg import JointState\n\no_path = '/root/catkin_ws/src/open_door_mpc/script'\no_path = o_path + '/utils'\nsys.path.append(o_path)\n\nfrom utils.omnirob_ur5_ros import OmniRobUr5Ros\nrob = OmniRobUr5Ros()\n\n# dt = 0.001  # 10ms\n\nagl_set = np.array([])\n# loop_count: every loop_count epoch(es) control arm\ndef ctrl_rob(cfg, u_base, gripper_config, loop_time, loop_count=1, gripper_state=1, real=False):\n    desired_world_positions = np.zeros(3, dtype=np.float64)\n    desired_arm_joint_angles = np.zeros(6, dtype=np.float64)\n\n    desired_world_positions[0] = cfg[0][0]\n    desired_world_positions[1] = cfg[0][1]\n    desired_world_positions[2] = cfg[0][2]\n\n    desired_arm_joint_angles[0] = cfg[0, 3]\n    desired_arm_joint_angles[1] = cfg[0, 4]\n    desired_arm_joint_angles[2] = cfg[0, 5]\n    desired_arm_joint_angles[3] = cfg[0, 6]\n    desired_arm_joint_angles[4] = cfg[0, 7]\n    desired_arm_joint_angles[5] = cfg[0, 8]\n    if not real:\n        rob.world_frame_set(desired_world_positions)\n        rob.arm_ctrl(desired_arm_joint_angles)\n    else:\n        rob.base_ctrl(x=u_base[0], y=0, theta=u_base[1])\n        rob.gripper_ctrl(gripper_config)\n        print(\"desired_arm_joint_angles: \", desired_arm_joint_angles)\n        global agl_set\n        agl_set = np.append(agl_set, desired_arm_joint_angles, axis=0)\n        if(agl_set.size==6*loop_count):\n            # rob.base_ctrl(x=0, y=0, theta=0)\n            print(\"agl_set:\", agl_set)\n            result = arm_control_client.arm_control_client(agl_set, loop_count)\n            print(result)\n            agl_set = np.array([])\n\n\nQ_x, Q_y, Q_theta = 0, 0, 0\n# state weight matrix (Q_X, Q_Y, Q_THETA, q1, ..., q6)[9]\nQ = ca.diagcat(Q_x, Q_y, Q_theta, 0, 0, 0, 0, 0, 0)\n# controls weights matrix\n# (v, theta, q1, ..., q6)[8]\nRv, Rw, Ra = 1, 1, 1\nR = ca.diagcat(Rv, Rw, Ra, Ra, Ra, Ra, Ra, Ra)\n\nstep_horizon = 0.1  # time between steps in seconds\nN = 30  # number of look ahead steps\nrob_diam = 0.3  # diameter of the robot\n\nsim_time = 1000  # simulation time\n\n# specs target\n# base\nx_init, y_init, theta_init = 0, 0, 0\n# car base\n# x_target, y_target, theta_target = -3.2, 3.8, pi / 2\nx_target, y_target, theta_target = 0.55, -0.65, pi / 4\n# arm tip\narm_tip_target_x = -3\narm_tip_target_y = 3\narm_tip_target_z = 1.0\n\n# q0 = rob.get_amr_pos()\n# while abs(q0[0]) <= 1e-4:\n#     print(\"q_init:\", q0)\n#     q0 = rob.get_amr_pos()\n#     rospy.sleep(0.1)\n\n# q0 = [1.5778058448580374, -1.996235163274898, -1.5911667359506643, 0, np.pi/2, 0]\n# q0 = [1.577, -1.996, -1.591, -0.293, -0.194, 1.735]\nq0 = [-1.591, -1.096, 1.577, -0.293, -0.194, 1.735]\nprint(\"q_init:\", q0)\n# assert 0\nstate_init = ca.DM([x_init, y_init, theta_init, q0[0], q0[1], q0[2], q0[3], q0[4], q0[5]])  # initial state\nstate_target = ca.DM([x_target, y_target, theta_target, 0, 0, 0, 0, 0, 0])  # target state\n\n\nQa_x, Qa_y, Qa_z = 200, 200, 200\nQa = ca.diag([Qa_x, Qa_y, Qa_z])\nQa_rot = ca.diag([10, 10, 10])\n\n# obs\nobs_x, obs_y, obs_z = 1.18, -0.5, 1.0 # hinge的右边\nobs_diam = 0.1\nQ_obs = ca.diag([0, 1, 0]) # 不管z轴\nQ_obs_base = ca.diag([1, 1])\nepi_obs = 0.1\n\n\n#\nv_max = 0.2\nv_min = -0.2\nv_arm_max = 0.1\nv_arm_min = -0.1\n\no_path = os.getcwd()\no_path = o_path + '/utils'\nprint(\"o_path:\", o_path)\nsys.path.append(o_path)\ndef shift_timestep(step_horizon, t0, state_init, u, f):\n    f_value = f(state_init, u[:, 0])\n    next_state = ca.DM.full(state_init + (step_horizon * f_value))\n\n    t0 = t0 + step_horizon\n    u0 = ca.horzcat(\n        u[:, 1:],\n        ca.reshape(u[:, -1], -1, 1)\n    )\n    return t0, next_state, u0\n\ndef DM2Arr(dm):\n    return np.array(dm.full())\n\n# state symbolic variables\nq_mm = ca.SX.sym('q_mm', 9)  # (x,y,theta,q1-q6)\nu_mm = ca.SX.sym('u_mm', 8)  # dot(v,w,q1-q6)\n\n# states symbolic variables\nn_states = q_mm.numel()\n# control symbolic variables\nn_controls = u_mm.numel()\n\n# matrix containing all states over all time steps +1 (each column is a state vector)\nX = ca.SX.sym('X', n_states, N + 1)\n\n# matrix containing all control actions over all time steps (each column is an action vector)\nU = ca.SX.sym('U', n_controls, N)\n\n# coloumn vector for storing initial state and target state\n# P = ca.SX.sym('P', n_states + n_states)\n# [base initial state and target state].T + [arm trajectory].T\n# i th arm trajectory = [x_i, y_i, z_i, angle_axis](6X1)\nP = ca.SX.sym('P', n_states + n_states + 2 + 2 + 7 * (N + 1))\n# P2 = ca.SX.sym('P', 6, N+1)\nP_gra = P[2 * n_states:2 * n_states + 2]\nP_dw_base = P[2 * n_states + 2:2 * n_states + 3]\nP_dw_arm = P[2 * n_states + 3:2 * n_states + 4]\nP_door = P[2 * n_states:2 * n_states + 4]\nP_arm = ca.reshape(P[4 + 2 * n_states:], 7, N + 1)\nprint(\"P_arm:\", P_arm)\n# # assert 0\ntheta = q_mm[2]\nv = u_mm[0]\nomega = u_mm[1]\nRHS = ca.vertcat(v * ca.cos(theta),\n                 v * ca.sin(theta),\n                 omega,\n                 u_mm[2],\n                 u_mm[3],\n                 u_mm[4],\n                 u_mm[5],\n                 u_mm[6],\n                 u_mm[7])\nf = ca.Function('f_mm', [q_mm, u_mm], [RHS])\n\n# 构建arm tip的障碍物RBF\njoint5_diam = 0.10\ntip_pos = ca.SX.sym('tip_pos', 3)  # (x,y)\ndh = tip_pos - ca.DM([obs_x, obs_y, obs_z])\nh = ca.sqrt(dh.T @ Q_obs @ dh) - joint5_diam / 2\nrbf_out = -epi_obs * ca.log(h)\nf_rbf = ca.Function('rbf', [tip_pos], [rbf_out], ['tip_pos'], ['rbf'])\n\n# 构建障碍物RBF1\nbase_pos = ca.SX.sym('base_pos', 2)  # (x,y)\ndh = base_pos - ca.DM([obs_x, obs_y])\n# dh = sdf.doorSDF()\nh = ca.sqrt(dh.T @ Q_obs_base @ dh) - obs_diam / 2 - rob_diam / 2\n# rbf_out = -epi_obs * ca.log(h)\n# rbf_base_out = ca.if_else(h < 3, -epi_obs * ca.log(h), 0) # 这儿分段会导致求解速度变慢\nrbf_base_out = -epi_obs * ca.log(h)\nf_rbf_base = ca.Function('rbf_base', [base_pos], [rbf_base_out], ['base_pos'], ['rbf_base'])\nprint(f\"f_rbf_base: {f_rbf_base}\")\n\n# 构建障碍物RBF2\nbase_pos = ca.SX.sym('base_pos', 2)  # (x,y)\ndh = base_pos - ca.DM([obs_x, 1])\nh = ca.sqrt(dh.T @ Q_obs_base @ dh) - obs_diam / 2 - rob_diam / 2\n# rbf_out = -epi_obs * ca.log(h)\n# rbf_base_out = ca.if_else(h < 3, -epi_obs * ca.log(h), 0) # 这儿分段会导致求解速度变慢\nrbf_base_out2 = -epi_obs * ca.log(h)\nf_rbf_base2 = ca.Function('rbf_base2', [base_pos], [rbf_base_out2], ['base_pos'], ['rbf_base'])\nprint(f\"f_rbf_base: {f_rbf_base2}\")\n\n# fake esdf\nbase_pos = ca.SX.sym('base_pos', 2)  # (x,y)\na = P_door[:2]\nb = P_door[2:]\nesdf_func = sdf.myLineSdfCasadi(base_pos, a, b) - rob_diam / 2\nepi_obs = 0.01\nrbf_base_out = -epi_obs * ca.log(esdf_func)\nf_rbf_esdf_base = ca.Function('f_rbf_esdf_base', [base_pos], [rbf_base_out], ['base_pos'], ['rbf_base'])\nprint(f\"f_rbf_esdf_base: {f_rbf_esdf_base}\")\n\n\n# =============================================== cost function ========================================================\ncost_fn = 0  # cost function\ng = X[:, 0] - P[:n_states]  # constraints in the equation\n\n# runge kutta\nfor k in range(N):\n    st = X[:, k]\n    con = U[:, k]\n    x_arm, R_arm = ur5_dh.RobFki(st)\n    # R_err = R_arm.T @ R_target\n    # from utils import mr_casadi as mc\n    # r_err = mc.RotationErrorByQuaternion(R_arm, R_target)\n    qua_arm = mc.RotationToQuaternion(R_arm)\n    r_err = mc.QuaternionError(qua_arm, P_arm[3:, k])\n    x_arm_t = np.array([arm_tip_target_x, arm_tip_target_y, arm_tip_target_z])\n    # ==============\n    # a = ca.DM([P_door[0], P_door[1]])\n    # b = ca.DM([P_door[2], P_door[3]])\n    # esdf_func = sdf.myLineSdfCasadi(st[:2], a, b) - rob_diam / 2\n    # rbf_base_out = -epi_obs * ca.log(esdf_func)\n    # ==============\n    # base_door_error = f_rbf_two_pos_error(ca.DM([st[0], st[1],x_arm[0],x_arm[1]]))\n    # print(base_door_error)\n    # + (x_arm - x_arm_t).T @ Qa @ (x_arm - x_arm_t) \\\n    # + (x_arm - P[:3, k + 1]).T @ Qa @ (x_arm - P[:3, k + 1]) \\\n    x_arm3, _ = ur5_dh.RobFki(st, 2)\n    x_arm4, _ = ur5_dh.RobFki(st, 3)\n    rbf_joints = f_rbf(x_arm3)\n    rbf_joints += f_rbf(x_arm4)\n    cost_fn = cost_fn \\\n                + con.T @ R @ con \\\n                + (x_arm - P_arm[:3, k]).T @ Qa @ (x_arm - P_arm[:3, k]) \\\n                + r_err.T @ Qa_rot @ r_err \n                # + f_rbf_esdf_base(st[:2])\n                # + P_dw_base * (st - P[n_statstate_inites:2 * n_states]).T @ Q @ (st - P[n_states:2 * n_states]) \\\n                # + rbf_joints\\\n    st_next = X[:, k + 1]\n    k1 = f(st, con)\n    k2 = f(st + step_horizon / 2 * k1, con)\n    k3 = f(st + step_horizon / 2 * k2, con)\n    k4 = f(st + step_horizon * k3, con)\n    st_next_RK4 = st + (step_horizon / 6) * (k1 + 2 * k2 + 2 * k3 + k4)\n    g = ca.vertcat(g, st_next - st_next_RK4)\n# Todo: Not Add Terminal Cost yet\n# =============================================== cost function ========================================================\nOPT_variables = ca.vertcat(\n    X.reshape((-1, 1)),  # Example: 3x11 ---> 33x1 where 3=states, 11=N+1\n    U.reshape((-1, 1))\n)\nnlp_prob = {\n    'f': cost_fn,\n    'x': OPT_variables,\n    'g': g,\n    'p': P\n}\n\nopts = {\n    'ipopt': {\n        'max_iter': 2000,\n        'print_level': 0,\n        'acceptable_tol': 1e-8,\n        'acceptable_obj_change_tol': 1e-6\n    },\n    'print_time': 0\n}\n\nsolver = ca.nlpsol('solver', 'ipopt', nlp_prob, opts)\n\nlbx = ca.DM.zeros((n_states * (N + 1) + n_controls * N, 1))\nubx = ca.DM.zeros((n_states * (N + 1) + n_controls * N, 1))\n\nlbx[0: n_states * (N + 1): n_states] = -ca.inf  # X lower bound\nlbx[1: n_states * (N + 1): n_states] = -ca.inf  # Y lower bound\nlbx[2: n_states * (N + 1): n_states] = -ca.inf  # theta lower bound\n\nam = hmqr5_agl_map()\nlbx[3: n_states * (N + 1): n_states] = am.limit[0][0]  # q1 lower bound\nlbx[4: n_states * (N + 1): n_states] = am.limit[1][0]  # q2 lower bound\nlbx[5: n_states * (N + 1): n_states] = am.limit[2][0]  # q3 lower bound\nlbx[6: n_states * (N + 1): n_states] = am.limit[3][0]  # q4 lower bound\nlbx[7: n_states * (N + 1): n_states] = am.limit[4][0]  # q5 lower bound\nlbx[8: n_states * (N + 1): n_states] = am.limit[5][0]  # q6 lower bound\n\n# print(\"limit 6:\", am.limit[5][0], am.limit[5][1])\n# assert 0\nubx[0: n_states * (N + 1): n_states] = ca.inf  # X upper bound\nubx[1: n_states * (N + 1): n_states] = ca.inf  # Y upper bound\nubx[2: n_states * (N + 1): n_states] = ca.inf  # theta upper bound\n\nubx[3: n_states * (N + 1): n_states] = am.limit[0][1]  # q1 upper bound\nubx[4: n_states * (N + 1): n_states] = am.limit[1][1]  # q2 upper bound\nubx[5: n_states * (N + 1): n_states] = am.limit[2][1]  # q3 upper bound\nubx[6: n_states * (N + 1): n_states] = am.limit[3][1]  # q4 upper bound\nubx[7: n_states * (N + 1): n_states] = am.limit[4][1]  # q5 upper bound\nubx[8: n_states * (N + 1): n_states] = am.limit[5][1]  # q6 upper bound\n\nlbx[n_states * (N + 1):] = v_arm_min  # u lower bound for all U\nubx[n_states * (N + 1):] = v_arm_max  # u upper bound for all U\nlbx[n_states * (N + 1): -1: n_controls] = v_min  # u upper bound for v\nlbx[n_states * (N + 1) + 1: -1: n_controls] = v_min  # u upper bound for w\nubx[n_states * (N + 1): -1: n_controls] = v_max  # u upper bound for v\nubx[n_states * (N + 1) + 1: -1: n_controls] = v_max  # u upper bound for w\n\nargs = {\n    'lbg': ca.DM.zeros((n_states * (N + 1), 1)),  # constraints lower bound\n    'ubg': ca.DM.zeros((n_states * (N + 1), 1)),  # constraints upper bound\n    'lbx': lbx,\n    'ubx': ubx\n}\n\nt0 = 0\n\n# xx = DM(state_init)\nt = ca.DM(t0)\n\nu0 = ca.DM.zeros((n_controls, N))  # initial control\nX0 = ca.repmat(state_init, 1, N + 1)  # initial state full\n\nmpc_iter = 0\ncat_states = DM2Arr(X0)\ncat_controls = DM2Arr(u0[:, 0])\ntimes = np.array([[0]])\n\narg_p_arm_mat = ca.DM.zeros(7, (N + 1))  # end effector trajectory\n\n\nesdf_gradient = ca.DM([0, 0])\nee_pos_record = []\n\nfrom utils import visualization as vl\nfrom visualization_msgs.msg import MarkerArray\nfrom nav_msgs.msg import Path\n\nee_planning_path_pub = rospy.Publisher('ee_planning_trajectory', Path, queue_size=50)\nbase_predict_path_pub = rospy.Publisher('base_predict_trajectory', Path, queue_size=50)\nee_real_path_pub = rospy.Publisher('ee_real_trajectory', Path, queue_size=50)\nrob_sphere_pub = rospy.Publisher(\"rob_sphere_pub\", MarkerArray, queue_size=10)\np_all_record = []\n\narg_p_arm = ca.DM.zeros(7 * (N + 1))\npose0 = [0,0,1,0,0.707,0,0.707] # [x,y,z, ox,oy,oz,ow]\nfor k in range(N + 1):\n        for j in range(7):\n            arg_p_arm[k * 7 + j] = pose0[j]   \n\ndef go_target_pose(pose_msg):\n    print(f\"Target:\\n{pose_msg}\")\n    global arg_p_arm, N\n    p, ori = pose_msg.position, pose_msg.orientation\n    args = [p.x, p.y, p.z, ori.x, ori.y, ori.z, ori.w]  \n    for k in range(N + 1):\n        for j in range(7):\n            arg_p_arm[k * 7 + j] = args[j]   \n    \n\n##############################################################################\nif __name__ == '__main__':\n    rospy.Subscriber(\"/mobile_manipulator_mpc_target\", Pose, go_target_pose)\n    rate = rospy.Rate(50)\n    while(not rospy.is_shutdown()):\n        main_loop = time.time()  # return time in sec\n        door_info = ca.DM([1.2, -0.65, 1.2, 0])\n        mpc_iter = 0\n        while (ca.norm_2(state_init - state_target) > 1e-1):\n                t1 = time.time()        \n                args['p'] = ca.vertcat(\n                    state_init,  # current state\n                    state_target,  # target state\n                    door_info,\n                    arg_p_arm\n                )\n                # optimization variable current state（sates and controls）\n                args['x0'] = ca.vertcat(\n                    ca.reshape(X0, n_states * (N + 1), 1),\n                    ca.reshape(u0, n_controls * N, 1)\n                )\n                sol = solver(\n                    x0=args['x0'],\n                    lbx=args['lbx'],\n                    ubx=args['ubx'],\n                    lbg=args['lbg'],\n                    ubg=args['ubg'],\n                    p=args['p']\n                )\n                u = ca.reshape(sol['x'][n_states * (N + 1):], n_controls, N)\n                X0 = ca.reshape(sol['x'][: n_states * (N + 1)], n_states, N + 1)\n                cat_states = np.dstack((\n                    cat_states,\n                    DM2Arr(X0)\n                ))\n                cat_controls = np.vstack((\n                    cat_controls,\n                    DM2Arr(u[:, 0])\n                ))\n                t = np.vstack((\n                    t,\n                    t0\n                ))\n                t0, state_init, u0 = shift_timestep(step_horizon, t0, state_init, u, f)\n                # ======================================== CTROL =====================================================\n                # ======================================== CTROL =====================================================\n                ctrl_rob(state_init.T, u[:2,0], 0, mpc_iter, loop_count=1, real=use_real_robot)\n                if use_real_robot:\n                    real_pose = rob.get_world_frame()\n                    state_init[0, 0] = real_pose[0]\n                    state_init[1, 0] = real_pose[1]\n                    state_init[2, 0] = real_pose[2]\n                # arm_pos = rob.get_amr_pos()\n                x_arm_now, R_arm_now = ur5_dh.RobFki(state_init)\n                ee_pos_record.append(x_arm_now)\n                # vl.DrawPath(ee_planning_path_pub, traj_ee, 'odom', 0)\n                vl.DrawBasePredictPath(base_predict_path_pub, X0, 'odom')\n                vl.PubEndEffectorRealTrajectory(ee_real_path_pub, x_arm_now)\n                p_all = vl.DrawRobSphere(rob_sphere_pub, state_init[:, 0], scale=0.1)\n                p_all_record.append(p_all)\n\n                X0 = ca.horzcat(\n                    X0[:, 1:],\n                    ca.reshape(X0[:, -1], -1, 1)\n                )\n                # xx ...\n                t2 = time.time()\n                # print(mpc_iter)\n                # print(\"Time cost:\", t2 - t1)\n                times = np.vstack((\n                    times,\n                    t2 - t1\n                ))\n                mpc_iter = mpc_iter + 1\n            # main_loop_time = time.time()\n            # ss_error = ca.norm_2(state_init - state_target)\n            # print('\\n\\n')\n            # print(f'Total time: {main_loop_time - main_loop} s.')\n            # print('Avg iteration time: ', np.array(times).mean() * 1000, 'ms')\n            # print('Final error: ', ss_error)\n            # print('ATE: ', ATE/mpc_iter)\n        rate.sleep()\n", "repo_name": "Matthew-WangT/mobile_manipulator_mpc", "sub_path": "open_door_mpc/script/diff_rob_mpc_ur5_demo.py", "file_name": "diff_rob_mpc_ur5_demo.py", "file_ext": "py", "file_size_in_byte": 16742, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.path.append", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "utils.omnirob_ur5_ros.OmniRobUr5Ros", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 58, "usage_type": "call"}, {"api_name": "utils.arm_control_client.arm_control_client", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.arm_control_client", "line_number": 62, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "casadi.diagcat", "line_number": 69, "usage_type": "call"}, {"api_name": "casadi.diagcat", "line_number": 73, "usage_type": "call"}, {"api_name": "casadi.pi", "line_number": 86, "usage_type": "name"}, {"api_name": "casadi.DM", "line_number": 103, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 104, "usage_type": "call"}, {"api_name": "casadi.diag", "line_number": 108, "usage_type": "call"}, {"api_name": "casadi.diag", "line_number": 109, "usage_type": "call"}, {"api_name": "casadi.diag", "line_number": 114, "usage_type": "call"}, {"api_name": "casadi.diag", "line_number": 115, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 125, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 128, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "casadi.DM.full", "line_number": 131, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 131, "usage_type": "attribute"}, {"api_name": "casadi.horzcat", "line_number": 134, "usage_type": "call"}, {"api_name": "casadi.reshape", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "casadi.SX.sym", "line_number": 144, "usage_type": "call"}, {"api_name": "casadi.SX", "line_number": 144, "usage_type": "attribute"}, {"api_name": "casadi.SX.sym", "line_number": 145, "usage_type": "call"}, {"api_name": "casadi.SX", "line_number": 145, "usage_type": "attribute"}, {"api_name": "casadi.SX.sym", "line_number": 153, "usage_type": "call"}, {"api_name": "casadi.SX", "line_number": 153, "usage_type": "attribute"}, {"api_name": "casadi.SX.sym", "line_number": 156, "usage_type": "call"}, {"api_name": "casadi.SX", "line_number": 156, "usage_type": "attribute"}, {"api_name": "casadi.SX.sym", "line_number": 162, "usage_type": "call"}, {"api_name": "casadi.SX", "line_number": 162, "usage_type": "attribute"}, {"api_name": "casadi.reshape", "line_number": 168, "usage_type": "call"}, {"api_name": "casadi.vertcat", "line_number": 174, "usage_type": "call"}, {"api_name": "casadi.cos", "line_number": 174, "usage_type": "call"}, {"api_name": "casadi.sin", "line_number": 175, "usage_type": "call"}, {"api_name": "casadi.Function", "line_number": 183, "usage_type": "call"}, {"api_name": "casadi.SX.sym", "line_number": 187, "usage_type": "call"}, {"api_name": "casadi.SX", "line_number": 187, "usage_type": "attribute"}, {"api_name": "casadi.DM", "line_number": 188, "usage_type": "call"}, {"api_name": "casadi.sqrt", "line_number": 189, "usage_type": "call"}, {"api_name": "casadi.log", "line_number": 190, "usage_type": "call"}, {"api_name": "casadi.Function", "line_number": 191, "usage_type": "call"}, {"api_name": "casadi.SX.sym", "line_number": 194, "usage_type": "call"}, {"api_name": "casadi.SX", "line_number": 194, "usage_type": "attribute"}, {"api_name": "casadi.DM", "line_number": 195, "usage_type": "call"}, {"api_name": "casadi.sqrt", "line_number": 197, "usage_type": "call"}, {"api_name": "casadi.log", "line_number": 200, "usage_type": "call"}, {"api_name": "casadi.Function", "line_number": 201, "usage_type": "call"}, {"api_name": "casadi.SX.sym", "line_number": 205, "usage_type": "call"}, {"api_name": "casadi.SX", "line_number": 205, "usage_type": "attribute"}, {"api_name": "casadi.DM", "line_number": 206, "usage_type": "call"}, {"api_name": "casadi.sqrt", "line_number": 207, "usage_type": "call"}, {"api_name": "casadi.log", "line_number": 210, "usage_type": "call"}, {"api_name": "casadi.Function", "line_number": 211, "usage_type": "call"}, {"api_name": "casadi.SX.sym", "line_number": 215, "usage_type": "call"}, {"api_name": "casadi.SX", "line_number": 215, "usage_type": "attribute"}, {"api_name": "utils.sdf.myLineSdfCasadi", "line_number": 218, "usage_type": "call"}, {"api_name": "utils.sdf", "line_number": 218, "usage_type": "name"}, {"api_name": "casadi.log", "line_number": 220, "usage_type": "call"}, {"api_name": "casadi.Function", "line_number": 221, "usage_type": "call"}, {"api_name": "utils.ur5_dh.RobFki", "line_number": 233, "usage_type": "call"}, {"api_name": "utils.ur5_dh", "line_number": 233, "usage_type": "name"}, {"api_name": "utils.mr_casadi.RotationToQuaternion", "line_number": 237, "usage_type": "call"}, {"api_name": "utils.mr_casadi", "line_number": 237, "usage_type": "name"}, {"api_name": "utils.mr_casadi.QuaternionError", "line_number": 238, "usage_type": "call"}, {"api_name": "utils.mr_casadi", "line_number": 238, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 239, "usage_type": "call"}, {"api_name": "utils.ur5_dh.RobFki", "line_number": 250, "usage_type": "call"}, {"api_name": "utils.ur5_dh", "line_number": 250, "usage_type": "name"}, {"api_name": "utils.ur5_dh.RobFki", "line_number": 251, "usage_type": "call"}, {"api_name": "utils.ur5_dh", "line_number": 251, "usage_type": "name"}, {"api_name": "casadi.vertcat", "line_number": 267, "usage_type": "call"}, {"api_name": "casadi.vertcat", "line_number": 270, "usage_type": "call"}, {"api_name": "casadi.nlpsol", "line_number": 291, "usage_type": "call"}, {"api_name": "casadi.DM.zeros", "line_number": 293, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 293, "usage_type": "attribute"}, {"api_name": "casadi.DM.zeros", "line_number": 294, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 294, "usage_type": "attribute"}, {"api_name": "casadi.inf", "line_number": 296, "usage_type": "attribute"}, {"api_name": "casadi.inf", "line_number": 297, "usage_type": "attribute"}, {"api_name": "casadi.inf", "line_number": 298, "usage_type": "attribute"}, {"api_name": "utils.hmqr5_agl_map.hmqr5_agl_map", "line_number": 300, "usage_type": "call"}, {"api_name": "casadi.inf", "line_number": 310, "usage_type": "attribute"}, {"api_name": "casadi.inf", "line_number": 311, "usage_type": "attribute"}, {"api_name": "casadi.inf", "line_number": 312, "usage_type": "attribute"}, {"api_name": "casadi.DM.zeros", "line_number": 329, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 329, "usage_type": "attribute"}, {"api_name": "casadi.DM.zeros", "line_number": 330, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 330, "usage_type": "attribute"}, {"api_name": "casadi.DM", "line_number": 338, "usage_type": "call"}, {"api_name": "casadi.DM.zeros", "line_number": 340, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 340, "usage_type": "attribute"}, {"api_name": "casadi.repmat", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 346, "usage_type": "call"}, {"api_name": "casadi.DM.zeros", "line_number": 348, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 348, "usage_type": "attribute"}, {"api_name": "casadi.DM", "line_number": 351, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 358, "usage_type": "call"}, {"api_name": "nav_msgs.msg.Path", "line_number": 358, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 359, "usage_type": "call"}, {"api_name": "nav_msgs.msg.Path", "line_number": 359, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 360, "usage_type": "call"}, {"api_name": "nav_msgs.msg.Path", "line_number": 360, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 361, "usage_type": "call"}, {"api_name": "visualization_msgs.msg.MarkerArray", "line_number": 361, "usage_type": "argument"}, {"api_name": "casadi.DM.zeros", "line_number": 364, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 364, "usage_type": "attribute"}, {"api_name": "rospy.Subscriber", "line_number": 382, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Pose", "line_number": 382, "usage_type": "argument"}, {"api_name": "rospy.Rate", "line_number": 383, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 384, "usage_type": "call"}, {"api_name": "time.time", "line_number": 385, "usage_type": "call"}, {"api_name": "casadi.DM", "line_number": 386, "usage_type": "call"}, {"api_name": "casadi.norm_2", "line_number": 388, "usage_type": "call"}, {"api_name": "time.time", "line_number": 389, "usage_type": "call"}, {"api_name": "casadi.vertcat", "line_number": 390, "usage_type": "call"}, {"api_name": "casadi.vertcat", "line_number": 397, "usage_type": "call"}, {"api_name": "casadi.reshape", "line_number": 398, "usage_type": "call"}, {"api_name": "casadi.reshape", "line_number": 399, "usage_type": "call"}, {"api_name": "casadi.reshape", "line_number": 409, "usage_type": "call"}, {"api_name": "casadi.reshape", "line_number": 410, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 411, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 419, "usage_type": "call"}, {"api_name": "utils.ur5_dh.RobFki", "line_number": 433, "usage_type": "call"}, {"api_name": "utils.ur5_dh", "line_number": 433, "usage_type": "name"}, {"api_name": "utils.visualization.DrawBasePredictPath", "line_number": 436, "usage_type": "call"}, {"api_name": "utils.visualization", "line_number": 436, "usage_type": "name"}, {"api_name": "utils.visualization.PubEndEffectorRealTrajectory", "line_number": 437, "usage_type": "call"}, {"api_name": "utils.visualization", "line_number": 437, "usage_type": "name"}, {"api_name": "utils.visualization.DrawRobSphere", "line_number": 438, "usage_type": "call"}, {"api_name": "utils.visualization", "line_number": 438, "usage_type": "name"}, {"api_name": "casadi.horzcat", "line_number": 441, "usage_type": "call"}, {"api_name": "casadi.reshape", "line_number": 443, "usage_type": "call"}, {"api_name": "time.time", "line_number": 446, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 449, "usage_type": "call"}]}
{"seq_id": "16857375438", "text": "import time\r\nimport asyncio\r\nimport aiohttp\r\nimport blog_spider\r\n\r\n# 单线程异步IO\r\nasync def async_spider(url):    # 协程\r\n\tprint(f\"crawl url: {url}\")\r\n\tasync with aiohttp.ClientSession() as session:   # 创建对象\r\n\t\tasync with session.get(url) as resp:\r\n\t\t\tresult = await resp.text()\r\n\t\t\tprint(f\"crawl url: {url}, {len(result)}\")\r\n\r\n\r\nloop = asyncio.get_event_loop()   # 超级循环\r\n\r\ntasks = [loop.create_task(async_spider(url)) for url in blog_spider.urls]\r\n\r\nstart = time.time()\r\nloop.run_until_complete(asyncio.wait(tasks))\r\nend = time.time()\r\nprint(\"use time:\", end - start)\r\n\r\n", "repo_name": "kongguhuiyin/python-Concurrency", "sub_path": "08.asyncio_spider.py", "file_name": "08.asyncio_spider.py", "file_ext": "py", "file_size_in_byte": 596, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "aiohttp.ClientSession", "line_number": 9, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 15, "usage_type": "call"}, {"api_name": "blog_spider.urls", "line_number": 17, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 19, "usage_type": "call"}, {"api_name": "asyncio.wait", "line_number": 20, "usage_type": "call"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}]}
{"seq_id": "17600886242", "text": "\"\"\"\r\nYou are given an m x n grid where each cell can have one of three values:\r\n\r\n0 representing an empty cell,\r\n1 representing a fresh orange, or\r\n2 representing a rotten orange.\r\nEvery minute, any fresh orange that is 4-directionally adjacent to a rotten orange becomes rotten.\r\n\r\nReturn the minimum number of minutes that must elapse until no cell has a fresh orange. If this is impossible, return -1.\r\n\r\nExample 1:\r\n\r\nInput: grid = [[2,1,1],[1,1,0],[0,1,1]]\r\nOutput: 4\r\nExample 2:\r\n\r\nInput: grid = [[2,1,1],[0,1,1],[1,0,1]]\r\nOutput: -1\r\nExplanation: The orange in the bottom left corner (row 2, column 0) is never rotten, because rotting only happens 4-directionally.\r\nExample 3:\r\n\r\nInput: grid = [[0,2]]\r\nOutput: 0\r\nExplanation: Since there are already no fresh oranges at minute 0, the answer is just 0.\r\n\r\nConstraints:\r\n\r\nm == grid.length\r\nn == grid[i].length\r\n1 <= m, n <= 10\r\ngrid[i][j] is 0, 1, or 2.\r\n\"\"\"\r\nfrom collections import deque\r\n\r\nclass Solution:\r\n#     def orangesRotting(self, grid: List[List[int]]) -> int:\r\n    def orangesRotting(self, grid):\r\n        ROWS = len(grid)\r\n        COLS = len(grid[0])\r\n        count = 0\r\n\r\n        # rotten = set()\r\n        queue = deque([])\r\n\r\n        for row in range(ROWS):\r\n            for col in range(COLS):\r\n                if grid[row][col] == 2:\r\n                    queue.append((row, col))\r\n        \r\n        while queue:\r\n            for _ in range(len(queue)):\r\n                curr_row, curr_col = queue.popleft()\r\n                positions = [(curr_row + 1, curr_col), (curr_row - 1, curr_col), (curr_row, curr_col + 1), (curr_row, curr_col - 1)]\r\n                for position in positions:\r\n                    pos_row, pos_col = position\r\n                    if pos_row in range(ROWS) and pos_col in range(COLS) and grid[pos_row][pos_col] == 1:\r\n                        queue.append((pos_row, pos_col))\r\n                        grid[pos_row][pos_col] = 2\r\n            count += 1\r\n\r\n        for row in range(ROWS):\r\n            for col in range(COLS):\r\n                if grid[row][col] == 1:\r\n                    return -1\r\n\r\n        return count - 1\r\n       \r\n        \r\n    \r\ndef main():\r\n    grid = [[2,1,1],[1,1,0],[0,1,1]]\r\n\r\n    solution = Solution()\r\n\r\n    result = solution.orangesRotting(grid)\r\n    \r\n    print(result)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()", "repo_name": "Przekazmierczak/my_codespace", "sub_path": "leetcode/0994_Rotting_Oranges.py", "file_name": "0994_Rotting_Oranges.py", "file_ext": "py", "file_size_in_byte": 2340, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.deque", "line_number": 43, "usage_type": "call"}]}
{"seq_id": "43190772775", "text": "# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nfrom typing import Optional\nfrom collections import deque\n\n\nclass Solution:\n    def widthOfBinaryTree(self, root: Optional[TreeNode]) -> int:\n        res = 0\n        if not root:\n            return res\n        # 二叉树性质：left = 2 * root   right = 2 * root + 1\n        # BFS，每次将 (编号，节点) 元组入队列，根据同层最大编号和最小编号的差值求 width\n        queue = deque([(1, root)])\n        while queue:\n            length = len(queue)\n            start, end = 0, 0\n            for i in range(length):\n                idx, node = queue.popleft()\n                if i == 0:\n                    start = idx\n                if i == length - 1:\n                    end = idx\n                if node.left:\n                    queue.append((2 * idx, node.left))\n                if node.right:\n                    queue.append((2 * idx + 1, node.right))\n            res = max(res, end - start + 1)\n        return res\n", "repo_name": "226wyj/LeetCode", "sub_path": "Medium/662.py", "file_name": "662.py", "file_ext": "py", "file_size_in_byte": 1143, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "typing.Optional", "line_number": 12, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "26742713867", "text": "import re, json, asyncio, argparse, coloredlogs, logging, secrets, pem, sys, base64, getpass, os\nfrom aio_tcpserver import tcp_server\nfrom cryptography.hazmat.backends import default_backend\nfrom cryptography.hazmat.primitives import serialization, hashes, padding, hmac\nfrom cryptography.hazmat.primitives.serialization import *\nfrom cryptography.hazmat.primitives.asymmetric import dh\nfrom cryptography.hazmat.primitives.asymmetric import padding as padding_assymmetric\nfrom cryptography.hazmat.primitives.kdf.hkdf import HKDF\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\nfrom cryptography import x509\nfrom cryptography.x509.oid import ExtensionOID, NameOID\nfrom datetime import datetime\n\nlogger = logging.getLogger('root')\n\nSTATE_CONNECT = 0\nSTATE_OPEN = 1\nSTATE_PICK_AUTHENTICATION = 2\nSTATE_AUTHENTICATION_SERVER = 3\nSTATE_AUTHENTICATION = 4\nSTATE_CHALLENGE = 5\nSTATE_SEND_FILE = 6\nSTATE_DATA = 7\nSTATE_CLOSE = 8\n\n#GLOBAL\nstorage_dir = 'files'\n\nclass ClientHandler(asyncio.Protocol):\n\tdef __init__(self, signal):\n\t\t\"\"\"\n\t\tDefault constructor\n\t\t\"\"\"\n\t\tself.signal = signal\n\t\tself.state = 0\n\t\tself.file = None\n\t\tself.file_name = None\n\t\tself.file_path = None\n\t\tself.storage_dir = storage_dir\n\t\tself.buffer = ''\n\t\tself.peername = ''\n\t\tself.salt_hash = b''\n\n\tdef connection_made(self, transport) -> None:\n\t\t\"\"\"\n\t\tCalled when a client connects\n\n\t\t:param transport: The transport stream to use with this client\n\t\t:return:\n\t\t\"\"\"\n\t\tself.peername = transport.get_extra_info('peername')\n\t\tlogger.info('\\n\\nConnection from {}'.format(self.peername))\n\t\tself.transport = transport\n\t\tself.state = STATE_CONNECT\n\n\n\tdef data_received(self, data: bytes) -> None:\n\t\t\"\"\"\n        Called when data is received from the client.\n        Stores the data in the buffer\n\n        :param data: The data that was received. This may not be a complete JSON message\n        :return:\n        \"\"\"\n\t\tlogger.debug('Received: {}'.format(data))\n\t\ttry:\n\t\t\tself.buffer += data.decode()\n\t\texcept:\n\t\t\tlogger.exception('Could not decode data from client')\n\n\t\tidx = self.buffer.find('\\r\\n')\n\n\t\twhile idx >= 0:  # While there are separators\n\t\t\tframe = self.buffer[:idx + 2].strip()  # Extract the JSON object\n\t\t\tself.buffer = self.buffer[idx + 2:]  # Removes the JSON object from the buffer\n\n\t\t\tself.on_frame(frame)  # Process the frame\n\t\t\tidx = self.buffer.find('\\r\\n')\n\n\t\tif len(self.buffer) > 4096 * 1024 * 1024:  # If buffer is larger than 4M\n\t\t\tlogger.warning('Buffer to large')\n\t\t\tself.buffer = ''\n\t\t\tself.transport.close()\n\n\n\tdef on_frame(self, frame: str) -> None:\n\t\t\"\"\"\n\t\tCalled when a frame (JSON Object) is extracted\n\n\t\t:param frame: The JSON object to process\n\t\t:return:\n\t\t\"\"\"\n\n\t\ttry:\n\t\t\tmessage = json.loads(frame)\n\t\texcept:\n\t\t\tlogger.exception(\"Could not decode JSON message: {}\".format(frame))\n\t\t\tself.transport.close()\n\t\t\treturn\n\n\t\tmtype = message.get('type', \"\").upper()\n\n\t\tif mtype == 'OPEN':\n\t\t\tret = self.process_open(message)\n\n\t\telif mtype == 'PICK_AUTHENTICATION':\n\t\t\tret = self.nonce_signature(message)\n\t\t\tlogger.info('Certificate sended')\n\t\t\tself.state = STATE_PICK_AUTHENTICATION\n\n\t\telif mtype == 'AUTHENTICATION_VALIDATION':\n\t\t\tret = self.check_validation(message)\n\t\t\tlogger.info(\"Client validate server\")\n\t\t\tself.state = STATE_AUTHENTICATION_SERVER\n\n\t\telif mtype == 'AUTHENTICATION':\n\t\t\tret = self.process_authentication(message)\n\t\t\tlogger.info(\"Nonce and salt sended\")\n\t\t\tself.state = STATE_AUTHENTICATION\n\n\t\telif mtype == 'CHALLENGE':\n\t\t\tret = self.process_challenge(message)\n\t\t\tlogger.info(\"Authentication finished and permissions verified\")\n\t\t\tself.state = STATE_CHALLENGE\n\n\t\telif mtype == 'DATA':\n\t\t\tret = self.process_data(message)\n\t\telif mtype == 'CLOSE':\n\t\t\tret = self.process_close(message)\n\t\telse:\n\t\t\tlogger.warning(\"Invalid message type: {}\".format(message['type']))\n\t\t\tret = False\n\n\t\tif not ret:\n\t\t\ttry:\n\t\t\t\tself._send({'type': 'ERROR', 'message': 'See server'})\n\t\t\texcept:\n\t\t\t\tpass # Silently ignore\n\n\t\t\tlogger.info(\"Closing transport\")\n\t\t\tif self.file is not None:\n\t\t\t\tself.file.close()\n\t\t\t\tself.file = None\n\n\t\t\tself.state = STATE_CLOSE\n\t\t\tself.transport.close()\n\n\n\tdef process_open(self, message: str) -> bool:\n\t\t\"\"\"\n\t\tProcesses an OPEN message from the client\n\t\tThis message should contain the filename\n\n\t\t:param message: The message to process\n\t\t:return: Boolean indicating the success of the operation\n\t\t\"\"\"\n\t\tlogger.debug(\"Process Open: {}\".format(message))\n\n\t\tif self.state != STATE_CONNECT:\n\t\t\tlogger.warning(\"Invalid state. Discarding\")\n\t\t\treturn False\n\n\t\tif not 'file_name' in message:\n\t\t\tlogger.warning(\"No filename in Open\")\n\t\t\treturn False\n\n\t\t# Only chars and letters in the filename\n\t\tfile_name = re.sub(r'[^\\w\\.]', '', message['file_name'])\n\t\tfile_path = os.path.join(self.storage_dir, file_name)\n\t\tif not os.path.exists(\"files\"):\n\t\t\ttry:\n\t\t\t\tos.mkdir(\"files\")\n\t\t\texcept:\n\t\t\t\tlogger.exception(\"Unable to create storage directory\")\n\t\t\t\treturn False\n\n\t\ttry:\n\t\t\tself.file = open(file_path, \"wb\")\n\t\t\tlogger.info(\"File open\")\n\t\texcept Exception:\n\t\t\tlogger.exception(\"Unable to open file\")\n\t\t\treturn False\n\n\t\tself._send({'type': 'OK'})\n\n\t\tself.file_name = file_name\n\t\tself.file_path = file_path\n\t\tself.state = STATE_OPEN\n\t\treturn True\n\n\tdef nonce_signature(self, message: str) -> bool:\n\t\tlogger.debug(\"Sign nonce: {}\".format(message))\n\n\t\tif self.state != STATE_OPEN:\n\t\t\tlogger.warning(\"Invalide state.\")\n\t\t\treturn False\n\n\t\ttry:\n\t\t\tdata = message.get('data', None)\n\t\t\tif data is None:\n\t\t\t\tlogger.debug(\"Invalid message - no data found\")\n\t\t\t\treturn False\n\t\t\tbdata = base64.b64decode(message['data'])\n\t\texcept:\n\t\t\tlogger.exception(\"Could not decode base64 content from message.data\")\n\t\t\treturn False\n\n\t\tnonce = bdata\n\t\tfile_cert = open(\"certServer.crt\", \"rb\")\n\t\tpem_data = file_cert.read()\n\t\tcert = x509.load_pem_x509_certificate(pem_data, default_backend())\n\n\t\tpriv_key_server = open(\"certServer.pem\", \"rb\")\n\t\tpriv_key = priv_key_server.read()\n\t\tpriv_key = serialization.load_pem_private_key(\n\t\t\tpriv_key,\n\t\t\tpassword=None,\n\t\t\tbackend=default_backend()\n\t\t)\n\n\t\tsignature = priv_key.sign(\n\t\t\tnonce,\n\t\t\tpadding_assymmetric.PSS(\n\t\t\t\tmgf=padding_assymmetric.MGF1(hashes.SHA256()),\n\t\t\t\tsalt_length=padding_assymmetric.PSS.MAX_LENGTH\n\t\t\t),\n\t\t\thashes.SHA256()\n\t\t)\n\n\t\tmessage = {'type': 'OK', 'certificate': None, 'signature': None}\n\t\tmessage['signature'] = base64.b64encode(signature).decode()\n\t\tmessage['certificate'] = base64.b64encode(cert.public_bytes(Encoding.PEM)).decode()\n\t\tself._send(message)\n\n\t\treturn True\n\n\tdef check_validation(self, message: str) -> bool:\n\t\tlogger.debug(\"See Validation: {}\".format(message))\n\n\t\tif self.state != STATE_PICK_AUTHENTICATION:\n\t\t\tlogger.warning(\"Invalide state. Discarding\")\n\t\t\treturn False\n\n\t\ttry:\n\t\t\tdata = message.get('data', None)\n\t\t\tif data is None:\n\t\t\t\tlogger.debug(\"Invalid message. No data found\")\n\t\t\t\treturn False\n\t\t\tbdata = base64.b64decode(message['data'])\n\t\texcept:\n\t\t\tlogger.exception(\"Could not decode base64 content from message.data\")\n\t\t\treturn False\n\n\t\tif bdata.decode() != \"OK\":\n\t\t\tlogger.warning(\"Authentication of the server failed. Client will close the connection\")\n\t\t\treturn False\n\n\t\tself._send({'type': 'OK'})\n\n\t\treturn True\n\n\tdef process_authentication(self, message: str) -> bool:\n\t\tlogger.debug(\"Process Authentication: {}\".format(message))\n\n\t\tif self.state != STATE_AUTHENTICATION_SERVER:\n\t\t\tlogger.warning(\"Invalide state. Discarding\")\n\t\t\treturn False\n\n\t\ttry:\n\t\t\tdata = message.get('data', None)\n\t\t\tif data is None:\n\t\t\t\tlogger.debug(\"Invalid message. No data found\")\n\t\t\t\treturn False\n\t\t\tbdata = base64.b64decode(message['data'])\n\t\texcept:\n\t\t\tlogger.exception(\"Could not decode base64 content from message.data\")\n\t\t\treturn False\n\n\t\tif \"password\" in bdata.decode():\n\t\t\tself.type_auth = \"password\"\n\t\t\tself.client = bdata.decode().split(\"password\")[1]\n\n\t\t\tif not self.user_verification():\n\t\t\t\tlogger.exception(\"User does not exist\")\n\t\t\t\tmessage = {'type': 'ERROR', 'data': \"User does not exist\"}\n\t\t\t\tself._send(message)\n\t\t\t\treturn False\n\t\telse:\n\t\t\tself.type_auth = \"cc\"\n\n\t\tself.nonce = (secrets.randbits(128)).to_bytes(64, byteorder='big')\n\t\tmessage = {'type': 'OK', 'data': base64.b64encode(self.nonce + self.salt_hash).decode()}\n\t\tself._send(message)\n\n\t\treturn True\n\n\tdef user_verification(self) -> bool:\n\t\tfile = open(\"users_db.txt\", \"r\")\n\t\tfor line in file:\n\t\t\tdata = line.split(\",\")\n\t\t\tif data[0] == self.client:\n\t\t\t\tself.client_hash = base64.b64decode(data[1].encode())\n\t\t\t\tself.salt_hash = base64.b64decode(data[2].encode())\n\t\t\t\treturn True\n\t\treturn False\n\n\tdef process_challenge(self, message: str) -> bool:\n\t\tlogger.debug(\"Process Challenge: {}\".format(message))\n\n\t\tif self.state != STATE_AUTHENTICATION:\n\t\t\tlogger.warning(\"Invalide state. Discarding\")\n\t\t\treturn False\n\n\t\ttry:\n\t\t\tdata = message.get('data', None)\n\t\t\tsignature = message.get('signature', None)\n\t\t\tcertificate = message.get('certificate', None)\n\n\t\t\tif data is None and self.type_auth == \"password\":\n\t\t\t\tlogger.debug(\"Invalid message. No data found\")\n\t\t\t\treturn False\n\t\t\telif data is not None and self.type_auth == 'password':\n\t\t\t\tbdata = base64.b64decode(message['data'])\n\n\t\t\tif (signature is None or certificate is None) and self.type_auth == \"cc\":\n\t\t\t\tlogger.debug(\"Invalid message. No data found\")\n\t\t\t\treturn False\n\n\t\texcept:\n\t\t\tlogger.exception(\"Could not decode base64 content from message.data\")\n\t\t\treturn False\n\n\t\tif self.type_auth == \"password\":\n\t\t\tif not self.password(bdata):\n\t\t\t\tlogger.exception(\"Authentication failed\")\n\t\t\t\tmessage = {'type': 'ERROR', 'data': \"Authentication failed\"}\n\t\t\t\tself._send(message)\n\t\t\t\treturn False\n\t\t\telif not self.permissions():\n\t\t\t\tlogger.exception(\"Permission denied\")\n\t\t\t\tmessage = {'type': 'ERROR', 'data': \"Permission denied\"}\n\t\t\t\tself._send(message)\n\t\t\t\treturn False\n\t\telse:\n\t\t\tif not self.cc(message):\n\t\t\t\tlogger.exception(\"Authentication failed\")\n\t\t\t\tmessage = {'type': 'ERROR', 'data': \"Authentication failed\"}\n\t\t\t\tself._send(message)\n\t\t\t\treturn False\n\t\t\telif not self.check_citizen_card_permissions():\n\t\t\t\tlogger.exception(\"Permission denied\")\n\t\t\t\tmessage = {'type': 'ERROR', 'data': \"Permission denied\"}\n\t\t\t\tself._send(message)\n\t\t\t\treturn False\n\n\t\tmessage = {'type': 'OK'}\n\t\tself._send(message)\n\t\tlogger.info(\"Successfully authenticated\")\n\t\tself.state = STATE_SEND_FILE\n\t\treturn True\n\n\tdef password(self, message: bytes) -> bool:\n\t\tdigest = hashes.Hash(hashes.SHA256(), backend=default_backend())\n\t\tdigest.update(self.nonce)\n\t\tdigest.update(self.client_hash)\n\t\thash = digest.finalize()\n\t\t\n\t\tif hash == message:\n\t\t\treturn True\n\t\treturn False\n\n\tdef permissions(self) -> bool:\n\t\tfile = open(\"users_db.txt\", \"r\")\n\t\tfor line in file:\n\t\t\tdata = line.split(\",\")\n\t\t\tif data[0] == self.client:\n\t\t\t\tpermission = data[3]\n\t\t\t\tif permission == 'TRUE':\n\t\t\t\t\treturn True\n\t\treturn False\n\n\tdef cc(self, message: str) -> bool:\n\t\tcert_cc = base64.b64decode(message['certificate'])\n\t\tcert = x509.load_pem_x509_certificate(cert_cc, default_backend())\n\t\tself.cert = cert\n\t\tverify_cert = self.check_certificate(cert)\n\n\t\tsignature = base64.b64decode(message['signature'])\n\t\tverify_sign = self.check_signature(signature, cert)\n\n\t\tif not verify_cert or not verify_sign:\n\t\t\treturn False\n\t\tlogger.info('Certificate chain and Signature verified')\n\t\treturn True\n\n\tdef check_signature(self, signature, client_cert) -> bool:\n\t\ttry:\n\t\t\tclient_cert.public_key().verify(\n\t\t\t\tsignature,\n\t\t\t\tself.nonce,\n\t\t\t\tpadding_assymmetric.PKCS1v15(),\n\t\t\t\thashes.SHA1()\n\t\t\t)\n\t\texcept:\n\t\t\treturn False\n\n\t\treturn True\n\n\tdef check_certificate(self, cert) -> bool:\n\t\t#Purpose - Citizen Card Certificate\n\t\tcitizen_card_cert = cert.extensions.get_extension_for_oid(ExtensionOID.KEY_USAGE).value.digital_signature\n\t\tif citizen_card_cert == False:\n\t\t\treturn False\n\n\t\tdic = self.build_dictionaries()\n\t\tif dic == False:\n\t\t\treturn False\n\n\t\t#Certificate Chain\n\t\tchain = self.build_chain(cert)\n\n\t\t#Check if each certificate is used to verify signatures\n\t\tfor i in range(1, len(chain)):\n\t\t\tpurpose = self.check_purpose(chain[i])\n\t\t\tif not purpose:\n\t\t\t\treturn False\n\n\t\t#Check if each certificate is revokated\n\t\tcrl = self.build_crl()\n\t\tfor cert in chain:\n\t\t\tif not self.check_crl(cert, crl):\n\t\t\t\tlogger.info(\"Revoked in crl.\")\n\t\t\t\treturn False\n\n\t\tfor i in range(0, len(chain)):\n\t\t\t#Verify Validity\n\t\t\tif chain[i].not_valid_after < datetime.now():\n\t\t\t\treturn False\n\t\t\t#Verify Signatures\n\t\t\tif i != len(chain) - 1 and not self.verify_cert_signature(chain[i], chain[i + 1].public_key()):\n\t\t\t\treturn False\n\t\t\t#Verify Root Signature\n\t\t\tif i == len(chain) - 1 and not self.verify_cert_signature(chain[i], chain[i].public_key()):\n\t\t\t\treturn False\n\n\t\treturn True\n\n\tdef build_dictionaries(self) -> bool:\n\t\tcerts = \"PTEID.pem\"\n\t\tcertificates = pem.parse_file(certs)\n\t\tself.certs = {}\n\t\tself.root = {}\n\n\t\t#PTEID.pem\n\t\tfor cert in certificates:\n\t\t\tcertificate = x509.load_pem_x509_certificate(cert.as_bytes(), default_backend())\n\t\t\tif certificate.not_valid_after > datetime.now():\n\t\t\t\tself.certs[certificate.subject.rfc4514_string()] = certificate\n\t\t\n\t\t#Load Root Certificate\n\t\troot_cert = \"/etc/ssl/certs/Baltimore_CyberTrust_Root.pem\"\n\t\twith open(root_cert, \"rb\") as baltimore:\n\t\t\tcertificate = x509.load_pem_x509_certificate(baltimore.read(), default_backend())\n\t\t\tself.root[certificate.subject.rfc4514_string()] = certificate\n\t\treturn True\n\n\tdef build_chain(self, cert, chain=[]):\n\t\tchain.append(cert)\n\n\t\tissuer = cert.issuer.rfc4514_string()\n\t\tsubject = cert.subject.rfc4514_string()\n\n\t\tif issuer == subject and subject in self.root:\n\t\t\treturn chain\n\n\t\tif issuer in self.certs:\n\t\t\treturn self.build_chain(self.certs[issuer], chain)\n\t\telif issuer in self.root:\n\t\t\treturn self.build_chain(self.root[issuer], chain)\n\n\t\treturn chain\n\n\tdef check_purpose(self, cert) -> bool:\n\t\tcert_sign = cert.extensions.get_extension_for_oid(ExtensionOID.KEY_USAGE).value.key_cert_sign\n\t\treturn cert_sign\n\n\tdef build_crl(self):\n\t\tcrl_folder = \"crl/\"\n\t\tfiles = [f for f in os.scandir(crl_folder)]\n\t\tcrl = []\n\t\tfor f in files:\n\t\t\twith open(f, \"rb\") as file:\n\t\t\t\tcrlist = x509.load_der_x509_crl(file.read(), default_backend())\n\t\t\t\tcrl.append(crlist)\n\t\treturn crl\n\n\tdef check_crl(self, cert, crl) -> bool:\n\t\tfor revocation_list in crl:\n\t\t\tif revocation_list.get_revoked_certificate_by_serial_number(cert.serial_number) is not None:\n\t\t\t\treturn False\n\t\treturn True\n\n\tdef verify_cert_signature(self, certificate, public_key_issuer) -> bool:\n\t\ttry:\n\t\t\tpublic_key_issuer.verify(\n\t\t\t\tcertificate.signature,\n\t\t\t\tcertificate.tbs_certificate_bytes,\n\t\t\t\tpadding_assymmetric.PKCS1v15(),\n\t\t\t\tcertificate.signature_hash_algorithm,\n\t\t\t)\n\t\t\treturn True\n\t\texcept Exception:\n\t\t\treturn False\n\n\tdef check_citizen_card_permissions(self) -> bool:\n\t\tcc_number = self.cert.subject.get_attributes_for_oid(NameOID.SERIAL_NUMBER)[0].value\n\t\tf = open(\"users_db.txt\", 'r')\n\n\t\tfor line in f:\n\t\t\tdata = line.split(\",\")\n\t\t\tpossible_number = 'BI' + data[4]\n\t\t\tif cc_number == possible_number:\n\t\t\t\tif data[3] == 'TRUE':\n\t\t\t\t\treturn True\n\t\treturn False\n\n\tdef process_data(self, message: str) -> bool:\n\t\t\"\"\"\n\t\tProcesses a DATA message from the client\n\t\tThis message should contain a chunk of the file\n\n\t\t:param message: The message to process\n\t\t:return: Boolean indicating the success of the operation\n\t\t\"\"\"\n\t\tlogger.debug(\"Process Data: {}\".format(message))\n\n\t\tif self.state == STATE_CHALLENGE:\n\t\t\tself.state = STATE_DATA\n\t\t\t# First Packet\n\n\t\telif self.state == STATE_DATA:\n\t\t\t# Next packets\n\t\t\tpass\n\n\t\telse:\n\t\t\tlogger.warning(\"Invalid state. Discarding\")\n\t\t\treturn False\n\n\t\ttry:\n\t\t\tdata = message.get('data', None)\n\t\t\tif data is None:\n\t\t\t\tlogger.debug(\"Invalid message. No data found\")\n\t\t\t\treturn False\n\n\t\t\tbdata = base64.b64decode(message['data'])\n\t\texcept:\n\t\t\tlogger.exception(\"Could not decode base64 content from message.data\")\n\t\t\treturn False\n\n\t\ttry:\n\t\t\tself.file.write(bdata)\n\t\t\tself.file.flush()\n\t\texcept:\n\t\t\tlogger.exception(\"Could not write to file\")\n\t\t\treturn False\n\n\t\treturn True\n\n\n\tdef process_close(self, message: str) -> bool:\n\t\t\"\"\"\n\t\tProcesses a CLOSE message from the client.\n\t\tThis message will trigger the termination of this session\n\n\t\t:param message: The message to process\n\t\t:return: Boolean indicating the success of the operation\n\t\t\"\"\"\n\t\tlogger.debug(\"Process Close: {}\".format(message))\n\n\t\tself.transport.close()\n\t\tif self.file is not None:\n\t\t\tself.file.close()\n\t\t\tself.file = None\n\n\t\tself.state = STATE_CLOSE\n\n\t\treturn True\n\n\n\tdef _send(self, message: str) -> None:\n\t\t\"\"\"\n\t\tEffectively encodes and sends a message\n\t\t:param message:\n\t\t:return:\n\t\t\"\"\"\n\t\tlogger.debug(\"Send: {}\".format(message))\n\n\t\tmessage_b = (json.dumps(message) + '\\r\\n').encode()\n\t\tself.transport.write(message_b)\n\ndef main():\n\tglobal storage_dir\n\n\tparser = argparse.ArgumentParser(description='Receives files from clients.')\n\tparser.add_argument('-v', action='count', dest='verbose',\n\t\t\t\t\t\thelp='Shows debug messages (default=False)',\n\t\t\t\t\t\tdefault=0)\n\tparser.add_argument('-p', type=int, nargs=1,\n\t\t\t\t\t\tdest='port', default=5000,\n\t\t\t\t\t\thelp='TCP Port to use (default=5000)')\n\n\tparser.add_argument('-d', type=str, required=False, dest='storage_dir',\n\t\t\t\t\t\tdefault='files',\n\t\t\t\t\t\thelp='Where to store files (default=./files)')\n\n\targs = parser.parse_args()\n\tstorage_dir = os.path.abspath(args.storage_dir)\n\tlevel = logging.DEBUG if args.verbose > 0 else logging.INFO\n\tport = args.port\n\tif port <= 0 or port > 65535:\n\t\tlogger.error(\"Invalid port\")\n\t\treturn\n\n\tif port < 1024 and not os.geteuid() == 0:\n\t\tlogger.error(\"Ports below 1024 require eUID=0 (root)\")\n\t\treturn\n\n\tcoloredlogs.install(level)\n\tlogger.setLevel(level)\n\n\tlogger.info(\"Port: {} LogLevel: {} Storage: {}\".format(port, level, storage_dir))\n\ttcp_server(ClientHandler, worker=2, port=port, reuse_port=True)\n\n\nif __name__ == '__main__':\n\tmain()\n\n\n", "repo_name": "joaogferreira/authentication-sio", "sub_path": "src/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 17590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "asyncio.Protocol", "line_number": 29, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 172, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 203, "usage_type": "call"}, {"api_name": "cryptography.x509.load_pem_x509_certificate", "line_number": 211, "usage_type": "call"}, {"api_name": "cryptography.x509", "line_number": 211, "usage_type": "name"}, {"api_name": "cryptography.hazmat.backends.default_backend", "line_number": 211, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.serialization.load_pem_private_key", "line_number": 215, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.serialization", "line_number": 215, "usage_type": "name"}, {"api_name": "cryptography.hazmat.backends.default_backend", "line_number": 218, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding.PSS", "line_number": 223, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding", "line_number": 223, "usage_type": "name"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding.MGF1", "line_number": 224, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding", "line_number": 224, "usage_type": "name"}, {"api_name": "cryptography.hazmat.primitives.hashes.SHA256", "line_number": 224, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.hashes", "line_number": 224, "usage_type": "name"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding.PSS", "line_number": 225, "usage_type": "attribute"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding", "line_number": 225, "usage_type": "name"}, {"api_name": "cryptography.hazmat.primitives.hashes.SHA256", "line_number": 227, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.hashes", "line_number": 227, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 231, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 232, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 249, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 274, "usage_type": "call"}, {"api_name": "secrets.randbits", "line_number": 291, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 292, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 302, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 303, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 323, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.hashes.Hash", "line_number": 363, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.hashes", "line_number": 363, "usage_type": "name"}, {"api_name": "cryptography.hazmat.primitives.hashes.SHA256", "line_number": 363, "usage_type": "call"}, {"api_name": "cryptography.hazmat.backends.default_backend", "line_number": 363, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 383, "usage_type": "call"}, {"api_name": "cryptography.x509.load_pem_x509_certificate", "line_number": 384, "usage_type": "call"}, {"api_name": "cryptography.x509", "line_number": 384, "usage_type": "name"}, {"api_name": "cryptography.hazmat.backends.default_backend", "line_number": 384, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 388, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding.PKCS1v15", "line_number": 401, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding", "line_number": 401, "usage_type": "name"}, {"api_name": "cryptography.hazmat.primitives.hashes.SHA1", "line_number": 402, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.hashes", "line_number": 402, "usage_type": "name"}, {"api_name": "cryptography.x509.oid.ExtensionOID.KEY_USAGE", "line_number": 411, "usage_type": "attribute"}, {"api_name": "cryptography.x509.oid.ExtensionOID", "line_number": 411, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 437, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 437, "usage_type": "name"}, {"api_name": "pem.parse_file", "line_number": 450, "usage_type": "call"}, {"api_name": "cryptography.x509.load_pem_x509_certificate", "line_number": 456, "usage_type": "call"}, {"api_name": "cryptography.x509", "line_number": 456, "usage_type": "name"}, {"api_name": "cryptography.hazmat.backends.default_backend", "line_number": 456, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 457, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 457, "usage_type": "name"}, {"api_name": "cryptography.x509.load_pem_x509_certificate", "line_number": 463, "usage_type": "call"}, {"api_name": "cryptography.x509", "line_number": 463, "usage_type": "name"}, {"api_name": "cryptography.hazmat.backends.default_backend", "line_number": 463, "usage_type": "call"}, {"api_name": "cryptography.x509.oid.ExtensionOID.KEY_USAGE", "line_number": 484, "usage_type": "attribute"}, {"api_name": "cryptography.x509.oid.ExtensionOID", "line_number": 484, "usage_type": "name"}, {"api_name": "os.scandir", "line_number": 489, "usage_type": "call"}, {"api_name": "cryptography.x509.load_der_x509_crl", "line_number": 493, "usage_type": "call"}, {"api_name": "cryptography.x509", "line_number": 493, "usage_type": "name"}, {"api_name": "cryptography.hazmat.backends.default_backend", "line_number": 493, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding.PKCS1v15", "line_number": 508, "usage_type": "call"}, {"api_name": "cryptography.hazmat.primitives.asymmetric.padding", "line_number": 508, "usage_type": "name"}, {"api_name": "cryptography.x509.oid.NameOID.SERIAL_NUMBER", "line_number": 516, "usage_type": "attribute"}, {"api_name": "cryptography.x509.oid.NameOID", "line_number": 516, "usage_type": "name"}, {"api_name": "base64.b64decode", "line_number": 555, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 598, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 604, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 617, "usage_type": "call"}, {"api_name": "os.path", "line_number": 617, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 618, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 618, "usage_type": "attribute"}, {"api_name": "os.geteuid", "line_number": 624, "usage_type": "call"}, {"api_name": "coloredlogs.install", "line_number": 628, "usage_type": "call"}, {"api_name": "aio_tcpserver.tcp_server", "line_number": 632, "usage_type": "call"}]}
{"seq_id": "27269402524", "text": "import logging\nfrom urllib.parse import urljoin\n\nimport requests\nfrom requests.cookies import cookiejar_from_dict\n\n\nlogger = logging.getLogger('test')\n\nMAX_RESPONSE_LENGTH = 500\n\n\nclass ResponseErrorException(Exception):\n    pass\n\n\nclass ResponseStatusCodeException(Exception):\n    pass\n\n\nclass InvalidLoginException(Exception):\n    pass\n\n\nclass ApiClient:\n\n    def __init__(self, base_url):\n        self.base_url = base_url\n        self.session = requests.Session()\n\n        self.csrf_token = None\n        self.sessionid_gtp = None\n\n    @staticmethod\n    def log_pre(method, url, headers, data, expected_status):\n        logger.info(f'Performing {method} request:\\n'\n                    f'URL: {url}\\n'\n                    f'HEADERS: {headers}\\n'\n                    f'DATA: {data}\\n\\n'\n                    f'expected status: {expected_status}\\n\\n')\n\n    @staticmethod\n    def log_post(response):\n        log_str = 'Got response:\\n' \\\n                  'RESPONSE STATUS: {response.status_code}'\n\n        if len(response.text) > MAX_RESPONSE_LENGTH:\n            if logger.level == logging.INFO:\n                logger.info(f'{log_str}\\n'\n                            f'RESPONSE CONTENT: COLLAPSED due to response size > {MAX_RESPONSE_LENGTH}. '\n                            f'Use DEBUG logging.\\n\\n')\n            elif logger.level == logging.DEBUG:\n                logger.debug(f'{log_str}\\n'\n                             f'RESPONSE CONTENT: {response.text}\\n\\n')\n        else:\n            logger.info(f'{log_str}\\n'\n                        f'RESPONSE CONTENT: {response.text}\\n\\n')\n\n    def _request(self, method, location, headers=None, data=None, expected_status=200, jsonify=True):\n        url = urljoin(self.base_url, location)\n\n        self.log_pre(method, url, headers, data, expected_status)\n        response = self.session.request(method, url, headers=headers, data=data)\n        self.log_post(response)\n\n        if response.status_code != expected_status:\n            raise ResponseStatusCodeException(f'Got {response.status_code} {response.reason} for URL \"{url}\"!\\n'\n                                              f'Expected status_code: {expected_status}.')\n\n        if jsonify:\n            json_response = response.json()\n            if json_response.get('bStateError'):\n                error = json_response.get('bErrorMsg', 'Unknown')\n                raise ResponseErrorException(f'Request \"{url}\" return error \"{error}\"!')\n            return json_response\n        return response\n\n    @property\n    def post_headers(self):\n        return {'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8'}\n\n    def get_token(self):\n        headers = self._request('GET', self.base_url, jsonify=False).headers['Set-Cookie'].split(';')\n        token_header = [h for h in headers if 'csrftoken' in h]\n        if not token_header:\n            raise Exception('CSRF token not found in main page headers')\n\n        token_header = token_header[0]\n        token = token_header.split('=')[-1]\n\n        return token\n\n    def post_login(self, user, password):\n        location = '/login/'\n\n        csrf_token = self.get_token()\n\n        headers = self.post_headers\n        headers['Cookie'] = f'csrftoken={csrf_token}'\n\n        data = {\n            'csrfmiddlewaretoken': csrf_token,\n            'login': user,\n            'password': password\n        }\n\n        result = self._request('POST', location, headers=headers, data=data, jsonify=False)\n        try:\n            response_cookies = result.headers['Set-Cookie'].split(';')\n        except Exception as e:\n            raise InvalidLoginException(e)\n\n        new_csrf_token = [c for c in response_cookies if 'csrftoken' in c][0].split('=')[-1]\n        sessionid_gtp = [c for c in response_cookies if 'sessionid_gtp' in c][0].split('=')[-1]\n\n        self.session.cookies = cookiejar_from_dict({'csrftoken': new_csrf_token, 'sessionid_gtp': sessionid_gtp})\n        self.csrf_token = new_csrf_token\n        self.sessionid_gtp = sessionid_gtp\n\n        return result\n\n    def post_topic_create(self, blog_id, title, text, publish=True):\n        location = f'/blog/{blog_id}/topic/create/'\n\n        data = {\n            'csrfmiddlewaretoken': self.csrf_token,\n            'blog': blog_id,\n            'title': title,\n            'text': text,\n            'publish': 'on' if publish else ''\n        }\n\n        return self._request('POST', location, headers=self.post_headers, data=data)\n\n    def get_topic_feed(self, feed_type='all'):\n        location = f'/feed/update/stream/?type={feed_type}'\n\n        return self._request('GET', location)\n\n    def post_topic_delete(self, topic_id):\n        location = f'/blog/topic/delete/{topic_id}/'\n\n        data = {\n            'csrfmiddlewaretoken': self.csrf_token,\n            'submit': 'Удалить'\n        }\n\n        return self._request('POST', location, headers=self.post_headers, data=data, jsonify=False)\n\n    def get_topic(self, topic_id, expected_status=200):\n        location = f'blog/topic/view/{topic_id}/'\n        return self._request('GET', location, expected_status=expected_status, jsonify=False)\n\n    def post_topic_vote(self, topic_id):\n        location = f'/blog/topic/vote/{topic_id}/'\n\n        data = {\n            'csrfmiddlewaretoken': self.csrf_token,\n            'vote': 1\n        }\n        return self._request('POST', location, headers=self.post_headers, data=data)", "repo_name": "frad1993/ivi_pytest_test_job_api", "sub_path": "pack/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 5391, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 48, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 52, "usage_type": "attribute"}, {"api_name": "urllib.parse.urljoin", "line_number": 60, "usage_type": "call"}, {"api_name": "requests.cookies.cookiejar_from_dict", "line_number": 116, "usage_type": "call"}]}
{"seq_id": "3416691010", "text": "# Usage:\n# python3 slide-downloader/main.py [presentation] [slides...]\n# If run with no arguments, will update all slides from all presentations.\n# If presentation is specified, will only update from that presentation.\n# If presentation _and_ slides are specified, will only update those slides from that presentation\n# (where slides are 1-indexed, as they appear in Google Slides and in the timettocode webapp).\n\nimport json\nimport os\nimport pickle\nimport subprocess\nimport sys\nimport time\n\nfrom dataclasses import dataclass\n\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import AuthorizedSession\n\n\n# Run everything relative to the git repository root:\nos.chdir(subprocess.run([\"git\", \"rev-parse\", \"--show-toplevel\"], check=True, capture_output=True, cwd=os.path.dirname(__file__)).stdout.strip())\n\n\n# This file needs to exist and point to a JSON auth file downloaded from\n# console.developers.google.com for a project which has the slides.googleapis.com API enabled.\n# Assuming you have such a project, you can download it by:\n#  * Navigate to https://console.developers.google.com\n#  * Click \"Credentials\" in the menu.\n#  * Click \"Create credentials\".\n#  * Select \"OAuth client ID\" from the drop-down.\n#  * Select \"Other\" as the Application type.\n#  * Click \"Create\".\n#  * Click the \"Download JSON\" link in the row that just showed up (looks like a down arrow).\n#\n# If in doubt, just ask @illicitonion for help.\nauth_json_file = \"auth.json\"\nif not os.path.exists(auth_json_file):\n    print(f\"File {auth_json_file} did not exist; see instructions in {__file__}\", file=sys.stderr)\n    sys.exit(1)\n\nif len(sys.argv) >= 2:\n    presentation_filter = lambda p: p.presentation_id == sys.argv[1]\nelse:\n    presentation_filter = lambda _: True\n\nif len(sys.argv) >= 3:\n    slides = {int(s) for s in sys.argv[2:]}\n    slide_filter = lambda s: s in slides\nelse:\n    slide_filter = lambda _: True\n\n@dataclass\nclass Config:\n    presentation_id: str\n    slides_dir: str\n    slides_controller_js_path: str\n\n    @property\n    def url(self):\n        return f\"https://slides.googleapis.com/v1/presentations/{self.presentation_id}\"\n\n    def slide_thumbnail_url(self, slide_id: str):\n        return f\"{self.url}/pages/{slide_id}/thumbnail\"\n\n\npresentations = [\n    Config(\n        presentation_id=\"1_aSkOhYu0u4qU8Y00e_nvaeTdzwb8rds5RlHb9uWEUE\",\n        slides_dir=\"slides/html-css-curriculum\",\n        slides_controller_js_path=\"js/slide_controllers_html_css.js\",\n    ),\n    Config(\n        presentation_id=\"1bHPc80Ifpd-gjj2_U3ihhcFsiYZvlHRKWd90O6brqhw\",\n        slides_dir=\"slides/js-curriculum\",\n        slides_controller_js_path=\"js/slide_controllers_js.js\",\n    ),\n]\nif len(sys.argv) >= 2:\n    presentation_id = sys.argv[1]\n    presentations = [p for p in presentations if presentation_filter(p)]\n\nflow = InstalledAppFlow.from_client_secrets_file(\n    auth_json_file, [\"https://www.googleapis.com/auth/presentations.readonly\"]\n)\ncredentials = flow.run_console()\nauthed_session = AuthorizedSession(credentials)\n\n\ndef fetch(url):\n    response = authed_session.get(url)\n    response.raise_for_status()\n    return response\n\n\nfor presentation in presentations:\n    with os.scandir(presentation.slides_dir) as dirents:\n        for dirent in sorted(dirents, key=lambda d: d.name):\n            if dirent.is_file() and dirent.name.lower().endswith(\".png\"):\n                slide_id = int(dirent.name[:-4])\n                if slide_filter(slide_id):\n                    print(f\"Removing {dirent.path}\")\n                    os.remove(dirent.path)\n\n    with open(presentation.slides_controller_js_path, \"w\") as controller_js:\n        controller_js.write(\n            \"\"\"// This file was auto-generated by the slide downloader - do not manually edit it!\nvar app = angular.module('SlideApp', ['ui.ace', 'LocalStorageModule']);\n\napp.directive('slideImage', function() {\n    return function(scope, element, attrs) {\n        attrs.$observe('slideImage', function(value) {\n            element.css({\n                'background-image': 'url(' + value +')',\n                'background-size' : 'cover'\n            });\n        });\n    }\n});\n\napp.controller('SlideController', function($scope, $sce, localStorageService) {\n    $scope.slideImages = [\n\"\"\"\n        )\n\n        slides_array = fetch(presentation.url).json()[\"slides\"]\n        for i, slide in enumerate(slides_array):\n            path = os.path.join(presentation.slides_dir, f\"{i+1:03d}.png\")\n            if slide_filter(i + 1):\n                slide_url = fetch(presentation.slide_thumbnail_url(slide[\"objectId\"])).json()[\n                    \"contentUrl\"\n                ]\n                print(f\"Downloading {slide_url} -> {path}\")\n                with open(path, \"wb\") as f:\n                    thumbnail_response = fetch(slide_url)\n                    f.write(thumbnail_response.content)\n                # We're rate-limited on the server side to 100 expensive read requests per 100 seconds.\n                # Avoid going over:\n                time.sleep(0.6)\n            print(f\"        '{path}',\", file=controller_js)\n\n        controller_js.write(\n            \"\"\"        ];\n    var html = localStorageService.get('html') || \"\";\n    $scope.data = {htmlString:html, trustedVersion:html}\n\n    var slideNos = localStorageService.get('currentSlideNum') || 0;\n    $scope.currentSlideNum = slideNos;\n    $scope.$watch('currentSlideNum', function(value){\n        localStorageService.add('currentSlideNum', value);\n    });\n\n\n\n\n    $scope.nextSlide = function() {\n        if($scope.currentSlideNum == $scope.slideImages.length-1) {\n            return;\n        }\n        $scope.currentSlideNum++;\n    }\n\n    $scope.prevSlide = function() {\n        if($scope.currentSlideNum == 0) {\n            return;\n        }\n        $scope.currentSlideNum--;\n    }\n\n    $scope.currentSlide = function() {\n        return $scope.slideImages[$scope.currentSlideNum];\n    }\n\n    $scope.progress = function() {\n        var current = parseInt($scope.currentSlideNum) + 1,\n            total = $scope.slideImages.length;\n        return current + \"/\" + total;\n    }\n\n    $scope.$watch(\"data.htmlString\", function(newValue) {\n        $scope.data.trustedVersion = $sce.trustAsHtml(newValue);\n        localStorageService.add('html', newValue);\n\n    }, true);\n});\n\"\"\"\n        )\n", "repo_name": "InclusiveTech/coding_class", "sub_path": "slide-downloader/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6316, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.chdir", "line_number": 22, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 40, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 48, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 53, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 79, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 80, "usage_type": "attribute"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow.from_client_secrets_file", "line_number": 83, "usage_type": "call"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow", "line_number": 83, "usage_type": "name"}, {"api_name": "google.auth.transport.requests.AuthorizedSession", "line_number": 87, "usage_type": "call"}, {"api_name": "os.scandir", "line_number": 97, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 139, "usage_type": "call"}]}
{"seq_id": "33565869466", "text": "import toml\nimport pandas as pd\nimport os\nfrom datetime import datetime\n\ndef load_config():\n    with open(\"config.toml\") as f:\n        return toml.load(f)\n\ndef time_from_filename(filename):\n    \"\"\"\n    Five? cases:\n        20210703_163126.jpg\n        IMG_20201002_133606.jpg\n        20210626_192712_HDR.jpg\n        20210606_095151_Burst01.jpg\n        IMG_20200902_083855_1.jpg\n    \"\"\"\n    stem = filename.split(\".\")[0]\n    if filename[16:19] == \"HDR\":\n        stem = stem[:15]\n    elif filename[:3] == \"IMG\":\n        stem = stem[4:]\n    elif filename[16:21] == \"Burst\":\n        stem = stem[:15]\n\n    stem = stem[:14] # removes the _1 for duplicates\n\n    dt = datetime.strptime(stem, \"%Y%m%d_%H%M%S\")\n    return dt.timestamp()\n\ndef main():\n    config = load_config()\n\n    pic_dir = config[\"sources\"][\"food_images\"]\n\n    data = []\n    for filename in os.listdir(pic_dir):\n        timestamp = time_from_filename(filename)\n        data.append([timestamp, filename])\n\n    df = pd.DataFrame(data, columns=[\"timestamp\", \"filename\"])\n    out = config[\"cleaned\"][\"food_images_meta\"]\n    df.to_csv(out, index=False)\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "rutrum/health-analysis", "sub_path": "src/clean/food_pictures.py", "file_name": "food_pictures.py", "file_ext": "py", "file_size_in_byte": 1146, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "toml.load", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "call"}]}
{"seq_id": "4854202708", "text": "\n# from experiment_utils import utils\n\n# import torch\nimport numpy as np\nimport argparse\n# from models.models import GraspSamplerDecoder, GraspEvaluator\n# from models.quaternion import quaternion_mult\n# from utils import density_utils\nimport pickle\nfrom graspsampler.GraspSampler import GraspSampler\nfrom graspsampler.common import PandaGripper, Scene, Object\n# from torch.utils.data import DataLoader, TensorDataset\n# import torch.nn.functional as F\n# from tqdm.auto import tqdm\n# from pathlib import Path\n# import time\nfrom scipy.spatial.transform import Rotation as R\nfrom graspsampler import utils\nfrom graspsampler.common import PandaGripper, Scene\n\ncat = \"mug\"\nidx = 14\n# grasp_idx = 0\ndata_path = f'../isaac/experiment4/{cat}/{cat}{idx:03}/'\nheuristic_initial_grasps_fname = f'{data_path}/grasps_heuristics_final.npz'\n# data_path = \"/home/user/isaac/grasps_sampled_for_positive_grasps/mug/mug000_isaac\"\n\n# prepare pointclouds\npcl_filename = f\"grasp_data/pcls/{cat}/{cat}{idx:03}.pkl\"\npcs_numpy = pickle.load(open(pcl_filename,'rb'))\npcs_numpy = np.concatenate([pcs_numpy[0],pcs_numpy[1],pcs_numpy[2],pcs_numpy[3]])\n\n\nimport trimesh\nscene = Scene()\nscene.add_geometry(trimesh.points.PointCloud(vertices=pcs_numpy))\n\n# for grasp_idx in range(1):\n\nmain_grasps_data = np.load(heuristic_initial_grasps_fname)\nmain_grasp_quaternion = main_grasps_data[\"quaternions\"]\nmain_grasp_translation = main_grasps_data[\"translations\"]\n\n    # sub_grasps_data = np.load(heuristic_initial_grasps_fname)\n    # sub_grasps_quaternions = sub_grasps_data[\"quaternions\"]\n    # sub_grasps_translations = sub_grasps_data[\"translations\"]\n    # sub_grasps_labels = sub_grasps_data[\"isaac_labels\"]\n\n    # add main grasp\nidx = 19\ngripper = utils.gripper_bd()\nr = R.from_quat(main_grasp_quaternion[idx])\ntransform = np.eye(4)\ntransform[:3,:3] = r.as_matrix()\ntransform[:3,3] = main_grasp_translation[idx]\ngripper.apply_transform(transform)\nscene.add_geometry(gripper)\nscene.show()\nexit()\nfor i in range(0,len(main_grasp_quaternion),20):\n    for j in range(i+15,i+20):\n        gripper = utils.gripper_bd()\n        r = R.from_quat(main_grasp_quaternion[j])\n        transform = np.eye(4)\n        transform[:3,:3] = r.as_matrix()\n        transform[:3,3] = main_grasp_translation[j]\n        gripper.apply_transform(transform)\n        scene.add_geometry(gripper)\n    scene.show()\n    scene = Scene()\n    scene.add_geometry(trimesh.points.PointCloud(vertices=pcs_numpy))\n\n    # for i in range(100):\n#         # if sub_grasps_labels[i] == 1:\n#         #     continue\n#         gripper = utils.gripper_bd(1)\n#         r = R.from_quat(sub_grasps_quaternions[i])\n#         transform = np.eye(4)\n#         transform[:3,:3] = r.as_matrix()\n#         transform[:3,3] = sub_grasps_translations[i]\n#         gripper.apply_transform(transform)\n#         scene.add_geometry(gripper)\n        \n# scene.show()\n\n# exit()\n# scene.add_geometry(obj_mesh)\n\n\n\n\n\n# number_of_grasps = 50\n# panda = PandaGripper()\n# panda.apply_transformation(transform)\n# scene.add_gripper(panda)\n\n\n# for i in range(translations_final.shape[0]):\n    \n#     gripper = utils.gripper_bd(1)\n#     r = R.from_quat(quaternions_final[i])\n#     transform = np.eye(4)\n#     transform[:3,:3] = r.as_matrix()\n#     transform[:3,3] = translations_final[i]\n\n#     gripper.apply_transform(transform)\n#     scene.add_geometry(gripper)\n\n    # transforms= utils.perturb_transform(transform, number_of_grasps, \n    #                 min_translation=(-0.01,-0.01,-0.01),\n    #                 max_translation=(0.01,0.01,0.01),\n    #                 min_rotation=(-0.125,-0.125,-0.125),\n    #                 max_rotation=(+0.125,+0.125,+0.125))\n    # print(len(transforms))\n\n    # for _trans in transforms:\n    #     gripper = utils.gripper_bd(0)\n    #     gripper.apply_transform(_trans)\n    #     scene.add_geometry(gripper)\n\n\n# for i in range(all_trans.shape[1]):\n#     gripper = utils.gripper_bd(1)\n#     r = R.from_euler('XYZ', all_eulers[-1][i])\n#     transform = np.eye(4)\n#     transform[:3,:3] = r.as_matrix()\n#     transform[:3,3] = all_trans[-1][i]\n#     gripper.apply_transform(transform)\n#     scene.add_geometry(gripper)\n\n# pc = trimesh.points.PointCloud(vertices=pcs_numpy)\n# scene.add_geometry(pc)\n# scene.show()\n\n\n# import matplotlib.pyplot as plt\n# all_trans_v = np.mean(all_trans_v, axis=1)\n# print(all_trans_v.shape)\n# plt.plot(all_trans_v[:,0], label='x')\n# plt.plot(all_trans_v[:,1], label='y')\n# plt.plot(all_trans_v[:,2], label='z')\n# # plt.show()\n\n# # all_quat_v = all_quat_v.squeeze(1)\n# # plt.plot(all_quat_v[:,0], 'r--')\n# # plt.plot(all_quat_v[:,1], 'r--')\n# # plt.plot(all_quat_v[:,2], 'r--')\n# # plt.plot(all_quat_v[:,3], 'r--')\n# # plt.show()\n\n# all_euler_v = np.mean(all_euler_v, axis=1)\n# plt.plot(all_euler_v[:,0], 'r--')\n# plt.plot(all_euler_v[:,1], 'r--')\n# plt.plot(all_euler_v[:,2], 'r--')\n# plt.legend()\n# plt.show()\n\n\n\n# all_success = all_success.squeeze()\n# print(all_success.shape)\n# plt.plot(all_success)\n# plt.show()", "repo_name": "patrickeala/GraspSampler", "sub_path": "all_files/grasps_visualizer2.py", "file_name": "grasps_visualizer2.py", "file_ext": "py", "file_size_in_byte": 4970, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pickle.load", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 32, "usage_type": "call"}, {"api_name": "graspsampler.common.Scene", "line_number": 36, "usage_type": "call"}, {"api_name": "trimesh.points.PointCloud", "line_number": 37, "usage_type": "call"}, {"api_name": "trimesh.points", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 41, "usage_type": "call"}, {"api_name": "graspsampler.utils.gripper_bd", "line_number": 52, "usage_type": "call"}, {"api_name": "graspsampler.utils", "line_number": 52, "usage_type": "name"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 54, "usage_type": "call"}, {"api_name": "graspsampler.utils.gripper_bd", "line_number": 63, "usage_type": "call"}, {"api_name": "graspsampler.utils", "line_number": 63, "usage_type": "name"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 64, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 65, "usage_type": "call"}, {"api_name": "graspsampler.common.Scene", "line_number": 71, "usage_type": "call"}, {"api_name": "trimesh.points.PointCloud", "line_number": 72, "usage_type": "call"}, {"api_name": "trimesh.points", "line_number": 72, "usage_type": "attribute"}]}
{"seq_id": "11336255359", "text": "# function_import --------------------\n\nfrom transformers import Swin2SRForConditionalGeneration\nfrom PIL import Image\n\n# function_code --------------------\n\ndef upscale_image(low_res_image_path: str, high_res_image_path: str):\n    \"\"\"\n    Upscale a low resolution image using the Swin2SRForConditionalGeneration model.\n\n    Args:\n        low_res_image_path (str): The path to the low resolution image.\n        high_res_image_path (str): The path to save the upscaled image.\n\n    Returns:\n        None\n    \"\"\"\n    # Load the low-resolution image\n    low_res_image = Image.open(low_res_image_path)\n\n    # Load the pre-trained model\n    model = Swin2SRForConditionalGeneration.from_pretrained('condef/Swin2SR-lightweight-x2-64')\n\n    # Upscale the image\n    high_res_image = model.upscale_image(low_res_image)\n\n    # Save the upscaled image\n    high_res_image.save(high_res_image_path)\n\n# test_function_code --------------------\n\ndef test_upscale_image():\n    \"\"\"\n    Test the upscale_image function.\n\n    Raises:\n        AssertionError: If the function does not work as expected.\n    \"\"\"\n    # Define the paths to the test images\n    low_res_image_path = 'test_low_res_image_path.jpg'\n    high_res_image_path = 'test_high_res_image_path.jpg'\n\n    # Call the function\n    upscale_image(low_res_image_path, high_res_image_path)\n\n    # Load the upscaled image\n    high_res_image = Image.open(high_res_image_path)\n\n    # Check that the image has been upscaled\n    assert high_res_image.size[0] > Image.open(low_res_image_path).size[0], 'The image has not been upscaled.'\n\n# call_test_function_code --------------------\n\ntest_upscale_image()", "repo_name": "vixuowis/Research-2309", "sub_path": "Exp-2/output/hf-eval-data-v2/f00196_upscale_image.py", "file_name": "f00196_upscale_image.py", "file_ext": "py", "file_size_in_byte": 1635, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "PIL.Image.open", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 20, "usage_type": "name"}, {"api_name": "transformers.Swin2SRForConditionalGeneration.from_pretrained", "line_number": 23, "usage_type": "call"}, {"api_name": "transformers.Swin2SRForConditionalGeneration", "line_number": 23, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 48, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 48, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 51, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 51, "usage_type": "name"}]}
{"seq_id": "41009276830", "text": "import asyncio\nimport logging\nfrom collections import Callable\nfrom datetime import datetime\nfrom typing import List\nfrom uuid import uuid4\nimport redis\nfrom tracardi.config import tracardi, memory_cache\nfrom tracardi.domain.entity import Entity\nfrom tracardi.domain.event_source import EventSource\nfrom tracardi.domain.value_object.operation import Operation\nfrom tracardi.process_engine.debugger import Debugger\nfrom tracardi.service.cache_manager import CacheManager\nfrom tracardi.service.console_log import ConsoleLog\nfrom tracardi.exceptions.log_handler import log_handler\nfrom tracardi.domain.console import Console\nfrom tracardi.exceptions.exception_service import get_traceback\nfrom tracardi.domain.profile import Profile\nfrom tracardi.domain.session import Session, SessionMetadata, SessionTime\nfrom tracardi.exceptions.exception import TracardiException, DuplicatedRecordException\nfrom tracardi.domain.payload.tracker_payload import TrackerPayload\nfrom tracardi.service.consistency.session_corrector import correct_session\nfrom tracardi.service.destination_orchestrator import DestinationOrchestrator\nfrom tracardi.service.storage.driver import storage\nfrom tracardi.service.synchronizer import ProfileTracksSynchronizer\nfrom tracardi.service.tracker_config import TrackerConfig\nfrom tracardi.service.tracking_manager import TrackingManager, TrackerResult\nfrom tracardi.service.utils.getters import get_entity_id\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(tracardi.logging_level)\nlogger.addHandler(log_handler)\ncache = CacheManager()\n\n\nclass TrackingOrchestrator:\n\n    def __init__(self,\n                 source: EventSource,\n                 tracker_config: TrackerConfig,\n                 console_log: ConsoleLog\n                 ):\n\n        self.tracker_config = tracker_config\n        self.source = source\n        self.console_log = console_log\n        self.locker = ProfileTracksSynchronizer(\n            wait=tracardi.sync_profile_tracks_wait,\n            max_repeats=tracardi.sync_profile_tracks_max_repeats\n        )\n\n    def __aenter__(self):\n        return self\n\n    def __aexit__(self, exc_type, exc_val, exc_tb):\n        await storage.driver.profile.refresh()\n        return self.unlock()\n\n    def unlock(self):\n        # Unlocking is done after data is saved\n        self.locker.unlock_entities()\n\n    async def invoke(self, tracker_payload: TrackerPayload) -> TrackerResult:\n\n        \"\"\"\n        Controls the synchronization of profiles and invokes the process.\n        \"\"\"\n\n        self.locker.set_entity_id(get_entity_id(tracker_payload.profile))\n        if self.locker.is_locked():\n            await self.locker.wait_for_unlock()\n        else:\n            self.locker.lock_entity()\n\n        return await self._invoke_track_process(tracker_payload)\n\n    async def _invoke_track_process(self, tracker_payload: TrackerPayload) -> TrackerResult:\n\n        tracker_payload.set_transitional(self.source)\n        tracker_payload.set_return_profile(self.source)\n\n        # Load session from storage\n        try:\n            if tracker_payload.session is None:\n\n                # If no session in tracker payload this means that we do not need session.\n                # But we may need an artificial session for workflow handling. We create\n                # one but will not save it.\n\n                session = Session(id=str(uuid4()), metadata=SessionMetadata())\n                tracker_payload.force_session(session)\n                tracker_payload.options.update({\n                    \"saveSession\": False\n                })\n\n            session = await cache.session(\n                session_id=tracker_payload.session.id,\n                ttl=memory_cache.session_cache_ttl\n            )\n\n            # Session can be none if not found in db. User defined a session this means he wanted it.\n            # At this point it is null. We handle it later.\n\n        except DuplicatedRecordException as e:\n\n            # There may be a case when we have 2 sessions with the same id.\n            logger.error(str(e))\n\n            # Try to recover sessions\n            list_of_profile_ids_referenced_by_session = await correct_session(tracker_payload.session.id)\n\n            # If there is duplicated session create new random session.\n            # As a consequence of this a new profile is created.\n            session = Session(\n                id=tracker_payload.session.id,\n                metadata=SessionMetadata(\n                    time=SessionTime(\n                        insert=datetime.utcnow()\n                    )\n                ),\n                operation=Operation(\n                    new=True\n                )\n            )\n\n            # If duplicated sessions referenced the same profile then keep it.\n            if len(list_of_profile_ids_referenced_by_session) == 1:\n                session.profile = Entity(id=list_of_profile_ids_referenced_by_session[0])\n\n        if self.tracker_config.static_profile_id is True:\n            # Get static profile - This is dangerous\n            profile, session = await tracker_payload.get_static_profile_and_session(\n                session,\n                storage.driver.profile.load_merged_profile,\n                tracker_payload.profile_less\n            )\n        else:\n            # Get profile\n            profile, session = await tracker_payload.get_profile_and_session(\n                session,\n                storage.driver.profile.load_merged_profile,\n                tracker_payload.profile_less\n            )\n\n            session.context['ip'] = self.tracker_config.ip\n\n        # Make profile copy\n        has_profile = not tracker_payload.profile_less and isinstance(profile, Profile)\n        profile_copy = profile.dict(exclude={\"operation\": ...}) if has_profile else None\n\n        if self.tracker_config.on_profile_ready is None:\n            tracking_manager = TrackingManager(\n                self.console_log,\n                self.tracker_config,\n                tracker_payload,\n                profile,\n                session\n            )\n\n            tracker_result = await tracking_manager.invoke_track_process()\n        else:\n            tracker_result = await self.tracker_config.on_profile_ready(\n                self.console_log,\n                tracker_payload,\n                profile,\n                session\n            )\n\n        # From now on do not use profile or session, use tracker_result.profile, tracker_result.session\n        # For security we override old values\n        profile = tracker_result.profile\n        session = tracker_result.session\n\n        # todo do not know is makes sense - async\n        if self.tracker_config.run_async:\n            pass\n\n        debug = tracker_payload.is_on('debugger', default=False)\n        await self.save_debug_data(tracker_result.debugger, debug, get_entity_id(tracker_result.profile))\n\n        # Send to destination\n        do = DestinationOrchestrator(\n            tracker_result.profile,\n            tracker_result.session,\n            tracker_result.events,\n            self.console_log\n        )\n        await do.sync_destination(\n            has_profile,\n            profile_copy,\n        )\n\n        # Save console log\n        await self.save_console_log()\n\n        # Add profile to response\n        if tracker_payload.return_profile():\n            raise NotImplementedError(\"Returning profile was removed from the system for security reasons.\")\n\n        return tracker_result\n\n    async def save_debug_data(self, debugger, debug: bool, profile_id):\n        try:\n            if tracardi.track_debug or debug:\n                if isinstance(debugger, Debugger) and debugger.has_call_debug_trace():\n                    # Save debug info in background\n                    asyncio.create_task(storage.driver.debug_info.save_debug_info(debugger))\n\n        except Exception as e:\n            message = \"Error during saving debug info: `{}`\".format(str(e))\n            logger.error(message)\n            self.console_log.append(\n                Console(\n                    flow_id=None,\n                    node_id=None,\n                    event_id=None,\n                    profile_id=profile_id,\n                    origin='profile',\n                    class_name='invoke_track_process_step_2',\n                    module=__name__,\n                    type='error',\n                    message=message,\n                    traceback=get_traceback(e)\n                )\n            )\n\n    async def save_console_log(self):\n        if self.console_log:\n            encoded_console_log = list(self.console_log.get_encoded())\n            # Save in background\n            asyncio.create_task(storage.driver.console_log.save_all(encoded_console_log))\n", "repo_name": "muhammad-hari/tracardi", "sub_path": "tracardi/service/tracking_orchestrator.py", "file_name": "tracking_orchestrator.py", "file_ext": "py", "file_size_in_byte": 8720, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "tracardi.config.tracardi.logging_level", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tracardi.config.tracardi", "line_number": 31, "usage_type": "name"}, {"api_name": "tracardi.exceptions.log_handler.log_handler", "line_number": 32, "usage_type": "argument"}, {"api_name": "tracardi.service.cache_manager.CacheManager", "line_number": 33, "usage_type": "call"}, {"api_name": "tracardi.domain.event_source.EventSource", "line_number": 39, "usage_type": "name"}, {"api_name": "tracardi.service.tracker_config.TrackerConfig", "line_number": 40, "usage_type": "name"}, {"api_name": "tracardi.service.console_log.ConsoleLog", "line_number": 41, "usage_type": "name"}, {"api_name": "tracardi.service.synchronizer.ProfileTracksSynchronizer", "line_number": 47, "usage_type": "call"}, {"api_name": "tracardi.config.tracardi.sync_profile_tracks_wait", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tracardi.config.tracardi", "line_number": 48, "usage_type": "name"}, {"api_name": "tracardi.config.tracardi.sync_profile_tracks_max_repeats", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tracardi.config.tracardi", "line_number": 49, "usage_type": "name"}, {"api_name": "tracardi.service.storage.driver.storage.driver.profile.refresh", "line_number": 56, "usage_type": "call"}, {"api_name": "tracardi.service.storage.driver.storage.driver", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tracardi.service.storage.driver.storage", "line_number": 56, "usage_type": "name"}, {"api_name": "tracardi.domain.payload.tracker_payload.TrackerPayload", "line_number": 63, "usage_type": "name"}, {"api_name": "tracardi.service.utils.getters.get_entity_id", "line_number": 69, "usage_type": "call"}, {"api_name": "tracardi.service.tracking_manager.TrackerResult", "line_number": 63, "usage_type": "name"}, {"api_name": "tracardi.domain.payload.tracker_payload.TrackerPayload", "line_number": 77, "usage_type": "name"}, {"api_name": "tracardi.domain.session.Session", "line_number": 90, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 90, "usage_type": "call"}, {"api_name": "tracardi.domain.session.SessionMetadata", "line_number": 90, "usage_type": "call"}, {"api_name": "tracardi.config.memory_cache.session_cache_ttl", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tracardi.config.memory_cache", "line_number": 98, "usage_type": "name"}, {"api_name": "tracardi.exceptions.exception.DuplicatedRecordException", "line_number": 104, "usage_type": "name"}, {"api_name": "tracardi.service.consistency.session_corrector.correct_session", "line_number": 110, "usage_type": "call"}, {"api_name": "tracardi.domain.session.Session", "line_number": 114, "usage_type": "call"}, {"api_name": "tracardi.domain.session.SessionMetadata", "line_number": 116, "usage_type": "call"}, {"api_name": "tracardi.domain.session.SessionTime", "line_number": 117, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 118, "usage_type": "name"}, {"api_name": "tracardi.domain.value_object.operation.Operation", "line_number": 121, "usage_type": "call"}, {"api_name": "tracardi.domain.entity.Entity", "line_number": 128, "usage_type": "call"}, {"api_name": "tracardi.service.storage.driver.storage.driver", "line_number": 134, "usage_type": "attribute"}, {"api_name": "tracardi.service.storage.driver.storage", "line_number": 134, "usage_type": "name"}, {"api_name": "tracardi.service.storage.driver.storage.driver", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tracardi.service.storage.driver.storage", "line_number": 141, "usage_type": "name"}, {"api_name": "tracardi.domain.profile.Profile", "line_number": 148, "usage_type": "argument"}, {"api_name": "tracardi.service.tracking_manager.TrackingManager", "line_number": 152, "usage_type": "call"}, {"api_name": "tracardi.service.utils.getters.get_entity_id", "line_number": 179, "usage_type": "call"}, {"api_name": "tracardi.service.destination_orchestrator.DestinationOrchestrator", "line_number": 182, "usage_type": "call"}, {"api_name": "tracardi.service.tracking_manager.TrackerResult", "line_number": 77, "usage_type": "name"}, {"api_name": "tracardi.config.tracardi.track_debug", "line_number": 204, "usage_type": "attribute"}, {"api_name": "tracardi.config.tracardi", "line_number": 204, "usage_type": "name"}, {"api_name": "tracardi.process_engine.debugger.Debugger", "line_number": 205, "usage_type": "argument"}, {"api_name": "asyncio.create_task", "line_number": 207, "usage_type": "call"}, {"api_name": "tracardi.service.storage.driver.storage.driver.debug_info.save_debug_info", "line_number": 207, "usage_type": "call"}, {"api_name": "tracardi.service.storage.driver.storage.driver", "line_number": 207, "usage_type": "attribute"}, {"api_name": "tracardi.service.storage.driver.storage", "line_number": 207, "usage_type": "name"}, {"api_name": "tracardi.domain.console.Console", "line_number": 213, "usage_type": "call"}, {"api_name": "tracardi.exceptions.exception_service.get_traceback", "line_number": 223, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 231, "usage_type": "call"}, {"api_name": "tracardi.service.storage.driver.storage.driver.console_log.save_all", "line_number": 231, "usage_type": "call"}, {"api_name": "tracardi.service.storage.driver.storage.driver", "line_number": 231, "usage_type": "attribute"}, {"api_name": "tracardi.service.storage.driver.storage", "line_number": 231, "usage_type": "name"}]}
{"seq_id": "41744975675", "text": "'''Dispersion relation for the symmetry preserving case\nAuthor: Huaiyu Duan (UNM)\nRef: arXiv:1901.01546\n'''\nimport numpy as np\nfrom scipy.integrate import quad\nfrom scipy.optimize import root, root_scalar, minimize_scalar\n\n\ndef _DR_func(K, Omega, G, int_opts={}):\n    '''The function D(K, Omega) that should equal 0. See Eq.34 of Ref. \n    K : wave number\n    Omega : wave frequency\n    G(u) : ELN distribution function\n    int_opts : dictionary of options to be passed on to the integrator\n\n    return : value of the function\n    '''\n    kr, ki = K.real, K.imag\n    wr, wi = Omega.real, Omega.imag\n    fr = lambda p, u: G(u)*(wr-kr*u)* u**p/((wr-kr*u)**2 + (wi-ki*u)**2)\n    fi = lambda p, u: G(u)*(-wi+ki*u)* u**p/((wr-kr*u)**2 + (wi-ki*u)**2)\n    J = [] # integral I in Eq.34 of Ref.\n    for p in range(3):\n        r, *_ = quad(lambda u: fr(p, u), -1, 1, **int_opts)\n        i, *_ = quad(lambda u: fi(p, u), -1, 1, **int_opts)\n        J.append(complex(r, i))\n\n    return (J[0]+1)*(J[2]-1) - J[1]**2\n\n\n\ndef _I_of_real_n(p, n, G, int_opts={}):\n    '''Compute the integral of [G(u) u^p / (1 - n u)] for u in [-1, 1] with real n. See Eq.50 of Ref.\n    p : integer\n    n : real refractive index\n    G(u) : ELN distribution function\n    int_opts : dictionary of options to be passed on to the integrator\n\n    return : integral value (real number)\n    '''\n    f = lambda u: G(u) * u**p / (1 - n*u)\n    res, *_ = quad(f, -1, 1, **int_opts)\n    return res\n\n\n\ndef _dI_dn(p, n, G, int_opts={}):\n    '''Compute the derivative of the above integral with respect to n.\n    p : integer\n    n : real refractive index\n    G(u) : ELN distribution function\n    int_opts : dictionary of options to be passed on to the integrator\n\n    return : real number\n    '''\n    f = lambda u: G(u) * u**(p+1) / (1 - n*u)**2\n    res, *_ = quad(f, -1, 1, **int_opts)\n    return res\n\n\n\ndef _Omega_of_real_n(n, G, sgn, int_opts={}):\n    '''Compute the frequency Omega of the real dispersion relation. See Eq.49 of Ref.\n    n : real refractive index\n    G(u) : ELN distribution function\n    sgn : a number indicating whether the plus or minus branch will be calculated\n    int_opts : dictionary of options to be passed on to the integrator\n\n    return : Omega, frequency of the wave\n    '''\n    sgn = np.sign(sgn)\n    I = [_I_of_real_n(p, n, G, int_opts) for p in range(3)]\n    D = 0 if sgn == 0 else (I[2] - I[0])**2 + 4*(I[2]*I[0] - I[1]**2)\n\n    return 0.5*(I[2] - I[0] + sgn*np.sqrt(D))\n\n\n\ndef _dOmega_dn(n, G, sgn, int_opts={}):\n    '''Calculate dOmega/dn of the real dispersion relation.\n    n : refractive index\n    G(u) : ELN distribution function for u in [-1, 1]\n    sgn : a number indicating whether the plus or minus branch will be calculated\n    int_opts : dictionary of options to be passed on to the integrator\n\n    return : dOmega/dn\n    '''\n    sgn = np.sign(sgn)\n    I = [_I_of_real_n(p, n, G, int_opts) for p in range(3)]\n    dIdn = [_dI_dn(p, n, G, int_opts) for p in range(3)]\n\n    D = (I[2] - I[0])**2 + 4*(I[2]*I[0] - I[1]**2)\n    assert D > 0, f\"Invalid n = {n} for real DR.\" \n    dDdn = 2*(I[2] - I[0])*(dIdn[2] - dIdn[0]) + 4*(dIdn[2]*I[0] + I[2]*dIdn[0] - 2*I[1]*dIdn[1])\n\n    return 0.5*(dIdn[2] - dIdn[0]) + 0.25*sgn*dDdn/np.sqrt(D)\n    \n\n\ndef _dK_dn(n, G, sgn, int_opts={}):\n    '''Calculate dK/dn=Omega + n dOmega/dn of the real dispersion relation.\n    n : refractive index\n    G(u) : ELN distribution function for u in [-1, 1]\n    sgn : a number indicating whether the plus or minus branch will be calculated\n    int_opts : dictionary of options to be passed on to the integrator\n\n    return : dK/dn\n    '''\n    sgn = np.sign(sgn)\n    I = [_I_of_real_n(p, n, G, int_opts) for p in range(3)]\n    dIdn = [_dI_dn(p, n, G, int_opts) for p in range(3)]\n\n    D = (I[2] - I[0])**2 + 4*(I[2]*I[0] - I[1]**2)\n    assert D > 0, f\"Invalid n = {n} for real DR.\" \n    dDdn = 2*(I[2] - I[0])*(dIdn[2] - dIdn[0]) + 4*(dIdn[2]*I[0] + I[2]*dIdn[0] - 2*I[1]*dIdn[1])\n\n    Omega = 0.5*(I[2] - I[0] + sgn*np.sqrt(D))\n    dOmegadn = 0.5*(dIdn[2] - dIdn[0]) + 0.25*sgn*dDdn/np.sqrt(D)\n    return Omega + n * dOmegadn\n\n\n\ndef _n_star(G, int_opts={}, eps=1e-5):\n    '''Compute the critical value of refractive index n where the two real DR branches join. See Sec.III.C.2 of Ref.\n    G(u) : ELN distribution function\n    int_opts : dictionary of options to be passed on to the integrator\n    eps : numerical error tolerance in n\n\n    return : the critical value of n\n    '''\n    assert G(1)<0 and G(-1)>0, \"There is no crossing.\"\n    def Delta(n): # discriminant\n        I = [_I_of_real_n(p, n, G, int_opts=int_opts) for  p in range(3)]\n        return (I[2] - I[0])**2 + 4*(I[2]*I[0] - I[1]**2)\n\n    # try to bracket the critical n between n0 and n1\n    dn = 0.5 \n    n0 = -1 + dn\n    n1 = 1 - dn\n    while Delta(n0) < 0: # make sure that n0 gives real DR\n        if dn < eps:  # fail to bracket\n            return None\n        n1 = n0\n        dn *= 0.5\n        n0 = -1 + dn\n    while Delta(n1) > 0: # make sure that n1 does NOT give real DR\n        n0 = n1\n        dn *= 0.5\n        n1 = 1 - dn\n\n    # find the critical n\n    sol = root_scalar(Delta, bracket=(n0, n1))\n    assert sol.converged, \"Cannot find the critical n.\"\n    return sol.root\n\n\ndef _n_of_real_K(K, G, sgn, int_opts={}, ns=None, Ks=None, eps=1e-5):\n    '''Compute the refractive index n that corresponds to the wave number K on the real dispersion relation.\n    K : wave number\n    G(u) : ELN distribution function\n    sgn : a number indicating whether the plus or minus branch will be calculated\n    int_opts : dictionary of options to be passed on to the integrator\n    ns : value of n where two real branches join\n    Ks : value of K where two real branches join\n    eps : numerical error tolerance in n\n\n    return : n, refractive index\n    '''\n    if K == 0: return 0.0\n    assert sgn != 0, \"sgn should should be a nonreal number indicating the real branch.\"\n\n    # try to bracket the desired value of n between n0 and nb\n    if ns == None:\n        n0 = 0; k0 = 0\n        nb = np.sign(sgn * K) # bound of n        \n    else:\n        assert Ks != None, \"Ks must be specified when ns is given.\"\n        n0 = ns; k0 = Ks\n        nb = -1\n    n1 = (nb + n0) * 0.5\n    k1 = n1 * _Omega_of_real_n(n1, G, sgn, int_opts=int_opts)\n    while (k1 - K) * (k0 - K) > 0: # make sure k0 and k1 bracket K\n        if abs(n0-nb) < eps:\n            print(f\"Cannot locate n exactly for K = {K} on the real branch. Use {nb} instead.\")\n            return nb\n        n0 = n1; k0 = k1\n        n1 = (nb + n0) * 0.5\n        k1 = n1 * _Omega_of_real_n(n1, G, sgn, int_opts=int_opts)\n\n    # find n\n    if ns == None: ns = 2 # guard against the situation where n == ns in function f below\n    f = lambda n: _Omega_of_real_n(n, G, sgn*(ns-n), int_opts=int_opts) * n - K\n    sol = root_scalar(f, bracket=(n0, n1))\n    assert sol.converged, f\"Cannot find n for K = {K}.\"\n    return sol.root\n\n\n    \ndef _extremalOmega(G, sgn, int_opts={}, eps=1e-5):\n    '''Find the extremal points of Omega(K) on the real dispersion relation.\n    G(u) : ELN distribution function for u in [-1, 1]\n    sgn : a number indicating whether the plus or minus branch will be calculated\n    int_opts : dictionary of options to be passed on to the integrator\n    eps : numerical error tolerance in n\n\n    return : [n, K, Omega], where n, K, and Omega are the values of refractive indices, wave numbers, and frequencies at the extremal points when there is no crossing or tuples when there is crossing.\n    '''\n    assert G(-1) > 0, \"G(-1) should be positive.\"\n \n    dOmega_dn = lambda n: _dOmega_dn(n, G, sgn, int_opts)   \n    if G(1) >= 0: # no crossing, one minimum point on each real branch\n        sol1 = root_scalar(dOmega_dn, bracket=(-1+eps, 1-eps))\n        assert sol1.converged, \"Couldn't find an extremal Omega point.\"\n        n1 = sol1.root # value of n where Omega(K) is extreme\n        w1 = _Omega_of_real_n(n1, G, sgn, int_opts)\n        return [n1, n1*w1, w1] # return n, K, and Omega\n\n    # crossing case\n    ns = _n_star(G, int_opts=int_opts, eps=eps) # critical n where two real branches join\n    if ns != None:\n        sol = minimize_scalar(lambda n: -dOmega_dn(n)*sgn, bounds=(-1+eps, ns-eps*(ns+1)), method=\"bounded\") \n        assert sol.success, \"Couldn't find the extremal Omega points.\"\n        n1 = sol.x # dOmega(n)/dn is extreme\n        w1 = _Omega_of_real_n(n1, G, sgn, int_opts)\n        val = dOmega_dn(n1)\n        if abs(val) <= eps*abs(w1): # degenerate extremal points\n            return [ [n1, n1], [w1*n1, w1*n1], [w1, w1]]\n        elif val*sgn > 0: # two separate extremal points\n            sol1 = root_scalar(dOmega_dn, bracket=(-1+eps, n1)) # find the minimum Omega point\n            sol2 = root_scalar(dOmega_dn, bracket=(n1, ns-eps)) # find the maximum Omega point\n            assert sol1.converged and sol2.converged, \"Couldn't find both extremal points. Crossing may be too shallow.\"\n            n1 = sol1.root; n2 = sol2.root # values of n where Omega(K) are extreme\n            w1 = _Omega_of_real_n(n1, G, sgn, int_opts)\n            w2 = _Omega_of_real_n(n2, G, sgn, int_opts)\n            return [ [n1, n2], [w1*n1, w2*n2], [w1, w2]]\n\n    # no extremal Omega points found\n    return [None, None, None]\n\n\n\ndef _extremalK(G, int_opts={}, eps=1e-5):\n    '''Find the extremal points of K(Omega) on the real dispersion relation.\n    G(u) : ELN distribution function for u in [-1, 1]\n    sgn : a number indicating whether the plus or minus branch will be calculated\n    int_opts : dictionary of options to be passed on to the integrator\n    eps : numerical error tolerance in n\n\n    return : [n, K, Omega], where n, K, and Omega are the values of refractive indices, wave numbers, and frequencies at the extremal points.\n    '''\n    assert G(-1) > 0, \"G(-1) should be positive.\"\n    if G(1) >= 0: # no crossing\n        return [None, None, None]\n    ns = _n_star(G, int_opts=int_opts) # critical point where two real branches join\n    if ns == None: # there is no real DR branch\n        return [None, None, None]\n\n    if ns > eps: # two extremal points on the plus and minus branches, respectively\n        n_list = []; k_list = []; w_list = [] # n, K, and Omega where K(Omega) are extreme \n        for sgn in [+1, -1]: # search the plus and minus branches\n            dK_dn = lambda n: _dK_dn(n, G, sgn, int_opts)\n            sol = root_scalar(dK_dn, bracket=(-1+eps, ns-eps))\n            assert sol.converged, f\"Error in searching extremal K point on the {'+' if sgn >0 else '-'} branch.\"\n            n = sol.root # value of n where K(Omega) is extreme\n            w = _Omega_of_real_n(n, G, sgn, int_opts)\n            n_list.append(n)\n            w_list.append(w)\n            k_list.append(n*w)\n    else: # search the plus branch only\n        dK_dn = lambda n: _dK_dn(n, G, +1, int_opts)\n        if abs(ns) <= eps: # ns is about 0 and is an extremal point\n            w2 = _Omega_of_real_n(0, G, 0, int_opts) # Omega at this point\n            # find the other point\n            sol1 = root_scalar(dK_dn, bracket=(-1+eps, -2*eps))\n            assert sol1.converged, \"Error in searching extremal K point on the + branch.\"\n            n1 = sol1.root\n            w1 = _Omega_of_real_n(n1, G, +1, int_opts)\n            n_list = [n1, 0.]\n            w_list = [w1, w2]\n            k_list = [n1*w1, 0.]\n        else:\n            # check if  there are two extremal points on the + branch\n            sol = minimize_scalar(dK_dn, bounds=(-1+eps, ns-eps), method='bounded')\n            assert sol.success, \"Error in searching extremal K points on the + branch.\"\n            n1 = sol.x # value of n where dK/dn is minimum\n            val = dK_dn(n1)\n            if val > eps: # no extremal point\n                n_list = k_list = w_list = None\n            elif abs(val) <= eps: # degenerate extremal points\n                w1 = _Omega_of_real_n(n1, G, +1, int_opts)\n                n_list = [n1, n1]\n                w_list = [w1, w1]\n                k_list = [w1*n1, w1*n1]\n            else: # two distinct extremal points\n                sol1 = root_scalar(dK_dn, bracket=(-1+eps, n1-eps))\n                sol2 = root_scalar(dK_dn, bracket=(n1+eps, ns-eps))\n                assert sol1.converged and sol2.converged, \"Error in searching extremal K points on the + branch.\"\n                n_list = [sol1.root, sol2.root]\n                w_list = [_Omega_of_real_n(n, G, +1, int_opts) for n in n_list]\n                k_list = [w_list[i]*n_list[i] for i in range(2)]\n\n    return [n_list, k_list, w_list] \n\n\n\ndef _xing(G, int_opts={}):\n    '''Find the crossing point of the ELN and the corresponding critical point in (K, Omega).\n    G(u) : ELN distribution function for u in [-1, 1]\n    int_opts : dictionary of options to be passed on to the integrator\n\n    return : [(n, K, Omega), ...], where n, K, and Omega are the values of refractive indices, wave numbers, and frequencies at the critical point.\n    '''\n    assert G(-1) > 0, \"G(-1) should be positive.\"\n    if G(1) >= 0: # no crossing\n        return [None, None, None]\n\n    # find the crossing point\n    sol = root_scalar(G, bracket=(-1, 1))\n    assert sol.converged, \"Cannot find the crossing point.\"\n    ux = sol.root # ELN crossing point\n\n    # K times integrals defined in Eq.36 in Ref.\n    I0, *_ = quad(G, -1, 1, weight='cauchy', wvar=sol.root, **int_opts)\n    I1, *_ = quad(lambda u: G(u)*u, -1, 1, weight='cauchy', wvar=sol.root, **int_opts)\n    I2, *_ = quad(lambda u: G(u)*u*u, -1, 1, weight='cauchy', wvar=sol.root, **int_opts)\n    D = (I2 - I0)**2 - 4*(I1*I1 - I0*I2)\n\n    # critical points\n    k_list = [ 0.5*(I0-I2+sgn*np.sqrt(D)) for sgn in [+1, -1]] # K\n    n = 1/ux if ux != 0.0 else np.inf # refractive index\n    w_list = [k*ux for k in k_list] # values of Omega\n    return [ [n, n], k_list, w_list]\n\n\ndef _cplx_K_of_0(G, int_opts={}):\n    '''Find the complex K with at Omega=0.\n    G(u) : ELN distribution function for u in [-1, 1]\n    int_opts : dictionary of options to be passed on to the integrator\n\n    return : K\n    '''\n    # K times integrals defined in Eq.36 of Ref at Omega = 0\n    I0, *_ = quad(G, -1, 1, weight='cauchy', wvar=0, **int_opts)\n    I0 += 1j * np.pi * G(0)\n    I1, *_ = quad(G, -1, 1, **int_opts)\n    I2, *_ = quad(lambda u: G(u)*u, -1, 1, **int_opts)\n    k_list = []\n    D = (I2 - I0)**2 - 4*(I1*I1 - I0*I2)\n    for sgn in [+1, -1]:\n        k0 = 0.5*(I0-I2+sgn*np.sqrt(D))\n        k_list.append(complex(k0.real, abs(k0.imag)))\n\n    # k_list = [ 0.5*(I0-I2+sgn*np.sqrt(D)) for sgn in [+1, -1]]\n    \n    return k_list\n\n\ndef _cplx_K_of_Omega(Omega, G, K0=0.1j, int_opts={}, rt_opts={}):\n    '''Find the complex K with given real Omega.\n    Omega : wave frequency\n    G(u) : ELN distribution function for u in [-1, 1]\n    K0 : initial guess of K\n    int_opts : dictionary of options to be passed on to the integrator\n    rt_opts : dictionary of options to be passed on to the root finder\n\n    return : K\n    '''\n    def eq(k): # equation for solving Omega\n        res = _DR_func(complex(k[0], k[1]), Omega, G, int_opts)\n        return res.real, res.imag\n    sol = root(eq, x0=[K0.real, K0.imag], **rt_opts)\n    assert sol.success, f\"{sol.message} Couldn't find a complex K solution at Omega = {Omega}. Current solution is K = {complex(sol.x[0], abs(sol.x[1]))}.\"\n    return complex(sol.x[0], abs(sol.x[1]))\n\n\ndef _cplx_K(Omega, G, K0=0.1j, int_opts={}, rt_opts={}):\n    '''Find the complex K with given list of real Omegas.\n    Omega[num] : NumPy array of wave frequencies\n    G(u) : ELN distribution function for u in [-1, 1]\n    K0 : initial guess of K\n    int_opts : dictionary of options to be passed on to the integrator\n    rt_opts : dictionary of options to be passed on to the root finder\n\n    return : K[num]\n    '''\n    kk = np.empty(len(Omega), dtype=np.cdouble)\n    for i in range(len(Omega)):\n        kk[i] = K0 = _cplx_K_of_Omega(Omega[i], G, K0, int_opts=int_opts)\n    return kk\n    \n\ndef _cplx_Omega_of_K(K, G, Omega0=0.1j, int_opts={}, rt_opts={}):\n    '''Find the complex Omega with given real K.\n    K : wave number\n    G(u) : ELN distribution function for u in [-1, 1]\n    Omega0 : guess value of Omega\n    int_opts : dictionary of options to be passed on to the integrator\n    rt_opts : dictionary of options to be passed on to the root finder\n\n    return : Omega\n    '''\n    def eq(w): # equation for solving Omega\n        res = _DR_func(K, complex(w[0], w[1]), G, int_opts)\n        return res.real, res.imag\n    sol = root(eq, x0=[Omega0.real, Omega0.imag], **rt_opts)\n    if not sol.success:\n        print(f\"WARNING: {sol.message} The complex Omega at K={K} may be off.\")\n    return complex(sol.x[0], abs(sol.x[1]))\n\n\ndef _cplx_Omega(K, G, Omega0=0.1j, int_opts={}, rt_opts={}):\n    '''Find the complex Omega with given list of real K.\n    K[num] : NumPy array of wave numbers\n    G(u) : ELN distribution function for u in [-1, 1]\n    Omega0 : guess value of Omega\n    int_opts : dictionary of options to be passed on to the integrator\n    rt_opts : dictionary of options to be passed on to the root finder\n\n    return : Omega[num]\n    '''\n    ww = np.empty(len(K), dtype=np.cdouble)\n    for i in range(len(K)):\n        ww[i] = Omega0 = _cplx_Omega_of_K(K[i], G, Omega0, int_opts=int_opts)\n    return ww\n\n\ndef DR_real(G, maxK=1, minK=-1, num_pts=100, int_opts={}, shift=True, eps=1e-5):\n    '''Calculate the real dispersion relation of the fast oscillation wave.\n    G(u) : ELN distribution function for u in [-1, 1]\n    maxK : maximum value of wave number K to compute\n    minK : minimum value of wave number K to compute\n    num_pts : number of points to calculate\n    int_opts : dictionary of options to be passed on to the integrator\n    shift : whether to shift the frequency and wave number so that the forbidden region centers at (0, 0)\n    eps : numerical error tolerance in n\n\n    return : [(K[num_pts], Omega[num_pts]), ...] with K and Omega being the NumPy arrays of the wave numbers and frequencies\n    '''\n    assert G(-1) > 0, \"G(-1) should be positive.\"\n\n    if not shift: # shift maxK and minK if necessary\n        w0, *_ = quad(G, -1, 1, **int_opts) # shift of the frequency\n        k0, *_ = quad(lambda u: G(u)*u, -1, 1, **int_opts) # shift of the wave number\n        maxK -= k0; minK -= k0\n    \n    dr = [] # dispersion relations\n    if G(1) >= 0: # no crossing, two real branches\n        for sgn in [+1, -1]:\n            na = _n_of_real_K(minK, G, sgn, int_opts=int_opts, eps=eps)\n            nb = _n_of_real_K(maxK, G, sgn, int_opts=int_opts, eps=eps)\n            nn = np.linspace(na, nb, num_pts)\n            ww = np.empty(num_pts, dtype=np.double)\n            kk = np.empty(num_pts, dtype=np.double)\n            kk[0] = minK; ww[0] = minK/na\n            kk[-1] = maxK; ww[-1] = maxK/nb\n            for i in range(1, num_pts-1):\n                ww[i] = _Omega_of_real_n(nn[i], G, sgn, int_opts=int_opts)\n                kk[i] = nn[i] * ww[i]\n            dr.append((kk, ww))\n    else: # crossing\n        ns = _n_star(G, int_opts=int_opts, eps=eps) # critical n where two real branches join\n        if (ns == None): return [] # deep crossing, no real DR\n        ws = _Omega_of_real_n(ns, G, 0, int_opts=int_opts)\n        ks = ns * ws\n        for sgn, Km in zip([+1, -1], [minK, maxK]): # compute the two branches\n            if (ks - Km)*sgn < 0: # no need to compute the branch\n                continue\n            nb = _n_of_real_K(Km, G, sgn, int_opts=int_opts, ns=ns, Ks=ks, eps=eps)\n            nn = np.linspace(ns, nb, num_pts)\n            ww = np.empty(num_pts, dtype=np.double)\n            kk = np.empty(num_pts, dtype=np.double)\n            kk[0] = ks; ww[0] = ws\n            kk[-1] = Km; ww[-1] = Km/nb\n            for i in range(1, num_pts-1):\n                ww[i] = _Omega_of_real_n(nn[i], G, sgn, int_opts=int_opts)\n                kk[i] = nn[i] * ww[i]\n            dr.append((kk, ww))\n\n    if not shift:  # shift the DR back if needed\n        for kk, ww in dr:\n            kk += k0\n            ww += w0\n\n    return dr\n\ndef _fix_K0(dr, br=(0, 1)):\n    '''Because of the uncertainty in the branch cut of sqrt, the end points of the plus and minus complex-K DRs at Omega = 0 may be swapped. This function fixes the problem.\n    dr : a list (K, Omega) with K and Omega being NumPy arrays\n    br : the indices of the DR pairs to check/fix\n    '''\n    dr0 = dr[br[0]]; dr1 = dr[br[1]] # two complex-K branches to compare\n    flag = 0\n    if abs(dr0[0][-1]-dr1[0][-2]) < abs(dr0[0][-1]-dr0[0][-2]): flag += 1\n    if abs(dr1[0][-1]-dr0[0][-2]) < abs(dr1[0][-1]-dr1[0][-2]): flag += 1\n    if flag == 2: # looks like K0 points are swapped\n        for i in range(2):\n            dr0[i][-1], dr1[i][-1] = dr1[i][-1], dr0[i][-1]\n    elif flag == 1:\n        print(\"WARNING: K at Omega=0 on one of the complex-K Dr may be off.\")\n\ndef DR_complexK(G, num_pts=100, int_opts={}, rt_opts={}, shift=True, eps=1e-5):\n    '''Calculate the complex K dispersion relation of the fast oscillation wave.\n    G(u) : ELN distribution function for u in [-1, 1]\n    num_pts : number of points to calculate\n    int_opts : dictionary of options to be passed on to the integrator\n    rt_opts : dictionary of options to be passed on to the root finder\n    shift : whether to shift the frequency and wave number so that the forbidden region centers at (0, 0)\n    eps : numerical error tolerance in n\n\n    return : [(K[num_pts], Omega[num_pts]), ...] with K and Omega being the NumPy arrays of the wave numbers and frequencies\n    '''\n    assert G(-1) > 0, \"G(-1) should be positive.\"\n    dr = [] # dispersion relations\n\n    if G(1) >= 0: # no crossing, two complex K branches\n        for sgn, k0 in zip([+1, -1], _cplx_K_of_0(G, int_opts=int_opts)):\n            nc, kc, wc = _extremalOmega(G, sgn, int_opts=int_opts, eps=eps) # extremal Omega points on the real branch\n            ww = np.linspace(wc, 0, num_pts)\n            kk = np.empty(num_pts, dtype=np.cdouble)\n            kk[0] = kc; kk[-1] = k0\n            kk[1:-1] = _cplx_K(ww[1:-1], G, kk[0]*(1+0.1j), int_opts=int_opts, rt_opts=rt_opts)\n            dr.append((kk, ww))\n        _fix_K0(dr) # fix possible end points error\n    else: # crossing\n        k0 = _cplx_K_of_0(G, int_opts) # complex K at Omega = 0\n        nx, kx, wx = _xing(G, int_opts) # critical points related to crossing\n        br = []\n        for i, sgn in enumerate([+1, -1]):\n            nc, kc, wc = _extremalOmega(G, sgn, int_opts=int_opts, eps=eps) # extremal Omega points on the real branch\n            if nc == None: # deep crossing, no extremal Omega point\n                assert wx[i] != 0.0, \"Cannot handle the case where ELN crosses at u = 0.\"\n                ww = np.linspace(wx[i], 0, num_pts)\n                kk = np.empty(num_pts, dtype=np.cdouble)\n                kk[0] = kx[i]; kk[-1] = k0[i]\n                kk[1:-1] = _cplx_K(ww[1:-1], G, kk[0]*(1+0.1j), int_opts=int_opts, rt_opts=rt_opts)\n                dr.append((kk, ww))\n                br.append(len(dr)-1)\n            else: # moderate crossing, two complex K branches\n                # first branch\n                ww = np.linspace(wc[0], 0, num_pts)\n                kk = np.empty(num_pts, dtype=np.cdouble)\n                kk[0] = kc[0]; kk[-1] = k0[i]\n                kk[1:-1] = _cplx_K(ww[1:-1], G, kk[0]*(1+0.1j), int_opts=int_opts, rt_opts=rt_opts)\n                dr.append((kk, ww))\n                br.append(len(dr)-1)\n                # second branch\n                ww = np.linspace(wx[i], wc[1], num_pts)\n                kk = np.empty(num_pts, dtype=np.cdouble)\n                kk[0] = kx[i]; kk[-1] = kc[1]\n                kk[1:-1] = _cplx_K(ww[1:-1], G, kk[0]*(1+0.1j), int_opts=int_opts, rt_opts=rt_opts)\n                dr.append((kk, ww))\n        # fix possible end points error\n        _fix_K0(dr, br)\n\n    if not shift: # shift back Omega and K if needed\n        w0, *_ = quad(G, -1, 1, **int_opts) # shift of the frequency\n        k0, *_ = quad(lambda u: G(u)*u, -1, 1, **int_opts) # shift of the wave number\n        for kk, ww in dr:\n            kk += k0\n            ww += w0\n    return dr\n\n\ndef DR_complexOmega(G, num_pts=100, int_opts={}, rt_opts={}, shift=True, eps=1e-5):\n    '''Calculate the complex Omega dispersion relation of the fast oscillation wave.\n    G(u) : ELN distribution function for u in [-1, 1]\n    num_pts : number of points to calculate\n    int_opts : dictionary of options to be passed on to the integrator\n    rt_opts : dictionary of options to be passed on to the root finder\n    shift : whether to shift the frequency and wave number so that the forbidden region centers at (0, 0)\n    eps : a small number to guard against rare cases\n\n    return : [(K[num_pts], Omega[num_pts]), ...] with K and Omega being the NumPy arrays of the wave numbers and frequencies\n    '''\n    assert G(-1) > 0, \"G(-1) should be positive.\"\n    dr = [] # dispersion relations\n    if G(1) < 0: # complex Omega branch exists only for crossing\n        nx, kx, wx = _xing(G, int_opts=int_opts) # critical points related to crossing\n        nc, kc, wc = _extremalK(G, int_opts=int_opts, eps=eps) # extremal K points on the real branch\n        if nc == None: # two complex Omega branches have joined\n            kk = np.linspace(kx[0], kx[1], num_pts)\n            ww = np.empty(num_pts, dtype=np.cdouble)\n            ww[0] = wx[0]; ww[-1] = wx[1]\n            ww[1:-1] = _cplx_Omega(kk[1:-1], G, ww[0]*(1+0.1j)+eps*1j, int_opts=int_opts, rt_opts=rt_opts)\n            dr.append((kk, ww))\n        else: # two separate complex Omega branches\n            for i in range(2):\n                kk = np.linspace(kx[i], kc[i], num_pts)\n                ww = np.empty(num_pts, dtype=np.cdouble)\n                ww[0] = wx[i]; ww[-1] = wc[i]\n                ww[1:-1] = _cplx_Omega(kk[1:-1], G, ww[0]*(1+0.1j), int_opts=int_opts, rt_opts=rt_opts)\n                dr.append((kk, ww))\n\n    if not shift: # shift back Omega and K if needed\n        w0, *_ = quad(G, -1, 1, **int_opts) # shift of the frequency\n        k0, *_ = quad(lambda u: G(u)*u, -1, 1, **int_opts) # shift of the wave number\n        for kk, ww in dr:\n            kk += k0\n            ww += w0\n\n    return dr", "repo_name": "NuCO-UNM/nugas", "sub_path": "src/nugas/f2e0d1a/trivialwave.py", "file_name": "trivialwave.py", "file_ext": "py", "file_size_in_byte": 26113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "scipy.integrate.quad", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 26, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 43, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 119, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 176, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 194, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 213, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize_scalar", "line_number": 222, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 230, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 231, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 263, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 275, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize_scalar", "line_number": 284, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 296, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 297, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 319, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 324, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 325, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 331, "usage_type": "attribute"}, {"api_name": "scipy.integrate.quad", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 345, "usage_type": "attribute"}, {"api_name": "scipy.integrate.quad", "line_number": 346, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 351, "usage_type": "call"}, {"api_name": "scipy.optimize.root", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.cdouble", "line_number": 387, "usage_type": "attribute"}, {"api_name": "scipy.optimize.root", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 422, "usage_type": "call"}, {"api_name": "numpy.cdouble", "line_number": 422, "usage_type": "attribute"}, {"api_name": "scipy.integrate.quad", "line_number": 443, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 444, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 452, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 453, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 454, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 454, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 470, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 471, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 471, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 472, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 472, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 519, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 520, "usage_type": "call"}, {"api_name": "numpy.cdouble", "line_number": 520, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 533, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 534, "usage_type": "call"}, {"api_name": "numpy.cdouble", "line_number": 534, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 541, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 542, "usage_type": "call"}, {"api_name": "numpy.cdouble", "line_number": 542, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 548, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 549, "usage_type": "call"}, {"api_name": "numpy.cdouble", "line_number": 549, "usage_type": "attribute"}, {"api_name": "scipy.integrate.quad", "line_number": 557, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 558, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 582, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 583, "usage_type": "call"}, {"api_name": "numpy.cdouble", "line_number": 583, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 589, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 590, "usage_type": "call"}, {"api_name": "numpy.cdouble", "line_number": 590, "usage_type": "attribute"}, {"api_name": "scipy.integrate.quad", "line_number": 596, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 597, "usage_type": "call"}]}
{"seq_id": "37941275333", "text": "import sys\nfrom collections import deque\n\nN = int(input())\n\ndata = []\n\nfor _ in range(N):\n    data.append(int(sys.stdin.readline().rstrip()))\n\n# 핵심은 순서로 상쇄되는 값을 최소값을 넣어서 상쇄값 최소화\ndata.sort(reverse = True)\ndata = deque(data)\n\nans = 0\ni = 0\n\nwhile len(data) > 0:\n    if data[0] > i:\n        ans += data.popleft() - i\n        i += 1\n    else:\n        data.popleft()\n\nprint(ans)", "repo_name": "algoitssm/algoritm_sungryul", "sub_path": "Baekjoon/Greedy/1758_Greedy_알바생강호.py", "file_name": "1758_Greedy_알바생강호.py", "file_ext": "py", "file_size_in_byte": 423, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.stdin.readline", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 9, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 13, "usage_type": "call"}]}
{"seq_id": "22687108659", "text": "from django.conf import settings\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render\nfrom querystring_parser import parser\n\nfrom csss.setup_logger import Loggers\nfrom csss.views.context_creation.create_authenticated_contexts import \\\n    create_context_for_updating_github_mappings_and_permissions\nfrom csss.views.privilege_validation.list_of_officer_details_from_past_specified_terms import \\\n    get_list_of_officer_details_from_past_specified_terms\nfrom csss.views.request_validation import validate_request_to_update_github_permissions\nfrom csss.views.views import ERROR_MESSAGES_KEY\nfrom csss.views_helper import there_are_multiple_entries\nfrom resource_management.models import NonOfficerGithubMember, OfficerPositionGithubTeam, \\\n    OfficerPositionGithubTeamMapping\nfrom .resource_apis.github.github_api import GitHubAPI\n\nGITHUB_RECORD_KEY = 'record_id'\nGITHUB_USERNAME_KEY = 'user_name'\nLEGAL_NAME_KEY = 'legal_name'\nGITHUB_TEAM_KEY = 'github_team'\n\nTAB_STRING = 'administration'\n\n\ndef index(request):\n    \"\"\"\n    shows the main page for google drive permission management\n    \"\"\"\n    context = create_context_for_updating_github_mappings_and_permissions(request, tab=TAB_STRING)\n    if ERROR_MESSAGES_KEY in request.session:\n        context[ERROR_MESSAGES_KEY] = request.session[ERROR_MESSAGES_KEY].split(\"<br>\")\n        del request.session[ERROR_MESSAGES_KEY]\n    context['non_officer_github_member'] = NonOfficerGithubMember.objects.all().filter().order_by('id')\n    context['GITHUB_RECORD_KEY'] = GITHUB_RECORD_KEY\n    context['GITHUB_USERNAME_KEY'] = GITHUB_USERNAME_KEY\n    context['LEGAL_NAME_KEY'] = LEGAL_NAME_KEY\n    context['GITHUB_TEAM_KEY'] = GITHUB_TEAM_KEY\n    return render(request, 'resource_management/github_management.html', context)\n\n\ndef add_non_officer_to_github_team(request):\n    \"\"\"\n    takes in the specified user and team from the user and attempts to give them the request github team\n    membership\n    \"\"\"\n    logger = Loggers.get_logger()\n    logger.info(f\"[resource_management/github_views.py add_non_officer_to_github_team()] request.POST={request.POST}\")\n    validate_request_to_update_github_permissions(request)\n    github = GitHubAPI()\n    if github.connection_successful:\n        post_dict = parser.parse(request.POST.urlencode())\n        if there_are_multiple_entries(post_dict, GITHUB_USERNAME_KEY):\n\n            number_of_entries = len(post_dict[GITHUB_USERNAME_KEY])\n            logger.info(\n                f\"[resource_management/github_views.py add_non_officer_to_github_team()] \"\n                f\"{number_of_entries} multiple \"\n                f\"entries detected\"\n            )\n            for index in range(number_of_entries):\n                team_name = post_dict[GITHUB_TEAM_KEY][index]\n                user_name = post_dict[GITHUB_USERNAME_KEY][index]\n                name = post_dict[LEGAL_NAME_KEY][index]\n                logger.info(f\"[resource_management/github_views.py add_non_officer_to_github_team()] \"\n                            f\"adding user \\\"{name}\\\" with github username \\\"{user_name}\\\" to team \\\"{team_name}\\\"\")\n                success, error_message = github.validate_user(user_name)\n                if not success:\n                    if ERROR_MESSAGES_KEY in request.session:\n                        request.session[ERROR_MESSAGES_KEY] += '{}<br>'.format(error_message)\n                    else:\n                        request.session[ERROR_MESSAGES_KEY] = '{}<br>'.format(error_message)\n                    continue\n                github.create_team(team_name)\n                success, message = github.add_users_to_a_team([user_name], team_name)\n                if not success:\n                    if ERROR_MESSAGES_KEY in request.session:\n                        request.session[ERROR_MESSAGES_KEY] += '{}<br>'.format(message)\n                    else:\n                        request.session[ERROR_MESSAGES_KEY] = '{}<br>'.format(message)\n                    continue\n                NonOfficerGithubMember(\n                    team_name=team_name,\n                    username=user_name,\n                    legal_name=name\n                ).save()\n        else:\n            logger.info(\n                \"[resource_management/github_views.py add_non_officer_to_github_team()] \"\n                f\"only one user detected: {post_dict[GITHUB_USERNAME_KEY]}\"\n            )\n            team_name = post_dict[GITHUB_TEAM_KEY]\n            user_name = post_dict[GITHUB_USERNAME_KEY]\n            name = post_dict[LEGAL_NAME_KEY]\n            logger.info(f\"[resource_management/github_views.py add_non_officer_to_github_team()] \"\n                        f\"adding user \\\"{name}\\\" with github username \\\"{user_name}\\\" to team \\\"{team_name}\\\"\")\n            success, error_message = github.validate_user(user_name)\n            if not success:\n                if ERROR_MESSAGES_KEY in request.session:\n                    request.session[ERROR_MESSAGES_KEY] += '{}<br>'.format(error_message)\n                else:\n                    request.session[ERROR_MESSAGES_KEY] = '{}<br>'.format(error_message)\n                return HttpResponseRedirect(f'{settings.URL_ROOT}resource_management/github')\n            github.create_team(team_name)\n            success, message = github.add_users_to_a_team([user_name], team_name)\n            if not success:\n                if ERROR_MESSAGES_KEY in request.session:\n                    request.session[ERROR_MESSAGES_KEY] += '{}<br>'.format(message)\n                else:\n                    request.session[ERROR_MESSAGES_KEY] = '{}<br>'.format(message)\n                return HttpResponseRedirect(f'{settings.URL_ROOT}resource_management/github')\n            NonOfficerGithubMember(\n                team_name=team_name,\n                username=user_name,\n                legal_name=name\n            ).save()\n    return HttpResponseRedirect(f'{settings.URL_ROOT}resource_management/github')\n\n\ndef update_github_non_officer(request):\n    \"\"\"\n    updates the specified github team membership, either changes the username or the team name that is associated\n    with the membership\n    \"\"\"\n    logger = Loggers.get_logger()\n    logger.info(f\"[resource_management/github_views.py update_github_non_officer()] request.POST={request.POST}\")\n    validate_request_to_update_github_permissions(request)\n    github = GitHubAPI()\n    if github.connection_successful:\n        if 'action' in request.POST:\n            if request.POST['action'] == 'update':\n                success = False\n                error_message = None\n                logger.info(\"[resource_management/github_views.py update_github_non_officer()] processing an update\")\n                github_user = NonOfficerGithubMember.objects.get(id=request.POST[GITHUB_RECORD_KEY])\n                if github_user.username != request.POST[GITHUB_USERNAME_KEY] \\\n                        and github_user.team_name != request.POST[GITHUB_TEAM_KEY]:\n                    logger.info(\n                        f\"[resource_management/github_views.py update_github_non_officer()] ll the fields for user\"\n                        f\" {github_user.username} with access to team {github_user.team_name} are different.\"\n                    )\n                    github.remove_users_from_a_team([github_user.username], github_user.team_name)\n                    success, error_message = github.add_users_to_a_team([request.POST[GITHUB_USERNAME_KEY]],\n                                                                        request.POST[GITHUB_TEAM_KEY])\n                if github_user.team_name != request.POST[GITHUB_TEAM_KEY]:\n                    logger.info(\n                        f\"[resource_management/github_views.py update_github_non_officer()] team for user\"\n                        f\" {github_user.username} has been changed from {github_user.team_name} to \"\n                        f\"{request.POST[GITHUB_TEAM_KEY]}.\"\n                    )\n                    github.remove_users_from_a_team([github_user.username], github_user.team_name)\n                    success, error_message = github.add_users_to_a_team([github_user.username],\n                                                                        request.POST[GITHUB_TEAM_KEY])\n                if github_user.username != request.POST[GITHUB_USERNAME_KEY]:\n                    logger.info(\n                        \"[resource_management/github_views.py update_github_non_officer()] team for user\"\n                        f\" {github_user.username} with permission to {github_user.team_name} has been \"\n                        f\"changed to {request.POST[GITHUB_USERNAME_KEY]}.\"\n                    )\n                    github.remove_users_from_a_team([github_user.username], github_user.team_name)\n                    success, error_message = github.add_users_to_a_team([request.POST[GITHUB_USERNAME_KEY]],\n                                                                        github_user.team_name)\n                if success:\n                    github_user.team_name = request.POST[GITHUB_TEAM_KEY]\n                    github_user.username = request.POST[GITHUB_USERNAME_KEY]\n                    github_user.legal_name = request.POST[LEGAL_NAME_KEY]\n                    github_user.save()\n                else:\n                    if ERROR_MESSAGES_KEY in request.session:\n                        request.session[ERROR_MESSAGES_KEY] += '{}<br>'.format(error_message)\n                    else:\n                        request.session[ERROR_MESSAGES_KEY] = '{}<br>'.format(error_message)\n            elif request.POST['action'] == 'delete':\n                github_user = NonOfficerGithubMember.objects.get(id=request.POST[GITHUB_RECORD_KEY])\n                logger.info(\n                    \"[resource_management/github_views.py update_github_non_officer()] \"\n                    f\"removing {github_user.username}'s access to {github_user.team_name}\"\n                )\n                github.remove_users_from_a_team([github_user.username], github_user.team_name)\n                github_user.delete()\n    return HttpResponseRedirect(f'{settings.URL_ROOT}resource_management/github')\n\n\ndef create_github_perms():\n    \"\"\"\n    Example of users_to_grant_permission_to_github_officers_team\n    {\n        \"githubUsername1\" : [\n            \"team_1\", \"team_2\", \"team_3\"\n        ],\n        \"githubUsername2\" : [\n            \"team_4\", \"team_5\", \"team_6\"\n        ],\n        \"githubUsername3\" : [\n            \"team_7\", \"team_8\", \"team_9\"\n        ],\n    }\n    \"\"\"\n    logger = Loggers.get_logger()\n    users_to_grant_permission_to_github_officers_team = {\n        'team_names': []\n    }\n    github_teams = OfficerPositionGithubTeam.objects.all()\n    for github_team in github_teams:\n        github_team_name = github_team.team_name.lower()\n        users_to_grant_permission_to_github_officers_team['team_names'].append(github_team_name)\n        officer_positions_with_access_to_team = [\n            position.officer_position_mapping.position_name\n            for position in OfficerPositionGithubTeamMapping.objects.all().filter(\n                github_team=github_team\n            )\n        ]\n        officer_github_usernames = get_list_of_officer_details_from_past_specified_terms(\n            relevant_previous_terms=github_team.relevant_previous_terms,\n            position_names=officer_positions_with_access_to_team,\n            filter_by_github=True\n        )\n        for officer_github_username in officer_github_usernames:\n            officer_github_username = officer_github_username.lower()\n            if officer_github_username not in users_to_grant_permission_to_github_officers_team:\n                users_to_grant_permission_to_github_officers_team[officer_github_username] = []\n            if github_team_name not in users_to_grant_permission_to_github_officers_team[officer_github_username]:\n                users_to_grant_permission_to_github_officers_team[officer_github_username].append(github_team_name)\n\n    non_officer_users_with_access = NonOfficerGithubMember.objects.all()\n    logger.info(\n        \"[resource_management/github_views.py create_github_perms()] non_officer_users_with_access\"\n        f\" = {non_officer_users_with_access}\"\n    )\n    for github_membership_for_non_officer in non_officer_users_with_access:\n        github_username = github_membership_for_non_officer.username.lower()\n        github_team_name = github_membership_for_non_officer.team_name.lower()\n        if github_team_name not in users_to_grant_permission_to_github_officers_team['team_names']:\n            users_to_grant_permission_to_github_officers_team['team_names'].append(github_team_name)\n        if github_username != \"\":\n            if github_username not in users_to_grant_permission_to_github_officers_team.keys():\n                # if this person's github username is not in the dict\n                # users_to_grant_permission_to_github_officers_team yet\n                users_to_grant_permission_to_github_officers_team[github_username] = [\n                    github_team_name\n                ]\n            else:\n                # if this person's github username is in the dict\n                # users_to_grant_permission_to_github_officers_team but another team needs to be added\n                users_to_grant_permission_to_github_officers_team[github_username].append(\n                    github_team_name\n                )\n    logger.info(\n        \"[resource_management/github_views.py create_github_perms()] \"\n        \"users_to_grant_permission_to_github_officers_team\"\n        f\" = {users_to_grant_permission_to_github_officers_team}\"\n    )\n    return users_to_grant_permission_to_github_officers_team\n", "repo_name": "CSSS/csss-site", "sub_path": "csss-site/src/resource_management/views/github_views.py", "file_name": "github_views.py", "file_ext": "py", "file_size_in_byte": 13663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "41", "api": [{"api_name": "csss.views.context_creation.create_authenticated_contexts.create_context_for_updating_github_mappings_and_permissions", "line_number": 30, "usage_type": "call"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 31, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 32, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 33, "usage_type": "name"}, {"api_name": "resource_management.models.NonOfficerGithubMember.objects.all", "line_number": 34, "usage_type": "call"}, {"api_name": "resource_management.models.NonOfficerGithubMember.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "resource_management.models.NonOfficerGithubMember", "line_number": 34, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "csss.setup_logger.Loggers.get_logger", "line_number": 47, "usage_type": "call"}, {"api_name": "csss.setup_logger.Loggers", "line_number": 47, "usage_type": "name"}, {"api_name": "csss.views.request_validation.validate_request_to_update_github_permissions", "line_number": 49, "usage_type": "call"}, {"api_name": "resource_apis.github.github_api.GitHubAPI", "line_number": 50, "usage_type": "call"}, {"api_name": "querystring_parser.parser.parse", "line_number": 52, "usage_type": "call"}, {"api_name": "querystring_parser.parser", "line_number": 52, "usage_type": "name"}, {"api_name": "csss.views_helper.there_are_multiple_entries", "line_number": 53, "usage_type": "call"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 69, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 70, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 72, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 77, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 78, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 80, "usage_type": "name"}, {"api_name": "resource_management.models.NonOfficerGithubMember", "line_number": 82, "usage_type": "call"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 99, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 100, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 102, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 103, "usage_type": "call"}, {"api_name": "django.conf.settings.URL_ROOT", "line_number": 103, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 103, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 107, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 108, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 110, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 111, "usage_type": "call"}, {"api_name": "django.conf.settings.URL_ROOT", "line_number": 111, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 111, "usage_type": "name"}, {"api_name": "resource_management.models.NonOfficerGithubMember", "line_number": 112, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 117, "usage_type": "call"}, {"api_name": "django.conf.settings.URL_ROOT", "line_number": 117, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 117, "usage_type": "name"}, {"api_name": "csss.setup_logger.Loggers.get_logger", "line_number": 125, "usage_type": "call"}, {"api_name": "csss.setup_logger.Loggers", "line_number": 125, "usage_type": "name"}, {"api_name": "csss.views.request_validation.validate_request_to_update_github_permissions", "line_number": 127, "usage_type": "call"}, {"api_name": "resource_apis.github.github_api.GitHubAPI", "line_number": 128, "usage_type": "call"}, {"api_name": "resource_management.models.NonOfficerGithubMember.objects.get", "line_number": 135, "usage_type": "call"}, {"api_name": "resource_management.models.NonOfficerGithubMember.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "resource_management.models.NonOfficerGithubMember", "line_number": 135, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 169, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 170, "usage_type": "name"}, {"api_name": "csss.views.views.ERROR_MESSAGES_KEY", "line_number": 172, "usage_type": "name"}, {"api_name": "resource_management.models.NonOfficerGithubMember.objects.get", "line_number": 174, "usage_type": "call"}, {"api_name": "resource_management.models.NonOfficerGithubMember.objects", "line_number": 174, "usage_type": "attribute"}, {"api_name": "resource_management.models.NonOfficerGithubMember", "line_number": 174, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 181, "usage_type": "call"}, {"api_name": "django.conf.settings.URL_ROOT", "line_number": 181, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 181, "usage_type": "name"}, {"api_name": "csss.setup_logger.Loggers.get_logger", "line_number": 199, "usage_type": "call"}, {"api_name": "csss.setup_logger.Loggers", "line_number": 199, "usage_type": "name"}, {"api_name": "resource_management.models.OfficerPositionGithubTeam.objects.all", "line_number": 203, "usage_type": "call"}, {"api_name": "resource_management.models.OfficerPositionGithubTeam.objects", "line_number": 203, "usage_type": "attribute"}, {"api_name": "resource_management.models.OfficerPositionGithubTeam", "line_number": 203, "usage_type": "name"}, {"api_name": "resource_management.models.OfficerPositionGithubTeamMapping.objects.all", "line_number": 209, "usage_type": "call"}, {"api_name": "resource_management.models.OfficerPositionGithubTeamMapping.objects", "line_number": 209, "usage_type": "attribute"}, {"api_name": "resource_management.models.OfficerPositionGithubTeamMapping", "line_number": 209, "usage_type": "name"}, {"api_name": "csss.views.privilege_validation.list_of_officer_details_from_past_specified_terms.get_list_of_officer_details_from_past_specified_terms", "line_number": 213, "usage_type": "call"}, {"api_name": "resource_management.models.NonOfficerGithubMember.objects.all", "line_number": 225, "usage_type": "call"}, {"api_name": "resource_management.models.NonOfficerGithubMember.objects", "line_number": 225, "usage_type": "attribute"}, {"api_name": "resource_management.models.NonOfficerGithubMember", "line_number": 225, "usage_type": "name"}]}
{"seq_id": "26329166713", "text": "import os\nfrom typing import List, Optional\n\nENV_TFIMM_HOME = \"TFIMM_HOME\"\nENV_XDG_CACHE_HOME = \"XDG_CACHE_HOME\"\nDEFAULT_CACHE_DIR = \"~/.cache\"\n_tfimm_dir = None\n_model_cache = {}\n\n\ndef get_dir() -> str:\n    \"\"\"\n    Get the ``tfimm`` cache directory used for storing downloaded models & weights.\n\n    If ``tfimm.set_dir`` has not been called, default path is ``$TFIMM_HOME``, where\n    environment variable ``$TFIMM_HOME`` defaults to ``$XDG_CACHE_HOME/tfimm``.\n    ``$XDG_CACHE_HOME`` follows the X Design Group specifications of the Linux\n    filesystem layout, with a default value ``~/.cache`` if the environment variable is\n    not set.\n    \"\"\"\n    if _tfimm_dir is not None:\n        return _tfimm_dir\n\n    cache_home = os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR)\n    tfimm_home = os.path.join(cache_home, \"tfimm\")\n    tfimm_home = os.getenv(ENV_TFIMM_HOME, tfimm_home)\n    tfimm_home = os.path.expanduser(tfimm_home)\n    return tfimm_home\n\n\ndef set_dir(d: str):\n    \"\"\"\n    Optionally set the ``tfimm`` models directory used to save downloaded models &\n    weights.\n\n    Args:\n        d: Path to a local folder to save downloaded models & weights.\n    \"\"\"\n    global _tfimm_dir\n    _tfimm_dir = d\n\n\ndef set_model_cache(model_name: str, model_path: str):\n    \"\"\"\n    Sets the cache location for ``model_name`` to ``model_path``.\n\n    Args:\n        model_name: Model name. Same name that is passed to\n            :py:func:`~tfimm.create_model`.\n        model_path: Path, where the model is stored.\n    \"\"\"\n    _model_cache[model_name] = model_path\n\n\ndef clear_model_cache(model_name: str):\n    \"\"\"\n    Removes the cache entry for ``model_name``.\n\n    Args:\n        model_name: Model name. Same name that is passed to\n            :py:func:`~tfimm.create_model`.\n    \"\"\"\n    if model_name in _model_cache:\n        del _model_cache[model_name]\n\n\ndef list_cached_models() -> List[str]:\n    \"\"\"\n    Lists all models in model-specific cache.\n\n    Returns:\n        List of model names.\n    \"\"\"\n    return sorted(_model_cache.keys())\n\n\ndef cached_model_path(model_name: str) -> Optional[str]:\n    \"\"\"\n    Checks if the weights for model ``model_name`` are cached. If so, we return the\n    path to the weights, otherwise return ``None``. We check the model-speficif cache\n    first before looking in the general cache directory.\n\n    Args:\n        model_name: Model to be queried.\n    \"\"\"\n    if model_name in _model_cache:\n        return _model_cache[model_name]\n    else:\n        # Now look in the general cache\n        model_path = os.path.join(get_dir(), model_name)\n        if os.path.exists(model_path):\n            return model_path\n        else:\n            return None\n", "repo_name": "martinsbruveris/tensorflow-image-models", "sub_path": "tfimm/utils/cache.py", "file_name": "cache.py", "file_ext": "py", "file_size_in_byte": 2678, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 270, "dataset": "github-code", "pt": "40", "api": [{"api_name": "os.getenv", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 67, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 77, "usage_type": "name"}]}
{"seq_id": "31497593025", "text": "from _decimal import Decimal\n\nfrom rest_framework import status\nfrom rest_framework.reverse import reverse\n\nfrom food_and_beverage.tests.factories import FoodFactory, BeverageFactory\nfrom orders.enums import OrderPreparationSatus\nfrom orders.models import Order\nfrom orders.tests.factories import OrderFactory\nfrom payments.tests.factories import TipFactory\nfrom utils.tests.test_base import StaffApiTestBase\n\n\nclass TestOrderViewSet(StaffApiTestBase):\n    def setUp(self):\n        super().setUp()\n        self.order_1 = OrderFactory()\n        self.order_2 = OrderFactory()\n        self.food_1 = FoodFactory()\n        self.food_2 = FoodFactory()\n        self.beverage_1 = BeverageFactory()\n        self.beverage_2 = BeverageFactory()\n        self.order_1.food_set.add(self.food_1)\n        self.order_1.beverage_set.add(self.beverage_1)\n        self.order_1.save()\n        self.order_2.food_set.add(self.food_2)\n        self.order_2.beverage_set.add(self.beverage_2)\n        self.order_2.save()\n        self.other_waiter = self.order_1.waiter\n\n    def _get_order_request_data(self, order: Order) -> dict:\n        food_set = [food.id for food in order.food_set.all()]\n\n        beverage_set = [beverage.id for beverage in order.beverage_set.all()]\n        data = {\n            \"table_id\": order.table.id,\n            \"waiter_id\": order.waiter.id,\n            \"food_set_ids\": food_set,\n            \"beverage_set_ids\": beverage_set,\n            \"payment_status\": order.payment_status,\n            \"preparation_status\": order.preparation_status,\n            \"ordering_type\": order.ordering_type,\n        }\n        return data\n\n    def test_list_orders_ok(self):\n        token = self._generate_token_for_existing_user(staff_user=self.manager_user)\n        response = self.client.get(\n            path=reverse(\"order-list\"),\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n        )\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n        data = response.data\n        self.assertEqual(2, len(data))\n        order_response_data = data[0]\n        self.assertEqual(order_response_data[\"id\"], self.order_2.id)\n        self.assertEqual(\n            order_response_data[\"table\"][\"number\"],\n            self.order_2.table.number,\n        )\n        self.assertEqual(\n            order_response_data[\"waiter\"][\"username\"],\n            self.order_2.waiter.user.username,\n        )\n        self.assertEqual(\n            order_response_data[\"food_set\"][0][\"name\"],\n            self.food_2.name,\n        )\n        self.assertEqual(\n            order_response_data[\"beverage_set\"][0][\"name\"],\n            self.beverage_2.name,\n        )\n\n    def test_list_filtered_by_id(self):\n        token = self._generate_token_for_existing_user(staff_user=self.manager_user)\n        response = self.client.get(\n            path=reverse(\"order-list\"),\n            data={\"id\": self.order_1.id},\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n        )\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n        self.assertEqual(1, len(response.data))\n        self.assertEqual(self.order_1.id, response.data[0][\"id\"])\n\n    def test_list_filtered_by_table(self):\n        token = self._generate_token_for_existing_user(staff_user=self.manager_user)\n        response_1 = self.client.get(\n            path=reverse(\"order-list\"),\n            data={\"table\": self.order_1.table.id},\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n        )\n\n        self.assertEqual(response_1.status_code, status.HTTP_200_OK)\n        data_1 = response_1.json()\n        self.assertEqual(len(data_1), 1)\n        self.assertEqual(data_1[0][\"id\"], self.order_1.id)\n\n        other_order = OrderFactory()  # Order to be filtered out\n        response_2 = self.client.get(\n            path=reverse(\"order-list\"),\n            data={\"table__in\": f\"{self.order_1.table.id},{self.order_2.table.id}\"},\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n        )\n\n        self.assertEqual(response_2.status_code, status.HTTP_200_OK)\n        data_2 = response_2.json()\n        self.assertEqual(len(data_2), 2)\n        self.assertEqual(data_2[0][\"id\"], self.order_2.id)\n        self.assertEqual(data_2[1][\"id\"], self.order_1.id)\n\n    def test_list_filtered_tipped(self):\n        token = self._generate_token_for_existing_user(staff_user=self.manager_user)\n        TipFactory(order=self.order_1)\n        response = self.client.get(\n            path=reverse(\"order-list\"),\n            data={\"is_tipped\": True},\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n        )\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        data = response.json()\n        self.assertEqual(len(data), 1)\n        self.assertEqual(data[0][\"id\"], self.order_1.id)\n\n    def test_filter_by_total_price(self):\n        additional_food = FoodFactory()\n        self.order_1.food_set.add(additional_food)\n        self.order_1.save()\n        order_with_no_items = OrderFactory()\n        token = self._generate_token_for_existing_user(staff_user=self.manager_user)\n        with self.subTest(msg=\"test_exact\"):\n            response = self.client.get(\n                path=reverse(\"order-list\"),\n                data={\"items_total_price\": \"20.00\"},\n                HTTP_AUTHORIZATION=f\"Token {token.key}\",\n            )\n            self.assertEqual(response.status_code, status.HTTP_200_OK)\n            data = response.json()\n            self.assertEqual(len(data), 1)\n            self.assertEqual(data[0][\"id\"], self.order_2.id)\n\n        with self.subTest(msg=\"test_lt\"):\n            with self.subTest(msg=\"test_exact\"):\n                response = self.client.get(\n                    path=reverse(\"order-list\"),\n                    data={\"items_total_price__lt\": \"25.00\"},\n                    HTTP_AUTHORIZATION=f\"Token {token.key}\",\n                )\n                self.assertEqual(response.status_code, status.HTTP_200_OK)\n                data = response.json()\n                self.assertEqual(len(data), 1)\n                self.assertEqual(data[0][\"id\"], self.order_2.id)\n\n        with self.subTest(msg=\"test_lt\"):\n            with self.subTest(msg=\"test_exact\"):\n                response = self.client.get(\n                    path=reverse(\"order-list\"),\n                    data={\"items_total_price__gt\": \"25.00\"},\n                    HTTP_AUTHORIZATION=f\"Token {token.key}\",\n                )\n                self.assertEqual(response.status_code, status.HTTP_200_OK)\n                data = response.json()\n                self.assertEqual(len(data), 1)\n                self.assertEqual(data[0][\"id\"], self.order_1.id)\n\n    def test_list_orders_invalid_token(self):\n        response = self.client.get(\n            path=reverse(\"order-list\"),\n            HTTP_AUTHORIZATION=f\"Token incorrect_token\",\n        )\n        self.assertEqual(status.HTTP_401_UNAUTHORIZED, response.status_code)\n\n    def test_detail_order_ok(self):\n        token = self._generate_token_for_existing_user(staff_user=self.manager_user)\n        response = self.client.get(\n            path=reverse(\"order-detail\", kwargs={\"pk\": self.order_1.id}),\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n        )\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n        data = response.data\n        self.assertEqual(self.order_1.id, data[\"id\"])\n        self.assertEqual(\n            self.order_1.waiter.user.username,\n            data[\"waiter\"][\"username\"],\n        )\n        self.assertEqual(self.food_1.name, data[\"food_set\"][0][\"name\"])\n        self.assertEqual(\n            self.beverage_1.name,\n            data[\"beverage_set\"][0][\"name\"],\n        )\n        self.assertEqual(self.order_1.payment_status, data[\"payment_status\"])\n        self.assertEqual(\n            self.order_1.preparation_status,\n            data[\"preparation_status\"],\n        )\n        self.assertEqual(self.order_1.ordering_type, data[\"ordering_type\"])\n        self.assertEqual(Decimal(\"0.00\"), data[\"tip\"])\n\n    def test_detail_order_not_found(self):\n        token = self._generate_token_for_existing_user(staff_user=self.manager_user)\n        response = self.client.get(\n            path=reverse(\"order-detail\", kwargs={\"pk\": self.order_2.id + 100}),\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n        )\n        self.assertEqual(status.HTTP_404_NOT_FOUND, response.status_code)\n\n    def test_detail_invalid_token_provided(self):\n        response = self.client.get(\n            path=reverse(\"order-detail\", kwargs={\"pk\": self.order_2.id + 100}),\n            HTTP_AUTHORIZATION=f\"Token invalid_token\",\n        )\n        self.assertEqual(status.HTTP_401_UNAUTHORIZED, response.status_code)\n\n    def test_update_order_ok(self):\n        token = self._generate_token_for_existing_user(staff_user=self.order_1.waiter)\n        self.assertEqual(OrderPreparationSatus.NEW, self.order_1.preparation_status)\n        data = self._get_order_request_data(self.order_1)\n        data[\"preparation_status\"] = OrderPreparationSatus.PREPARING\n        response = self.client.put(\n            path=reverse(\"order-detail\", kwargs={\"pk\": self.order_1.id}),\n            data=data,\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n            content_type=\"application/json\",\n        )\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n        self.order_1.refresh_from_db()\n        self.assertEqual(\n            OrderPreparationSatus.PREPARING,\n            self.order_1.preparation_status,\n        )\n\n    def test_update_order_by_manager(self):\n        token = self._generate_token_for_existing_user(staff_user=self.manager_user)\n        self.assertEqual(OrderPreparationSatus.NEW, self.order_1.preparation_status)\n        data = self._get_order_request_data(self.order_1)\n        data[\"preparation_status\"] = OrderPreparationSatus.PREPARING\n        response = self.client.put(\n            path=reverse(\"order-detail\", kwargs={\"pk\": self.order_1.id}),\n            data=data,\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n            content_type=\"application/json\",\n        )\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n\n    def test_update_order_no_permission(self):\n        token = self._generate_token_for_existing_user(staff_user=self.waiter_user)\n        data = self._get_order_request_data(self.order_1)\n        response = self.client.put(\n            path=reverse(\"order-detail\", kwargs={\"pk\": self.order_1.id}),\n            data=data,\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n            content_type=\"application/json\",\n        )\n        self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code)\n\n    def test_update_order_not_all_data_provided(self):\n        token = self._generate_token_for_existing_user(staff_user=self.order_1.waiter)\n        data = self._get_order_request_data(self.order_1)\n        data.pop(\"table_id\")\n        response = self.client.put(\n            path=reverse(\"order-detail\", kwargs={\"pk\": self.order_1.id}),\n            data=data,\n            HTTP_AUTHORIZATION=f\"Token {token.key}\",\n            content_type=\"application/json\",\n        )\n        self.assertEqual(status.HTTP_400_BAD_REQUEST, response.status_code)\n", "repo_name": "ARROM2405/online-waiter", "sub_path": "orders/tests/tests_staff_user_api.py", "file_name": "tests_staff_user_api.py", "file_ext": "py", "file_size_in_byte": 11154, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "utils.tests.test_base.StaffApiTestBase", "line_number": 14, "usage_type": "name"}, {"api_name": "orders.tests.factories.OrderFactory", "line_number": 17, "usage_type": "call"}, {"api_name": "orders.tests.factories.OrderFactory", "line_number": 18, "usage_type": "call"}, {"api_name": "food_and_beverage.tests.factories.FoodFactory", "line_number": 19, "usage_type": "call"}, {"api_name": "food_and_beverage.tests.factories.FoodFactory", "line_number": 20, "usage_type": "call"}, {"api_name": "food_and_beverage.tests.factories.BeverageFactory", "line_number": 21, "usage_type": "call"}, {"api_name": "food_and_beverage.tests.factories.BeverageFactory", "line_number": 22, "usage_type": "call"}, {"api_name": "orders.models.Order", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 49, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 52, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 52, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 77, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 81, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 81, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 88, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 93, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 93, "usage_type": "name"}, {"api_name": "orders.tests.factories.OrderFactory", "line_number": 98, "usage_type": "call"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 100, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 105, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 105, "usage_type": "name"}, {"api_name": "payments.tests.factories.TipFactory", "line_number": 113, "usage_type": "call"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 115, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 119, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 119, "usage_type": "name"}, {"api_name": "food_and_beverage.tests.factories.FoodFactory", "line_number": 125, "usage_type": "call"}, {"api_name": "orders.tests.factories.OrderFactory", "line_number": 128, "usage_type": "call"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 132, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 136, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 136, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 144, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 148, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 148, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 156, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 160, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 160, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 167, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 170, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 170, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 175, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 178, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 178, "usage_type": "name"}, {"api_name": "_decimal.Decimal", "line_number": 196, "usage_type": "call"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 201, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 204, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 204, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 208, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 211, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 211, "usage_type": "name"}, {"api_name": "orders.enums.OrderPreparationSatus.NEW", "line_number": 215, "usage_type": "attribute"}, {"api_name": "orders.enums.OrderPreparationSatus", "line_number": 215, "usage_type": "name"}, {"api_name": "orders.enums.OrderPreparationSatus.PREPARING", "line_number": 217, "usage_type": "attribute"}, {"api_name": "orders.enums.OrderPreparationSatus", "line_number": 217, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 219, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 224, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 224, "usage_type": "name"}, {"api_name": "orders.enums.OrderPreparationSatus.PREPARING", "line_number": 227, "usage_type": "attribute"}, {"api_name": "orders.enums.OrderPreparationSatus", "line_number": 227, "usage_type": "name"}, {"api_name": "orders.enums.OrderPreparationSatus.NEW", "line_number": 233, "usage_type": "attribute"}, {"api_name": "orders.enums.OrderPreparationSatus", "line_number": 233, "usage_type": "name"}, {"api_name": "orders.enums.OrderPreparationSatus.PREPARING", "line_number": 235, "usage_type": "attribute"}, {"api_name": "orders.enums.OrderPreparationSatus", "line_number": 235, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 237, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 242, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 242, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 248, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 253, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 253, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 260, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 265, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 265, "usage_type": "name"}]}
{"seq_id": "12731859637", "text": "#!/usr/bin/python3\n'''\nFunctions for repairing ISBNs\nFunctions for looking up on Google API - use isbn_safe_lookup - it does extra check if the API is working\n'''\n\nimport logging\n\n\nlogger = logging.getLogger(\"book_engine\")\n\n\ndef isbn_strip_non_digits(isbn: str) -> str:\n    '''\n    Some ISBNs include '-', remove all non-digits, except \"X\" and \"x\", which might be part of ISBN\n    '''\n    import re\n    return re.sub('[^0-9X]','', isbn.upper())\n\n\ndef isbn_add_leading_zeroes(isbn: str) -> str:\n    '''\n    Leading zeroes might get lost in \"str\" -> \"int\" conversion\n    '''\n    len_isbn = len(isbn)\n    if len_isbn < 10:\n        return \"0\"*(10-len_isbn)+isbn # add leading zeroes\n    elif 10 < len_isbn < 13:\n        return \"0\"*(13-len_isbn)+isbn # add leading zeroes\n    return isbn\n\n\ndef isbn_prepare(isbn: str) -> str:\n    '''\n    Strip non digits -> add leading zeroes, if necessary\n    '''\n    return isbn_add_leading_zeroes(isbn_strip_non_digits(isbn))\n\n\ndef isbn_lookup(isbn: str, api_key: str=None) -> tuple:\n    '''\n    Function that takes ISBN and check if it is in Google database, return book title and author\n    '''\n    import urllib.request\n    import urllib.error\n    import json\n\n    base_api_link = \"https://www.googleapis.com/books/v1/volumes?q=isbn:\"\n    \n    # Try to fix corrupted ISBN\n    isbn = isbn_prepare(isbn)\n\n\n    # Add api_key if we have it\n    if api_key:\n        api_key_interface = \"&key=\"\n        api_link = base_api_link + isbn + api_key_interface + api_key\n    else:\n        api_link = base_api_link + isbn\n    \n\n    # Fetch JSON from Google API\n    try:\n        with urllib.request.urlopen(api_link) as f:\n            text = f.read()\n        decoded_text = text.decode(\"utf-8\")\n        obj = json.loads(decoded_text) # deserializes decoded_text to a Python object\n        volume_info = obj[\"items\"][0]\n        title = volume_info[\"volumeInfo\"][\"title\"]\n        authors = \",\".join(obj[\"items\"][0][\"volumeInfo\"][\"authors\"])\n        try:\n            return title, authors\n        except TypeError:\n            logger.critical('Title, authors not found...')\n            raise ConnectionError\n\n\n    except urllib.error.HTTPError:\n        logger.critical('HTTP error, retrying...')\n        raise ConnectionError\n\n    except urllib.error.ContentTooShortError:\n        logger.critical('Error: truncated data, retrying...')\n        raise ConnectionError\n\n    # Base exception\n    except urllib.error.URLError:\n        logger.critical('URLError error, retrying...')\n        raise ConnectionError\n\n    except UnboundLocalError:\n        logger.critical('UnboundLocalError error, retrying...')\n        raise ConnectionError\n\n    # ISBN not found\n    except KeyError:\n        logger.critical('ISBN not found...')\n        raise ConnectionError\n\n\ndef isbn_safe_lookup(isbn: str, api_key: str=None) -> tuple:\n    '''\n    Does regular isbn_lookup, if it fails, it keeps trying to fetch book that is known to exist as confirmation\n    '''\n\n    try:\n        # ISBN found - no problem, pass it over\n        return isbn_lookup(isbn, api_key)\n    \n    \n    except ConnectionError:\n\n        # isbn_lookup threw exception, lets see if book, that we know exists can be found via API\n        correct_isbn = \"0345417623\"\n        correct_response = ('Timeline', 'Michael Crichton')\n        correct_isbn_result = isbn_lookup(correct_isbn, api_key)\n\n        # isbn_lookup found the test book, high chance of wrong ISBN\n        if correct_isbn_result == correct_response:\n            logger.critical(\"Looks like API works, but the ISBN is wrong\")\n            raise LookupError\n\n        # isbn_lookup did NOT found the test book, API is broken, keep trying until it works\n        else:\n            while True:\n                logger.critical(\"Trying to establish API connection...\")\n                correct_isbn_result = isbn_lookup(correct_isbn, api_key)\n                if correct_isbn_result == correct_response:\n                    return isbn_lookup(isbn, api_key)\n", "repo_name": "TomasHricina/book_engine", "sub_path": "book_engine/src/helpers/isbn_helpers.py", "file_name": "isbn_helpers.py", "file_ext": "py", "file_size_in_byte": 3974, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 18, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 64, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 64, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 64, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 67, "usage_type": "call"}, {"api_name": "urllib.request.error", "line_number": 78, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 78, "usage_type": "name"}, {"api_name": "urllib.request.error", "line_number": 82, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 82, "usage_type": "name"}, {"api_name": "urllib.request.error", "line_number": 87, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 87, "usage_type": "name"}]}
{"seq_id": "73123688763", "text": "import time\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom pytorch_grad_cam.utils.image import show_cam_on_image\nfrom pytorch_grad_cam.utils.model_targets import SemanticSegmentationTarget\nfrom torch.nn import Conv2d\n\nfrom model.unet_model import UNet\nfrom model.enet import ENet\nfrom model.Models import AttU_Net, AttU_Net_min\nfrom utils.dataset import ISBI_Loader\nfrom torch import optim\nimport torch.nn as nn\nimport torch\nfrom tqdm import tqdm\nfrom torchvision.models.segmentation import fcn_resnet50, deeplabv3_resnet50, lraspp_mobilenet_v3_large\n\n# from torch.utils.tensorboard import SummaryWriter\nimport numpy as np\nfrom pytorch_grad_cam import GradCAM\n\n\ndef train_net(net, device, data_path, epochs=40, batch_size=1, lr=0.00001):\n    # 加载训练集\n    isbi_dataset = ISBI_Loader(data_path)\n    print(isbi_dataset)\n    per_epoch_num = len(isbi_dataset) / batch_size\n\n\n    train_loader = torch.utils.data.DataLoader(dataset=isbi_dataset,\n                                               batch_size=batch_size,\n                                               shuffle=True)\n    # 定义RMSprop算法\n    optimizer = optim.RMSprop(net.parameters(), lr=lr, weight_decay=1e-8, momentum=0.9)\n    # 定义Loss算法\n    criterion = nn.BCEWithLogitsLoss()\n    # best_loss统计，初始化为正无穷\n    best_loss = float('inf')\n    # 训练epochs次\n    #target_layers = [net.down4.maxpool_conv[-1]]\n    # writer = SummaryWriter('logs/minpool-logsigmoid')\n    loss2=[]\n    starttime=time.time()\n    with tqdm(total=epochs*per_epoch_num) as pbar:\n        for epoch in range(epochs):\n            # 训练模式\n            net.train()\n            # 按照batch_size开始训练\n            loss1=0\n            for image, label in train_loader:\n                optimizer.zero_grad()\n                #plt.imshow(np.transpose(image.cpu()[1], (1, 2, 0)), interpolation='nearest',cmap='gray')\n                # 将数据拷贝到device中\n                image = image.to(device=device, dtype=torch.float32)\n                #plt.imshow(np.transpose(image.cpu()[1], (1, 2, 0)), interpolation='nearest', cmap='gray')\n\n                # print(image.size())\n                # plt.imshow(image.cpu()[1][0],cmap='gray')\n                #plt.show()\n\n                label = label.to(device=device, dtype=torch.float32)\n                # 使用网络参数，输出预测结果\n                pred = net(image)\n\n                # 计算loss\n                #loss = criterion(pred['out'], label)\n                loss = criterion(pred, label)\n                #losses.append(loss)\n                loss1=loss1+loss.item()\n                # writer.add_images('raw_images',image, epoch)\n                # writer.add_images('pred_images', pred, epoch)\n                #\n                # writer.add_images('labeled_images', label, epoch)\n\n                #print(losses)\n                # print('{}/{}：Loss/train'.format(epoch + 1, epochs), loss.item())\n                # 保存loss值最小的网络参数\n                if loss1 < best_loss:\n                    best_loss = loss1\n                    torch.save(net.state_dict(), 'best_model.pth')\n                # 更新参数\n                loss.backward()\n                optimizer.step()\n                pbar.update(1)\n            # writer.add_scalar('loss', loss1, epoch)\n            loss2.append(loss1)\n    # writer.close()\n    endtime = time.time()\n\n\n    dict = {'loss': loss2}\n    dict = pd.DataFrame(dict)\n    dict.to_csv('./results/train_loss.csv')\n    print('trainingtime:', endtime - starttime)\n\n\nif __name__ == \"__main__\":\n    # 选择设备，有cuda用cuda，没有就用cpu\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n    '''\n    Choose a deep learning method:\n\n    \n\n    1. net = AttU_Net(img_ch=1, output_ch=1)\n\n    2. net = AttU_Net_min(img_ch=1, output_ch=1)\n    \n    3. net = UNet(n_channels=1, n_classes=1)\n\n    4. net = fcn_resnet50(num_classes=1)\n\n    5.  net = deeplabv3_resnet50(num_classes= 1)\n\n    6. net = lraspp_mobilenet_v3_large(num_classes=1)\n\n\n\n    '''\n    # 加载网络，图片单通道1，分类为1。\n    # original net\n    # net = UNet(n_channels=1, n_classes=1)  # todo edit input_channels n_classes\n    # attention unet\n    # net = AttU_Net(img_ch=1, output_ch=1)\n    # FCN\n    # net = fcn_resnet50(num_classes=1)\n#-------------------------\n    # net = deeplabv3_resnet50(num_classes= 1)\n\n    # net = fcn_resnet50(num_classes=1)\n    net = AttU_Net(img_ch=1, output_ch=1)\n\n\n    # net = lraspp_mobilenet_v3_large(num_classes=1)\n    # #\n    # # # net.classifier._modules['6'] = nn.Linear(4096, 4)#for vgg16, alexnet\n    # net.backbone.conv1 = Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)  # for vgg16, alexnet\n    #\n    #net.backbone._modules['0']._modules['0'] = Conv2d(1, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n\n\n#--------------------------\n    # net.to(device)\n\n\n\n    # 将网络拷贝到deivce中\n    net.to(device=device)\n    # 指定训练集地址，开始训练\n    data_path = \"./images/cracks\" # todo: your training datasets\n    '''\n    four choices: \n    \"./images/cracks\"\n    \"./images/cracks_tradition\"\n    \"./images/cracks_DCGAN\"\n    \"./images/cracks_APCGAN\"\n    '''\n    train_net(net, device, data_path, epochs=300, batch_size=16)\n", "repo_name": "tjboise/APCGAN-AttuNet", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 5324, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "utils.dataset.ISBI_Loader", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.optim.RMSprop", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "time.time", "line_number": 44, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 81, "usage_type": "call"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 100, "usage_type": "attribute"}, {"api_name": "model.Models.AttU_Net", "line_number": 133, "usage_type": "call"}]}
{"seq_id": "14158520063", "text": "import cv2\nimport pytesseract\n\npytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'\n\ndef preprocess_image(img):\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    blur = cv2.GaussianBlur(gray, (3, 3), 0)\n    thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]\n    return thresh\n\ndef extract_text(img):\n    cards_text = []\n    processed = preprocess_image(img)\n    contours, hierarchy = cv2.findContours(processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n    for contour in contours:\n        x, y, w, h = cv2.boundingRect(contour)\n        if w > 50 and h > 50:\n            card = img[y:y+h, x:x+w]\n            card_text = pytesseract.image_to_string(card)\n            cards_text.append(card_text.strip())\n    return cards_text\n\nfilename = 'sample_cards.jpg'\nimg = cv2.imread(filename)\ncards_text = extract_text(img)\nprint(cards_text)\n", "repo_name": "aalberty/mtg", "sub_path": "ocr2.py", "file_name": "ocr2.py", "file_ext": "py", "file_size_in_byte": 876, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pytesseract.pytesseract", "line_number": 4, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 17, "usage_type": "call"}, {"api_name": "pytesseract.image_to_string", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "86449155232", "text": "import requests\nfrom lxml import etree\nimport csv\nimport codecs\nimport time\nimport random\nimport pymysql\nfrom fake_useragent import UserAgent\n# ++++\ntry:\n    # 数据存储 mysql\n    conn = pymysql.connect(host='localhost', port=3306, user=\"root\", password=\"914673123\", database=\"xiaoshuo\")\n    cur = conn.cursor()\n    print(\"数据库 ok\")\n    start = int(input(\"请输入起始页：\"))\n    end = int(input(\"请输入结束页：\"))\n    tb_name = input(\"请输入贴吧名字：\")\n    # 贴吧后边是 f\n    url = 'https://tieba.baidu.com/f'\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (compatible; WOW64; MSIE 10.0; Windows NT 6.2)'\n    }\n    for page in range(start, end + 1):\n        print('page:', page)\n        params = {\n            'kw': tb_name,\n            'ie': 'utf-8',\n            'pn': (page - 1) * 50\n        }\n        response = requests.get(url, params=params, headers=headers)\n        html = response.text\n\n        # 二.数据提取\n        html = etree.HTML(html)  # 二.内容解析 解析text\n        ls = html.xpath('//li[@class=\" j_thread_list clearfix\"]')\n        print(len(ls))\n        for each in ls:\n            # 标题\n            title = each.xpath('.//a[@class =\"j_th_tit \"]/text()')[0]\n            title = str(title)\n            print(\"标题\", title)\n            print(type(title))\n\n            # 回复数 .strip()移除首位空格\n            answer_count = each.xpath('.//span[@class =\"threadlist_rep_num center_text\"]/text()')[0].strip()\n            print(\"回复数\", answer_count)\n\n            # 作者\n            author = each.xpath('.//span[@class =\"frs-author-name-wrap\"]/a/text()')[0].strip()\n            print(\"作者\", author)\n\n            # 链接\n            url_t = \"https://tieba.baidu.com\" + each.xpath('.//a[@class =\"j_th_tit \"]/@href')[0]\n            print(\"链接\", url_t)\n\n            # # 数据存储.csv格式codecs.\n            # # 第一步 打开\n            # with codecs.open('./data/tieba_' + tb_name + '.csv', 'a', encoding='utf-8') as file:\n            #     # 第二步 对csv操作，需要生成csv的writer对象\n            #     wr = csv.writer(file)\n            #     # 一般写法\n            #     # wr.writerow([title, t3, t1, t2)\n            #\n            #     # 优化写法\n            #     wr.writerow(f\"标题：{title}\")\n            #     wr.writerow(f\"链接：{answer_count}\")\n            #     wr.writerow(f\"作者：{author}\")\n            #     wr.writerow(f\"人数：{url_t}\")\n            #     wr.writerow(\"######################################\")\n            #     wr.writerow([title, answer_count, author, url_t])\n\n            sql = \"insert into tieba_one VALUE (0,%s,%s,%s,%s)\"\n            params = [title, answer_count, author, url_t]\n            cur.execute(sql, params)\n            conn.commit()\n        time.sleep(random.random())\n    time.sleep(random.random())\n\n\nexcept Exception as e:\n    print(e)\n", "repo_name": "A1079146598/pc_pc", "sub_path": "requests_project/exp_百度贴吧.py", "file_name": "exp_百度贴吧.py", "file_ext": "py", "file_size_in_byte": 2909, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pymysql.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 34, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 34, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 76, "usage_type": "call"}, {"api_name": "random.random", "line_number": 76, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "random.random", "line_number": 77, "usage_type": "call"}]}
{"seq_id": "11197467676", "text": "import json\nfrom hashlib import md5\nfrom urllib.request import Request\nfrom urllib.request import urlopen\n\nfrom bs4 import BeautifulSoup\nfrom tqdm import tqdm\n\n\n# read the make and model from a file\n# file is in format of:\n# make,model\ndef readCars(carFile):\n    cars = dict()\n    mm = open(carFile, 'r')\n    allmm = mm.readlines()\n    mm.close()\n    for item in allmm:\n        me, ml = item.split(',', 1)\n        make = me.strip()\n        model = ml.strip()\n\n        if make not in cars:\n            cars[make] = set()\n\n        cars[make].add(model)\n    return cars\n\n\n# read the acceptable colors from a file\n# file is in the format of:\n# color\ndef readColors(colorFile):\n    colors = list()\n    c = open(colorFile, 'r')\n    allc = c.readlines()\n    c.close()\n    for i in allc:\n        colors.append(i.strip())\n    return colors\n\n\n# download a file with a given user-agent string\ndef get_soup(url, header):\n    # return BeautifulSoup(urllib2.urlopen(urllib2.Request(url,\n    # headers=header)), 'html.parser')\n    return BeautifulSoup(urlopen(Request(url, headers=header)), 'html.parser')\n\n\n# format item metadata\ndef makeLine(color, make, model, img, Type):\n    d = dict()\n    d['make'] = make\n    d['model'] = model\n    d['color'] = color\n    text = '' + color + make + model + img\n    m = md5()\n    m.update(text.encode('utf-8'))\n    d['hash'] = m.hexdigest()\n    fname = '{0}/{1}/{2}.{3}'.format(color, make, d['hash'], Type)\n    d['filename'] = fname.replace(' ', '_')\n    d['url'] = img\n    return d\n\n\n# for a specific make model and color of car attempt to get num images\ndef getCAR(color, makeIn, modelIn, num, outFile, outError):\n    make = makeIn.replace(' ', '+')\n    model = modelIn.replace(' ', '+')\n    query = '{0}+{1}+{2}'.format(color, make, model)\n    # query with no usage rights\n    # url = 'https://www.google.co.in/search?q=' + query + '&source=lnms&tbm=isch'\n\n    # return images with appropriate usage rights\n    url = 'https://www.google.co.in/search?q=' + query + '&tbs=sur:fc&tbm=isch'\n    header = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36'}\n    try:\n        soup = get_soup(url, header)\n        ActualImages = []\n        for a in soup.find_all('div', {'class': 'rg_meta'}):\n            link, Type = json.loads(a.text)['ou'], json.loads(a.text)['ity']\n            ActualImages.append((link, Type))\n\n        for i, (img, Type) in enumerate(ActualImages[:num]):\n            if Type is not None:\n                # write out where to get the image from\n                data = makeLine(color, makeIn, modelIn, img, Type)\n                outFile.write(json.dumps(data) + '\\n')\n\n    except:\n        # spout some error messages when things go poorly\n        data = makeLine(color, makeIn, modelIn, img, 'FAIL')\n        outError.write(json.puts(data) + '\\n')\n\n\ndef main(imagesPerMakeModel=100):\n    cars = readCars('cars')\n    colors = readColors('colors')\n\n    outFile = open('dataset', 'w')\n    outError = open('errorset', 'w')\n\n    for color in tqdm(colors):\n        for make in cars.keys():\n            for model in cars[make]:\n                # time.sleep(2)\n                getCAR(\n                    color,\n                    make,\n                    model,\n                    imagesPerMakeModel,\n                    outFile,\n                    outError)\n                outFile.flush()\n                outError.flush()\n\n    outFile.close()\n    outError.close()\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "d-grossman/scrapeCars", "sub_path": "scripts/gis.py", "file_name": "gis.py", "file_ext": "py", "file_size_in_byte": 3528, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 35, "dataset": "github-code", "pt": "41", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 47, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 47, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 47, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 57, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 82, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 89, "usage_type": "call"}, {"api_name": "json.puts", "line_number": 94, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 104, "usage_type": "call"}]}
{"seq_id": "22673042465", "text": "from django import forms\nfrom .models import Item\n\nfrom menu.models import ContactDetails\n\n\nclass ItemForm(forms.ModelForm):\n    class Meta:\n        model = Item\n        fields = [\n            'name',\n            'contact',\n            'image',\n            'public'\n\n        ]\n\n    def __init__(self, user=None, *args, **kwargs):\n        print(user)\n        # print(kwargs.pop('instants'))\n        super(ItemForm, self).__init__(*args, **kwargs)\n        self.fields['contact'].queryset = ContactDetails.objects.filter(owner=user)\n\n", "repo_name": "lauramott/MemoryAssistant", "sub_path": "facescan/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.forms.ModelForm", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "models.Item", "line_number": 9, "usage_type": "name"}, {"api_name": "menu.models.ContactDetails.objects.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "menu.models.ContactDetails.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "menu.models.ContactDetails", "line_number": 22, "usage_type": "name"}]}
{"seq_id": "40705871785", "text": "from dataclasses import fields\nfrom django import forms\nfrom blog.models import Post,Comment\n\nclass PostForm(forms.ModelForm):\n    class Meta():\n        model=Post\n        fields=('title','short_desc','text','image')\n        widgets = {\n            'title': forms.TextInput(attrs={'class': 'textinputclass'}),\n            'text': forms.Textarea(attrs={'class': 'editable medium-editor-textarea postcontent'}),\n            'short_desc': forms.Textarea(attrs={'class': 'editable medium-editor-textarea postcontent'}),\n\n        }\nclass CommentForm(forms.ModelForm):\n    class Meta():\n        model=Comment\n        fields=('text',)\n        widgets = {\n            # 'author': forms.TextInput(attrs={'class': 'textinputclass'}),\n            'text': forms.Textarea(attrs={'class': 'editable medium-editor-textarea'}),\n        }\n", "repo_name": "mushaid01/aws_port", "sub_path": "blog/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 822, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "django.forms.ModelForm", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "blog.models.Post", "line_number": 7, "usage_type": "name"}, {"api_name": "dataclasses.fields", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 10, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 11, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 11, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 15, "usage_type": "name"}, {"api_name": "blog.models.Comment", "line_number": 17, "usage_type": "name"}, {"api_name": "dataclasses.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "33759237422", "text": "from WordleGuessScorer import WordleGuessScorer\r\nfrom CorpusCreator import CORPUS_OUT_PATH, GUESSES_OUT_PATH\r\nfrom enum import Enum\r\nimport re\r\nimport pickle\r\nimport random\r\n\r\nclass WordleGuesser:\r\n    class Color(Enum):\r\n        GRAY = 0\r\n        YELLOW = 1\r\n        GREEN = 2\r\n    \r\n    def __init__(self, corpus, silentInit=False):\r\n        self.originalCorpus = corpus\r\n        self.letterPools = self._initLetterPools()\r\n        self.yellowConstraints = set()\r\n        self.corpus = self._getFiveLetterWordCorpus(corpus.copy())\r\n        if not silentInit:\r\n            self.printBestGuesses(5)\r\n\r\n    # Getters\r\n    def getCorpus(self):\r\n        return self.corpus\r\n\r\n    def getPools(self):\r\n        return self.letterPools\r\n\r\n    def getYellowConstraints(self):\r\n        return self.yellowConstraints\r\n    \r\n    def getScorer(self):\r\n        return WordleGuessScorer(self.corpus, self.letterPools, self.yellowConstraints)\r\n\r\n    # Public Methods\r\n    def machineGuess(self, word, letterColors):\r\n        for i, char in enumerate(word):\r\n            if letterColors[i] == self.Color.GRAY:\r\n                self.addGrayLetterConstraint(char)\r\n            elif letterColors[i] == self.Color.YELLOW:\r\n                self.addYellowLetterConstraint(i, char)\r\n            else:\r\n                self.addGreenLetterConstraint(i, char)\r\n        \r\n        self.corpus = self.filterCorpus(self.corpus)\r\n    \r\n    def makeGuess(self, word):\r\n        if len(word) != 5:\r\n            print(\"Guess must be 5 letters\")\r\n            return\r\n        \r\n        print(f\"You guessed {word}.\")\r\n        i = 0\r\n        while i < 5:\r\n            char = word[i]\r\n            color = input(f\"What color was {char}?\").lower()\r\n            if color in ['gray', 'grey']:\r\n                self.addGrayLetterConstraint(char)\r\n            elif color == 'yellow':\r\n                self.addYellowLetterConstraint(i, char)\r\n            elif color == 'green':\r\n                self.addGreenLetterConstraint(i, char)\r\n            else:\r\n                print(\"Sorry, that's not a valid color. Choose gray, yellow, or green\")\r\n                continue\r\n            i += 1\r\n\r\n        self.corpus = self.filterCorpus(self.corpus)\r\n        print(f\"There are {len(self.corpus)} possible words left.\")\r\n        self.printBestGuesses(5)\r\n\r\n    def getCorpusSize(self):\r\n        return len(self.corpus)\r\n\r\n    def reset(self, silentInit=False):\r\n        self.__init__(self.originalCorpus, silentInit)\r\n\r\n    def printPoolsAndConstraints(self):\r\n        for pool in self.letterPools:\r\n            print(''.join(pool))\r\n        print(f\"Yellow: {''.join(self.yellowConstraints)}\")\r\n\r\n    # Public - Guess methods\r\n    def printBestGuesses(self, numGuesses):\r\n        guesses = self.getBestNGuesses(numGuesses)\r\n        print(f\"Best {numGuesses} guesses:\")\r\n        for word, score in guesses:\r\n            print(f\"{word}: {score:.3f}\")\r\n\r\n    def getBestGuess(self):\r\n        '''return highest scoring word. Chooses randomly when multiple words are best'''\r\n        scorer = self.getScorer()\r\n        bestWords, bestScore = [], 0\r\n        for word, score in scorer.calculateSortedScoredWords():\r\n            if bestScore == 0:\r\n                bestScore = score\r\n            \r\n            if score == bestScore:\r\n                bestWords.append(word)\r\n            else:\r\n                break\r\n        \r\n        return random.choice(bestWords)\r\n\r\n    def getBestNGuesses(self, n):\r\n        scorer = self.getScorer()\r\n        if len(self.corpus) < n:\r\n            n = len(self.corpus)\r\n        return scorer.calculateSortedScoredWords()[:n]\r\n\r\n    # Supporting, Public Methods\r\n    def addGreenLetterConstraint(self, index, letter):\r\n        self.letterPools[index] = set({letter})\r\n\r\n    def addYellowLetterConstraint(self, index, letter):\r\n        try:\r\n            self.letterPools[index].remove(letter)\r\n            self.yellowConstraints.add(letter)\r\n        except KeyError:\r\n            pass\r\n\r\n    def addGrayLetterConstraint(self, letter):\r\n        for pool in self.letterPools:\r\n            try:\r\n                pool.remove(letter)\r\n            except KeyError:\r\n                pass\r\n\r\n    def filterCorpus(self, corpus):\r\n        patterns = self._assemblePatterns()\r\n        filtered = corpus\r\n        for pattern in patterns:\r\n            filtered = set(filter(lambda word: bool(pattern.match(word)), filtered))\r\n        return set(filtered)\r\n\r\n    # Private Methods\r\n    def _getFiveLetterWordCorpus(self, corpus):\r\n        fiveLetterWords = set(filter(lambda x: len(x) == 5, corpus))\r\n        return self.filterCorpus(fiveLetterWords)\r\n\r\n    def _assemblePatterns(self):\r\n        patterns = [self._assembleMainPattern()]\r\n        for letter in self.yellowConstraints:\r\n            patterns.append(re.compile(f\".*{letter}.*\"))\r\n        return patterns\r\n\r\n    def _assembleMainPattern(self):\r\n        patternString = ''\r\n        for pool in self.letterPools:\r\n            patternString += f\"[{''.join(pool)}]\"\r\n        return re.compile(patternString)\r\n\r\n    def _initLetterPools(self):\r\n        pool = []\r\n        alphabet = set('abcdefghijklmnopqrstuvwxyz')\r\n        for i in range(5):\r\n            pool.append(alphabet.copy())\r\n        return pool\r\n\r\n\r\n\r\n    \r\n\r\nif __name__ == \"__main__\":\r\n    corpus = None\r\n    guesses = None\r\n    with open(CORPUS_OUT_PATH, 'rb') as file:\r\n        corpus = pickle.load(file)\r\n    \r\n    with open(GUESSES_OUT_PATH, 'rb') as file:\r\n        guesses = pickle.load(file)\r\n        \r\n    guesser = WordleGuesser(corpus)\r\n    fullGuesser = WordleGuesser(guesses)", "repo_name": "TheBrodsky/wordle-guesser", "sub_path": "WordleGuesser.py", "file_name": "WordleGuesser.py", "file_ext": "py", "file_size_in_byte": 5568, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "enum.Enum", "line_number": 9, "usage_type": "name"}, {"api_name": "WordleGuessScorer.WordleGuessScorer", "line_number": 33, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 103, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 144, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 151, "usage_type": "call"}, {"api_name": "CorpusCreator.CORPUS_OUT_PATH", "line_number": 167, "usage_type": "argument"}, {"api_name": "pickle.load", "line_number": 168, "usage_type": "call"}, {"api_name": "CorpusCreator.GUESSES_OUT_PATH", "line_number": 170, "usage_type": "argument"}, {"api_name": "pickle.load", "line_number": 171, "usage_type": "call"}]}
{"seq_id": "72094767485", "text": "from rest_framework import serializers\nfrom music.models import Song, Comment\n\n\nclass SongRetrieveSerializer(serializers.ModelSerializer):\n    user_liked = serializers.SerializerMethodField()\n\n    def get_user_liked(self, obj):\n        return obj.users.filter(pk=self.context['request'].user.pk).exists()\n\n    class Meta:\n        model = Song\n        fields = '__all__'\n\n\nclass SongListSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Song\n        exclude = ('text',)\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n    user = serializers.SerializerMethodField()\n    created_on = serializers.CharField(source='tms_created_on', read_only=True)\n\n    def get_user(self, obj):\n        return (obj.user.get_full_name() or obj.user.username)\n\n    class Meta:\n        model = Comment\n        exclude = ('parent', 'song', 'active')\n        read_only_fields = ('reply_count', 'user', 'id')\n", "repo_name": "m0hammadhossein/django-music", "sub_path": "music/api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 913, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "41", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 5, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 5, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 6, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "music.models.Song", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 16, "usage_type": "name"}, {"api_name": "music.models.Song", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 23, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 23, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 24, "usage_type": "name"}, {"api_name": "music.models.Comment", "line_number": 30, "usage_type": "name"}]}
{"seq_id": "12186131264", "text": "import numpy as np\nfrom selenium import webdriver\n\n# open a browser - http://chromedriver.chromium.org/downloads\noptions = webdriver.ChromeOptions()\noptions.add_argument( '--kiosk' )\ndriver = webdriver.Chrome( executable_path='/Users/meigarom/chromedriver', options=options )\n\n# flask application address\ndriver.get( 'http://127.0.0.1:5000/home' )\n\n# find the buttons and click on them\nclicks = 100\nfor click in range( clicks ):\n    if np.random.random() < 0.5:\n        driver.find_element_by_name( 'yescheckbox' ).click()\n        driver.find_element_by_id( 'yesbtn' ).click()\n    else:\n        driver.find_element_by_name( 'nocheckbox' ).click()\n        driver.find_element_by_id( 'nobtn' ).click()\n\n", "repo_name": "Meigarom/Practical-Artifical-Intelligence-For-AB-Testing", "sub_path": "AI-agent-look/webscraper_look.py", "file_name": "webscraper_look.py", "file_ext": "py", "file_size_in_byte": 701, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "41", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 5, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 5, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 7, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.random.random", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}]}
{"seq_id": "22350699945", "text": "import streamlit as st\nfrom st_pages import Page, Section, show_pages, add_page_title\nfrom st_pages import show_pages_from_config\n\nfrom langchain import PromptTemplate, LLMChain\nfrom langchain.embeddings import HuggingFaceEmbeddings\nfrom langchain.chains import ConversationChain\nfrom langchain.memory import ConversationBufferMemory\nfrom streamlit_chat import message\nimport textwrap\n\nimport together\nfrom togetherllm import TogetherLLM\nfrom time import sleep\n\nfrom langchain.chains.conversation.memory import ConversationEntityMemory\nfrom langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE\n\ntemplate = \"\"\"[INST] <<SYS>>\nYou are helpful assistant, you always only answer for the assistant then you stop, read the chat history to get the context\n{chat_history}\n\nQuestion: {user_input}\n<</SYS>>\n\n[/INST]\"\"\"\n\nprint(template)\n\nst.set_page_config(page_title='­ЪДаMemoryBot­Ъцќ', layout='wide')\n\nst.title('­Ъјѕ Llama 2 Chatbot | Conversation Chain with Memory')\n\nllm = TogetherLLM(\nmodel= \"togethercomputer/llama-2-7b-chat\",\ntemperature=0,\nmax_tokens=512\n)\n\n# Initialize session states\nif \"generated\" not in st.session_state:\n    st.session_state[\"generated\"] = []\nif \"past\" not in st.session_state:\n    st.session_state[\"past\"] = []\nif \"input\" not in st.session_state:\n    st.session_state[\"input\"] = \"\"\nif \"stored_session\" not in st.session_state:\n    st.session_state[\"stored_session\"] = []\n\n# Define function to get user input\ndef get_text():\n    \"\"\"\n    Get the user input text.\n\n    Returns:\n        (str): The text entered by the user\n    \"\"\"\n    input_text = st.text_input(\"You: \", st.session_state[\"input\"], key=\"input\",\n                            placeholder=\"Your AI assistant here! Ask me anything ...\", \n                            label_visibility='hidden')\n    return input_text\n\n# Define function to start a new chat\ndef new_chat():\n    \"\"\"\n    Clears session state and starts a new chat.\n    \"\"\"\n    save = []\n    for i in range(len(st.session_state['generated'])-1, -1, -1):\n        save.append(\"User:\" + st.session_state[\"past\"][i])\n        save.append(\"Bot:\" + st.session_state[\"generated\"][i])        \n    st.session_state[\"stored_session\"].append(save)\n    st.session_state[\"generated\"] = []\n    st.session_state[\"past\"] = []\n    st.session_state[\"input\"] = \"\"\n    st.session_state.entity_memory.entity_store = {}\n    st.session_state.entity_memory.buffer.clear()\n\n# Set up sidebar with various options\nwith st.sidebar.expander(\"­ЪЏа№ИЈ \", expanded=False):\n    st.title(\"X-Chat\")\n    st.header(\"Settings\")\n    add_replicate_api = st.text_input(\"Enter your password here\", type='password')\n\n    if not (add_replicate_api.startswith('jl')):\n        st.warning('Please enter your credentials', icon='Рџа№ИЈ')\n    else:\n        st.success('Proceed to entering your prompt message!', icon='­ЪЉЅ')\n \n    # Option to preview memory store\n    if st.checkbox(\"Preview memory store\"):\n        #with st.expander(\"Memory-Store\", expanded=False):\n        st.info(st.session_state.entity_memory.store)\n        \n    # Option to preview memory buffer\n    if st.checkbox(\"Preview memory buffer\"):\n        #with st.expander(\"Bufffer-Store\", expanded=False):\n        st.info(st.session_state.entity_memory.buffer)\n        \n    K = st.number_input(' (#)Summary of prompts to consider',min_value=3,max_value=1000)\n    \n# Create a ConversationEntityMemory object if not already created\nif 'entity_memory' not in st.session_state:\n    st.session_state.entity_memory = ConversationEntityMemory(llm=llm, k=K )\n    \nConversation = ConversationChain(\n    llm=llm,\n    prompt = ENTITY_MEMORY_CONVERSATION_TEMPLATE,\n    memory = st.session_state.entity_memory\n)\n\n# Add a button to start a new chat\nst.sidebar.button(\"New Chat\", on_click = new_chat, type='primary')\n\nuser_input = get_text()\n\nif user_input:\n    output = Conversation.run(input=user_input)\n    st.session_state.past.append(user_input)\n    st.session_state.generated.append(output)\n    \n# Allow to download as well\ndownload_str = []\n# Display the conversation history using an expander, and allow the user to download it\nwith st.expander(\"Conversation\", expanded=True):\n    for i in range(len(st.session_state['generated'])-1, -1, -1):\n        st.info(st.session_state[\"past\"][i],icon=\"­ЪДљ\")\n        st.success(st.session_state[\"generated\"][i], icon=\"­Ъцќ\")\n        download_str.append(st.session_state[\"past\"][i])\n        download_str.append(st.session_state[\"generated\"][i])\n    \n    # Can throw error - requires fix\n    download_str = '\\n'.join(download_str)\n    if download_str:\n        st.download_button('Download',download_str)\n        \n# Display stored conversation sessions in the sidebar\nfor i, sublist in enumerate(st.session_state.stored_session):\n        with st.sidebar.expander(label= f\"Conversation-Session:{i}\"):\n            st.write(sublist)\n\n# Allow the user to clear all stored conversation sessions\nif st.session_state.stored_session:   \n    if st.sidebar.checkbox(\"Clear-all\"):\n        del st.session_state.stored_session", "repo_name": "bet0x/Llama2", "sub_path": "llama2_projects/llama2_memory/streamlit_conversationchain_together_entity_memory.py", "file_name": "streamlit_conversationchain_together_entity_memory.py", "file_ext": "py", "file_size_in_byte": 5025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "streamlit.set_page_config", "line_number": 30, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 32, "usage_type": "call"}, {"api_name": "togetherllm.TogetherLLM", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 41, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 42, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 43, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 44, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 45, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 46, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 47, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 48, "usage_type": "attribute"}, {"api_name": "streamlit.text_input", "line_number": 58, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 58, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 69, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 70, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 71, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 72, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 73, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 74, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 75, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 76, "usage_type": "attribute"}, {"api_name": "streamlit.session_state.entity_memory.buffer.clear", "line_number": 77, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 77, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.expander", "line_number": 80, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 80, "usage_type": "attribute"}, {"api_name": "streamlit.title", "line_number": 81, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 82, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 83, "usage_type": "call"}, {"api_name": "streamlit.warning", "line_number": 86, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 88, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 91, "usage_type": "call"}, {"api_name": "streamlit.info", "line_number": 93, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 93, "usage_type": "attribute"}, {"api_name": "streamlit.checkbox", "line_number": 96, "usage_type": "call"}, {"api_name": "streamlit.info", "line_number": 98, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 98, "usage_type": "attribute"}, {"api_name": "streamlit.number_input", "line_number": 100, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 103, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 104, "usage_type": "attribute"}, {"api_name": "langchain.chains.conversation.memory.ConversationEntityMemory", "line_number": 104, "usage_type": "call"}, {"api_name": "langchain.chains.ConversationChain", "line_number": 106, "usage_type": "call"}, {"api_name": "langchain.chains.conversation.prompt.ENTITY_MEMORY_CONVERSATION_TEMPLATE", "line_number": 108, "usage_type": "name"}, {"api_name": "streamlit.session_state", "line_number": 109, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.button", "line_number": 113, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 113, "usage_type": "attribute"}, {"api_name": "streamlit.session_state.past.append", "line_number": 119, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 119, "usage_type": "attribute"}, {"api_name": "streamlit.session_state.generated.append", "line_number": 120, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 120, "usage_type": "attribute"}, {"api_name": "streamlit.expander", "line_number": 125, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 126, "usage_type": "attribute"}, {"api_name": "streamlit.info", "line_number": 127, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 127, "usage_type": "attribute"}, {"api_name": "streamlit.success", "line_number": 128, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 128, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 129, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 130, "usage_type": "attribute"}, {"api_name": "streamlit.download_button", "line_number": 135, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 138, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.expander", "line_number": 139, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 139, "usage_type": "attribute"}, {"api_name": "streamlit.write", "line_number": 140, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 143, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.checkbox", "line_number": 144, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 144, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 145, "usage_type": "attribute"}]}
{"seq_id": "2670646494", "text": "#coding=utf-8\nimport logging\nfrom logging.handlers import TimedRotatingFileHandler\nfrom service.common.config import LOG_FILE\n\n\ndef get_logger(name=None):\n    if not name:\n        name = \"dealer_model_service\"\n    logger = logging.getLogger(name)\n\n    if not getattr(logger, 'handlers', None):\n        logger.propagate = False\n        pattern = '%(asctime)s %(levelname)s %(module)s.%(funcName)s Line:%(lineno)d %(message)s'\n        formatter = logging.Formatter(pattern)\n        handler = TimedRotatingFileHandler(filename=LOG_FILE, when='midnight', interval=1, backupCount=365)\n        handler.setFormatter(formatter)\n        logger.addHandler(handler)\n        logger.setLevel(logging.WARNING)\n\n    return logger", "repo_name": "iloveppxbaby/test_api", "sub_path": "common/logutils.py", "file_name": "logutils.py", "file_ext": "py", "file_size_in_byte": 714, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.handlers.TimedRotatingFileHandler", "line_number": 16, "usage_type": "call"}, {"api_name": "service.common.config.LOG_FILE", "line_number": 16, "usage_type": "name"}, {"api_name": "logging.WARNING", "line_number": 19, "usage_type": "attribute"}]}
{"seq_id": "22629768613", "text": "import torch\nimport torch.utils.data as data\nfrom PIL import Image\nimport os\nimport math\nimport functools\nimport copy\nimport random\n\ndef pil_loader(path):\n    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\n    with open(path, 'rb') as f:\n        with Image.open(f) as img:\n            return img.convert('RGB')\n\n\ndef accimage_loader(path):\n    try:\n        return accimage.Image(path)\n    except IOError:\n        # Potentially a decoding problem, fall back to PIL.Image\n        return pil_loader(path)\n\n\ndef get_default_image_loader():\n    from torchvision import get_image_backend\n    if get_image_backend() == 'accimage':\n        import accimage\n        return accimage_loader\n    else:\n        return pil_loader\n\n\ndef video_loader(video_dir_path, frame_indices, image_loader):\n    video = []\n    for i in frame_indices:\n        image_path = os.path.join(video_dir_path, 'image_{:05d}.jpg'.format(i))\n        if os.path.exists(image_path):\n            video.append(image_loader(image_path))\n        else:\n            return video\n    return video\n\n\ndef get_default_video_loader():\n    image_loader = get_default_image_loader()\n    return functools.partial(video_loader, image_loader=image_loader)\n\n\ndef make_dataset(video_listfile, sample_duration, n_samples_for_each_video):\n    dataset = []\n    video_list = []\n    with open(video_listfile, 'r') as f:\n        for lines in f.readlines():\n            video_list.append(lines.strip('\\n').split(' '))\n    for video_path, n_frames, label in video_list:\n        n_frames = int(n_frames)\n        begin_t = 1\n        end_t = n_frames\n        sample = {\n            'video': video_path,\n            'segment': [begin_t, end_t],\n            'n_frames': n_frames,\n            'label': int(label)\n        }\n        if n_samples_for_each_video == 1:\n            sample['frame_indices'] = list(range(1, n_frames + 1))\n            dataset.append(sample)\n        else:\n            if n_samples_for_each_video > 1:\n                step = max(1, math.ceil((n_frames - 1 - sample_duration) /\n                                        (n_samples_for_each_video - 1)))\n            else:\n                step = sample_duration\n            for i in range(1, n_frames, step):\n                sample_i = copy.deepcopy(sample)\n                sample_i['frame_indices'] = list(range(i, min(n_frames, i + sample_duration)))\n                dataset.append(sample_i)\n    return dataset\n\nclass Video(data.Dataset):\n    def __init__(self, video_listfile,\n                 spatial_transform=None, temporal_transform=None,\n                 sample_duration=16, n_samples_for_each_video=1, get_loader=get_default_video_loader):\n        self.data = make_dataset(video_listfile, sample_duration, n_samples_for_each_video)\n        self.spatial_transform = spatial_transform\n        self.temporal_transform = temporal_transform\n        self.loader = get_loader()\n\n    def __getitem__(self, index):\n        \"\"\"\n        Args:\n            index (int): Index\n        Returns:\n            tuple: (image, target) where target is class_index of the target class.\n        \"\"\"\n        video_path = self.data[index]['video']\n        label = self.data[index]['label']\n        frame_indices = self.data[index]['frame_indices']\n        if self.temporal_transform is not None:\n            frame_indices = self.temporal_transform(frame_indices)\n        clip = self.loader(video_path, frame_indices)\n        if self.spatial_transform is not None:\n            clip = [self.spatial_transform(img) for img in clip]\n        clip = torch.stack(clip, 0).permute(1, 0, 2, 3)\n        return clip, label, video_path\n\n    def __len__(self):\n        return len(self.data)\n", "repo_name": "shuangshuangguo/3D-ResNet-Pytorch", "sub_path": "dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 3709, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "41", "api": [{"api_name": "PIL.Image.open", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 13, "usage_type": "name"}, {"api_name": "torchvision.get_image_backend", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 47, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 71, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 105, "usage_type": "call"}]}
{"seq_id": "39695860213", "text": "from time import time, sleep\nfrom copy import copy\nfrom os import path\nfrom glob import glob\nfrom random import randint\n\nimport cv2\nimport numpy as np\nfrom scipy.cluster.vq import vq, kmeans \nfrom scipy.cluster.hierarchy import fclusterdata\n\nfrom modules.misc_funcs import get_time_stamp, writeFile, chk_msg_q, calc_pt_line_dist, calc_line_angle, calc_angle_diff, load_img\n\nflag_window = True # create an opencv window or not\nflag_video_rec = False # video recording\n\n# ======================================================\n\nclass CVProc:\n    def __init__(self, parent):\n        self.parent = parent\n\n        self.fourcc = cv2.cv.CV_FOURCC('x', 'v', 'i', 'd')\n        #self.fourcc = cv2.VideoWriter_fourcc('x', 'v', 'i', 'd') #cv2.cv.CV_FOURCC('x', 'v', 'i', 'd')\n        self.video_rec = None # video recorder\n        self.fSize = (960, 540) # default frame size\n        self.gmpts = [] # good matched points found by ORB\n        self.cluster_cols = [(125,125,125), (0,0,255), (255,0,0), (0,255,255), (255,100,100), \n                             (0,255,0), (0,0,0), (50,255,50), (100,100,255), (0,125,255)]\n        self.video_rec = None\n\n    # --------------------------------------------------\n\n    def start_video_rec(self, video_fp, frame_arr):\n        self.fSize= (frame_arr.shape[1], frame_arr.shape[0])\n        self.video_fSize = (int(self.fSize[0]/2), int(self.fSize[1]/2)) # output video frame size\n        self.video_rec = cv2.VideoWriter( video_fp, self.fourcc, self.parent.vFPS, self.video_fSize, 1 )\n\n    # --------------------------------------------------\n\n    def stop_video_rec(self):\n        self.video_rec.release()\n        self.video_rec = None\n\n    # --------------------------------------------------\n    \n    def proc_img(self, prev_frame_arr, frame_arr, hPos=None, bPos=None, imgType='RGB-image', animalSp='Marmoset'):\n        hD = None\n        status_msg = \"%i/ %i, \"%(self.parent.fi, self.parent.frame_cnt)\n        \n        ### find the subject's body (or its parts) by comparing the current frame with the background image \n        diff = cv2.absdiff( frame_arr, self.bg ) # difference from the background image \n        diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)\n        kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3,3)) \n        diff = cv2.morphologyEx(diff, cv2.MORPH_OPEN, kernel, iterations=self.mExOIter) # decrease noise & minor features\n        #diff = cv2.morphologyEx(diff, cv2.MORPH_CLOSE, kernel, iterations=self.mExCIter) # closing small holes (useful for intensify minor features)\n        __, diff = cv2.threshold(diff, self.thParam, 255, cv2.THRESH_BINARY) # make the recognized parts clear\n\n        ### edge detection on the recognized parts\n        edged = cv2.Canny(diff, 150, 150)\n        (cnts, _) = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # get contours of difference\n        cnt_info = [] # contour info (size, center-X, center-Y)\n        cntsBr = [] # bounding rect(x1,y1,x2,y2) for all contours\n        for ci in range(len(cnts)):\n            mr = cv2.boundingRect(cnts[ci])\n            if mr[2]+mr[3] < self.contourTh: continue\n            cv2.circle(edged, (mr[0]+mr[2]/2,mr[1]+mr[3]/2), mr[2]/2, 125, 1)\n            cnt_info.append( (mr[2]+mr[3], mr[0]+mr[2]/2, mr[1]+mr[3]/2) )\n            if cntsBr == []: cntsBr = [mr[0], mr[1], mr[0]+mr[2], mr[1]+mr[3]]\n            else:\n                if mr[0] < cntsBr[0]: cntsBr[0] = mr[0]\n                if mr[1] < cntsBr[1]: cntsBr[1] = mr[1]\n                if mr[0]+mr[2] > cntsBr[2]: cntsBr[2] = mr[0]+mr[2]\n                if mr[1]+mr[3] > cntsBr[3]: cntsBr[3] = mr[1]+mr[3]\n        if cntsBr == []: cpt = (-1, -1)\n        else: cpt = ( cntsBr[0]+(cntsBr[2]-cntsBr[0])/2, cntsBr[1]+(cntsBr[3]-cntsBr[1])/2 ) # center point of all contours\n        if self.parent.fi == 1: self.last_motion_frame = frame_arr.copy()\n        \n        if self.parent.fi > 1:\n            self.prev_hd = self.parent.oData[self.parent.fi-1][\"hD\"] # head direction of previous frame\n            self.prev_hPos = self.parent.oData[self.parent.fi-1][\"hPos\"] # head position of previous frame\n            self.prev_bPos = self.parent.oData[self.parent.fi-1][\"bPos\"] # base position of previous frame\n        else:\n            self.prev_hd = None\n            self.prev_hPos = None\n            self.prev_bPos = None\n        \n        if hPos != None and bPos != None: # head postion and base position is given by user\n            hD = calc_line_angle(bPos, hPos)\n        elif self.parent.oData[self.parent.fi][\"mHD\"] == True: # head direction is manually fixed\n            hD = self.parent.oData[self.parent.fi][\"hD\"]\n            hPos = self.parent.oData[self.parent.fi][\"hPos\"]\n            bPos = self.parent.oData[self.parent.fi][\"bPos\"]\n        elif self.parent.chk_manual.GetValue() == True: # manual input is checked\n            hD = self.prev_hd \n            hPos = self.prev_hPos\n            bPos = self.prev_bPos\n        else:\n            if self.parent.fi == 1: bPos = copy(cpt)\n            ### motion detection\n            m_diff = cv2.absdiff( frame_arr, self.last_motion_frame) # difference between the current and last motion frame \n            if self.motionTh[0] <= np.sqrt(np.sum(m_diff)/255) < self.motionTh[1]: # thresholds for accepting motion\n            # there's a motion\n                self.last_motion_frame = frame_arr.copy()\n                ### determine hPos, bPos, and hD \n                if hPos == None or bPos == None: # it's not manually given by user\n                    if animalSp == 'Marmoset': hPos, bPos, hD = self.proc_marmoset(cnt_info)\n                    elif animalSp == 'Alligator': hPos, bPos, hD, frame_arr = self.proc_alligator(frame_arr, diff, cntsBr)\n                    elif animalSp == 'Gerbil': hPos, bPos, hD, frame_arr = self.proc_alligator(frame_arr, diff, cntsBr) # gerbil can use alligator algorithm, with different parameters (determined in onChangeAnimalSp function of hc.py)\n\n            else: # no motion detection\n                if self.parent.fi > 1: # not the first frame\n                    hD = self.parent.oData[self.parent.fi-1][\"hD\"] # set head direction as the previous head direction\n                    if type(hD) == int:\n                        bPos = self.prev_bPos \n                        hPos = self.calc_hPos(hD, bPos) # calculate hPos\n     \n        if imgType == 'Greyscale(Diff)': frame_arr = cv2.cvtColor(diff, cv2.COLOR_GRAY2BGR)\n        elif imgType == 'Greyscale(Edge)': frame_arr = cv2.cvtColor(edged, cv2.COLOR_GRAY2BGR)\n        if type(hD) == int: # head direction available\n            cv2.line(frame_arr, hPos, bPos, (0,255,0), 2)\n            cv2.circle(frame_arr, hPos, 3, (0,125,255), -1)\n       \n        status_msg += 'HD %s'%str(hD)\n        \n        cv2.putText(frame_arr, status_msg, (10,25), cv2.FONT_HERSHEY_PLAIN, fontScale=1.5, color=(0,250,0), thickness=2) # write status\n        self.video_rec.write( cv2.resize(frame_arr,self.video_fSize) )\n\n        if hPos==None: hPos=('None','None')\n        if bPos==None: bPos=('None','None')\n        if hD==None: hD='None'\n        return frame_arr, hPos, bPos, hD\n\n    # --------------------------------------------------\n    \n    def proc_marmoset(self, cnt_info):\n        hPos=None; bPos=None; hD=None\n        ept1 = None; ept2 = None\n        cnt_info = sorted(cnt_info, reverse=True) # sort contours by size (largest first)\n        if len(cnt_info) >= 2: # there are more than 2 contours\n            ept1 = (cnt_info[0][1], cnt_info[0][2]) # first two (largest) contours\n            ept2 = (cnt_info[1][1], cnt_info[1][2]) # first two (largest) contours\n            if ept1[0] > ept2[0]: tmp_ = copy(ept2); ept2 = copy(ept1); ept1 = tmp_\n        if ept1 != None:\n            ang_ = calc_line_angle(ept1, ept2) # angle of a line connecting two ears\n            s = np.sin(np.deg2rad(ang_))\n            c = np.cos(np.deg2rad(ang_))\n            l_ = np.sqrt( (ept1[0]-ept2[0])**2+(ept1[1]-ept2[1])**2 ) / 2 # length of line\n            bPos = ( int(ept1[0]+l_*c), int(ept1[1]-l_*s) )\n            ### determine two possible head directions\n            if -90 <= ang_ <= 90: hD1 = ang_+90; hD2 = ang_-90\n            elif 91 <= ang_ <= 180: hD1 = -(180-(ang_+90)%180); hD2 = ang_-90\n            elif -91 > ang_ >= -180: hD1 = ang_+90; hD2 = (ang_-90)%180 \n            ### determine head direction which is closer to the previous one\n            if type(self.prev_hd) == int: # head direction in the previous hD is available\n                ang_diff1 = calc_angle_diff(self.prev_hd, hD1)\n                ang_diff2 = calc_angle_diff(self.prev_hd, hD2)\n                if ang_diff1 <= ang_diff2:\n                    if ang_diff1 <= self.degTh: hD = hD1\n                    else: hD = self.prev_hd\n                elif ang_diff1 > ang_diff2:\n                    if ang_diff2 <= self.degTh: hD = hD2\n                    else: hD = self.prev_hd\n            else:\n                hD = hD1\n        else: # ear position is not available\n            hD = self.prev_hd\n            if type(hD) == int: bPos = self.prev_bPos \n        if type(hD) == int: # head direction is available\n            if bPos == None: bPos = self.prev_bPos\n            hPos = self.calc_hPos(hD, bPos) # calculate hPos\n            \n        return hPos, bPos, hD\n   \n    # --------------------------------------------------\n\n    def proc_alligator(self, frame_arr, diff, cntsBr):\n        hPos=None; bPos=None; hD=None\n        if type(self.prev_hd) == int: \n            ### draw a point with the known head direction from the center of subject        \n            s = np.sin(np.deg2rad(self.prev_hd))\n            c = np.cos(np.deg2rad(self.prev_hd))\n            w_=abs(cntsBr[2]-cntsBr[0]); h_=abs(cntsBr[3]-cntsBr[1])\n            l_ = max(w_,h_) # length of line\n            hpt_ = ( int(self.prev_bPos[0]+l_*c), int(self.prev_bPos[1]-l_*s) ) # point further away from head\n            cv2.circle(frame_arr, hpt_, 5, (50,50,50), -1)\n            ### cluster subject points \n            dPts = np.where(diff==255)\n            dPts = np.hstack( (dPts[1].reshape((dPts[1].shape[0],1)),dPts[0].reshape((dPts[0].shape[0],1))) ) # coordinates of all the 255 pixels of diff\n            centroids, __ = kmeans( dPts, self.nKMC ) # kmeans clustering\n            ### calculate distances between the hpt_ and centroids of clusters. the closest cluster is supposed to be the head cluster\n            d_cents = [] # (distance to hpt_, x, y)\n            for ci in range(len(centroids)):\n                d_ =  np.sqrt((centroids[ci][0]-hpt_[0])**2+(centroids[ci][1]-hpt_[1])**2)\n                d_cents.append( [d_, centroids[ci][0], centroids[ci][1]] )\n            d_cents = sorted(d_cents) # sort by distance\n            centroids = np.array(d_cents, dtype=np.uint16)[:,1:]\n            ### get points of the head cluster                            \n            idx, __ = vq( dPts, np.asarray(centroids) )\n            pts_ = dPts[np.where(idx==0)[0]]\n            #frame_arr[pts_[:,1],pts_[:,0]] = self.cluster_cols[0]\n            ### determine hPos as the closest point in the head cluster\n            d_ = []\n            for hci in range(len(pts_)):\n                d_.append(  np.sqrt((pts_[hci][0]-hpt_[0])**2+(pts_[hci][1]-hpt_[1])**2) )\n            i_ = d_.index(min(d_))\n            hPos = ( int(pts_[i_][0]), int(pts_[i_][1]) )\n            cv2.circle(frame_arr, hPos, 3, (200,200,200), -1)\n            ### draw each cluster centroids and calculates distances between the head cluster and other clusters \n            d_ = [] # distances between the head cluster and other clusters\n            for ci in range(1, len(centroids)):\n                cv2.circle(frame_arr, tuple(centroids[ci]), 3, (200,200,200), -1)\n                d_.append( np.sqrt((centroids[0][0]-int(centroids[ci][0]))**2+(centroids[0][1]-int(centroids[ci][1]))**2) )\n                pts_ = dPts[np.where(idx==ci)[0]]\n                frame_arr[pts_[:,1],pts_[:,0]] = self.cluster_cols[ci]\n            bi_ = d_.index(min(d_))+1 # index of the closets cluster to the head cluster\n            bPos = ( int(centroids[bi_][0]), int(centroids[bi_][1]) ) \n            #cv2.circle(frame_arr, hPos, 5, self.cluster_cols[0], -1)\n            #cv2.circle(frame_arr, bPos, 5, self.cluster_cols[1], -1)\n            #cv2.line(frame_arr, hPos, bPos, (200,200,200), 2)\n        if hPos == None and bPos == None:\n            hD = self.prev_hd \n        else:\n            hD = calc_line_angle(bPos, hPos)\n            if self.parent.fi > 1: # not the first frame\n                if type(self.prev_hd) == int:\n                    if calc_angle_diff(self.prev_hd, hD) > self.degTh: hD = self.prev_hd # differnce is too big. use the previous hD\n        if type(hD) == int and bPos != None: hPos = self.calc_hPos(hD, bPos) # calculate hPos with hD\n        return hPos, bPos, hD, frame_arr\n\n    # --------------------------------------------------\n\n    def proc_alligator2(self, frame_arr, diff, cntsBr):\n        ''' Difference between proc_alligator and proc_alligator2 is that \n        bPos is the red tag in the middle of head\n        '''\n        hPos=None; bPos=None; hD=None\n        if type(self.prev_hd) == int: \n            ### draw a point with the known head direction from the center of subject        \n            s = np.sin(np.deg2rad(self.prev_hd))\n            c = np.cos(np.deg2rad(self.prev_hd))\n            w_=abs(cntsBr[2]-cntsBr[0]); h_=abs(cntsBr[3]-cntsBr[1])\n            l_ = max(w_,h_) # length of line\n            hpt_ = ( int(self.prev_bPos[0]+l_*c), int(self.prev_bPos[1]-l_*s) ) # point further away from head\n            cv2.circle(frame_arr, hpt_, 5, (50,50,50), -1)\n            ### cluster subject points \n            dPts = np.where(diff==255)\n            dPts = np.hstack( (dPts[1].reshape((dPts[1].shape[0],1)),dPts[0].reshape((dPts[0].shape[0],1))) ) # coordinates of all the 255 pixels of diff\n            centroids, __ = kmeans( dPts, self.nKMC ) # kmeans clustering\n            ### calculate distances between the hpt_ and centroids of clusters. the closest cluster is supposed to be the head cluster\n            d_cents = [] # (distance to hpt_, x, y)\n            for ci in range(len(centroids)):\n                d_ =  np.sqrt((centroids[ci][0]-hpt_[0])**2+(centroids[ci][1]-hpt_[1])**2)\n                d_cents.append( [d_, centroids[ci][0], centroids[ci][1]] )\n            d_cents = sorted(d_cents) # sort by distance\n            centroids = np.array(d_cents, dtype=np.uint16)[:,1:]\n            ### get points of the head cluster                            \n            idx, __ = vq( dPts, np.asarray(centroids) )\n            pts_ = dPts[np.where(idx==0)[0]]\n            #frame_arr[pts_[:,1],pts_[:,0]] = self.cluster_cols[0]\n            ### determine hPos as the closest point in the head cluster\n            d_ = []\n            for hci in range(len(pts_)):\n                d_.append(  np.sqrt((pts_[hci][0]-hpt_[0])**2+(pts_[hci][1]-hpt_[1])**2) )\n            i_ = d_.index(min(d_))\n            hPos = ( int(pts_[i_][0]), int(pts_[i_][1]) )\n            cv2.circle(frame_arr, hPos, 3, (200,200,200), -1)\n            ### find red tag in the middle head\n            tmp_grey_img = self.find_color((0,0,frame_arr.shape[1],frame_arr.shape[0]), frame_arr, (175,100,90), (180,255,255))\n            M = cv2.moments(tmp_grey_img)\n            cx = int(M['m10']/M['m00'])\n            cy = int(M['m01']/M['m00'])\n            bPos = (cx, cy)\n        if hPos == None and bPos == None:\n            hD = self.prev_hd \n        else:\n            hD = calc_line_angle(bPos, hPos)\n            if self.parent.fi > 1: # not the first frame\n                if type(self.prev_hd) == int:\n                    if calc_angle_diff(self.prev_hd, hD) > self.degTh: hD = self.prev_hd # differnce is too big. use the previous hD\n        if type(hD) == int and bPos != None: hPos = self.calc_hPos(hD, bPos) # calculate hPos with hD\n        return hPos, bPos, hD, frame_arr\n\n    # --------------------------------------------------\n    \n    def calc_hPos(self, hD, bPos):\n        s = np.sin(np.deg2rad(hD))\n        c = np.cos(np.deg2rad(hD))\n        l_ = self.hdLineLen # length of line\n        hPos = ( int(bPos[0]+l_*c), int(bPos[1]-l_*s) )\n        return hPos\n\n    # --------------------------------------------------\n\n    def calc_bPos(self, hD, hPos):\n        fhD = hD-180 # flip head direction\n        if fhD <= -180: fhD += 360 \n        s = np.sin(np.deg2rad(fhD))\n        c = np.cos(np.deg2rad(fhD))\n        l_ = self.hdLineLen # length of line\n        bPos = ( int(hPos[0]+l_*c), int(hPos[1]-l_*s) )\n        return bPos\n\n    # --------------------------------------------------\n    \n    def find_color(self, rect, inImage, HSV_min, HSV_max):\n    # Find a color(range: 'HSV_min' ~ 'HSV_max') in an area('rect') of an image('inImage')\n    # 'rect' here is (x1,y1,x2,y2)\n        pts_ = [ (rect[0], rect[1]), (rect[0], rect[3]), (rect[2], rect[3]), (rect[2], rect[1]) ] # Upper Left, Lower Left, Lower Right, Upper Right\n        mask = np.zeros( (inImage.shape[0], inImage.shape[1]) , dtype=np.uint8 )\n        tmp_grey_img = np.zeros( (inImage.shape[0], inImage.shape[1]) , dtype=np.uint8 )\n        cv2.fillConvexPoly(mask, np.asarray(pts_), 255)\n        tmp_col_img = cv2.bitwise_and(inImage, inImage, mask=mask )\n        HSV_img = cv2.cvtColor(tmp_col_img, cv2.COLOR_BGR2HSV)\n        tmp_grey_img = cv2.inRange(HSV_img, HSV_min, HSV_max)\n        ret, tmp_grey_img = cv2.threshold(tmp_grey_img, 50, 255, cv2.THRESH_BINARY)\n        return tmp_grey_img\n\n    # --------------------------------------------------\n\n    def clustering(self, pt_list, threshold):\n        pt_arr = np.asarray(pt_list)\n        result = []\n        try: result = list(fclusterdata(pt_arr, threshold, 'distance'))\n        except: pass\n        number_of_groups = 0\n        groups = []\n        if result != []:\n            groups = []\n            number_of_groups = max(result)\n            for i in range(number_of_groups): groups.append([])\n            for i in range(len(result)):\n                 groups[result[i]-1].append(pt_list[i])\n        return number_of_groups, groups\n\n\n# ======================================================\n\nif __name__ == '__main__':\n    pass\n\n", "repo_name": "jinook0707/HDC", "sub_path": "modules/cv_proc.py", "file_name": "cv_proc.py", "file_ext": "py", "file_size_in_byte": 18201, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "cv2.cv.CV_FOURCC", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.cv", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.absdiff", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.getStructuringElement", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.Canny", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 61, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 67, "usage_type": "call"}, {"api_name": "modules.misc_funcs.calc_line_angle", "line_number": 89, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.absdiff", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 118, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 118, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 119, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 126, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_PLAIN", "line_number": 126, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 127, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 143, "usage_type": "call"}, {"api_name": "modules.misc_funcs.calc_line_angle", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 148, "usage_type": "call"}, {"api_name": "modules.misc_funcs.calc_angle_diff", "line_number": 156, "usage_type": "call"}, {"api_name": "modules.misc_funcs.calc_angle_diff", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 182, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 189, "usage_type": "call"}, {"api_name": "scipy.cluster.vq.kmeans", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 197, "usage_type": "attribute"}, {"api_name": "scipy.cluster.vq.vq", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 205, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 208, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 214, "usage_type": "call"}, {"api_name": "modules.misc_funcs.calc_line_angle", "line_number": 224, "usage_type": "call"}, {"api_name": "modules.misc_funcs.calc_angle_diff", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 241, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 248, "usage_type": "call"}, {"api_name": "scipy.cluster.vq.kmeans", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 256, "usage_type": "attribute"}, {"api_name": "scipy.cluster.vq.vq", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 264, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 267, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 270, "usage_type": "call"}, {"api_name": "modules.misc_funcs.calc_line_angle", "line_number": 277, "usage_type": "call"}, {"api_name": "modules.misc_funcs.calc_angle_diff", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 310, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 311, "usage_type": "attribute"}, {"api_name": "cv2.fillConvexPoly", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 312, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 313, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 314, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 314, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 315, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 316, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 316, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 322, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.fclusterdata", "line_number": 324, "usage_type": "call"}]}
{"seq_id": "26094781649", "text": "from __future__ import print_function\nfrom django.contrib.auth import authenticate, login\nfrom django.contrib.auth import logout\nfrom django.http import JsonResponse\nfrom django.shortcuts import render, get_object_or_404\nfrom django.db.models import Q\nfrom .forms import cvemappingForm, UploadForm\nfrom .models import cvemapping\nfrom .cvesearch import handle_uploaded_file\n\n\nUPLOAD_FILE_TYPES = ['csv', 'txt', 'xlsx']\n\n\ndef create_cvemapping(request):\n        form = cvemappingForm(request.POST or None, request.FILES or None)\n        if form.is_valid():\n            cvemapping = form.save(commit=False)\n            cvemapping.cvemapping_logo = request.FILES['cvemapping_logo']\n            file_type = cvemapping.cvemapping_logo.url.split('.')[-1]\n            file_type = file_type.lower()\n            print(file_type)\n            if (file_type not in UPLOAD_FILE_TYPES) :\n                context = {\n                    'cvemapping': cvemapping,\n                    'form': form,\n                    'error_message': 'Unsupported File Format Selected',\n                }\n                #return subprocess.check_output(['python', 'cvesearch.py', handle_uploaded_file(\"1\")]).decode('utf-8')\n                return render(request, 'CVE/create_cvemapping.html', context)\n            if (file_type!=\"txt\" and cvemapping.artist==2):\n                context = {\n                    'cvemapping': cvemapping,\n                    'form': form,\n                    'error_message': 'Check the source Option - Please Upload Valid TXT File',\n                }\n                #return subprocess.check_output(['python', 'cvesearch.py', handle_uploaded_file(\"1\")]).decode('utf-8')\n                return render(request, 'CVE/create_cvemapping.html', context)\n            if (file_type!=\"csv\" and cvemapping.artist==0):\n                context = {\n                    'cvemapping': cvemapping,\n                    'form': form,\n                    'error_message': 'Check the source Option - Please Upload Valid CSV File',\n                }\n                #return subprocess.check_output(['python', 'cvesearch.py', handle_uploaded_file(\"1\")]).decode('utf-8')\n                return render(request, 'CVE/create_cvemapping.html', context)\n            if (file_type!=\"xlsx\" and cvemapping.artist==1) :\n                context = {\n                    'cvemapping': cvemapping,\n                    'form': form,\n                    'error_message': 'Check the source Option - Please Upload Valid XLSX File',\n                }\n                #return subprocess.check_output(['python', 'cvesearch.py', handle_uploaded_file(\"1\")]).decode('utf-8')\n                return render(request, 'CVE/create_cvemapping.html', context)\n            cvemapping.save()\n            #handle_uploaded_file(cvemapping.artist, cvemapping.cvemapping_logo.url,cvemapping.update_check, cvemapping.choice)\n            dict1={'total':'','found':'','exploitdb':'','nmap':''}\n            fin=[]\n            cvemapping.val1,cvemapping.val2,cvemapping.val3,cvemapping.val4=handle_uploaded_file(request,cvemapping.artist, cvemapping.cvemapping_logo.url,cvemapping.update_check, cvemapping.choice)\n#            cvemapping.choice=fin[2]\n#            cvemapping.genre=fin[3]\n            return render(request, 'CVE/detail.html', {'cvemapping': cvemapping})\n        context = {\n            \"form\": form,\n        }\n        return render(request, 'CVE/create_cvemapping.html', context)\n\n\n\ndef create_file(request):\n        form = UploadForm(request.POST or None, request.FILES or None)\n        if form.is_valid():\n            files = form.save(commit=False)\n            files.file = request.FILES\n            files.save()\n            return render(request, 'CVE/index.html', {'file': files})\n        context = {\n            'form': form,\n        }\n        return render(request, 'CVE/index.html', context)\n\n\ndef delete_cvemapping(request, cvemapping_id):\n    cvemapping = cvemapping.objects.get(pk=cvemapping_id)\n    cvemapping.delete()\n    cvemappings = cvemapping.objects.filter(user=request.user)\n    return render(request, 'CVE/index.html', {'cvemappings': cvemappings})\n\n\ndef delete_song(request, cvemapping_id, song_id):\n    cvemapping = get_object_or_404(cvemapping, pk=cvemapping_id)\n    song = Song.objects.get(pk=song_id)\n    song.delete()\n    return render(request, 'CVE/detail.html', {'cvemapping': cvemapping})\n\n\ndef detail(request, cvemapping_id):\n    if not request.user.is_authenticated():\n        return render(request, 'CVE/login.html')\n    else:\n        user = request.user\n        cvemapping = get_object_or_404(cvemapping, pk=cvemapping_id)\n        return render(request, 'CVE/detail.html', {'cvemapping': cvemapping, 'user': user})\n\n\ndef favorite(request, song_id):\n    song = get_object_or_404(Song, pk=song_id)\n    try:\n        if song.is_favorite:\n            song.is_favorite = False\n        else:\n            song.is_favorite = True\n        song.save()\n    except (KeyError, Song.DoesNotExist):\n        return JsonResponse({'success': False})\n    else:\n        return JsonResponse({'success': True})\n\n\ndef favorite_cvemapping(request, cvemapping_id):\n    cvemapping = get_object_or_404(cvemapping, pk=cvemapping_id)\n    try:\n        if cvemapping.is_favorite:\n            cvemapping.is_favorite = False\n        else:\n            cvemapping.is_favorite = True\n        cvemapping.save()\n    except (KeyError, cvemapping.DoesNotExist):\n        return JsonResponse({'success': False})\n    else:\n        return JsonResponse({'success': True})\n\n\ndef index(request):\n    if not request.user.is_authenticated():\n        return render(request, 'CVE/login.html')\n    else:\n        cvemappings = cvemapping.objects.filter(user=request.user)\n        song_results = Song.objects.all()\n        query = request.GET.get(\"q\")\n        if query:\n            cvemappings = cvemappings.filter(\n                Q(cvemapping_title__icontains=query) |\n                Q(artist__icontains=query)\n            ).distinct()\n            song_results = song_results.filter(\n                Q(song_title__icontains=query)\n            ).distinct()\n            return render(request, 'CVE/index.html', {\n                'cvemappings': cvemappings,\n                'songs': song_results,\n            })\n        else:\n            return render(request, 'CVE/index.html', {'cvemappings': cvemappings})\n\n\ndef logout_user(request):\n    logout(request)\n    form = UserForm(request.POST or None)\n    context = {\n        \"form\": form,\n    }\n    return render(request, 'CVE/login.html', context)\n\n\ndef login_user(request):\n    if request.method == \"POST\":\n        username = request.POST['username']\n        password = request.POST['password']\n        user = authenticate(username=username, password=password)\n        if user is not None:\n            if user.is_active:\n                login(request, user)\n                cvemappings = cvemapping.objects.filter(user=request.user)\n                return render(request, 'CVE/index.html', {'cvemappings': cvemappings})\n            else:\n                return render(request, 'CVE/login.html', {'error_message': 'Your account has been disabled'})\n        else:\n            return render(request, 'CVE/login.html', {'error_message': 'Invalid login'})\n    return render(request, 'CVE/login.html')\n\n\ndef register(request):\n    form = UserForm(request.POST or None)\n    if form.is_valid():\n        user = form.save(commit=False)\n        username = form.cleaned_data['username']\n        password = form.cleaned_data['password']\n        user.set_password(password)\n        user.save()\n        user = authenticate(username=username, password=password)\n        if user is not None:\n            if user.is_active:\n                login(request, user)\n                cvemappings = cvemapping.objects.filter(user=request.user)\n                return render(request, 'CVE/index.html', {'cvemappings': cvemappings})\n    context = {\n        \"form\": form,\n    }\n    return render(request, 'CVE/register.html', context)\n\n\ndef songs(request, filter_by):\n    if not request.user.is_authenticated():\n        return render(request, 'CVE/login.html')\n    else:\n        try:\n            song_ids = []\n            for cvemapping in cvemapping.objects.filter(user=request.user):\n                for song in cvemapping.song_set.all():\n                    song_ids.append(song.pk)\n            users_songs = Song.objects.filter(pk__in=song_ids)\n            if filter_by == 'favorites':\n                users_songs = users_songs.filter(is_favorite=True)\n        except cvemapping.DoesNotExist:\n            users_songs = []\n        return render(request, 'CVE/songs.html', {\n            'song_list': users_songs,\n            'filter_by': filter_by,\n        })\n", "repo_name": "nallamuthu/CVE-EXPLOITDB-Mapping", "sub_path": "CVE/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "forms.cvemappingForm", "line_number": 16, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 18, "usage_type": "name"}, {"api_name": "models.cvemapping.cvemapping_logo", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 19, "usage_type": "name"}, {"api_name": "models.cvemapping.cvemapping_logo.url.split", "line_number": 20, "usage_type": "call"}, {"api_name": "models.cvemapping.cvemapping_logo", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 20, "usage_type": "name"}, {"api_name": "models.cvemapping", "line_number": 25, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "models.cvemapping.artist", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 31, "usage_type": "name"}, {"api_name": "models.cvemapping", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "models.cvemapping.artist", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 39, "usage_type": "name"}, {"api_name": "models.cvemapping", "line_number": 41, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "models.cvemapping.artist", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 47, "usage_type": "name"}, {"api_name": "models.cvemapping", "line_number": 49, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 54, "usage_type": "call"}, {"api_name": "models.cvemapping.save", "line_number": 55, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 55, "usage_type": "name"}, {"api_name": "models.cvemapping.val1", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 59, "usage_type": "name"}, {"api_name": "models.cvemapping.val2", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.cvemapping.val3", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.cvemapping.val4", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cvesearch.handle_uploaded_file", "line_number": 59, "usage_type": "call"}, {"api_name": "models.cvemapping.artist", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.cvemapping.cvemapping_logo", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.cvemapping.update_check", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.cvemapping.choice", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 62, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 62, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 66, "usage_type": "call"}, {"api_name": "forms.UploadForm", "line_number": 71, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 80, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 84, "usage_type": "name"}, {"api_name": "models.cvemapping.objects.get", "line_number": 84, "usage_type": "call"}, {"api_name": "models.cvemapping.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "models.cvemapping.delete", "line_number": 85, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 85, "usage_type": "name"}, {"api_name": "models.cvemapping.objects.filter", "line_number": 86, "usage_type": "call"}, {"api_name": "models.cvemapping.objects", "line_number": 86, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 86, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 87, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 91, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 91, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 94, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 94, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 99, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 102, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 102, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 103, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 103, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 107, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 115, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 117, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 121, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 121, "usage_type": "call"}, {"api_name": "models.cvemapping.is_favorite", "line_number": 123, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 123, "usage_type": "name"}, {"api_name": "models.cvemapping.is_favorite", "line_number": 124, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 124, "usage_type": "name"}, {"api_name": "models.cvemapping.is_favorite", "line_number": 126, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 126, "usage_type": "name"}, {"api_name": "models.cvemapping.save", "line_number": 127, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 127, "usage_type": "name"}, {"api_name": "models.cvemapping.DoesNotExist", "line_number": 128, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 128, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 129, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 131, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 136, "usage_type": "call"}, {"api_name": "models.cvemapping.objects.filter", "line_number": 138, "usage_type": "call"}, {"api_name": "models.cvemapping.objects", "line_number": 138, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 138, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 143, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 144, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 147, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 149, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 154, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 158, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 163, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 170, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 173, "usage_type": "call"}, {"api_name": "models.cvemapping.objects.filter", "line_number": 174, "usage_type": "call"}, {"api_name": "models.cvemapping.objects", "line_number": 174, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 174, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 175, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 177, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 179, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 180, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 191, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 194, "usage_type": "call"}, {"api_name": "models.cvemapping.objects.filter", "line_number": 195, "usage_type": "call"}, {"api_name": "models.cvemapping.objects", "line_number": 195, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 195, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 196, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 200, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 205, "usage_type": "call"}, {"api_name": "models.cvemapping", "line_number": 209, "usage_type": "name"}, {"api_name": "models.cvemapping.objects.filter", "line_number": 209, "usage_type": "call"}, {"api_name": "models.cvemapping.objects", "line_number": 209, "usage_type": "attribute"}, {"api_name": "models.cvemapping.song_set.all", "line_number": 210, "usage_type": "call"}, {"api_name": "models.cvemapping.song_set", "line_number": 210, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 210, "usage_type": "name"}, {"api_name": "models.cvemapping.DoesNotExist", "line_number": 215, "usage_type": "attribute"}, {"api_name": "models.cvemapping", "line_number": 215, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 217, "usage_type": "call"}]}
{"seq_id": "8579094217", "text": "import writeToPostgreSQL\r\nimport writeToXMLfilesForPsql\r\nimport writeToErrorsInLogFiles\r\nimport os\r\nimport sys\r\nimport datetime\r\nfrom timeit import default_timer\r\nimport sendToMailPsql\r\nimport multiprocessing\r\nimport psutil\r\ndef main():\r\n\r\n    print(\"*************************************\")\r\n    user=\"\"\r\n    password=\"\"\r\n    host=\"\"\r\n    port=\"\"\r\n    dbName=\"\"\r\n    nesne1=writeToPostgreSQL.WPsql()\r\n    while True:\r\n        user=input(\"user                                --\")\r\n        password=input(\"password                            --\")\r\n        host=input(\"host                                --\")\r\n        port=input(\"port                                --\")\r\n        dbName=input(\"Database Name                       --\")\r\n        psqlIsConn=nesne1.connDB(user,password,host,port,dbName)\r\n        if(psqlIsConn==True):\r\n            print(\"CONNECTED\\n\")\r\n            break\r\n        else:\r\n            print(\"Database not founded !!! \\n\")\r\n            os.system(\"pause\")\r\n            os.system(\"cls\")\r\n            print(\"*************************************\")\r\n    while True:\r\n        fileName=input(\"File Name                           --\")\r\n        if(os.path.exists(fileName)):\r\n            break\r\n        else:\r\n            print(\"File not exists. Please enter again file name!!!\\n\")\r\n            writeToErrorsInLogFiles.writeErrorLogFile(fileName+\" -> file not exists\")\r\n    while True:\r\n        tableName=input(\"Table Name                          --\")\r\n        tableName=tableName.lower()\r\n        if(nesne1.tableControl(dbName,tableName)==True):\r\n           break\r\n        else:\r\n           print(\"Table not exists. Please create table!!!\\n\")\r\n           writeToErrorsInLogFiles.writeErrorLogFile(tableName+\" -> table not exists in database = {}\".format(dbName))\r\n    while True:\r\n        headerInfo=input(\"Header(0)/ No Header(1)             --\")\r\n        if(headerInfo==\"0\" or headerInfo==\"1\"):\r\n            break\r\n        else:\r\n            print(\"Header(0)/ No Header(1) wrong choice!Please enter again!\")\r\n    while True:\r\n        columnState=input(\"Required Columns(0)/ All Columns(1) --\")\r\n        if(columnState==\"0\"):\r\n            desiredColumnIndexes=input(\"Index of Required Columns           --\")\r\n            break\r\n        elif(columnState==\"1\"):\r\n            desiredColumnIndexes=\"\"\r\n            break\r\n        else:\r\n            print(\"Required Columns(0)/ All Columns(1) is wrong choice!Please enter again!\")\r\n\r\n    sep=','\r\n \r\n    toMail=input(\"Enter the Gmail address             --\")\r\n    print(\"*************************************\")\r\n    sendToMailPsql.sendMailStart(toMail)\r\n    rowCount=nesne1.rowCounter(fileName,headerInfo)\r\n    print(\"Row Count: {}\".format(rowCount))\r\n    core_count=multiprocessing.cpu_count()\r\n    ram_available=(psutil.virtual_memory().available/(1024*1024*1024))\r\n    calculate_chunk_size=int(ram_available/(core_count*2)*1000000)\r\n    if (rowCount/core_count)<calculate_chunk_size:\r\n        chunk_size=int(rowCount/core_count)\r\n        chunk_size=max(chunk_size,1)\r\n    else:\r\n        chunk_size=calculate_chunk_size\r\n        chunk_size=max(chunk_size,1)\r\n    init_size=0\r\n    count=multiprocessing.cpu_count()\r\n    size=0\r\n    procs=[]\r\n    if(columnState==\"0\" ):\r\n        start_time = default_timer()\r\n        nesne1.seperatingColumns(dbName,desiredColumnIndexes,fileName,tableName,headerInfo)\r\n    else:\r\n        nesne1.disconnDB()\r\n        start_time = default_timer()\r\n        while True:\r\n            try:\r\n                if size<count:\r\n                    if init_size+1>rowCount:\r\n                        break                                                     \r\n                    proc=multiprocessing.Process(target =nesne1.copyAll, args = (user,password,host,port,dbName,fileName,tableName,headerInfo,sep,init_size,chunk_size))\r\n                    procs.append(proc)\r\n                    proc.start()\r\n                    init_size +=chunk_size\r\n                    size +=1\r\n                    for one_proc in procs:\r\n                        if one_proc.is_alive()== False:\r\n                            procs.remove(one_proc)\r\n                            size -=1\r\n                if init_size+1>rowCount:\r\n                    break\r\n            except:\r\n                break\r\n            for one_proc in procs:\r\n                if one_proc.is_alive()== False:\r\n                    procs.remove(one_proc)\r\n                    size -=1\r\n\r\n    while True:\r\n        if len(procs)==0:\r\n                break\r\n        for one_proc in procs:\r\n            if one_proc.is_alive()== False:\r\n                procs.remove(one_proc)\r\n                size -=1\r\n            if len(procs)==0:\r\n                break\r\n    duration = default_timer() - start_time\r\n    print(\"Lines per second: \",rowCount/duration)\r\n\r\n    writeToXMLfilesForPsql.writeXML(dbName,tableName,datetime.datetime.now(),user,password,host,port) \r\n    nesne1.disconnDB()\r\n    \r\n    \r\n    \r\n    sendToMailPsql.sendMailFinish(toMail,str(duration),rowCount,rowCount/duration)\r\n    print(\"DURATION: \",duration)\r\n    print(\"PostgreSQL Database Operation Finished\\n\")\r\n", "repo_name": "sametsalgin/MEPTT", "sub_path": "MainPostgreSQL.py", "file_name": "MainPostgreSQL.py", "file_ext": "py", "file_size_in_byte": 5108, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "writeToPostgreSQL.WPsql", "line_number": 19, "usage_type": "call"}, {"api_name": "os.system", "line_number": 32, "usage_type": "call"}, {"api_name": "os.system", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "writeToErrorsInLogFiles.writeErrorLogFile", "line_number": 41, "usage_type": "call"}, {"api_name": "writeToErrorsInLogFiles.writeErrorLogFile", "line_number": 49, "usage_type": "call"}, {"api_name": "sendToMailPsql.sendMailStart", "line_number": 71, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 74, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 75, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 84, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 88, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 92, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 98, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 125, "usage_type": "call"}, {"api_name": "writeToXMLfilesForPsql.writeXML", "line_number": 128, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 128, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 128, "usage_type": "attribute"}, {"api_name": "sendToMailPsql.sendMailFinish", "line_number": 133, "usage_type": "call"}]}
{"seq_id": "19663535023", "text": "#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Apr 30 21:40:25 2018\r\nhttps://blog.csdn.net/luoganttcc/article/details/80152807\r\nPython 量化(四)计算股票的移动平均线\r\n@author: luogan\r\n\"\"\"\r\nimport tushare as ts\r\nimport talib\r\nfrom matplotlib import pyplot as plt\r\n#通过tushare获取股票信息\r\ndf=ts.get_k_data('002230',start='2018-01-12',end='2019-04-30')\r\n#提取收盘价\r\nclosed=df['close'].values\r\n#获取均线的数据，通过timeperiod参数来分别获取 5,10,20 日均线的数据。\r\nma5=talib.SMA(closed,timeperiod=30)\r\nma10=talib.SMA(closed,timeperiod=60)\r\nma20=talib.SMA(closed,timeperiod=250)\r\n\r\n#打印出来每一个数据\r\nprint (closed)\r\nprint (ma5)\r\nprint (ma10)\r\nprint (ma20)\r\n\r\n\r\nplt.figure(figsize=(12,8))\r\n#通过plog函数可以很方便的绘制出每一条均线\r\nplt.plot(closed)\r\nplt.plot(ma5)\r\nplt.plot(ma10)\r\nplt.plot(ma20)\r\n#添加网格，可有可无，只是让图像好看点\r\nplt.grid()\r\n#记得加这一句，不然不会显示图像\r\nplt.show()\r\n", "repo_name": "chntylz/a_stock", "sub_path": "test_graph/test_ma_display.py", "file_name": "test_ma_display.py", "file_ext": "py", "file_size_in_byte": 1022, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "tushare.get_k_data", "line_number": 13, "usage_type": "call"}, {"api_name": "talib.SMA", "line_number": 17, "usage_type": "call"}, {"api_name": "talib.SMA", "line_number": 18, "usage_type": "call"}, {"api_name": "talib.SMA", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}]}
{"seq_id": "35291571538", "text": "# -*- coding: utf-8 -*-\n# -*- coding: 850 -*-\n\nfrom reportlab.platypus import *\nfrom reportlab.lib import colors\nfrom reportlab.lib.pagesizes import A4\nfrom reportlab.platypus import Image, Paragraph, SimpleDocTemplate, Table\nfrom reportlab.lib.styles import getSampleStyleSheet\nfrom PyQt5.QtCore import QThread\n\n\nclass Test_Report(QThread):\n    def __init__(self):\n        QThread.__init__(self)\n\n\n    def run(self):\n        self.elements = []\n\n        styleSheet = getSampleStyleSheet()\n\n        self.I = Image('/home/luciano/Escritorio/Trabajos/Drive Train Services/HMI/drive_train_logo.png',\n                       width=120, height=40)\n        self.I.hAlign = 'RIGHT'\n\n        self.MainTitle = Paragraph('''<para alignment=center><b>HIDRAULIC PUMP TEST RESULT</b></para>''',\n                                   styleSheet[\"Heading1\"])\n\n        self.CompanyBrief = Paragraph('''<para alignment=justify><b>Thank you for doing busines with us.\n         We have individually tested this product\n         to meet or exceed the O.E.M. specifications to ensure exceptional quality and performance</b></para>''',\n                                      styleSheet[\"Heading4\"])\n\n        self.SpecialNotes = Paragraph('''<b>Special Notes</b>''', styleSheet[\"Heading4\"])\n\n        self.Instructions = Paragraph('''<para alignment=center><b>*** Start up instructions *** \n        to help prevent pump damage</b></para>''', styleSheet['BodyText'])\n        self.Instructions.hAlign = 'CENTER'\n\n        _point_1 = Paragraph('Use oil with the proper viscosity at operating temperature',\n                             styleSheet['BodyText'], bulletText='>')\n        _point_2 = Paragraph('Fill the pump at least half full with filtered hydraulic fluid before start up',\n                             styleSheet['BodyText'], bulletText='>')\n        _point_3 = Paragraph('use an air bleed valve if the circuit has blocked flow on start up',\n                             styleSheet['BodyText'], bulletText='>')\n        _point_4 = Paragraph('use check valve in line service to prevent pump reversal',\n                             styleSheet['BodyText'], bulletText='>')\n        _point_5 = Paragraph('Do not press coupling halves tightly together!'\n                             ' Allow clearance between hub and insert to prevent end of thrust into pump shaft',\n                             styleSheet['BodyText'], bulletText='>')\n\n        self.Instruction_list = [_point_1, _point_2, _point_3, _point_4, _point_5]\n\n        self.Final_Setting = Paragraph('''<para alignment=justify><i>This unit's control has a final setting \n        of \"nanu nanu\" for shipment.</i></para>''',\n                                       styleSheet['BodyText'])\n        self.StandBy_Setting = Paragraph('''<para alignment=justify><i>This unit's control stand by pressure is set at \n        \"nanu nanu\" for shipment.</i></para>''',\n                                         styleSheet['BodyText'])\n        self.Date_report = Paragraph('''<para alignment=left><b>THIS TEST REPORT COMPLETE ON</b></para>''',\n                                     styleSheet['BodyText'])\n\n        self.t_2 = [['Test Pressure', 'Pump Flow']]\n\n    def fill_test_table(self, _data):\n        row_0 = ['Brand:', _data[0], 'Model Code:', _data[1]]\n        row_1 = ['Oil Temp:', str(_data[2]), '°F', 'Rotation:', _data[5]]\n        row_2 = ['Test Speed:', str(_data[3]), 'RPM', 'Peak Test\\nPressure:', str(_data[6]), _data[7]]\n        row_3 = ['Test Duration:', str(_data[4]), 'min', 'Test Cycles:', str(_data[8])]\n\n        self.t_1 = [row_0, row_1, row_2, row_3]\n\n        self.table_1 = Table(self.t_1, colWidths=80, style=[('GRID', (0, 0), (-1, -1), 0.5, colors.white),\n                                                       ('BACKGROUND', (0, 0), (0, -1), colors.lightgrey),\n                                                       ('BACKGROUND', (3, 1), (3, 3), colors.lightgrey),\n                                                       ('BACKGROUND', (2, 0), (2, 0), colors.lightgrey),\n                                                       ('ALIGN', (0, 0), (-1, -1), 'RIGHT'),\n                                                       ('ALIGN', (2, 1), (2, 3), 'LEFT'),\n                                                       ('ALIGN', (-1, 2), (-1, 2), 'LEFT')])\n\n    def fill_sample_table(self, _list): # read a list of lists with shape [n-sample][pressure,flow]\n        for sample in _list:\n            self.t_2.append([sample[0], sample[1]])\n\n        self.table_2 = Table(self.t_2, colWidths=80, style=[('GRID', (0, 0), (-1, -1), 0.5, colors.white),\n                                                       ('BACKGROUND', (0, 0), (1, 0), colors.lightgrey)])\n\n        self.table_3 = Table(self.t_2, colWidths=80, style=[('GRID', (0, 0), (-1, -1), 0.5, colors.white),\n                                                       ('BACKGROUND', (0, 0), (1, 0), colors.lightgrey)])\n\n    def update(self, filename):\n        self.doc = SimpleDocTemplate(filename, pagesize=A4)\n\n        self.elements.append(self.I)\n        self.elements.append(Spacer(200, 10))\n        self.elements.append(self.MainTitle)\n        self.elements.append(self.CompanyBrief)\n        self.elements.append(Spacer(200, 10))\n        self.elements.append(self.table_1)\n        self.elements.append(Spacer(200, 10))\n        self.elements.append(self.table_2)\n        self.elements.append(Spacer(200, 10))\n        self.elements.append(self.table_3)\n        self.elements.append(Spacer(200, 8))\n        self.elements.append(self.SpecialNotes)\n        self.elements.append(Spacer(200, 8))\n        self.elements.append(self.Instructions)\n        self.elements.extend(self.Instruction_list)\n        self.elements.append(Spacer(200, 8))\n        self.elements.append(self.Final_Setting)\n        self.elements.append(self.StandBy_Setting)\n        self.elements.append(Spacer(200, 8))\n        self.elements.append(self.Date_report)\n\n    def build(self):\n        self.doc.build(self.elements)\n        if self.isRunning():\n            self.stop()", "repo_name": "DrGhonzo/Tester", "sub_path": "tables.py", "file_name": "tables.py", "file_ext": "py", "file_size_in_byte": 6023, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "PyQt5.QtCore.QThread", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThread.__init__", "line_number": 14, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 14, "usage_type": "name"}, {"api_name": "reportlab.lib.styles.getSampleStyleSheet", "line_number": 20, "usage_type": "call"}, {"api_name": "reportlab.platypus.Image", "line_number": 22, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 26, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 29, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 34, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 36, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 40, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 42, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 44, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 46, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 48, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 54, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 57, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 60, "usage_type": "call"}, {"api_name": "reportlab.platypus.Table", "line_number": 73, "usage_type": "call"}, {"api_name": "reportlab.lib.colors.white", "line_number": 73, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 73, "usage_type": "name"}, {"api_name": "reportlab.lib.colors.lightgrey", "line_number": 74, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 74, "usage_type": "name"}, {"api_name": "reportlab.lib.colors.lightgrey", "line_number": 75, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 75, "usage_type": "name"}, {"api_name": "reportlab.lib.colors.lightgrey", "line_number": 76, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 76, "usage_type": "name"}, {"api_name": "reportlab.platypus.Table", "line_number": 85, "usage_type": "call"}, {"api_name": "reportlab.lib.colors.white", "line_number": 85, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 85, "usage_type": "name"}, {"api_name": "reportlab.lib.colors.lightgrey", "line_number": 86, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 86, "usage_type": "name"}, {"api_name": "reportlab.platypus.Table", "line_number": 88, "usage_type": "call"}, {"api_name": "reportlab.lib.colors.white", "line_number": 88, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 88, "usage_type": "name"}, {"api_name": "reportlab.lib.colors.lightgrey", "line_number": 89, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 89, "usage_type": "name"}, {"api_name": "reportlab.platypus.SimpleDocTemplate", "line_number": 92, "usage_type": "call"}, {"api_name": "reportlab.lib.pagesizes.A4", "line_number": 92, "usage_type": "name"}]}
{"seq_id": "17074259390", "text": "\"\"\"This contains all the database models used by the Reporting application.\"\"\"\n\n# Standard Libraries\nimport json\nimport logging\nimport os\n\n# Django Imports\nfrom django.conf import settings\nfrom django.core.files.storage import FileSystemStorage\nfrom django.core.validators import MinValueValidator\nfrom django.db import models\nfrom django.urls import reverse\n\n# 3rd Party Libraries\nfrom taggit.managers import TaggableManager\n\n# Ghostwriter Libraries\nfrom ghostwriter.reporting.validators import validate_evidence_extension\n\n# Using __name__ resolves to ghostwriter.reporting.models\nlogger = logging.getLogger(__name__)\n\n\nclass Severity(models.Model):\n    \"\"\"Stores an individual severity rating.\"\"\"\n\n    def get_default_weight():\n        \"\"\"\n        Return the default weight for a new :model:`reporting.Severity` instance.\n        \"\"\"\n        return Severity.objects.count() + 1\n\n    severity = models.CharField(\n        \"Severity\",\n        max_length=255,\n        unique=True,\n        help_text=\"Name for this severity rating (e.g., High, Low)\",\n    )\n    weight = models.IntegerField(\n        \"Severity Weight\",\n        default=get_default_weight,\n        validators=[MinValueValidator(1)],\n        help_text=\"Weight for sorting severity categories in reports (lower numbers are more severe)\",\n    )\n    color = models.CharField(\n        \"Severity Color\",\n        max_length=6,\n        default=\"7A7A7A\",\n        help_text=\"Six character hex color code associated with this severity for reports (e.g., FF7E79)\",\n    )\n\n    def count_findings(self):\n        \"\"\"Return the number of :model:`reporting.Finding` associated with an instance.\"\"\"\n        return Finding.objects.filter(severity=self).count()\n\n    @property\n    def color_rgb(self):\n        \"\"\"Return the severity color code as a list of RGB values.\"\"\"\n        return tuple(int(self.color[i : i + 2], 16) for i in (0, 2, 4))\n\n    @property\n    def color_hex(self):\n        \"\"\"Return the severity color code as a list of hexadecimal.\"\"\"\n        n = 2\n        return tuple(hex(int(self.color[i : i + n], 16)) for i in range(0, len(self.color), n))\n\n    count = property(count_findings)\n\n    class Meta:\n        ordering = [\"weight\", \"severity\"]\n        verbose_name = \"Severity rating\"\n        verbose_name_plural = \"Severity ratings\"\n\n    def __str__(self):\n        return f\"{self.severity}\"\n\n    def clean(self):\n        old_entry = None\n        if self.pk:\n            try:\n                old_entry = self.__class__.objects.get(pk=self.pk)\n            except self.__class__.DoesNotExist:\n                pass\n\n        # A ``pre_save`` Signal is connected to this model and runs this ``clean()`` method\n        # whenever ``save()`` is called\n\n        # The following adjustments use the queryset ``update()`` method (direct SQL statement)\n        # instead of calling ``save()`` on the individual model instance\n        # This avoids forever looping through position changes\n\n        severity_queryset = self.__class__.objects.all()\n        if old_entry:\n            old_weight = old_entry.weight\n            if old_weight != self.weight:\n                self.weight = max(self.weight, 1)\n                if self.weight > severity_queryset.count():\n                    self.weight = severity_queryset.count()\n\n                counter = 1\n                if severity_queryset:\n                    for category in severity_queryset:\n                        if not self.pk == category.pk:\n                            if self.weight == counter:\n                                counter += 1\n                            severity_queryset.filter(id=category.id).update(weight=counter)\n                            counter += 1\n                        else:\n                            pass\n                else:\n                    self.weight = 1\n        else:\n            self.weight = severity_queryset.count() + 1\n\n\nclass FindingType(models.Model):\n    \"\"\"Stores an individual finding type.\"\"\"\n\n    finding_type = models.CharField(\"Type\", max_length=255, unique=True, help_text=\"Type of finding (e.g. network)\")\n\n    def count_findings(self):\n        \"\"\"Return the number of :model:`reporting.Finding` associated with an instance.\"\"\"\n        return Finding.objects.filter(finding_type=self).count()\n\n    count = property(count_findings)\n\n    class Meta:\n        ordering = [\"finding_type\"]\n        verbose_name = \"Finding type\"\n        verbose_name_plural = \"Finding types\"\n\n    def __str__(self):\n        return f\"{self.finding_type}\"\n\n\nclass Finding(models.Model):\n    \"\"\"Stores an individual finding, related to :model:`reporting.Severity` and :model:`reporting.FindingType`.\"\"\"\n\n    title = models.CharField(\n        \"Title\",\n        max_length=255,\n        unique=True,\n        help_text=\"Enter a title for this finding that will appear in reports\",\n    )\n    description = models.TextField(\n        \"Description\",\n        null=True,\n        blank=True,\n        help_text=\"Provide a description for this finding that introduces it\",\n    )\n    impact = models.TextField(\n        \"Impact\",\n        help_text=\"Describe the impact of this finding on the affected entities\",\n        null=True,\n        blank=True,\n    )\n    mitigation = models.TextField(\n        \"Mitigation\",\n        null=True,\n        blank=True,\n        help_text=\"Describe how this finding can be resolved or addressed\",\n    )\n    replication_steps = models.TextField(\n        \"Replication Steps\",\n        null=True,\n        blank=True,\n        help_text=\"Provide an explanation for how the reader may reproduce this finding\",\n    )\n    host_detection_techniques = models.TextField(\n        \"Host Detection Techniques\",\n        null=True,\n        blank=True,\n        help_text=\"Describe how this finding can be detected on an endpoint - leave blank if this does not apply\",\n    )\n    network_detection_techniques = models.TextField(\n        \"Network Detection Techniques\",\n        null=True,\n        blank=True,\n        help_text=\"Describe how this finding can be detected on a network - leave blank if this does not apply\",\n    )\n    references = models.TextField(\n        \"References\",\n        null=True,\n        blank=True,\n        help_text=\"Provide solid references for this finding, such as links to tools and white papers\",\n    )\n    finding_guidance = models.TextField(\n        \"Finding Guidance\",\n        null=True,\n        blank=True,\n        help_text=\"Provide notes for your team that describes how the finding is intended to be used or edited during editing\",\n    )\n    cvss_score = models.FloatField(\n        \"CVSS Score v3.0\",\n        blank=True,\n        null=True,\n        help_text=\"Set the CVSS score for this finding\",\n    )\n    cvss_vector = models.CharField(\n        \"CVSS Vector v3.0\",\n        blank=True,\n        null=True,\n        max_length=54,\n        help_text=\"Set the CVSS vector for this finding\",\n    )\n    tags = TaggableManager(blank=True)\n    # Foreign Keys\n    severity = models.ForeignKey(\n        \"Severity\",\n        default=1,\n        on_delete=models.PROTECT,\n        help_text=\"Select a severity rating for this finding that reflects its role in a system compromise\",\n    )\n    finding_type = models.ForeignKey(\n        \"FindingType\",\n        default=1,\n        on_delete=models.PROTECT,\n        help_text=\"Select a finding category that fits\",\n    )\n\n    class Meta:\n        ordering = [\"severity\", \"-cvss_score\", \"finding_type\", \"title\"]\n        verbose_name = \"Finding\"\n        verbose_name_plural = \"Findings\"\n\n    def get_absolute_url(self):\n        return reverse(\"reporting:finding_detail\", args=[str(self.id)])\n\n    def __str__(self):\n        return f\"[{self.severity}] {self.title}\"\n\n\nclass DocType(models.Model):\n    \"\"\"Stores an individual document type, related to :model:`reporting.ReportTemplate`.\"\"\"\n\n    doc_type = models.CharField(\n        \"Document Type\",\n        max_length=5,\n        unique=True,\n        help_text=\"Enter a file extension for a report template filetype\",\n    )\n\n    class Meta:\n        ordering = [\n            \"doc_type\",\n        ]\n        verbose_name = \"Document type\"\n        verbose_name_plural = \"Document types\"\n\n    def __str__(self):\n        return f\"{self.doc_type}\"\n\n\nclass ReportTemplate(models.Model):\n    \"\"\"Stores an individual report template file, related to :model:`reporting.Report`.\"\"\"\n\n    # Direct template uploads to ``TEMPLATE_LOC`` instead of ``MEDIA``\n    template_storage = FileSystemStorage(location=settings.TEMPLATE_LOC)\n\n    document = models.FileField(storage=template_storage, blank=True)\n    name = models.CharField(\n        \"Template Name\",\n        null=True,\n        max_length=255,\n        help_text=\"Provide a name to be used when selecting this template\",\n    )\n    upload_date = models.DateField(\n        \"Upload Date\",\n        auto_now=True,\n        help_text=\"Date and time the template was first uploaded\",\n    )\n    last_update = models.DateField(\n        \"Last Modified\",\n        auto_now=True,\n        help_text=\"Date and time the report was last modified\",\n    )\n    description = models.TextField(\n        \"Description\",\n        blank=True,\n        help_text=\"Provide a description of this template\",\n    )\n    protected = models.BooleanField(\n        \"Protected\",\n        default=False,\n        help_text=\"Only administrators can edit this template\",\n    )\n    lint_result = models.JSONField(\n        \"Template Linter Results\",\n        null=True,\n        blank=True,\n        help_text=\"Results returned by the linter for this template in JSON format\",\n    )\n    changelog = models.TextField(\n        \"Template Change Log\",\n        null=True,\n        blank=True,\n        help_text=\"Add a line explaining any file changes\",\n    )\n    landscape = models.BooleanField(\n        \"Landscape Orientation\",\n        default=False,\n        help_text=\"Flag this document as landscape orientation\",\n    )\n    tags = TaggableManager(blank=True)\n    # Foreign Keys\n    uploaded_by = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.SET_NULL, null=True, blank=True)\n    client = models.ForeignKey(\n        \"rolodex.Client\",\n        on_delete=models.CASCADE,\n        null=True,\n        blank=True,\n        help_text=\"Template will only be displayed for this client\",\n    )\n    doc_type = models.ForeignKey(\n        \"reporting.DocType\",\n        on_delete=models.SET_NULL,\n        null=True,\n        blank=True,\n        help_text=\"Select the filetype for this template\",\n    )\n    p_style = models.CharField(\n        \"New Paragraph Style\",\n        max_length=255,\n        null=True,\n        blank=True,\n        default=None,\n        help_text=\"Provide the name of a style in your template to use for new paragraphs (Word only).\",\n    )\n\n    class Meta:\n        ordering = [\"doc_type\", \"client\", \"name\"]\n        verbose_name = \"Report template\"\n        verbose_name_plural = \"Report templates\"\n\n    def get_absolute_url(self):\n        return reverse(\"reporting:template_detail\", args=[str(self.id)])\n\n    def __str__(self):\n        return f\"{self.name}\"\n\n    @property\n    def filename(self):\n        return os.path.basename(self.document.name)\n\n    def get_status(self):\n        result_code = \"unknown\"\n        if self.lint_result:\n            try:\n                result_code = self.lint_result[\"result\"]\n            except json.decoder.JSONDecodeError:  # pragma: no cover\n                logger.exception(\"Could not decode data in model as JSON: %s\", self.lint_result)\n            except Exception:  # pragma: no cover\n                logger.exception(\n                    \"Encountered an exception while trying to decode this as JSON: %s\",\n                    self.lint_result,\n                )\n        return result_code\n\n\nclass Report(models.Model):\n    \"\"\"Stores an individual report, related to :model:`rolodex.Project` and :model:`users.User`.\"\"\"\n\n    title = models.CharField(\n        \"Title\",\n        max_length=255,\n        default=\"New Report\",\n        help_text=\"Provide a meaningful title for this report - this is only seen in Ghostwriter\",\n    )\n    creation = models.DateField(\"Creation Date\", auto_now_add=True, help_text=\"Date the report was created\")\n    last_update = models.DateField(\"Last Update\", auto_now=True, help_text=\"Date the report was last touched\")\n    complete = models.BooleanField(\"Completed\", default=False, help_text=\"Mark the report as complete\")\n    archived = models.BooleanField(\"Archived\", default=False, help_text=\"Mark the report as archived\")\n    tags = TaggableManager(blank=True)\n    # Foreign Keys\n    project = models.ForeignKey(\n        \"rolodex.Project\",\n        on_delete=models.CASCADE,\n        null=True,\n        help_text=\"Select the project tied to this report\",\n    )\n    docx_template = models.ForeignKey(\n        \"ReportTemplate\",\n        related_name=\"reporttemplate_docx_set\",\n        on_delete=models.SET_NULL,\n        limit_choices_to={\n            \"doc_type__doc_type__iexact\": \"docx\",\n        },\n        null=True,\n        help_text=\"Select the Word template to use for this report\",\n    )\n    pptx_template = models.ForeignKey(\n        \"ReportTemplate\",\n        related_name=\"reporttemplate_pptx_set\",\n        on_delete=models.SET_NULL,\n        limit_choices_to={\"doc_type__doc_type__iexact\": \"pptx\"},\n        null=True,\n        help_text=\"Select the PowerPoint template to use for this report\",\n    )\n    created_by = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.SET_NULL, null=True, blank=True)\n    delivered = models.BooleanField(\"Delivered\", default=False, help_text=\"Delivery status of the report\")\n\n    class Meta:\n        ordering = [\"-creation\", \"-last_update\", \"project\"]\n        verbose_name = \"Report\"\n        verbose_name_plural = \"Reports\"\n\n    def get_absolute_url(self):\n        return reverse(\"reporting:report_detail\", args=[str(self.id)])\n\n    def __str__(self):\n        return f\"{self.title}\"\n\n\nclass ReportFindingLink(models.Model):\n    \"\"\"\n    Stores an individual copy of a finding added to a :model:`reporting.Report` with\n    :model:`reporting.Severity`, :model:`reporting.FindingType`, and :model:`users.User`.\n    \"\"\"\n\n    title = models.CharField(\n        \"Title\",\n        max_length=255,\n        help_text=\"Enter a title for this finding that will appear in the reports\",\n    )\n    position = models.IntegerField(\n        \"Report Position\",\n        default=1,\n        validators=[MinValueValidator(1)],\n    )\n    affected_entities = models.TextField(\n        \"Affected Entities\",\n        null=True,\n        blank=True,\n        help_text=\"Provide a list of the affected entities (e.g. domains, hostnames, IP addresses)\",\n    )\n    description = models.TextField(\n        \"Description\",\n        null=True,\n        blank=True,\n        help_text=\"Provide a description for this finding that introduces it\",\n    )\n    impact = models.TextField(\n        \"Impact\",\n        null=True,\n        blank=True,\n        help_text=\"Describe the impact of this finding on the affected entities\",\n    )\n    mitigation = models.TextField(\n        \"Mitigation\",\n        null=True,\n        blank=True,\n        help_text=\"Describe how this finding can be resolved or addressed\",\n    )\n    replication_steps = models.TextField(\n        \"Replication Steps\",\n        null=True,\n        blank=True,\n        help_text=\"Provide an explanation for how the reader may reproduce this finding\",\n    )\n    host_detection_techniques = models.TextField(\n        \"Host Detection Techniques\",\n        null=True,\n        blank=True,\n        help_text=\"Describe how this finding can be detected on an endpoint - leave blank if this does not apply\",\n    )\n    network_detection_techniques = models.TextField(\n        \"Network Detection Techniques\",\n        null=True,\n        blank=True,\n        help_text=\"Describe how this finding can be detected on a network - leave blank if this does not apply\",\n    )\n    references = models.TextField(\n        \"References\",\n        null=True,\n        blank=True,\n        help_text=\"Provide solid references for this finding, such as links to reference materials, tooling, and white papers\",\n    )\n    finding_guidance = models.TextField(\n        \"Finding Guidance\",\n        null=True,\n        blank=True,\n        help_text=\"Provide notes for your team that describes this finding within this report\",\n    )\n    complete = models.BooleanField(\n        \"Completed\",\n        default=False,\n        help_text=\"Mark the finding as ready for a QA review\",\n    )\n    added_as_blank = models.BooleanField(\n        \"Added as Blank\",\n        default=False,\n        help_text=\"Identify a finding that was created for this report instead of copied from the library\",\n    )\n    tags = TaggableManager(blank=True)\n    # Foreign Keys\n    severity = models.ForeignKey(\n        \"Severity\",\n        default=1,\n        on_delete=models.PROTECT,\n        help_text=\"Select a severity rating for this finding that reflects its role in a system compromise\",\n    )\n    finding_type = models.ForeignKey(\n        \"FindingType\",\n        default=1,\n        on_delete=models.PROTECT,\n        help_text=\"Select a finding category that fits\",\n    )\n    report = models.ForeignKey(\"Report\", on_delete=models.CASCADE, null=True)\n    assigned_to = models.ForeignKey(\n        settings.AUTH_USER_MODEL,\n        on_delete=models.SET_NULL,\n        null=True,\n        blank=True,\n        help_text=\"Assign the task of editing this finding to a specific operator - defaults to the operator that added it to the report\",\n    )\n    cvss_score = models.FloatField(\n        \"CVSS Score v3.0\",\n        blank=True,\n        null=True,\n        help_text=\"Set the CVSS score for this finding\",\n    )\n    cvss_vector = models.CharField(\n        \"CVSS Vector v3.0\",\n        blank=True,\n        null=True,\n        max_length=54,\n        help_text=\"Set the CVSS vector for this finding\",\n    )\n\n    class Meta:\n        ordering = [\"report\", \"severity__weight\", \"position\"]\n        verbose_name = \"Report finding\"\n        verbose_name_plural = \"Report findings\"\n\n    def __str__(self):\n        return f\"{self.title}\"\n\n\nclass Evidence(models.Model):\n    \"\"\"\n    Stores an individual evidence file, related to :model:`reporting.ReportFindingLink`\n    and :model:`users.User`.\n    \"\"\"\n\n    def set_upload_destination(self, filename):\n        \"\"\"Sets the `upload_to` destination to the evidence folder for the associated report ID.\"\"\"\n        return os.path.join(\"evidence\", str(self.finding.report.id), filename)\n\n    document = models.FileField(\n        upload_to=set_upload_destination,\n        validators=[validate_evidence_extension],\n        blank=True,\n    )\n    friendly_name = models.CharField(\n        \"Friendly Name\",\n        null=True,\n        max_length=255,\n        help_text=\"Provide a simple name to be used to reference this evidence\",\n    )\n    upload_date = models.DateField(\n        \"Upload Date\",\n        auto_now=True,\n        help_text=\"Date and time the evidence was uploaded\",\n    )\n    caption = models.CharField(\n        \"Caption\",\n        max_length=255,\n        blank=True,\n        help_text=\"Provide a one line caption to be used in the report - keep it brief\",\n    )\n    description = models.TextField(\n        \"Description\",\n        blank=True,\n        help_text=\"Describe this evidence to your team\",\n    )\n    tags = TaggableManager(blank=True)\n    # Foreign Keys\n    finding = models.ForeignKey(\"ReportFindingLink\", on_delete=models.CASCADE)\n    uploaded_by = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.SET_NULL, null=True, blank=True)\n\n    class Meta:\n        ordering = [\"finding\", \"document\"]\n        verbose_name = \"Evidence\"\n        verbose_name_plural = \"Evidence\"\n\n    def get_absolute_url(self):\n        return reverse(\"reporting:evidence_detail\", args=[str(self.id)])\n\n    def __str__(self):\n        return f\"{self.document.name}\"\n\n    @property\n    def filename(self):\n        return os.path.basename(self.document.name)\n\n\nclass Archive(models.Model):\n    \"\"\"Stores an individual archived report, related to :model:`rolodex.Project.\"\"\"\n\n    report_archive = models.FileField()\n    project = models.ForeignKey(\"rolodex.Project\", on_delete=models.CASCADE, null=True)\n\n    @property\n    def filename(self):\n        return os.path.basename(self.report_archive.name)\n\n    class Meta:\n        ordering = [\"project\"]\n        verbose_name = \"Archived report\"\n        verbose_name_plural = \"Archived reports\"\n\n    def __str__(self):\n        return f\"{self.report_archive.name}\"\n\n\nclass FindingNote(models.Model):\n    \"\"\"Stores an individual finding note, related to :model:`reporting.Finding`.\"\"\"\n\n    timestamp = models.DateField(\"Timestamp\", auto_now_add=True, help_text=\"Creation timestamp\")\n    note = models.TextField(\n        \"Notes\",\n        null=True,\n        blank=True,\n        help_text=\"Provide additional information about the finding\",\n    )\n    # Foreign Keys\n    finding = models.ForeignKey(\"Finding\", on_delete=models.CASCADE, null=False)\n    operator = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.SET_NULL, null=True, blank=True)\n\n    class Meta:\n        ordering = [\"finding\", \"-timestamp\"]\n        verbose_name = \"Finding note\"\n        verbose_name_plural = \"Finding notes\"\n\n    def __str__(self):\n        return f\"{self.finding} {self.timestamp}: {self.note}\"\n\n\nclass LocalFindingNote(models.Model):\n    \"\"\"Stores an individual finding note in a report, related to :model:`reporting.ReportFindingLink`.\"\"\"\n\n    timestamp = models.DateField(\"Timestamp\", auto_now_add=True, help_text=\"Creation timestamp\")\n    note = models.TextField(\n        \"Notes\",\n        null=True,\n        blank=True,\n        help_text=\"Provide additional information about the finding\",\n    )\n    # Foreign Keys\n    finding = models.ForeignKey(\"ReportFindingLink\", on_delete=models.CASCADE, null=False)\n    operator = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.SET_NULL, null=True, blank=True)\n\n    class Meta:\n        ordering = [\"finding\", \"-timestamp\"]\n        verbose_name = \"Local finding note\"\n        verbose_name_plural = \"Local finding notes\"\n\n    def __str__(self):\n        return f\"{self.finding} {self.timestamp}: {self.note}\"\n", "repo_name": "GhostManager/Ghostwriter", "sub_path": "ghostwriter/reporting/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 22125, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1085, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 117, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 117, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 120, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 120, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 137, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 137, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 140, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 140, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 146, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 146, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 152, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 152, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 158, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 158, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 164, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 164, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 170, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 170, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 176, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 176, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 182, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 182, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 188, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 188, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 194, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 194, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 200, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 200, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 207, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 209, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 209, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 212, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 212, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 215, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 215, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 218, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 218, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 228, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 234, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 234, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 237, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 237, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 255, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 255, "usage_type": "name"}, {"api_name": "django.core.files.storage.FileSystemStorage", "line_number": 259, "usage_type": "call"}, {"api_name": "django.conf.settings.TEMPLATE_LOC", "line_number": 259, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 259, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 261, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 261, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 262, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 262, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 268, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 268, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 273, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 273, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 278, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 278, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 283, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 283, "usage_type": "name"}, {"api_name": "django.db.models.JSONField", "line_number": 288, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 288, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 294, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 294, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 300, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 300, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 305, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 307, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 307, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 307, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 307, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 307, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 308, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 308, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 310, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 310, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 315, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 315, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 317, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 317, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 322, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 322, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path", "line_number": 344, "usage_type": "attribute"}, {"api_name": "json.decoder", "line_number": 351, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 361, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 361, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 364, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 364, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 370, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 370, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 371, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 371, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 372, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 372, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 373, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 373, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 374, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 376, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 376, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 378, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 378, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 382, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 382, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 385, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 385, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 392, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 392, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 395, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 395, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 400, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 400, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 400, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 400, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 400, "usage_type": "attribute"}, {"api_name": "django.db.models.BooleanField", "line_number": 401, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 401, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 409, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 415, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 415, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 421, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 421, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 426, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 426, "usage_type": "name"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 429, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 431, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 431, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 437, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 437, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 443, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 443, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 449, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 449, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 455, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 455, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 461, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 461, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 467, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 467, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 473, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 473, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 479, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 479, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 485, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 485, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 490, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 490, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 495, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 497, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 497, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 500, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 500, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 503, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 503, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 506, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 506, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 509, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 509, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 509, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 510, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 510, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 511, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 511, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 512, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 512, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 517, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 517, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 523, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 523, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 540, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 540, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 548, "usage_type": "call"}, {"api_name": "os.path", "line_number": 548, "usage_type": "attribute"}, {"api_name": "django.db.models.FileField", "line_number": 550, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 550, "usage_type": "name"}, {"api_name": "ghostwriter.reporting.validators.validate_evidence_extension", "line_number": 552, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 555, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 555, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 561, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 561, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 566, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 566, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 572, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 572, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 577, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 579, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 579, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 579, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 580, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 580, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 580, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 580, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 580, "usage_type": "attribute"}, {"api_name": "django.urls.reverse", "line_number": 588, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 595, "usage_type": "call"}, {"api_name": "os.path", "line_number": 595, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 598, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 598, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 601, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 601, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 602, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 602, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 602, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 606, "usage_type": "call"}, {"api_name": "os.path", "line_number": 606, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 617, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 617, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 620, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 620, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 621, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 621, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 628, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 628, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 628, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 629, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 629, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 629, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 629, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 629, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 640, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 640, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 643, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 643, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 644, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 644, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 651, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 651, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 651, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 652, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 652, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 652, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 652, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 652, "usage_type": "attribute"}]}
{"seq_id": "37843601526", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Dec  7 11:10:05 2020\r\n\r\n@author: 月球来的火星人\r\n\"\"\"\r\n# =============================================================================\r\n# catboost版本为0.24.1\r\n# =============================================================================\r\n\r\nimport pandas as pd\r\nimport gc\r\nimport re \r\nimport jieba\r\nimport numpy as np\r\nimport seaborn as sns \r\nimport matplotlib.pyplot as plt\r\nimport catboost as cbt\r\nimport xgboost as xgb\r\nfrom tqdm import tqdm\r\nimport lightgbm as lgb\r\nfrom datetime import datetime, timedelta\r\nfrom scipy.special import boxcox1p\r\nfrom scipy.stats import skew, norm\r\nfrom sklearn.model_selection import train_test_split\r\nfrom scipy.stats import boxcox_normmax\r\nimport category_encoders as ce\r\nfrom collections import Counter\r\nfrom sklearn.model_selection import KFold,StratifiedKFold\r\nfrom sklearn.metrics import  roc_auc_score,accuracy_score,f1_score\r\nimport warnings\r\nfrom sklearn.decomposition import TruncatedSVD\r\nfrom sklearn.metrics import f1_score\r\nfrom catboost import CatBoostClassifier\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\r\nfrom gensim.models import Word2Vec\r\nfrom sklearn.preprocessing import LabelEncoder\r\n#import toad\r\nplt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体为黑体\r\nplt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题\r\nwarnings.filterwarnings('ignore')\r\n\r\n# 读取数据\r\nbase_info = pd.read_csv('risk-prediction-of-illegal/base_info.csv')\r\ntax_info = pd.read_csv('risk-prediction-of-illegal/tax_info.csv')\r\nchange_info = pd.read_csv('risk-prediction-of-illegal/change_info.csv')\r\nother_info = pd.read_csv('risk-prediction-of-illegal/other_info.csv')\r\nnews_info = pd.read_csv('risk-prediction-of-illegal/news_info.csv')\r\nentprise_info = pd.read_csv('risk-prediction-of-illegal/entprise_info.csv')\r\nannual_report_info = pd.read_csv('risk-prediction-of-illegal/annual_report_info.csv')\r\ntest_evaluate = pd.read_csv('entprise_evaluate.csv')\r\ntest_submit = pd.read_csv('entprise_submit.csv')\r\n\r\n\r\nfeature = base_info.merge(entprise_info, how='left')\r\n\r\n# =============================================================================\r\n# 第二张表\r\n# =============================================================================\r\n\r\nfor f in tqdm(['FUNDAM', 'EMPNUM', 'COLGRANUM','RETSOLNUM','DISPERNUM','UNENUM','COLEMPLNUM','RETEMPLNUM']):\r\n    df_temp = annual_report_info.groupby('id')[f].agg(**{\r\n        'annual_{}_mean'.format(f): 'mean',\r\n        'annual_{}_std'.format(f): 'std',\r\n        'annual_{}_max'.format(f): 'max',\r\n        'annual_{}_min'.format(f): 'min',\r\n        'annual_{}_sum'.format(f): 'sum',\r\n        'annual_{}_count'.format(f): 'count', \r\n#         'annual_{}_skew'.format(f): 'skew',\r\n    }).reset_index()\r\n    feature = feature.merge(df_temp, how='left')\r\n\r\ndef unique_num(x):\r\n    return len(np.unique(x))\r\nfor i in tqdm(['WEBSITSIGN', 'STATE']): \r\n    df_temp = annual_report_info.groupby('id',sort=False)[i].agg(**{'annual_{}_unique_num'.format(i): unique_num,}).reset_index()\r\n    feature = feature.merge(df_temp, how='left')\r\n\r\n\r\n\r\n# tax_info['START_DATE'] = pd.to_datetime(tax_info['START_DATE'])\r\n# tax_info['END_DATE'] = pd.to_datetime(tax_info['END_DATE'])\r\n# tax_info['DATE_gap'] = tax_info['END_DATE'].apply(lambda x: x.day) - tax_info['START_DATE'].apply(lambda x: x.day)\r\n# def w2v_feat(df, group_id, feat, length):\r\n#     print('start word2vec ...')\r\n#     df[feat] = df[feat].astype('str')\r\n#     data_frame = df.groupby(group_id)[feat].agg(list).reset_index()\r\n#     model = Word2Vec(data_frame[feat].values, size=length, window=5, min_count=1, sg=1,workers=1, hs=1, iter=10, seed=1)\r\n#     data_frame[feat] = data_frame[feat].apply(lambda x: pd.DataFrame([model[c] for c in x]))\r\n#     for m in range(length): \r\n#         data_frame['w2v_{}_mean'.format(m)] = data_frame[feat].apply(lambda x: x[m].mean())\r\n#     del data_frame[feat]\r\n#     return data_frame\r\n# feature = feature.merge(w2v_feat(change_info, 'id', 'bgxmdm', 5), how='left')\r\n# def gen_user_group_amount_features(df, value):\r\n#     group_df = df.pivot_table(index='id',\r\n#                               columns=value,\r\n#                               values='FUNDAM',\r\n#                               dropna=False,\r\n#                               aggfunc=['count','sum','mean']).fillna(0)\r\n#     group_df.columns = ['id_{}_{}_FUNDAM_{}'.format(value, f[1], f[0]) for f in group_df.columns]\r\n#     group_df.reset_index(inplace=True)\r\n#     return group_df \r\n# # annual_report_info['WEBSITSIGN'] = annual_report_info['WEBSITSIGN'].fillna(-1)\r\n# feature = feature.merge(gen_user_group_amount_features(df=annual_report_info, value='ANCHEYEAR'), how='left')\r\n# def gen_user_countvec_features(df, value):\r\n#     df[value] = df[value].astype(str)\r\n#     df[value].fillna('-1', inplace=True)\r\n#     group_df = df.groupby(['id']).apply(lambda x: x[value].tolist()).reset_index()\r\n#     group_df.columns = ['id', 'list']\r\n#     group_df['list'] = group_df['list'].apply(lambda x: ','.join(x))\r\n#     enc_vec = CountVectorizer()\r\n#     tfidf_vec = enc_vec.fit_transform(group_df['list'])\r\n#     svd_enc = TruncatedSVD(n_components=10, n_iter=20, random_state=2020)\r\n#     vec_svd = svd_enc.fit_transform(tfidf_vec)\r\n#     vec_svd = pd.DataFrame(vec_svd)\r\n#     vec_svd.columns = ['svd_countvec_{}_{}'.format(value, i) for i in range(vec_svd.shape[1])]\r\n#     group_df = pd.concat([group_df, vec_svd], axis=1)\r\n#     del group_df['list']\r\n#     return group_df\r\n# feature = feature.merge(gen_user_countvec_features(df=annual_report_info, value='FUNDAM'), how='left')\r\n# def w2v_feat(df, group_id, feat, length):\r\n#     print('start word2vec ...')\r\n#     df[feat] = df[feat].astype('str')\r\n#     data_frame = df.groupby(group_id)[feat].agg(list).reset_index()\r\n#     model = Word2Vec(data_frame[feat].values, size=length, window=5, min_count=1, sg=1,workers=1, hs=1, iter=10, seed=1)\r\n#     data_frame[feat] = data_frame[feat].apply(lambda x: pd.DataFrame([model[c] for c in x]))\r\n#     for m in range(length): \r\n#         data_frame['w2v_{}_mean'.format(m)] = data_frame[feat].apply(lambda x: x[m].mean())\r\n#     del data_frame[feat]\r\n#     return data_frame\r\n# feature = feature.merge(w2v_feat(annual_report_info, 'id', 'EMPNUM', 5), how='left')\r\n# groups = annual_report_info.groupby('id')\r\n# indexs = annual_report_info['id'].unique()\r\n# annual_fe = []\r\n# for i in tqdm(['EMPNUMSIGN','STOCKTRANSIGN']): #,,'WEBSITSIGN','FORINVESTSIGN','STOCKTRANSIGN'\r\n#     temp = [groups.get_group(g)[i].tolist()[-1] for g in groups.groups]\r\n#     df_temp = pd.DataFrame({'id':indexs,f'{i}_state':temp})\r\n#     annual_fe.append(f'{i}_state')\r\n#     feature = feature.merge(df_temp, how='left')\r\n\r\n# =============================================================================\r\n# base\r\n# =============================================================================\r\n#删除缺失值99%以上的\r\ndel feature['midpreindcode'],feature['ptbusscope'],feature['protype'],feature['forregcap'],feature['forreccap']\r\nfeature['dom_len'] = feature['dom'].apply(lambda x: len(x))\r\nfeature['opscope_len'] = feature['opscope'].apply(lambda x: len(x))\r\nfeature['oploc_len'] = feature['oploc'].apply(lambda x: len(x))\r\nfeature['opform_len'] = feature['opform'].apply(lambda x: len(x) if type(x)==str else x)\r\ndef date_deal(df):\r\n    df['opfrom'] = pd.to_datetime(df['opfrom'])\r\n    df['opto'] = pd.to_datetime(df['opto'])\r\n    df['date_gap'] = df['opto'].apply(lambda x: x.year) - df['opfrom'].apply(lambda x: x.year)\r\n#     df['_date_gap'] = 4\r\n#     df[df['date_gap']==30]['_date_gap'],df[df['date_gap']==40]['_date_gap'],df[df['date_gap']==50]['_date_gap'] = 1,2,3\r\n    df['opfrom_year'] = df['opfrom'].apply(lambda x: x.year)\r\n    df['opto_year'] = df['opto'].apply(lambda x: x.year)\r\n    df['opfrom_month'] = df['opfrom'].apply(lambda x: x.month)\r\n    df['opto_month'] = df['opto'].apply(lambda x: x.month)\r\n    del df['opfrom'],df['opto']\r\n\r\n    return df\r\nfeature = date_deal(feature)\r\n\r\n# def f2(x):\r\n#     if x==0.0:\r\n#         return 1\r\n#     elif x==1000.0:\r\n#         return 2\r\n#     else:\r\n#         return 3\r\n# feature['reccap_'] = feature['reccap'].apply(lambda x: f1(x))a\r\n# feature[feature['label']==1][''opscope''].count()\r\n# Counter(feature[feature['label']==1]['regcap']).most_common(50)\r\n# Counter(feature[feature['label']==0]['regcap']).most_common(50)\r\n# sns.distplot(feature[feature['label']==1]['regcap'], color=\"b\", bins=10)\r\n# sns.distplot(feature[feature['label']==0]['regcap'], color=\"b\", bins=10)\r\n# def w2v_feat(df, feat, length):\r\n#     print('start word2vec ...')\r\n#     data_frame = df.copy()\r\n# #     df[feat] = df[feat].astype('str')\r\n# #     data_frame = df.groupby(group_id)[feat].agg(list).reset_index()\r\n#     model = Word2Vec(data_frame[feat].values, size=length, window=5, min_count=1, sg=1,workers=1, hs=1, iter=10, seed=1)\r\n#     data_frame[feat] = data_frame[feat].apply(lambda x: pd.DataFrame([model[c] for c in x]))\r\n#     for m in range(length): \r\n#         data_frame['w2v_{}_mean'.format(m)] = data_frame[feat].apply(lambda x: x[m].mean())\r\n#     del data_frame[feat]\r\n#     return data_frame\r\n# df_ = w2v_feat(feature, 'opscope', 30)\r\n# 读取停用词数据\r\n# stopwords = pd.read_csv('../input/english-and-chinese-stopwords/cn_stopwords.txt', encoding='utf8', names=['stopword'], index_col=False)\r\n# # 转化词列表\r\n# stop_list = stopwords['stopword'].tolist()\r\n\r\n# r =  \"\\\\【.*?】+|\\\\《.*?》+|\\\\#.*?#+|[.!/_,$&%^*()<>+\"\"'?@|:~{}#]+|[——！\\\\\\，。=？、：；“”‘’￥……（）《》【】]\"\r\n# seg = pkuseg.pkuseg() \r\n# feature['opscope'] = feature['opscope'].apply(lambda x: [i for i in seg.cut(re.sub(r,'',x)) if i not in stop_list])\r\n# def f1(x):\r\n#     if '投资' in x:\r\n#         return 1\r\n#     elif '咨询' in x:\r\n#         return 2\r\n#     elif '融资' in x:\r\n#         return 3\r\n#     else:\r\n#         return 4\r\n# feature['opscope_word'] = feature['opscope'].apply(lambda x: f1(x))\r\n# def skew_deal(df,*cols):\r\n#     for col in cols:\r\n#         df[col] = df[col].apply(lambda x: np.log(1+x))\r\n#     return df\r\n# feature = skew_deal(feature,['empnum','regcap','exenum'])\r\n# # 缺失值统计，统计存在缺失值的特征，构造缺失值相关计数特征\r\n# loss_fea = ['enttypeitem', 'empnum', 'compform', 'venind', 'enttypeminu',\r\n#        'reccap', 'opform_len', 'date_gap']\r\n# for i in tqdm(loss_fea):\r\n#     feature[i] = feature[i].fillna(-999)\r\n#     a = feature.loc[feature[i]==-999]\r\n#     e = a.groupby(['industryphy'])['id'].count().reset_index(name=i+'_industryphy_count') \r\n#     feature = feature.merge(e,on='industryphy',how='left')\r\n    \r\n#     d = a.groupby(['loanProduct'])['id'].count().reset_index(name=i+'_loan_count') \r\n#     data = data.merge(d,on='loanProduct',how='left')\r\n    \r\n#     m = a.groupby(['job'])['id'].count().reset_index(name=i+'_job_count') \r\n#     data = data.merge(m,on='job',how='left')\r\n    \r\n#     data['certloss_'+i] = data[i+'_certId_count']/data['certId_count']\r\n#     data['jobloss_'+i] = data[i+'_job_count']/data['job_count']\r\n#rank_fe = ['oplocdistrict','industryco','enttype','enttypeitem','enttypeminu']\r\n# for i in tqdm(cross_fe):\r\n#     feature[f'{i}_unique'] = feature.groupby([i])['id'].transform('nunique')\r\n# dense_features = ['empnum','parnum','exenum','regcap','reccap']\r\n# for f1 in tqdm(dense_features):\r\n#     for f2 in dense_features:\r\n#         if f1==f2:\r\n#             continue\r\n#         if '{}_add_{}'.format(f1,f2) not in feature.columns and '{}_add_{}'.format(f2,f1) not in feature.columns:\r\n#             feature['{}_add_{}'.format(f1, f2)] = feature[f1] + feature[f2]\r\n#             feature['{}_Mul_{}'.format(f1, f2)] = feature[f1] / feature[f2]\r\n#feature['reg/rec'] = feature['regcap'] / feature['reccap']\r\n#feature['emp/exe'] = feature['empnum'] / feature['exenum']\r\n#feature['par/exe'] = feature['parnum'] / feature['exenum']\r\nfeature['opform'] = feature['opform'].replace('01', '01-以个人财产出资').replace('02', '02-以家庭共有财产作为个人出资')\r\n\r\nfeature['opform_'] = feature['opform'].fillna(-1)\r\ndef f(x):\r\n    if x=='10':\r\n        return 1\r\n    elif x=='01-以个人财产出资':\r\n        return 2\r\n    elif x==-1:\r\n        return 3\r\n    else:\r\n        return 4\r\nfeature['opform_'] = feature['opform_'].apply(lambda x: f(x))\r\n#筛选类别特征\r\nfeature_list = ['oplocdistrict','industryphy','enttype','state','adbusign','townsign',\r\n                'regtype','opform','enttypegb','dom_len','oploc_len'] #'opscope_len'\r\ncross_fe = []\r\nfor c in tqdm(feature_list):\r\n    for c1 in ['industryphy']:\r\n        if c1==c:\r\n            continue\r\n        if 'cross_{}_{}'.format(c,c1) not in feature.columns and 'cross_{}_{}'.format(c1,c) not in feature.columns:\r\n            feature['cross_{}_{}'.format(c,c1)]=feature[c].astype(str)+feature[c1].astype(str)\r\n            cross_fe.append('cross_{}_{}'.format(c,c1))\r\nfor i in tqdm(feature_list):\r\n    feature[f'{i}_count'] = feature.groupby([i])['id'].transform('count')\r\n           \r\nTE_encoder = ce.TargetEncoder(cols = feature_list)\r\nfeature = TE_encoder.fit_transform(feature, feature['label'])            \r\npre_col = ['id','opscope','dom','label','oploc']\r\ncat_fe = [i for i in feature.select_dtypes('object').columns if i not in pre_col]\r\nfor f in tqdm(cat_fe):\r\n    lbl = LabelEncoder()\r\n    feature[f] = lbl.fit_transform(feature[f].astype(str))            \r\n            \r\ndf_train = feature[~feature['label'].isnull()].copy().reset_index(drop=True)\r\ntarget = df_train['label']\r\ndf_test = feature[feature['label'].isnull()].copy().reset_index(drop=True)            \r\n            \r\ncol = [i for i in feature.columns if i not in pre_col]            \r\n            \r\n# =============================================================================\r\n# CatBoostClassifier\r\n# =============================================================================\r\nycol = 'label'\r\nseed = 2020\r\nnum_folds=5\r\nprediction = df_test[['id']]\r\nprediction['score'] = 0\r\noffline_score = []\r\n\r\nfor i,model_seed in enumerate(range(num_model_seed)):\r\n    kfold = StratifiedKFold(n_splits=num_folds, random_state=seeds[model_seed], shuffle=True).split(df_train[col], df_train[ycol])\r\n    oof_probs = np.zeros(df_train.shape[0])\r\n    for fold, (train_idx, valid_idx) in enumerate(kfold):\r\n        X_train, y_train = df_train[col].iloc[train_idx], df_train[ycol].iloc[train_idx]\r\n        X_valid, y_valid = df_train[col].iloc[valid_idx], df_train[ycol].iloc[valid_idx]\r\n\r\n        model=CatBoostClassifier(\r\n            loss_function=\"Logloss\",\r\n            eval_metric=\"F1\",\r\n            task_type=\"CPU\",\r\n            learning_rate=0.01,\r\n            iterations=10000,\r\n            random_seed=2020,\r\n            od_type=\"Iter\",\r\n            depth=8,\r\n            early_stopping_rounds=500)\r\n\r\n        clf = model.fit(X_train,y_train, eval_set=(X_valid,y_valid),verbose=500)\r\n        yy_pred_valid=clf.predict(X_valid)\r\n        y_pred_valid = clf.predict(X_valid,prediction_type='Probability')[:,-1]\r\n        oof_probs[valid_idx] = y_pred_valid\r\n        offline_score.append(f1_score(y_valid, yy_pred_valid))\r\n        pred_test = clf.predict(df_test[col],prediction_type='Probability')[:,-1]\r\n        prediction['score'] += pred_test / num_folds\r\n        \r\n    print('OOF-MEAN-F1:%.6f, OOF-STD-F1:%.6f' % (np.mean(offline_score), np.std(offline_score)))\r\n    oof_probs += oof_probs / num_model_seed\r\n    prediction['score'] += prediction['score'] / num_model_seed\r\n", "repo_name": "weichenlong-boy/CCF", "sub_path": "code/ctb0.8425.py", "file_name": "ctb0.8425.py", "file_ext": "py", "file_size_in_byte": 15553, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "41", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 39, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 40, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "warnings.filterwarnings", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 52, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 74, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 152, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 153, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 262, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 269, "usage_type": "call"}, {"api_name": "category_encoders.TargetEncoder", "line_number": 272, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 276, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 277, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 298, "usage_type": "call"}, {"api_name": "catboost.CatBoostClassifier", "line_number": 303, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 322, "usage_type": "call"}]}
{"seq_id": "33166926239", "text": "from django.template.defaultfilters import register\nfrom django.contrib.auth.models import Group\n\nepsilon = 0.00000001\n@register.filter(name='lookup')\ndef lookup(dict, index):\n    if index in dict:\n        return dict[index]\n    return ''\n\n@register.filter(name='currency')\ndef currency(value):\n    try:\n        v = float(value)\n        if v < epsilon:\n            return 0\n        strValue = '{:,.2f}'.format(v)\n        \n        return strValue if strValue != \"0.00\" else crypto(value)\n    except:\n        pass    \n        \n    return value\n\n@register.filter(name='crypto')\ndef crypto(value):\n    try:\n        v = float(value)\n        if v < epsilon:\n            return 0\n        strValue = '{:,.8f}'.format(v)\n        return strValue if strValue != \"0.00000000\" else str(value)\n    except:\n        pass\n        \n    return value\n\n@register.filter(name='has_group')\ndef has_group(user, group_name):\n    try:\n        group = Group.objects.get(name=group_name)\n        return group in user.groups.all()\n    except:\n        return False\n\n@register.filter(name='zip')\ndef zip_lists(a, b):\n  return zip(a, b)", "repo_name": "adgalad/Cash4Home", "sub_path": "app/templatetags/filters.py", "file_name": "filters.py", "file_ext": "py", "file_size_in_byte": 1104, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.template.defaultfilters.register.filter", "line_number": 5, "usage_type": "call"}, {"api_name": "django.template.defaultfilters.register", "line_number": 5, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.register.filter", "line_number": 11, "usage_type": "call"}, {"api_name": "django.template.defaultfilters.register", "line_number": 11, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.register.filter", "line_number": 25, "usage_type": "call"}, {"api_name": "django.template.defaultfilters.register", "line_number": 25, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.get", "line_number": 41, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 41, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.register.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "django.template.defaultfilters.register", "line_number": 38, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.register.filter", "line_number": 46, "usage_type": "call"}, {"api_name": "django.template.defaultfilters.register", "line_number": 46, "usage_type": "name"}]}
{"seq_id": "23564858167", "text": "from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n\n    path('admin_page/', views.admin_page, name='admin_page'),\n    path('', views.gateway, name='gateway'),  # one only ... blank ('')\n\n    path('create_contract/', views.create_contract, name='create_contract'),\n\n    path('billing/', views.billing, name='billing'),\n\n    path('payment_individual/', views.payment_individual, name='payment_individual'),\n    path('pay_rent/str<bref>/', views.pay_rent, name='pay_rent'),\n\n    path('report_type/', views.report_type, name='report_type'),\n    path('report_parameters/', views.report_parameters, name='report_parameters'),\n    path('current_tenant/', views.current_tenant, name='current_tenant'),\n    path('extra_service/', views.extra_service, name='extra_service'),\n    path('elec_cpu_change/', views.elec_cpu_change, name='elec_cpu_change'),\n    path('water_cpu_change/', views.water_cpu_change, name='water_cpu_change'),\n    path('room_type_rate/', views.room_type_rate, name='room_type_rate'),\n    path('current_tenant/', views.current_tenant, name='current_tenant'),\n    path('vacant_rooms/', views.vacant_rooms, name='vacant_rooms'),\n    path('monthly_report_mini/', views.monthly_report_mini, name='monthly_report_mini'),\n\n    path('misc_contents/', views.misc_contents, name='misc_contents'),\n    path('manage_users/', views.manage_users, name='manage_users'),\n    path('user_list_to_delete/', views.user_list_to_delete, name='user_list_to_delete'),\n    path('delete_user/<str:rmn>/', views.delete_user, name='delete_user'),\n    path('confirm_delete_user/<str:k>/', views.confirm_delete_user, name='confirm_delete_user'),\n    path('register/', views.Register.as_view(), name='register'),\n    path('register/done/', views.register_done, name='register_done'),\n    path('change_password/', views.change_password, name='change_password'),\n\n    path('maintenance_charge/', views.maintenance_charge, name='maintenance_charge'),\n\n    path('new_tenant/', views.new_tenant, name='new_tenant'),\n    path('tenant_profile/', views.tenant_profile, name='tenant_profile'),\n    path('tenant_bill/', views.tenant_bill, name='tenant_bill'),\n    path('tenant_feedback/', views.tenant_feedback, name='tenant_feedback'),\n\n    # path('payment/', views.payment, name='payment'),\n    # path('register new users/', views.Register_new_users, name='register_new_users'),\n    # path('confirm_delete/', views.confirm_delete, name='confirm_delete'),\n    # path('reset_password/', views.reset_password, name='reset_password'),\n    # path('tenant_page/', views.tenant_page, name='tenant_page'),\n    # path('monthly_report/', views.monthly_report, name='monthly_report'),\n    # path('tenant_comment/', views.tenant_comment, name='tenant_comment'),\n    # path('send_sms_to_individual_room/', views.send_sms_to_individual_room, name='send_sms_to_individual_room'),\n    # path('send_general_sms/', views.send_general_sms, name='send_general_sms'),\n    # path('send_bill_sms_to_all_tenants/', views.send_bill_sms_to_all_tenants, name='send_bill_sms_to_all_tenants'),\n    # path('tenant_bill/', views.tenant_bill, name='tenant_bill'),\n\n    # path('confirmation/', views.send_sms_confirmation, name='confirmation'),\n    # path('send_sms_execution/', views.send_sms_execution, name='send_sms_execution'),\n\n]\n", "repo_name": "pchab2458/PB_Django_Template", "sub_path": "my_app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 3292, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}]}
{"seq_id": "9208815148", "text": "from matplotlib import pyplot as plt\nimport cv2\nimport numpy as np\n\nw=2000\nh=1100\npts1=np.float32([[33,1133],[4472,297],[1111,2959],[4345,2211]])\npts2=np.float32([[0,0],[w,0],[0,h],[w,h]])\nM=cv2.getPerspectiveTransform(pts1,pts2)\nprint(M)\n\nimg=cv2.imread('pc.jpg')\ndst=cv2.warpPerspective(img,M,(w,h))\nimg = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\ndst = cv2.cvtColor(dst, cv2.COLOR_RGB2BGR)\nplt.subplot(121),plt.imshow(img),plt.title('Input')\nplt.subplot(122),plt.imshow(dst),plt.title('Output')\nplt.show()\n\n", "repo_name": "yeejlan/learning_stuff", "sub_path": "dl/homography/homography.py", "file_name": "homography.py", "file_ext": "py", "file_size_in_byte": 505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.float32", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 15, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}]}
{"seq_id": "71865982203", "text": "#!/usr/bin/python3.8\n\nimport socketio\nfrom readchar import readkey\nfrom time import sleep\nfrom sys import exit\n\n\ndef incRed():\n    sio.emit('inc', {'x': 'red', 'y': 'blue'})\n\ndef incBlue():\n    sio.emit('inc', {'x': 'blue', 'y': 'red'})\n\ndef decRed():\n    sio.emit('dec', {'x': 'red', 'y': 'blue'})\n\ndef decBlue():\n    sio.emit('dec', {'x': 'blue', 'y': 'red'})\n\ndef reset():\n    sio.emit('reset')\n\ndef stop():\n    global listenting\n    listenting = False\n\nCODE_MAP = {\n    '\\x1b[H': stop,\n    '\\x1b[F': stop,\n    '0': incBlue,           # numpad 0\n    '.': incBlue,           # numpad .\n    '1': incBlue,           # numpad 1\n    '2': incBlue,           # numpad 2\n    '3': incBlue,           # numpad 3\n    '\\x1b[D': incRed,       # arrow left\n    '\\x1b[B': incRed,       # arrow down\n    '\\x1b[C': incRed,       # arrow right\n    '\\x1b[A': incRed,       # arrow up\n    '\\x1b[6': decRed,       # page down\n    '\\x1b[5': decBlue,      # page up\n    ' ': reset\n}\n\nconnected = False\nretry = 10\nwhile retry > 0:\n    try:\n        sio = socketio.Client()\n        sio.connect('http://localhost:4001')\n        connected = True\n        break\n    except:\n        print('connected to server!')\n        print('couldnt connect to server, trying again')\n        sleep(1)\n    finally:\n        retry -= 1\n\nif not connected:\n    print('couldnt connect to server, exitting')\n    exit(1)\n\nprint('use HOME, HOME, Ctrl^C to quit')\n\nlistenting = True\nwhile listenting:\n    key = readkey()\n\n    # this isn't really working?\n    if key == '\\x1b[H' or key == '\\x1b[F':\n        listenting = False\n\n    elif key in CODE_MAP:\n        CODE_MAP[key]()\n        sleep(.4)\n", "repo_name": "dbusteed/score-tracker", "sub_path": "clients/keyboard_client.py", "file_name": "keyboard_client.py", "file_ext": "py", "file_size_in_byte": 1642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "socketio.Client", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 62, "usage_type": "call"}, {"api_name": "readchar.readkey", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 76, "usage_type": "call"}]}
{"seq_id": "12525265039", "text": "import json\r\nfrom database_monitor import Database\r\n\r\nclass Log:\r\n    def __init__(self, database):\r\n        self.db = Database(database)\r\n\r\n    def post(self, json_data):\r\n        http_status_code = 500\r\n        message = \"Unknow\"\r\n        data = None\r\n        try:\r\n            host = json_data['host']\r\n            tag = json_data['tag']\r\n            msg = json_data['msg']\r\n        except:\r\n            http_status_code = 500\r\n            message = \"Only acept fields: host, tag, msg\"\r\n            data = None\r\n            json_response = {\"status\": http_status_code, \"message\": message, \"data\": data}\r\n            json_response = json.dumps(json_response)\r\n            json_response = json.loads(json_response)\r\n            return json_response\r\n        sql=\"insert into logs(host,tag,datetime,msg) values('%s','%s', NOW(),'%s');\"%(host, tag, msg)\r\n        status, message, data_table = self.db.execute_non_query(sql)\r\n        if status == 1:\r\n            http_status_code = 500\r\n            message = message\r\n            data = None\r\n        if status == 0:\r\n            http_status_code = 201\r\n            message = message\r\n            data = data_table\r\n        json_response = {\"status\": http_status_code, \"message\": message, \"data\": data}\r\n        json_response = json.dumps(json_response)\r\n        json_response = json.loads(json_response)\r\n        return json_response\r\n\r\n", "repo_name": "hhthuongbtr/monitor", "sub_path": "DAL/database_log.py", "file_name": "database_log.py", "file_ext": "py", "file_size_in_byte": 1386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "41", "api": [{"api_name": "database_monitor.Database", "line_number": 6, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 21, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "37956301853", "text": "import glob\nimport hashlib\nimport os\nimport sys\nimport argparse\n\nclass FileComparer:\n    def __init__(self, folders, extensions):\n        self.folders = folders\n        self.extensions = extensions\n\n    def run(self):\n        self.results = {}\n        for folder in self.folders:\n            files = [file for file in glob.glob(os.path.join(folder, \"*\")) if os.path.isfile(file)]\n            for file in files:\n                ext = os.path.splitext(file)[1][1:]\n                if ext in self.extensions:\n                    md5_hash = hashlib.md5()\n                    with open(file, 'rb') as f:\n                        for chunk in iter(lambda: f.read(4096), b\"\"):\n                            md5_hash.update(chunk)\n                    self.results.setdefault(md5_hash.hexdigest(), []).append(file)\n        return self\n\n    def report(self, target=sys.stdout):\n        for files in self.results.values():\n            if len(files) > 1:\n                print(\", \".join(files), file=target)\n        return self\n\ndef main():\n    parser = argparse.ArgumentParser(description='Compare files in two folders.')\n    parser.add_argument('folders', metavar='F', type=str, nargs='+', help='a list of folders to compare')\n    parser.add_argument('--extensions', type=str, nargs='+', default=['jpg', 'mp3', 'epub'], help='a list of file extensions to compare')\n    args = parser.parse_args()\n\n    comparer = FileComparer(args.folders, args.extensions)\n    comparer.run().report()\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "cuber-it/aktueller-kurs", "sub_path": "Tag5/filecompare.py", "file_name": "filecompare.py", "file_ext": "py", "file_size_in_byte": 1510, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "glob.glob", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "hashlib.md5", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 26, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 33, "usage_type": "call"}]}
{"seq_id": "8314976653", "text": "\"\"\"Author : Omkar Damle\n\tDate : 24th May 2017\n\tThis scraper is used to scrape only the geo tagged tweets in LA area\n\t\"\"\"\t\nfrom __future__ import print_function\nimport settings_geo\nimport tweepy\nimport dataset\nfrom textblob import TextBlob\nfrom sqlalchemy.exc import ProgrammingError\nimport json\nfrom nltk.stem import *\nfrom nltk.stem.snowball import SnowballStemmer\nimport string\nfrom string import maketrans\nimport unicodedata\n\ndef startStream():\n\tcount = 0\n\twhile(True):\n\t\tprint(count)\n\t\tcount += 1\n\t\ttry:\n\t\t\tstream.filter(locations=[-118.5,33.8,-118,34.2], languages=[\"en\"])\n\t\texcept Exception as e:\n\t\t\tprint('Exception raised : ' + str(e))\n\t\t\tcontinue\n\n\ndb = dataset.connect(settings_geo.CONNECTION_STRING)        #????\n\nclass MyStreamListener(tweepy.StreamListener):\n\n\tdef on_status(self, status):\n\t\t\n#\t\tprint('-----------------------------------------------')\n\n\t\tif status.retweeted:\n\t\t\treturn\n\n\t\tprint('Original tweet : ' + status.text)\n\n#ensures that only tweets with coordinates are stored\n\t\tif status.coordinates is None:\n\t\t\treturn \n\n\t\tdescription = status.user.description\n\t\tloc = status.user.location\n\t\ttext = status.text\n\t\tcoords = status.coordinates\n\t\tgeo = status.geo\n\t\tname = status.user.screen_name\n\t\tuser_created = status.user.created_at\n\t\tfollowers = status.user.followers_count\n\t\tid_str = status.id_str\n\t\tcreated = status.created_at\n\t\tretweets = status.retweet_count\n\t\tbg_color = status.user.profile_background_color\n\t\tblob = TextBlob(text)\t\t\t\t\t\t\t\t#sentiment analysis\n\t\tsent = blob.sentiment\n\n\t\tif geo is not None:\n\t\t\tgeo = json.dumps(geo)\n\n\t\tif coords is not None:\n\t\t\tcoords = json.dumps(coords)\n\n\t\ttable = db[settings_geo.TABLE_NAME]\n\t\ttry:\n\t\t\ttable.insert(dict(\n\t\t\t\tuser_description=description,\n\t\t\t\tuser_location=loc,\n\t\t\t\tcoordinates=coords,\n\t\t\t\ttext=text,\n\t\t\t\tgeo=geo,\n\t\t\t\tuser_name=name,\n\t\t\t\tuser_created=user_created,\n\t\t\t\tuser_followers=followers,\n\t\t\t\tid_str=id_str,\n\t\t\t\tcreated=created,\n\t\t\t\tretweet_count=retweets,\n\t\t\t\tuser_bg_color=bg_color,\n\t\t\t\tpolarity=sent.polarity,\n\t\t\t\tsubjectivity=sent.subjectivity,\n\t\t\t))\n\t\texcept ProgrammingError as err:\n\t\t\tprint(err)\n\n\tdef on_error(self, status_code):\n\t\tprint(type(self))\n\n\t\tif status_code == 420:\n\t\t\t#returning False in on_data disconnects the stream\n#\t\t\tprint(\"here\")\n\t\t\treturn False\n#print(\"i am here\")\nauth = tweepy.OAuthHandler(settings_geo.TWITTER_APP_KEY, settings_geo.TWITTER_APP_SECRET)\nauth.set_access_token(settings_geo.TWITTER_KEY, settings_geo.TWITTER_SECRET)\napi = tweepy.API(auth)\n\"\"\"\npublic_tweets = api.home_timeline()\nfor tweet in public_tweets:\n\tprint tweet.text\n\nprint \"now let us see the streams\"\n\"\"\"\nstream_listener = MyStreamListener()\nstream = tweepy.Stream(auth=api.auth, listener=stream_listener)\n#stream.filter(track=settings.TRACK_TERMS, languages=[\"en\"])\n\nstartStream()\n\n", "repo_name": "scc-usc/LAcrime", "sub_path": "viterbi work/twitter-scrape-master/scraper_geo.py", "file_name": "scraper_geo.py", "file_ext": "py", "file_size_in_byte": 2772, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "dataset.connect", "line_number": 30, "usage_type": "call"}, {"api_name": "settings_geo.CONNECTION_STRING", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tweepy.StreamListener", "line_number": 32, "usage_type": "attribute"}, {"api_name": "textblob.TextBlob", "line_number": 59, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 63, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 66, "usage_type": "call"}, {"api_name": "settings_geo.TABLE_NAME", "line_number": 68, "usage_type": "attribute"}, {"api_name": "sqlalchemy.exc.ProgrammingError", "line_number": 86, "usage_type": "name"}, {"api_name": "tweepy.OAuthHandler", "line_number": 97, "usage_type": "call"}, {"api_name": "settings_geo.TWITTER_APP_KEY", "line_number": 97, "usage_type": "attribute"}, {"api_name": "settings_geo.TWITTER_APP_SECRET", "line_number": 97, "usage_type": "attribute"}, {"api_name": "settings_geo.TWITTER_KEY", "line_number": 98, "usage_type": "attribute"}, {"api_name": "settings_geo.TWITTER_SECRET", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tweepy.API", "line_number": 99, "usage_type": "call"}, {"api_name": "tweepy.Stream", "line_number": 108, "usage_type": "call"}]}
{"seq_id": "3083131093", "text": "import numpy as np\n# import matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression\nfrom data import *\n\noption_num = 0 # P V T options\nclass_num = 2\nford_num = 10\ndata, label = dataloader(class_num, option_num)\nX,Y = shuffle_2arr(data, label)\nX_train, Y_train, X_test, Y_test = split_train_test(X,Y, option_num, ford_num)\nX_test, Y_test = valence_class(X_test, Y_test, class_num)\n# X_train, Y_train = valence_class(X_train, Y_train, class_num)\nprint(X_train.shape, X_test.shape)\n\nif class_num == 2:\n    logreg = LogisticRegression(solver='lbfgs')\nelif class_num == 3:\n    logreg = LogisticRegression(solver='lbfgs', multi_class='multinomial') # multinomial / auto/ ovr\nlogreg.max_iter = 1000\n# we create an instance of Neighbours Classifier and fit the data.\nlogreg.fit(X_train, Y_train)\n\nPred = logreg.predict(X_test)\nprint('label\\t:',Y_test)\nprint('predict :',Pred)\ntotal_num = len(Y_test)\ncorrect_answer = 0\nfor i in range(total_num):\n    if Y_test[i] == Pred[i]:\n        correct_answer += 1\n\nprint('the probability is ')\nprob = correct_answer*100 / total_num\nprint(prob)\n\n\n# assert False\n# # Plot the decision boundary. For that, we will assign a color to each\n# # point in the mesh [x_min, x_max]x[y_min, y_max].\n# x_min, x_max = X_train[:, 0].min() - .5, X_train[:, 0].max() + .5\n# y_min, y_max = X_train[:, 1].min() - .5, X_train[:, 1].max() + .5\n# print(x_min)\n# h = .02  # step size in the mesh\n# xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n# print(xx.ravel())\n# Pred = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n#\n# # Put the result into a color plot\n# Pred = Pred.reshape(xx.shape)\n# plt.figure(1, figsize=(4, 3))\n# plt.pcolormesh(xx, yy, Pred, cmap=plt.cm.Paired)\n#\n# # Plot also the training points\n# plt.scatter(X_train[:, 0], X_train[:, 1], c=Y_train, edgecolors='k', cmap=plt.cm.Paired)\n# plt.xlabel('Sepal length')\n# plt.ylabel('Sepal width')\n#\n# plt.xlim(xx.min(), xx.max())\n# plt.ylim(yy.min(), yy.max())\n# plt.xticks(())\n# plt.yticks(())\n#\n# plt.show()\n", "repo_name": "oopil/save_ubuntu_20190423", "sub_path": "chosun_AD/logistic_regression.py", "file_name": "logistic_regression.py", "file_ext": "py", "file_size_in_byte": 2030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "12975363873", "text": "from typing import Union\n\nfrom flask import render_template, url_for\nfrom werkzeug.utils import redirect\n\nfrom app import db\nfrom app.main import bp\nfrom app.main.forms import SearchForm\nfrom app.models import Bookmark, Post, User\n\n\n@bp.route('/')\ndef feed():\n    posts: Post = Post.query\n\n    bookmarks_count: int = Bookmark.query.count()\n\n    search_form: SearchForm = SearchForm(csrf_enabled=False)\n\n    return render_template(\n        'index.html',\n        posts=posts,\n        bookmarks_count=bookmarks_count,\n        search_form=search_form,\n    )\n\n\n@bp.route('/posts/<int:post_id>')\ndef post(post_id: int):\n    post: Post = Post.query.filter_by(id=post_id).first_or_404()\n\n    return render_template(\n        'post.html',\n        post=post,\n        title='post',\n    )\n\n\n@bp.route('/search')\ndef search():\n    search_form: SearchForm = SearchForm(csrf_enabled=False)\n\n    posts: Union[list, Post] = []\n\n    if search_form.validate():\n        posts = Post.search_by_like(search_form.s.data, limit=10)\n\n    return render_template(\n        'search.html',\n        posts=posts,\n        search_form=search_form,\n        title='search',\n    )\n\n\n@bp.route('/user/<string:username>')\ndef user_feed(username: str):\n    user: User = User.query.filter_by(username=username).first_or_404()\n\n    return render_template(\n        'user-feed.html',\n        user=user,\n        title='user feed',\n    )\n\n\n@bp.route('/tag/<string:tag_name>')\ndef tag_feed(tag_name: str):\n    posts: Post = Post.search_by_like(tag_name, limit=None)\n\n    return render_template(\n        'tag-feed.html',\n        tag_name=tag_name,\n        posts=posts,\n        title='tag'\n    )\n\n\n@bp.route('/bookmarks/add/<int:post_id>')\ndef add_post_to_bookmarks(post_id: int):\n    bookmark: Bookmark = Bookmark.query.filter_by(post_id=post_id).first()\n\n    if bookmark:\n        return redirect(url_for('main.feed'), code=302)\n\n    post: Post = Post.query.filter_by(id=post_id).first_or_404()\n\n    db.session.add(Bookmark(post_id=post.id))\n    db.session.commit()\n\n    return redirect(url_for('main.feed'), code=302)\n\n\n@bp.route('/bookmarks/remove/<int:bookmark_id>')\ndef remove_post_from_bookmarks(bookmark_id: int):\n    bookmark: Bookmark = Bookmark.query.filter_by(id=bookmark_id).first_or_404()\n\n    db.session.delete(bookmark)\n    db.session.commit()\n\n    return redirect(url_for('main.feed'), code=302)\n\n\n@bp.route('/bookmarks')\ndef bookmarks():\n    bookmarks: Bookmark = Bookmark.query\n\n    return render_template(\n        'bookmarks.html',\n        bookmarks=bookmarks\n    )\n\n\n@bp.route('/raise_500')\ndef raise_500():\n    raise Exception\n", "repo_name": "pribit/skyprogram", "sub_path": "app/main/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 2598, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "app.models.Post", "line_number": 14, "usage_type": "name"}, {"api_name": "app.models.Post.query", "line_number": 14, "usage_type": "attribute"}, {"api_name": "app.models.Bookmark.query.count", "line_number": 16, "usage_type": "call"}, {"api_name": "app.models.Bookmark.query", "line_number": 16, "usage_type": "attribute"}, {"api_name": "app.models.Bookmark", "line_number": 16, "usage_type": "name"}, {"api_name": "app.main.forms.SearchForm", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "app.main.bp.route", "line_number": 12, "usage_type": "call"}, {"api_name": "app.main.bp", "line_number": 12, "usage_type": "name"}, {"api_name": "app.models.Post", "line_number": 30, "usage_type": "name"}, {"api_name": "app.models.Post.query.filter_by", "line_number": 30, "usage_type": "call"}, {"api_name": "app.models.Post.query", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "app.main.bp.route", "line_number": 28, "usage_type": "call"}, {"api_name": "app.main.bp", "line_number": 28, "usage_type": "name"}, {"api_name": "app.main.forms.SearchForm", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 43, "usage_type": "name"}, {"api_name": "app.models.Post", "line_number": 43, "usage_type": "name"}, {"api_name": "app.models.Post.search_by_like", "line_number": 46, "usage_type": "call"}, {"api_name": "app.models.Post", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 48, "usage_type": "call"}, {"api_name": "app.main.bp.route", "line_number": 39, "usage_type": "call"}, {"api_name": "app.main.bp", "line_number": 39, "usage_type": "name"}, {"api_name": "app.models.User", "line_number": 58, "usage_type": "name"}, {"api_name": "app.models.User.query.filter_by", "line_number": 58, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 60, "usage_type": "call"}, {"api_name": "app.main.bp.route", "line_number": 56, "usage_type": "call"}, {"api_name": "app.main.bp", "line_number": 56, "usage_type": "name"}, {"api_name": "app.models.Post", "line_number": 69, "usage_type": "name"}, {"api_name": "app.models.Post.search_by_like", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 71, "usage_type": "call"}, {"api_name": "app.main.bp.route", "line_number": 67, "usage_type": "call"}, {"api_name": "app.main.bp", "line_number": 67, "usage_type": "name"}, {"api_name": "app.models.Bookmark", "line_number": 81, "usage_type": "name"}, {"api_name": "app.models.Bookmark.query.filter_by", "line_number": 81, "usage_type": "call"}, {"api_name": "app.models.Bookmark.query", "line_number": 81, "usage_type": "attribute"}, {"api_name": "werkzeug.utils.redirect", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 84, "usage_type": "call"}, {"api_name": "app.models.Post", "line_number": 86, "usage_type": "name"}, {"api_name": "app.models.Post.query.filter_by", "line_number": 86, "usage_type": "call"}, {"api_name": "app.models.Post.query", "line_number": 86, "usage_type": "attribute"}, {"api_name": "app.db.session.add", "line_number": 88, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 88, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 88, "usage_type": "name"}, {"api_name": "app.models.Bookmark", "line_number": 88, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 89, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 89, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 89, "usage_type": "name"}, {"api_name": "werkzeug.utils.redirect", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 91, "usage_type": "call"}, {"api_name": "app.main.bp.route", "line_number": 79, "usage_type": "call"}, {"api_name": "app.main.bp", "line_number": 79, "usage_type": "name"}, {"api_name": "app.models.Bookmark", "line_number": 96, "usage_type": "name"}, {"api_name": "app.models.Bookmark.query.filter_by", "line_number": 96, "usage_type": "call"}, {"api_name": "app.models.Bookmark.query", "line_number": 96, "usage_type": "attribute"}, {"api_name": "app.db.session.delete", "line_number": 98, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 98, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 98, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 99, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 99, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 99, "usage_type": "name"}, {"api_name": "werkzeug.utils.redirect", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 101, "usage_type": "call"}, {"api_name": "app.main.bp.route", "line_number": 94, "usage_type": "call"}, {"api_name": "app.main.bp", "line_number": 94, "usage_type": "name"}, {"api_name": "app.models.Bookmark", "line_number": 106, "usage_type": "name"}, {"api_name": "app.models.Bookmark.query", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 108, "usage_type": "call"}, {"api_name": "app.main.bp.route", "line_number": 104, "usage_type": "call"}, {"api_name": "app.main.bp", "line_number": 104, "usage_type": "name"}, {"api_name": "app.main.bp.route", "line_number": 114, "usage_type": "call"}, {"api_name": "app.main.bp", "line_number": 114, "usage_type": "name"}]}
{"seq_id": "39012374337", "text": "import datetime\nimport dateutil\nfrom money import Money\nfrom api.config import Configuration\nfrom api.circulation_exceptions import *\nfrom core.model.patron import Patron\nfrom core.util import MoneyUtility\nfrom core.util.datetime_helpers import utc_now\n\nclass PatronUtility(object):\n    \"\"\"Apply circulation-specific logic to Patron model objects.\"\"\"\n\n    @classmethod\n    def needs_external_sync(cls, patron):\n        \"\"\"Could this patron stand to have their metadata synced with the\n        remote?\n\n        By default, all patrons get synced once every twelve\n        hours. Patrons who lack borrowing privileges can always stand\n        to be synced, since their privileges may have just been\n        restored.\n        \"\"\"\n        if not patron.last_external_sync:\n            # This patron has never been synced.\n            return True\n\n        now = utc_now()\n        if cls.has_borrowing_privileges(patron):\n            # A patron who has borrowing privileges gets synced every twelve\n            # hours. Their account is unlikely to change rapidly.\n            check_every = Patron.MAX_SYNC_TIME\n        else:\n            # A patron without borrowing privileges might get synced\n            # every time they make a request. It's likely they are\n            # taking action to get their account reinstated and we\n            # don't want to make them wait twelve hours to get access.\n            check_every = datetime.timedelta(seconds=5)\n        expired_at = patron.last_external_sync + check_every\n        if now > expired_at:\n            return True\n        return False\n\n    @classmethod\n    def has_borrowing_privileges(cls, patron):\n        \"\"\"Is the given patron allowed to check out books?\n\n        :return: A boolean\n        \"\"\"\n        try:\n            cls.assert_borrowing_privileges(patron)\n            return True\n        except CannotLoan as e:\n            return False\n\n    @classmethod\n    def assert_borrowing_privileges(cls, patron):\n        \"\"\"Raise an exception unless the patron currently has borrowing\n        privileges.\n\n        :raises AuthorizationExpired: If the patron's authorization has expired.\n        :raises OutstandingFines: If the patron has too many outstanding fines.\n\n        \"\"\"\n        if not cls.authorization_is_active(patron):\n            # The patron's card has expired.\n            raise AuthorizationExpired()\n\n        if cls.has_excess_fines(patron):\n            raise OutstandingFines()\n\n        from api.authenticator import PatronData\n        if patron.block_reason is not None:\n            if patron.block_reason is PatronData.EXCESSIVE_FINES:\n                # The authentication mechanism itself may know that\n                # the patron has outstanding fines, even if the circulation\n                # manager is not configured to make that deduction.\n                raise OutstandingFines()\n            raise AuthorizationBlocked()\n\n    @classmethod\n    def has_excess_fines(cls, patron):\n        \"\"\"Does this patron have fines in excess of the maximum fine amount set for their library?\n\n        :param a Patron:\n        :return: A boolean\n        \"\"\"\n        if not patron.fines:\n            return False\n\n        if isinstance(patron.fines, Money):\n            patron_fines = patron.fines\n        else:\n            patron_fines = MoneyUtility.parse(patron.fines)\n        actual_fines = patron_fines.amount\n        max_fines = Configuration.max_outstanding_fines(patron.library)\n        if max_fines is not None and actual_fines > max_fines.amount:\n            return True\n        return False\n\n    @classmethod\n    def authorization_is_active(cls, patron):\n        \"\"\"Return True unless the patron's authorization has expired.\"\"\"\n        # Unlike pretty much every other place in this app, we use\n        # (server) local time here instead of UTC. This is to make it\n        # less likely that a patron's authorization will expire before\n        # they think it should.\n        now_local = datetime.datetime.now(tz=dateutil.tz.tzlocal())\n        if (patron.authorization_expires\n            and cls._to_date(patron.authorization_expires)\n            < cls._to_date(now_local)):\n            return False\n        return True\n\n    @classmethod\n    def _to_date(cls, x):\n        \"\"\"Convert a datetime into a date. Leave a date alone.\"\"\"\n        if isinstance(x, datetime.datetime):\n            return x.date()\n        return x\n", "repo_name": "NYPL-Simplified/circulation", "sub_path": "api/util/patron.py", "file_name": "patron.py", "file_ext": "py", "file_size_in_byte": 4395, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "41", "api": [{"api_name": "core.util.datetime_helpers.utc_now", "line_number": 27, "usage_type": "call"}, {"api_name": "core.model.patron.Patron.MAX_SYNC_TIME", "line_number": 31, "usage_type": "attribute"}, {"api_name": "core.model.patron.Patron", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 37, "usage_type": "call"}, {"api_name": "api.authenticator.PatronData.EXCESSIVE_FINES", "line_number": 73, "usage_type": "attribute"}, {"api_name": "api.authenticator.PatronData", "line_number": 73, "usage_type": "name"}, {"api_name": "money.Money", "line_number": 90, "usage_type": "argument"}, {"api_name": "core.util.MoneyUtility.parse", "line_number": 93, "usage_type": "call"}, {"api_name": "core.util.MoneyUtility", "line_number": 93, "usage_type": "name"}, {"api_name": "api.config.Configuration.max_outstanding_fines", "line_number": 95, "usage_type": "call"}, {"api_name": "api.config.Configuration", "line_number": 95, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "attribute"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 107, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 107, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 117, "usage_type": "attribute"}]}
{"seq_id": "22355407365", "text": "from typing import Union\n# SMTP\nfrom smtplib import SMTP\n# Message utils\nfrom .EmailMessage import EmailAttachment, EmailMessageFactory\n\n\nclass EmailManager:\n    \"\"\"\n    @version 1.0.1\n\n    Class that provides email functionality, by encapsulating all the process and exposing only the \n    facade and setters we are able to hide the underlying complexity of this process, making it a lot\n    easier to use.\n    It supports basic text messages, as well as attachments. One important thing about attachments is\n    that we don't need to worry about the MIMETypes, because they're inferred automatically, also, they\n    are processed and attached automatically, so we just need to provide the path to the attachment, and\n    the class will do all the required stuff under the hood.\n    \"\"\"\n    \n    def __init__(\n        self,\n        server: str,\n        sender: str = None,\n        target: Union[str, list] = None,\n        subject: str = None,\n        email_body: str = '',\n        attachments: list = []\n    ):\n        \"\"\"\n        @param {str} server The SMTP server IP or domain.\n        @param {str} sender The email user that sends the message.\n        @param {str} target The email target user of the message.\n        @param {str} subject The email subject.\n        @param {str} email_body The email text body.\n        @param {str} attachments The attachments list (path to the attachments, actually).\n        \"\"\"\n        self.server = server\n        self.sender = sender\n        self.target = target\n        self.subject = subject\n        self.email_body = email_body\n        self.attachments = attachments\n        # To be set later\n        self.root_message = None\n\n    # Setters \n\n    def set_participants(\n        self,\n        sender: str,\n        target: Union[str, list]\n    ):\n        \"\"\"\n        Sets the participants of the mail (to and from).\n        \"\"\"\n        self.sender = sender\n        self.target = target\n\n    def set_subject(self, subject):\n        \"\"\"\n        Sets the mail subject.\n        \"\"\"\n        self.mail_subject = subject\n\n    def set_email_body(self, email_body):\n        \"\"\"\n        Sets the mail text body.\n        \"\"\"\n        self.email_body = email_body\n    \n    def add_attachment(\n        self, \n        attachment_path: str\n    ):\n        \"\"\"\n        Adds an attachment to the attachments list.\n        \"\"\"\n        self.attachments.append(attachment_path)\n\n    # Facade\n\n    def send(self):\n        \"\"\"\n        Facade method to send the email. It calls all the necessary method to, first validate the email\n        components, create and initialize the root message (or email tree), attach the text message body,\n        attach the attachments and send the message via the SMTP server.\n        \"\"\"\n        # We perform a validation of the mandatory mail components (to, from and body)\n        self.__validate_mail_components()\n        # We create and initialize the root message (the email tree, which will contain the text body and the attachments)\n        self.__initialize_root_message()\n        # We attach the message body (provided as text) to the root message\n        self.__attach_text_message_body()\n        # We add the attachments to the root message\n        self.__incorporate_attachments()\n        # Finally, we actually send the mail\n        self.__send_root_message_via_smtp()\n\n    # Private methods\n\n    def __initialize_root_message(self):\n        \"\"\"\n        Creates and initialize a root message (MIMEMultipart), to support text and attachments.\n        Also, the email data is set, such as email_to, email_from and the subject.\n        \"\"\"\n        self.root_message = EmailMessageFactory.create_by_type(EmailMessageFactory.MULTIPART)\n        self.root_message['To'] = self.target\n        self.root_message['From'] = self.sender\n        self.root_message['Subject'] = self.subject\n\n    def __attach_text_message_body(self):\n        \"\"\"\n        Attaches the provided text email body to the root message.\n        \"\"\"\n        text_message_body = EmailMessageFactory.create_text_message(self.email_body)\n        self.root_message.attach(text_message_body)\n\n    def __incorporate_attachments(self):\n        \"\"\"\n        Iterates through the attachments list, creating an EmailAttachment instance for each (this\n        class prepares the attachment to be ready to be attached directly to the root message, without\n        us having to worry about setting the content type, subtype, the file content or the encoding and\n        headers).\n        \"\"\"\n        for attachment_path in self.attachments:\n            attachment = EmailAttachment(attachment_path).create()\n            self.root_message.attach(attachment)\n\n    def __send_root_message_via_smtp(self):\n        \"\"\"\n        Method to finally send the message via the SMTP protocol, using the specified server.\n        \"\"\"\n        with SMTP(self.server) as server:\n            server.ehlo()\n            server.sendmail(\n                self.sender, \n                self.target, \n                self.root_message.as_string()\n            )\n\n    # Validations\n    def __validate_mail_components(self):\n        \"\"\"\n        Validates that the mandatory data is not empty, throwing an exception if that's the case.\n        \"\"\"\n        if (\n            not self.sender or\n            not self.target or\n            not self.email_body\n        ):\n            raise Exception('The mail cannot be send without the mandatory data (to, from and payload).')", "repo_name": "rulerdo/esa_commons", "sub_path": "esalib/infrastructure/email_manager/EmailManager.py", "file_name": "EmailManager.py", "file_ext": "py", "file_size_in_byte": 5456, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "typing.Union", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 52, "usage_type": "name"}, {"api_name": "EmailMessage.EmailMessageFactory.create_by_type", "line_number": 107, "usage_type": "call"}, {"api_name": "EmailMessage.EmailMessageFactory", "line_number": 107, "usage_type": "name"}, {"api_name": "EmailMessage.EmailMessageFactory.MULTIPART", "line_number": 107, "usage_type": "attribute"}, {"api_name": "EmailMessage.EmailMessageFactory.create_text_message", "line_number": 116, "usage_type": "call"}, {"api_name": "EmailMessage.EmailMessageFactory", "line_number": 116, "usage_type": "name"}, {"api_name": "EmailMessage.EmailAttachment", "line_number": 127, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 134, "usage_type": "call"}]}
{"seq_id": "3062955917", "text": "import sys\nimport json\nimport platform\nimport subprocess\nimport os\nimport PySide2\nfrom PyQt5 import QtWidgets\nfrom PyQt5.Qt import *\nfrom PyQt5.QtGui import QIcon\n\nfrom QT_windows import blank_window\nfrom QT_windows.A_Shaitu import shaitu_window\nfrom QT_windows.B_Biaotu import biaotu_window\nfrom QT_windows.C_Biaozhutongji import biaozhutongji_window\nfrom QT_windows.D_Dianjian import dianjian_window\nfrom QT_windows.E_Biaozhunbiangeng import biaozhunbiangeng_window\nfrom database_moudle.data_show_page import DataGrid\n\nfrom QT_style.style import StackWidget_Style, Mini_Exit_Button_Style, Still_Button_Style, Normal_Button_Style\nfile_path = r'QT_style/Real_main_window.json'\nstyle_dict = json.load(open(file_path, 'r'))\n\ndirname = os.path.dirname(PySide2.__file__)\nplugin_path = os.path.join(dirname, 'plugins', 'platforms')\nos.environ['QT_QPA_PLATFORM_PLUGIN_PATH'] = plugin_path\n\nclass MainWidget(QtWidgets.QMainWindow, QWidget):\n\n    def __init__(self):\n        super(MainWidget, self).__init__()\n        self.app = QApplication(sys.argv)\n        self.scaleRate = 1\n        self.init_ui(self.scaleRate)\n        self.setup_ui()\n        self.add_shadow()\n\n    def init_ui(self, scaleRate):\n\n        self.resize(1322 * scaleRate, 830 * scaleRate)\n\n    def setup_ui(self):\n\n        # self.setWindowOpacity(0.97)  # 设置窗口透明度\n        self.setAttribute(Qt.WA_TranslucentBackground)  # 设置窗口背景透明----\n        self.setWindowFlag(Qt.FramelessWindowHint)  # 隐藏边框\n        self.setWindowIcon(QIcon('./images/shuguang.png'))\n\n        self.base_widget = QtWidgets.QWidget()  # 创建透明窗口\n        self.base_widget.setObjectName('base_widget')\n        self.base_layout = QtWidgets.QGridLayout()\n        self.base_widget.setLayout(self.base_layout)\n        self.base_widget.setAttribute(Qt.WA_TranslucentBackground)\n\n        self.main_widget = QtWidgets.QWidget()\n        self.main_widget.setObjectName('main_widget')\n        self.main_widget.setStyleSheet('QWidget#main_widget{border-top-left-radius:25px;border-bottom-left-radius:25px;'\n                                       'border-top-right-radius:0px;border-bottom-right-radius:0px;'\n                                        'background-image: url(./images/background7.png);'\n                                       '}' )\n        self.base_layout.addWidget(self.main_widget,0,0)\n\n        self.main_layout = QtWidgets.QGridLayout()\n        self.main_layout.setContentsMargins(105, 0, 0, 0)\n        self.main_widget.setLayout(self.main_layout)\n\n        self.setCentralWidget(self.base_widget)  # 设置窗口主部件\n\n        # 退出及最小化按钮\n        self.exit = Mini_Exit_Button_Style(self, **style_dict['Exit_Button_Style'])\n        self.exit.clicked.connect(self.exit_onClick_Button)\n\n        self.mini = Mini_Exit_Button_Style(self, **style_dict['Mini_Button_Style'])\n        self.mini.clicked.connect(lambda: self.showMinimized())\n\n        self.refresh = Mini_Exit_Button_Style(self, **style_dict['Refresh_Button_Style'])\n        self.refresh.clicked.connect(self.refresh_onClick_Button)\n\n        self.homepage = Mini_Exit_Button_Style(self, **style_dict['Homepage_Button_Style'])\n        self.homepage.clicked.connect(self.homepage_onClick_Button)\n\n        # 右侧“切换界面”窗口\n        self.Init_window_StackWidget = StackWidget_Style(self, **style_dict['Init_window_StackWidget_Style'])\n        self.main_layout.addWidget(self.Init_window_StackWidget)\n\n        #左侧菜单栏图标\n        self.button1 = Still_Button_Style(self, **style_dict['Shaitu_Still_Button_Style'])\n        self.button1.clicked.connect(self.button1_onClick_Button)\n\n        self.button2 = Still_Button_Style(self, **style_dict['Biaotu_Still_Button_Style'])\n        self.button2.clicked.connect(self.button2_onClick_Button)\n\n        self.button3 = Still_Button_Style(self, **style_dict['Biaozhutongji_Still_Button_Style'])\n        self.button3.clicked.connect(self.button3_onClick_Button)\n\n        self.button4 = Still_Button_Style(self, **style_dict['Dianjian_Still_Button_Style'])\n        self.button4.clicked.connect(self.button4_onClick_Button)\n\n        self.button5 = Still_Button_Style(self, **style_dict['Biaozhun_Still_Button_Style'])\n        self.button5.clicked.connect(self.button5_onClick_Button)\n\n        self.button6 = Normal_Button_Style(self, **style_dict['Database_Button_Style'])\n        self.button6.clicked.connect(self.button6_onClick_Button)\n\n        self.button7 = Normal_Button_Style(self, **style_dict['VIA_Button_Style'])\n        self.button7.clicked.connect(self.button7_onClick_Button)\n\n        tools_str = [getattr(blank_window, \"WindowDemo\"),\n                     getattr(shaitu_window, \"Shaitu_window_StackWidget\"),\n                     getattr(biaotu_window, \"Biaozhu_window_StackWidget\"),\n                     getattr(biaozhutongji_window, \"Biaozhujiancha_window_StackWidget\"),\n                     getattr(dianjian_window, \"Dianjian_window_StackWidget\"),\n                     getattr(biaozhunbiangeng_window, \"Biaozhunbiangeng_window_StackWidget\"),\n                     ]\n        for i in range(6):\n            self.Init_window_StackWidget.addWidget(tools_str[i]())\n\n    def add_shadow(self):\n        # 添加阴影\n        self.effect_shadow = QtWidgets.QGraphicsDropShadowEffect(self)\n        self.effect_shadow.setOffset(0, 0)  # 偏移\n        self.effect_shadow.setBlurRadius(10)  # 阴影半径\n        self.effect_shadow.setColor(Qt.gray)  # 阴影颜色\n        self.main_widget.setGraphicsEffect(self.effect_shadow)  # 将设置套用到widget窗口中\n\n    def mousePressEvent(self, event):\n        if event.button() == Qt.LeftButton:\n            self.m_flag = True\n            self.m_Position = event.globalPos() - self.pos()  # 获取鼠标相对窗口的位置\n            event.accept()\n            self.setCursor(QCursor(Qt.OpenHandCursor))  # 更改鼠标图标\n\n    def mouseMoveEvent(self, QMouseEvent):\n        if Qt.LeftButton and self.m_flag:\n            self.move(QMouseEvent.globalPos() - self.m_Position)  # 更改窗口位置\n            QMouseEvent.accept()\n            if len(self.app.screens()) == 1:\n                self.scaleRate = 1\n            else:\n                self.scaleRate = self.app.screens()[1].logicalDotsPerInch() / 96\n            self.init_ui(self.scaleRate)\n            self.add_shadow()\n\n    def mouseReleaseEvent(self, QMouseEvent):\n        self.m_flag = False\n        self.setCursor(QCursor(Qt.ArrowCursor))\n\n    def exit_onClick_Button(self):\n        app = QApplication.instance()  # 退出应用程序\n        app.quit()\n\n    def refresh_onClick_Button(self):\n        QApplication.processEvents()  # 刷新界面\n        status_bool = [False, False, False, False, False, False]\n        i = self.Init_window_StackWidget.currentIndex()\n        status_bool[i - 1] = True\n        self.button1.setAutoExclusive(False)\n        self.button1.setChecked(status_bool[0])\n        self.button1.setAutoExclusive(True)\n        self.button2.setAutoExclusive(False)\n        self.button2.setChecked(status_bool[1])\n        self.button2.setAutoExclusive(True)\n        self.button3.setAutoExclusive(False)\n        self.button3.setChecked(status_bool[2])\n        self.button3.setAutoExclusive(True)\n        self.button4.setAutoExclusive(False)\n        self.button4.setChecked(status_bool[3])\n        self.button4.setAutoExclusive(True)\n        self.button5.setAutoExclusive(False)\n        self.button5.setChecked(status_bool[4])\n        self.button5.setAutoExclusive(True)\n        self.button6.setAutoExclusive(False)\n        self.button6.setChecked(status_bool[5])\n        self.button6.setAutoExclusive(True)\n\n    def homepage_onClick_Button(self):\n        self.Init_window_StackWidget.setCurrentIndex(0)\n        self.button1.setAutoExclusive(False)\n        self.button1.setChecked(False)\n        self.button1.setAutoExclusive(True)\n        self.button2.setAutoExclusive(False)\n        self.button2.setChecked(False)\n        self.button2.setAutoExclusive(True)\n        self.button3.setAutoExclusive(False)\n        self.button3.setChecked(False)\n        self.button3.setAutoExclusive(True)\n        self.button4.setAutoExclusive(False)\n        self.button4.setChecked(False)\n        self.button4.setAutoExclusive(True)\n        self.button5.setAutoExclusive(False)\n        self.button5.setChecked(False)\n        self.button5.setAutoExclusive(True)\n        self.button6.setAutoExclusive(False)\n        self.button6.setChecked(False)\n        self.button6.setAutoExclusive(True)\n\n    def button1_onClick_Button(self):\n        self.Init_window_StackWidget.setCurrentIndex(1)\n\n    def button2_onClick_Button(self):\n        self.Init_window_StackWidget.setCurrentIndex(2)\n\n    def button3_onClick_Button(self):\n        self.Init_window_StackWidget.setCurrentIndex(3)\n\n    def button4_onClick_Button(self):\n        self.Init_window_StackWidget.setCurrentIndex(4)\n\n    def button5_onClick_Button(self):\n        self.Init_window_StackWidget.setCurrentIndex(5)\n\n    def button6_onClick_Button(self):\n        self.datapage = DataGrid()\n        self.datapage.show()\n\n    def button7_onClick_Button(self):\n        fp = r'via-src-2.0.11/dist/via.html'\n        systemType: str = platform.platform()  # 获取系统类型\n        if 'mac' in systemType:  # 判断以下当前系统类型\n            fp: str = fp.replace(\"\\\\\", \"/\")  # mac系统下,遇到`\\\\`让路径打不开,不清楚为什么哈,觉得没必要的话自己可以删掉啦,18行那条也是\n            subprocess.call([\"open\", fp])\n        else:\n            fp: str = fp.replace(\"/\", \"\\\\\")  # win系统下,有时`/`让路径打不开\n            os.startfile(fp)\n\n\ndef main():\n\n    QApplication.setAttribute(Qt.AA_EnableHighDpiScaling)\n    app = QApplication(sys.argv)\n    main_wnd = MainWidget()\n    main_wnd.show()\n    app.exec()\n\n\nif __name__ == '__main__':\n    main()", "repo_name": "trouvaille1620/SunBreak", "sub_path": "src/Real_main_window.py", "file_name": "Real_main_window.py", "file_ext": "py", "file_size_in_byte": 9875, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PySide2.__file__", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 27, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 48, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 48, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 50, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 50, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 54, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 54, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 62, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 62, "usage_type": "name"}, {"api_name": "QT_style.style.Mini_Exit_Button_Style", "line_number": 69, "usage_type": "call"}, {"api_name": "QT_style.style.Mini_Exit_Button_Style", "line_number": 72, "usage_type": "call"}, {"api_name": "QT_style.style.Mini_Exit_Button_Style", "line_number": 75, "usage_type": "call"}, {"api_name": "QT_style.style.Mini_Exit_Button_Style", "line_number": 78, "usage_type": "call"}, {"api_name": "QT_style.style.StackWidget_Style", "line_number": 82, "usage_type": "call"}, {"api_name": "QT_style.style.Still_Button_Style", "line_number": 86, "usage_type": "call"}, {"api_name": "QT_style.style.Still_Button_Style", "line_number": 89, "usage_type": "call"}, {"api_name": "QT_style.style.Still_Button_Style", "line_number": 92, "usage_type": "call"}, {"api_name": "QT_style.style.Still_Button_Style", "line_number": 95, "usage_type": "call"}, {"api_name": "QT_style.style.Still_Button_Style", "line_number": 98, "usage_type": "call"}, {"api_name": "QT_style.style.Normal_Button_Style", "line_number": 101, "usage_type": "call"}, {"api_name": "QT_style.style.Normal_Button_Style", "line_number": 104, "usage_type": "call"}, {"api_name": "QT_windows.blank_window", "line_number": 107, "usage_type": "argument"}, {"api_name": "QT_windows.A_Shaitu.shaitu_window", "line_number": 108, "usage_type": "argument"}, {"api_name": "QT_windows.B_Biaotu.biaotu_window", "line_number": 109, "usage_type": "argument"}, {"api_name": "QT_windows.C_Biaozhutongji.biaozhutongji_window", "line_number": 110, "usage_type": "argument"}, {"api_name": "QT_windows.D_Dianjian.dianjian_window", "line_number": 111, "usage_type": "argument"}, {"api_name": "QT_windows.E_Biaozhunbiangeng.biaozhunbiangeng_window", "line_number": 112, "usage_type": "argument"}, {"api_name": "PyQt5.QtWidgets.QGraphicsDropShadowEffect", "line_number": 119, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 119, "usage_type": "name"}, {"api_name": "database_moudle.data_show_page.DataGrid", "line_number": 212, "usage_type": "call"}, {"api_name": "platform.platform", "line_number": 217, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 220, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 223, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 229, "usage_type": "attribute"}]}
{"seq_id": "12763569454", "text": "import pytest\nfrom selenium import webdriver\nimport time\nimport math\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\n\n@pytest.fixture(scope=\"function\")\ndef browser():\n    print(\"\\nstart browser for test..\")\n    browser = webdriver.Chrome()\n    yield browser\n    time.sleep(3)\n    print(\"\\nquit browser..\")\n    browser.quit()\n\n@pytest.mark.parametrize('url', [\"236895\", \"236896\", \"236897\", \"236898\", \"236899\", \"236903\", \"236904\", \"236905\"])\ndef test_guest_should_see_login_link(browser, url):\n    link = f\"https://stepik.org/lesson/{url}/step/1\"\n    browser.get(link)\n    textarea =  WebDriverWait(browser, 10).until(EC.visibility_of_element_located((By.CLASS_NAME, \"textarea\")))\n    #textarea = browser.find_element_by_css_selector(\"textarea\")\n    answer = math.log(int(time.time()))\n    textarea.send_keys(str(answer))\n    button = WebDriverWait(browser, 5).until(EC.element_to_be_clickable((By.CLASS_NAME, \"submit-submission\")))\n    button.click()\n    WebDriverWait(browser, 15).until(EC.visibility_of_element_located((By.CLASS_NAME, \"smart-hints__hint\")))\n    result_test = browser.find_element_by_class_name(\"smart-hints__hint\") \n    assert result_test.text == \"Correct!\", \"Incorrect: Test has not been passed\"\n    \n\n", "repo_name": "dangerous3/stepik-selenium_course", "sub_path": "correct-test-param.py", "file_name": "correct-test-param.py", "file_ext": "py", "file_size_in_byte": 1339, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 15, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 23, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.visibility_of_element_located", "line_number": 23, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 23, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 23, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 23, "usage_type": "name"}, {"api_name": "math.log", "line_number": 25, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 27, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 27, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 27, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 27, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 27, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 29, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.visibility_of_element_located", "line_number": 29, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 29, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 29, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 29, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 19, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 19, "usage_type": "attribute"}]}
{"seq_id": "9592344642", "text": "from PyQt5 import QtCore, QtWidgets\r\nimport sys\r\nimport os\r\nimport strTool\r\nimport threading\r\nfrom PyQt5.QtCore import QBasicTimer\r\nfrom PyQt5.QtCore import QThread, pyqtSignal\r\nimport time\r\n##########################################\r\nglobal path\r\npath = \"\"\r\nstartpath = \"F:/summerImmunedroid2021/spring2022/lotsOfApp/strava\"\r\ntime_in_sec = 0\r\n##########################################\r\n#ui界面设置\r\n\r\nclass Thread(QThread):\r\n    _signal = pyqtSignal(int) #定义信号类型为整型\r\n    def __init__(self):\r\n        super(Thread, self).__init__()\r\n    def __del__(self):\r\n        self.wait()\r\n    def run(self):\r\n        for i in range(1,101):\r\n            global time_in_sec\r\n            time.sleep(time_in_sec/100)\r\n            self._signal.emit(i)  #发射信号\r\n\r\n\r\nclass Ui_MainWindow(object):\r\n\r\n    def setupUi(self, MainWindow):\r\n    \r\n        #主窗口参数设置\r\n        MainWindow.setObjectName(\"MainWindow\")\r\n        MainWindow.resize(848, 721)\r\n        self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n        self.centralwidget.setObjectName(\"centralwidget\")\r\n\r\n        # 设置分析按键参数\r\n        self.analyze = QtWidgets.QPushButton(self.centralwidget)\r\n        # Qrect(a,b,c,d):以(a,b)为左上角，给定宽度c和高度d的矩形\r\n        self.analyze.setGeometry(QtCore.QRect(57, 260, 175, 28))\r\n        self.analyze.setObjectName(\"file\")\r\n        self.analyze.setStyleSheet(\"background-color:rgb(111,180,219)\")\r\n        self.analyze.setStyleSheet(\r\n            \"QPushButton{background-color:rgb(111,180,219)}\"  # 按键背景色\r\n            \"QPushButton:hover{color:green}\"  # 光标移动到上面后的前景色\r\n            \"QPushButton{border-radius:6px}\"  # 圆角半径\r\n            \"QPushButton:pressed{background-color:rgb(180,180,180);border: None;}\"  # 按下时的样式\r\n        )\r\n\r\n        # 设置进度条参数\r\n        self.pbar = QtWidgets.QProgressBar(self.centralwidget)\r\n        self.pbar.setGeometry(200,400,480,28)\r\n        self.pbar.setValue(0)\r\n\r\n\r\n        # 设置路径按钮参数\r\n        self.file = QtWidgets.QPushButton(self.centralwidget)\r\n        self.file.setGeometry(QtCore.QRect(57, 200, 175, 28))\r\n        self.file.setObjectName(\"analyze\")\r\n        self.file.setStyleSheet(\"background-color:rgb(111,180,219)\")\r\n        self.file.setStyleSheet(\r\n            \"QPushButton{background-color:rgb(111,180,219)}\"  # 按键背景色\r\n            \"QPushButton:hover{color:green}\"  # 光标移动到上面后的前景色\r\n            \"QPushButton{border-radius:6px}\"  # 圆角半径\r\n            \"QPushButton:pressed{background-color:rgb(180,180,180);border: None;}\"  # 按下时的样式\r\n        )\r\n\r\n        # 设置显示窗口参数\r\n        self.fileT = QtWidgets.QPushButton(self.centralwidget)\r\n        self.fileT.setGeometry(QtCore.QRect(300, 200, 480, 28))\r\n        self.fileT.setObjectName(\"file\")\r\n        self.fileT.setStyleSheet(\"background-color:rgb(111,180,219)\")\r\n        self.fileT.setStyleSheet(\r\n            \"QPushButton{background-color:rgb(111,180,219)}\"  # 按键背景色\r\n            \"QPushButton:hover{color:green}\"  # 光标移动到上面后的前景色\r\n            \"QPushButton{border-radius:6px}\"  # 圆角半径\r\n            \"QPushButton:pressed{background-color:rgb(180,180,180);border: None;}\"  # 按下时的样式\r\n        )\r\n\r\n\r\n        #主窗口及菜单栏标题栏设置\r\n        MainWindow.setCentralWidget(self.centralwidget)\r\n        self.menubar = QtWidgets.QMenuBar(MainWindow)\r\n        self.menubar.setGeometry(QtCore.QRect(0, 0, 848, 26))\r\n        self.menubar.setObjectName(\"menubar\")\r\n        MainWindow.setMenuBar(self.menubar)\r\n        self.statusbar = QtWidgets.QStatusBar(MainWindow)\r\n        self.statusbar.setObjectName(\"statusbar\")\r\n        MainWindow.setStatusBar(self.statusbar)\r\n\r\n        self.retranslateUi(MainWindow)\r\n        QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n\r\n        ################button按钮点击事件回调函数################\r\n\r\n        self.file.clicked.connect(self.msg)\r\n        self.analyze.clicked.connect(self.androanalyze)\r\n\r\n\r\n    def retranslateUi(self, MainWindow):\r\n        _translate = QtCore.QCoreApplication.translate\r\n        MainWindow.setWindowTitle(_translate(\"MainWindow\", \"ImmuneDroid\"))\r\n        self.file.setText(_translate(\"MainWindow\", \"choose\"))\r\n        self.fileT.setText(_translate(\"MainWindow\", \"\"))\r\n        self.analyze.setText(_translate(\"MainWindow\", \"start\"))\r\n\r\n    #########选择图片文件夹#########\r\n\r\n    def msg(self,Filepath):\r\n        global path\r\n        path = QtWidgets.QFileDialog.getOpenFileName(None,\"choose file\",startpath,\"All Files (*);;Text Files (*.txt)\")  # 起始路径\r\n        # print(m)\r\n        self.fileT.setText(path[0]) #m[0]是包含文件的路径\r\n\r\n    #########分析函数入口#########\r\n    def androanalyze(self):\r\n        local_path = \"\"\r\n        local_path = repr(path[0])\r\n        case = local_path.split(\"/\")\r\n        apk_name = case[-1]\r\n        dir_path = local_path.replace(apk_name,\"\")\r\n        file_size = os.path.getsize(local_path.replace(\"'\",\"\"))\r\n        print(\"apk_name: \" + apk_name)\r\n        print(\"dir_path: \" + dir_path)\r\n        print(\"file_size: \" + str(file_size))\r\n        global time_in_sec\r\n        time_in_sec = (int)(file_size/15100)\r\n        print(\"time_in_sec: \" + str(time_in_sec))\r\n        t = threading.Thread(target=strTool.main_analysis,args = (dir_path,apk_name))\r\n        t.setDaemon(True) #不必等待子线程\r\n        t.start()\r\n        self.fileT.setText(\"start! please wait!\")\r\n        self.thread = Thread()\r\n        self.thread._signal.connect(self.signal_accept)\r\n        self.thread.start() # 调用自定义Thread类型中run函数\r\n    def signal_accept(self,msg):\r\n        self.pbar.setValue(int(msg))\r\n        if self.pbar.value() == 100:\r\n            self.fileT.setText(\"finish!\")\r\n\r\n#########主函数入口 #########\r\n\r\nif __name__ == '__main__':\r\n    app = QtWidgets.QApplication(sys.argv)\r\n    mainWindow = QtWidgets.QMainWindow()\r\n    ui = Ui_MainWindow()\r\n    ui.setupUi(mainWindow)\r\n    mainWindow.show()\r\n    sys.exit(app.exec_())\r\n\r\n", "repo_name": "Nicholas-wei/Immunedroid", "sub_path": "JanFeb/show_path.py", "file_name": "show_path.py", "file_ext": "py", "file_size_in_byte": 5916, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "PyQt5.QtCore.QThread", "line_number": 17, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 18, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 41, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QProgressBar", "line_number": 54, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 54, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 60, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 60, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 61, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 72, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 72, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 73, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 73, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMenuBar", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 86, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 87, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 87, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStatusBar", "line_number": 90, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QMetaObject.connectSlotsByName", "line_number": 95, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QMetaObject", "line_number": 95, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 95, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 104, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 104, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 114, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 114, "usage_type": "name"}, {"api_name": "os.path.getsize", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 132, "usage_type": "call"}, {"api_name": "strTool.main_analysis", "line_number": 132, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 147, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 147, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 147, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 148, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 148, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 152, "usage_type": "call"}]}
{"seq_id": "23653006977", "text": "'''\r\nCreated on Oct 6, 2014\r\n\r\n@author: Tuan\r\n'''\r\nimport codecs\r\nimport json\r\nimport os\r\n\r\nfrom src.utils.constant import QUALIA_CLASS_FILE, WRITING_MODE, CODEC, \\\r\n    READING_MODE\r\nfrom src.utils.helper import Alphabet\r\n\r\n\r\nclass ClassAlphabet(object):\r\n    def __init__(self):\r\n        if os.path.isfile(QUALIA_CLASS_FILE):\r\n            with codecs.open(QUALIA_CLASS_FILE, READING_MODE, CODEC) as file_handler:\r\n                self.class_alphabet = Alphabet.json_loads(json.load(file_handler))\r\n        else:\r\n            ClassAlphabet.initiate_alphabet()\r\n    \r\n    def size(self):\r\n        return self.class_alphabet.num_labels\r\n    \r\n    def get_class_index(self, label_str):\r\n        if self.class_alphabet.has_label(label_str.lower()):\r\n            return self.class_alphabet.get_index(label_str.lower())\r\n        return None\r\n    \r\n    def get_class(self, class_index):\r\n        return self.class_alphabet.get_label(class_index)\r\n    \r\n    @classmethod\r\n    def initiate_alphabet(cls):\r\n        class_alphabet = Alphabet()\r\n        class_alphabet.add('formal')\r\n        class_alphabet.add('telic')\r\n        class_alphabet.add('agentive')\r\n        class_alphabet.add('constitutive')\r\n        with codecs.open(QUALIA_CLASS_FILE, WRITING_MODE, CODEC) as file_handler:\r\n            json.dump(class_alphabet.json_dumps(), file_handler)", "repo_name": "tuandnvn/QualiaClassification", "sub_path": "src/algorithm/class_alphabet.py", "file_name": "class_alphabet.py", "file_ext": "py", "file_size_in_byte": 1341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "os.path.isfile", "line_number": 17, "usage_type": "call"}, {"api_name": "src.utils.constant.QUALIA_CLASS_FILE", "line_number": 17, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 18, "usage_type": "call"}, {"api_name": "src.utils.constant.QUALIA_CLASS_FILE", "line_number": 18, "usage_type": "argument"}, {"api_name": "src.utils.constant.READING_MODE", "line_number": 18, "usage_type": "argument"}, {"api_name": "src.utils.constant.CODEC", "line_number": 18, "usage_type": "argument"}, {"api_name": "src.utils.helper.Alphabet.json_loads", "line_number": 19, "usage_type": "call"}, {"api_name": "src.utils.helper.Alphabet", "line_number": 19, "usage_type": "name"}, {"api_name": "json.load", "line_number": 19, "usage_type": "call"}, {"api_name": "src.utils.helper.Alphabet", "line_number": 36, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 41, "usage_type": "call"}, {"api_name": "src.utils.constant.QUALIA_CLASS_FILE", "line_number": 41, "usage_type": "argument"}, {"api_name": "src.utils.constant.WRITING_MODE", "line_number": 41, "usage_type": "argument"}, {"api_name": "src.utils.constant.CODEC", "line_number": 41, "usage_type": "argument"}, {"api_name": "json.dump", "line_number": 42, "usage_type": "call"}]}
{"seq_id": "29413749468", "text": "#!/usr/bin/env python3\n\nfrom restaurant_crud import RestaurantCRUD\nfrom flask import Flask, render_template, request, url_for, redirect, jsonify, flash\nimport bleach\n\n# Oauth2 imports\nfrom flask import session as login_session\nfrom flask import make_response\nimport string, random\nfrom oauth2client.client import flow_from_clientsecrets\nfrom oauth2client.client import FlowExchangeError\nimport httplib2, json, requests\n\napp = Flask(__name__)\ncrud = RestaurantCRUD()\n\n\nCLIENT_ID = json.loads(\n    open('client_secrets.json', 'r').read())['web']['client_id']\nAPPLICATION_NAME = \"Restaurant Menu Application\"\n\n\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef showLogin():\n    state = \"\".join(random.choice(string.ascii_letters + string.digits) for x in range(32))\n    login_session[\"state\"] = state\n    data = request.form\n    if request.method == \"POST\":\n        return redirect(url_for(\"restaurant\"))\n    return render_template(\"login.html\", STATE=state)\n\n@app.route('/gconnect', methods=['POST'])\ndef gconnect():\n    # Validate state token\n    if request.args.get('state') != login_session['state']:\n        response = make_response(json.dumps('Invalid state parameter.'), 401)\n        response.headers['Content-Type'] = 'application/json'\n        return response\n    # Obtain authorization code\n    code = request.data\n\n    try:\n        # Upgrade the authorization code into a credentials object\n        oauth_flow = flow_from_clientsecrets('client_secrets.json', scope='')\n        oauth_flow.redirect_uri = 'postmessage'\n        credentials = oauth_flow.step2_exchange(code)\n    except FlowExchangeError:\n        response = make_response(\n            json.dumps('Failed to upgrade the authorization code.'), 401)\n        response.headers['Content-Type'] = 'application/json'\n        return response\n\n    # Check that the access token is valid.\n    access_token = credentials.access_token\n    url = ('https://www.googleapis.com/oauth2/v1/tokeninfo?access_token=%s'\n           % access_token)\n    h = httplib2.Http()\n    result = json.loads(h.request(url, 'GET')[1])\n    # If there was an error in the access token info, abort.\n    if result.get('error') is not None:\n        response = make_response(json.dumps(result.get('error')), 500)\n        response.headers['Content-Type'] = 'application/json'\n        return response\n\n    # Verify that the access token is used for the intended user.\n    gplus_id = credentials.id_token['sub']\n    if result['user_id'] != gplus_id:\n        response = make_response(\n            json.dumps(\"Token's user ID doesn't match given user ID.\"), 401)\n        response.headers['Content-Type'] = 'application/json'\n        return response\n\n    # Verify that the access token is valid for this app.\n    if result['issued_to'] != CLIENT_ID:\n        response = make_response(\n            json.dumps(\"Token's client ID does not match app's.\"), 401)\n        print (\"Token's client ID does not match app's.\")\n        response.headers['Content-Type'] = 'application/json'\n        return response\n\n    stored_access_token = login_session.get('access_token')\n    stored_gplus_id = login_session.get('gplus_id')\n    if stored_access_token is not None and gplus_id == stored_gplus_id:\n        response = make_response(json.dumps('Current user is already connected.'),\n                                 200)\n        response.headers['Content-Type'] = 'application/json'\n        return response\n\n    # Store the access token in the session for later use.\n    login_session['access_token'] = credentials.access_token\n    login_session['gplus_id'] = gplus_id\n\n    # Get user info\n    userinfo_url = \"https://www.googleapis.com/oauth2/v1/userinfo\"\n    params = {'access_token': credentials.access_token, 'alt': 'json'}\n    answer = requests.get(userinfo_url, params=params)\n\n    data = answer.json()\n\n    login_session['username'] = data['name']\n    login_session['picture'] = data['picture']\n    login_session['email'] = data['email']\n    # ADD PROVIDER TO LOGIN SESSION\n    login_session['provider'] = 'google'\n\n    # see if user exists, if it doesn't make a new one\n    user_id = crud.getUserID(data[\"email\"])\n    if not user_id:\n        user_id = crud.createUser(login_session)\n    login_session['user_id'] = user_id\n\n    output = ''\n    output += '<h1>Welcome, '\n    output += login_session['username']\n    output += '!</h1>'\n    output += '<img src=\"'\n    output += login_session['picture']\n    output += ' \" style = \"width: 300px; height: 300px;border-radius: 150px;-webkit-border-radius: 150px;-moz-border-radius: 150px;\"> '\n    flash(\"you are now logged in as %s\" % login_session['username'])\n    print (\"done!\")\n    return jsonify(output={\"body\": output})\n\n@app.route('/fbconnect', methods=['POST'])\ndef fbconnect():\n    if request.args.get('state') != login_session['state']:\n        response = make_response(json.dumps('Invalid state parameter.'), 401)\n        response.headers['Content-Type'] = 'application/json'\n        return response\n    access_token = request.data\n    print (\"access token received %s \" % access_token)\n\n\n    app_id = json.loads(open('fb_client_secrets.json', 'r').read())[\n        'web']['app_id']\n    app_secret = json.loads(\n        open('fb_client_secrets.json', 'r').read())['web']['app_secret']\n    url = 'https://graph.facebook.com/oauth/access_token?grant_type=fb_exchange_token&client_id=%s&client_secret=%s&fb_exchange_token=%s' % (\n        app_id, app_secret, access_token)\n    h = httplib2.Http()\n    result = h.request(url, 'GET')[1]\n\n\n    # Use token to get user info from API\n    userinfo_url = \"https://graph.facebook.com/v2.8/me\"\n    '''\n        Due to the formatting for the result from the server token exchange we have to\n        split the token first on commas and select the first index which gives us the key : value\n        for the server access token then we split it on colons to pull out the actual token value\n        and replace the remaining quotes with nothing so that it can be used directly in the graph\n        api calls\n    '''\n    print(result)\n    token = result.split(',')[0].split(':')[1].replace('\"', '')\n\n    url = 'https://graph.facebook.com/v2.8/me?access_token=%s&fields=name,id,email' % token\n    h = httplib2.Http()\n    result = h.request(url, 'GET')[1]\n    # print \"url sent for API access:%s\"% url\n    # print \"API JSON result: %s\" % result\n    data = json.loads(result)\n    login_session['provider'] = 'facebook'\n    login_session['username'] = data[\"name\"]\n    login_session['email'] = data[\"email\"]\n    login_session['facebook_id'] = data[\"id\"]\n\n    # The token must be stored in the login_session in order to properly logout\n    login_session['access_token'] = token\n\n    # Get user picture\n    url = 'https://graph.facebook.com/v2.8/me/picture?access_token=%s&redirect=0&height=200&width=200' % token\n    h = httplib2.Http()\n    result = h.request(url, 'GET')[1]\n    data = json.loads(result)\n\n    login_session['picture'] = data[\"data\"][\"url\"]\n\n    # see if user exists\n    user_id = crud.getUserID(login_session['email'])\n    if not user_id:\n        user_id = crud.createUser(login_session)\n    login_session['user_id'] = user_id\n\n    output = ''\n    output += '<h1>Welcome, '\n    output += login_session['username']\n\n    output += '!</h1>'\n    output += '<img src=\"'\n    output += login_session['picture']\n    output += ' \" style = \"width: 300px; height: 300px;border-radius: 150px;-webkit-border-radius: 150px;-moz-border-radius: 150px;\"> '\n\n    flash(\"Now logged in as %s\" % login_session['username'])\n    return jsonify(output={\"body\": output})\n\ndef gdisconnect():\n    # Only disconnect a connected user.\n    access_token = login_session.get('access_token')\n    if access_token is None:\n        response = make_response(\n            json.dumps('Current user not connected.'), 401)\n        response.headers['Content-Type'] = 'application/json'\n        return response\n    url = 'https://accounts.google.com/o/oauth2/revoke?token=%s' % access_token\n    h = httplib2.Http()\n    result = h.request(url, 'GET')[0]\n    if result['status'] == '200':\n        response = make_response(json.dumps('Successfully disconnected.'), 200)\n        response.headers['Content-Type'] = 'application/json'\n        return response\n    else:\n        response = make_response(json.dumps('Failed to revoke token for given user.'))#, 400))\n        response.headers['Content-Type'] = 'application/json'\n        return response\n\ndef fbdisconnect():\n    facebook_id = login_session['facebook_id']\n    # The access token must me included to successfully logout\n    access_token = login_session['access_token']\n    url = 'https://graph.facebook.com/%s/permissions?access_token=%s' % (facebook_id,access_token)\n    h = httplib2.Http()\n    result = h.request(url, 'DELETE')[1]\n    return \"you have been logged out\"\n\n# Disconnect based on provider\n@app.route('/disconnect')\ndef disconnect():\n    print(login_session)\n    if 'provider' in login_session:\n        if login_session['provider'] == 'google':\n            gdisconnect()\n            del login_session['gplus_id']\n            del login_session['access_token']\n        if login_session['provider'] == 'facebook':\n            fbdisconnect()\n            del login_session['facebook_id']\n        del login_session['username']\n        del login_session['email']\n        del login_session['picture']\n        del login_session['user_id']\n        del login_session['provider']\n        flash(\"You have successfully been logged out.\")\n        return redirect(url_for('restaurant'))\n    else:\n        flash(\"You are not logged in\")\n        return redirect(url_for('restaurant'))\n\n@app.route(\"/\")\n@app.route(\"/restaurant\")\ndef restaurant():\n    restaurants = crud.get_all_restaurants()\n    return render_template(\"restaurant.html\", restaurants=restaurants, session=login_session)\n\n@app.route(\"/restaurant/new\", methods=[\"GET\", \"POST\"])\ndef newRestaurant():\n    if \"username\" not in login_session:\n        return redirect(\"/login\")\n\n    if request.method == \"POST\":\n        # store form data\n        data = request.form\n\n        # check which button is clicked - either CREATE or CANCEL\n        if data[\"action\"] == \"CREATE\":\n            # clean data\n            rest_data = bleach.clean(data[\"name\"])\n            # perform create operation on database\n            crud.create_restaurant(rest_data)\n            # flash message to inform user\n            flash(\"New Restaurant {} Created!\".format(rest_data))\n\n        # redirect to home page for both action of either CREATE or CANCEL\n        return redirect(url_for('restaurant'))\n    else: # else GET request\n        return render_template(\"restaurant_op.html\", restaurant=\"\", op=\"add\")\n\n@app.route(\"/restaurant/<int:r_id>/edit\", methods=[\"GET\", \"POST\"])\ndef editRestaurant(r_id):\n    if \"username\" not in login_session:\n        return redirect(\"/login\")\n\n    if request.method == \"POST\":\n        data = request.form\n        # check which button is clicked - either UPDATE or CANCEL\n        if data[\"action\"] == \"UPDATE\":\n            rest_data = bleach.clean(data[\"name\"])\n            crud.update_restaurant_name(r_id, rest_data)\n            flash(\"Restaurant {} Updated!\".format(rest_data))\n\n        return redirect(url_for('restaurant'))\n    else:\n        r = crud.get_restaurant(r_id)\n        return render_template(\"restaurant_op.html\", restaurant=r, op=\"edit\")\n\n@app.route(\"/restaurant/<int:r_id>/delete\", methods=[\"GET\", \"POST\"])\ndef deleteRestaurant(r_id):\n    if \"username\" not in login_session:\n        return redirect(\"/login\")\n\n    if request.method == \"POST\":\n        data = request.form\n\n        # check which button is clicked - either DELETE or CANCEL\n        if data[\"action\"] == \"DELETE\":\n            r = crud.get_restaurant(r_id)\n            crud.delete_restaurant(r_id)\n            flash(\"Restaurant {} Deleted!\".format(r.name))\n\n        return redirect(url_for('restaurant'))\n    else:\n        r = crud.get_restaurant(r_id)\n        return render_template(\"restaurant_op.html\", restaurant=r, op=\"delete\")\n\n@app.route(\"/restaurant/<int:r_id>/\", methods =[\"GET\", \"POST\"])\ndef restaurantMenuItem(r_id):\n    # read restaurant and its menu items from database\n    r = crud.get_restaurant(r_id)\n    m = crud.get_rest_menu_items(r_id)\n\n    # collect all courses provided by restaurant\n    courses =[]\n    for i in m:\n        # prepare a course list\n        courses.append(i.course)\n\n    # get unique set of courses\n    courses = list(set(courses))\n\n    return render_template(\"menu.html\", courses=courses, restaurant=r, items=m)\n\n@app.route(\"/restaurant/<int:r_id>/<int:m_id>/edit\", methods=[\"GET\", \"POST\"])\ndef editMenuItem(r_id, m_id):\n    if \"username\" not in login_session:\n        return redirect(\"/login\")\n\n    r = crud.get_restaurant(r_id)\n    m = crud.get_menu_item(r.id, m_id)\n    m_all = crud.get_rest_menu_items(r_id)\n\n    # prepare course list\n    courses = []\n    for i in m_all:\n        courses.append(i.course)\n    courses = list(set(courses))\n\n    if request.method == \"POST\":\n        data = request.form\n\n        # check if button submit is UPDATE or CANCEL\n        if data[\"action\"] == \"UPDATE\":\n            item_name = bleach.clean(data[\"name\"])\n            item_desc = bleach.clean(data[\"description\"])\n            item_price = bleach.clean(data[\"price\"])\n            item_course = bleach.clean(data[\"course\"])\n\n            # check and update the new-course to item_course\n            if item_course == \"OTHER\":\n                new_course = bleach.clean(data[\"new-course\"])\n                # update the item_course to value new-course\n                item_course = new_course if item_course == \"OTHER\" else item_course\n\n            crud.update_menu_item(r.id, m.id, item_name, item_course, item_desc, item_price)\n            flash(\"Menu Item {} Updated!\".format(item_name))\n        return redirect(url_for('restaurantMenuItem', r_id=r.id))\n    else:\n        return render_template(\"menuitem_op.html\", restaurant=r, courses=courses, item=m, op=\"edit\")\n\n@app.route(\"/restaurant/<int:r_id>/<int:m_id>/delete\", methods=[\"GET\", \"POST\"])\ndef deleteMenuItem(r_id, m_id):\n    if \"username\" not in login_session:\n        return redirect(\"/login\")\n\n    r = crud.get_restaurant(r_id)\n    m = crud.get_menu_item(r.id, m_id)\n    if request.method == \"POST\":\n        data = request.form\n        if data[\"action\"] == \"DELETE\":\n            crud.delete_menu_item(r.id, m.id)\n            flash(\"Menu Item {} Deleted!\".format(m.name))\n        return redirect(url_for('restaurantMenuItem', r_id=r.id))\n    else:\n        return render_template(\"menuitem_op.html\", restaurant=r, item=m, op=\"delete\")\n\n@app.route(\"/restaurant/<int:r_id>/new\", methods=[\"GET\", \"POST\"])\ndef newMenuItem(r_id):\n    if \"username\" not in login_session:\n        return redirect(\"/login\")\n\n    r = crud.get_restaurant(r_id)\n    m = crud.get_rest_menu_items(r_id)\n\n    # get all courses\n    courses =[]\n    for i in m:\n        courses.append(i.course)\n    courses = list(set(courses))\n\n    if request.method == \"POST\":\n        data = request.form\n        if data[\"action\"] == \"CREATE\":\n            item_name = bleach.clean(data[\"name\"])\n            item_desc = bleach.clean(data[\"description\"])\n            item_price = bleach.clean(data[\"price\"])\n            course = bleach.clean(data[\"course\"])\n            item_course = bleach.clean(data[\"course\"])\n\n            # check and update the new-course to item_course\n            if item_course == \"OTHER\":\n                new_course =bleach.clean(data[\"new-course\"])\n                item_course = new_course if item_course == \"OTHER\" else item_course\n\n            crud.create_menu_item(r.id, item_name, item_desc, item_course, item_price)\n            flash(\"New Menu Item {} Created!\".format(item_name))\n        return redirect(url_for('restaurantMenuItem', r_id=r.id))\n    else:\n        return render_template(\"menuitem_op.html\", restaurant=r, courses=courses, item='', op=\"add\")\n\n@app.route(\"/restaurant/<int:r_id>/menu/JSON\", methods=[\"GET\"])\ndef getRestaurantMenuItemJSON(r_id):\n    menu_items = crud.get_rest_menu_items(r_id)\n    return jsonify(MenuItems=[{\n        \"name\":i.name,\n        \"price\":i.price,\n        \"course\":i.course,\n        \"description\":i.description\n        } for i in menu_items])\n\nif __name__ == \"__main__\":\n    # super secure key for flash messaging\n    app.secret_key = \"super secret key\"\n    # debug mode\n    app.debug = True\n    app.run(host=\"127.0.0.1\", port=8000)", "repo_name": "ravi-2912/fullstack-restaurant-app", "sub_path": "flask-server.py", "file_name": "flask-server.py", "file_ext": "py", "file_size_in_byte": 16398, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "flask.Flask", "line_number": 15, "usage_type": "call"}, {"api_name": "restaurant_crud.RestaurantCRUD", "line_number": 16, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 19, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 26, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 26, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 37, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "oauth2client.client.flow_from_clientsecrets", "line_number": 45, "usage_type": "call"}, {"api_name": "oauth2client.client.FlowExchangeError", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 49, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 50, "usage_type": "call"}, {"api_name": "httplib2.Http", "line_number": 58, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 62, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 69, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 76, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 85, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 92, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 102, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 110, "usage_type": "argument"}, {"api_name": "flask.session", "line_number": 111, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 115, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 120, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 122, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 126, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 126, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 126, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 127, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 130, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 130, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 134, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 136, "usage_type": "call"}, {"api_name": "httplib2.Http", "line_number": 140, "usage_type": "call"}, {"api_name": "httplib2.Http", "line_number": 157, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 161, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 163, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 164, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 165, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 168, "usage_type": "name"}, {"api_name": "httplib2.Http", "line_number": 172, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 174, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 176, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 179, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 181, "usage_type": "argument"}, {"api_name": "flask.session", "line_number": 182, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 186, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 190, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 193, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 193, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 194, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 198, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 198, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 200, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 201, "usage_type": "call"}, {"api_name": "httplib2.Http", "line_number": 205, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 208, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 208, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 212, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 212, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 217, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 219, "usage_type": "name"}, {"api_name": "httplib2.Http", "line_number": 221, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 228, "usage_type": "argument"}, {"api_name": "flask.session", "line_number": 229, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 230, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 232, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 233, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 234, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 236, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 237, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 238, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 239, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 240, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 241, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 242, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 243, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 243, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 245, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 246, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 246, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 252, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 252, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 256, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 257, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 259, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 259, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 261, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 261, "usage_type": "name"}, {"api_name": "bleach.clean", "line_number": 266, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 270, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 273, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 273, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 275, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 279, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 280, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 282, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 282, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 283, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 283, "usage_type": "name"}, {"api_name": "bleach.clean", "line_number": 286, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 288, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 290, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 290, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 293, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 297, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 298, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 300, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 300, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 301, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 301, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 307, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 309, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 309, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 312, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 329, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 333, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 334, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 346, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 346, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 347, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 347, "usage_type": "name"}, {"api_name": "bleach.clean", "line_number": 351, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 352, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 353, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 354, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 358, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 363, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 364, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 364, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 366, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 370, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 371, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 375, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 375, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 376, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 376, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 379, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 380, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 380, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 382, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 386, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 387, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 398, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 398, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 399, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 399, "usage_type": "name"}, {"api_name": "bleach.clean", "line_number": 401, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 402, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 403, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 404, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 405, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 409, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 413, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 414, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 414, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 416, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 421, "usage_type": "call"}]}
{"seq_id": "18107805951", "text": "from PySide2 import QtWidgets, QtGui, QtCore \nfrom PySide2.QtGui import QIcon\nfrom PySide2.QtCore import Qt\nfrom ...hall.hall import Hall\nimport sqlite3 \nclass AddHallDialog(QtWidgets.QDialog):\n    \"\"\"\n    Dijalog za dodavanje nove hale.\n    \"\"\"\n    def __init__(self, parent=None):\n        \"\"\" \n        Inicijalizator dijaloga za dodavanje novog hale.\n\n        :param parent: roditeljski widget.\n        :type parent: QWidget\n        \"\"\"\n        super().__init__(parent)\n        self.setWindowTitle(\"Dodaj halu\")\n        self.setWindowIcon(QIcon(\"resources/icons/plus.png\")) \n        self.vbox_layout = QtWidgets.QVBoxLayout()\n        self.form_layout = QtWidgets.QFormLayout()\n        self.places_total_input = QtWidgets.QSpinBox(self)\n        self.places_total_input.setGeometry(QtCore.QRect(400, 50, 101, 22))\n        self.places_total_input.setMaximum(100000)\n        self.places_total_input.setSingleStep(100)\n        \n        self.hall_type = QtWidgets.QComboBox(self)\n        self.hall_type.addItem(\"sobna temperatura (19°C do 25°C)\")\n        self.hall_type.addItem(\"rashladna hala (1°C do 18°C)\")\n        self.hall_type.addItem(\"hala za zamrzavanje (-10°C do 0°C)\")\n        \n        self.button_box = QtWidgets.QDialogButtonBox(QtWidgets.QDialogButtonBox.Ok \n            | QtWidgets.QDialogButtonBox.Cancel, parent=self)\n        \n        \n        self.form_layout.addRow(\"Mesta u hali:\", self.places_total_input)\n        \n        self.form_layout.addRow(\"Tip Hale: \", self.hall_type)\n        \n        self.vbox_layout.addLayout(self.form_layout)\n        self.vbox_layout.addWidget(self.button_box)\n\n        self.button_box.accepted.connect(self._on_accept)\n        self.button_box.rejected.connect(self.reject)\n\n        self.setLayout(self.vbox_layout)\n        self.new_hall = None\n\n    def _on_accept(self):\n        \"\"\"\n        Metoda koja se poziva kada se pritisne na dugme ok.\n        Prvo proverava popunjenost forme. \n        Ukoliko neko polje nije popunjeno korisniku se prikazuje upozorenje.\n        \"\"\"\n        if self.places_total_input.text() == \"0\":\n            QtWidgets.QMessageBox.warning(self, \n            \"Provera ukupno mesta\", \"Polje mora biti popunjeno!\", QtWidgets.QMessageBox.Ok)\n            return\n        elif int(self.places_total_input.text()) > 100000:\n            QtWidgets.QMessageBox.warning(self, \n            \"Provera ukupno mesta\", \"Polje mora biti manje od 100000!\", QtWidgets.QMessageBox.Ok)\n            return\n        elif int(self.places_total_input.text()) % 100 != 0:\n            QtWidgets.QMessageBox.warning(self, \n            \"Provera ukupno mesta\", \"Polje mora biti deljiv sa 100!\", QtWidgets.QMessageBox.Ok)\n            return\n        self.new_hall = self.get_hall()\n        self.accept()\n        \n    def _hall_type(self, hall_type):\n        \"\"\"\n        Metoda koja dobavlja podatke tabele i vraća kojeg je tipa hala.\n\n        :param type: tip hale može biti određeni tekst\n        :type ps: str\n        :returns: int -- tip hale 1,2,3\n        \"\"\"   \n        if hall_type == \"sobna temperatura (19°C do 25°C)\":\n            return 1\n        elif hall_type == \"rashladna hala (1°C do 18°C)\":\n            return 2\n        else:\n            return 3\n\n    def get_hall(self):\n        \"\"\"\n        Metoda koja dobavlja podatke tabele i inicializuje halu.\n\n        :returns: Hall -- nova kreirana hala.\n        \"\"\"   \n        conn = sqlite3.connect('plugins\\\\rs_ac_singidunum_magacin\\\\db\\\\warehouse.db')\n        c = conn.cursor()\n        for i in  c.execute('SELECT seq FROM sqlite_sequence WHERE name = \"halls\"'):\n            number = int(str(i).replace(\"(\",\"\").replace(\",)\",\"\").replace(\"('\",\"\").replace(\"',)\",\"\"))\n        conn.commit()\n        conn.close() \n\n        return Hall(\"Hala \" + str(number+1), 0, int(self.places_total_input.text()), self._hall_type(self.hall_type.currentText()))\n\n\n\n", "repo_name": "mmbond/MRS", "sub_path": "Projekat - Magacin/plugins/rs_ac_singidunum_magacin/widgets/dialogs/add_hall_dialog.py", "file_name": "add_hall_dialog.py", "file_ext": "py", "file_size_in_byte": 3857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "PySide2.QtWidgets.QDialog", "line_number": 6, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 6, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QIcon", "line_number": 19, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 20, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 20, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QFormLayout", "line_number": 21, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 21, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QSpinBox", "line_number": 22, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 22, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QRect", "line_number": 23, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 23, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QComboBox", "line_number": 27, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 27, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QDialogButtonBox", "line_number": 32, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 32, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QDialogButtonBox", "line_number": 33, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 33, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QMessageBox.warning", "line_number": 56, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QMessageBox", "line_number": 56, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 56, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QMessageBox", "line_number": 57, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 57, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QMessageBox.warning", "line_number": 60, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QMessageBox", "line_number": 60, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 60, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QMessageBox", "line_number": 61, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 61, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QMessageBox.warning", "line_number": 64, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QMessageBox", "line_number": 64, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 64, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QMessageBox", "line_number": 65, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 65, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 91, "usage_type": "call"}, {"api_name": "hall.hall.Hall", "line_number": 98, "usage_type": "call"}]}
{"seq_id": "32284409835", "text": "from collections import defaultdict\nimport sys\n\ninput = sys.stdin.readline\n\nn= int(input())\ntimes=[0]*(n+1)\ngraph=defaultdict(list)\nfor i in range(1,n+1):\n    lst = list(map(int,input().split()))\n    times[i]= lst[0]\n    if lst[1]==0:\n        continue\n    for j in lst[2:]:\n        graph[i].append(j)\n\nfor i in range(1,n+1):\n    if i in graph:\n        time =0\n        for j in graph[i]:\n            time = max(time,times[j])\n            print(i,time)\n        print()\n        times[i]+=time\n        print(times)\n\nprint(times)\nprint(graph)", "repo_name": "kimth007kim/python_algorithm", "sub_path": "코테스터디/2022/0522/0517/2056 작업_해설.py", "file_name": "2056 작업_해설.py", "file_ext": "py", "file_size_in_byte": 537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.stdin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 8, "usage_type": "call"}]}
{"seq_id": "13694839341", "text": "from pathlib import Path\n\ndef get_nums():\n    text = Path(\"problem_01.txt\").read_text().strip()\n    return [int(line) for line in text.split(\"\\n\")]\n\ndef part_a():\n    nums = get_nums()\n    count = 0\n    for i in range(1, len(nums)):\n        if nums[i] > nums[i-1]:\n            count += 1\n    return count\n\ndef part_b():\n    nums = get_nums()\n    count = 0\n    for range_end in range(3, len(nums)):\n        if nums[range_end] > nums[range_end-3]:\n            count += 1\n    return count\n\n\nprint(part_a())\nprint(part_b())\n", "repo_name": "NakulK48/aoc-2021", "sub_path": "problem_01.py", "file_name": "problem_01.py", "file_ext": "py", "file_size_in_byte": 520, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pathlib.Path", "line_number": 4, "usage_type": "call"}]}
{"seq_id": "37690645337", "text": "#!/usr/bin/env python3\n\"\"\"Transcode python streams\n\nUsage:\n  transcode.py inout <inpath> <outpath>\n  transcode.py glob_base <glob_expr> <out_base>\n\nOptions:\n  -h --help     Show this screen.\n\"\"\"\nfrom docopt import docopt\n\nimport pandas as pd\nimport os.path\nimport os\nimport struct\nfrom glob import glob\nfrom jsonlinesreader import JsonLinesReader\nfrom fact.credentials import create_factdb_engine\nfrom tqdm import tqdm\n\ndb = create_factdb_engine()\nevent_integer_value_order = [\n    'Night',\n    'Run',\n    'Event',\n    'Trigger',\n]\n\nNAMES = None\n\nevent_double_value_order = ['Az_deg', 'Zd_deg']\n\n\ndef main(inpath, outpath):\n    night, run = night_run_from_inpath(inpath)\n    runinfo = get_runinfo(night, run)\n\n    os.makedirs(os.path.split(outpath)[0], exist_ok=True)\n    with open(outpath, 'wb') as file:\n        put_version_header(file)\n        dump_runinfo_binary(runinfo, file)\n        with JsonLinesReader(inpath) as infile:\n            for event in infile:\n                dump_event_binary(event, file)\n\n\ndef get_runinfo(night, run):\n    return pd.read_sql(\n        '''\n        SELECT *\n        FROM RunInfo\n        WHERE\n            fNight={night} AND\n            fRunID={run}\n        '''.format(night=night, run=run),\n        con=db)\n\n\ndef put_version_header(file):\n    file.write(bytes([0xfa, 0xc7, 0x01]))\n\n\ndef dump_runinfo_binary(runinfo, file):\n    ''' write all entries from runinfo into file as 64bit 'd'oubles'''\n    d = {}\n    for n in runinfo.columns:\n        x = runinfo.iloc[0][n]\n\n        if isinstance(x, pd.tslib.Timestamp):\n            x = x.asm8.astype('f8')\n        try:\n            d[n] = float(x)\n        except:\n            pass\n\n    names = sorted(d.keys())\n\n    global NAMES\n    if NAMES is None:\n        NAMES = set(names)\n    else:\n        if not set(names) == NAMES:\n            raise \"OMG\"\n\n    values = [d[name] for name in names]\n\n    _bytes = struct.pack('d'*len(values), *values)\n    file.write(_bytes)\n\n\ndef dump_event_binary(event, file):\n    integer_values = [event[name] for name in event_integer_value_order]\n    file.write(struct.pack('i'*len(integer_values), *integer_values))\n\n    double_values = [event[name] for name in event_double_value_order]\n    file.write(struct.pack('d'*len(double_values), *double_values))\n\n    unixtime = event['UnixTime_s_us'][0] + event['UnixTime_s_us'][1]/1e6\n    file.write(struct.pack('d', unixtime))\n\n    dump_arrival_times(event['PhotonArrivals_500ps'], file)\n    dump_saturated_pixel(event['SaturatedPixels'], file)\n\n\ndef dump_arrival_times(s, file):\n    total_number_of_entries = sum(len(x) for x in s)  # 1byte per entry\n    number_of_sublists = len(s)   # 2bytes before each sublist\n    total_legth = (\n        total_number_of_entries * 1 +\n        number_of_sublists * 2\n    )\n    file.write(struct.pack('I', total_legth))\n    for sublist in s:\n        file.write(struct.pack('H', len(sublist)))\n        file.write(struct.pack('B'*len(sublist), *sublist))\n\n\ndef dump_saturated_pixel(s, file):\n    file.write(struct.pack('H', len(s)))\n    file.write(struct.pack('H'*len(s), *s))\n\n\ndef make_path(night, run, base, ext):\n    year = night[0:4]\n    month = night[4:6]\n    day = night[6:8]\n    filename = \"{night}_{run}{ext}\".format(night=night, run=run, ext=ext)\n    return os.path.join(base, year, month, day, filename)\n\n\ndef night_run_from_inpath(p):\n    filename = os.path.split(p)[1]\n    basename = filename.split('.')[0]\n    night = basename[0:8]\n    run = basename[9:12]\n    return night, run\n\nif __name__ == '__main__':\n    arguments = docopt(__doc__, version='Transcode')\n    print(arguments)\n    if arguments['inout']:\n        main(arguments['<inpath>'], arguments['<outpath>'])\n    elif arguments['glob_base']:\n        for inpath in sorted(glob(arguments['<glob_expr>'])):\n            night, run = night_run_from_inpath(inpath)\n            outpath = make_path(\n                night, run, arguments['<out_base>'], ext='.phs.bin')\n            main(inpath, outpath)\n\n\n", "repo_name": "dneise/transcode_phs_to_binary", "sub_path": "transcode.py", "file_name": "transcode.py", "file_ext": "py", "file_size_in_byte": 3958, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "fact.credentials.create_factdb_engine", "line_number": 22, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "jsonlinesreader.JsonLinesReader", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.tslib", "line_number": 70, "usage_type": "attribute"}, {"api_name": "struct.pack", "line_number": 88, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 94, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 97, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 100, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 113, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 115, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 116, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 120, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "docopt.docopt", "line_number": 140, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 145, "usage_type": "call"}]}
{"seq_id": "25763720798", "text": "# AutoEncoder : 訓練重建原生data的能力 -> 平常的data我有信心回復平常data原來的樣子(reconstruction error最小化)；但異常的data我就沒辦法有信心回復了!\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport os\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_absolute_error\nfrom tensorflow.keras import layers, losses\nfrom tensorflow.keras.datasets import fashion_mnist\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.models import load_model\n\n# 1. 分割資料成訓練、測試集\n(X_train,_),(X_test, _) = fashion_mnist.load_data()\n\n# 2. 歸一化(MinMax Normalization)\nx_train = X_train.astype('float32') / 255.\nx_test = X_test.astype('float32') / 255.\n\nprint (x_train.shape)\nprint (x_test.shape)\n\n# 定義AutoEncoder模型架構\nn_bottle_neck = 64\n\nclass Autoencoder(Model): # 繼承Model的屬性\n  def __init__(self, latent_dim):\n    super(Autoencoder, self).__init__() # 繼承Model的方法\n    self.latent_dim = latent_dim \n    # 建立 Encoder (Compressing the input)  \n    self.encoder = tf.keras.Sequential([\n      layers.Flatten(),\n      layers.Dense(latent_dim, activation='relu'),\n    ])\n    # 建立 Decoder (Reconstructing the compressed input Encoder made)\n    self.decoder = tf.keras.Sequential([\n      layers.Dense(784, activation='linear'),\n      layers.Reshape((28, 28))\n    ])\n\n  def call(self, x): # pic -> encoding -> decoding\n    encoded = self.encoder(x)\n    decoded = self.decoder(encoded)\n    return decoded\n  \n\ndef get_model():\n  return Autoencoder(n_bottle_neck)\n\n# 3. 新增模型架構並載入訓練好的權重\n## 讀取方法I : 建立相同模型並載入權重\n# checkpoint_path = 'Autencoder_training_1/ckpt'\n# autoencoder = get_model()\n# autoencoder.compile(optimizer='adam', loss='mse')\n# # Initialize the variables used by optimizers\n# autoencoder.train_on_batch(x_train[:1], x_train[:1])\n# # Load the state of the Old model\n# autoencoder.load_weights(checkpoint_path)\n\n# 讀取方法II : 讀取整個模型，不用再建立一個新的模型架構\nmodel_path = 'Autencoder_training_1/ckpt.h5'\nautoencoder = load_model(model_path)\n# 4. 使用AutoEncoder 的 Encoder 以及 Decoder\ndecoded_imgs = np.array(autoencoder.predict(x_test))\n\n# 5. 畫圖顯示\nn = 10\nplt.figure(figsize=(20, 4))\nfor i in range(n):\n  # display original\n  ax = plt.subplot(2, n, i + 1)\n  plt.imshow(X_test[i])\n  plt.title(\"original\")\n  plt.gray()\n  ax.get_xaxis().set_visible(False)\n  ax.get_yaxis().set_visible(False)\n\n  # display reconstruction\n  ax = plt.subplot(2, n, i + 1 + n)\n  plt.imshow(decoded_imgs[i])\n  plt.title(\"reconstructed\")\n  plt.gray()\n  ax.get_xaxis().set_visible(False)\n  ax.get_yaxis().set_visible(False)\nplt.show()", "repo_name": "joshsmiththenoob/DeepLearning_Knowledge", "sub_path": "AutoEncoder_predcting_TFdoc.py", "file_name": "AutoEncoder_predcting_TFdoc.py", "file_ext": "py", "file_size_in_byte": 2869, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "tensorflow.keras.datasets.fashion_mnist.load_data", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.fashion_mnist", "line_number": 16, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 28, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 34, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 35, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 39, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Reshape", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 40, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}]}
{"seq_id": "35947428558", "text": "from .pytest_compiler import pytest_compiles\nfrom .question_parser import parse_question\nimport json\n\n\ndef compile(args):\n\n    rubric = parse_question(args.question_file)\n\n    if args.language == \"py\" and args.framework == \"pytest_scforge\":\n        data = pytest_compiles(args)\n    else:\n        raise Exception(\n            f\"Compilation is not yet supported for {args.language} {args.framework}\")\n\n    with open(args.output, \"w\") as f:\n        json.dump({\n            \"title\": \"\",\n            \"difficulty\": 1,\n            \"className\": \"\",\n            \"methodName\": \"\",\n            \"rubric\": rubric,\n            **data,\n        }, f, indent=4)\n", "repo_name": "tituszban/showcode_forge", "sub_path": "showcode_forge/compilers/compilers.py", "file_name": "compilers.py", "file_ext": "py", "file_size_in_byte": 645, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "40", "api": [{"api_name": "question_parser.parse_question", "line_number": 8, "usage_type": "call"}, {"api_name": "pytest_compiler.pytest_compiles", "line_number": 11, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "70833241719", "text": "import logging\nimport torch\nimport shutil\n\n\ndef configure_logging(log_filename):\n    logger = logging.getLogger(log_filename)\n    # logger = logging.getLogger(\"my_logger\")\n    logger.setLevel(logging.DEBUG)\n    # Format for our log lines\n    formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n\n    # Setup console logging\n    ch = logging.StreamHandler()\n    ch.setLevel(logging.DEBUG)\n    ch.setFormatter(formatter)\n    logger.addHandler(ch)\n\n    # Setup file logging\n    fh = logging.FileHandler(log_filename)\n    fh.setLevel(logging.DEBUG)\n    fh.setFormatter(formatter)\n    logger.addHandler(fh)\n    return logger\n\n\ndef save_checkpoint(state, is_best, epoch, checkpoint='checkpoint', filename='checkpoint.pth.tar'):\n    filePath = checkpoint + filename + 'epoch:' + str(epoch)\n    torch.save(state, filePath)\n    if is_best:\n        best_filePath = checkpoint + filename + 'best'\n        shutil.copyfile(filePath, best_filePath)\n\n\ndef get_learning_rate(optimizer):\n    lr = []\n    for param_group in optimizer.param_groups:\n        lr += [param_group['lr']]\n    return lr\n", "repo_name": "alfassy/kaggle-dbi", "sub_path": "scripts/configure_logging.py", "file_name": "configure_logging.py", "file_ext": "py", "file_size_in_byte": 1111, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 29, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 32, "usage_type": "call"}]}
{"seq_id": "14851300022", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[9]:\n\n\nimport pandas as pd\nimport requests\n\n# ask user for X-RapidAPI-Key, app_key, and app_id\nx_rapidapi_key = input(\"Enter your X-RapidAPI-Key: \")\napp_key = input(\"Enter your app_key: \")\napp_id = input(\"Enter your app_id: \")\n\n# set up API request parameters\nurl = \"https://edamam-food-and-grocery-database.p.rapidapi.com/api/food-database/v2/parser\"\nquerystring = {\"app_key\": app_key, \"app_id\": app_id, \"ingr\": \"champagne\"}\nheaders = {\n    \"X-RapidAPI-Key\": x_rapidapi_key,\n    \"X-RapidAPI-Host\": \"edamam-food-and-grocery-database.p.rapidapi.com\"\n}\n\n# send API request and get response\nresponse = requests.request(\"GET\", url, headers=headers, params=querystring)\n\n# convert response json to a pandas dataframe\nresults = response.json()[\"hints\"]\ndf = pd.json_normalize(results)\ndf_sortie = df[['food.foodId','food.label','food.category','food.foodContentsLabel','food.image']]\ndf_sortie = df_sortie.head(10)\n\n# save results to csv file\ndf_sortie.to_csv('extraction_10_premiers_produits_champagne.csv')\n\n\n", "repo_name": "manuelmartin67/nlp_computer_vision_classification", "sub_path": "Martin_Manuel_3_script_Python_022023.py", "file_name": "Martin_Manuel_3_script_Python_022023.py", "file_ext": "py", "file_size_in_byte": 1048, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "requests.request", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "32044512940", "text": "import os\nimport configparser\n\n# config_fname = 'test_params.ini'\nconfig_fname = 'planck2018_params.ini'\nconfig_path, _ = os.path.split(__file__)\n\nconfig = configparser.ConfigParser()\nconfig.read(config_path + '/' + config_fname)\n\n# Want printout?\nverbose = True\n\n# Definitions\nmpctokm = 3.08568e19\nmpctocm = mpctokm*1e5\ndeltac = 0\ndeltab = 4\ndeltat = 8\nreal = 0\nimag = 1\nvelreal = 2\nvelimag = 3\n\ndelc = 0\ndelb = 2\nvel = 1\ndelt = 4\n\n# Constants\nTcmb = 2.726              # CMB temperature at z=0\nmproton = 1.6726e-24      # proton mass in g\nmelectron = 9.11e-28      # electron mass in g\nmel_ev = 5.11e5           # electron mass in eV/c^2\nlightspeed = 3.0e10       # c in cm/s\ncritdensity = 1.8791e-29  # rho_c in g/cm^3\nsigmat = 0.665e-24        # Thomson cross-section in cm^2\nyhe = 0.25                # Helium fraction?\nboltzk = 1.3806e-16       # Boltzmann's constant in erg/K\nmub = 1.22                # Mean molecular weight, primordial (?)\naSB = 7.56e-15            # u = aSB T^4 => Stefan's constant in ?\nu_cmb = aSB*(Tcmb**4)     # energy stored CMB\nt_gamma = 3*lightspeed*melectron/(8*sigmat*u_cmb)  # t_gamma in s\n\n# Interpolation redshifts for photon density fluctuations, not used currently\n# zs = [1000, 900, 800, 700, 600, 500, 400, 300, 200]\n\n# Parameters (runtime, CICsASS)\n# Only a few of the commented ones are needed, and the rest are\n# input dynamically\n# boxsize = 0.2\n# size = 128\n# vstream = 0.0\n# zstart = 1000.0\n# zinit = 50.0\n# dz = 3.0\n\n# THIS WILL BREAK IF ARCHIVED\nimport os\nbase, _ = os.path.split(__file__)\ncamb_base = config.get('filenames', 'camb_base')\ntf_base = os.path.join(base, 'tfs', '{0}_z{1:03d}.dat')\nrf_base = os.path.join(base, 'recfast', config.get('filenames', 'rf_base'))\njustcamb = 0\n\n# Parameters (cosmological)\nhconst = float(config.get('cosmology', 'hconst'))\nomega_m = float(config.get('cosmology', 'omega_m'))\nomega_b = float(config.get('cosmology', 'omega_b'))\nsigma_8 = float(config.get('cosmology', 'sigma_8'))\nns = float(config.get('cosmology', 'ns'))\nomega_r = 4.15e-5/(hconst**2)  # Dodelson (2002) Eq. 2.86\ncosth = float(config.get('cosmology', 'costh'))\n", "repo_name": "lconaboy/drft", "sub_path": "py_vbc/constants.py", "file_name": "constants.py", "file_ext": "py", "file_size_in_byte": 2118, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "os.path.split", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}]}
{"seq_id": "41168011180", "text": "from flask import render_template, url_for, flash, redirect, request, Blueprint\nfrom bookreview.readinglist.forms import ListForm, SearchnaddForm, SearchForm\nfrom bookreview.models import User, Readinglist\nfrom bookreview import create_app,db, bcrypt\nfrom flask_login import login_user, current_user, logout_user, login_required\nimport requests\nimport json\n\nreadinglist = Blueprint('readinglist', __name__)\n\n# Home page functionality that lists all the current reviews by the user\n@readinglist.route('/list')\n@readinglist.route('/')\n@login_required\ndef home():\n    user = User.query.filter_by(id=current_user.id).first_or_404()\n    readinglist = Readinglist.query.filter_by(author=user).all()\n    return render_template(\"books.html\", readinglist=readinglist, title = \"Bookhead\")\n\n# Functionality to add a new review by the user\n@readinglist.route(\"/list/new\", methods=['GET', 'POST'])\n@login_required\ndef new_list():\n    form = ListForm()\n    if form.validate_on_submit():\n        alist = Readinglist(bookname=form.bookname.data, review = form.review.data, \n            isbn = form.isbn.data,libid = form.libraryid.data,author=current_user)       \n        db.session.add(alist)\n        db.session.commit()\n        flash('Your list has been created!', 'success')\n        return redirect(url_for('readinglist.home',user_id=current_user.id))\n    return render_template('create_list.html', title='New List',\n                           form=form, legend='New List')\n\n# Functionality view a particular review\n@readinglist.route(\"/book/<int:list_id>\")\n@login_required\ndef alist(list_id):\n    alist = Readinglist.query.get_or_404(list_id)\n    return render_template('alist.html', title=alist.bookname, alist=alist)\n\n# Functionality to update a review\n@readinglist.route(\"/book/<int:list_id>/update\", methods=['GET', 'POST'])\n@login_required\ndef update_list(list_id):\n    readinglist = Readinglist.query.get_or_404(list_id)\n    if readinglist.author != current_user:\n        abort(403)\n    form = ListForm()\n    if form.validate_on_submit():\n        readinglist.bookname = form.bookname.data\n        readinglist.review = form.review.data\n        readinglist.isbn = form.isbn.data\n        readinglist.libid = form.libraryid.data\n        db.session.commit()\n        flash('Your review has been updated!', 'success')\n        return redirect(url_for('readinglist.home',user_id=current_user.id))\n    elif request.method == 'GET':\n        form.bookname.data = readinglist.bookname\n        form.review.data = readinglist.review\n        form.isbn.data = readinglist.isbn\n        form.libraryid.data = readinglist.libid\n    return render_template('create_list.html', title='Update List',\n                           form=form, legend='Update List')\n\n# Functionality to delete a review\n@readinglist.route(\"/book/<int:list_id>/delete\", methods=['POST'])\n@login_required\ndef delete_list(list_id):\n    readinglist = Readinglist.query.get_or_404(list_id)\n    if readinglist.author != current_user:\n        abort(403)\n    db.session.delete(readinglist)\n    db.session.commit()\n    flash('Your post has been deleted!', 'success')\n    return redirect(url_for('readinglist.home',user_id=current_user.id))\n\n# Functionality to add a book review\n#@readinglist.route(\"/book/<int:list_id>/add\")\n#def addbook(list_id):\n    #alist = Readinglist.query.get_or_404(list_id)\n#    booklist = [\"abcbook\",\"1234book\"]\n#    return render_template('addbook.html', title='abc', booklist=booklist)\n\n# Functionality to search a book from external API\n@readinglist.route(\"/book/<bookname>/search\",methods=['GET', 'POST'])\ndef booknamesearch(bookname):\n    form = SearchnaddForm()\n    if form.validate_on_submit():\n        alist = Readinglist(bookname=form.bookname.data, review = form.review.data, \n            isbn = form.isbn.data,libid = form.libraryid.data,author=current_user)        \n        db.session.add(alist)\n        db.session.commit()\n        flash('Your list has been created!', 'success')\n        return redirect(url_for('readinglist.home',user_id=current_user.id))\n    response_book = requests.get(\"http://openlibrary.org/search.json?title=\"+bookname)\n    json_response = response_book.json()\n    docs = json_response[\"docs\"]\n    booklist = []\n    for line in docs:\n        if \"isbn\" in line and \"lending_edition_s\" in line :\n            booklist.append(line[\"title\"])\n            booklist.append(line[\"isbn\"][0])\n            booklist.append(line[\"lending_edition_s\"])        \n    lngthoflist = len(booklist)\n    return render_template(\"addbook.html\", booklist=booklist, lngthoflist = lngthoflist, title = \"Add Book\",form=form)\n\n# Functionality to add a book review after searching\n@readinglist.route(\"/list/search\", methods=['GET', 'POST'])\n@login_required\ndef searchbook():\n    form = SearchForm()\n    if form.validate_on_submit():\n        bookname = form.bookname.data,\n        flash('Your search results below', 'success')\n        return redirect(url_for('readinglist.booknamesearch',bookname=bookname))\n    return render_template('searchbook.html', title='New List',\n                           form=form, legend='New List')\n\n", "repo_name": "abhixcode/BookReviewApp", "sub_path": "bookreview/readinglist/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 5097, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "flask.Blueprint", "line_number": 9, "usage_type": "call"}, {"api_name": "bookreview.models.User.query.filter_by", "line_number": 16, "usage_type": "call"}, {"api_name": "bookreview.models.User.query", "line_number": 16, "usage_type": "attribute"}, {"api_name": "bookreview.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 16, "usage_type": "name"}, {"api_name": "bookreview.models.Readinglist.query.filter_by", "line_number": 17, "usage_type": "call"}, {"api_name": "bookreview.models.Readinglist.query", "line_number": 17, "usage_type": "attribute"}, {"api_name": "bookreview.models.Readinglist", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 14, "usage_type": "name"}, {"api_name": "bookreview.readinglist.forms.ListForm", "line_number": 24, "usage_type": "call"}, {"api_name": "bookreview.models.Readinglist", "line_number": 26, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 27, "usage_type": "name"}, {"api_name": "bookreview.db.session.add", "line_number": 28, "usage_type": "call"}, {"api_name": "bookreview.db.session", "line_number": 28, "usage_type": "attribute"}, {"api_name": "bookreview.db", "line_number": 28, "usage_type": "name"}, {"api_name": "bookreview.db.session.commit", "line_number": 29, "usage_type": "call"}, {"api_name": "bookreview.db.session", "line_number": 29, "usage_type": "attribute"}, {"api_name": "bookreview.db", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 31, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 22, "usage_type": "name"}, {"api_name": "bookreview.models.Readinglist.query.get_or_404", "line_number": 39, "usage_type": "call"}, {"api_name": "bookreview.models.Readinglist.query", "line_number": 39, "usage_type": "attribute"}, {"api_name": "bookreview.models.Readinglist", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 37, "usage_type": "name"}, {"api_name": "bookreview.models.Readinglist.query.get_or_404", "line_number": 46, "usage_type": "call"}, {"api_name": "bookreview.models.Readinglist.query", "line_number": 46, "usage_type": "attribute"}, {"api_name": "bookreview.models.Readinglist", "line_number": 46, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 47, "usage_type": "name"}, {"api_name": "bookreview.readinglist.forms.ListForm", "line_number": 49, "usage_type": "call"}, {"api_name": "bookreview.db.session.commit", "line_number": 55, "usage_type": "call"}, {"api_name": "bookreview.db.session", "line_number": 55, "usage_type": "attribute"}, {"api_name": "bookreview.db", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 57, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 63, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 44, "usage_type": "name"}, {"api_name": "bookreview.models.Readinglist.query.get_or_404", "line_number": 70, "usage_type": "call"}, {"api_name": "bookreview.models.Readinglist.query", "line_number": 70, "usage_type": "attribute"}, {"api_name": "bookreview.models.Readinglist", "line_number": 70, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 71, "usage_type": "name"}, {"api_name": "bookreview.db.session.delete", "line_number": 73, "usage_type": "call"}, {"api_name": "bookreview.db.session", "line_number": 73, "usage_type": "attribute"}, {"api_name": "bookreview.db", "line_number": 73, "usage_type": "name"}, {"api_name": "bookreview.db.session.commit", "line_number": 74, "usage_type": "call"}, {"api_name": "bookreview.db.session", "line_number": 74, "usage_type": "attribute"}, {"api_name": "bookreview.db", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 76, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 76, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 68, "usage_type": "name"}, {"api_name": "bookreview.readinglist.forms.SearchnaddForm", "line_number": 88, "usage_type": "call"}, {"api_name": "bookreview.models.Readinglist", "line_number": 90, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 91, "usage_type": "name"}, {"api_name": "bookreview.db.session.add", "line_number": 92, "usage_type": "call"}, {"api_name": "bookreview.db.session", "line_number": 92, "usage_type": "attribute"}, {"api_name": "bookreview.db", "line_number": 92, "usage_type": "name"}, {"api_name": "bookreview.db.session.commit", "line_number": 93, "usage_type": "call"}, {"api_name": "bookreview.db.session", "line_number": 93, "usage_type": "attribute"}, {"api_name": "bookreview.db", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 95, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 95, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 95, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 106, "usage_type": "call"}, {"api_name": "bookreview.readinglist.forms.SearchForm", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 117, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 110, "usage_type": "name"}]}
{"seq_id": "19029830194", "text": "import sys\r\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QHeaderView, QDialog, QMessageBox\r\nfrom PyQt5.QtCore import QPropertyAnimation, QEasingCurve \r\nfrom PyQt5 import QtCore, QtWidgets, uic \r\nfrom PyQt5.uic import loadUi\r\nfrom PyQt5.QtGui import QDoubleValidator\r\nimport sqlite3\r\n\r\nclass Pagina7():\r\n    def __init__(self):\r\n        #Conectar a la base de datos#\r\n        self.conn = sqlite3.connect(\"PrgogramaInventarios.db\")\r\n\r\n    def __str__(self):\r\n        Datos = self.ConsultarLista()\r\n        Aux = \"\"\r\n\r\n        for row in Datos:\r\n            Aux = Aux + str(row) + \"\\n\"\r\n        return Aux\r\n\r\n    def ConsultarLista(self):\r\n        c = self.conn.cursor()\r\n        c.execute(\"SELECT * FROM Lista\")\r\n\r\n        Dato = c.fetchall()\r\n        #c.close()\r\n        return Dato\r\n\r\n    def BuscarLista(self, Referencia):\r\n        c = self.conn.cursor()\r\n        buscar = \"SELECT * FROM Lista WHERE Referencia= '{}'\".format(Referencia)\r\n        c.execute(buscar)\r\n\r\n        Dato = c.fetchall()\r\n        c.close()\r\n        return Dato\r\n\r\n    def BuscarListaN(self, Clave):\r\n        c = self.conn.cursor()\r\n        buscar = \"SELECT * FROM Lista WHERE Descripcion= '{}'\".format(Clave)\r\n        c.execute(buscar)\r\n\r\n        Dato = c.fetchall()\r\n        c.close()\r\n        return Dato\r\n\r\n    def AgregarLista(self, ID, Referencia, Descripcion, Unidad, Cantidad, Ancho, Largo, Lugar, FolioREQ, Numero, Marca, Color, Proveedor, Stock, TPMov):\r\n        c = self.conn.cursor()\r\n        Agregar = \"INSERT INTO Lista (ID, Referencia, Descripcion, Unidad, Cantidad, Ancho, Largo, Lugar, FolioREQ, Numero, Marca, Color, Proveedor, Stock, TPMov ) VALUES('{}', '{}', '{}', '{}', {}, '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', {}, '{}')\".format(ID, Referencia, Descripcion, Unidad, Cantidad, Ancho, Largo, Lugar, FolioREQ, Numero, Marca, Color, Proveedor, Stock, TPMov)\r\n        c.execute(Agregar)\r\n        n = c.rowcount\r\n        self.conn.commit()\r\n        c.close()\r\n        return n\r\n    \r\n    def EliminarLista(self, ID):\r\n        c = self.conn.cursor()\r\n        Elimiar = \"DELETE FROM Lista WHERE ID='{}'\".format(ID)\r\n        c.execute(Elimiar)\r\n        n = c.rowcount\r\n        self.conn.commit()\r\n        c.close()\r\n        return n\r\n    \r\n    def ActualizarLista(self, ID, Referencia, Descripcion, Unidad, Cantidad, Ancho, Largo, Lugar, FolioREQ, Numero, Marca, Color, Proveedor, Stock, TPMov):\r\n        c = self.conn.cursor()\r\n        Actualizar = \"UPDATE Lista SET Referencia='{}', Descripcion='{}', Unidad='{}', Cantidad={}, Ancho='{}', Largo='{}', Lugar='{}', FolioREQ='{}', Numero='{}', Marca='{}', Color='{}', Proveedor='{}', Stock={}, TPMov='{}' WHERE ID={}\".format(Referencia, Descripcion, Unidad, Cantidad, Ancho, Largo, Lugar, FolioREQ, Numero, Marca, Color, Proveedor, Stock, TPMov, ID)\r\n        c.execute(Actualizar)\r\n        n = c.rowcount\r\n        self.conn.commit()\r\n        c.close()\r\n        return n", "repo_name": "Isaac-Monted/Programa-Inventarios-Domotica", "sub_path": "Scrips/IngresarLista.py", "file_name": "IngresarLista.py", "file_ext": "py", "file_size_in_byte": 2926, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sqlite3.connect", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "20369748891", "text": "from telegram.ext import Updater, CommandHandler\n\n\ndef get_webhook_info(update, context):\n    user_pic = context.bot.get_webhook_info()\n    update.message.reply_text(f\"webhook info : \\n\\n{user_pic}\")\n\n\ndef runner():\n    updater = Updater(token=\"5333086108:AAGOz98WcjgaJ5SEg208C_XS7rYVZJ8-eT4\")\n    dispatcher = updater.dispatcher\n\n    dispatcher.add_handler(CommandHandler('start', get_webhook_info))\n\n    updater.start_polling()\n    updater.idle()\n\n\nif __name__ == '__main__':\n    print(\"ishga tushdi ...\")\n    runner()", "repo_name": "IqrorjonCoder/all-telegrambot-functions", "sub_path": "learn/context/bot/get_webhook_info.py", "file_name": "get_webhook_info.py", "file_ext": "py", "file_size_in_byte": 520, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "41", "api": [{"api_name": "telegram.ext.Updater", "line_number": 10, "usage_type": "call"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 13, "usage_type": "call"}]}
{"seq_id": "36504861053", "text": "from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport os.path, sys\nimport numpy as np\n\nfrom . import util\nfrom ... import binary_type, reraise_context, text_type, PKError\nfrom ...cli import check_usage, wrong_usage, warn, die\nfrom ...kwargv import ParseKeywords, Custom\n\n# Keep the tasks alphabetized!\n\n__all__ = (b'''\napplycal applycal_cli ApplycalConfig\nbpplot bpplot_cli BpplotConfig\nclearcal clearcal_cli\nconcat concat_cli\ndelcal delcal_cli\ndelmod delmod_cli\ndftphotom_cli\nextractbpflags extractbpflags_cli\nflagmanager_cli\nflaglist flaglist_cli FlaglistConfig\nflagzeros flagzeros_cli FlagzerosConfig\nfluxscale fluxscale_cli FluxscaleConfig\nft ft_cli FtConfig\ngaincal gaincal_cli GaincalConfig\ngencal gencal_cli GencalConfig\ngetopacities getopacities_cli\ngpplot gpplot_cli GpplotConfig\nimage2fits image2fits_cli\nimportevla importevla_cli\nlistobs listobs_cli\nmfsclean mfsclean_cli MfscleanConfig\nplotants plotants_cli\nplotcal plotcal_cli plotcal_multipage_page PlotcalConfig\nsetjy setjy_cli SetjyConfig\nsplit split_cli SplitConfig\nspwglue_cli\nuvsub uvsub_cli UvsubConfig\ncmdline_driver\n''').split ()\n\n\n# Some utilities\n\nfrom .util import sanitize_unicode as b\n\nprecal_doc = \\\n\"\"\"\n*** Pre-applied calibrations:\n\ngaintable=\n  Comma-separated list of calibration tables to apply on-the-fly\n  before solving\n\ngainfield=\n  SEMICOLON-separated list of field selections to apply for each gain table.\n  If there are fewer items than there are gaintable items, the list is\n  padded with blank items, implying no selection by field.\n\ninterp=\n  COMMA-separated list of interpolation types to use for each gain\n  table. If there are fewer items, the list is padded with 'linear'\n  entries. Allowed values:\n    nearest linear cubic spline\n\nspwmap=\n  SEMICOLON-separated list of spectral window mappings for each\n  existing gain table; each record is a COMMA-separated list of\n  integers. For the i'th spw in the dataset, spwmap[i] specifies\n  the record in the gain table to use. For instance [0, 0, 1, 1]\n  maps four spws in the UV data to just two spectral windows in\n  the preexisting gain table.\n\nopacity=\n  Comma-separated list of opacities in nepers. One for each spw; if\n  there are more spws than entries, the last entry is used for the\n  remaining spws.\n\ngaincurve=\n  Whether to apply VLA-specific built in gain curve correction\n  (default: false)\n\nparang=\n  Whether to apply parallactic angle rotation correction\n  (default: false)\n\"\"\"\n\nstdsel_doc = \\\n\"\"\"\n*** Standard data selection keywords:\n\nantenna=\narray=\ncorrelation=\nfield=\nintent=\nobservation=\nscan=\nspw=\ntaql=\ntimerange=\nuvrange=\n\"\"\"\n\nloglevel_doc = \\\n\"\"\"\nloglevel=\n  Level of detail from CASA logging system. Default: warn; allowed:\n    severe warn info info1 info2 info3 info4 info5 debug1 debug2 debugging\n\"\"\"\n\ndef extractmsselect (cfg, havearray=False, havecorr=False, haveintent=True,\n                     intenttoscanintent=False, taqltomsselect=True,\n                     observationtoobs=False):\n    # expects cfg to have:\n    #  antenna [correlation] field intent observation scan spw taql timerange uvrange\n    # fills a dict with:\n    #  baseline [correlation] field intent (msselect|taql) observation scan spw time uvrange\n\n    selkws = {}\n\n    direct = 'field scan spw uvrange'.split ()\n    indirect = 'antenna:baseline timerange:time'.split ()\n\n    if havearray:\n        indirect.append ('array:subarray')\n\n    if havecorr:\n        direct.append ('correlation')\n\n    if haveintent:\n        if intenttoscanintent:\n            indirect.append ('intent:scanintent')\n        else:\n            direct.append ('intent')\n\n    if observationtoobs:\n        indirect.append ('observation:obs')\n    else:\n        direct.append ('observation')\n\n    if taqltomsselect:\n        indirect.append ('taql:msselect')\n    else:\n        direct.append ('taql')\n\n    for k in direct:\n        selkws[k] = getattr (cfg, k) or ''\n\n    for p in indirect:\n        ck, sk = p.split (':')\n        selkws[sk] = getattr (cfg, ck) or ''\n\n    return selkws\n\n\ndef applyonthefly (cb, cfg):\n    # expects cfg to have:\n    #   gaintable gainfield interp spwmap opacity gaincurve parang\n\n    n = len (cfg.gaintable)\n\n    # fill in missing values, taking care not to mutate cfg.\n\n    gainfields = list (cfg.gainfield)\n    interps = list (cfg.interp)\n    spwmaps = list (cfg.spwmap)\n\n    if len (gainfields) < n:\n        gainfields += [''] * (n - len (gainfields))\n    elif len (gainfields) > n:\n        raise ValueError ('more \"gainfield\" entries than \"gaintable\" entries')\n\n    if len (interps) < n:\n        interps += ['linear'] * (n - len (interps))\n    elif len (interps) > n:\n        raise ValueError ('more \"interp\" entries than \"gaintable\" entries')\n\n    if len (spwmaps) < n:\n        spwmaps += [[-1]] * (n - len (spwmaps))\n    elif len (spwmaps) > n:\n        raise ValueError ('more \"spwmap\" entries than \"gaintable\" entries')\n\n    for table, field, interp, spwmap in zip (cfg.gaintable, gainfields,\n                                             interps, spwmaps):\n        cb.setapply (table=b(table), field=b(field), interp=b(interp), spwmap=b(spwmap),\n                     t=0., calwt=True)\n\n    if len (cfg.opacity):\n        cb.setapply (type=b'TOPAC', opacity=b(cfg.opacity), t=-1, calwt=True)\n\n    if cfg.gaincurve:\n        cb.setapply (type=b'GAINCURVE', t=-1, calwt=True)\n\n    if cfg.parang:\n        cb.setapply (type=b'P')\n\n\ndef makekwcli (doc, cfgclass, impl):\n    def kwclifunc (argv):\n        check_usage (doc, argv, usageifnoargs=True)\n        cfg = cfgclass ().parse (argv[1:])\n        util.logger (cfg.loglevel)\n        impl (cfg)\n    return kwclifunc\n\n\n# applycal\n\napplycal_doc = \\\n\"\"\"\ncasatask applycal vis=<MS> [keywords]\n\nFill in the CORRECTED_DATA column of a visibility dataset using\nthe raw data and a set of calibration tables.\n\nvis=\n  The MS to modify\n\ncalwt=\n  Write out calibrated weights as well as calibrated visibilities.\n  Default: false\n\"\"\" + precal_doc + stdsel_doc + loglevel_doc\n\n\nclass ApplycalConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    calwt = False\n    # skipping: flagbackup\n\n    gaintable = [str]\n    gainfield = Custom ([str], sep=';')\n    interp = [str]\n    @Custom ([str], sep=';')\n    def spwmap (v):\n        return [map (int, e.split (',')) for e in v]\n    opacity = [float]\n    gaincurve = False\n    parang = False\n\n    antenna = str\n    field = str\n    intent = str\n    observation = str\n    scan = str\n    spw = str\n    taql = str\n    timerange = str\n    uvrange = str\n\n    applymode = 'calflag' # almost never want to change this\n\n    loglevel = 'warn'\n\n\ndef applycal (cfg):\n    cb = util.tools.calibrater ()\n    cb.open (filename=b(cfg.vis), compress=False, addcorr=True, addmodel=False)\n\n    selkws = extractmsselect (cfg)\n    selkws['chanmode'] = 'none' # ?\n    cb.selectvis (**b(selkws))\n\n    applyonthefly (cb, cfg)\n\n    cb.correct (b(cfg.applymode))\n    cb.close ()\n\n\napplycal_cli = makekwcli (applycal_doc, ApplycalConfig, applycal)\n\n\n# bpplot\n#\n# plotcal() can do this, but it is slow, ugly, and has not-very-informative\n# output. Yes, CASA is so crappy that I can even speed up bandpass plotting\n# by an order of magnitude.\n\nbpplot_doc = \\\n\"\"\"\ncasatask bpplot caltable= dest=\n\nPlot a bandpass calibration table. Currently, the supported format is a series\nof pages showing amplitude and phase against normalized channel number, with\neach page showing a particular antenna and polarization. Polarizations are\nalways reported as \"R\" and \"L\" since the relevant information is not stored\nwithin the bandpass data set.\n\ncaltable=MS\n  The input calibration Measurement Set\n\ndest=PATH\n  If specified, plots are saved to this file -- the format is inferred\n  from the extension, which must allow multiple pages to be saved. If\n  unspecified, the plots are displayed using a Gtk3 backend.\n\ndims=WIDTH,HEIGHT\n  If saving to a file, the dimensions of a each page. These are in points\n  for vector formats (PDF, PS) and pixels for bitmaps (PNG). Defaults to\n  1000, 600.\n\nmargins=TOP,RIGHT,LEFT,BOTTOM\n  If saving to a file, the plot margins in the same units as the dims.\n  The default is 4 on every side.\n\"\"\" + loglevel_doc\n\n\nclass BpplotConfig (ParseKeywords):\n    caltable = Custom (str, required=True)\n    dest = str\n    dims = [1000, 600]\n    margins = [4, 4, 4, 4]\n    loglevel = 'warn'\n\n\ndef bpplot (cfg):\n    import omega as om, omega.render\n    from ... import numutil\n\n    if isinstance (cfg.dest, omega.render.Pager):\n        # This is for non-CLI invocation.\n        pager = cfg.dest\n    elif cfg.dest is None:\n        import omega.gtk3\n        pager = om.makeDisplayPager ()\n    else:\n        pager = om.makePager (cfg.dest,\n                              dims=cfg.dims,\n                              margins=cfg.margins,\n                              style=om.styles.ColorOnWhiteVector ())\n\n    tb = util.tools.table ()\n\n    tb.open (binary_type (cfg.caltable), nomodify=True)\n    spws = tb.getcol (b'SPECTRAL_WINDOW_ID')\n    ants = tb.getcol (b'ANTENNA1')\n    vals = tb.getcol (b'CPARAM')\n    flags = tb.getcol (b'FLAG')\n    tb.close ()\n\n    tb.open (binary_type (os.path.join (cfg.caltable, 'ANTENNA')), nomodify=True)\n    names = tb.getcol (b'NAME')\n    tb.close ()\n\n    npol, nchan, nsoln = vals.shape\n\n    # see what we've got\n\n    antpols = {}\n    seenspws = set ()\n\n    for ipol in xrange (npol):\n        for isoln in xrange (nsoln):\n            if not flags[ipol,:,isoln].all ():\n                k = (ants[isoln], ipol)\n                byspw = antpols.get (k)\n                if byspw is None:\n                    antpols[k] = byspw = []\n\n                byspw.append ((spws[isoln], isoln))\n                seenspws.add (spws[isoln])\n\n    seenspws = sorted (seenspws)\n    spw_to_offset = dict ((spwid, spwofs * nchan)\n                          for spwofs, spwid in enumerate (seenspws))\n\n    # normalize phases to avoid distracting wraps\n\n    for iant, ipol in sorted (antpols.iterkeys ()):\n        for ispw, isoln in antpols[iant,ipol]:\n            f = flags[ipol,:,isoln]\n            meanph = np.angle (vals[ipol,~f,isoln].mean ())\n            vals[ipol,:,isoln] *= np.exp (-1j * meanph)\n\n    # find plot limits\n\n    okvals = vals[np.where (~flags)]\n\n    max_am = np.abs (okvals).max ()\n    min_am = np.abs (okvals).min ()\n    span = max_am - min_am\n    max_am += 0.05 * span\n    min_am -= 0.05 * span\n\n    max_ph = np.angle (okvals, deg=True).max ()\n    min_ph = np.angle (okvals, deg=True).min ()\n    span = max_ph - min_ph\n    max_ph += 0.05 * span\n    min_ph -= 0.05 * span\n    if max_ph > 160:\n        max_ph = 180\n    if min_ph < -160:\n        min_ph = -180\n\n    polnames = 'RL' # XXX: identification doesn't seem to be stored in cal table\n\n    # plot away\n\n    for iant, ipol in sorted (antpols.iterkeys ()):\n        p_am = om.RectPlot ()\n        p_ph = om.RectPlot ()\n\n        for ispw, isoln in antpols[iant,ipol]:\n            f = flags[ipol,:,isoln]\n            a = np.abs (vals[ipol,:,isoln])\n            p = np.angle (vals[ipol,:,isoln], deg=True)\n            w = np.where (~f)[0]\n\n            for s in numutil.slice_around_gaps (w, 1):\n                wsub = w[s]\n                if wsub.size == 0:\n                    continue # Should never happen, but eh.\n                else:\n                    # It'd also be pretty weird to have a spectral window\n                    # containing just one (valid) channel, but it could\n                    # happen.\n                    lines = (wsub.size > 1)\n\n                p_am.addXY (wsub + spw_to_offset[ispw], a[wsub], None,\n                            lines=lines, dsn=ispw)\n                p_ph.addXY (wsub + spw_to_offset[ispw], p[wsub], None,\n                            lines=lines, dsn=ispw)\n\n        p_am.setBounds (xmin=0,\n                        xmax=len (seenspws) * nchan,\n                        ymin=min_am,\n                        ymax=max_am)\n        p_ph.setBounds (xmin=0,\n                        xmax=len (seenspws) * nchan,\n                        ymin=min_ph,\n                        ymax=max_ph)\n        p_am.addKeyItem ('%s %s' % (names[iant], polnames[ipol]))\n\n        p_am.bpainter.paintLabels = False\n        p_am.setYLabel ('Amplitude')\n        p_ph.setLabels ('Normalized channel', 'De-meaned Phase (deg)')\n\n        vb = om.layout.VBox (2)\n        vb[0] = p_am\n        vb[1] = p_ph\n        vb.setWeight (0, 2.5)\n        pager.send (vb)\n\nbpplot_cli = makekwcli (bpplot_doc, BpplotConfig, bpplot)\n\n\n# clearcal\n\nclearcal_doc = \\\n\"\"\"\ncasatask clearcal [-w] <vis1> [more vises...]\n\nFill the imaging and calibration columns (MODEL_DATA, CORRECTED_DATA,\nIMAGING_WEIGHT) of each measurement set with default values, creating\nthe columns if necessary.\n\nIf you want to reset calibration models, these days you probably want\ndelmod. If you want to quickly make the columns go away, you probably\nwant delcal.\n\n-w - only create and fill the IMAGING_WEIGHT column\n\"\"\"\n\nclearcal_imaging_col_tmpl = {'_c_order': True,\n                             'comment': '',\n                             'dataManagerGroup': '',\n                             'dataManagerType': '',\n                             'keywords': {},\n                             'maxlen': 0,\n                             'ndim': 1,\n                             'option': 0,\n                             'shape': None,\n                             'valueType': 'float'}\nclearcal_imaging_dminfo_tmpl = {'TYPE': 'TiledShapeStMan',\n                                'SPEC': {'DEFAULTTILESHAPE': [32, 128]},\n                                'NAME': 'imagingweight'}\n\ndef clearcal (vis, weightonly=False):\n    tb = util.tools.table ()\n    cb = util.tools.calibrater ()\n\n    # cb.open() will create the tables if they're not present, so\n    # if that's the case, we don't actually need to run initcalset()\n\n    tb.open (b(vis), nomodify=False)\n    colnames = tb.colnames ()\n    needinit = ('MODEL_DATA' in colnames) or ('CORRECTED_DATA' in colnames)\n    if 'IMAGING_WEIGHT' not in colnames:\n        c = dict (clearcal_imaging_col_tmpl)\n        c['shape'] = tb.getcell (b'DATA', 0).shape[-1:]\n        tb.addcols ({b'IMAGING_WEIGHT': c}, clearcal_imaging_dminfo_tmpl)\n    tb.close ()\n\n    if not weightonly:\n        cb.open (b(vis))\n        if needinit:\n            cb.initcalset ()\n        cb.close ()\n\n\ndef clearcal_cli (argv):\n    check_usage (clearcal_doc, argv, usageifnoargs=True)\n\n    argv = list (argv)\n    weightonly = '-w' in argv\n    if weightonly:\n        sys.argv.remove ('-w')\n\n    if len (argv) < 2:\n        wrong_usage (clearcal_doc, 'need at least one argument')\n\n    util.logger ()\n    for vis in argv[1:]:\n        clearcal (b(vis), weightonly=weightonly)\n\n\n# concat\n\nconcat_doc = \\\n\"\"\"\ncasatask concat [-s] <input vises ...> <output vis>\n\nConcatenate the visibility measurement sets.\n\n-s - sort the output in time\n\"\"\"\n\nconcat_freqtol = 1e-5\nconcat_dirtol = 1e-5\n\ndef concat (invises, outvis, timesort=False):\n    tb = util.tools.table ()\n    ms = util.tools.ms ()\n\n    if os.path.exists (outvis):\n        raise RuntimeError ('output \"%s\" already exists' % outvis)\n\n    for invis in invises:\n        if not os.path.isdir (invis):\n            raise RuntimeError ('input \"%s\" does not exist' % invis)\n\n    tb.open (b(invises[0]))\n    tb.copy (b(outvis), deep=True, valuecopy=True)\n    tb.close ()\n\n    ms.open (b(outvis), nomodify=False)\n\n    for invis in invises[1:]:\n        ms.concatenate (msfile=b(invis), freqtol=b(concat_freqtol),\n                        dirtol=b(concat_dirtol))\n\n    ms.writehistory (message=b'taskname=tasklib.concat', origin=b'tasklib.concat')\n    ms.writehistory (message=b('vis = ' + ', '.join (invises)), origin=b'tasklib.concat')\n    ms.writehistory (message=b('timesort = ' + 'FT'[int (timesort)]), origin=b'tasklib.concat')\n\n    if timesort:\n        ms.timesort ()\n\n    ms.close ()\n\n\ndef concat_cli (argv):\n    check_usage (concat_doc, argv, usageifnoargs=True)\n\n    argv = list (argv)\n    timesort = '-s' in argv\n    if timesort:\n        sys.argv.remove ('-s')\n\n    if len (argv) < 3:\n        wrong_usage (concat_doc, 'need at least two arguments')\n\n    util.logger ()\n    concat (argv[1:-1], argv[-1], timesort)\n\n\n# delcal\n#\n# Not a CASA task. Delmod on steroids, at least when delmod\n# is operating on scratch columns and not OTF models.\n\ndelcal_doc = \\\n\"\"\"\ncasatask delcal <ms> [mses...]\n\nDelete the MODEL_DATA and CORRECTED_DATA columns from MSes.\n\"\"\"\n\n\ndef delcal (mspath):\n    wantremove = 'MODEL_DATA CORRECTED_DATA'.split ()\n    tb = util.tools.table ()\n    tb.open (b(mspath), nomodify=False)\n    cols = frozenset (tb.colnames ())\n    toremove = [b(c) for c in wantremove if c in cols]\n    if len (toremove):\n        tb.removecols (toremove)\n    tb.close ()\n    return toremove\n\n\ndef delcal_cli (argv):\n    check_usage (delcal_doc, argv, usageifnoargs=True)\n    util.logger ()\n\n    for mspath in argv[1:]:\n        removed = delcal (mspath)\n        if len (removed):\n            print ('%s: removed %s' % (mspath, ', '.join (removed)))\n        else:\n            print ('%s: nothing to remove' % mspath)\n\n\n# delmod\n\ndelmod_doc = \\\n\"\"\"\ncasatask delmod <MS...>\n\nDelete the \"on-the-fly\" model information from the specified\nMeasurement Set(s).\n\nIf you want to delete the scratch columns, use delcal. If you\nwant to clear the scratch columns, use clearcal. I'm torn\nbetween wanting better terminology and not wanting to be\ngratuitously different from CASA.\n\"\"\"\n\ndef delmod_cli (argv, alter_logger=True):\n    check_usage (delmod_doc, argv, usageifnoargs=True)\n    if alter_logger:\n        util.logger ()\n\n    cb = util.tools.calibrater ()\n\n    for mspath in argv[1:]:\n        cb.open (b(mspath), addcorr=False, addmodel=False)\n        cb.delmod (otf=True, scr=False)\n        cb.close ()\n\n\n# dftphotom\n#\n# Shim for a separate module\n\ndef dftphotom_cli (argv):\n    from .dftphotom import dftphotom_cli\n    dftphotom_cli (argv)\n\n\n# extractbpflags\n#\n# Not a CASA task, but I've found this to be very useful.\n\nextractbpflags_doc = \\\n\"\"\"\nWhen CASA encounters flagged channels in bandpass calibration tables, it\ninterpolates over them as best it can -- even if interp='<any>,nearest'. This\nmeans that if certain channels are unflagged in some target data but entirely\nflagged in your BP cal, they'll get multiplied by some number that may or may\nnot be reasonable, not flagged. This is scary if, for instance, you're using\nan automated system to find RFI, or you flag edge channels in some uneven way.\n\nThis script writes out a list of flagging commands corresponding to the\nflagged channels in the bandpass table to ensure that the data without\nbandpass solutions are flagged.\n\nNote that, because we can't select by antpol, we can't express a situation in\nwhich the R and L bandpass solutions have different flags. But in CASA the\nflags seem to always be the same.\n\nWe're assuming that the channelization of the bandpass solution and the data\nare the same.\n\"\"\"\n\ndef extractbpflags (calpath, deststream):\n    tb = util.tools.table ()\n    tb.open (b(os.path.join (calpath, 'ANTENNA')))\n    antnames = tb.getcol (b'NAME')\n    tb.close ()\n\n    tb.open (b(calpath))\n    try:\n        t = tb.getkeyword (b'VisCal')\n    except RuntimeError:\n        raise PKError ('no \"VisCal\" keyword in %s; it doesn\\'t seem to be a '\n                       'bandpass calibration table', calpath)\n\n    if t != 'B Jones':\n        raise PKError ('table %s doesn\\'t seem to be a bandpass calibration '\n                       'table; its type is \"%s\"', calpath, t)\n\n    def emit (antidx, spwidx, chanstart, chanend):\n        # Channel ranges are inclusive, unlike Python syntax.\n        print (\"antenna='%s&*' spw='%d:%d~%d' reason='BANDPASS_FLAGGED'\" % \\\n               (antnames[antidx], spwidx, chanstart, chanend), file=deststream)\n\n    for row in xrange (tb.nrows ()):\n        ant = tb.getcell (b'ANTENNA1', row)\n        spw = tb.getcell (b'SPECTRAL_WINDOW_ID', row)\n        flag = tb.getcell (b'FLAG', row)\n\n        # This is the logical 'or' of the two polarizations: i.e., anything that\n        # is flagged in either poln is flagged in this.\n        sqflag = ~((~flag).prod (axis=0, dtype=np.bool))\n\n        runstart = None\n\n        for i in xrange (sqflag.size):\n            if sqflag[i]:\n                # This channel is flagged. Start a run if not already in one.\n                if runstart is None:\n                    runstart = i\n            elif runstart is not None:\n                # The current run just ended.\n                emit (ant, spw, runstart, i - 1)\n                runstart = None\n\n        if runstart is not None:\n            emit (ant, spw, runstart, i)\n\n    tb.close ()\n\n\ndef extractbpflags_cli (argv):\n    check_usage (extractbpflags_doc, argv, usageifnoargs='long')\n\n    if len (argv) != 2:\n        wrong_usage (extractbpflags_doc, 'expect one MS name as an argument')\n\n    extractbpflags (argv[1], sys.stdout)\n\n\n# flagmanager. Not really complicated enough to make it worth making a\n# modular implementation to be driven from the CLI.\n\nflagmanager_doc = \\\n\"\"\"\ncasatask flagmanager list <ms>\ncasatask flagmanager save <ms> <name>\ncasatask flagmanager restore <ms> <name>\ncasatask flagmanager delete <ms> <name>\n\"\"\"\n\ndef flagmanager_cli (argv, alter_logger=True):\n    check_usage (flagmanager_doc, argv, usageifnoargs=True)\n\n    if len (argv) < 3:\n        wrong_usage (flagmanager_doc, 'expect at least a mode and an MS name')\n\n    mode = argv[1]\n    ms = argv[2]\n\n    if alter_logger:\n        if mode == 'list':\n            util.logger ('info')\n        elif mode == 'delete':\n            # it WARNs 'deleting version xxx' ... yargh\n            util.logger ('severe')\n        else:\n            util.logger ()\n\n    try:\n        factory = util.tools.agentflagger\n    except AttributeError:\n        factory = util.tools.testflagger\n\n    af = factory ()\n    af.open (b(ms))\n\n    if mode == 'list':\n        if len (argv) != 3:\n            wrong_usage (flagmanager_doc, 'expect exactly one argument in list mode')\n        af.getflagversionlist ()\n    elif mode == 'save':\n        if len (argv) != 4:\n            wrong_usage (flagmanager_doc, 'expect exactly two arguments in save mode')\n        from time import strftime\n        name = argv[3]\n        af.saveflagversion (versionname=b(name), merge=b'replace',\n                            comment=b('created %s (casatask flagmanager)'\n                                      % strftime ('%Y-%m-%dT%H:%M:%SZ')))\n    elif mode == 'restore':\n        if len (argv) != 4:\n            wrong_usage (flagmanager_doc, 'expect exactly two arguments in restore mode')\n        name = argv[3]\n        af.restoreflagversion (versionname=b(name), merge=b'replace')\n    elif mode == 'delete':\n        if len (argv) != 4:\n            wrong_usage (flagmanager_doc, 'expect exactly two arguments in delete mode')\n        name = argv[3]\n\n        if not os.path.isdir (os.path.join (ms + '.flagversions', 'flags.' + name)):\n            # This condition only results in a WARN from deleteflagversion ()!\n            raise RuntimeError ('version \"%s\" doesn\\'t exist in \"%s.flagversions\"'\n                                % (name, ms))\n\n        af.deleteflagversion (versionname=b(name))\n    else:\n        wrong_usage (flagmanager_doc, 'unknown flagmanager mode \"%s\"' % mode)\n\n    af.done ()\n\n\n# flaglist. Not quite a CASA task; something like\n# flagcmd (vis=, inpmode='list', inpfile=, flagbackup=False)\n#\n# We have to reproduce a lot of the dumb logic from the flaghelper.py module\n# because we can't import it because it drags in the whole casapy pile of\n# stuff.\n\nflaglist_doc = \\\n\"\"\"\ncasatask flaglist vis= inpfile= [datacol=]\n\nFlag data using a list of flagging commands stored in a text file. This\nis approximately equivalent to 'flagcmd(vis=, inpfile=, inpmode='list',\nflagbackup=False)'.\n\nThis implementation must emulate the CASA modules that load up the\nflagging commands and may not be precisely compatible with the CASA\nversion.\n\"\"\"\n\nclass FlaglistConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    inpfile = Custom (str, required=True)\n    datacol = 'data'\n    loglevel = 'warn'\n\n\ndef flaglist (cfg):\n    from ast import literal_eval\n\n    try:\n        factory = util.tools.agentflagger\n    except AttributeError:\n        factory = util.tools.testflagger\n\n    af = factory ()\n    af.open (b(cfg.vis), 0.0)\n    af.selectdata ()\n\n    for row, origline in enumerate (open (cfg.inpfile)):\n        origline = origline.rstrip ()\n        if not len (origline):\n            continue\n        if origline[0] == '#':\n            continue\n\n        # emulating flaghelper.py here and elsewhere ...\n        bits = origline.replace ('true', 'True').replace ('false', 'False').split (' ')\n        params = {}\n        lastkey = None\n\n        for bit in bits:\n            subbits = bit.split ('=', 1)\n\n            if len (subbits) == 1:\n                assert lastkey is not None, 'illegal flag list syntax'\n                params[lastkey] += ' ' + bit\n            else:\n                params[subbits[0]] = subbits[1]\n                lastkey = subbits[0]\n\n        assert 'ntime' not in params, 'cannot handle \"ntime\" flag key'\n\n        for key in params.keys ():\n            val = params[key]\n\n            try:\n                val = literal_eval (val)\n            except ValueError:\n                val = val.strip ('\\'\"')\n\n            params[key] = val\n\n        params['name'] = 'agent_%d' % row\n        params['datacolumn'] = cfg.datacol.upper ()\n        params['apply'] = True\n\n        params.setdefault ('mode', 'manual')\n\n        if not af.parseagentparameters (b(params)):\n            raise Exception ('cannot parse flag line: %s' % origline)\n\n    af.init ()\n    # A summary report would be nice. run() should return\n    # info but I can't get it to do so. (I'm just trying to\n    # copy the task_flagdata.py implementation.)\n    af.run (True, True)\n    af.done ()\n\n\nflaglist_cli = makekwcli (flaglist_doc, FlaglistConfig, flaglist)\n\n\n# flagzeros. Not quite a CASA task; something like\n# flagdata (vis=, mode='clip', clipzeros=True, flagbackup=False)\n\nflagzeros_doc = \\\n\"\"\"\ncasatask flagzeros vis= [datacol=]\n\nFlag zero data points in the specified data column.\n\"\"\"\n\nclass FlagzerosConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    datacol = 'data'\n    loglevel = 'warn'\n\n\ndef flagzeros (cfg):\n    try:\n        factory = util.tools.agentflagger\n    except AttributeError:\n        factory = util.tools.testflagger\n\n    af = factory ()\n    af.open (b(cfg.vis), 0.0)\n    af.selectdata ()\n    pars = dict (datacolumn=cfg.datacol.upper (),\n                 clipzeros=True, name='CLIP', mode='clip',\n                 apply=True)\n    af.parseagentparameters (pars)\n    af.init ()\n    # A summary report would be nice. run() should return\n    # info but I can't get it to do so. (I'm just trying to\n    # copy the task_flagdata.py implementation.)\n    af.run (True, True)\n    af.done ()\n\n\nflagzeros_cli = makekwcli (flagzeros_doc, FlagzerosConfig, flagzeros)\n\n\n# fluxscale\n\nfluxscale_doc = \\\n\"\"\"\ncasatask fluxscale vis=<MS> caltable=<MS> fluxtable=<new MS> reference=<fields> transfer=<fields> [keywords]\n\nWrite a new calibation table determining the fluxes for intermediate calibrators\nfrom known reference sources\n\nvis=\n  The visibility dataset. (Shouldn't be needed, but ...)\n\ncaltable=\n  The preexisting calibration table with gains associated with more than one source.\n\nfluxtable=\n  The path of a new calibration table to create\n\nreference=\n  Comma-separated names of sources whose model fluxes are assumed to be well-known.\n\ntransfer=\n  Comma-separated names of sources whose fluxes should be computed from the gains.\n\nlistfile=\n  If specified, write out flux information to this path.\n\nappend=\n  Boolean, default false. If true, append to existing cal table rather than\n  overwriting.\n\nrefspwmap=\n  Comma-separated list of integers. If gains are only available for some spws,\n  map from the data to the gains. For instance, refspwmap=1,1,3,3 means that spw 0\n  will have its flux calculated using the gains for spw 1.\n\"\"\" + loglevel_doc\n\n# Not supported in CASA 3.4:\n#incremental=\n#  Boolean, default false. If true, create an \"incremental\" table where the amplitudes\n#  are correction factors, not absolute gains. (I.e., for the reference sources,\n#  the amplitudes will be unity.)\n\n\nclass FluxscaleConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    caltable = Custom (str, required=True)\n    fluxtable = Custom (str, required=True)\n    reference = Custom ([str], required=True)\n    transfer = Custom ([str], required=True)\n\n    listfile = str\n    append = False\n    refspwmap = [int]\n    #incremental = False\n\n    loglevel = 'warn'\n\n\ndef fluxscale (cfg):\n    cb = util.tools.calibrater ()\n\n    reference = cfg.reference\n    if isinstance (reference, (list, tuple)):\n        reference = ','.join (reference)\n\n    transfer = cfg.transfer\n    if isinstance (transfer, (list, tuple)):\n        transfer = ','.join (transfer)\n\n    refspwmap = cfg.refspwmap\n    if not len (refspwmap):\n        refspwmap = [-1]\n\n    cb.open (b(cfg.vis), compress=False, addcorr=False, addmodel=False)\n    result = cb.fluxscale (tablein=b(cfg.caltable), tableout=b(cfg.fluxtable),\n                           reference=b(reference), transfer=b(transfer),\n                           listfile=b(cfg.listfile or ''),\n                           append=cfg.append, refspwmap=b(refspwmap))\n                           #incremental=cfg.incremental)\n    cb.close ()\n    return result\n\n\nfluxscale_cli = makekwcli (fluxscale_doc, FluxscaleConfig, fluxscale)\n\n\n# ft\n#\n# We derive 'nterms' from len(model), and always derive reffreq\n# from the model images. These seem like safe constraints?\n\nft_doc = \\\n\"\"\"\ncasatask ft vis=<MS> [keywords]\n\nFill in (or update) the MODEL_DATA column of a Measurement Set with\nvisibilities computed from an image or list of components.\n\nvis=\n  The path to the measurement set\n\nmodel=\n  Comma-separated list of model images, each giving successive\n  Taylor terms of a spectral model for each source. (It's fine\n  to have just one model, and this will do what you want.) The\n  reference frequency for the Taylor expansion is taken from\n  the first image.\n\ncomplist=\n  Path to a CASA ComponentList Measurement Set to use in the modeling.\n  I don't know what happens if you specify both this and \"model\".\n  They might both get applied?\n\nincremental=\n  Bool, default false, meaning that the MODEL_DATA column will be\n  replaced with the new values computed here. If true, the new values\n  are added to whatever's already in MODEL_DATA.\n\nwprojplanes=\n  Optional integer. If provided, W-projection will be used in the computation\n  of the model visibilities, using the specified number of planes. Note that\n  this *does* make a difference even now that this task only embeds information\n  in a MS to enable later on-the-fly computation of the UV model.\n\nusescratch=\n  Foo.\n\"\"\" + stdsel_doc + loglevel_doc\n\n\nclass FtConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    model = [str]\n    complist = str\n    incremental = False\n    wprojplanes = int\n    usescratch = False\n\n    antenna = str\n    field = str\n    observation = str\n    scan = str\n    spw = str\n    taql = str\n    timerange = str\n    uvrange = str\n\n    loglevel = 'warn'\n\n\ndef ft (cfg):\n    im = util.tools.imager ()\n\n    im.open (b(cfg.vis), usescratch=cfg.usescratch)\n    im.selectvis (**b(extractmsselect (cfg, haveintent=False,\n                                       taqltomsselect=False)))\n    nmodel = len (cfg.model)\n\n    if nmodel > 1:\n        ia = util.tools.image ()\n        ia.open (b(cfg.model[0]))\n        # This gives Hz:\n        reffreq = ia.coordsys ().referencevalue (type=b'spectral')['numeric'][0]\n        ia.close ()\n        im.settaylorterms (ntaylorterms=nmodel, reffreq=reffreq)\n\n    if cfg.wprojplanes is not None:\n        im.defineimage ()\n        im.setoptions (ftmachine=b'wproject', wprojplanes=cfg.wprojplanes)\n\n    im.ft (model=b(cfg.model),\n           complist=b(cfg.complist or ''),\n           incremental=cfg.incremental)\n    im.close ()\n\n\nft_cli = makekwcli (ft_doc, FtConfig, ft)\n\n\n# gaincal\n#\n# I've folded in the bandpass functionality since there's\n# so much overlap. Some limitations that this leads to:\n#\n# - bandpass solint has a frequency component that we don't support\n# - bandpass combine defaults to 'scan'\n\ngaincal_doc = \\\n\"\"\"\ncasatask gaincal vis=<MS> caltable=<TBL> [keywords]\n\nCompute calibration parameters from data. Encompasses the functionality\nof CASA tasks 'gaincal' *and* 'bandpass'.\n\nvis=\n  Input dataset\n\ncaltable=\n  Output calibration table (can exist if append=True)\n\ngaintype=\n  Kind of gain solution:\n    G       - gains per poln and spw (default)\n    T       - like G, but one value for all polns\n    GSPLINE - like G, with a spline fit. \"Experimental\"\n    B       - bandpass\n    BPOLY   - bandpass with polynomial fit. \"Somewhat experimental\"\n    K       - antenna-based delays\n    KCROSS  - global cross-hand delay ; use parang=True\n    D       - solve for instrumental leakage\n    Df      - above with per-channel leakage terms\n    D+QU    - solve for leakage and apparent source polarization\n    Df+QU   - above with per-channel leakage terms\n    X       - solve for absolute position angle phase term\n    Xf      - above with per-channel phase terms\n    D+X     - D and X. \"Not normally done\"\n    Df+X    - Df and X. Presumably also not normally done.\n    XY+QU   - ?\n    XYf+QU  - ?\n\ncalmode=\n  What parameters to solve for: amplitude (\"a\"), phase (\"p\"), or both\n  (\"ap\"). Default is \"ap\". Not used in bandpass solutions.\n\nsolint=\n  Solution interval; this is an upper bound, but solutions\n  will be broken across certain boundaries according to \"combine\".\n  'inf'    - solutions as long as possible (the default)\n  'int'    - one solution per integration\n  (str)    - a specific time with units (e.g. '5min')\n  (number) - a specific time in seconds\n\ncombine=\n  Comma-separated list of boundary types; solutions will NOT be\n  broken across these boundaries. Types are:\n    field scan spw\n\nrefant=\n  Comma-separated list of reference antennas in decreasing\n  priority order.\n\nsolnorm=\n  Normalize solution amplitudes to 1 after solving (only applies\n  to gaintypes G, T, B). Also normalizes bandpass phases to zero\n  when solving for bandpasses. Default: false.\n\nappend=\n  Whether to append solutions to an existing table. If the table\n  exists and append=False, the table is overwritten! (Default: false)\n\"\"\" + precal_doc + \"\"\"\n*** Low-level parameters:\n\nminblperant=\n  Number of baselines for each ant in order to solve (default: 4)\n\nminsnr=\n  Min. SNR for acceptable solutions (default: 3.0)\n\npreavg=\n  Interval for pre-averaging data within each solution interval,\n  in seconds. Default is -1, meaning not to pre-average.\n\"\"\" + stdsel_doc + loglevel_doc\n\n\nclass GaincalConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    caltable = Custom (str, required=True)\n    gaintype = 'G'\n    calmode = 'ap'\n\n    solint = 'inf'\n    combine = [str]\n    refant = [str]\n    solnorm = False\n    append = False\n    minblperant = 4\n    minsnr = 3.0\n    preavg = -1.0\n\n    gaintable = [str]\n    gainfield = Custom ([str], sep=';')\n    interp = [str]\n    opacity = [float]\n    gaincurve = False\n    parang = False\n\n    @Custom ([str], sep=';')\n    def spwmap (v):\n        return [map (int, e.split (',')) for e in v]\n\n    # gaincal keywords: splinetime npointaver phasewrap smodel\n    # bandpass keywords: fillgaps degamp degphase visnorm maskcenter\n    #   maskedge\n\n    antenna = str\n    field = str\n    intent = str\n    observation = str\n    scan = str\n    spw = str\n    taql = str # msselect\n    timerange = str\n    uvrange = str\n\n    loglevel = 'warn' # teeny hack for CLI\n\n\ndef gaincal (cfg):\n    cb = util.tools.calibrater ()\n    cb.open (filename=b(cfg.vis), compress=False, addcorr=False, addmodel=False)\n\n    selkws = extractmsselect (cfg)\n    selkws['chanmode'] = 'none' # ?\n    cb.selectvis (**b(selkws))\n\n    applyonthefly (cb, cfg)\n\n    # Solve\n\n    solkws = {}\n\n    for k in 'append preavg minblperant minsnr solnorm'.split ():\n        solkws[k] = getattr (cfg, k)\n\n    for p in 'caltable:table calmode:apmode'.split ():\n        ck, sk = p.split (':')\n        solkws[sk] = getattr (cfg, ck)\n\n    if isinstance (cfg.solint, (int, float)):\n        solkws['t'] = '%fs' % cfg.solint # sugar\n    else:\n        solkws['t'] = str (cfg.solint)\n\n    if isinstance (cfg.refant, basestring):\n        solkws['refant'] = cfg.refant\n    else:\n        solkws['refant'] = ','.join (cfg.refant)\n\n    solkws['combine'] = ','.join (cfg.combine)\n    solkws['phaseonly'] = False\n    solkws['type'] = cfg.gaintype.upper ()\n\n    if solkws['type'] == 'GSPLINE':\n        cb.setsolvegainspline (**b(solkws))\n    elif solkws['type'] == 'BPOLY':\n        cb.setsolvebandpoly (**b(solkws))\n    else:\n        cb.setsolve (**b(solkws))\n\n    cb.solve ()\n    cb.close ()\n\n\ngaincal_cli = makekwcli (gaincal_doc, GaincalConfig, gaincal)\n\n\n# gencal\n\ngencal_doc = \\\n\"\"\"\ncasatask gencal vis=<MS> caltable=<TBL> caltype=<TYPE> [keywords...]\n\nGenerate certain calibration tables that don't need to be solved for from\nthe actual data.\n\nvis=\n  Input dataset\n\ncaltable=\n  Output calibration table (appended to if preexisting)\n\ncaltype=\n  The kind of table to generate:\n  amp       - generic amplitude correction; needs parameter(s)\n  ph        - generic phase correction; needs parameter(s)\n  sbd       - single-band delay: phase slope for each SPW; needs parameter(s)\n  mbd       - multi-band delay: phase slope for all SPWs; needs parameter(s)\n  antpos    - antenna position corrections in ITRF; what you want; accepts parameter(s)\n  antposvla - antenna position corrections in VLA frame; not what you want; accepts parameter(s)\n  tsys      - tsys from ALMA syscal table\n  swpow     - EVLA switched-power and requantizer gains (\"experimental\")\n  opac      - tropospheric opacity; needs parameter\n  gc        - (E)VLA elevation-dependent gain curve\n  eff       - (E)VLA antenna efficiency correction\n  gceff     - combination of \"gc\" and \"eff\"\n  rq        - EVLA requantizer gains; not what you want\n  swp/rq    - EVLA switched-power gains divided by \"rq\"; not what you want\n\nparameter=\n  Custom parameters for various caltypes. Dimensionality depends on selections applied.\n  amp       - gain; dimensionless\n  ph        - phase; degrees\n  sbd       - delay; nanosec\n  mbd       - delay; nanosec\n  antpos    - position offsets; ITRF meters (or look up automatically for EVLA if unspecified)\n  antposvla - position offsets; meters in VLA reference frame\n  opac      - opacity; dimensionless (nepers?)\n\nantenna=\npol=\nspw=\n  Selection keywords governing which solutions to generate and controlling shape\n  of \"parameter\" keyword.\n\"\"\" + loglevel_doc\n\n\nclass GencalConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    caltable = Custom (str, required=True)\n    caltype = Custom (str, required=True)\n    parameter = [float]\n\n    antenna = str\n    pol = str\n    spw = str\n\n    loglevel = 'warn'\n\n\ndef gencal (cfg):\n    cb = util.tools.calibrater ()\n    cb.open (filename=b(cfg.vis), compress=False, addcorr=False, addmodel=False)\n\n    antenna = cfg.antenna or ''\n    parameter = cfg.parameter\n\n    if cfg.caltype == 'antpos' and cfg.antenna is None:\n        # There's a Python module in casapy that implements this; I don't want\n        # to shadow it entirely ...\n        import cPickle as pickle\n        from .scripting import CasapyScript\n\n        script = os.path.join (os.path.dirname (__file__), 'cscript_genantpos.py')\n\n        with CasapyScript (script, vis=cfg.vis) as cs:\n            try:\n                with open (os.path.join (cs.workdir, 'info.pkl'), 'rb') as f:\n                    antenna = pickle.load (f)\n                    parameter = pickle.load (f)\n            except Exception:\n                reraise_context ('interal casapy script seemingly failed; no info.pkl')\n\n    cb.specifycal (caltable=b(cfg.caltable), time=b'', spw=b(cfg.spw or ''),\n                   antenna=b(antenna), pol=b(cfg.pol or ''), caltype=b(cfg.caltype),\n                   parameter=b(parameter))\n    cb.close ()\n\n\ngencal_cli = makekwcli (gencal_doc, GencalConfig, gencal)\n\n\n# getopacities\n#\n# This is a casapy script.\n\ngetopacities_doc = \\\n\"\"\"\ncasatask getopacities <MS> <plotdest> [spwwidth1,spwwidth2]\n\nCalculate atmospheric opacities in the MS's spectral windows from its weather\ndata. Output the opacities and save a plot of the weather conditions. Optionally\noutput opacities averaged over spectral windows; for instance, in a standard\nVLA wideband setup, in which the data come in 16 spectral windows,\n\n  casatask getopacities unglued.ms weather.png 8,8\n\nwill print 2 values, averaged over 8 spectral windows each.\n\"\"\"\n\ndef getopacities (ms, plotdest):\n    import cPickle as pickle\n    from .scripting import CasapyScript\n\n    script = os.path.join (os.path.dirname (__file__), 'cscript_getopacities.py')\n\n    with CasapyScript (script, ms=ms, plotdest=plotdest) as cs:\n        try:\n            with open (os.path.join (cs.workdir, 'opac.npy'), 'rb') as f:\n                opac = pickle.load (f)\n        except Exception:\n            reraise_context ('interal casapy script seemingly failed; no opac.npy')\n\n    return opac\n\n\ndef getopacities_cli (argv):\n    check_usage (getopacities_doc, argv, usageifnoargs=True)\n\n    if len (argv) not in (3, 4):\n        wrong_usage (getopacities_doc, 'expected 2 or 3 arguments')\n\n    opac = getopacities (argv[1], argv[2])\n\n    if len (argv) > 3:\n        spwwidths = [int (x) for x in argv[3].split (',')]\n        averaged = []\n        idx = 0\n\n        for width in spwwidths:\n            averaged.append (opac[idx:idx+width].mean ())\n            idx += width\n\n        opac = averaged\n\n    print ('opacity = [%s]' % (', '.join ('%.5f' % q for q in opac)))\n\n\n# gpplot\n#\n# See bpplot() -- CASA plotcal can do this in a certain sense, but it's slow\n# and ugly.\n\ngpplot_doc = \\\n\"\"\"\ncasatask gpplot caltable= dest=\n\nPlot a gain calibration table. Currently, the supported format is a series of\npages showing amplitude and phase against time, with each page showing a\nparticular antenna and polarization. Polarizations are always reported as \"R\"\nand \"L\" since the relevant information is not stored within the bandpass data\nset.\n\ncaltable=MS\n  The input calibration Measurement Set\n\ndest=PATH\n  If specified, plots are saved to this file -- the format is inferred\n  from the extension, which must allow multiple pages to be saved. If\n  unspecified, the plots are displayed using a Gtk3 backend.\n\ndims=WIDTH,HEIGHT\n  If saving to a file, the dimensions of a each page. These are in points\n  for vector formats (PDF, PS) and pixels for bitmaps (PNG). Defaults to\n  1000, 600.\n\nmargins=TOP,RIGHT,LEFT,BOTTOM\n  If saving to a file, the plot margins in the same units as the dims.\n  The default is 4 on every side.\n\"\"\" + loglevel_doc\n\n\nclass GpplotConfig (ParseKeywords):\n    caltable = Custom (str, required=True)\n    dest = str\n    dims = [1000, 600]\n    margins = [4, 4, 4, 4]\n    loglevel = 'warn'\n\n\ndef gpplot (cfg):\n    import omega as om, omega.render\n    from ... import numutil\n\n    if isinstance (cfg.dest, omega.render.Pager):\n        # This is for non-CLI invocation.\n        pager = cfg.dest\n    elif cfg.dest is None:\n        import omega.gtk3\n        pager = om.makeDisplayPager ()\n    else:\n        pager = om.makePager (cfg.dest,\n                              dims=cfg.dims,\n                              margins=cfg.margins,\n                              style=om.styles.ColorOnWhiteVector ())\n\n    tb = util.tools.table ()\n\n    tb.open (binary_type (cfg.caltable), nomodify=True)\n    fields = tb.getcol (b'FIELD_ID')\n    spws = tb.getcol (b'SPECTRAL_WINDOW_ID')\n    ants = tb.getcol (b'ANTENNA1')\n    vals = tb.getcol (b'CPARAM')\n    flags = tb.getcol (b'FLAG')\n    times = tb.getcol (b'TIME')\n    tb.close ()\n\n    tb.open (binary_type (os.path.join (cfg.caltable, 'ANTENNA')), nomodify=True)\n    names = tb.getcol (b'NAME')\n    tb.close ()\n\n    npol, unused, nsoln = vals.shape\n    assert unused == 1, 'unexpected gain table structure!'\n    vals = vals[:,0,:]\n    flags = flags[:,0,:]\n\n    # see what we've got\n\n    def seen_values (data):\n        return [idx for idx, count in enumerate (np.bincount (data)) if count]\n\n    any_ok = ~(np.all (flags, axis=0)) # mask for solns where at least one pol is unflagged\n    seenfields = seen_values (fields[any_ok])\n    seenspws = seen_values (spws[any_ok])\n    seenants = seen_values (ants[any_ok])\n\n    field_offsets = dict ((fieldid, idx) for idx, fieldid in enumerate (seenfields))\n    spw_offsets = dict ((spwid, idx) for idx, spwid in enumerate (seenspws))\n\n    # normalize phases to avoid distracting wraps\n\n    for iant in seenants:\n        for ipol in xrange (npol):\n            filter = (ants == iant) & ~flags[ipol]\n\n            for ispw in seenspws:\n                sfilter = filter & (spws == ispw)\n                if not sfilter.any ():\n                    continue\n\n                meanph = np.angle (vals[ipol,sfilter].mean ())\n                vals[ipol,sfilter] *= np.exp (-1j * meanph)\n\n    # find plot limits\n\n    times /= 86400. # convert to MJD\n    min_time = times[any_ok].min ()\n    max_time = times[any_ok].max ()\n    mjdref = int (np.floor (min_time))\n    times -= mjdref # convert to delta-MJD\n    min_time = (min_time - mjdref)\n    max_time = (max_time - mjdref)\n    span = max_time - min_time\n    max_time += 0.05 * span\n    min_time -= 0.05 * span\n\n    okvals = vals[np.where (~flags)]\n    max_am = np.abs (okvals).max ()\n    min_am = np.abs (okvals).min ()\n    span = max_am - min_am\n    max_am += 0.05 * span\n    min_am -= 0.05 * span\n\n    max_ph = np.angle (okvals, deg=True).max ()\n    min_ph = np.angle (okvals, deg=True).min ()\n    span = max_ph - min_ph\n    max_ph += 0.05 * span\n    min_ph -= 0.05 * span\n    if max_ph > 160:\n        max_ph = 180\n    if min_ph < -160:\n        min_ph = -180\n\n    polnames = 'RL' # XXX: identification doesn't seem to be stored in cal table\n    maxtimegap = 10. / 1440 # 10 minutes, in units of days\n\n    for iant in seenants:\n        for ipol in xrange (npol):\n            filter = (ants == iant) & ~flags[ipol]\n            p_am = om.RectPlot ()\n            p_ph = om.RectPlot ()\n            anyseen = False\n\n            for ispw in seenspws:\n                # XXX: do something by field\n                sfilter = filter & (spws == ispw)\n                t = times[sfilter]\n                if not t.size:\n                    continue\n\n                v = vals[ipol,sfilter]\n                a = np.abs (v)\n                p = np.angle (v, deg=True)\n                kt = '%s %s spw#%d' % (names[iant], polnames[ipol], ispw)\n\n                for s in numutil.slice_around_gaps (t, maxtimegap):\n                    tsub, asub, psub = t[s], a[s], p[s]\n                    if tsub.size == 0:\n                        continue # Should never happen, but eh.\n                    else:\n                        lines = (tsub.size > 1)\n\n                    p_am.addXY (tsub, asub, kt, lines=lines, dsn=spw_offsets[ispw])\n                    p_ph.addXY (tsub, psub, None, lines=lines, dsn=spw_offsets[ispw])\n                    anyseen = True\n                    kt = None\n\n            if not anyseen:\n                continue\n\n            p_am.setBounds (xmin=min_time,\n                            xmax=max_time,\n                            ymin=min_am,\n                            ymax=max_am)\n            p_ph.setBounds (xmin=min_time,\n                            xmax=max_time,\n                            ymin=min_ph,\n                            ymax=max_ph)\n\n            p_am.bpainter.paintLabels = False\n            p_am.setYLabel ('Amplitude')\n            p_ph.setLabels ('Time (MJD - %d)' % mjdref, 'De-meaned Phase (deg)')\n\n            vb = om.layout.VBox (2)\n            vb[0] = p_am\n            vb[1] = p_ph\n            vb.setWeight (0, 2.5)\n            pager.send (vb)\n\ngpplot_cli = makekwcli (gpplot_doc, GpplotConfig, gpplot)\n\n\n# image2fits\n#\n# This is basically the \"exportfits\" task with fewer options and a slightly\n# clearer name (which is also consistent with the pwimage2fits script).\n\nimage2fits_doc = \\\n\"\"\"\ncasatask image2fits <input MS image> <output FITS image>\n\nConvert an image in MS format to FITS format.\n\"\"\"\n\ndef image2fits (mspath, fitspath):\n    ia = util.tools.image ()\n    ia.open (b(mspath))\n    ia.tofits (outfile=b(fitspath), velocity=False, optical=False, bitpix=-32,\n               minpix=0, maxpix=-1, overwrite=False, dropstokes=False,\n               stokeslast=True, history=True)\n    ia.close ()\n\n\ndef image2fits_cli (argv):\n    check_usage (image2fits_doc, argv, usageifnoargs=True)\n    util.logger ()\n\n    if len (argv) != 3:\n        wrong_usage (image2fits_doc, 'expected exactly 2 arguments')\n\n    image2fits (argv[1], argv[2])\n\n\n# importevla\n#\n# This is a casapy script. We don't reeeallly need to be, but there's enough\n# logic in CASA's task_importevla.py that I'm not thrilled to copy/paste it\n# all.\n\nimportevla_doc = \\\n\"\"\"\ncasatask importevla <ASDM> <MS>\n\nConvert an EVLA low-level ASDM dataset to Measurement Set format. This\nimplementation automatically infers the value of the \"tbuff\" parameter.\n\"\"\"\n\ndef importevla (asdm, ms):\n    from .scripting import CasapyScript\n\n    # Here's the best way I can figure to find the recommended value of tbuff\n    # (= 1.5 * integration time). Obviously you might have different\n    # integration times in the dataset and such, and we're just going to\n    # ignore that possibility.\n\n    bdfstem = os.listdir (os.path.join (asdm, 'ASDMBinary'))[0]\n    bdf = os.path.join (asdm, 'ASDMBinary', bdfstem)\n    tbuff = None\n\n    with open (bdf) as f:\n        for linenum, line in enumerate (f):\n            if linenum > 60:\n                raise PKError ('cannot find integration time info in %s', bdf)\n\n            if not line.startswith ('<sdmDataSubsetHeader'):\n                continue\n\n            try:\n                i1 = line.index ('<interval>') + len ('<interval>')\n                i2 = line.index ('</interval>')\n                if i2 <= i1:\n                    raise ValueError ()\n            except ValueError:\n                raise PKError ('cannot parse integration time info in %s', bdf)\n\n            tbuff = float (line[i1:i2]) * 1.5e-9 # nanosecs, and want 1.5x\n            break\n\n    if tbuff is None:\n        raise PKError ('found no integration time info')\n\n    print ('importevla: %s -> %s with tbuff=%.2f' % (asdm, ms, tbuff))\n\n    script = os.path.join (os.path.dirname (__file__), 'cscript_importevla.py')\n    with CasapyScript (script, asdm=asdm, ms=ms, tbuff=tbuff) as cs:\n        pass\n\n\ndef importevla_cli (argv):\n    check_usage (importevla_doc, argv, usageifnoargs=True)\n\n    if len (argv) != 3:\n        wrong_usage (importevla_doc, 'expected exactly 2 arguments')\n\n    importevla (argv[1], argv[2])\n\n\n# listobs\n#\n# This one is mostly about the CLI.\n\nlistobs_doc = \\\n\"\"\"\ncasatask listobs <MS>\n\nGenerate a standard \"listobs\" listing of visibility MS contents. If\nstandard output is a TTY, the listing will be paged.\n\"\"\"\n\ndef listobs (vis):\n    \"\"\"Generates a set of lines of output. Errors are only detected by looking\n    at the output.\"\"\"\n\n    def inner_list (sink):\n        try:\n            ms = util.tools.ms ()\n            ms.open (vis)\n            ms.summary (verbose=True)\n            ms.close ()\n        except Exception as e:\n            sink.post (b'listobs failed: %s' % e, priority=b'SEVERE')\n\n    for line in util.forkandlog (inner_list):\n        info = line.rstrip ().split ('\\t', 3) # date, priority, origin, message\n        if len (info) > 3:\n            yield info[3]\n        else:\n            yield ''\n\n\ndef listobs_cli (argv):\n    check_usage (listobs_doc, argv, usageifnoargs=True)\n\n    if len (argv) != 2:\n        wrong_usage (listobs_doc, 'expect exactly one argument, the MS path')\n\n    vis = argv[1]\n\n    proc = None\n    out = sys.stdout\n\n    if sys.stdout.isatty () or \\\n           (hasattr (sys.stdout, 'stream') and sys.stdout.stream.isatty ()):\n        # Send our output to a pager!\n        import subprocess\n        pager = os.environ.get ('PAGER') or 'less -SRFX'\n        try:\n            proc = subprocess.Popen (pager, stdin=subprocess.PIPE, close_fds=True,\n                                     shell=True)\n        except Exception as e:\n            warn ('couldn\\'t start pager %r: %s', pager, e)\n        else:\n            out = proc.stdin\n\n    for line in listobs (vis):\n        print (line, file=out)\n\n    if proc is not None:\n        proc.stdin.close ()\n        proc.wait () # ignore return code\n\n# mfsclean\n#\n# This isn't a CASA task, but we're pulling out a subset of the functionality\n# of clean, which has a bajillion options and has a really gross implementation\n# in the library.\n\nmfsclean_doc = \\\n\"\"\"\ncasatask mfsclean vis=[] [keywords]\n\nDrive the CASA imager with a very restricted set of options.\n\nFor W-projection, set ftmachine='wproject' and wprojplanes=64 (or so).\n\nvis=\n  Input visibility MS\n\nimbase=\n  Base name of output files. We create files named \"imbaseEXT\"\n  where EXT is all of \"mask\", \"modelTT\", \"imageTT\", \"residualTT\",\n  and \"psfTT\", and TT is empty if nterms = 1, and \"ttN.\" otherwise.\n\ncell = 1 [arcsec]\nftmachine = 'ft' or 'wproject'\ngain = 0.1\nimsize = 256,256\nmask = (blank) or path of CASA-format region text file\nniter = 500\nnterms = 1\nphasecenter = (blank) or 'J2000 12h34m56.7 -12d34m56.7'\nreffreq = 0 [GHz]\nstokes = I\nthreshold = 0 [mJy]\nweighting = 'briggs' (robust=0.5) or 'natural'\nwprojplanes = 1\n\n\"\"\" + stdsel_doc + loglevel_doc.replace ('warn;', 'info;')\n\nclass MfscleanConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    imbase = Custom (str, required=True)\n\n    cell = 1. # arcsec\n    ftmachine = 'ft'\n    gain = 0.1\n    imsize = [256, 256]\n    mask = str\n    minpb = 0.2\n    niter = 500\n    nterms = 1\n    phasecenter = str\n    reffreq = 0. # GHz; 0 -> be sensible\n    stokes = 'I'\n    threshold = 0. # mJy\n    weighting = 'briggs'\n    wprojplanes = 1\n\n    # allowchunk = False\n    # cyclefactor = 1.5\n    # cyclespeedup = -1\n    # imagermode = csclean\n    # interactive = False\n    # gridmode = '' -- related to ftmachine keyword\n    # mode = mfs\n    # modelimage = []\n    # multiscale = []\n    # nchan = -1\n    # npixels = 0\n    # pbcor = False\n    # psfmode = clark\n    # restoringbeam = []\n    # robust = 0.5\n    # smallscalebias = 0.6\n    # usescratch = False\n    # uvtaper = False\n    # veltype = radio\n    # width = 1\n\n    antenna = str\n    field = str\n    observation = str\n    scan = str\n    spw = str\n    timerange = str\n    uvrange = str\n    taql = str\n\n    loglevel = 'info'\n\n\nspecframenames = 'REST LSRK LSRD BARY GEO TOPO GALACTO LGROUP CMB'.split ()\n\n\ndef mfsclean (cfg):\n    ms = util.tools.ms ()\n    im = util.tools.imager ()\n    tb = util.tools.table ()\n    qa = util.tools.quanta ()\n    ia = util.tools.image ()\n\n    # Filenames. TODO: make sure nothing exists\n\n    mask = cfg.imbase + 'mask'\n    pb = cfg.imbase + 'flux'\n\n    if cfg.nterms == 1:\n        models = [cfg.imbase + 'model']\n        restoreds = [cfg.imbase + 'image']\n        resids = [cfg.imbase + 'residual']\n        psfs = [cfg.imbase + 'psf']\n    else:\n        # Note: the names for the 'alpha' and 'alpha.error' images are\n        # generated automatically inside the C++ stuff by looking for image\n        # names ending in 'tt0', so we're limited in our naming flexibility\n        # here.\n        models, restoreds, resids, psfs = [], [], [], []\n\n        for i in xrange (cfg.nterms):\n            models.append (cfg.imbase + 'model.tt%d' % i)\n            restoreds.append (cfg.imbase + 'image.tt%d' % i)\n            resids.append (cfg.imbase + 'residual.tt%d' % i)\n            psfs.append (cfg.imbase + 'psf.tt%d' % i)\n\n    # Get info on our selected data for various things we need to figure\n    # out later.\n\n    selkws = extractmsselect (cfg, havearray=False, haveintent=False, taqltomsselect=False)\n    ms.open (b(cfg.vis))\n    ms.msselect (b(selkws))\n    rangeinfo = ms.range (b'data_desc_id field_id'.split ())\n    ddids = rangeinfo['data_desc_id']\n    fields = rangeinfo['field_id']\n\n    # Get the spectral frame from the first spw of the selected data\n\n    tb.open (b(os.path.join (cfg.vis, 'DATA_DESCRIPTION')))\n    ddspws = tb.getcol (b'SPECTRAL_WINDOW_ID')\n    tb.close ()\n    spw0 = ddspws[ddids[0]]\n\n    tb.open (b(os.path.join (cfg.vis, 'SPECTRAL_WINDOW')))\n    specframe = specframenames[tb.getcell (b'MEAS_FREQ_REF', spw0)]\n    tb.close ()\n\n    # Choose phase center\n\n    if cfg.phasecenter is None:\n        phasecenter = int (fields[0])\n    else:\n        phasecenter = cfg.phasecenter\n\n        if ':' in phasecenter:\n            # phasecenter='J2000 19:06:48.568 40:11:06.68'\n            # parses to \"07:06:48.57 297.13.19.80\"\n            # Using hm/dm instead of ::/:: as separators makes it parse\n            # correctly. No idea how the colons manage to parse both\n            # without warning and totally wrongly.\n            warn ('you moron, who uses colons in sexagesimal?')\n            tmp1 = phasecenter.split ()\n            twiddled = False\n            if len (tmp1) == 3:\n                tmp2 = tmp1[1].split (':')\n                tmp3 = tmp1[2].split (':')\n                if len (tmp2) == 3 and len (tmp3) == 3:\n                    tmp2 = tmp2[0] + 'h' + tmp2[1] + 'm' + tmp2[2]\n                    tmp3 = tmp3[0] + 'd' + tmp3[1] + 'm' + tmp3[2]\n                    phasecenter = ' '.join ([tmp1[0], tmp2, tmp3])\n                    twiddled = True\n            if twiddled:\n                warn ('attempted to fix it up: \"%s\"\\n\\n', phasecenter)\n            else:\n                warn ('couldn\\'t parse, left as-is; good luck\\n\\n')\n\n    # Set up all of this junk\n\n    im.open (b(cfg.vis), usescratch=False)\n    im.selectvis (nchan=-1, start=0, step=1, usescratch=False, writeaccess=False, **b(selkws))\n    im.defineimage (nx=cfg.imsize[0], ny=cfg.imsize[1],\n                    cellx=qa.quantity (b(cfg.cell), b'arcsec'),\n                    celly=qa.quantity (b(cfg.cell), b'arcsec'),\n                    outframe=b(specframe), phasecenter=b(phasecenter),\n                    stokes=cfg.stokes,\n                    spw=-1, # to verify: selectvis (spw=) good enough?\n                    restfreq=b'', mode=b'mfs', veltype=b'radio',\n                    nchan=-1, start=0, step=1, facets=1)\n\n    if cfg.weighting == 'briggs':\n        im.weight (type=b'briggs', rmode=b'norm', robust=0.5, npixels=0) #noise=, mosaic=\n    elif cfg.weighting == 'natural':\n        im.weight (type=b'natural', rmode=b'none')\n    else:\n        raise ValueError ('unknown weighting type \"%s\"' % cfg.weighting)\n\n    # im.filter (...)\n    im.setscales (scalemethod=b'uservector', uservector=[0])\n    im.setsmallscalebias (0.6)\n    im.setmfcontrol ()\n    im.setvp (dovp=True)\n    im.makeimage (type=b'pb', image=b(pb), compleximage=b'', verbose=False)\n    im.setvp (dovp=False, verbose=False)\n    im.setoptions (ftmachine=b(cfg.ftmachine), wprojplanes=b(cfg.wprojplanes),\n                   freqinterp=b'linear', padding=1.2, pastep=360.0, pblimit=b(cfg.minpb),\n                   applypointingoffsets=False, dopbgriddingcorrections=True)\n\n    if cfg.nterms > 1:\n        im.settaylorterms (ntaylorterms=cfg.nterms, reffreq=cfg.reffreq * 1e9)\n\n    im.setmfcontrol (stoplargenegatives=-1, cyclefactor=1.5,\n                     cyclespeedup=-1, minpb=cfg.minpb)\n\n    # Create the mask\n\n    if cfg.mask is None:\n        maskstr = ''\n    else:\n        maskstr = mask\n        im.make (b(mask))\n        ia.open (b(mask))\n        maskcs = ia.coordsys ()\n        maskcs.setreferencecode (b(specframe), b'spectral', True)\n        ia.setcoordsys (maskcs.torecord ())\n\n        if cfg.mask is not None:\n            rg = util.tools.regionmanager ()\n            regions = rg.fromtextfile (filename=b(cfg.mask),\n                                       shape=ia.shape (),\n                                       csys=maskcs.torecord ())\n            im.regiontoimagemask (mask=b(mask), region=regions)\n\n        ia.close ()\n\n    # Create blank model(s). Diverging from task_clean even more\n    # significantly than usual here.\n\n    for model in models:\n        im.make (b(model))\n\n    # Go!\n\n    im.clean (algorithm=b'msmfs', niter=cfg.niter, gain=cfg.gain,\n              threshold=qa.quantity (b(cfg.threshold), b'mJy'),\n              model=b(models), residual=b(resids), image=b(restoreds),\n              psfimage=b(psfs), mask=b(maskstr), interactive=False)\n    im.close ()\n\n\nmfsclean_cli = makekwcli (mfsclean_doc, MfscleanConfig, mfsclean)\n\n\n# plotants\n\nplotants_doc = \\\n\"\"\"\ncasatask plotants <MS> <figfile>\n\nPlot the physical layout of the antennas described in the MS.\n\"\"\"\n\ndef plotants (vis, figfile):\n    from .scripting import CasapyScript\n\n    script = os.path.join (os.path.dirname (__file__), 'cscript_plotants.py')\n\n    with CasapyScript (script, vis=vis, figfile=figfile) as cs:\n        pass\n\n\ndef plotants_cli (argv):\n    check_usage (plotants_doc, argv, usageifnoargs=True)\n\n    if len (argv) != 3:\n        wrong_usage (plotants_doc, 'expect exactly two arguments')\n\n    plotants (argv[1], argv[2])\n\n\n# plotcal\n\nplotcal_doc = \\\n\"\"\"\ncasatask plotcal caltable=<MS> [keywords]\n\nPlot values from a calibration dataset in any of a variety of ways.\n\ncaltable=\n  The calibration MS to plot\n\nxaxis=\n  amp antenna chan freq imag phase real snr time\n\nyaxis=\n  amp antenna imag phase real snr\n\niteration=\n  antenna field spw time\n\n*** Data selection\n\nantenna=\nfield=\npoln=\n  RL R L XY X Y '/'\nspw=\ntimerange=\n\n*** Plot appearance options\n\nplotsymbol=\nplotcolor=\nfontsize=\nfigfile=\n\n\"\"\" + loglevel_doc\n\n\nclass PlotcalConfig (ParseKeywords):\n    caltable = Custom (str, required=True)\n    xaxis = 'time'\n    yaxis = 'amp'\n    iteration = ''\n\n    # not implemented: subplot, overplot, clearpanel, plotrange,\n    # showflags, showgui\n\n    plotsymbol = '.'\n    plotcolor = 'blue'\n    markersize = 5.\n    fontsize = 10.\n    figfile = str\n\n    antenna = ''\n    field = ''\n    poln = 'RL'\n    spw = ''\n    timerange = ''\n\n\ndef plotcal (cfg):\n    # casa-tools plotting relies on invoking matplotlib in an internal Python\n    # interpreter (!), and it uses a very old version of matplotlib that's\n    # essentially incompatible with what's available in any up-to-date Python\n    # environment (e.g. Anaconda). Therefore we have to launch any plotcal\n    # operations as casapy scripts to ensure compatibility.\n\n    from .scripting import CasapyScript\n\n    script = os.path.join (os.path.dirname (__file__), 'cscript_plotcal.py')\n\n    selectcals = b(dict (antenna = cfg.antenna,\n                         field = cfg.field,\n                         poln = cfg.poln.upper (),\n                         spw = cfg.spw,\n                         time = cfg.timerange))\n\n    plotoptions = b(dict (iteration = cfg.iteration,\n                          plotrange = [0.0]*4,\n                          plotsymbol = cfg.plotsymbol,\n                          plotcolor = cfg.plotcolor,\n                          markersize = cfg.markersize,\n                          fontsize = cfg.fontsize))\n\n    with CasapyScript (script,\n                       caltable=b(cfg.caltable),\n                       selectcals=selectcals,\n                       plotoptions=plotoptions,\n                       xaxis=b(cfg.xaxis.upper ()),\n                       yaxis=b(cfg.yaxis.upper ()),\n                       figfile=b(cfg.figfile)) as cs:\n        pass\n\n\ndef plotcal_cli (argv):\n    check_usage (plotcal_doc, argv, usageifnoargs=True)\n    cfg = PlotcalConfig ().parse (argv[1:])\n    plotcal (cfg)\n\n\n# setjy\n\nsetjy_doc = \\\n\"\"\"\ncasatask setjy vis= [keywords]\n\nInsert model data into a measurement set. We force usescratch=False\nand scalebychan=True. You probably want to specify \"field\".\n\nfluxdensity=\n  Up to four comma-separated numbers giving Stokes IQUV intensities in\n  Jy. Default values are [-1, 0, 0, 0]. If the Stokes I intensity is\n  negative (i.e., the default), a \"sensible default\" will be used:\n  detailed spectral models if the source is known (see \"standard\"), or\n  1 otherwise. If it is zero and \"modimage\" is used, the flux density\n  of the model image is used. The built-in standards do NOT have\n  polarimetric information, so for pol cal you do need to manually\n  specify the flux density information -- or see the program\n  \"mspolmodel\".\n\nmodimage=\n  An image to use as the basis for the source's spatial structure and,\n  potentialy, flux density (if fluxdensity=0). Only usable for Stokes\n  I.  If the verbatim value of \"modimage\" can't be opened as a path,\n  it is assumed to be relative to the CASA data directory; a typical\n  value might be \"nrao/VLA/CalModels/3C286_C.im\".\n\nspindex=\nreffreq=\n  If using fluxdensity, these specify the spectral dependence of the\n  values, such that S = fluxdensity * (freq/reffreq)**spindex. Reffreq\n  is in GHz. Default values are 0 and 1, giving no spectral\n  dependence.\n\nstandard='Perley-Butler 2013'\n  Solar-system standards are not supported by this implementation, so\n  acceptable values are: Baars, Perley 90, Perley-Taylor 95,\n  Perley-Taylor 99, Perley-Butler 2010, Perley-Butler 2013.\n\n*** Supported data selection keywords:\n\nfield=\nobservation=\nscan=\nspw=\ntimerange=\n\"\"\" + loglevel_doc\n\nclass SetjyConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    modimage = str\n    fluxdensity = [-1., 0., 0., 0.]\n    spindex = 0.\n    reffreq = 1. # GHz\n    standard = 'Perley-Butler 2013'\n\n    field = str\n    observation = str\n    scan = str\n    spw = str\n    timerange = str\n\n    loglevel = 'warn'\n\n\ndef setjy (cfg):\n    kws = {}\n\n    for kw in 'field fluxdensity observation scan spw standard'.split ():\n        kws[kw] = getattr (cfg, kw) or ''\n\n    kws['time'] = cfg.timerange or ''\n    kws['reffreq'] = str (cfg.reffreq) + 'GHz'\n    kws['spix'] = cfg.spindex\n    kws['scalebychan'] = True # don't think you'd ever want false??\n\n    if cfg.modimage is None:\n        kws['modimage'] = ''\n    else:\n        if os.path.isdir (cfg.modimage):\n            mi = cfg.modimage\n        else:\n            mi = util.datadir (cfg.modimage)\n            if not os.path.isdir (mi):\n                raise RuntimeError ('no model image \"%s\" or \"%s\"' % (cfg.modimage, mi))\n        kws['modimage'] = mi\n\n    im = util.tools.imager ()\n    im.open (b(cfg.vis), usescratch=False) # don't think you'll ever want True?\n    im.setjy (**b(kws))\n    im.close ()\n\n\nsetjy_cli = makekwcli (setjy_doc, SetjyConfig, setjy)\n\n\n# split\n#\n# note: spw=999 -> exception; scan=999 -> no output, or error, generated\n\nsplit_doc = \\\n\"\"\"\ncasatask split vis=<MS> out=<MS> [keywords...]\n\ntimebin=\n  Time-average data into bins of \"timebin\" seconds; defaults to no averaging\n\nstep=\n  Frequency-average data in bins of \"step\" channels; defaults to no averaging\n\ncol=all\n  Extract the column \"col\" as the DATA column. If \"all\", copy all available\n  columns without renaming. Possible values:\n    all DATA MODEL_DATA CORRECTED_DATA FLOAT_DATA LAG_DATA\n\ncombine=[col1,col2,...]\n  When time-averaging, don't start a new bin when the specified columns change.\n  Acceptable column names:\n    scan state\n\"\"\" + stdsel_doc + loglevel_doc\n\nclass SplitConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    out = Custom (str, required=True)\n\n    timebin = float # seconds\n    step = 1\n    col = 'all'\n    combine = [str]\n\n    antenna = str\n    array = str\n    correlation = str\n    field = str\n    intent = str\n    observation = str # renamed from obs for consistency\n    scan = str\n    spw = str\n    taql = str\n    timerange = str\n    uvrange = str\n\n    loglevel = 'warn'\n\n\ndef split (cfg):\n    import tempfile, shutil\n\n    kws = extractmsselect (cfg, havearray=True, havecorr=True,\n                           observationtoobs=True, taqltomsselect=False)\n    kws['whichcol'] = cfg.col\n    kws['combine'] = ','.join (cfg.combine)\n    kws['step'] = [cfg.step] # can be done on per-spw basis; we skip that\n\n    if cfg.timebin is None:\n        kws['timebin'] = '-1s'\n    else:\n        kws['timebin'] = str (cfg.timebin) + 's'\n\n    ms = util.tools.ms ()\n    ms.open (b(cfg.vis))\n\n    # split() will merrily overwrite an existing MS, which I think is\n    # very bad behavior. We try to prevent this in two steps: 1) claim\n    # the desired output name in a way that will error out if it\n    # already exists; 2) tell split() to create its outputs in an\n    # empty temporary directory, to minimize the chances of blowing\n    # away anything preexisting. In the pathological case, there's a\n    # chance for someone with our UID to move something into the\n    # temporary directory with our target name and have us delete\n    # it. There's nothing we can do about that so long as split() is\n    # happy to overwrite existing data.\n    #\n    # It's also possible for someone with our UID to spoil our rename\n    # by changing the permissions on our placeholder output directory\n    # and stuffing something in it, but this failure mode doesn't\n    # involve data loss.\n    #\n    # We put the temporary working directory adjacent to the destination\n    # to make sure it's on the same device.\n\n    didntmakeit = True\n    renamed = False\n    workdir = None\n\n    try:\n        didntmakeit = os.mkdir (cfg.out, 0) # error raised if already exists.\n\n        try:\n            workdir = tempfile.mkdtemp (dir=os.path.dirname (cfg.out),\n                                        prefix=os.path.basename (cfg.out) + '_')\n            kws['outputms'] = os.path.join (workdir, os.path.basename (cfg.out))\n            ms.split (**b(kws))\n            os.rename (kws['outputms'], cfg.out)\n            renamed = True\n        finally:\n            if workdir is not None:\n                shutil.rmtree (workdir, ignore_errors=True)\n    finally:\n        if not didntmakeit and not renamed:\n            try:\n                os.rmdir (cfg.out)\n            except:\n                pass\n\n    ms.close ()\n\n\nsplit_cli = makekwcli (split_doc, SplitConfig, split)\n\n\n# spwglue\n#\n# Shim for a separate module\n\ndef spwglue_cli (argv):\n    from .spwglue import spwglue_cli\n    spwglue_cli (argv)\n\n\n# uvsub\n#\n# We add UV selection keywords not supported by the CASA task.\n# I assume that they're honored ...\n\nuvsub_doc = \\\n\"\"\"\ncasatask uvsub vis= [keywords]\n\nSet the CORRECTED_DATA column to the difference of DATA and MODEL_DATA.\n\nvis=\n  The input data set.\n\nreverse=\n  Boolean, default false, which means to set CORRECTED = DATA - MODEL. If\n  true, CORRECTED = DATA + MODEL.\n\"\"\" + stdsel_doc + loglevel_doc\n\n\nclass UvsubConfig (ParseKeywords):\n    vis = Custom (str, required=True)\n    reverse = False\n\n    antenna = str\n    array = str\n    field = str\n    intent = str\n    observation = str\n    scan = str\n    spw = str\n    timerange = str\n    uvrange = str\n    taql = str\n\n    loglevel = 'warn'\n\n\ndef uvsub (cfg):\n    ms = util.tools.ms ()\n\n    ms.open (b(cfg.vis), nomodify=False)\n    ms.msselect (b(extractmsselect (cfg,\n                                    havearray=True,\n                                    intenttoscanintent=True,\n                                    taqltomsselect=False)))\n    ms.uvsub (reverse=cfg.reverse)\n    ms.close ()\n\n\nuvsub_cli = makekwcli (uvsub_doc, UvsubConfig, uvsub)\n\n\n# Driver for command-line access. I wrote this before multitool, and it\n# doesn't seem particularly valuable to convert them to Multitool since the\n# current system works fine.\n\ndef cmdline_usage (stream, exitcode):\n    print ('usage: casatask <task> [task-specific arguments]', file=stream)\n    print (file=stream)\n    print ('Supported tasks:', file=stream)\n    print (file=stream)\n\n    for name in sorted (globals ().iterkeys ()):\n        if name.endswith ('_cli'):\n            print (name[:-4], file=stream)\n\n    raise SystemExit (exitcode)\n\n\ndef commandline (argv=None):\n    if argv is None:\n        argv = sys.argv\n        from ... import cli\n        cli.propagate_sigint ()\n        cli.backtrace_on_usr1 ()\n        cli.unicode_stdio ()\n\n    if len (argv) < 2 or argv[1] == '--help':\n        cmdline_usage (sys.stdout, 0)\n\n    driver = globals ().get (argv[1] + '_cli')\n    if driver is None:\n        die ('unknown task \"%s\"; run with no arguments for a list', argv[1])\n\n    subargv = [' '.join (argv[:2])] + argv[2:]\n    driver (subargv)\n", "repo_name": "BunnyBuster/pwkit", "sub_path": "pwkit/environments/casa/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 72251, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "40", "api": [{"api_name": "util.sanitize_unicode", "line_number": 188, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 192, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 203, "usage_type": "call"}, {"api_name": "util.logger", "line_number": 205, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 228, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 229, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 234, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 236, "usage_type": "call"}, {"api_name": "util.tools.calibrater", "line_number": 259, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 259, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 260, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 264, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 268, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 310, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 311, "usage_type": "call"}, {"api_name": "omega.render", "line_number": 322, "usage_type": "attribute"}, {"api_name": "omega.makeDisplayPager", "line_number": 327, "usage_type": "call"}, {"api_name": "omega.makePager", "line_number": 329, "usage_type": "call"}, {"api_name": "omega.styles.ColorOnWhiteVector", "line_number": 332, "usage_type": "call"}, {"api_name": "omega.styles", "line_number": 332, "usage_type": "attribute"}, {"api_name": "util.tools.table", "line_number": 334, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 334, "usage_type": "attribute"}, {"api_name": "os.path.path.join", "line_number": 343, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 343, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 343, "usage_type": "name"}, {"api_name": "numpy.angle", "line_number": 374, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 388, "usage_type": "call"}, {"api_name": "omega.RectPlot", "line_number": 402, "usage_type": "call"}, {"api_name": "omega.RectPlot", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 409, "usage_type": "call"}, {"api_name": "omega.layout.VBox", "line_number": 440, "usage_type": "call"}, {"api_name": "omega.layout", "line_number": 440, "usage_type": "attribute"}, {"api_name": "util.tools.table", "line_number": 481, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 481, "usage_type": "attribute"}, {"api_name": "util.tools.calibrater", "line_number": 482, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 482, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 487, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 497, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 504, "usage_type": "call"}, {"api_name": "sys.argv.remove", "line_number": 509, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 509, "usage_type": "attribute"}, {"api_name": "cli.wrong_usage", "line_number": 512, "usage_type": "call"}, {"api_name": "util.logger", "line_number": 514, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 516, "usage_type": "call"}, {"api_name": "util.tools.table", "line_number": 534, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 534, "usage_type": "attribute"}, {"api_name": "util.tools.ms", "line_number": 535, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 535, "usage_type": "attribute"}, {"api_name": "os.path.path.exists", "line_number": 537, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 537, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 537, "usage_type": "name"}, {"api_name": "os.path.path.isdir", "line_number": 541, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 541, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 541, "usage_type": "name"}, {"api_name": "util.sanitize_unicode", "line_number": 544, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 545, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 548, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 551, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 552, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 555, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 556, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 565, "usage_type": "call"}, {"api_name": "sys.argv.remove", "line_number": 570, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 570, "usage_type": "attribute"}, {"api_name": "cli.wrong_usage", "line_number": 573, "usage_type": "call"}, {"api_name": "util.logger", "line_number": 575, "usage_type": "call"}, {"api_name": "util.tools.table", "line_number": 594, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 594, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 595, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 597, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 605, "usage_type": "call"}, {"api_name": "util.logger", "line_number": 606, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 632, "usage_type": "call"}, {"api_name": "util.logger", "line_number": 634, "usage_type": "call"}, {"api_name": "util.tools.calibrater", "line_number": 636, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 636, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 639, "usage_type": "call"}, {"api_name": "dftphotom.dftphotom_cli", "line_number": 650, "usage_type": "call"}, {"api_name": "util.tools.table", "line_number": 679, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 679, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 680, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 680, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 680, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 680, "usage_type": "name"}, {"api_name": "util.sanitize_unicode", "line_number": 684, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 707, "usage_type": "attribute"}, {"api_name": "cli.check_usage", "line_number": 728, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 731, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 733, "usage_type": "attribute"}, {"api_name": "cli.check_usage", "line_number": 748, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 751, "usage_type": "call"}, {"api_name": "util.logger", "line_number": 758, "usage_type": "call"}, {"api_name": "util.logger", "line_number": 761, "usage_type": "call"}, {"api_name": "util.logger", "line_number": 763, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 766, "usage_type": "attribute"}, {"api_name": "util.tools", "line_number": 768, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 771, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 775, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 779, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 782, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 783, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 784, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 787, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 789, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 792, "usage_type": "call"}, {"api_name": "os.path.path.isdir", "line_number": 795, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 795, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 795, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 795, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 800, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 802, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 827, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 828, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 829, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 838, "usage_type": "attribute"}, {"api_name": "util.tools", "line_number": 840, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 843, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 874, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 886, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 910, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 911, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 918, "usage_type": "attribute"}, {"api_name": "util.tools", "line_number": 920, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 923, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 984, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 985, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 986, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 987, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 988, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 989, "usage_type": "call"}, {"api_name": "util.tools.calibrater", "line_number": 1000, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1000, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 1014, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1015, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1016, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1017, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1018, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 1070, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 1071, "usage_type": "call"}, {"api_name": "util.tools.imager", "line_number": 1091, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1091, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 1093, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1094, "usage_type": "call"}, {"api_name": "util.tools.image", "line_number": 1099, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1099, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 1100, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1110, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1111, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 1204, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 1205, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 1206, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 1220, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 1226, "usage_type": "call"}, {"api_name": "util.tools.calibrater", "line_number": 1248, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1248, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 1249, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1253, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1283, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1285, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1287, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 1346, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 1347, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 1348, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 1349, "usage_type": "call"}, {"api_name": "util.tools.calibrater", "line_number": 1360, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1360, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 1361, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 1372, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1372, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1372, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 1372, "usage_type": "call"}, {"api_name": "scripting.CasapyScript", "line_number": 1374, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 1376, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1376, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1376, "usage_type": "name"}, {"api_name": "cPickle.load", "line_number": 1377, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 1378, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1382, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1383, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1384, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 1413, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1413, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1413, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 1413, "usage_type": "call"}, {"api_name": "scripting.CasapyScript", "line_number": 1415, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 1417, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1417, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1417, "usage_type": "name"}, {"api_name": "cPickle.load", "line_number": 1418, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 1426, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 1429, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 1481, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 1482, "usage_type": "call"}, {"api_name": "omega.render", "line_number": 1493, "usage_type": "attribute"}, {"api_name": "omega.makeDisplayPager", "line_number": 1498, "usage_type": "call"}, {"api_name": "omega.makePager", "line_number": 1500, "usage_type": "call"}, {"api_name": "omega.styles.ColorOnWhiteVector", "line_number": 1503, "usage_type": "call"}, {"api_name": "omega.styles", "line_number": 1503, "usage_type": "attribute"}, {"api_name": "util.tools.table", "line_number": 1505, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1505, "usage_type": "attribute"}, {"api_name": "os.path.path.join", "line_number": 1516, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1516, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1516, "usage_type": "name"}, {"api_name": "numpy.bincount", "line_number": 1528, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 1530, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 1549, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 1550, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 1557, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 1565, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1566, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1567, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 1572, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 1573, "usage_type": "call"}, {"api_name": "omega.RectPlot", "line_number": 1588, "usage_type": "call"}, {"api_name": "omega.RectPlot", "line_number": 1589, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 1600, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 1601, "usage_type": "call"}, {"api_name": "omega.layout.VBox", "line_number": 1632, "usage_type": "call"}, {"api_name": "omega.layout", "line_number": 1632, "usage_type": "attribute"}, {"api_name": "util.tools.image", "line_number": 1654, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1654, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 1655, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1656, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 1663, "usage_type": "call"}, {"api_name": "util.logger", "line_number": 1664, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 1667, "usage_type": "call"}, {"api_name": "os.path.listdir", "line_number": 1694, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1694, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 1694, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1694, "usage_type": "attribute"}, {"api_name": "os.path.path.join", "line_number": 1695, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1695, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1695, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 1722, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1722, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1722, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 1722, "usage_type": "call"}, {"api_name": "scripting.CasapyScript", "line_number": 1723, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 1728, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 1731, "usage_type": "call"}, {"api_name": "util.tools.ms", "line_number": 1754, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1754, "usage_type": "attribute"}, {"api_name": "util.forkandlog", "line_number": 1761, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 1770, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 1773, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 1778, "usage_type": "attribute"}, {"api_name": "sys.stdout.isatty", "line_number": 1780, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 1780, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 1781, "usage_type": "attribute"}, {"api_name": "sys.stdout.stream.isatty", "line_number": 1781, "usage_type": "call"}, {"api_name": "os.path.environ.get", "line_number": 1784, "usage_type": "call"}, {"api_name": "os.path.environ", "line_number": 1784, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1784, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 1786, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 1786, "usage_type": "attribute"}, {"api_name": "cli.warn", "line_number": 1789, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 1838, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 1839, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 1840, "usage_type": "call"}, {"api_name": "util.tools.ms", "line_number": 1894, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1894, "usage_type": "attribute"}, {"api_name": "util.tools.imager", "line_number": 1895, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1895, "usage_type": "attribute"}, {"api_name": "util.tools.table", "line_number": 1896, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1896, "usage_type": "attribute"}, {"api_name": "util.tools.quanta", "line_number": 1897, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1897, "usage_type": "attribute"}, {"api_name": "util.tools.image", "line_number": 1898, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 1898, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 1927, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1928, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1935, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 1935, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1935, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1935, "usage_type": "name"}, {"api_name": "util.sanitize_unicode", "line_number": 1940, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 1940, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 1940, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 1940, "usage_type": "name"}, {"api_name": "cli.warn", "line_number": 1957, "usage_type": "call"}, {"api_name": "cli.warn", "line_number": 1969, "usage_type": "call"}, {"api_name": "cli.warn", "line_number": 1971, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1975, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1976, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1978, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1979, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1980, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 1998, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2000, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2001, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2016, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2017, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2019, "usage_type": "call"}, {"api_name": "util.tools.regionmanager", "line_number": 2023, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 2023, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 2024, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2027, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2035, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2040, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2041, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2042, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 2061, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 2061, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 2061, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 2061, "usage_type": "call"}, {"api_name": "scripting.CasapyScript", "line_number": 2063, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 2068, "usage_type": "call"}, {"api_name": "cli.wrong_usage", "line_number": 2071, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 2115, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 2116, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 2146, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 2146, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 2146, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 2146, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2148, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2154, "usage_type": "call"}, {"api_name": "scripting.CasapyScript", "line_number": 2161, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2162, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2165, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2166, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2167, "usage_type": "call"}, {"api_name": "cli.check_usage", "line_number": 2172, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 2225, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 2226, "usage_type": "call"}, {"api_name": "os.path.path.isdir", "line_number": 2256, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 2256, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 2256, "usage_type": "name"}, {"api_name": "util.datadir", "line_number": 2259, "usage_type": "call"}, {"api_name": "os.path.path.isdir", "line_number": 2260, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 2260, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 2260, "usage_type": "name"}, {"api_name": "util.tools.imager", "line_number": 2264, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 2264, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 2265, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2266, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 2298, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 2299, "usage_type": "call"}, {"api_name": "kwargv.Custom", "line_number": 2300, "usage_type": "call"}, {"api_name": "util.tools.ms", "line_number": 2336, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 2336, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 2337, "usage_type": "call"}, {"api_name": "os.path.mkdir", "line_number": 2363, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2363, "usage_type": "name"}, {"api_name": "tempfile.mkdtemp", "line_number": 2366, "usage_type": "call"}, {"api_name": "os.path.path.dirname", "line_number": 2366, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 2366, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 2366, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 2367, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 2367, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 2367, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 2368, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 2368, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 2368, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 2368, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2369, "usage_type": "call"}, {"api_name": "os.path.rename", "line_number": 2370, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2370, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 2374, "usage_type": "call"}, {"api_name": "os.path.rmdir", "line_number": 2378, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2378, "usage_type": "name"}, {"api_name": "spwglue.spwglue_cli", "line_number": 2394, "usage_type": "call"}, {"api_name": "kwargv.ParseKeywords", "line_number": 2417, "usage_type": "name"}, {"api_name": "kwargv.Custom", "line_number": 2418, "usage_type": "call"}, {"api_name": "util.tools.ms", "line_number": 2436, "usage_type": "call"}, {"api_name": "util.tools", "line_number": 2436, "usage_type": "attribute"}, {"api_name": "util.sanitize_unicode", "line_number": 2438, "usage_type": "call"}, {"api_name": "util.sanitize_unicode", "line_number": 2439, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 2469, "usage_type": "attribute"}, {"api_name": "cli.propagate_sigint", "line_number": 2471, "usage_type": "call"}, {"api_name": "cli.backtrace_on_usr1", "line_number": 2472, "usage_type": "call"}, {"api_name": "cli.unicode_stdio", "line_number": 2473, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 2476, "usage_type": "attribute"}, {"api_name": "cli.die", "line_number": 2480, "usage_type": "call"}]}
{"seq_id": "6356968506", "text": "import math\nimport pygame\n\nfrom background.Sound import play_sound\nfrom color.Colors import Colors\nfrom text.Text import Text\nfrom typing import List, Union\nfrom weapons.Weapon import Weapon\n\nclass Player():\n    \"\"\"\n    Represents a player character in the pygame\n    \"\"\"\n\n    def __init__(self, image_path: str, x_start: int, y_start: int, start_scrolling_pos_x: int, stage_width: int, game_width: int, y_top_threshold: int, y_bottom_threshold: int, health: int):\n        \"\"\"\n        Initialize a player character\n        :param image_path: file path of player image\n        :param x_start:\n        :param y_start:\n        :param start_scrolling_pos_x: point at which background scrolls/moves, not the player\n        :param stage_width: width of stage (a few times the background image)\n        :param game_width: width of game window itself\n        :param y_top_threshold: top threshold where character cannot go above\n        :param y_bottom_threshold: bottom threshold where character cannot go below\n        :param health: hit points of character\n        \"\"\"\n        self.image = pygame.image.load(image_path)\n        self.image_width = self.image.get_width()\n\n        self.x = x_start\n        self.y = y_start\n        self.x_velocity = 0.0\n        self.y_velocity = 0.0\n        self.real_x_position = self.image_width\n\n        self.start_scrolling_pos_x = start_scrolling_pos_x\n        self.stage_width = stage_width\n        self.game_width = game_width\n\n        self.y_top_threshold = y_top_threshold\n        self.y_bottom_threshold = y_bottom_threshold\n\n        self.health = health\n\n        self.is_left_facing = False\n\n        self.weapons: List[Weapon] = []\n        self.current_weapon = 0\n\n        self.points = 0\n\n    def draw(self, screen) -> None:\n        \"\"\"\n        draws player and metadata onto the pygame window, changing orientation if necessary\n        :param screen: pygame display\n        :return: None\n        \"\"\"\n        # print character\n        if self.is_left_facing: screen.blit(self.image, (self.real_x_position, self.y))\n        else: screen.blit(pygame.transform.flip(self.image, True, False), (self.real_x_position, self.y))\n\n        # print health\n        Text.render(screen, str(int(self.health)) + \" HP\", Text.Font.Euro_Horror, 15, Colors.Red, (self.real_x_position + 20, self.y + 70))\n\n        # print weapon\n        if len(self.weapons) > 0: self.get_current_weapon().draw(screen)\n\n    def set_x_velocity(self, new_velocity: float) -> None:\n        \"\"\"\n        Changes x velocity field\n        :param new_velocity:\n        :return:\n        \"\"\"\n        self.x_velocity = new_velocity\n        \n        # change player's orientation/direction\n        if self.x_velocity == 0: return\n        else: self.is_left_facing = False if self.x_velocity > 0 else True\n\n    def set_y_velocity(self, new_velocity: float) -> None:\n        \"\"\"\n        Changes y velocity field\n        :param new_velocity:\n        :return:\n        \"\"\"\n        self.y_velocity = new_velocity\n\n    def move(self) -> None:\n        \"\"\"\n        Moves player character according to current position and velocities\n        This was super helpful: https://www.youtube.com/watch?v=AX8YU2hLBUg\n        :return:\n        \"\"\"\n        self.x += self.x_velocity\n        self.y += self.y_velocity\n\n        # makes sure player doesn't go beyond stage to the right\n        if self.x > self.stage_width - self.image_width: self.x = self.stage_width - self.image_width\n        # makes sure player doesn't go beyond stage to the left\n        if self.x < 0: self.x = 0\n        # makes sure player doesn't go above top threshold\n        if self.y < self.y_top_threshold: self.y = self.y_top_threshold\n        # makes sure player doesn't go below bottom threshold\n        if self.y > self.y_bottom_threshold: self.y = self.y_bottom_threshold\n\n\n        # where x position of player is less than scrolling threshold\n        if self.x < self.start_scrolling_pos_x:\n            self.real_x_position = self.x\n        # where stage no longer scrolls, but the player moves to the end\n        elif self.x > self.stage_width - self.start_scrolling_pos_x:\n            self.real_x_position = self.x - self.stage_width + self.game_width\n        # scroll stage (handled elsewhere), but keep player \"still\" in the middle area\n        else:\n            self.real_x_position = self.start_scrolling_pos_x\n\n    def take_damage(self, damage: float) -> None:\n        \"\"\"\n        Character hitpoints get removed\n        :param damage: number of hitpoints to be removed\n        :return:\n        \"\"\"\n        self.health -= abs(damage)\n\n    def add_weapon(self, w: Weapon) -> None:\n        \"\"\"\n        Adds a weapon to the player's inventory\n        :param w:\n        :return:\n        \"\"\"\n        self.weapons.append(w)\n\n    def add_mystery_weapon(self, weapon: Weapon) -> None:\n        \"\"\"\n        Adds a mystery weapon to the player's kit\n        :param weapon: weapon to be added\n        :return:\n        \"\"\"\n        if len(self.weapons) <= 1:\n            self.add_weapon(weapon)\n            self.switch_to_next_weapon()\n        else:\n            self.weapons[self.current_weapon] = weapon\n\n    def switch_to_next_weapon(self) -> None:\n        \"\"\"\n        Switches to the next weapon in inventory, if available and current weapon isn't being used\n        :return:\n        \"\"\"\n        if len(self.weapons) == 0:\n            self.current_weapon = 0\n            return\n        \n        old_curr = self.current_weapon\n        if not self.get_current_weapon().is_being_used():\n            self.current_weapon += 1\n            self.current_weapon %= len(self.weapons)\n            if old_curr != self.current_weapon:\n                play_sound(\"background/swap_weapon.wav\")\n\n    def get_current_weapon(self) -> Union[Weapon, None]:\n        \"\"\"\n        Gets current weapon from weapons list\n        :return: Weapon\n        \"\"\"\n        if len(self.weapons) == 0: return None\n\n        if self.current_weapon >= len(self.weapons):\n            self.current_weapon = 0\n\n        return self.weapons[self.current_weapon]\n    \n    def remove_current_weapon(self):\n        \"\"\"\n        Removes current weapon from player's hands\n        \"\"\"\n        del self.weapons[self.current_weapon]\n        self.switch_to_next_weapon()\n\n    def fire_current_weapon(self) -> None:\n        \"\"\"\n        Fires current weapon, if able to\n        :return:\n        \"\"\"\n        if self.get_current_weapon() == None: return\n        else: self.get_current_weapon().fire()\n\n    def add_points(self, amount: int) -> None:\n        \"\"\"\n        Adds amount to points\n        :param: amount to add\n        :return:\n        \"\"\"\n        self.points += amount\n\n    def remove_points(self, amount: int) -> None:\n        \"\"\"\n        Removes amount from points\n        :param amount:\n        :return:\n        \"\"\"\n        self.points -= amount", "repo_name": "Quikks1lver/sweet-retreat", "sub_path": "characters/Player.py", "file_name": "Player.py", "file_ext": "py", "file_size_in_byte": 6850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pygame.image.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 48, "usage_type": "name"}, {"api_name": "weapons.Weapon.Weapon", "line_number": 48, "usage_type": "name"}, {"api_name": "pygame.transform.flip", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 61, "usage_type": "attribute"}, {"api_name": "text.Text.Text.render", "line_number": 64, "usage_type": "call"}, {"api_name": "text.Text.Text", "line_number": 64, "usage_type": "name"}, {"api_name": "text.Text.Text.Font", "line_number": 64, "usage_type": "attribute"}, {"api_name": "color.Colors.Colors.Red", "line_number": 64, "usage_type": "attribute"}, {"api_name": "color.Colors.Colors", "line_number": 64, "usage_type": "name"}, {"api_name": "weapons.Weapon.Weapon", "line_number": 126, "usage_type": "name"}, {"api_name": "weapons.Weapon.Weapon", "line_number": 134, "usage_type": "name"}, {"api_name": "background.Sound.play_sound", "line_number": 160, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 162, "usage_type": "name"}, {"api_name": "weapons.Weapon.Weapon", "line_number": 162, "usage_type": "name"}]}
{"seq_id": "13081933537", "text": "from PIL import Image\nfrom skimage import io, img_as_float\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom resizeimage import resizeimage\n\ndef resize_image(input_image_path,\n                 output_image_path,\n                 size):\n    original_image = Image.open(input_image_path)\n    width, height = original_image.size\n    print('The original image size is {wide} wide x {height} '\n          'high'.format(wide=width, height=height))\n\n    resized_image = original_image.resize(size)\n    width, height = resized_image.size\n    print('The resized image size is {wide} wide x {height} '\n          'high'.format(wide=width, height=height))\n    resized_image.save(output_image_path)\n\nif __name__==\"__main__\":\n    # im = Image.open('frozenblack.jpg') # Can be many different formats.\n    # pix = im.load()\n    # print(im.size)  # Get the width and hight of the image for iterating over\n    # width, height = im.size\n    # background={}\n    # for x in range(0, width):\n    #     for y in range(0, height):\n    #         if pix[x, y][0]<130 and pix[x, y][1]<130 and pix[x, y][2]<130:\n    #             pix[x, y]=(255, 255, 255)\n    # for x in range(width-70, width):\n    #     for y in range(0, height):\n    #         pix[x, y]=(255, 255, 255)\n    # im.save('frozen.png')  # Save the modified pixels as .pngr\n    #\n    # for k in range(1, 5):\n    #     im = Image.open('frozen.png')\n    #     pix = im.load()\n    #     if k!=1:\n    #         for x in range(0, int(width/2)):\n    #             for y in range(0, int(height/2)):\n    #                 pix[x, y]=(255, 255, 255)\n    #     if k!=2:\n    #         for x in range(int(width/2), width):\n    #             for y in range(0, int(height/2)):\n    #                 pix[x, y]=(255, 255, 255)\n    #     if k!=3:\n    #         for x in range(0, int(width/2)):\n    #             for y in range(int(height/2), height):\n    #                 pix[x, y]=(255, 255, 255)\n    #     if k!=4:\n    #         for x in range(int(width/2), width):\n    #             for y in range(int(height/2), height):\n    #                 pix[x, y]=(255, 255, 255)\n    #     im.save('piece'+str(k)+'.png')\n\n    image = img_as_float(io.imread('piece1.png'))\n\n    # Select all pixels almost equal to white\n    # (almost, because there are some edge effects in jpegs\n    # so the boundaries may not be exactly white)\n    white = np.array([1, 1, 1])\n    mask = np.abs(image - white).sum(axis=2) < 0.05\n\n    # Find the bounding box of those pixels\n    coords = np.array(np.nonzero(~mask))\n    top_left = np.min(coords, axis=1)\n    bottom_right = np.max(coords, axis=1)\n\n    out = image[top_left[0]:bottom_right[0],\n          top_left[1]:bottom_right[1]]\n\n    plt.imsave(\"piece1cropped.png\", out)\n\n    img = Image.open('piece1cropped.png')\n    img = img.convert(\"RGBA\")\n    datas = img.getdata()\n\n    newData = []\n    for item in datas:\n        if item[0] == 255 and item[1] == 255 and item[2] == 255:\n            newData.append((255, 255, 255, 0))\n        else:\n            newData.append(item)\n\n    img.putdata(newData)\n    img.save(\"piece1cropped2.png\", \"PNG\")\n    resize_image(input_image_path='piece1cropped2.png',\n                 output_image_path='piece1cropped3.png',\n                 size=(151, 219))", "repo_name": "ethannguonly/Jigsaw-Puzzle-Solving-with-a-Robotic-Arm-and-Computer-Vision", "sub_path": "blackbackground.py", "file_name": "blackbackground.py", "file_ext": "py", "file_size_in_byte": 3235, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "PIL.Image.open", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 10, "usage_type": "name"}, {"api_name": "skimage.img_as_float", "line_number": 57, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 57, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 75, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 75, "usage_type": "name"}]}
{"seq_id": "22149191925", "text": "from junitparser import JUnitXml, Element, Attr, TestCase\nimport argparse\nimport glob, os\n\nclass ClassNameTestCase(TestCase):\n    classname = Attr('classname')\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"search_path\")\nparser.add_argument(\"output_name\")\nargs = parser.parse_args()\n\nxml = JUnitXml()\nfor file in glob.glob(args.search_path + \"/**/*.xml\", recursive=True):\n    xml += JUnitXml.fromfile(file)\n\nfor suite in xml:\n    for case in suite:\n        classname_case = ClassNameTestCase.fromelem(case)\n        if classname_case.name == 'test_all':\n            classname_case.name = classname_case.classname\nxml.write(args.output_name)\n", "repo_name": "shotsan/Man-in-middle_4G_5G", "sub_path": ".ci/utils/format_devtest_junitxml.py", "file_name": "format_devtest_junitxml.py", "file_ext": "py", "file_size_in_byte": 650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "junitparser.TestCase", "line_number": 5, "usage_type": "name"}, {"api_name": "junitparser.Attr", "line_number": 6, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "junitparser.JUnitXml", "line_number": 13, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 14, "usage_type": "call"}, {"api_name": "junitparser.JUnitXml.fromfile", "line_number": 15, "usage_type": "call"}, {"api_name": "junitparser.JUnitXml", "line_number": 15, "usage_type": "name"}]}
{"seq_id": "72773760761", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse,JsonResponse\nfrom django.contrib import messages\nimport language_tool_python\nimport docx2txt\nimport spacy\nimport re\nfrom bs4 import BeautifulSoup\nimport requests\nimport re\nimport multiprocessing\nimport time\nimport nltk\nfrom nltk.stem.snowball import SnowballStemmer\nimport numpy as np\nimport pandas as pd\nfrom sklearn import feature_extraction\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom PyDictionary import PyDictionary\nnltk.download('words')\nnltk.download('stopwords')\nnltk.download('punkt')\ntry: \n  from googlesearch import search \nexcept ImportError:  \n  print(\"No module named 'google' found\") \nimport pyrebase\n# Create your views here.\n# from spellchecker import SpellChecker\n\nnlp = spacy.load(\"en_core_web_md\")\n\nconfig = {\n        \"apiKey\": \"AIzaSyBYJJBXd5Fzfo3KOfOn_L6pC5gLk8cFir8\",\n        \"authDomain\": \"vrite4u-aa22c.firebaseapp.com\",\n        \"projectId\": \"vrite4u-aa22c\",\n        \"storageBucket\": \"vrite4u-aa22c.appspot.com\",\n        \"messagingSenderId\": \"887727054072\",\n        \"appId\": \"1:887727054072:web:977987de4a5bdf561d6f0d\",\n        \"measurementId\": \"G-FSTHP6N3DZ\",\n        \"databaseURL\":\"https://vrite4u-aa22c-default-rtdb.firebaseio.com/\"\n\n    }\nfirebase=pyrebase.initialize_app(config) \nauthe = firebase.auth() \ndatabase=firebase.database() \n\nemail = \"\"\n\ndef index(request):\n    email  = database.child('users').child('0').child('email').get().val()\n    phone = database.child('users').child('0').child('phone').get().val()\n    return render(request,'main_page.html')\n\ndef profile(request):\n    arr = range(0,12)\n    # em = request.GET['email']\n    email = request.GET['email']\n    return render(request,'user_profile.html',{'arr': arr,'email':email })\n\n\ndef check(request):\n    p = \"\"\n    lis = []\n    tool = language_tool_python.LanguageTool('en-US')\n    p = request.GET['para']\n    p=p.lstrip()\n    p=p.rstrip()\n    print(p)\n    err_corr={}\n    matches = tool.check(p)\n    \n    # score\n    for i in range(len(matches)):\n        rules=matches[i]\n        d={}\n        if len(rules.replacements)>0:\n            d['start']=rules.offset\n            d['end']=rules.errorLength+rules.offset\n            d['error_text']=p[rules.offset:rules.errorLength+rules.offset]\n            if((d['error_text']=='')or(d['error_text']==' ')):\n                continue\n            d['corr']=rules.replacements\n            if((d['corr']==' ')or(len(d['corr'])==0)):\n                continue\n            d['cat']=rules.category\n            d['first'] = rules.sentence[:rules.offset]\n            d['last'] = p[rules.errorLength+rules.offset:len(rules.sentence)]\n            err_corr[i+1]=d\n            print(d)\n    links = []\n    import re\n    n_words = len(re.findall(r'\\w+', p))\n    err_words=0\n    for i in err_corr:\n        err_txt=err_corr[i]['error_text']\n    #print(err_txt)\n        err_words += len(re.findall(r'\\w+', err_txt))\n\n    score=int(((n_words-err_words)/n_words)*100)\n    # links = webScraper1(p)\n    # import docx2txt\n    # unseendocx = docx2txt.process(p)\n    vocab_dict , arr = textProcessing(p)\n    tf = computeTF(vocab_dict,arr)\n    idf = computeIDF([vocab_dict])\n    tfidf = computeTfidf(tf,idf)\n    print(\"Finished TFIDF\")\n    NER = ner(p)\n    print(\"Finished NER\")\n    #terms = clustering(p)\n    #print(\"Finished Clustering\")\n    print(list(NER)+list(tfidf)) #theme NER-named entities TFIDF-freq TERMS-clustering\n    queries = list(NER)+list(tfidf)\n    for i in range(len(queries)):\n        queries[i] = queries[i].lower() \n    queries = list(set(queries))#theme remove duplicates\n    print(\"Score:\",score)\n    #v = scrape_queries(queries,unseen_document) #links %time print the time\n    return render(request,'main_page.html',{'content': p,'matches':matches,'finals':err_corr,'num':len(err_corr),'themes':queries, 'themes_len':len(queries),'score':score})\n\ndef signIn(request): \n    return render(request,\"login.html\") \ndef home(request): \n    return render(request,\"main_page.html\") \n  \ndef postsignIn(request): \n    email=request.POST.get('email') \n    pasw=request.POST.get('pass') \n    try: \n        # if there is no error then signin the user with given email and password \n        user=authe.sign_in_with_email_and_password(email,pasw) \n    except: \n        message=\"Invalid Credentials!!Please ChecK your Data\"\n        print(message)\n        return render(request,\"login.html\",{\"message\":message}) \n    session_id=user['idToken'] \n    request.session['uid']=str(session_id) \n    return render(request,'main_page.html',{\"email\":email}) \n  \ndef logout(request): \n    try: \n        del request.session['uid'] \n    except: \n        pass\n    return render(request,\"login.html\") \n  \ndef signUp(request): \n    return render(request,\"register.html\") \n  \ndef postsignUp(request): \n    email = request.POST.get('email') \n    passs = request.POST.get('pass') \n        #  name = request.POST.get('name') \n    try: \n            # creating a user with the given email and password \n        user=authe.create_user_with_email_and_password(email,passs) \n        uid = user['localId'] \n        idtoken = request.session['uid'] \n        print(uid) \n    except:\n        # print('hello') \n        return render(request, \"login.html\") \n    return render(request,\"login.html\")\n\ndef about(request):\n    return render(request,'about.html')\n\ndef ner(unseen_document): #named entity\n  nlp = spacy.load(\"en_core_web_md\")\n  doc = nlp(unseen_document)\n  queries = set({}) #search keywords\n  for ent in doc.ents: \n    if(ent.label_ in ['PERSON', 'NORP', 'FAC', 'ORG', 'GPE', 'LOC', 'PRODUCT', 'EVENT', 'WORK_OF_ART', 'LAW', 'LANGUAGE']):\n      queries.add(ent.text)\n  return queries #txt of named entities\n\ndef scrape_helper(url,text):\n  page = requests.get(url)\n  soup = BeautifulSoup(page.content, 'html.parser') #DOM retrieval\n  page_body = soup.body\n  regex = \"<(\\\"[^\\\"]*\\\"|'[^']*'|[^'\\\">])*>\"\n  words = set(nltk.corpus.words.words())\n  page_body = \" \".join(w for w in nltk.wordpunct_tokenize(str(page_body)) \\\n           if w.lower() in words or not w.isalpha()) #extraxting meaningful words\n  page_body = re.sub(regex,'',str(page_body)).replace('\\n','') #rem HTML tags\n  page_body = page_body.replace('\\t',' ')\n\n  # print(page_body)\n  text[url] = page_body #entire scrape helper\n\ndef scrape(url):\n  manager = multiprocessing.Manager()\n  return_dict = manager.dict()\n  # text = multiprocessing.Value(c_char_p, \"\".encode(), lock=False)\n  p = multiprocessing.Process(target=scrape_helper, args=(url,return_dict,))\n  p.start()\n\n  p.join(10)\n\n  if p.is_alive():\n    p.terminate()\n    p.join()\n  return return_dict[url]\n\ndef scrape_queries(q,unseen_document):\n  to_ret = {}\n  doc=nlp(unseen_document)\n  links = search(q, tld=\"co.in\", num=1, stop=5, pause=2)\n  visited = []\n  for j in links:\n    if j in visited:\n        break\n    visited.append(j)\n    try:\n        print(j)\n        scrape_nlp = nlp(str(scrape(j)))\n        # print(scrape_nlp)\n        sim = scrape_nlp.similarity(doc)\n        print(sim,j)\n        if (sim>0.7):\n            to_ret[sim]=j\n            #print(to_ret[sim])\n        else:\n            break\n    except:\n        continue\n  print(to_ret)\n  return to_ret\n\ndef textProcessing(doc):\n    # Reference: https://github.com/AjayJohnAlex/Keyword-Extraction/blob/master/model.py\n    '''Prepocessing of input text with \n    1. tokenisation and Lemmatisation\n    2. Removing stop words \n\n    3. Creating and removing custom stop words.\n    4. Generating required Vocabulary from input\n    5. Preprocessing the input \n    '''\n    Nouns = []\n    Noun_set = []\n    trimmed_noun_set = []\n    removing_duplicates = []\n    arr = []\n    vocab = []\n    vocab_dict = {}\n\n    doc = nlp(doc.upper())\n\n    for possible_nouns in doc:\n        if possible_nouns.pos_ in [\"NOUN\",\"PROPN\"] :\n            Nouns.append([possible_nouns , [child for child in possible_nouns.children]])\n       \n    \n    for i,j in Nouns:\n        for k in j:\n            Noun_set.append([k,i])\n\n    \n    for i , j in Noun_set:\n        if i.pos_ in ['PROPN','NOUN','ADJ']:\n            trimmed_noun_set.append([i ,j])\n            \n    \n    for word in trimmed_noun_set:\n        if word not in removing_duplicates:\n            removing_duplicates.append(word)\n    \n    \n    for i in removing_duplicates:\n        strs = ''\n        for j in i:\n            strs += str(j)+\" \"\n        arr.append(strs.strip())\n\n    \n    for word in Noun_set:\n        string = ''\n        for j in word:\n            string+= str(j)+ \" \"\n        vocab.append(string.strip())\n\n    \n    for word in vocab:\n        vocab_dict[word]= 0\n        \n    for word in arr:\n        vocab_dict[word]+= 1\n\n    return vocab_dict , arr\n\ndef computeTF(wordDict,bow):\n    '''Computing TF(Term Frequency of the vocab) '''\n    tfDict = {}\n    bowCount = len(bow)\n    for word, count in wordDict.items():\n        tfDict[word] = count/float(bowCount)\n    return tfDict\n\n\ndef computeIDF(doclist):\n    '''Computing IDF for the vocab '''\n    import math \n    count = 0\n    idfDict = {}\n    for element in doclist:\n        for j in element:\n            count+=1\n    N = count\n\n    # count no of documents that contain the word w\n    idfDict = dict.fromkeys(doclist[0].keys(),0)\n\n    for doc in doclist:\n        for word,val in doc.items():\n            if val>0:\n                idfDict[word]+= 1\n\n    # divide N by denominator above\n    for word,val in idfDict.items():\n        if val == 0:\n            idfDict[word] = 0.0\n        else:\n            idfDict[word] = math.log(N / float(val))\n\n    return idfDict\n\ndef computeTfidf(tf,idf):\n    '''Computing TF-IDF for the words in text '''\n    tfidf = {}\n    sorted_list = []\n    for word , val in tf.items():\n        tfidf[word] = val * idf[word]\n\n    ranking_list  = sorted(tfidf.items(),reverse=True, key = lambda kv:(kv[1], kv[0]))[:10]\n    for i, _ in ranking_list:\n        sorted_list.append(i)\n\n    return sorted_list\n\ndef synlinks(request):\n    query=request.GET.get('keyword')\n    p=request.GET.get('para')\n    ldict = scrape_queries(query,p)\n    links=[]\n    \n    for i in sorted(ldict.keys()):\n        links.append(ldict[i])\n    if(len(links)==0):\n        links.append(\"No relevant links found\")\n    elif(len(links)>5):\n        links=links[:5]\n    ql=query.split()\n    s=[]\n    for q in ql:\n        s+=getsyn(q)\n    print(links)\n    res={'links':links,'synonyms':s}\n    print(res)\n    return JsonResponse({'Result':res})\n\ndef getsyn(word):\n    dictionary=PyDictionary()\n    l=list(dictionary.synonym(word))\n    if(len(l)>5):\n        l=l[:5]\n    return l\n\ndef upload_files(request):\n    p=\"\"\n    if(request.method == \"POST\"):\n        n = request.FILES['notesfile']\n        p=n.read().decode('utf-8')\n        p=p.strip('\\n')\n    #p = Notes()\n    #p.notesfile = n\n    return render(request,'main_page.html',{'content':p})\n", "repo_name": "harsha270501/Online_Essay_Checker", "sub_path": "online_essay_checker/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 10794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "nltk.download", "line_number": 20, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 21, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 22, "usage_type": "call"}, {"api_name": "spacy.load", "line_number": 31, "usage_type": "call"}, {"api_name": "pyrebase.initialize_app", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}, {"api_name": "language_tool_python.LanguageTool", "line_number": 65, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 93, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 98, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 120, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 123, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 125, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 136, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 139, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 146, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 149, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 163, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 164, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 167, "usage_type": "call"}, {"api_name": "spacy.load", "line_number": 170, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 179, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 180, "usage_type": "call"}, {"api_name": "nltk.corpus.words.words", "line_number": 183, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 183, "usage_type": "attribute"}, {"api_name": "nltk.wordpunct_tokenize", "line_number": 184, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 186, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 193, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 196, "usage_type": "call"}, {"api_name": "googlesearch.search", "line_number": 209, "usage_type": "call"}, {"api_name": "math.log", "line_number": 325, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 361, "usage_type": "call"}, {"api_name": "PyDictionary.PyDictionary", "line_number": 364, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 378, "usage_type": "call"}]}
{"seq_id": "69905948296", "text": "#from django.conf import settings\nfrom django.conf.urls.defaults import url, include, patterns\n#from django.conf.urls.defaults import * # * needed for 404 handler\n#from django.views.generic import create_update #, list_detail\n#from django.contrib import admin\nfrom django.contrib.sites.models import Site\nimport dorsale.views as dv\n\ndef makepatterns(mymodule, mymodels, urlpatterns=None):\n    if not urlpatterns:\n        urlpatterns = patterns('',)    \n    \n    for mymodel in mymodels:\n        name = mymodel.__name__.lower()\n        eco = {\n            'site': Site.objects.get_current,\n            'object_example': mymodel(),\n        }\n        urlpatterns.append(url(r'^'+name+r'/$', dv.list_items, {'app_name':mymodule, 'model_name':name}, name='%s-%s-list' % (mymodule, name)))\n        urlpatterns.append(url(r'^'+name+r'/(?P<object_id>\\d+)/$', dv.show_item, {'app_name':mymodule, 'model_name':name}, name='%s-%s-display' % (mymodule, name)))\n        urlpatterns.append(url(r'^'+name+r'/(?P<object_id>\\d+)/edit/$', dv.edit_item, {'app_name':mymodule, 'model_name':name}, name='%s-%s-edit' % (mymodule, name)))\n        urlpatterns.append(url(r'^'+name+r'/(?P<object_id>\\d+)/delete/$', dv.delete_item, {'app_name':mymodule, 'model_name':name}, name='%s-%s-delete' % (mymodule, name)))\n        urlpatterns.append(url(r'^'+name+'/new/$', '%s.views.new_%s' % (mymodule, name), name='%s-%s-new' % (mymodule, name)))\n#        urlpatterns.append(url(r'^'+name+r'/$', list_detail.object_list, {\n#            'queryset': mymodel.objects.all(),\n#            'template_name': 'list_items.html',\n#            'paginate_by': int(getattr(mymodel, 'items_per_page', getattr(settings, 'ITEMS_PER_PAGE', 10))),\n#            'extra_context': eco,\n#            }, name='%s-%s-list' % (mymodule, name)))\n#        urlpatterns.append(url(r'^'+name+r'/(?P<object_id>\\d+)/$', list_detail.object_detail, {\n#            'queryset': mymodel.objects.all(),\n#            'template_name': 'show_item.html',\n#            'extra_context': eco,\n#            }, name='%s-%s-display' % (mymodule, name)))\n#        urlpatterns.append(url(r'^'+name+r'/(?P<object_id>\\d+)/edit/$', create_update.update_object, {\n#            'model': mymodel,\n#            'template_name': 'edit_item.html',\n#            'extra_context': eco,\n#            }, name='%s-%s-edit' % (mymodule, name)))\n        \n    return urlpatterns\n\n", "repo_name": "pombredanne/fiee-dorsale", "sub_path": "dorsale/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2381, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "django.conf.urls.defaults.patterns", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.sites.models.Site.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.sites.models.Site", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 19, "usage_type": "call"}, {"api_name": "dorsale.views.list_items", "line_number": 19, "usage_type": "attribute"}, {"api_name": "dorsale.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 20, "usage_type": "call"}, {"api_name": "dorsale.views.show_item", "line_number": 20, "usage_type": "attribute"}, {"api_name": "dorsale.views", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 21, "usage_type": "call"}, {"api_name": "dorsale.views.edit_item", "line_number": 21, "usage_type": "attribute"}, {"api_name": "dorsale.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 22, "usage_type": "call"}, {"api_name": "dorsale.views.delete_item", "line_number": 22, "usage_type": "attribute"}, {"api_name": "dorsale.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 23, "usage_type": "call"}]}
{"seq_id": "15660581203", "text": "from random import randint\n\nfrom matplotlib.pyplot import imshow\nfrom src.dataset.MonkeyDataset import ImageTransform\n\nname2id = {\n    'alouatta_palliata': 0,\n    'erythrocebus_patas': 1,\n    'cacajao_calvus': 2,\n    'macaca_fuscata': 3,\n    'cebuella_pygmea': 4,\n    'cebus_capucinus': 5,\n    'mico_argentatus': 6,\n    'saimiri_sciureus': 7,\n    'aotus_nigriceps': 8,\n    'trachypithecus_johnii': 9,\n}\n\n\ndef get_random_monkey(data, cat_num, pic_num, random=True, resize=False):\n    maximum = len(data)\n    # Pic at random\n    if random:\n        rand = randint(0, maximum)\n        x, y = data[rand]\n    # Not Random and sb gave cat and pic number\n    else:\n        j = 1\n        # I have to iterate through examples, It could be nicer if we have dataset_loader setup?\n        for i in range(0, maximum):\n            x, y = data[i]\n            if y == cat_num and j == pic_num:\n                break\n            elif y == cat_num and j != pic_num:\n                j += 1\n    # Adding string label\n    for name, idx in name2id.items():\n        if idx == y:\n            name_display = name\n            print(\"y = \" + str(y) + \"-\" + name_display)\n            if resize:\n                x = ImageTransform(200, 0, 1)(x)\n            return imshow(x.permute(1, 2, 0).numpy())\n", "repo_name": "mra1ster/MonkeysKaggle", "sub_path": "src/dataset/RandomImage.py", "file_name": "RandomImage.py", "file_ext": "py", "file_size_in_byte": 1269, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "src.dataset.MonkeyDataset.ImageTransform", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 43, "usage_type": "call"}]}
{"seq_id": "36860651426", "text": "import logging\nimport sys\n\nfrom helper_functions.initialize_spark_session import initialize_spark_session\nfrom pyspark.sql.types import StructField, StructType, StringType\nfrom constants import dict_dbs_locations, edge_node_path\nfrom sql_queries.landing_zone_ddl import list_landing_zone_standard_lookups\nfrom helper_functions.zip_csv_to_gzip_parquet import zip_csv_to_gzip_parquet\nfrom helper_functions.loop_files import loop_files\nimport os\n\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s: %(levelname)s: %(message)s \")\n\n\ndef load_lz_standard_lookup(table_name):\n    landing_zone_location = dict_dbs_locations.get('LANDING_ZONE_LOC')\n\n    # Initializing a Spark session\n    spark = initialize_spark_session('load_lz' + table_name)\n\n    # Loading the standard lookups with the same schema in landing_zone\n    try:\n\n        # Standard schema for the standard lookups\n        schema_lookups_schema = StructType(\n            [\n                StructField(\"Code\", StringType(), True),\n                StructField(\"Description\", StringType(), True)\n            ]\n        )\n\n        df_file = spark \\\n            .read \\\n            .schema(schema_lookups_schema) \\\n            .option(\"header\", \"true\") \\\n            .csv(os.path.join(edge_node_path, table_name, '*.csv'))\n\n        df_file.write.format(\"csv\") \\\n            .mode(\"overwrite\") \\\n            .option(\"sep\", \",\") \\\n            .option('header', 'true') \\\n            .save(os.path.join(landing_zone_location, table_name))\n\n        logging.info(f'{table_name} has been loaded in the landing zone.')\n\n    except Exception as e:\n        logging.error(f\"Failed to load {table_name} in the landing zone,{e}\")\n\n\nif __name__ == '__main__':\n\n    table_name = None\n\n    # finding and parsing table_name arg to be provided to the function\n    for arg in sys.argv:\n        if 'table_name' in arg:\n            table_name = arg.split('=')[1]\n\n    if table_name is not None:\n\n        load_lz_standard_lookup(table_name)\n\n    else:\n        raise Exception(\n            'table_name argument is required to start loading, ex: table_name=L_AIRLINE_ID')\n", "repo_name": "OmarRehan/Multi-Layer-Data-Lake-Pipelines-on-AWS", "sub_path": "load_landing_zone/load_landing_zone_standard_lookup.py", "file_name": "load_landing_zone_standard_lookup.py", "file_ext": "py", "file_size_in_byte": 2108, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "45", "api": [{"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 12, "usage_type": "attribute"}, {"api_name": "constants.dict_dbs_locations.get", "line_number": 16, "usage_type": "call"}, {"api_name": "constants.dict_dbs_locations", "line_number": 16, "usage_type": "name"}, {"api_name": "helper_functions.initialize_spark_session.initialize_spark_session", "line_number": 19, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructType", "line_number": 25, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 27, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 27, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 28, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "constants.edge_node_path", "line_number": 36, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 55, "usage_type": "attribute"}]}
{"seq_id": "72814253881", "text": "import json\n\nfrom django.db import models\nfrom django.db.models.signals import post_save\nfrom django.dispatch import receiver\nfrom django_fsm import FSMField, transition\n\nfrom src.constants import CREATED, DELIVER, DISPATCH, PICK, READY_TO_DISPATCH, RECEIVE, SHELVE\nfrom .utils import TRANSITIONS, transition_filename\n\n\nclass Order(models.Model):\n    \"\"\"\n    Workflow for OrderFlow\n    \"\"\"\n\n    state = FSMField(default=CREATED, protected=True)\n    awb = models.CharField(max_length=20, unique=True, null=False, blank=False)\n\n    @transition(field=state, source=TRANSITIONS.get(PICK), target=PICK,\n                custom=dict(verbose='order is Picked from the Client'))\n    def pick(self):\n        \"\"\"\n        Pick Orders\n        :param by:\n        :return:\n        \"\"\"\n        return {'success': True, 'message': 'Picked successfully'}\n\n    @transition(field=state, source=TRANSITIONS.get(RECEIVE), target=RECEIVE,\n                custom=dict(verbose='order is received from the Client'))\n    def receive(self):\n        \"\"\"\n        Receive Orders\n        :return:\n        \"\"\"\n        return {'success': True, 'message': 'Received successfully'}\n\n    @transition(field=state, source=TRANSITIONS.get(SHELVE), target=SHELVE,\n                custom=dict(verbose='Order is shelved in our warehouse'))\n    def shelve(self):\n        \"\"\"\n        Shelve Orders\n        :return:\n        \"\"\"\n        return {'success': True, 'message': 'Shelved successfully'}\n\n    @transition(field=state, source=TRANSITIONS.get(READY_TO_DISPATCH), target=READY_TO_DISPATCH, on_error=SHELVE)\n    def ready_to_dispatch(self):\n        \"\"\"\n        Ready to dispatch orders\n        :return:\n        \"\"\"\n        return {'success': True, 'message': 'Ready to dispatch successful'}\n\n    @transition(field=state, source=TRANSITIONS.get(DISPATCH), target=DISPATCH,\n                on_error=READY_TO_DISPATCH)\n    def dispatch(self):\n        \"\"\"\n        Dispatch Orders\n        :return:\n        \"\"\"\n        return {'success': True, 'message': 'Dispatched successfully'}\n\n    @transition(field=state, source=TRANSITIONS.get(DELIVER), target=DELIVER,\n                on_error=DISPATCH)\n    def deliver(self):\n        \"\"\"\n        Deliver Orders\n        :return:\n        \"\"\"\n        return {'success': True, 'message': 'delivered successfully'}\n\n    class Meta:\n        verbose_name = \"Order\"\n        verbose_name_plural = \"Orders\"\n\n\nclass State(models.Model):\n    \"\"\"\n    States name and codes\n    \"\"\"\n\n    name = models.CharField(max_length=130, verbose_name='State Name')\n    code = models.CharField(max_length=10, verbose_name='State Code', unique=True)\n\n    class Meta:\n        verbose_name_plural = 'States'\n\n    def __str__(self):\n        return \"{} ({})\".format(self.name, self.code)\n\n\nclass StateTransition(models.Model):\n    \"\"\"\n    Holds transitions for Orders\n    \"\"\"\n\n    source = models.ForeignKey(State, related_name='state_flow_source', on_delete=models.CASCADE)\n    target = models.ForeignKey(State, related_name='state_flow_target', on_delete=models.CASCADE)\n\n    class Meta:\n        unique_together = ('source', 'target')\n\n    def __str__(self):\n        return \"{} -- {}\".format(self.source, self.target)\n\n\n@receiver(post_save, sender=StateTransition)\ndef update_and_create_transitions_json(sender, instance, **kwargs):\n    \"\"\"\n    Creates a json file for transitions\n    \"\"\"\n    transitions = {}\n    for target, source in StateTransition.objects.values_list('target__code', 'source__code'):\n        if target in transitions:\n            transitions[target].append(source)\n        else:\n            transitions[target] = [source]\n\n    with open(transition_filename, 'w+') as file:\n        file.write(json.dumps(transitions))\n", "repo_name": "asgarzee/logistics-flow", "sub_path": "src/apps/flow/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3704, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "django.db.models.Model", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django_fsm.FSMField", "line_number": 17, "usage_type": "call"}, {"api_name": "src.constants.CREATED", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django_fsm.transition", "line_number": 20, "usage_type": "call"}, {"api_name": "utils.TRANSITIONS.get", "line_number": 20, "usage_type": "call"}, {"api_name": "src.constants.PICK", "line_number": 20, "usage_type": "argument"}, {"api_name": "utils.TRANSITIONS", "line_number": 20, "usage_type": "name"}, {"api_name": "django_fsm.transition", "line_number": 30, "usage_type": "call"}, {"api_name": "utils.TRANSITIONS.get", "line_number": 30, "usage_type": "call"}, {"api_name": "src.constants.RECEIVE", "line_number": 30, "usage_type": "argument"}, {"api_name": "utils.TRANSITIONS", "line_number": 30, "usage_type": "name"}, {"api_name": "django_fsm.transition", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.TRANSITIONS.get", "line_number": 39, "usage_type": "call"}, {"api_name": "src.constants.SHELVE", "line_number": 39, "usage_type": "argument"}, {"api_name": "utils.TRANSITIONS", "line_number": 39, "usage_type": "name"}, {"api_name": "django_fsm.transition", "line_number": 48, "usage_type": "call"}, {"api_name": "utils.TRANSITIONS.get", "line_number": 48, "usage_type": "call"}, {"api_name": "src.constants.READY_TO_DISPATCH", "line_number": 48, "usage_type": "argument"}, {"api_name": "utils.TRANSITIONS", "line_number": 48, "usage_type": "name"}, {"api_name": "src.constants.SHELVE", "line_number": 48, "usage_type": "name"}, {"api_name": "django_fsm.transition", "line_number": 56, "usage_type": "call"}, {"api_name": "utils.TRANSITIONS.get", "line_number": 56, "usage_type": "call"}, {"api_name": "src.constants.DISPATCH", "line_number": 56, "usage_type": "argument"}, {"api_name": "utils.TRANSITIONS", "line_number": 56, "usage_type": "name"}, {"api_name": "src.constants.READY_TO_DISPATCH", "line_number": 57, "usage_type": "name"}, {"api_name": "django_fsm.transition", "line_number": 65, "usage_type": "call"}, {"api_name": "utils.TRANSITIONS.get", "line_number": 65, "usage_type": "call"}, {"api_name": "src.constants.DELIVER", "line_number": 65, "usage_type": "argument"}, {"api_name": "utils.TRANSITIONS", "line_number": 65, "usage_type": "name"}, {"api_name": "src.constants.DISPATCH", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 79, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 84, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 85, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 85, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 94, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 94, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 99, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 99, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 100, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 100, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 100, "usage_type": "attribute"}, {"api_name": "utils.transition_filename", "line_number": 121, "usage_type": "argument"}, {"api_name": "json.dumps", "line_number": 122, "usage_type": "call"}, {"api_name": "django.dispatch.receiver", "line_number": 109, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 109, "usage_type": "argument"}]}
{"seq_id": "40756436012", "text": "#Import JSON file and then swap it's keys and values only if they consist of alphabetical characters\n#Ensure that the JSON file is flat (Only has 1 level)\n\n#Import json library\nimport json\n\n#Create variables\noutput_dict = {}\n\n#Read the example JSON file and load it as a variable\nwith open('example.json') as f:\n    input_dict = json.load(f)\n\nprint(input_dict)\n\n#Parse through JSON file and switch keys and values which consist of only alphabetical characters\nfor key in input_dict: \n    if(key.isalpha() and input_dict[key].isalpha()):\n       output_dict.update({input_dict[key] : key})\n    else: \n       output_dict.update({key : input_dict[key]})\n\n#Print the new JSON file\nprint(output_dict)\n", "repo_name": "mikebrowne12/JSONKeyFlipper", "sub_path": "FoodGeniusChallenge.py", "file_name": "FoodGeniusChallenge.py", "file_ext": "py", "file_size_in_byte": 695, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "json.load", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "40132767492", "text": "from collections import defaultdict\nfrom collections import deque\n##https://leetcode.com/problems/longest-repeating-character-replacement/\n\"\"\"\ns=\"AABABBA\"\n\nstep 1: keep track of the (start,end,length) of the repeating letters\n'A': [[0, 1, 2], [3, 3, 1], [6, 6, 1]], 'B': [[2, 2, 1], [4, 5, 2]]\n\nstep 2: for each letter, use two queues, and keep track of how many k left, end_idx,\nthe first element in the queue_2 is always the start point, the first element in queue_1 is always the subarray of letters that need to be combined with subarray in queue_1\nqueue_1:'A': [[0, 1, 2], [3, 3, 1], [6, 6, 1]]\nqueue_2: []\nif queue_two is empty, queue_2.append(queue_1.popleft)\nif distance between first subarray in queue_1 and end_idx <= left, we just combine the\nsubarray in queue_1 and update the left_k, end_idx,cur_max\n\nif not: means we don't and enough k to combine the 1st subarray in queue_1 and queue_2,\nthen end_idx += left_k, left_k=0\n\nif left_k == 0, then we just cut the 1st subarray in queue_2, and make more left_k\n\nuntil we pop out all elements in queue_1,\nmax_length = min (current_max + left_k, len(string))\n\"\"\"\nclass Solution:\n    def maxLength(self,s,k)->int:\n        pre = None\n        pre_idx = None\n        letter_to_idx = defaultdict(list)\n        for idx,e in enumerate(s):\n            if (not pre and not pre_idx) or (pre and pre != e):\n                pre = e\n                pre_idx = idx\n                letter_to_idx[e].append([idx,idx,1])\n\n            elif pre and pre == e:\n                letter_to_idx[pre][-1][1] += 1\n                letter_to_idx[pre][-1][2] += 1\n\n        global_max=0\n        for key,val in letter_to_idx.items():\n            queue = deque(val)\n            queue_2 = deque([queue.popleft()])\n            end,cur_max = queue_2[0][1],queue_2[0][2]\n            left = k\n            while(queue):\n                if left == 0:\n                    if len(queue_2)>=2:\n                        intv = queue_2[1][0]-queue_2[0][1]-1\n                        left += intv\n                    queue_2.popleft()\n\n                if not queue_2:\n                    end = queue[0][1]\n                    left = k\n                    queue_2.append(queue.popleft())\n\n                else:\n                    interval = queue[0][0]-end-1\n                    if queue[0][0]-end-1 <= left: ##interval <= left\n                        end = queue[0][1]\n                        left -= interval\n                        queue_2.append(queue.popleft())\n                    else: ##interval > left\n                        end += left\n                        left = 0\n\n                cur_max = max(cur_max,end-queue_2[0][0]+1)\n\n            cur_max = max(cur_max, min(end-queue_2[0][0]+1+left,len(s)))\n            global_max = max(global_max,cur_max)\n        return global_max\n\n    def characterReplacement(self, s: str, k: int) -> int:\n        return self.maxLength(s,k)", "repo_name": "zhangsisi0717/Leetcode", "sub_path": "LongestRepeatingCharacterReplacement.py", "file_name": "LongestRepeatingCharacterReplacement.py", "file_ext": "py", "file_size_in_byte": 2890, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "collections.defaultdict", "line_number": 30, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 43, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 44, "usage_type": "call"}]}
{"seq_id": "19873715686", "text": "import asyncio\nimport binascii\n\nfrom bleak import BleakClient, BleakScanner\n\n# 设备的Characteristic UUID，这是蓝牙设备的一种特性，具备通知属性(Notify)\npar_notification_characteristic = \"0000ffe1-0000-1000-8000-00805f9b34fb\"\n\n# 设备的Characteristic UUID，这是蓝牙设备的一种特性，具备写属性(Write)\npar_write_characteristic = \"0000ffe1-0000-1000-8000-00805f9b34fb\"\n\n# 设备的MAC地址，这是蓝牙设备的唯一标识\npar_device_addr = \"98:DA:20:05:35:A9\"\n\n# 标志变量，指示是否成功发送数据\ndata_sent = False\n\nasync def send_data():\n    global data_sent\n    async with BleakClient(par_device_addr) as client:\n        # 将数据转换为十六进制\n        data = b\"A\"\n        hex_data = binascii.hexlify(data).decode()\n\n        try:\n            # 发送数据\n            await client.write_gatt_char(par_write_characteristic, bytearray.fromhex(hex_data))\n            data_sent = True\n        except Exception as e:\n            print(f\"Failed to send data: {e}\")\n            data_sent = False\n\n\nasync def receive_data():\n    async with BleakScanner() as scanner:\n        devices = await scanner.discover()\n        device = next((d for d in devices if d.address == par_device_addr), None)\n\n        if device:\n            async with BleakClient(device) as client:\n                while True:\n                    # 接收数据通知\n                    notification = await client.read_gatt_char(par_notification_characteristic)\n                    if notification:\n                        # 解码并显示接收到的数据\n                        received_data = binascii.unhexlify(notification.decode()).decode()\n                        print(f\"Received data: {received_data}\")\n                    else:\n                        # 没有接收到数据\n                        print(\"No data received.\")\n\n                    await asyncio.sleep(1)\n\n# 创建事件循环\nloop = asyncio.get_event_loop()\n\n# 创建任务列表\ntasks = [\n    loop.create_task(send_data()),\n    loop.create_task(receive_data())\n]\n\n# 执行任务并等待完成\ntry:\n    loop.run_until_complete(asyncio.wait(tasks))\nexcept KeyboardInterrupt:\n    pass\nfinally:\n    # 关闭事件循环\n    loop.close()\n\n# 显示数据发送情况\nif data_sent:\n    print(\"成功发送数据.\")\nelse:\n    print(\"Failed to send data.\")\n", "repo_name": "Acui001/AI-car", "sub_path": "booth.py", "file_name": "booth.py", "file_ext": "py", "file_size_in_byte": 2356, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "bleak.BleakClient", "line_number": 20, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 23, "usage_type": "call"}, {"api_name": "bleak.BleakScanner", "line_number": 35, "usage_type": "call"}, {"api_name": "bleak.BleakClient", "line_number": 40, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 46, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 55, "usage_type": "call"}, {"api_name": "asyncio.wait", "line_number": 65, "usage_type": "call"}]}
{"seq_id": "13667197114", "text": "import requests\nimport datetime\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.http import JsonResponse\nfrom django.views.generic import TemplateView\n\nimport urllib.request\n\nimport numpy as np\nimport pandas as pd\nimport datetime as dt\nimport re\n\nimport pytz\n\n\ndef get_bus_time(url, station, line, direction):\n    page = urllib.request.urlopen(url).read().decode()\n    df_all = pd.read_html(page)\n    today_type = re.search('(..)ダイヤ</a>で運行しております。',\n                           page)[1]\n\n    tz = pytz.timezone('Asia/Tokyo')\n    list_times = []\n\n    for df in df_all:\n        header = {re.sub('\\.[0-9]+', '', str(c))\n                  for c in df.columns if c != '時'}\n        if header == {f'【{station}】 {line} {direction}行（{today_type}）'}:\n\n            now = tz.localize(dt.datetime.now())\n\n            df = (df.set_index('時')\n                  .dropna(axis='index', how='all')\n                  .dropna(axis='columns', how='all')\n                  .replace('[^0-9]', '', regex=True)\n                  .astype(float))\n\n            for r in df.iterrows():\n                hour = r[0]\n                for minute in r[1]:\n                    if np.isnan(minute):\n                        continue\n                    bus_time = tz.localize(\n                        dt.datetime.combine(dt.date.today(),\n                                            dt.time(hour, int(minute)))\n                    )\n                    if bus_time > now:\n                        list_times.append(bus_time)\n    return list_times\n\n\ndef get_time_to_next_hana(_):\n    requests\n    url = 'https://tobus.jp/blsys/navi?LCD=&VCD=cresultrsi&ECD=aprslt&slst=1235'\n    page = requests.get(\n        url,\n        headers={'Cache-Control': 'max-age=0'}\n    ).content.decode('UTF-8')\n\n    status = pd.read_html(page)[2]\n    is_soon = status.iloc[0, 1] == '新宿駅西口行まもなく'\n    time_list = status.iloc[0].str.extract(r'新宿駅西口行([0-9]+)分待').dropna()\n\n    if is_soon:\n        bus_stop = 0\n        bus_wait = 0\n    else:\n        try:\n            bus_stop = int(time_list.iloc[0].name / 2)\n            bus_wait = f'{int(time_list.iloc[0])}'\n        except IndexError:\n            bus_stop = -1\n            bus_wait = '-'\n\n    return JsonResponse({\n        'real_next_bus_stop': bus_stop,\n        'real_next_bus_wait': bus_wait,\n    })\n\n\nclass BusTimeMain(TemplateView):\n    template_name = \"bustime/bustime_main.html\"\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        stationMain = self.request.GET.get('station', '新宿駅西口')\n        from_shinjuku = stationMain == '新宿駅西口'\n        stationOther = '花園町' if from_shinjuku else '新宿駅西口'\n\n        if from_shinjuku:\n            url = 'https://tobus.jp/blsys/navi?LCD=&VCD=cresultttbl&ECD=show' \\\n                  '&slst=702&pl=8&RTMCD=122&lrid=2&tgo=1'\n            class_main = 'is-info'\n            class_other = 'is-success'\n        else:\n            url = 'https://tobus.jp/blsys/navi?LCD=&VCD=cresultttbl&ECD=show' \\\n                  '&slst=1235&pl=1&RTMCD=122&lrid=1&tgo=1'\n            class_main = 'is-success'\n            class_other = 'is-info'\n\n        line = '白６１'\n        direction = '練馬駅・練馬車庫前' if from_shinjuku else '新宿駅西口'\n        context['list_times'] = get_bus_time(url, stationMain, line, direction)\n        context['busStopMain'] = {'name': stationMain,\n                                  'class': class_main}\n        context['busStopOther'] = {'name': stationOther,\n                                   'class': class_other}\n        return context\n\n", "repo_name": "RepoKK/kukan", "sub_path": "bustime/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "45", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 18, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 18, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 18, "usage_type": "name"}, {"api_name": "pandas.read_html", "line_number": 19, "usage_type": "call"}, {"api_name": "re.search", "line_number": 20, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 23, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 45, "usage_type": "attribute"}, {"api_name": "datetime.time", "line_number": 46, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 61, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 76, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 82, "usage_type": "name"}]}
{"seq_id": "17219535666", "text": "from django.conf import settings\nimport pathlib, io, uuid, tarfile, os, shutil\nfrom django.db.utils import IntegrityError\nfrom .models import EtchingPlate\nfrom .exceptions import *\nimport copy, json\n\nclass RestrictAccess:\n    def make_private(self):\n        pass\n\n    def grant_access(self):\n        pass\n\n    def has_access(self, user):\n        pass\n\n\nclass Beam:\n    def __init__(self, id, root=None, incoming=False):\n        if root is None:\n            root = get_static_root()\n        self.root = pathlib.Path(root)\n        self.incoming = incoming\n        if incoming:\n            self.tmp_id = id\n        else:\n            self.etched(id)\n\n    def etched(self, id):\n        self.id = id\n        self.path = self.root / id\n        self.read_config()\n\n    def get_artifact(self, id):\n        return Artifact(self, id)\n\n    def get_artifacts(self):\n        return [Artifact(self, int(id)) for id in os.listdir(self.path) if id.isdigit()]\n\n    def read_config(self):\n        pass\n\n    def etch(self, user, archive, meta):\n        self.tmp_path = tmp = pathlib.Path(\"/tmp/ndsv-\" + self.tmp_id)\n        with tarfile.open(fileobj=archive.open(mode=\"rb\"), mode=\"r\") as arc:\n            if not arc.next().isdir():\n                raise PayloadError(\"The toplevel tarfile should be a directory.\")\n            arc.extractall(str(tmp))\n        payload_root = self.validate_payload()\n        return self.etch_payload(user, payload_root, meta)\n\n    def validate_payload(self):\n        toplevel = os.listdir(self.tmp_path)\n        if len(toplevel) != 1 or not (self.tmp_path / toplevel[0]).is_dir():\n            raise PayloadError(\"There may only be 1 toplevel object and it must be a directory.\")\n        payload_root = self.tmp_path / toplevel[0]\n        return payload_root\n\n    def etch_payload(self, user, payload, meta):\n        id = self.tmp_id\n        reserved = {'id', 'beam'}\n        fields = set(field.name for field in EtchingPlate._meta.get_fields())\n        valid = fields - {'beam_id', 'id'}\n        supplied = set(meta.keys())\n        invalid = supplied - valid\n        if invalid:\n            raise PayloadError(f\"Invalid meta keys {invalid} supplied. Valid meta keys: {valid}\")\n        while True:\n            try:\n                etch = EtchingPlate.objects.create(etched_by=user, beam_id=id, **meta)\n                break\n            except IntegrityError:\n                id = str(uuid.uuid4())\n        self.etched(id)\n        shutil.move(payload, self.path)\n        # Make sure the uploading user has access to its own artifacts\n        for artifact in self.get_artifacts():\n            access = artifact.json.get(\"access_list\", [])\n            if user.username not in access:\n                print(f\"Adding uploading user '{user.username}' to access list\")\n                access.append(user.username)\n                artifact.json({\"access_list\": access})\n        return etch\n\n    @classmethod\n    def receive(cls, user, archive, meta):\n        tmp_id = str(uuid.uuid4())\n        beam = cls(tmp_id, incoming=True)\n        beam.etch(user, archive, meta)\n        return beam\n\n\nclass ArtifactJson(dict):\n    def __init__(self, path, initial):\n        self.path = path\n        self.update(copy.deepcopy(initial))\n\n    def __call__(self, update):\n        self.update(copy.deepcopy(update))\n        with open(self.path, \"w\") as f:\n            json.dump(self, f)\n\n\nclass Artifact:\n    def __init__(self, beam, id):\n        self.id = id\n        self.beam = beam\n        self.root = beam.path\n        self.path = beam.path / str(id)\n        self.read_json()\n\n    def read_json(self):\n        json_path = self.path / \"artifact.json\"\n        with open(json_path, \"r\") as f:\n            self.json = ArtifactJson(json_path, json.load(f))\n\n    def has_access(self, user):\n        return user.is_staff or self.json[\"public_access\"] or user.username in self.json[\"access_list\"]\n\n    def as_response(self, file):\n        from django.http import FileResponse\n\n        file = self.path / file\n        if not self.is_file(file):\n            raise IOError(f\"'{file}' is not an artifact file of {self.beam.id}/{self.id}\")\n        return FileResponse(open(file, 'rb'))\n\n    def is_file(self, file):\n        return path_is_parent(self.path, file) and os.path.exists(file)\n\n    def get_scheme(self):\n        return self.json.get(\"scheme\", \"infiltrator\")\n\ndef path_is_parent(parent_path, child_path):\n    parent_path = os.path.realpath(parent_path)\n    child_path = os.path.realpath(child_path)\n    return parent_path == os.path.commonpath([parent_path, child_path])\n\ndef get_static_root():\n    path = pathlib.Path(settings.NDSV_STORAGE_VAULT)\n    if not path.exists():\n        raise RuntimeError(f\"Static NDSV file root '{path}' does not exist.\")\n    return path\n", "repo_name": "dbbs-lab/ndsv", "sub_path": "ndsv/artifacts.py", "file_name": "artifacts.py", "file_ext": "py", "file_size_in_byte": 4754, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pathlib.Path", "line_number": 23, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 39, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 45, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 46, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 54, "usage_type": "call"}, {"api_name": "models.EtchingPlate._meta.get_fields", "line_number": 63, "usage_type": "call"}, {"api_name": "models.EtchingPlate._meta", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.EtchingPlate", "line_number": 63, "usage_type": "name"}, {"api_name": "models.EtchingPlate.objects.create", "line_number": 71, "usage_type": "call"}, {"api_name": "models.EtchingPlate.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.EtchingPlate", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.utils.IntegrityError", "line_number": 73, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 74, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 76, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 88, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 97, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 102, "usage_type": "call"}, {"api_name": "json.load", "line_number": 116, "usage_type": "call"}, {"api_name": "django.http.FileResponse", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.path.commonpath", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 141, "usage_type": "call"}, {"api_name": "django.conf.settings.NDSV_STORAGE_VAULT", "line_number": 141, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 141, "usage_type": "name"}]}
{"seq_id": "12690116203", "text": "from __future__ import print_function\n\nfrom datetime import datetime, timedelta\n\nimport os\nimport json\nfrom LargeOffice import LargeOffice\nimport sys\nimport math\n\ntry:\n    import fncs\nexcept:\n    print('fncs problem')\n    pass\nfrom metrics_collector import MetricsCollector, MetricsStore\n\n\n# def startLOSimulation(startDay, duration, timeStep=60, metricsRecordInterval=86400):\ndef startLOSimulation(configFile):\n    # startDay        = 1  # day of year --> 1=Jan; 32=Feb; 60=Mar; 91=Apr; 121=May; 152=Jun; 182=Jul; 213=Aug; 244=Sep; 274=Oct; 305=Nov; 335=Dec;\n    # duration        = 2   # number of days\n    if os.path.isfile(configFile):\n        with open(configFile, 'r') as stream:\n            try:\n                conf = json.load(stream)\n                name = str(conf['name'])\n                duration = conf['duration']\n                StartTime = conf['StartTime']\n                timeFormat = '%Y-%m-%d %H:%M:%S'\n                dtStart = datetime.strptime(StartTime, timeFormat)\n                timeDeltaStr = conf['time_delta']\n                metricsRecordInterval = conf['metricsRecordInterval']\n                metricsWriteOutInterval = conf['metricsWriteOutInterval']\n            except ValueError as ex:\n                print(ex)\n    else:\n        print('could not open CONFIG FILE for largeBuilding.')\n        sys.exit()\n\n    startDay = dtStart.timetuple().tm_yday\n    duration = int(filter(lambda x: x.isdigit(), duration))\n    timeStep = convertTimeToSeconds(timeDeltaStr)\n    metricsAddInterval = convertTimeToSeconds(metricsRecordInterval)\n    metricsWriteOutInterval = convertTimeToSeconds(metricsWriteOutInterval)\n    startTime = (int(startDay) - 1) * 86400\n    print(\"start time: \", startTime)\n    stopTime = (int(startDay) - 1 + int(duration)) * 86400\n    print(\"stop time: \", stopTime)\n    timeElapsed = 0\n    print(\"current time: \", timeElapsed)\n    tnext_write_metrics = metricsWriteOutInterval\n    print(\"metrics record interval: \", tnext_write_metrics)\n    tnext_add_metrics = metricsAddInterval\n\n    # ------temporary read in from CSVs, eventually read in from FNCS---------------------\n    # TO = np.genfromtxt('./core/_temp/TO.csv', delimiter=',',max_rows=(startDay - 1 + duration)*1440+1)[(startDay-1)*1440+1:,1]\n\n    # weather_current = {\"TO\":0,\"windSpeed\":0}\n    # initialize a large office model\n\n    TO_current = None\n    windSpeed = None\n    voltageAB = 0\n    voltageBC = 0\n    voltageCA = 0\n    # weather_current = {}\n    fncs.initialize()\n    print('FNCS initialized')\n\n    while timeElapsed < stopTime - startTime:\n        events = fncs.get_events()\n        for topic in events:\n            value = fncs.get_value(topic)\n            if topic == 'temperature':\n                TO_current = float(value)\n            if topic == 'wind_speed':\n                windSpeed = float(value)\n            if topic == 'voltageAB':\n                p, q = parse_complex(value)\n                voltageAB = math.sqrt(p ** 2 + q ** 2)\n            if topic == 'voltageBC':\n                p, q = parse_complex(value)\n                voltageBC = math.sqrt(p ** 2 + q ** 2)\n            if topic == 'voltageCA':\n                p, q = parse_complex(value)\n                voltageCA = math.sqrt(p ** 2 + q ** 2)\n            print(topic)\n            print(value)\n        if TO_current is None:\n            print(\"TO_current is None\")\n        if windSpeed is None:\n            print(\"windSpeed is None\")\n        if TO_current is not None and windSpeed is not None:\n            break\n        print(\"current time: \", timeElapsed)\n        timeElapsed = timeElapsed + timeStep\n        nextFNCSTime = min(timeElapsed, stopTime)\n        print('nextFNCSTime: ', nextFNCSTime)\n        time_granted = fncs.time_request(nextFNCSTime)\n        print('time granted: ', time_granted)\n        timeElapsed = time_granted\n        print(\"current time after time granted: \", timeElapsed)\n\n    print(\"weather info found!\")\n    weather_current = {\"TO\": TO_current, \"windSpeed\": windSpeed}\n    voltage = {\"voltage_AB\": voltageAB, \"voltage_BC\": voltageBC, \"voltage_CA\": voltageCA}\n\n    # adding the metrics collector object\n    metrics_collector_obj = MetricsCollector.factory(start_time=StartTime, write_hdf5=False)\n\n    # interval for metrics recording\n    metrics_interval_cnt = 1\n    tnext_write_metrics += timeElapsed\n    tnext_add_metrics += timeElapsed\n\n    # define metadata for metric files\n    metrics_collector = MetricsStore(\n        name_units_pairs= [\n            ('total_power', ['kW']),\n            # ('room_temperature', [u\"\\u2103\".encode('utf-8')] * 19)\n            ('room_temperature', ['degF'] * 19)\n            # ('room_temperature', [u'\\N{DEGREE SIGN}'.encode('utf-8') + 'C'] * 19)\n        ],\n        file_string='large_office_{}_interval'.format(metricsRecordInterval),\n        collector=metrics_collector_obj,\n    )\n    # print(u\"\\u2103\")u\"\\u00B0\"\n    # lo_metrics_meta = {'total_power': {'units': str(['kW']), 'index': 0},\n    #                    'room_temperature': {'units': str(['c'] * 19), 'index': 1}}\n\n    # add metric data to collector\n    # metrics_collector_obj.add_metric('large_office_' + str(metricsRecordInterval) + '_interval_', 'large_office_' + str(metricsRecordInterval) + '_interval_', lo_metrics_meta)\n\n    LO1 = LargeOffice(int(startDay), int(duration), weather_current)\n    # LO2 = LargeOffice(startDay, duration,initation2)\n\n    # start simulation\n    model_time = LO1.startTime + timeElapsed\n    print('model_time: ', model_time)\n    time_stop = LO1.stopTime\n\n    control_inputs = {}  # use default control inputs, or define dynamic values here\n    if weather_current:  # ['TO'] and weather_current['windSpeed']:\n        P_total, T_room = LO1.step(model_time, weather_current, control_inputs, voltage)\n        # print(P_total)\n        fncs.publish('room_temps', T_room)\n        fncs.publish('total_power', str(list(P_total)))\n        if time_granted >= tnext_add_metrics or time_granted >= stopTime - startTime:\n            metrics_collector.append_data(\n                time_granted,\n                name,\n                P_total.tolist(),\n                [convertTemperatureFromFtoC(t) for t in T_room]\n            )\n            tnext_add_metrics+=metricsAddInterval\n        # metrics_collector_obj.add_data('large_office_' + str(metricsRecordInterval) + '_interval_', time_granted, {'total_power': [P_total.tolist()], 'room temperature': [T_room.tolist()]})\n        # metrics_collector_obj.add_data('large_office_' + str(metricsRecordInterval) + '_interval_', time_granted, {'name': [P_total.tolist(), T_room.tolist()]})\n        # metrics_collector_obj.add_data('large_office_' + str(metricsRecordInterval) + '_interval_', time_granted, {'name': [P_total.tolist() + T_room.tolist()]})\n        print('P_total: ', P_total, ', T_room: ', T_room)\n    # model_time = model_time + timeStep\n    # nextFNCSTime = min(model_time, time_stop)\n    # print('nextFNCSTime: ', nextFNCSTime)\n    # time_granted = fncs.time_request(nextFNCSTime)\n    # print('time granted: ', time_granted)\n    # model_time = time_granted\n\n    print(\"current time: \", timeElapsed)\n    timeElapsed = timeElapsed + timeStep\n    nextFNCSTime = min(timeElapsed, stopTime - startTime)\n    print('nextFNCSTime: ', nextFNCSTime)\n    time_granted = fncs.time_request(nextFNCSTime)\n    print('time granted: ', time_granted)\n    timeElapsed = time_granted\n    print(\"current time after time granted: \", timeElapsed)\n    model_time = LO1.startTime + timeElapsed\n    print('model_time: ', model_time)\n\n    while (timeElapsed < stopTime - startTime):  # fmu uses second of year as model_time\n        # currentDay = int(model_time/86400)\n        # currentHour = int((model_time-currentDay*86400)%86400/3600)\n        # currentMin = int((model_time-(currentDay*86400+currentHour*3600))/60)\n        # currentSec = int((model_time-(currentDay*86400+currentHour*3600))%60)\n\n        events = fncs.get_events()\n        for topic in events:\n            value = fncs.get_value(topic)\n            if topic == 'temperature':\n                weather_current['TO'] = float(value)\n            if topic == 'wind_speed':\n                weather_current['windSpeed'] = float(value)\n            if topic == 'voltageAB':\n                p, q = parse_complex(value)\n                voltage['voltage_AB'] = math.sqrt(p ** 2 + q ** 2)\n            if topic == 'voltageBC':\n                p, q = parse_complex(value)\n                voltage['voltage_BC'] = math.sqrt(p ** 2 + q ** 2)\n            if topic == 'voltageCA':\n                p, q = parse_complex(value)\n                voltage['voltage_CA'] = math.sqrt(p ** 2 + q ** 2)\n            print(topic)\n            print(value)\n        # weather_current={'TO':TO_current,'windSpeed':windSpeed}\n        control_inputs = {}  # use default control inputs, or define dynamic values here\n        if weather_current:  # ['TO'] and weather_current['windSpeed']:\n            P_total, T_room = LO1.step(model_time, weather_current, control_inputs, voltage)\n            # print(P_total)\n            fncs.publish('room_temps', T_room)\n            fncs.publish('total_power', str(list(P_total)))\n            if time_granted >= tnext_add_metrics or time_granted >= stopTime - startTime:\n                metrics_collector.append_data(\n                    time_granted,\n                    name,\n                    P_total.tolist(),\n                    [convertTemperatureFromFtoC(t) for t in T_room]\n                )\n                tnext_add_metrics+=metricsAddInterval\n            # metrics_collector_obj.add_data('large_office_' + str(metricsRecordInterval) + '_interval_', time_granted, {'total_power': [P_total.tolist()], 'room temperature': [T_room.tolist()]})\n            # metrics_collector_obj.add_data('large_office_' + str(metricsRecordInterval) + '_interval_', time_granted, {'name': [P_total.tolist(), T_room.tolist()]})\n            # metrics_collector_obj.add_data('large_office_' + str(metricsRecordInterval) + '_interval_', time_granted, {'name': [P_total.tolist() + T_room.tolist()]})\n            print('P_total: ', P_total, ', T_room: ', T_room)\n        print(\"current time: \", timeElapsed)\n        timeElapsed = timeElapsed + timeStep\n        nextFNCSTime = min(timeElapsed, stopTime - startTime)\n        print('nextFNCSTime: ', nextFNCSTime)\n        time_granted = fncs.time_request(nextFNCSTime)\n        print('time granted: ', time_granted)\n        timeElapsed = time_granted\n        print(\"current time after time granted: \", timeElapsed)\n        model_time = LO1.startTime + timeElapsed\n        print('model_time: ', model_time)\n\n        # ----------------------------------------------------------------------------------------------------\n        # ------------------------------------ Write metrics -------------------------------------------------\n        # ----------------------------------------------------------------------------------------------------\n        if time_granted >= tnext_write_metrics or time_granted >= stopTime - startTime:\n            print(\"-- writing metrics --\")\n            # write all known metrics to disk\n            # metrics_collector_obj.write_metrics(metrics_interval_cnt)\n            metrics_collector_obj.write_metrics()\n            tnext_write_metrics += metricsWriteOutInterval\n            # metrics_interval_cnt += 1\n\n    LO1.terminate()\n    print(\"=======================Simulation Done=======================\")\n    print('finalizing FNCS')\n    fncs.finalize()\n\n\ndef parse_complex(arg):\n    \"\"\" Helper function to parse P+jQ from a FNCS value\n\n    Args:\n      arg (str): FNCS value in rectangular format\n\n    Returns:\n      float, float: P [MW] and Q [MVAR]\n    \"\"\"\n    tok = arg.strip('; MWVAKdrij')\n    bLastDigit = False\n    bParsed = False\n    vals = [0.0, 0.0]\n    for i in xrange(len(tok)):\n        if tok[i] == '+' or tok[i] == '-':\n            if bLastDigit:\n                vals[0] = float(tok[: i])\n                vals[1] = float(tok[i:])\n                bParsed = True\n                break\n        bLastDigit = tok[i].isdigit()\n    if not bParsed:\n        vals[0] = float(tok)\n\n    if 'd' in arg:\n        vals[1] *= (math.pi / 180.0)\n        p = vals[0] * math.cos(vals[1])\n        q = vals[0] * math.sin(vals[1])\n    elif 'r' in arg:\n        p = vals[0] * math.cos(vals[1])\n        q = vals[0] * math.sin(vals[1])\n    else:\n        p = vals[0]\n        q = vals[1]\n\n    if 'KVA' in arg:\n        p /= 1000.0\n        q /= 1000.0\n    elif 'MVA' in arg:\n        p *= 1.0\n        q *= 1.0\n    else:  # VA\n        p /= 1000000.0\n        q /= 1000000.0\n    return p, q\n\n\ndef usage():\n    # print(        \"usage: python largeBuilding.py <startDay of year> <duration by day> <time step by seconds(optional, default to 60 seconds)> <metrics record interval by seconds(optional, default to 300 seconds)>\")\n    print(\n        \"usage: python largeBuilding.py <configFile>\")\n\n\ndef convertTimeToSeconds(time):\n    \"\"\"Convert time string with unit to integer in seconds\n\n    It only parse unit in day, hour, minute and second.\n    It will not recognize week, month, year, millisecond, microsecond or nanosecond, they can be added if needed.\n\n    :param time: str\n        time with unit\n    :return: int\n        represent the input time in second\n    \"\"\"\n    unit = filter(lambda x: x.isalpha(), time)\n    timeNum = int(filter(lambda x: x.isdigit(), time))\n    if \"d\" == unit or \"day\" == unit or \"days\" == unit:\n        return 24 * 60 * 60 * timeNum\n    elif \"h\" == unit or \"hour\" == unit or \"hours\" == unit:\n        return 60 * 60 * timeNum\n    elif \"m\" == unit or \"min\" == unit or \"minute\" == unit or \"minutes\" == unit:\n        return 60 * timeNum\n    elif 's' == unit or \"sec\" == unit or \"second\" == unit or \"seconds\" == unit:\n        return timeNum\n    else:\n        raise Exception(\"unrecognized time unit '\" + unit + \"'.\")\n\n\ndef convertTemperatureFromFtoC(t):\n    return t * 9.0 / 5.0 + 32.0\n\nif __name__ == '__main__':\n    # argLength = len(sys.argv)\n    # if argLength == 3:\n    #     startDay = sys.argv[1]\n    #     duration = sys.argv[2]\n    #     startLOSimulation(startDay, duration)\n    # elif argLength == 4:\n    #     startDay = sys.argv[1]\n    #     duration = sys.argv[2]\n    #     timeStep = int(sys.argv[3])\n    #     startLOSimulation(startDay, duration, timeStep)\n    # elif argLength == 5:\n    #     startDay = sys.argv[1]\n    #     duration = sys.argv[2]\n    #     timeStep = int(sys.argv[3])\n    #     metricsRecordInterval = int(sys.argv[4])\n    #     startLOSimulation(startDay, duration, timeStep, metricsRecordInterval)\n    # else:\n    #     usage()\n    #     sys.exit()\n    argLength = len(sys.argv)\n    if argLength == 2:\n        configFile = sys.argv[1]\n        startLOSimulation(configFile)\n    else:\n        usage()\n        sys.exit()", "repo_name": "renzedec/tesp", "sub_path": "third_party/bldgrp/tesp_example/largeBuilding.py", "file_name": "largeBuilding.py", "file_ext": "py", "file_size_in_byte": 14752, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "40", "api": [{"api_name": "os.path.isfile", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}, {"api_name": "fncs.initialize", "line_number": 68, "usage_type": "call"}, {"api_name": "fncs.get_events", "line_number": 72, "usage_type": "call"}, {"api_name": "fncs.get_value", "line_number": 74, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 81, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 84, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 87, "usage_type": "call"}, {"api_name": "fncs.time_request", "line_number": 100, "usage_type": "call"}, {"api_name": "metrics_collector.MetricsCollector.factory", "line_number": 110, "usage_type": "call"}, {"api_name": "metrics_collector.MetricsCollector", "line_number": 110, "usage_type": "name"}, {"api_name": "metrics_collector.MetricsStore", "line_number": 118, "usage_type": "call"}, {"api_name": "LargeOffice.LargeOffice", "line_number": 135, "usage_type": "call"}, {"api_name": "fncs.publish", "line_number": 147, "usage_type": "call"}, {"api_name": "fncs.publish", "line_number": 148, "usage_type": "call"}, {"api_name": "metrics_collector.append_data", "line_number": 150, "usage_type": "call"}, {"api_name": "fncs.time_request", "line_number": 172, "usage_type": "call"}, {"api_name": "fncs.get_events", "line_number": 185, "usage_type": "call"}, {"api_name": "fncs.get_value", "line_number": 187, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 194, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 197, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 200, "usage_type": "call"}, {"api_name": "fncs.publish", "line_number": 208, "usage_type": "call"}, {"api_name": "fncs.publish", "line_number": 209, "usage_type": "call"}, {"api_name": "metrics_collector.append_data", "line_number": 211, "usage_type": "call"}, {"api_name": "fncs.time_request", "line_number": 226, "usage_type": "call"}, {"api_name": "fncs.finalize", "line_number": 247, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 275, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 276, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 277, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 279, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 280, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 351, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 353, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 357, "usage_type": "call"}]}
{"seq_id": "35168676200", "text": "import math\nimport os\nimport sys\nfrom urllib.request import urlretrieve\nimport torch\nimport torch.nn as nn\nimport torch.utils.model_zoo as model_zoo\nmodel_urls = {\n    'resnet18': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet18-imagenet.pth',\n    'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth',\n}\n\n\ndef conv3x3(in_channels, out_channels, stride=1):\n    return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride,\n                     padding=1, bias=False)\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(self, in_channels, out_channels, stride=1, downsample=None):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(in_channels, out_channels, stride)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(out_channels, out_channels)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        if self.downsample is not None:\n            residual = self.downsample(x)\n        out += residual\n        out = self.relu(out)\n        return out\n\n\nclass BottleNeck(nn.Module):\n    expansion = 4\n\n    def __init__(self, in_channels, out_channels, stride=1, downsample=None):\n        # kernel size 1, 3, 1\n        # stride 1, 1, 1\n        # padding 0, 1, 0\n        super(BottleNeck, self).__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.conv3 = nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(out_channels * BottleNeck.expansion)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n        return out\n\n\nclass ResNet(nn.Module):\n    def __init__(self, block, num_block, num_classes=512):\n        super(ResNet, self).__init__()\n        self.in_channels = 128\n        self.conv1 = conv3x3(3, 64, stride=2)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.conv2 = conv3x3(64, 64)\n        self.bn2 = nn.BatchNorm2d(64)\n\n        self.conv3 = conv3x3(64, 128)\n        self.bn3 = nn.BatchNorm2d(128)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n        self.layer1 = self._make_layer(block, 64, num_block[0])\n        self.layer2 = self._make_layer(block, 128, num_block[1], 2)\n        self.layer3 = self._make_layer(block, 256, num_block[2], 2)\n        self.layer4 = self._make_layer(block, 512, num_block[3], 2)\n\n        # self.avgpool = nn.AvgPool2d(7)\n        # self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2. / n))\n            elif isinstance(m, nn.BatchNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n\n    def _make_layer(self, block, out_channels, num_block, stride=1):\n        downsample = None\n        if stride != 1 or self.in_channels != out_channels * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.in_channels, out_channels * block.expansion,\n                          kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(out_channels * block.expansion),\n            )\n        layers = []\n        layers.append(block(self.in_channels, out_channels, stride, downsample))\n        self.in_channels = out_channels * block.expansion\n        for i in range(1, num_block):\n            layers.append(block(self.in_channels, out_channels))\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n\n        x = self.conv2(x)\n        x = self.bn2(x)\n        x = self.relu(x)\n\n        x = self.conv3(x)\n        x = self.bn3(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n\n        x2 = self.layer1(x)\n        x3 = self.layer2(x2)\n        x4 = self.layer3(x3)\n        x5 = self.layer4(x4)\n        return x2, x3, x4, x5\n\n\ndef resnet18(pretrained=False):\n    model = ResNet(BasicBlock, [2, 2, 2, 2])\n    if pretrained:\n        print('load from imagenet')\n        model.load_state_dict(load_url(model_urls['resnet18']), strict=False)\n    return model\n\n\ndef resnet50(pretrained=False):\n    model = ResNet(BottleNeck, [3, 4, 6, 3])\n    if pretrained:\n        model.load_state_dict(load_url(model_urls['resnet50']), strict=False)\n    return model\n\n\ndef load_url(url, model_dir='./pre_model', map_location=None):\n    if not os.path.exists(model_dir):\n        os.makedirs(model_dir)\n    filename = url.split('/')[-1]\n    cached_file = os.path.join(model_dir, filename)\n    if not os.path.exists(cached_file):\n        sys.stderr.write('Downloading: {} to {}\\n'.format(url, cached_file))\n        urlretrieve(url, cached_file)\n    return torch.load(cached_file, map_location=map_location)\n\n\nif __name__ == '__main__':\n    model = resnet18(pretrained=False)\n    img = torch.randn(1, 3, 640, 640)\n    output = model(img)\n    for i in range(4):\n        print(output[i].size())\n", "repo_name": "hieptran1812/VCMS-Backend", "sub_path": "src/models_all/ocr/det_module/model/resnet_old.py", "file_name": "resnet_old.py", "file_ext": "py", "file_size_in_byte": 6135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "torch.nn.Conv2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 110, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 130, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path", "line_number": 172, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 174, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 174, "usage_type": "attribute"}, {"api_name": "urllib.request.urlretrieve", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 181, "usage_type": "call"}]}
{"seq_id": "7772521983", "text": "import numpy as np\nimport matplotlib\nfrom matplotlib import pyplot as plt\nfrom astropy import units as u\nfrom astropy.coordinates import SkyCoord\nfrom datetime import date, timedelta\nimport h5py\n\n# from tqdm import tqdm as progress_bar\nfrom multiprocessing import Pool, cpu_count\n\nfrom fancy.interfaces.stan import coord_to_uv, uv_to_coord\n\nfrom fancy.plotting import AllSkyMap\nfrom fancy.interfaces.utils import get_nucleartable, fischer_int_eq_P\n\ntry:\n\n    import crpropa\n\nexcept ImportError:\n\n    crpropa = None\n\n__all__ = [\"Uhecr\"]\n\n\nclass Uhecr:\n    \"\"\"\n    Stores the data and parameters for UHECRs\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Initialise empty container.\n        \"\"\"\n\n        self.properties = None\n        self.source_labels = None\n\n        self.nuc_table = get_nucleartable()\n        self.nthreads = int(0.75 * cpu_count())\n\n    def _get_angerr(self):\n        \"\"\"Get angular reconstruction uncertainty from label\"\"\"\n\n        if self.label == \"TA2015\":\n            from fancy.detector.TA2015 import sig_omega\n        elif self.label == \"auger2014\":\n            from fancy.detector.auger2014 import sig_omega\n        elif self.label == \"auger2010\":\n            from fancy.detector.auger2010 import sig_omega\n        else:\n            raise Exception(\"Undefined detector type!\")\n\n        return np.deg2rad(sig_omega)\n\n    def from_data_file(\n        self, filename, label, ptype=\"p\", gmf_model=\"JF12\", exp_factor=1.0\n    ):\n        \"\"\"\n        Define UHECR from data file of original information.\n\n        Handles calculation of observation periods and\n        effective areas assuming the UHECR are detected\n        by the Pierre Auger Observatory or TA.\n\n        :param filename: name of the data file\n        :param label: reference label for the UHECR data set\n        \"\"\"\n\n        self.label = label\n\n        with h5py.File(filename, \"r\") as f:\n\n            data = f[self.label]\n\n            self.year = data[\"year\"][()]\n            self.day = data[\"day\"][()]\n            self.zenith_angle = np.deg2rad(data[\"theta\"][()])\n            self.energy = data[\"energy\"][()]\n            self.N = len(self.energy)\n            glon = data[\"glon\"][()]\n            glat = data[\"glat\"][()]\n            self.coord = self.get_coordinates(glon, glat)\n\n            # check if we can extract exposure of UHECR (auger2022 dataset)\n            if \"exposure\" in data:\n                self.exposure = data[\"exposure\"][()]\n\n            self.unit_vector = coord_to_uv(self.coord)\n            self.period = self._find_period()\n            self.A = self._find_area(exp_factor)\n\n            self.ptype = ptype\n            gmf_model = \"JF12\" if gmf_model == \"None\" else gmf_model\n            if \"kappa_gmf\" in data:\n                self.kappa_gmf = data[\"kappa_gmf\"][gmf_model][ptype][\"kappa_gmf\"][()]\n\n    def _get_properties(self):\n        \"\"\"\n        Convenience function to pack object into dict.\n        \"\"\"\n\n        self.properties = {}\n        self.properties[\"label\"] = self.label\n        self.properties[\"N\"] = self.N\n        self.properties[\"unit_vector\"] = self.unit_vector\n        self.properties[\"energy\"] = self.energy\n        self.properties[\"A\"] = self.A\n        self.properties[\"zenith_angle\"] = self.zenith_angle\n        self.properties[\"ptype\"] = self.ptype\n        self.properties[\"kappa_gmf\"] = self.kappa_gmf\n\n        # Only if simulated UHECRs\n        # if isinstance(self.source_labels, (list, np.ndarray)):\n        #     self.properties['source_labels'] = self.source_labels\n\n    def from_properties(self, uhecr_properties):\n        \"\"\"\n        Define UHECR from properties dict.\n\n        :param uhecr_properties: dict containing UHECR properties.\n        :param label: identifier\n        \"\"\"\n\n        self.label = uhecr_properties[\"label\"]\n\n        # Read from input dict\n        self.N = uhecr_properties[\"N\"]\n        self.unit_vector = uhecr_properties[\"unit_vector\"]\n        self.energy = uhecr_properties[\"energy\"]\n        self.zenith_angle = uhecr_properties[\"zenith_angle\"]\n        self.A = uhecr_properties[\"A\"]\n        self.kappa_gmf = uhecr_properties[\"kappa_gmf\"]\n\n        # decode byte string if uhecr_properties is read from h5 file\n        ptype_from_file = uhecr_properties[\"ptype\"]\n        self.ptype = (\n            ptype_from_file.decode(\"UTF-8\")\n            if isinstance(ptype_from_file, bytes)\n            else ptype_from_file\n        )\n\n        # Only if simulated UHECRs\n        # try:\n        #     self.source_labels = uhecr_properties['source_labels']\n        # except:\n        #     pass\n\n        # Get SkyCoord from unit_vector\n        self.coord = uv_to_coord(self.unit_vector)\n\n    def from_simulation(self, uhecr_properties):\n        \"\"\"\n        Define UHECR from properties dict, evaluated from simulating\n        dataset.\n\n        Only real difference to from_properties() is in kappa_gmf,\n        since evaluation of it depends on the parameters initialized\n        for Uhecr().\n\n        :param uhecr_properties: dict containing UHECR properties.\n        :param label: identifier\n        \"\"\"\n\n        self.label = uhecr_properties[\"label\"]\n\n        # Read from input dict\n        self.N = uhecr_properties[\"N\"]\n        self.unit_vector = uhecr_properties[\"unit_vector\"]\n        self.energy = uhecr_properties[\"energy\"]\n        self.zenith_angle = uhecr_properties[\"zenith_angle\"]\n        self.A = uhecr_properties[\"A\"]\n\n        # decode byte string if uhecr_properties is read from h5 file\n        ptype_from_file = uhecr_properties[\"ptype\"]\n        self.ptype = (\n            ptype_from_file.decode(\"UTF-8\")\n            if isinstance(ptype_from_file, bytes)\n            else ptype_from_file\n        )\n\n        # Only if simulated UHECRs\n        # try:\n        #     self.source_labels = uhecr_properties['source_labels']\n        # except:\n        #     pass\n\n        # Get SkyCoord from unit_vector\n        self.coord = uv_to_coord(self.unit_vector)\n        # kappa_gmf set to zero array by default, if joint+gmf then\n        # evaluated in analysis.simulate\n        self.kappa_gmf = np.zeros(self.N)\n\n    def plot(self, skymap: AllSkyMap, size=2):\n        \"\"\"\n        Plot the Uhecr instance on a skymap.\n\n        Called by Data.show()\n\n        :param skymap: the AllSkyMap\n        :param size: tissot radius\n        :param source_labels: source labels (int)\n        \"\"\"\n\n        lons = self.coord.galactic.l.deg\n        lats = self.coord.galactic.b.deg\n\n        alpha_level = 0.7\n\n        # If source labels are provided, plot with colour\n        # indicating the source label.\n        if isinstance(self.source_labels, (list, np.ndarray)):\n\n            Nc = max(self.source_labels)\n\n            # Use a continuous cmap\n            cmap = plt.cm.get_cmap(\"plasma\", Nc)\n\n            write_label = True\n\n            for lon, lat, lab in np.nditer([lons, lats, self.source_labels]):\n                color = cmap(lab)\n                if write_label:\n                    skymap.tissot(\n                        lon,\n                        lat,\n                        size,\n                        npts=30,\n                        color=color,\n                        lw=0,\n                        alpha=0.5,\n                        label=self.label,\n                    )\n                    write_label = False\n                else:\n                    skymap.tissot(\n                        lon, lat, size, npts=30, color=color, lw=0, alpha=0.5\n                    ),\n\n        # Otherwise, use the cmap to show the UHECR energy.\n        else:\n\n            # use colormap for energy\n            norm_E = matplotlib.colors.Normalize(min(self.energy), max(self.energy))\n            cmap = plt.cm.get_cmap(\"viridis\", len(self.energy))\n\n            write_label = True\n            for E, lon, lat in np.nditer([self.energy, lons, lats]):\n\n                color = cmap(norm_E(E))\n\n                if write_label:\n                    skymap.tissot(\n                        lon,\n                        lat,\n                        size,\n                        npts=30,\n                        color=color,\n                        lw=0,\n                        alpha=alpha_level,\n                        label=self.label,\n                    )\n                    write_label = False\n                else:\n                    skymap.tissot(\n                        lon,\n                        lat,\n                        size,\n                        npts=30,\n                        color=color,\n                        lw=0,\n                        alpha=alpha_level,\n                    )\n\n    def save(self, file_handle):\n        \"\"\"\n        Save to the passed H5py file handle,\n        i.e. something that cna be used with\n        file_handle.create_dataset()\n\n        :param file_handle: file handle\n        \"\"\"\n\n        self._get_properties()\n\n        for key, value in self.properties.items():\n            file_handle.create_dataset(key, data=value)\n\n    def _find_area(self, exp_factor):\n        \"\"\"\n        Find the effective area of the observatory at\n        the time of detection.\n\n        Possible areas are calculated from the exposure reported\n        in Abreu et al. (2010) or Collaboration et al. 2014.\n        \"\"\"\n\n        if self.label == \"auger2010\":\n            from ..detector.auger2010 import A1, A2, A3\n\n            possible_areas = [A1, A2, A3]\n            area = [possible_areas[i - 1] * exp_factor for i in self.period]\n\n        elif self.label == \"auger2014\":\n            from ..detector.auger2014 import (\n                A1,\n                A2,\n                A3,\n                A4,\n                A1_incl,\n                A2_incl,\n                A3_incl,\n                A4_incl,\n            )\n\n            possible_areas_vert = [A1, A2, A3, A4]\n            possible_areas_incl = [A1_incl, A2_incl, A3_incl, A4_incl]\n\n            # find area depending on period and incl\n            area = []\n            for i, p in enumerate(self.period):\n                if self.zenith_angle[i] <= 60:\n                    area.append(possible_areas_vert[p - 1] * exp_factor)\n                if self.zenith_angle[i] > 60:\n                    area.append(possible_areas_incl[p - 1] * exp_factor)\n\n        elif self.label == \"auger2022\":\n            from ..detector.auger2022 import M, period_start\n            \n            # get period for each event - in years, taking into account days\n            start_julianyear = period_start.year + period_start.day / 365.25\n            deltats = (self.year + self.day / 365.25) - start_julianyear\n\n            area = self.exposure / (M * deltats)\n\n        elif self.label == \"TA2015\":\n            from ..detector.TA2015 import A1, A2\n\n            possible_areas = [A1, A2]\n            area = [possible_areas[i - 1] * exp_factor for i in self.period]\n\n        else:\n            print(\"Error: effective areas and periods not defined\")\n\n        return area\n\n    def _find_period(self):\n        \"\"\"\n        For a given year or day, find UHECR period based on dates\n        in table 1 in Abreu et al. (2010) or in Collaboration et al. 2014.\n        \"\"\"\n\n        period = []\n        if self.label == \"auger2014\":\n            from ..detector.auger2014 import (\n                period_1_start,\n                period_1_end,\n                period_2_start,\n                period_2_end,\n                period_3_start,\n                period_3_end,\n                period_4_start,\n                period_4_end,\n            )\n\n            # check dates\n            for y, d in np.nditer([self.year, self.day]):\n                d = int(d)\n                test_date = date(y, 1, 1) + timedelta(d)\n\n                if period_1_start <= test_date <= period_1_end:\n                    period.append(1)\n                elif period_2_start <= test_date <= period_2_end:\n                    period.append(2)\n                elif period_3_start <= test_date <= period_3_end:\n                    period.append(3)\n                elif test_date >= period_3_end:\n                    period.append(4)\n                else:\n                    print(\"Error: cannot determine period for year\", y, \"and day\", d)\n\n        elif self.label == \"TA2015\":\n            from ..detector.TA2015 import (\n                period_1_start,\n                period_1_end,\n                period_2_start,\n                period_2_end,\n            )\n\n            for y, d in np.nditer([self.year, self.day]):\n                d = int(d)\n                test_date = date(y, 1, 1) + timedelta(d)\n\n                if period_1_start <= test_date <= period_1_end:\n                    period.append(1)\n                elif period_2_start <= test_date <= period_2_end:\n                    period.append(2)\n                elif test_date >= period_2_end:\n                    period.append(2)\n                else:\n                    print(\"Error: cannot determine period for year\", y, \"and day\", d)\n\n        return period\n\n    def select_period(self, period):\n        \"\"\"\n        Select certain periods for analysis, other periods will be discarded.\n        \"\"\"\n\n        # find selected periods\n        if len(period) == 1:\n            selection = np.where(np.asarray(self.period) == period[0])\n        if len(period) == 2:\n            selection = np.concatenate(\n                [\n                    np.where(np.asarray(self.period) == period[0]),\n                    np.where(np.asarray(self.period) == period[1]),\n                ],\n                axis=1,\n            )\n\n        # keep things as lists\n        selection = selection[0].tolist()\n\n        # make selection\n        self.A = [self.A[i] for i in selection]\n        self.period = [self.period[i] for i in selection]\n        self.energy = [self.energy[i] for i in selection]\n        self.incidence_angle = [self.incidence_angle[i] for i in selection]\n        self.unit_vector = [self.unit_vector[i] for i in selection]\n\n        self.N = len(self.period)\n\n        self.day = [self.day[i] for i in selection]\n        self.year = [self.year[i] for i in selection]\n\n        self.coord = self.coord[selection]\n\n    def select_energy(self, Eth):\n        \"\"\"\n        Select out only UHECRs above a certain energy.\n        \"\"\"\n\n        selection = np.where(np.asarray(self.energy) >= Eth)\n        selection = selection[0].tolist()\n\n        # make selection\n        self.A = [self.A[i] for i in selection]\n        self.period = [self.period[i] for i in selection]\n        self.energy = [self.energy[i] for i in selection]\n        self.incidence_angle = [self.incidence_angle[i] for i in selection]\n        self.unit_vector = [self.unit_vector[i] for i in selection]\n\n        self.N = len(self.period)\n\n        self.day = [self.day[i] for i in selection]\n        self.year = [self.year[i] for i in selection]\n\n        self.coord = self.coord[selection]\n\n    def get_coordinates(self, glon, glat, D=None):\n        \"\"\"\n        Convert glon and glat to astropy SkyCoord\n        Add distance if possible (allows conversion to cartesian coords)\n\n        :return: astropy.coordinates.SkyCoord\n        \"\"\"\n\n        if D:\n            return SkyCoord(\n                l=glon * u.degree,\n                b=glat * u.degree,\n                frame=\"galactic\",\n                distance=D * u.mpc,\n            )\n        else:\n            return SkyCoord(l=glon * u.degree, b=glat * u.degree, frame=\"galactic\")\n\n    # Anatoli 2022.04.30: The GMF propagation has been moved out from this class\n    # because it doesn't really belong here and it makes things more complicated\n    # to maintain backpropagation.\n\n    # def build_kappa_gmf(self, uhecr_file=None, particle_type=\"all\", args=None):\n    #     \"\"\"\n    #     Evaluate spread parameter for GMF for each UHECR dataset.\n    #     Note that as of now, UHECR dataset label coincides with\n    #     names from fancy/detector. The output is written to uhecr_file,\n    #     which can then be accessed later with analysis.use_tables().\n\n    #     If all_particles is True, create kappa_gmf tables for each element given in\n    #     fancy/interfaces/nuclear_table.pkl.\n\n    #     All arrival directions are given in terms of galactic coordinates (lon, lat)\n    #     with respect to mpl: lon \\in [-pi,pi], lat \\in [-pi/2, pi/2]\n    #     \"\"\"\n    #     # there must be a better way to do this...\n    #     if args is not None:\n    #         Nrand, gmf, plot_true = args\n    #     else:\n    #         Nrand, gmf, plot_true = 100, \"JF12\", False\n\n    #     omega_true = np.zeros((len(self.coord.galactic.l.rad), 2))\n    #     omega_true[:, 0] = np.pi - self.coord.galactic.l.rad\n    #     omega_true[:, 1] = self.coord.galactic.b.rad\n\n    #     if particle_type == \"all\":\n    #         kappa_gmf_args_list = [\n    #             (ptype, Nrand, gmf, plot_true) for ptype in list(self.nuc_table.keys())\n    #         ]\n\n    #         with Pool(self.nthreads) as mpool:\n    #             results = list(\n    #                 progress_bar(\n    #                     mpool.imap(self.build_kappa_gmf_ptype, kappa_gmf_args_list),\n    #                     total=len(kappa_gmf_args_list),\n    #                     desc=\"Precomputing kappa_gmf for each composition\",\n    #                 )\n    #             )\n\n    #         with h5py.File(uhecr_file, \"r+\") as f:\n    #             uhecr_dataset_group = f[self.label]\n\n    #             if \"kappa_gmf\" in list(uhecr_dataset_group.keys()):\n    #                 kappa_gmf_group = f[self.label + \"/kappa_gmf\"]\n    #             else:\n    #                 kappa_gmf_group = uhecr_dataset_group.create_group(\"kappa_gmf\")\n\n    #             gmf_field_group = kappa_gmf_group.create_group(gmf)\n\n    #             for i, ptype in enumerate(list(self.nuc_table.keys())):\n    #                 particle_group = gmf_field_group.create_group(ptype)\n    #                 particle_group.create_dataset(\"kappa_gmf\", data=results[i][0])\n    #                 particle_group.create_dataset(\"omega_gal\", data=results[i][1])\n    #                 particle_group.create_dataset(\"omega_rand\", data=results[i][2])\n    #                 particle_group.create_dataset(\"omega_true\", data=omega_true)\n    #                 particle_group.create_dataset(\"kappa_gmf_rand\", data=results[i][3])\n\n    #     else:\n    #         kappa_gmf, omega_rand, omega_gal, kappa_gmf_rand = self.eval_kappa_gmf(\n    #             particle_type, Nrand, gmf, plot_true\n    #         )\n\n    #         if uhecr_file:\n    #             with h5py.File(uhecr_file, \"r+\") as f:\n    #                 uhecr_dataset_group = f[self.label]\n    #                 if \"kappa_gmf\" in list(uhecr_dataset_group.keys()):\n    #                     kappa_gmf_group = f[self.label + \"/kappa_gmf\"]\n    #                 else:\n    #                     kappa_gmf_group = uhecr_dataset_group.create_group(\"kappa_gmf\")\n    #                 gmf_field_group = kappa_gmf_group.create_group(gmf)\n    #                 particle_group = gmf_field_group.create_group(particle_type)\n    #                 particle_group.create_dataset(\"kappa_gmf\", data=kappa_gmf)\n    #                 particle_group.create_dataset(\"omega_gal\", data=omega_gal)\n    #                 particle_group.create_dataset(\"omega_rand\", data=omega_rand)\n    #                 particle_group.create_dataset(\"omega_true\", data=omega_true)\n    #                 particle_group.create_dataset(\"kappa_gmf_rand\", data=kappa_gmf_rand)\n\n    # def build_kappa_gmf_ptype(self, kappa_gmf_args):\n    #     \"\"\"Simple wrapper around eval_kappa_gmf so that Pool works.\"\"\"\n    #     (\n    #         kappa_gmf_ptype,\n    #         defl_arrdirs_ptype,\n    #         rand_arrdirs_ptype,\n    #         kappa_gmf_rand_ptype,\n    #     ) = self.eval_kappa_gmf(*kappa_gmf_args)\n\n    #     return (\n    #         kappa_gmf_ptype,\n    #         defl_arrdirs_ptype,\n    #         rand_arrdirs_ptype,\n    #         kappa_gmf_rand_ptype,\n    #     )\n\n    # def eval_kappa_gmf(self, particle_type=\"p\", Nrand=100, gmf=\"JF12\", plot=False):\n    #     \"\"\"\n    #     Evaluate spread parameter between arrival direction\n    #     and deflected direction at galactic magnetic field.\n    #     Returns kappa_gmf, deflected and undeflected arrival vectors\n    #     \"\"\"\n\n    #     # get angular reconstruction uncertainty\n    #     ang_err = self._get_angerr()\n    #     # evaluate vector3d object of arrival direction\n    #     uhecr_vector3d = self.coord_to_vector3d()\n    #     # prepare inputs / initializations for CRPropa simulation\n    #     sim, pid, R, pos = self._prepare_crpropasim(particle_type, gmf)\n\n    #     # arrival directions, mainly for plotting\n    #     rand_arrdirs = np.zeros((self.N, Nrand, 2))\n    #     defl_arrdirs = np.zeros((self.N, Nrand, 2))\n\n    #     # evaluated kappa_gmf\n    #     kappa_gmfs = np.zeros((self.N, Nrand))\n\n    #     for i, arr_dir in enumerate(uhecr_vector3d):\n    #         energy = self.energy[i] * crpropa.EeV\n    #         for j in range(Nrand):\n    #             rand_arrdir = R.randVectorAroundMean(arr_dir, ang_err)\n    #             c = crpropa.Candidate(\n    #                 crpropa.ParticleState(pid, energy, pos, rand_arrdir)\n    #             )\n    #             sim.run(c)\n\n    #             defl_dir = c.current.getDirection()\n\n    #             # append longitudes and latitudes\n    #             # need to append np.pi / 2 - theta for latitude\n    #             # also append the randomized arrival direction in lons and lats\n    #             rand_arrdirs[i, j, :] = (\n    #                 rand_arrdir.getPhi(),\n    #                 np.pi / 2.0 - rand_arrdir.getTheta(),\n    #             )\n    #             defl_arrdirs[i, j, :] = (\n    #                 defl_dir.getPhi(),\n    #                 np.pi / 2.0 - defl_dir.getTheta(),\n    #             )\n\n    #             # evaluate dot product between arrival direction (randomized) and deflected vector\n    #             # dot exists with Vector3d() objects\n    #             cos_theta = rand_arrdir.dot(defl_dir)\n\n    #             # use scipy.optimize.root to get kappa_gmf using dot product\n    #             # P = 0.683 as per Soiaporn paper\n    #             sol = root(fischer_int_eq_P, x0=1, args=(cos_theta, 0.683))\n    #             # print(sol)   # check solution\n    #             kappa_gmf = sol.x[0]\n\n    #             if kappa_gmf < 0:\n    #                 kappa_gmf = 1e-15  # set some lower limit\n\n    #             kappa_gmfs[i, j] = kappa_gmf\n\n    #     if plot:\n    #         self.plot_deflections(defl_arrdirs, rand_arrdirs)\n\n    #     # evaluate mean kappa_gmf for each uhecr\n    #     kappa_gmf_mean = np.mean(kappa_gmfs, axis=1)\n    #     # set lower limit to ~0 due to how vMF distribution is defined\n    #     kappa_gmf_mean[kappa_gmf_mean < 0] = 1e-15  # ~0 due to log(kappa) evaluation\n\n    #     return kappa_gmf_mean, defl_arrdirs, rand_arrdirs, kappa_gmfs\n\n    def coord_to_vector3d(self):\n        \"\"\"Convert from SkyCoord array to Vector3d list\"\"\"\n\n        if not crpropa:\n\n            raise ImportError(\"CRPropa3 must be installed to use this functionality\")\n\n        uhecr_vector3d = []\n        # due to how SkyCoord defines coordinates,\n        # lon_vector3d = pi - lon_skycoord\n        # lat_vector3d = pi/2 - lat_skycoord\n        for coord in self.coord:\n            v = crpropa.Vector3d()\n            v.setRThetaPhi(\n                1, np.pi / 2.0 - coord.galactic.b.rad, np.pi - coord.galactic.l.rad\n            )\n            uhecr_vector3d.append(v)\n        return uhecr_vector3d\n\n    # def _prepare_crpropasim(self, particle_type=\"p\", model_name=\"JF12\", seed=691342):\n    #     \"\"\"Set up CRPropa simulation with some magnetic field model\"\"\"\n    #     sim = crpropa.ModuleList()\n\n    #     # setup magnetic field\n    #     if model_name == \"JF12\":  # JanssonFarrar2012\n    #         gmf = crpropa.JF12Field()\n    #         gmf.randomStriated(seed)\n    #         gmf.randomTurbulent(seed)\n\n    #     elif model_name == \"PT11\":  # Pshirkov2011\n    #         gmf = crpropa.PT11Field()\n    #         # gmf.setUseASS(True)  # Axisymmetric\n\n    #     else:  # default with JF12\n    #         gmf = crpropa.JF12Field()\n    #         gmf.randomStriated(seed)\n    #         gmf.randomTurbulent(seed)\n\n    #     # Propagation model, parameters: (B-field model, target error, min step, max step)\n    #     sim.add(\n    #         crpropa.PropagationCK(gmf, 1e-4, 0.1 * crpropa.parsec, 100 * crpropa.parsec)\n    #     )\n    #     obs = crpropa.Observer()\n\n    #     # observer at galactic boundary (20 kpc)\n    #     obs.add(\n    #         crpropa.ObserverSurface(\n    #             crpropa.Sphere(crpropa.Vector3d(0), 20 * crpropa.kpc)\n    #         )\n    #     )\n    #     sim.add(obs)\n\n    #     # A, Z = self.get_AZ(nuc=particle_type)\n    #     A, Z = self.nuc_table[particle_type]\n\n    #     # composition\n    #     pid = -crpropa.nucleusId(A, Z)\n\n    #     # CRPropa random number generator\n    #     crpropa_randgen = crpropa.Random()\n\n    #     # position of earth in galactic coordinates\n    #     pos_earth = crpropa.Vector3d(-8.5, 0, 0) * crpropa.kpc\n\n    #     return sim, pid, crpropa_randgen, pos_earth\n\n    def plot_deflections(self, defl_arrdirs, rand_arrdirs):\n        \"\"\"Plot deflections for particular UHECR dataset\"\"\"\n        # check with basic mpl mollweide projection\n        plt.figure(figsize=(12, 7))\n        ax = plt.subplot(111, projection=\"mollweide\")\n\n        # get lon and lat arrays for future reference\n        # shift lons by 180. due to how its defined in mpl\n        uhecr_lons = np.pi - self.coord.galactic.l.rad\n        uhecr_lats = self.coord.galactic.b.rad\n\n        ax.scatter(\n            uhecr_lons,\n            uhecr_lats,\n            color=\"k\",\n            marker=\"+\",\n            s=10.0,\n            alpha=1.0,\n            label=\"True\",\n        )\n        for i in range(self.N):\n            ax.scatter(\n                defl_arrdirs[i, :, 0],\n                defl_arrdirs[i, :, 1],\n                color=\"b\",\n                alpha=0.05,\n                s=4.0,\n            )\n            ax.scatter(\n                rand_arrdirs[i, :, 0],\n                rand_arrdirs[i, :, 1],\n                color=\"r\",\n                alpha=0.05,\n                s=4.0,\n            )\n\n        # TODO: add labels based on color\n        ax.grid()\n", "repo_name": "cescalara/fancy", "sub_path": "fancy/interfaces/uhecr.py", "file_name": "uhecr.py", "file_ext": "py", "file_size_in_byte": 26182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "40", "api": [{"api_name": "fancy.interfaces.utils.get_nucleartable", "line_number": 41, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 56, "usage_type": "call"}, {"api_name": "fancy.detector.auger2010.sig_omega", "line_number": 56, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 80, "usage_type": "call"}, {"api_name": "fancy.interfaces.stan.coord_to_uv", "line_number": 91, "usage_type": "call"}, {"api_name": "fancy.interfaces.stan.uv_to_coord", "line_number": 152, "usage_type": "call"}, {"api_name": "fancy.interfaces.stan.uv_to_coord", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 194, "usage_type": "call"}, {"api_name": "fancy.plotting.AllSkyMap", "line_number": 196, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 214, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 219, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "numpy.nditer", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 246, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 247, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "numpy.nditer", "line_number": 250, "usage_type": "call"}, {"api_name": "detector.auger2010.A1", "line_number": 303, "usage_type": "name"}, {"api_name": "detector.auger2010.A2", "line_number": 303, "usage_type": "name"}, {"api_name": "detector.auger2010.A3", "line_number": 303, "usage_type": "name"}, {"api_name": "detector.auger2014.A1", "line_number": 318, "usage_type": "name"}, {"api_name": "detector.auger2014.A2", "line_number": 318, "usage_type": "name"}, {"api_name": "detector.auger2014.A3", "line_number": 318, "usage_type": "name"}, {"api_name": "detector.auger2014.A4", "line_number": 318, "usage_type": "name"}, {"api_name": "detector.auger2014.A1_incl", "line_number": 319, "usage_type": "name"}, {"api_name": "detector.auger2014.A2_incl", "line_number": 319, "usage_type": "name"}, {"api_name": "detector.auger2014.A3_incl", "line_number": 319, "usage_type": "name"}, {"api_name": "detector.auger2014.A4_incl", "line_number": 319, "usage_type": "name"}, {"api_name": "detector.auger2022.period_start.year", "line_number": 333, "usage_type": "attribute"}, {"api_name": "detector.auger2022.period_start", "line_number": 333, "usage_type": "name"}, {"api_name": "detector.auger2022.period_start.day", "line_number": 333, "usage_type": "attribute"}, {"api_name": "detector.auger2022.M", "line_number": 336, "usage_type": "name"}, {"api_name": "detector.TA2015.A1", "line_number": 341, "usage_type": "name"}, {"api_name": "detector.TA2015.A2", "line_number": 341, "usage_type": "name"}, {"api_name": "numpy.nditer", "line_number": 369, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 371, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 371, "usage_type": "call"}, {"api_name": "detector.auger2014.period_1_start", "line_number": 373, "usage_type": "name"}, {"api_name": "detector.auger2014.period_1_end", "line_number": 373, "usage_type": "name"}, {"api_name": "detector.auger2014.period_2_start", "line_number": 375, "usage_type": "name"}, {"api_name": "detector.auger2014.period_2_end", "line_number": 375, "usage_type": "name"}, {"api_name": "detector.auger2014.period_3_start", "line_number": 377, "usage_type": "name"}, {"api_name": "detector.auger2014.period_3_end", "line_number": 377, "usage_type": "name"}, {"api_name": "detector.auger2014.period_3_end", "line_number": 379, "usage_type": "name"}, {"api_name": "numpy.nditer", "line_number": 392, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 394, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 394, "usage_type": "call"}, {"api_name": "detector.TA2015.period_1_start", "line_number": 396, "usage_type": "name"}, {"api_name": "detector.TA2015.period_1_end", "line_number": 396, "usage_type": "name"}, {"api_name": "detector.TA2015.period_2_start", "line_number": 398, "usage_type": "name"}, {"api_name": "detector.TA2015.period_2_end", "line_number": 398, "usage_type": "name"}, {"api_name": "detector.TA2015.period_2_end", "line_number": 400, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 446, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 446, "usage_type": "call"}, {"api_name": "astropy.coordinates.SkyCoord", "line_number": 472, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 473, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 473, "usage_type": "name"}, {"api_name": "astropy.units.degree", "line_number": 474, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 474, "usage_type": "name"}, {"api_name": "astropy.units.mpc", "line_number": 476, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 476, "usage_type": "name"}, {"api_name": "astropy.coordinates.SkyCoord", "line_number": 479, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 479, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 479, "usage_type": "name"}, {"api_name": "crpropa.Vector3d", "line_number": 657, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 659, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 714, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 714, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 715, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 715, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 719, "usage_type": "attribute"}]}
{"seq_id": "38282561371", "text": "\n\nfrom django.contrib import admin\nfrom django.urls import path\n\nfrom . import views\napp_name = \"courses\"\n\nurlpatterns = [\n    \n\tpath('', views.courses, name = 'list'),\n\tpath('<int:id>/details', views.course_details, name = 'details'),\n\tpath('create', views.course_create, name = 'create'),\n\tpath('<int:id>/update', views.course_update, name = 'update'),\n\tpath('<int:id>/delete', views.course_delete, name = 'delete'),\n\n]\n", "repo_name": "tarekhasan2/E-learning", "sub_path": "learncoding/courses/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 422, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}]}
{"seq_id": "1966044210", "text": "\"\"\"\nIn a given grid, each cell can have one of three values:\n\n-> the value 0 representing an empty cell;\n-> the value 1 representing a fresh orange;\n-> the value 2 representing a rotten orange.\n\nEvery minute, any fresh orange that is adjacent (4-directionally) to a rotten orange becomes rotten.\n\nReturn the minimum number of minutes that must elapse until no cell has a fresh orange.  If this\nis impossible, return -1 instead.\n\"\"\"\n\nfrom typing import List, Tuple\nfrom collections import deque\n\n\ndef get_neighbours(i: int,\n                   j: int,\n                   length: int,\n                   width: int) -> List[Tuple[int, int]]:\n\n    neighbours = []\n\n    if i > 0:\n        neighbours.append((i - 1, j))\n\n    if i < length - 1:\n        neighbours.append((i + 1, j))\n\n    if j > 0:\n        neighbours.append((i, j - 1))\n\n    if j < width - 1:\n        neighbours.append((i, j + 1))\n\n    return neighbours\n\n\ndef get_minutes_to_rot(oranges: List[List[int]]):\n\n    if not oranges or not oranges[0]:\n        # null grid edge case\n        return 0\n\n    visited = set()\n    node_queue = deque()\n    fresh_orange_count = 0\n    minutes = 0\n\n    for i in range(len(oranges)):\n        for j in range(len(oranges[0])):\n\n            if oranges[i][j] == 1:\n                fresh_orange_count += 1\n            elif oranges[i][j] == 2:\n                node_queue.append((i, j))\n                visited.add((i, j))\n\n    if fresh_orange_count == 0:\n        # no oranges to rot...\n        return 0\n\n    while True:\n        counter = len(node_queue)\n        while counter > 0:\n            current = node_queue.popleft()\n            neighbours = get_neighbours(*current, len(oranges), len(oranges[0]))\n            for node in neighbours:\n                if node not in visited:\n                    visited.add(node)\n                    if oranges[node[0]][node[1]] == 1:\n                        node_queue.append(node)\n                        fresh_orange_count -= 1\n\n            counter -= 1\n\n        if node_queue:\n            minutes += 1\n        else:\n            break\n\n    if fresh_orange_count > 0:\n        return - 1\n    else:\n        return minutes\n\n\nclass Solution:\n    def orangesRotting(self, grid: List[List[int]]) -> int:\n        return get_minutes_to_rot(grid)\n\n\nif __name__ == \"__main__\":\n\n    arr = [[0, 2]]\n    print(get_minutes_to_rot(arr))\n", "repo_name": "JoeLove100/leet-code", "sub_path": "misc/rotting_oranges.py", "file_name": "rotting_oranges.py", "file_ext": "py", "file_size_in_byte": 2347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 47, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 90, "usage_type": "name"}]}
{"seq_id": "35663477755", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n\"\"\"\nListing页面广告位置分析\ndate:2022-06-27\n\"\"\"\n# In[5]:\n\n\nimport time\nimport re\nimport pandas as pd\nfrom pyquery import PyQuery as pq\nfrom selenium import webdriver\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\n\n\n# In[6]:\n\n\ndef select_area_code(driver, code):\n    while True:\n        try:\n            driver.find_element(By.ID, 'glow-ingress-line1').click()\n            time.sleep(2)\n        except Exception as e:\n            driver.refresh()\n            time.sleep(10)\n            continue\n        try:\n            driver.find_element(By.ID, \"GLUXChangePostalCodeLink\").click()\n            time.sleep(1)\n        except:\n            pass\n        try:\n            driver.find_element(By.ID, 'GLUXZipUpdateInput').send_keys(code)\n            time.sleep(1)\n            break\n        except Exception as NoSuchElementException:\n            try:\n                driver.find_element(By.ID, 'GLUXZipUpdateInput_0').send_keys(\n                    postal.split('-')[0])\n                time.sleep(1)\n                driver.find_element(By.ID, 'GLUXZipUpdateInput_1').send_keys(\n                    postal.split('-')[1])\n                time.sleep(1)\n                break\n            except Exception as NoSuchElementException:\n                driver.refresh()\n                time.sleep(5)\n                continue\n        print(\"input area code again\")\n    driver.find_element(By.ID, 'GLUXZipUpdate').click()\n    time.sleep(1)\n    driver.refresh()\n    time.sleep(1)\n\n\n# In[7]:\n\n\ndef driver(asin='B08ZYJ8CRX'):\n    options = webdriver.ChromeOptions()\n    prefs = {\"profile.managed_default_content_settings.images\": 2}\n    options.add_experimental_option(\"prefs\", prefs)\n    options.add_argument(\"--disable-blink-features=AutomationControlled\")\n    options.add_experimental_option(\"excludeSwitches\", ['enable-automation'])\n    driver = webdriver.Chrome(options=options)\n    driver.execute_cdp_cmd(\"Page.addScriptToEvaluateOnNewDocument\", {\n        \"source\": \"\"\"\n                    Object.defineProperty(navigator, 'webdriver', {\n                      get: () => undefined\n                    })\n                  \"\"\"\n    })\n    #     driver = webdriver.Chrome(options=options)\n    driver.implicitly_wait(3)\n    wait = WebDriverWait(driver, 3)\n\n    #get target html\n    area = '10010'\n    tar_url = 'https://www.amazon.com/dp/{}?th=1'.format(asin)\n    driver.get(tar_url)\n    select_area_code(driver, area)\n\n    #ajax 懒加载\n    js = \"return action=document.body.scrollHeight\"\n    height = 0\n    new_height = driver.execute_script(js)\n    while height < new_height:\n        # 将滚动条调整至页面底部\n        for i in range(height, new_height, 100):\n            driver.execute_script('window.scrollTo(0, {})'.format(i))\n            time.sleep(0.1)\n        height = new_height\n        #         time.sleep(2)\n        new_height = driver.execute_script(js)\n    wait.until(\n        EC.presence_of_element_located(\n            (By.CSS_SELECTOR, \"div#dp-ads-center-promo_feature_div\")))\n\n    #define containers\n    sp_label = []\n    con_asin = []\n    con_title = []\n    con_rating = []\n    con_review = []\n    con_price = []\n\n    #Pyquery analysis\n    doc = pq(driver.page_source)\n    items = doc('div[id^=sp_detail][data-a-carousel-options]').items()\n    elements = driver.find_elements(\n        By.CSS_SELECTOR, 'div[id^=sp_detail][data-a-carousel-options]')\n    for item, element in zip(items, elements):\n        label = item('h2.a-carousel-heading').text()\n        label = re.findall(r'(.*)\\n.sp', label)\n        #         print('label:', label)\n        try:\n            max_page = int(item('span.a-carousel-page-max').text())\n        except:\n            break\n        for i in range(max_page):\n            wait.until(\n                EC.presence_of_element_located((\n                    By.CSS_SELECTOR,\n                    'div[id^=sp_detail][data-a-carousel-options] li div[data-asin]'\n                )))\n            doc_child = pq(driver.page_source)\n            items_child = doc_child(\n                'div[id^=sp_detail][data-a-carousel-options]').items()\n            for child in items_child:\n                #                 print('label1:', child('h2').text())\n                #                 return 0\n                label_child = child('h2.a-carousel-heading').text()\n                label_child = re.findall(r'(.*)\\n.sp', label_child)\n                if label_child != label:\n                    break\n                else:\n                    items_child = child('li div[data-asin]').items()\n                    for li in items_child:\n                        asin = li.attr('data-asin')\n                        price = li('span.a-size-medium.a-color-price').text()\n                        title = li('div.sponsored-products-truncator-truncated'\n                                   ).text()\n                        rating = li('i.a-icon.a-icon-star').attr(\n                            'class')  #[-3:].replace('-', '.')\n                        review = li('span.a-color-link').text()\n                        #save\n                        sp_label.append(label)\n                        con_asin.append(asin)\n                        con_title.append(title)\n                        con_rating.append(rating)\n                        con_review.append(review)\n                        con_price.append(price)\n            element.find_element(\n                By.CSS_SELECTOR,\n                'div.a-carousel-right span.a-button-inner').click()\n            time.sleep(2)\n#         break\n\n    res = pd.DataFrame({\n        'SP Label': sp_label,\n        'Asin': con_asin,\n        'Price': con_price,\n        'Title': con_title,\n        'Rating': con_rating,\n        'Review': con_review\n    })\n    return res\n\n\n# In[8]:\n\n\nres = driver('B081MYC174')\nres\n\n\n# In[9]:\n\n\n#导出自己Listing页面的ASIN\nres.to_csv('Listing页面的ASIN.csv')\n\n\n# In[ ]:\n\n\n\n\n", "repo_name": "Wangjingfang/AmamaScript", "sub_path": "AmaScript/Listing页面广告位置分析 2022-06-27.py", "file_name": "Listing页面广告位置分析 2022-06-27.py", "file_ext": "py", "file_size_in_byte": 6032, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 27, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 27, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 34, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 34, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 39, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 39, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 44, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 44, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 47, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 47, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 56, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 56, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 59, "usage_type": "call"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 66, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 66, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 71, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 71, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 81, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 97, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 102, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 102, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 103, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 103, "usage_type": "name"}, {"api_name": "pyquery.PyQuery", "line_number": 114, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 117, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 117, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 120, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 128, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 128, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 129, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 129, "usage_type": "name"}, {"api_name": "pyquery.PyQuery", "line_number": 132, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 139, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 160, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 160, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 162, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 165, "usage_type": "call"}]}
{"seq_id": "2993250211", "text": "import os\n\nfrom cs50 import SQL\nimport sqlite3\nfrom flask import Flask, flash, redirect, render_template, request, session\nfrom flask_session import Session\nfrom tempfile import mkdtemp\nfrom werkzeug.security import check_password_hash, generate_password_hash\n\nfrom helpers import apology, login_required, lookup, usd\n\n# Configure application\napp = Flask(__name__)\n\n# Ensure templates are auto-reloaded\napp.config[\"TEMPLATES_AUTO_RELOAD\"] = True\n\n# Custom filter\napp.jinja_env.filters[\"usd\"] = usd\n\n# Configure session to use filesystem (instead of signed cookies)\napp.config[\"SESSION_PERMANENT\"] = False\napp.config[\"SESSION_TYPE\"] = \"filesystem\"\nSession(app)\n\n# Configure CS50 Library to use SQLite database\ndb = SQL(\"sqlite:///finance.db\")\n\n#Heroku postgres DB\n#uri = os.getenv(\"postgres://lihwoixnwuffzj:35d43f4c69271cc00c4f0531336275819487765c55d9eefe1b42bba292f40b68@ec2-54-208-104-27.compute-1.amazonaws.com:5432/d9pl5vl57f70v7\")\n#if uri.startswith(\"postgres://\"):\n#    uri = uri.replace(\"postgres://\", \"postgresql://\")\n#db = SQL(uri)\n\n# Make sure API key is set\nif not os.environ.get(\"API_KEY\"):\n    raise RuntimeError(\"API_KEY not set\")\n\n@app.after_request\ndef after_request(response):\n    \"\"\"Ensure responses aren't cached\"\"\"\n    response.headers[\"Cache-Control\"] = \"no-cache, no-store, must-revalidate\"\n    response.headers[\"Expires\"] = 0\n    response.headers[\"Pragma\"] = \"no-cache\"\n    return response\n\n@app.route(\"/\")\n@login_required\ndef index():\n    username = session[\"username\"]\n    #Update Stock Prices\n    update_portfolio = db.execute(\"SELECT * FROM purchased WHERE username = ?\", username)\n    for row in update_portfolio:\n        #updates the price\n        db.execute(\"UPDATE purchased SET stock_price = ? WHERE username = ? AND symbol = ?\", lookup(row[\"symbol\"])[\"price\"],username,row[\"symbol\"])\n        db.execute(\"UPDATE purchased SET total_current = ? WHERE username = ? AND purchase_id =?\", (row[\"stock_price\"] * row[\"number_shares\"]),username, row[\"purchase_id\"])\n\n    #Get values from database\n    current_portfolio = db.execute(\"SELECT symbol, number_shares, SUM(number_shares), SUM(total_price_bought), stock_price FROM purchased WHERE username = ? GROUP BY symbol\", username)\n\n    #if no stocks show site\n    if not current_portfolio:\n        #share for placeholder\n        return render_template(\"index.html\")\n\n    #get total\n    total_portfolio = db.execute(\"SELECT SUM(total_price_bought),SUM(total_current) FROM purchased WHERE username = ?\", username)\n\n    #get user info\n    user_data = db.execute(\"SELECT * FROM users WHERE username = ?\", username)\n\n    return render_template(\"index.html\", portfolio=current_portfolio, total_portfolio_shares=total_portfolio, user_info = user_data)\n\n@app.route(\"/buy\", methods=[\"GET\", \"POST\"])\n@login_required\ndef buy():\n    if request.method == \"POST\":\n        username = session[\"username\"]\n\n        #Getting the symbol into global variable so it can pass\n\n        symbol_pre = request.form.get(\"symbol\")\n        symbol = symbol_pre.strip()\n        session[\"symbol\"] = symbol\n\n        shares = request.form.get(\"shares\",type=int)\n        session[\"shares\"] = shares\n\n        if not symbol:\n            return apology(\"Must provide ticker\", 403)\n\n        elif not lookup(symbol):\n            return apology(\"Must provide VALID ticker\", 403)\n\n        elif not shares:\n            return apology(\"Put number of shares\", 403)\n\n        elif (shares < 1):\n            return apology(\"Number of shares must be above 1\", 403)\n\n        #Calculate total price of shares requested to be purchased\n        #print((lookup(symbol)[\"price\"])*shares)\n        total_price = (lookup(symbol)[\"price\"]) * shares\n\n        #Check how much money is in the account\n        cash = db.execute(\"SELECT cash FROM users WHERE id = ?\", session[\"user_id\"])\n        user_cash = cash[0][\"cash\"]\n\n        #If money - shares total price > 0 then make transaction go through and figure out new balance of account and update\n        if ((user_cash - total_price) > 0):\n            new_balance = user_cash - total_price\n            db.execute(\"UPDATE users SET cash = ? WHERE id = ?\", new_balance, session[\"user_id\"])\n        else:\n            return apology(\"You don't have enough money\", 403)\n\n        #insert purchase into the database\n        db.execute(\"INSERT INTO purchased (username, symbol, number_shares, total_price_bought,stock_price,total_current) VALUES (?,?,?,?,?,?)\", username, lookup(symbol)[\"symbol\"],shares,total_price,lookup(symbol)[\"price\"],total_price)\n\n        #insert purchase into history\n        db.execute(\"INSERT INTO history (username, symbol, number_shares, total_price_bought,stock_price,total_current) VALUES (?,?,?,?,?,?)\", username, lookup(symbol)[\"symbol\"],shares,total_price,lookup(symbol)[\"price\"],total_price)\n\n        return redirect(\"/purchased\")\n\n    else:\n        return render_template(\"buy.html\")\n\n@app.route(\"/purchased\", methods=[\"GET\", \"POST\"])\n@login_required\ndef purchased():\n    #Rendering the purchased page\n    balance = db.execute(\"SELECT cash FROM users WHERE id=?\", session[\"user_id\"])\n    current_balance = balance[0][\"cash\"]\n    shares = session[\"shares\"]\n    buystock = lookup(session[\"symbol\"])\n\n    return render_template(\"purchased.html\", buystock=buystock, current_balance=current_balance, shares=shares)\n\n\n# Working on the history page!!!\n\n@app.route(\"/history\", methods=[\"GET\"])\n@login_required\ndef history():\n\n    #get username\n    #current_user = db.execute(\"SELECT username FROM users WHERE id = ?\", session[\"user_id\"])\n    #user = current_user[0][\"username\"]\n\n    #Get values from database\n\n    history = db.execute(\"SELECT symbol, number_shares, total_price_bought, stock_price, datestamp FROM history WHERE username = ?\", session[\"username\"])\n    HISTORY_LOG = history\n\n    #for row in history:\n        #HISTORY_LOG = row\n\n    return render_template(\"history.html\", history_log=HISTORY_LOG)\n\n\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n    \"\"\"Log user in\"\"\"\n\n    # Forget any user_id\n    session.clear()\n\n    # User reached route via POST (as by submitting a form via POST)\n    if request.method == \"POST\":\n\n        # Ensure username was submitted\n        if not request.form.get(\"username\"):\n            return apology(\"must provide username\", 403)\n\n        # Ensure password was submitted\n        elif not request.form.get(\"password\"):\n            return apology(\"must provide password\", 403)\n\n        # Query database for username\n        rows = db.execute(\"SELECT * FROM users WHERE username = ?\", request.form.get(\"username\"))\n\n        # Ensure username exists and password is correct\n        if len(rows) != 1 or not check_password_hash(rows[0][\"hash\"], request.form.get(\"password\")):\n            return apology(\"invalid username and/or password\", 403)\n\n        # Remember which user has logged in\n        session[\"user_id\"] = rows[0][\"id\"]\n        session[\"username\"] = rows[0][\"username\"]\n\n        # Redirect user to home page\n        return redirect(\"/\")\n\n    # User reached route via GET (as by clicking a link or via redirect)\n    else:\n        return render_template(\"login.html\")\n\n\n@app.route(\"/logout\")\ndef logout():\n    \"\"\"Log user out\"\"\"\n\n    # Forget any user_id\n    session.clear()\n\n    # Redirect user to login form\n    return redirect(\"/\")\n\n\n@app.route(\"/quote\", methods=[\"GET\", \"POST\"])\n@login_required\ndef quote():\n    if request.method == \"POST\":\n\n        # Ensure stock was submitted\n        if not request.form.get(\"symbol\"):\n            return apology(\"must provide stock ticker\", 403)\n\n        #print(session[\"user_id\"])\n\n        # Submission\n        symbol = request.form.get(\"symbol\")\n        session[\"symbol\"] = symbol\n\n         # Redirect user to quoted page\n        return redirect(\"/quoted\")\n\n    # User reached route via GET (as by clicking a link or via redirect)\n    else:\n        return render_template(\"quote.html\")\n\n\n\n@app.route(\"/quoted\", methods=[\"GET\", \"POST\"])\n@login_required\ndef quoted():\n        #print(SYMBOL)\n        return render_template(\"quoted.html\", quotestock=lookup(session[\"symbol\"]))\n\n\n@app.route(\"/register\", methods=[\"GET\", \"POST\"])\ndef register():\n    if request.method == \"POST\":\n\n        # Ensure username was submitted\n        if not request.form.get(\"username\"):\n            return apology(\"must provide username\", 403)\n\n        # Ensure password was submitted\n        elif not request.form.get(\"password\"):\n            return apology(\"must provide password\", 403)\n\n        # Submission\n        username = request.form.get(\"username\")\n        password = generate_password_hash(request.form.get(\"password\"))\n\n        # Query database for username\n        db.execute(\"INSERT INTO users (username,hash) VALUES (?,?)\", username, password)\n\n        # Redirect user to home page\n        return redirect(\"/\")\n\n    # User reached route via GET (as by clicking a link or via redirect)\n    else:\n        return render_template(\"register.html\")\n\n\n@app.route(\"/sell\", methods=[\"GET\", \"POST\"])\n@login_required\ndef sell():\n    username = session[\"username\"]\n    current_portfolio = db.execute(\"SELECT symbol,SUM(number_shares) FROM purchased WHERE username = ? GROUP BY symbol\", username)\n\n    if request.method == \"POST\":\n\n        symbol = request.form.get(\"symbol\")\n        shares = request.form.get(\"shares\",type=int)\n\n        session[\"symbol\"] = symbol\n        session[\"shares\"] = shares\n\n        if not symbol:\n            return apology(\"Must provide ticker\", 403)\n\n        elif not lookup(symbol):\n            return apology(\"Must provide VALID ticker\", 403)\n\n        #elif not in PORTFOLIO:\n            #return apology(\"Must provide ticker in your portfolio\", 403)\n\n        elif not shares:\n            return apology(\"Put number of shares\", 403)\n\n        elif (shares < 1):\n            return apology(\"Number of shares must be above 1\", 403)\n\n        #update balance\n        balance = db.execute(\"SELECT cash FROM users WHERE id=?\", session[\"user_id\"])\n        CURRENT_BALANCE = balance[0][\"cash\"]\n        session[\"balance\"] = CURRENT_BALANCE\n\n        #price of stocks sold\n        total_price = lookup(symbol)[\"price\"] * shares\n\n        #adding sold quantity to blanace\n        CURRENT_BALANCE = CURRENT_BALANCE + total_price\n\n        db.execute(\"UPDATE users SET cash=? WHERE id=?\",CURRENT_BALANCE,session[\"user_id\"])\n\n        #add transaction to list\n        db.execute(\"INSERT INTO purchased (username, symbol, number_shares, total_price_bought,stock_price,total_current) VALUES (?,?,?,?,?,?)\", username, lookup(symbol)[\"symbol\"],(shares*-1),(total_price*-1),lookup(symbol)[\"price\"],(total_price*-1))\n\n        #add transaction to history\n        db.execute(\"INSERT INTO history (username, symbol, number_shares, total_price_bought,stock_price,total_current) VALUES (?,?,?,?,?,?)\", username, lookup(symbol)[\"symbol\"],(shares*-1),(total_price*-1),lookup(symbol)[\"price\"],(total_price*-1))\n\n        stock_zero = db.execute(\"SELECT SUM(number_shares) FROM purchased WHERE symbol = ?\", symbol)\n\n        if (stock_zero[0][\"SUM(number_shares)\"]) == 0:\n            db.execute(\"DELETE FROM purchased WHERE symbol = ?\", symbol)\n\n        return redirect(\"/sold\")\n\n    else:\n        current_portfolio = db.execute(\"SELECT symbol,SUM(number_shares) FROM purchased WHERE username = ? GROUP BY symbol\", username)\n\n        if not current_portfolio:\n            return render_template(\"sell.html\")\n        else:\n            return render_template(\"sell.html\", portfolio=current_portfolio)\n\n\n@app.route(\"/sold\", methods=[\"GET\", \"POST\"])\n@login_required\ndef sold():\n    return render_template(\"sold.html\", sellstock=lookup(session[\"symbol\"]),current_balance=session[\"balance\"], shares=session[\"shares\"])\n\n@app.route(\"/cash\", methods=[\"GET\", \"POST\"])\n@login_required\ndef cash():\n    if request.method == \"POST\":\n        cash_request = request.form.get(\"cash_request\", type=int)\n        session[\"cash_request\"] = request.form.get(\"cash_request\", type=int)\n\n        if not cash_request:\n            return apology(\"Provide numeric value\", 403)\n        elif cash_request < 0:\n            return apology(\"Must provide number greater than 0\", 403)\n        else:\n            balance = db.execute(\"SELECT cash FROM users WHERE id=?\", session[\"user_id\"])\n\n            db.execute(\"UPDATE users SET cash = (? + ?) WHERE id=?\", balance[0][\"cash\"],cash_request, session[\"user_id\"])\n            return redirect(\"/cash_confirmation\")\n\n    else:\n        #Rendering GET cash page\n        balance = db.execute(\"SELECT cash FROM users WHERE id=?\", session[\"user_id\"])\n        CURRENT_BALANCE = balance[0][\"cash\"]\n\n        return render_template(\"cash.html\", current_balance=CURRENT_BALANCE)\n\n@app.route(\"/cash_confirmation\", methods=[\"GET\", \"POST\"])\n@login_required\ndef cash_confirmation():\n    balance = db.execute(\"SELECT cash FROM users WHERE id=?\", session[\"user_id\"])\n    CURRENT_BALANCE = balance[0][\"cash\"]\n    CASH_REQUEST = session[\"cash_request\"]\n\n    return render_template(\"cash_confirmation.html\", current_balance=CURRENT_BALANCE, cash_request=CASH_REQUEST)", "repo_name": "tonjoey/finance", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 13015, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "helpers.usd", "line_number": 19, "usage_type": "name"}, {"api_name": "flask_session.Session", "line_number": 24, "usage_type": "call"}, {"api_name": "cs50.SQL", "line_number": 27, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 36, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 50, "usage_type": "name"}, {"api_name": "helpers.lookup", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 72, "usage_type": "call"}, {"api_name": "helpers.login_required", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 87, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 90, "usage_type": "call"}, {"api_name": "helpers.lookup", "line_number": 92, "usage_type": "call"}, {"api_name": "helpers.apology", "line_number": 93, "usage_type": "call"}, {"api_name": "helpers.apology", "line_number": 96, "usage_type": "call"}, {"api_name": "helpers.apology", "line_number": 99, "usage_type": "call"}, {"api_name": "helpers.lookup", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 112, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 114, "usage_type": "call"}, {"api_name": "helpers.lookup", "line_number": 117, "usage_type": "call"}, {"api_name": "helpers.lookup", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 122, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 125, "usage_type": "call"}, {"api_name": "helpers.login_required", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 133, "usage_type": "name"}, {"api_name": "helpers.lookup", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 136, "usage_type": "call"}, {"api_name": "helpers.login_required", "line_number": 128, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 151, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 157, "usage_type": "call"}, {"api_name": "helpers.login_required", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.session.clear", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 165, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 168, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 168, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 171, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 171, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 171, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 172, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 175, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 175, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 175, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 176, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 179, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 179, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 179, "usage_type": "name"}, {"api_name": "werkzeug.security.check_password_hash", "line_number": 182, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 182, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 182, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 182, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 183, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 186, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 187, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 190, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 194, "usage_type": "call"}, {"api_name": "flask.session.clear", "line_number": 202, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 202, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 205, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 211, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 211, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 214, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 214, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 214, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 215, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 220, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 220, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 220, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 221, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 224, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 228, "usage_type": "call"}, {"api_name": "helpers.login_required", "line_number": 209, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 236, "usage_type": "call"}, {"api_name": "helpers.lookup", "line_number": 236, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 236, "usage_type": "name"}, {"api_name": "helpers.login_required", "line_number": 233, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 241, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 241, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 244, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 244, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 244, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 245, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 248, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 248, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 248, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 249, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 252, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 252, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 252, "usage_type": "name"}, {"api_name": "werkzeug.security.generate_password_hash", "line_number": 253, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 253, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 253, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 253, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 259, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 263, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 269, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 272, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 272, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 274, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 274, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 274, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 275, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 275, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 275, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 277, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 278, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 281, "usage_type": "call"}, {"api_name": "helpers.lookup", "line_number": 283, "usage_type": "call"}, {"api_name": "helpers.apology", "line_number": 284, "usage_type": "call"}, {"api_name": "helpers.apology", "line_number": 290, "usage_type": "call"}, {"api_name": "helpers.apology", "line_number": 293, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 296, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 298, "usage_type": "name"}, {"api_name": "helpers.lookup", "line_number": 301, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 306, "usage_type": "name"}, {"api_name": "helpers.lookup", "line_number": 309, "usage_type": "call"}, {"api_name": "helpers.lookup", "line_number": 312, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 319, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 325, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 327, "usage_type": "call"}, {"api_name": "helpers.login_required", "line_number": 267, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 333, "usage_type": "call"}, {"api_name": "helpers.lookup", "line_number": 333, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 333, "usage_type": "name"}, {"api_name": "helpers.login_required", "line_number": 331, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 338, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 338, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 339, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 339, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 339, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 340, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 340, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 340, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 340, "usage_type": "name"}, {"api_name": "helpers.apology", "line_number": 343, "usage_type": "call"}, {"api_name": "helpers.apology", "line_number": 345, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 347, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 349, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 350, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 354, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 357, "usage_type": "call"}, {"api_name": "helpers.login_required", "line_number": 336, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 362, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 364, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 366, "usage_type": "call"}, {"api_name": "helpers.login_required", "line_number": 360, "usage_type": "name"}]}
{"seq_id": "72334580936", "text": "from django.urls import path\nfrom . import views\n\napp_name = \"home\"\nurlpatterns = [\n    path('people/',views.people_list),\n    path('people/<int:id>/',views.person_detail),\n\n    path('articles/',views.article_list),\n    path('articles/<int:id>/',views.artcile_detail),\n\n    path('skills/',views.skill_list),\n    path('skills/<int:id>/',views.skill_detail),\n\n    path('education/',views.education_list),\n    path('educations/<int:id>/',views.education_detail),\n\n    path('projects/',views.project_list),\n    path('projects/<int:id>/',views.project_detail),\n\n    path(\"worked-at/\",views.worked_at),\n    path(\"worked-at/<int:id>/\",views.worked_at_detail),\n\n    path(\"images/\",views.image_list),\n    path(\"images/<int:id>/\",views.image_detail),\n]", "repo_name": "mohammadhosseini3/Portfolio", "sub_path": "home/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 742, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "7808447058", "text": "from ariadne import ObjectType, QueryType, gql, make_executable_schema\n\nfrom .tag_service import TagService\n\ntype_defs = gql(\"\"\"\n\n    type Query {\n        datasets(uris: [String], tags: [String], limit: Int, skip: Int): [Dataset]!\n    }\n\n\n    enum DatasetType {\n        tiled\n        file\n        web\n    }\n\n    type Tag {\n\n        uid: String\n        name: String!\n        locator: String\n        confidence: Float\n    }\n\n    type TagSource{\n        \" The entity that created a Tag \"\n        type: String!\n        name: String\n\n    }\n\n    type TaggingEvent {\n        tagger: TagSource\n    }\n\n\n    type Dataset {\n        \" Dataset model \"\n        uid: String!\n        uri: String!\n        type: DatasetType!\n        tags: [Tag]\n    }\n\n\"\"\")\n\nquery = QueryType()\n\n\ndef set_gql_tag_service(new_tag_svc: TagService):\n    global tag_svc\n    tag_svc = new_tag_svc\n\n\n@query.field(\"datasets\")\ndef resolve_datasets(self, *_, tags=None, uris=None, limit=10, skip=0):\n    datasets = list(tag_svc.find_datasets(tags=tags, uris=uris, offset=skip, limit=limit))\n    return datasets\n\n\ndataset = ObjectType(\"Dataset\")\nschema = make_executable_schema(type_defs, query)\n", "repo_name": "als-computing/splash-ml", "sub_path": "tagging/graphql.py", "file_name": "graphql.py", "file_ext": "py", "file_size_in_byte": 1152, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "45", "api": [{"api_name": "ariadne.gql", "line_number": 5, "usage_type": "call"}, {"api_name": "ariadne.QueryType", "line_number": 48, "usage_type": "call"}, {"api_name": "tag_service.TagService", "line_number": 51, "usage_type": "name"}, {"api_name": "ariadne.ObjectType", "line_number": 62, "usage_type": "call"}, {"api_name": "ariadne.make_executable_schema", "line_number": 63, "usage_type": "call"}]}
{"seq_id": "21557747218", "text": "from django.urls import path\r\n\r\nfrom common.views import browse_category, LandingView, BrowseView, checkout, orders_details, accept_order, \\\r\n    dismiss_order, SearchResultsView\r\n\r\nurlpatterns = [\r\n    path('', BrowseView.as_view(), name='browse'),\r\n    path('about_us/', LandingView.as_view(), name='landing'),\r\n    path('category/<str:category>', browse_category, name='browse category'),\r\n    path('checkout/', checkout, name='checkout'),\r\n    path('orders/', orders_details, name='orders'),\r\n    path('accept_order/<int:pk>', accept_order, name='accept order'),\r\n    path('dismiss_order/<int:pk>', dismiss_order, name='dismiss order'),\r\n    path('search/', SearchResultsView.as_view(), name='search results')\r\n\r\n]\r\n", "repo_name": "todorpetkov11/riddance_ecom", "sub_path": "common/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 720, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "common.views.BrowseView.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "common.views.BrowseView", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "common.views.LandingView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "common.views.LandingView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "common.views.browse_category", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "common.views.checkout", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "common.views.orders_details", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "common.views.accept_order", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "common.views.dismiss_order", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "common.views.SearchResultsView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "common.views.SearchResultsView", "line_number": 14, "usage_type": "name"}]}
{"seq_id": "36605225319", "text": "# -*- coding: utf-8 -*-\nfrom datetime import datetime, timedelta\nfrom typing import List\n\nfrom progress.bar import Bar as Bar\n\nfrom .exceptions import TooLargeBoarders\nfrom .methods import OrdersMethods\nfrom .request import RequestApi\n\nMAX_COUNT_CUSTOMERS_PER_REQUEST = 500\n\n\nclass OrdersAPI:\n    def __init__(self, request_api: RequestApi):\n        self.request_api = request_api\n\n    def get(\n            self,\n            date_from: datetime = None,\n            date_to: datetime = None,\n            order_statuses: list = None,\n            ids: List[int] = None,\n            internal_numbers: List[int] = None,\n            customer_id: int = None,\n            count: int = 500,\n            offset: int = 0\n    ):\n        if order_statuses is None:\n            order_statuses = []\n        if count > MAX_COUNT_CUSTOMERS_PER_REQUEST:\n            raise TooLargeBoarders(\n                f'Количество запрашиваемых клиентов за раз должно быть меньше {MAX_COUNT_CUSTOMERS_PER_REQUEST}'\n            )\n        out_statuses = []\n        for status in order_statuses:\n            if isinstance(status, int):\n                out_statuses.append({'id': status})\n            elif isinstance(status, str):\n                out_statuses.append({'name': status})\n            else:\n                raise TypeError(f'Ожидалось int или str, получил {type(status)}')\n\n        data = {\n            'dateFrom': date_from.strftime('%Y-%m-%d') if date_from else None,\n            'dateTill': (date_to + timedelta(days=1)).strftime(\n                '%Y-%m-%d') if date_to else None,\n            'orderStatuses': out_statuses,\n            'customerId': customer_id,\n            'ids': ids,\n            'internalNumbers': internal_numbers,\n            'pageSize': count,\n            'startRowNumber': offset,\n        }\n        response = self.request_api.send(\n            OrdersMethods.get,\n            data=data\n        )\n\n        return OrdersResponse(response)\n\n    def get_all(\n            self,\n            date_from: datetime = None,\n            date_to: datetime = None,\n            order_statuses: list = None,\n            ids: List[int] = None,\n            internal_numbers: List[int] = None,\n            customer_id: int = None,\n    ) -> List[dict]:\n\n        items = []\n        count = MAX_COUNT_CUSTOMERS_PER_REQUEST\n        offset = 0\n\n        r = self.get(\n            date_from, date_to, order_statuses,\n            ids, internal_numbers, customer_id,\n            count=1, offset=0\n        )\n        total_count = r.not_returned_count + r.count\n\n        if total_count == 0:\n            return []\n\n        with Bar(f'Orders | {self.request_api.login}',\n                 max=total_count, fill='█', empty_fill='░') as bar:\n            while len(items) < total_count:\n                r = self.get(\n                    date_from, date_to, order_statuses,\n                    ids, internal_numbers, customer_id,\n                    count, offset\n                )\n                items.extend(r.orders)\n                offset += count\n                bar.next(r.count)\n        return items\n\n\nclass OrdersResponse:\n    def __init__(self, response: dict):\n        self.count: int = response['count']\n        self.not_returned_count: int = response['notReturnedCount']\n        self.orders: list = response['orders']\n        self.response: dict = response\n\n    def __repr__(self):\n        return str(self.response)\n", "repo_name": "thehighestmath/bluesales-python-sdk", "sub_path": "bluesalespy/ordersapi.py", "file_name": "ordersapi.py", "file_ext": "py", "file_size_in_byte": 3473, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "request.RequestApi", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 24, "usage_type": "name"}, {"api_name": "exceptions.TooLargeBoarders", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 46, "usage_type": "call"}, {"api_name": "methods.OrdersMethods.get", "line_number": 56, "usage_type": "attribute"}, {"api_name": "methods.OrdersMethods", "line_number": 56, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 64, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 68, "usage_type": "name"}, {"api_name": "progress.bar.Bar", "line_number": 86, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 70, "usage_type": "name"}]}
{"seq_id": "3333755878", "text": "from scipy.spatial import distance\nimport numpy as np\n\n\ndef compute_cluster_sense_frequency(cluster_labels, embeddings_epoch_label, epoch_labels):\n    n_cluster = len(set(cluster_labels))\n    cluster_epoch_combined = list(zip(cluster_labels, embeddings_epoch_label))\n    sense_frequencies_task1 = {epoch_label: [] for epoch_label in epoch_labels}\n    sense_frequencies_task2 = {epoch_label: [] for epoch_label in epoch_labels}\n    for epoch in epoch_labels:\n        count_epoch_total = sum(int(epoch == epoch_label) for cluster_label, epoch_label in cluster_epoch_combined)\n        for sense_label in range(n_cluster):\n            count_sense_epoch = sum(int(cluster_label == sense_label and epoch == epoch_label)\n                                    for cluster_label, epoch_label in cluster_epoch_combined)\n            sense_frequency_epoch = count_sense_epoch / count_epoch_total\n            sense_frequencies_task2[epoch].append(sense_frequency_epoch)\n            sense_frequencies_task1[epoch].append(count_sense_epoch)\n    return sense_frequencies_task1, sense_frequencies_task2\n\n\ndef compute_task1_answer(sense_frequencies, k, n):\n    for epoch_1_count, epoch_2_count in zip(*sense_frequencies.values()):\n        if (epoch_1_count <= k and epoch_2_count > n) or (epoch_2_count <= k and epoch_1_count > n):\n            return 1\n    return 0\n\n\ndef compute_task1_answer_alt(sense_frequencies, k, n):\n    for epoch_1_count, epoch_2_count in zip(*sense_frequencies.values()):\n        if (epoch_1_count <= k/100 and epoch_2_count > n/100) or (epoch_2_count <= k/100 and epoch_1_count > n/100):\n            return 1\n    return 0\n\n\ndef compute_task2_answer(sense_frequencies):\n    return distance.jensenshannon(sense_frequencies[0], sense_frequencies[1], 2.0)\n\n\ndef compute_task_answers(cluster_labels, embeddings_epoch_label, epoch_labels, k, n):\n    sf1, sf2 = compute_cluster_sense_frequency(cluster_labels, embeddings_epoch_label, epoch_labels)\n    task_2_answer = compute_task2_answer(sf2)\n    if np.isnan(task_2_answer):\n        task_2_answer = 0.5\n\n    return compute_task1_answer_alt(sf2, k, n), task_2_answer\n\n", "repo_name": "DavidRother/semeval2020-task1", "sub_path": "semeval2020/model/model_utilities.py", "file_name": "model_utilities.py", "file_ext": "py", "file_size_in_byte": 2117, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "45", "api": [{"api_name": "scipy.spatial.distance.jensenshannon", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 42, "usage_type": "call"}]}
{"seq_id": "23508825526", "text": "# 作业2： 高斯噪声\nimport random\n\nimport cv2\n\nimg = cv2.imread(\"lenna.png\")\ndst = img.copy()\nheight, width, channels = img.shape\n# 1、输入参数 sigma 和 mean 以及分布噪点的百分比（直接影响噪点数量和分布）\nmean = 0\nsigma = 10\npercent = 0.8\n# 2、计算噪点数\nnoise = int(percent * height * width)\nfor channel in range(channels):\n    for i in range(noise):\n        # 3、取任意随机点([0, width - 1], [0, height - 1])，高斯噪声的图片 边缘 不做处理\n        rand_x = random.randint(0, width - 1)\n        rand_y = random.randint(0, height - 1)\n        # 4、生成高斯随机数\n        num = random.gauss(mean, sigma)\n        # 5、在原像素值基础上 加上 随机数\n        dst[rand_x, rand_y, channel] = img[rand_x, rand_y, channel] + num\n        # 6、若像素值小于0则置为0，大于255则置为255\n        if dst[rand_x, rand_y, channel] < 0:\n            dst[rand_x, rand_y, channel] = 0\n        elif dst[rand_x, rand_y, channel] > 255:\n            dst[rand_x, rand_y, channel] = 255\ncv2.imshow('origin', img)\ncv2.imshow('gauss', dst)\ncv2.waitKey(0)\n", "repo_name": "OMG1-1/badou-ai-special-2023", "sub_path": "115-张恩慈-广东/20231103/高斯噪声_mine.py", "file_name": "高斯噪声_mine.py", "file_ext": "py", "file_size_in_byte": 1122, "program_lang": "python", "lang": "zh", "doc_type": "code", "dataset": "github-code", "pt": "40", "api": [{"api_name": "cv2.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 18, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "random.gauss", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 31, "usage_type": "call"}]}
{"seq_id": "38698518505", "text": "import random\nfrom sys import exit\n\nimport cocos\nfrom cocos.director import director\nfrom cocos.actions import *\n\nfrom pyglet import gl\n\nWIDTH = 1024\nHEIGHT = 768\n\n\nclass DragDropStrings(cocos.layer.Layer):\n    \"\"\"\n    DragDropStrings - слой, в котором происходит  перетаскивание строчек кода.\n    Тут же и часть игровой логики\n    \"\"\"\n    def __init__(self, strings):\n        \"\"\" Конструктор\"\"\"\n        super(DragDropStrings, self).__init__()\n\n        self.dragged_line = None  # таскаемая в данный момент строчка кода (RectWithText)\n        self.original_strings = strings[:]  # оригинальный код, который нужно собрать\n        self.lines = []  # lines - список строк RectWithText\n        self.positions = []  # positions - список позиций для строк RectPosition\n        global background\n        self.background = background\n        self.add(self.background)\n        # Перемешиваем строки и добавляем их к слою для отрисовки. Также добавляем позиции\n        i = 0\n        strings = random.sample(strings, len(strings))\n        for string in strings:\n            self.lines.append(RectWithText(string, (0.3, 0.3, 1, 1), 100, HEIGHT-100-20*i))\n            self.positions.append(RectPosition(WIDTH/2, HEIGHT - 100 - 20 * i))\n            i += 1\n\n        for line in self.lines:\n            self.add(line, z=1)\n\n        for pos in self.positions:\n            self.add(pos, z=0)\n        # Добавляем строку, сообщающую о выигрыше, в спрятанном состоянии\n        self.winning_line = cocos.text.Label(\"Поздравляю! Ты собрал правильный код!\", (WIDTH/2 - 100, 100))\n        self.winning_line.do(Hide())\n        self.add(self.winning_line)\n\n    def is_line_in_unoccupied_position(self, x, y):\n        \"\"\" Функция для проверки, попадает ли строка в незанятую позицию\n            Если попадает, возвращается ссылку на объект позиции (RectPosition)\n            Если нет, возвращается False\n        \"\"\"\n        for position in self.positions:\n            posx = position.x\n            posy = position.y\n            posx2 = position.x + position.w\n            posy2 = position.y + position.h\n            if (x >= posx) and (y >= posy) and (x <= posx2) and (y <= posy2) and not position.is_occupied:\n                return position\n        return False\n\n    def goal(self):\n        \"\"\" Функция проверяет, собран ли правильно код. Составляется список занятых строк и сравнивается\n            с оригинальным списком\n            Возвращает True/False соответственно\n        \"\"\"\n        attempt = []\n        for pos in self.positions:\n            attempt.append(pos.occupying_line)\n        if attempt == self.original_strings:\n            return True\n        return False\n\n\nclass RectWithText(cocos.layer.Layer):\n    \"\"\" RectWithText - слой, который содержит строчку собираемого кода, и всё для работы с ней.\n        Является обработчиком событий\n    \"\"\"\n\n    is_event_handler = True\n\n    def __init__(self, text, color, x, y):\n        \"\"\" Конструктор\"\"\"\n        super(RectWithText, self).__init__()\n\n        self.is_dragged = False  # флаг, показывает, тащит ли юзер эту строчку мышкой в данный момент\n\n        self.x = x  # x координата\n        self.y = y  # y координата\n        self.startx = x  # x координата стартовой позиции. Для возвращения строки на исходное место\n        self.starty = y  # y координата стартовой позиции. Для возвращения строки на исходное место\n\n        self.w = len(text)*10  # Длина прямоугольника со строкой.\n        self.h = 18  # Высота прямоугольника со строкой.\n\n        self.line = text  # Строковое значение - одна из строк кода. Используется для записи в соотв. поле позиции\n        self.in_position = None  # ссылка на объект позиции (RectPosition) в которой находится строка\n\n        self.rectangle = Rectangle(color, 0, 0, self.w, self.h)  # объект цветная подложка под текстом (Rectangle)\n        self.text = cocos.text.Label(text, x=2, y=4)  # объект cocos.label.label с текстом строки кода\n        # Добавляем в слой подложку и сам текст\n        self.add(self.rectangle)\n        self.add(self.text)\n\n    def on_mouse_press(self, x, y, buttons, modifiers):\n        \"\"\" Обработчик события  - нажатие кнопки мыши\n        x, y - физические координаты курсора\n        buttons - нажатые кнопки. Опытным путем установлено: 1 - левая, 4 - правая, 2 - средняя\n        \"\"\"\n        # Если нажата левая кнопка, проверяем, попали ли в одну из таскаемых строчек, если да - начинаем таскать\n        if buttons == 1:\n            x, y = director.get_virtual_coordinates(x, y)  # перевод из физических координат в виртуальные\n            posx = self.x\n            posy = self.y\n            posx2 = self.x + self.w\n            posy2 = self.y + self.h\n            if (x >= posx) and (y >= posy) and (x <= posx2) and (y <= posy2) and not(self.are_actions_running()):\n                # Тут для того, чтобы поменять z-уровень строки, она удаляется из списка, а потом добавляется заново\n                self.is_dragged = True\n                # hack:\n                dragged = self\n                par = self.parent\n                self.kill()\n                par.add(dragged, z=1)\n                # /hack\n                self.parent.dragged_line = self\n\n    def on_mouse_release(self, x, y, buttons, modifiers):\n        \"\"\" Обработчик события  - отпускание кнопки мыши\n        x, y - физические координаты курсора\n        buttons - нажатые кнопки. Опытным путем установлено: 1 - левая, 4 - правая, 2 - средняя\n        \"\"\"\n        if buttons == 1:  # если нажата ЛКМ\n            x, y = director.get_virtual_coordinates(x, y)  # перевод из физических координат в виртуальные\n            if self.is_dragged:  # если строчку таскают\n                pos = self.parent.is_line_in_unoccupied_position(x, y)\n                if pos:  # проверяем, попали ли в незанятую позицию\n                    self.is_dragged = False\n                    self.do(MoveTo((pos.x, pos.y), 0.1))\n                    self.parent.dragged_line = self\n                    if self.in_position:  # если строка уже была в позиции, очищаем позицию\n                        self.in_position.is_occupied = False\n                        self.in_position.occupying_line = \"\"\n                        self.in_position = None\n                    pos.is_occupied = True\n                    pos.occupying_line = self.line\n                    self.in_position = pos\n                else:  # если не попала - возвращаем строку на исходную\n                    self.is_dragged = False\n                    self.do(MoveTo((self.startx, self.starty), 0.25))\n                    self.parent.dragged_line = None\n                    if self.in_position:\n                        self.in_position.is_occupied = False\n                        self.in_position.occupying_line = \"\"\n                        self.in_position = None\n        if self.parent.goal():  # проверяем, не достигнута ли цель\n            self.parent.winning_line.do(Show())\n            self.parent.parent.next_button.is_active = True\n            self.parent.parent.next_button.do(Show())\n        else:\n            self.parent.winning_line.do(Hide())\n            self.parent.parent.next_button.is_active = False\n            self.parent.parent.next_button.do(Hide())\n\n    def on_mouse_drag(self, x, y, dx, dy, buttons, modifiers):\n        \"\"\" Обработчик события  - таскание мышью\n            x, y - физические координаты курсора\n            buttons - нажатые кнопки. Опытным путем установлено: 1 - левая, 4 - правая, 2 - средняя\n        \"\"\"\n        if self.is_dragged:  # если именно эту строчку таскают - таскаемся =)\n            x, y = director.get_virtual_coordinates(x, y)\n            self.do(Place((x - self.w/2, y - self.h/2)))  # за серединку\n\n\nclass Button(cocos.layer.Layer):\n    \"\"\" Button - слой, который содержит кнопку.\n        Является обработчиком событий\n    \"\"\"\n\n    is_event_handler = True\n\n    def __init__(self, text, color=(0, 0, 0, 0), color_pressed=(0.5, 0.5, 0.5, 1), x=0, y=0, w=1, h=1,\n                 active=True, func=None, *args, **kwargs):\n        \"\"\" Конструктор\"\"\"\n        super(Button, self).__init__()\n        self.is_active = active  # активна кнопка или нет\n        self.x = x  # x координата\n        self.y = y  # y координата\n        self.w = w  # Длина кнопки\n        self.h = h  # Высота кнопки\n        self.line = text  # Надпись на кнопке\n        self.func = func  # функция или метод, которая должна выполнится при нажатии на кнопку\n        self.args = args\n        self.kwargs = kwargs\n        self.color = color\n        self.color_pressed = color_pressed\n        self.rectangle = Rectangle(color, 0, 0, self.w, self.h)  # объект цветная подложка под текстом (Rectangle)\n        self.text = cocos.text.Label(text, x=2, y=4, font_size=16)  # объект cocos.label.label с текстом\n        # Добавляем в слой подложку и сам текст\n        self.add(self.rectangle)\n        self.add(self.text)\n\n    def on_mouse_press(self, x, y, buttons, modifiers):\n        \"\"\" Обработчик события  - нажатие кнопки мыши\n        x, y - физические координаты курсора\n        buttons - нажатые кнопки. Опытным путем установлено: 1 - левая, 4 - правая, 2 - средняя\n        \"\"\"\n        if buttons == 1:\n            x, y = director.get_virtual_coordinates(x, y)  # перевод из физических координат в виртуальные\n            posx = self.x\n            posy = self.y\n            posx2 = self.x + self.w\n            posy2 = self.y + self.h\n            if (x >= posx) and (y >= posy) and (x <= posx2) and (y <= posy2) and self.is_active:\n                self.rectangle.layer_color = self.color_pressed\n\n    def on_mouse_release(self, x, y, buttons, modifiers):\n        \"\"\" Обработчик события  - отпускание кнопки мыши\n        x, y - физические координаты курсора\n        buttons - нажатые кнопки. Опытным путем установлено: 1 - левая, 4 - правая, 2 - средняя\n        \"\"\"\n        if buttons == 1:\n            x, y = director.get_virtual_coordinates(x, y)  # перевод из физических координат в виртуальные\n            posx = self.x\n            posy = self.y\n            posx2 = self.x + self.w\n            posy2 = self.y + self.h\n            if (x >= posx) and (y >= posy) and (x <= posx2) and (y <= posy2) and self.is_active:\n                if self.func:\n                    self.func(*self.args, **self.kwargs)\n            else:\n                self.rectangle.layer_color = self.color\n\n    def on_mouse_drag(self, x, y, dx, dy, buttons, modifiers):\n        \"\"\" Обработчик события  - таскание мышью\n            x, y - физические координаты курсора\n            buttons - нажатые кнопки. Опытным путем установлено: 1 - левая, 4 - правая, 2 - средняя\n        \"\"\"\n        if buttons == 1:\n            x, y = director.get_virtual_coordinates(x, y)  # перевод из физических координат в виртуальные\n            posx = self.x\n            posy = self.y\n            posx2 = self.x + self.w\n            posy2 = self.y + self.h\n            # проверяем, попадает ли курсор, если да - изменяем цвет прямоугольника\n            if (x >= posx) and (y >= posy) and (x <= posx2) and (y <= posy2) and self.is_active:\n                self.rectangle.layer_color = self.color_pressed\n            else:\n                self.rectangle.layer_color = self.color\n\n\nclass StartPicture(cocos.layer.Layer):\n\n    is_event_handler = True\n\n    def __init__(self):\n        \"\"\" Конструктор\"\"\"\n        super(StartPicture, self).__init__()\n        self.picture = cocos.sprite.Sprite(image='data/start.jpg', position=(WIDTH / 2, HEIGHT / 2))\n        self.add(self.picture)\n\n    def on_mouse_release(self, x, y, buttons, modifiers):\n        global scene_main_menu\n        director.run(scene_main_menu)  # запускаем сцену главного меню\n\n\nclass RectPosition(cocos.layer.Layer):\n    \"\"\" RectPosition - слой, который содержит позицию, в которой может находится строчка кода, и всё для работы с ней.\n        Является обработчиком событий\n    \"\"\"\n\n    is_event_handler = True\n\n    def __init__(self, x, y):\n        \"\"\" Конструктор \"\"\"\n        super(RectPosition, self).__init__()\n\n        self.is_occupied = False  # флаг занятости позиции\n        self.occupying_line = \"\"  # строка текста, если позиция занята. Из них собирается список для проверки\n\n        self.x = x  # x - координата\n        self.y = y  # y - координата\n        self.w = 250  # ширина позиции\n        self.h = 18  # высота позиции\n\n        self.rectangle = Rectangle((1, 0.2, 0.2, 1), 0, 0, self.w, self.h)  # закрашений прямоугольник (Rectangle)\n\n        self.add(self.rectangle)\n\n    def on_mouse_drag(self, x, y, dx, dy, buttons, modifiers):\n        \"\"\" Обработчик события  - таскание мышью\n            x, y - физические координаты курсора\n            buttons - нажатые кнопки. Опытным путем установлено: 1 - левая, 4 - правая, 2 - средняя\n        \"\"\"\n        x, y = director.get_virtual_coordinates(x, y)  # перевод из физических координат в виртуальные\n        posx = self.x\n        posy = self.y\n        posx2 = self.x + self.w\n        posy2 = self.y + self.h\n        # проверяем, попадает ли курсор в позицию, если да - изменяем цвет прямоугольника\n        if (x >= posx) and (y >= posy) and (x <= posx2) and (y <= posy2) and not self.is_occupied:\n            self.rectangle.layer_color = (0.2, 1, 0.2, 1)  # зелёненький\n        else:\n            self.rectangle.layer_color = (1, 0.2, 0.2, 1)  # красненький\n\n    def on_mouse_release(self, x, y, buttons, modifiers):\n        \"\"\" Обработчик события  - отпускание кнопки мыши\n        x, y - физические координаты курсора\n        buttons - нажатые кнопки. Опытным путем установлено: 1 - левая, 4 - правая, 2 - средняя\n        \"\"\"\n        self.rectangle.layer_color = (1, 0.2, 0.2, 1)  # меняем цвет на красный в любом случае при отпускании мыши\n\n\nclass Rectangle(cocos.layer.Layer):\n\n    \"\"\"Rectangle (color, x, y, w, h) : слой, который рисует прямоугольник в (x,y) нужного цвета, ширины и высоты\"\"\"\n\n    def __init__(self, color, x, y, w, h):\n        \"\"\" Конструктор \"\"\"\n        super(Rectangle, self).__init__()\n\n        self.x = x  # x - координата\n        self.y = y  # y - координата\n        self.w = w  # высота прямоугольника\n        self.h = h  # ширина прямоугольника\n        self.layer_color = color  # цвет прямоугольника (в формате OpenGL - (R<от 0 до 1>,G,B,A - альфа)  )\n\n    def draw(self):\n        \"\"\" Функция рисования прямоугольника, с использованием pyglet.gl \"\"\"\n        super(Rectangle, self).draw()\n\n        gl.glColor4f(*self.layer_color)\n        x, y = self.x, self.y\n        w = x + self.w\n        h = y + self.h\n        gl.glBegin(gl.GL_QUADS)\n        gl.glVertex2f(x, y)\n        gl.glVertex2f(x, h)\n        gl.glVertex2f(w, h)\n        gl.glVertex2f(w, y)\n        gl.glEnd()\n        gl.glColor4f(1, 1, 1, 1)\n\n\nclass SceneStart(cocos.scene.Scene):\n    \"\"\" Сцена, которая показывает картинку при запуске\n            Является обработчиком событий\n    \"\"\"\n\n    def __init__(self):\n        super(SceneStart, self).__init__()\n        self.add(StartPicture())\n\n\nclass SceneTheory(cocos.scene.Scene):\n    def __init__(self, picture_file, progress, prev_scene=None, next_scene=None):\n        super(SceneTheory, self).__init__()\n        self.progress = progress\n        self.prev_scene = prev_scene\n        self.next_scene = next_scene\n        self.background = cocos.sprite.Sprite(image='code-1024x768.jpg', position=(WIDTH/2, HEIGHT/2))\n        self.add(self.background)\n        self.picture = cocos.sprite.Sprite(image=picture_file, position=(WIDTH/2, HEIGHT/2))\n        self.add(self.picture)\n        self.prev_button = Button('Назад', (0, 0.7, 0, 1), (0.5, 0.7, 0, 1), WIDTH / 5, 50, 100, 20, True,\n                                  self.on_prev)\n        self.add(self.prev_button)\n        self.next_button = Button('Дальше', (0, 0.7, 0, 1), (0.5, 0.7, 0, 1), WIDTH / 5 * 3, 50, 100, 20, True,\n                                  self.on_next)\n        self.add(self.next_button)\n\n    def on_next(self):\n        global progress\n        if progress < self.progress:\n            progress = self.progress\n        if self.next_scene == 'themes_menu':\n            global background\n            self.next_scene = cocos.scene.Scene(background, ThemesMenu())\n        director.replace(self.next_scene)\n\n    def on_prev(self):\n        if self.prev_scene == 'themes_menu':\n            global background\n            self.prev_scene = cocos.scene.Scene(background, ThemesMenu())\n        director.replace(self.prev_scene)\n\n\nclass SceneCode(cocos.scene.Scene):\n    def __init__(self, code_file, task_file, progress, prev_scene=None, next_scene=None):\n        super(SceneCode, self).__init__()\n        self.progress = progress\n        self.prev_scene = prev_scene\n        self.next_scene = next_scene\n        self.background = cocos.sprite.Sprite(image='code-1024x768.jpg', position=(WIDTH/2, HEIGHT/2))\n        self.add(self.background)\n        self.strings = DragDropStrings([line.strip() for line in open(code_file, 'r')])\n        self.add(self.strings)\n        self.prev_button = Button('Назад', (0, 0.7, 0, 1), (0.5, 0.7, 0, 1), WIDTH / 5, 50, 100, 20, True,\n                                  self.on_prev)\n        self.add(self.prev_button)\n        self.next_button = Button('Дальше', (0, 0.7, 0, 1), (0.5, 0.7, 0, 1), WIDTH / 5 * 3, 50, 100, 20, True,\n                                  self.on_next)\n        self.next_button.is_active = False\n        self.next_button.do(Hide())\n        self.add(self.next_button)\n        self.task = cocos.text.Label(''.join([line for line in open(task_file, 'r')]), (50, HEIGHT-20),\n                                     multiline=True, width=WIDTH-100)\n        self.add(self.task)\n\n    def on_next(self):\n        global progress\n        if progress < self.progress:\n            progress = self.progress\n        if self.next_scene == 'themes_menu':\n            global background\n            self.next_scene = cocos.scene.Scene(background, ThemesMenu())\n        director.replace(self.next_scene)\n\n    def on_prev(self):\n        if self.prev_scene == 'themes_menu':\n            global background\n            self.prev_scene = cocos.scene.Scene(background, ThemesMenu())\n        director.replace(self.prev_scene)\n\n\nclass MainMenu(cocos.menu.Menu):\n    def __init__(self):\n        super(MainMenu, self).__init__('Конструктор программ Pascal')\n        self.menu_valign = cocos.menu.CENTER\n        self.menu_halign = cocos.menu.CENTER\n\n        menu_items = [\n            (cocos.menu.MenuItem(\"Задания\", self.on_puzzles)),\n            (cocos.menu.MenuItem(\"Сбросить прогресс\", self.on_clear_progress)),\n            (cocos.menu.MenuItem(\"Выход\", self.on_quit))]\n        self.create_menu(menu_items)\n\n    def on_puzzles(self):\n        global background\n        director.run(cocos.scene.Scene(background, ThemesMenu()))\n\n    def on_clear_progress(self):\n        global progress\n        progress = 0\n\n    def on_quit(self):\n        global progress\n        open('progress', 'w').write(str(progress))\n        exit()\n\n\nclass ThemesMenu(cocos.menu.Menu):\n    def __init__(self):\n        global progress\n        super(ThemesMenu, self).__init__('Выбери тему')\n        self.menu_valign = cocos.menu.CENTER\n        self.menu_halign = cocos.menu.CENTER\n        menu_items = []\n        if progress >= 0:\n            menu_items.append(cocos.menu.MenuItem(\"Линейные программы\", self.on_theme, 1))\n        if progress >= 1:\n            menu_items.append(cocos.menu.MenuItem(\"Условный переход\", self.on_theme, 2))\n        if progress >= 2:\n            menu_items.append(cocos.menu.MenuItem(\"Оператор CASE\", self.on_theme, 3))\n        if progress >= 3:\n            menu_items.append(cocos.menu.MenuItem(\"Цикл FOR\", self.on_theme, 4))\n        if progress >= 4:\n            menu_items.append(cocos.menu.MenuItem(\"Цикл WHILE\", self.on_theme, 5))\n        if progress >= 5:\n            menu_items.append(cocos.menu.MenuItem(\"Табулирование\", self.on_theme, 6))\n        if progress >= 6:\n            menu_items.append(cocos.menu.MenuItem(\"Массивы\", self.on_theme, 7))\n        if progress >= 7:\n            menu_items.append(cocos.menu.MenuItem(\"Сортировка массивов\", self.on_theme, 8))\n        menu_items.append(cocos.menu.MenuItem(\"Назад\", self.on_back))\n        self.create_menu(menu_items)\n\n    def on_theme(self, theme):\n        global background\n        if theme == 1:\n            sc1 = SceneTheory('data/theme1pic1.jpg', 0, 'themes_menu')\n            sc2 = SceneCode('puzzles/theme1code1.txt', 'puzzles/theme1task1.txt', 0)\n            sc3 = SceneCode('puzzles/theme1code2.txt', 'puzzles/theme1task2.txt', 1)\n            sc1.next_scene = sc2\n            sc2.prev_scene = sc1\n            sc2.next_scene = sc3\n            sc3.prev_scene = sc2\n            sc3.next_scene = 'themes_menu'\n            director.run(sc1)\n        if theme == 2:\n            sc1 = SceneTheory('data/theme2pic1.jpg', 0, 'themes_menu')\n            sc2 = SceneCode('puzzles/theme2code1.txt', 'puzzles/theme2task1.txt', 0)\n            sc3 = SceneCode('puzzles/theme2code2.txt', 'puzzles/theme2task2.txt', 2)\n            sc1.next_scene = sc2\n            sc2.prev_scene = sc1\n            sc2.next_scene = sc3\n            sc3.prev_scene = sc2\n            sc3.next_scene = 'themes_menu'\n            director.run(sc1)\n        if theme == 3:\n            sc1 = SceneTheory('data/theme3pic1.jpg', 0, 'themes_menu')\n            sc2 = SceneCode('puzzles/theme3code1.txt', 'puzzles/theme3task1.txt', 0)\n            sc3 = SceneCode('puzzles/theme3code2.txt', 'puzzles/theme3task2.txt', 3)\n            sc1.next_scene = sc2\n            sc2.prev_scene = sc1\n            sc2.next_scene = sc3\n            sc3.prev_scene = sc2\n            sc3.next_scene = 'themes_menu'\n            director.run(sc1)\n        if theme == 4:\n            sc1 = SceneTheory('data/theme4pic1.jpg', 0, 'themes_menu')\n            sc2 = SceneCode('puzzles/theme4code1.txt', 'puzzles/theme4task1.txt', 0)\n            sc3 = SceneCode('puzzles/theme4code2.txt', 'puzzles/theme4task2.txt', 0)\n            sc4 = SceneCode('puzzles/theme4code3.txt', 'puzzles/theme4task3.txt', 4)\n            sc1.next_scene = sc2\n            sc2.prev_scene = sc1\n            sc2.next_scene = sc3\n            sc3.prev_scene = sc2\n            sc3.next_scene = sc4\n            sc4.prev_scene = sc3\n            sc4.next_scene = 'themes_menu'\n            director.run(sc1)\n        if theme == 5:\n            sc1 = SceneTheory('data/theme5pic1.jpg', 0, 'themes_menu')\n            sc2 = SceneCode('puzzles/theme5code1.txt', 'puzzles/theme5task1.txt', 0)\n            sc3 = SceneCode('puzzles/theme5code2.txt', 'puzzles/theme5task2.txt', 5)\n            sc1.next_scene = sc2\n            sc2.prev_scene = sc1\n            sc2.next_scene = sc3\n            sc3.prev_scene = sc2\n            sc3.next_scene = 'themes_menu'\n            director.run(sc1)\n        if theme == 6:\n            sc1 = SceneTheory('data/theme6pic1.jpg', 0, 'themes_menu')\n            sc2 = SceneCode('puzzles/theme6code1.txt', 'puzzles/theme6task1.txt', 0)\n            sc3 = SceneCode('puzzles/theme6code2.txt', 'puzzles/theme6task2.txt', 6)\n            sc1.next_scene = sc2\n            sc2.prev_scene = sc1\n            sc2.next_scene = sc3\n            sc3.prev_scene = sc2\n            sc3.next_scene = 'themes_menu'\n            director.run(sc1)\n        if theme == 7:\n            sc1 = SceneTheory('data/theme7pic1.jpg', 0, 'themes_menu')\n            sc2 = SceneCode('puzzles/theme7code1.txt', 'puzzles/theme7task1.txt', 0)\n            sc3 = SceneCode('puzzles/theme7code2.txt', 'puzzles/theme7task2.txt', 7)\n            sc1.next_scene = sc2\n            sc2.prev_scene = sc1\n            sc2.next_scene = sc3\n            sc3.prev_scene = sc2\n            sc3.next_scene = 'themes_menu'\n            director.run(sc1)\n        if theme == 8:\n            sc1 = SceneTheory('data/theme8pic1.jpg', 0, 'themes_menu')\n            sc2 = SceneCode('puzzles/theme8code1.txt', 'puzzles/theme8task1.txt', 8)\n            sc1.next_scene = sc2\n            sc2.prev_scene = sc1\n            sc2.next_scene = 'themes_menu'\n            director.run(sc1)\n\n    def on_back(self):\n        global scene_main_menu\n        director.run(scene_main_menu)\n\n    def on_quit(self):\n        global progress\n        open('progress', 'w').write(str(progress))\n        exit()\n\n# Тут начинается выполнение программы\ntry:\n    progress = int(open('progress', 'r').read())\nexcept FileNotFoundError:\n    progress = 0\ndirector.init(width=1024, height=768, caption=\"Code Puzzle\", fullscreen=False)  # инициализация окна игры\nbackground = cocos.sprite.Sprite(image='code-1024x768.jpg', position=(WIDTH/2, HEIGHT/2))\nscene_start = SceneStart()\nscene_main_menu = cocos.scene.Scene(background, MainMenu())\ndirector.run(scene_start)  # запускаем сцену\n", "repo_name": "Code-Puzzle/CodePuzzle", "sub_path": "codepuzzle.py", "file_name": "codepuzzle.py", "file_ext": "py", "file_size_in_byte": 28511, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "cocos.layer", "line_number": 14, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 32, "usage_type": "call"}, {"api_name": "cocos.text.Label", "line_number": 44, "usage_type": "call"}, {"api_name": "cocos.text", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cocos.layer", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cocos.text.Label", "line_number": 100, "usage_type": "call"}, {"api_name": "cocos.text", "line_number": 100, "usage_type": "attribute"}, {"api_name": "cocos.director.director.get_virtual_coordinates", "line_number": 112, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 112, "usage_type": "name"}, {"api_name": "cocos.director.director.get_virtual_coordinates", "line_number": 134, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 134, "usage_type": "name"}, {"api_name": "cocos.director.director.get_virtual_coordinates", "line_number": 171, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 171, "usage_type": "name"}, {"api_name": "cocos.layer", "line_number": 175, "usage_type": "attribute"}, {"api_name": "cocos.text.Label", "line_number": 198, "usage_type": "call"}, {"api_name": "cocos.text", "line_number": 198, "usage_type": "attribute"}, {"api_name": "cocos.director.director.get_virtual_coordinates", "line_number": 209, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 209, "usage_type": "name"}, {"api_name": "cocos.director.director.get_virtual_coordinates", "line_number": 223, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 223, "usage_type": "name"}, {"api_name": "cocos.director.director.get_virtual_coordinates", "line_number": 240, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 240, "usage_type": "name"}, {"api_name": "cocos.layer", "line_number": 252, "usage_type": "attribute"}, {"api_name": "cocos.sprite.Sprite", "line_number": 259, "usage_type": "call"}, {"api_name": "cocos.sprite", "line_number": 259, "usage_type": "attribute"}, {"api_name": "cocos.director.director.run", "line_number": 264, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 264, "usage_type": "name"}, {"api_name": "cocos.layer", "line_number": 267, "usage_type": "attribute"}, {"api_name": "cocos.director.director.get_virtual_coordinates", "line_number": 295, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 295, "usage_type": "name"}, {"api_name": "cocos.layer", "line_number": 314, "usage_type": "attribute"}, {"api_name": "pyglet.gl.glColor4f", "line_number": 332, "usage_type": "call"}, {"api_name": "pyglet.gl", "line_number": 332, "usage_type": "name"}, {"api_name": "pyglet.gl.glBegin", "line_number": 336, "usage_type": "call"}, {"api_name": "pyglet.gl", "line_number": 336, "usage_type": "name"}, {"api_name": "pyglet.gl.GL_QUADS", "line_number": 336, "usage_type": "attribute"}, {"api_name": "pyglet.gl.glVertex2f", "line_number": 337, "usage_type": "call"}, {"api_name": "pyglet.gl", "line_number": 337, "usage_type": "name"}, {"api_name": "pyglet.gl.glVertex2f", "line_number": 338, "usage_type": "call"}, {"api_name": "pyglet.gl", "line_number": 338, "usage_type": "name"}, {"api_name": "pyglet.gl.glVertex2f", "line_number": 339, "usage_type": "call"}, {"api_name": "pyglet.gl", "line_number": 339, "usage_type": "name"}, {"api_name": "pyglet.gl.glVertex2f", "line_number": 340, "usage_type": "call"}, {"api_name": "pyglet.gl", "line_number": 340, "usage_type": "name"}, {"api_name": "pyglet.gl.glEnd", "line_number": 341, "usage_type": "call"}, {"api_name": "pyglet.gl", "line_number": 341, "usage_type": "name"}, {"api_name": "pyglet.gl.glColor4f", "line_number": 342, "usage_type": "call"}, {"api_name": "pyglet.gl", "line_number": 342, "usage_type": "name"}, {"api_name": "cocos.scene", "line_number": 345, "usage_type": "attribute"}, {"api_name": "cocos.scene", "line_number": 355, "usage_type": "attribute"}, {"api_name": "cocos.sprite.Sprite", "line_number": 361, "usage_type": "call"}, {"api_name": "cocos.sprite", "line_number": 361, "usage_type": "attribute"}, {"api_name": "cocos.sprite.Sprite", "line_number": 363, "usage_type": "call"}, {"api_name": "cocos.sprite", "line_number": 363, "usage_type": "attribute"}, {"api_name": "cocos.scene.Scene", "line_number": 378, "usage_type": "call"}, {"api_name": "cocos.scene", "line_number": 378, "usage_type": "attribute"}, {"api_name": "cocos.director.director.replace", "line_number": 379, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 379, "usage_type": "name"}, {"api_name": "cocos.scene.Scene", "line_number": 384, "usage_type": "call"}, {"api_name": "cocos.scene", "line_number": 384, "usage_type": "attribute"}, {"api_name": "cocos.director.director.replace", "line_number": 385, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 385, "usage_type": "name"}, {"api_name": "cocos.scene", "line_number": 388, "usage_type": "attribute"}, {"api_name": "cocos.sprite.Sprite", "line_number": 394, "usage_type": "call"}, {"api_name": "cocos.sprite", "line_number": 394, "usage_type": "attribute"}, {"api_name": "cocos.text.Label", "line_number": 406, "usage_type": "call"}, {"api_name": "cocos.text", "line_number": 406, "usage_type": "attribute"}, {"api_name": "cocos.scene.Scene", "line_number": 416, "usage_type": "call"}, {"api_name": "cocos.scene", "line_number": 416, "usage_type": "attribute"}, {"api_name": "cocos.director.director.replace", "line_number": 417, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 417, "usage_type": "name"}, {"api_name": "cocos.scene.Scene", "line_number": 422, "usage_type": "call"}, {"api_name": "cocos.scene", "line_number": 422, "usage_type": "attribute"}, {"api_name": "cocos.director.director.replace", "line_number": 423, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 423, "usage_type": "name"}, {"api_name": "cocos.menu", "line_number": 426, "usage_type": "attribute"}, {"api_name": "cocos.menu", "line_number": 429, "usage_type": "attribute"}, {"api_name": "cocos.menu", "line_number": 430, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 433, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 433, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 434, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 434, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 435, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 435, "usage_type": "attribute"}, {"api_name": "cocos.director.director.run", "line_number": 440, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 440, "usage_type": "name"}, {"api_name": "cocos.scene.Scene", "line_number": 440, "usage_type": "call"}, {"api_name": "cocos.scene", "line_number": 440, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 449, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 452, "usage_type": "attribute"}, {"api_name": "cocos.menu", "line_number": 456, "usage_type": "attribute"}, {"api_name": "cocos.menu", "line_number": 457, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 460, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 460, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 462, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 462, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 464, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 464, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 466, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 466, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 468, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 468, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 470, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 470, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 472, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 472, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 474, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 474, "usage_type": "attribute"}, {"api_name": "cocos.menu.MenuItem", "line_number": 475, "usage_type": "call"}, {"api_name": "cocos.menu", "line_number": 475, "usage_type": "attribute"}, {"api_name": "cocos.director.director.run", "line_number": 489, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 489, "usage_type": "name"}, {"api_name": "cocos.director.director.run", "line_number": 499, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 499, "usage_type": "name"}, {"api_name": "cocos.director.director.run", "line_number": 509, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 509, "usage_type": "name"}, {"api_name": "cocos.director.director.run", "line_number": 522, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 522, "usage_type": "name"}, {"api_name": "cocos.director.director.run", "line_number": 532, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 532, "usage_type": "name"}, {"api_name": "cocos.director.director.run", "line_number": 542, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 542, "usage_type": "name"}, {"api_name": "cocos.director.director.run", "line_number": 552, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 552, "usage_type": "name"}, {"api_name": "cocos.director.director.run", "line_number": 559, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 559, "usage_type": "name"}, {"api_name": "cocos.director.director.run", "line_number": 563, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 563, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 568, "usage_type": "call"}, {"api_name": "cocos.director.director.init", "line_number": 575, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 575, "usage_type": "name"}, {"api_name": "cocos.sprite.Sprite", "line_number": 576, "usage_type": "call"}, {"api_name": "cocos.sprite", "line_number": 576, "usage_type": "attribute"}, {"api_name": "cocos.scene.Scene", "line_number": 578, "usage_type": "call"}, {"api_name": "cocos.scene", "line_number": 578, "usage_type": "attribute"}, {"api_name": "cocos.director.director.run", "line_number": 579, "usage_type": "call"}, {"api_name": "cocos.director.director", "line_number": 579, "usage_type": "name"}]}
{"seq_id": "73484338363", "text": "import argparse\r\nimport matplotlib.pyplot as plt\r\nimport torch\r\nimport numpy as np\r\nfrom torch.utils.data import DataLoader\r\nfrom model import ae_flow\r\nfrom tqdm import tqdm\r\nfrom HYPERPARAMETER import alpha, beta, batch_size, epochs\r\nfrom dataloader import ChestXrayDataset\r\nfrom sklearn.metrics import accuracy_score, f1_score, confusion_matrix, auc, roc_curve\r\nfrom model.utils import optimize_threshold\r\n\r\n\r\ndef metrics(true, anomaly_scores, threshold):\r\n    results = {}\r\n\r\n    # Calculate true positive (tp), false positive (fp), false negative (fn), true negative (tn)\r\n    tp = sum(true[i] == 1 and anomaly_scores[i] >= threshold for i in range(len(true)))\r\n    fp = sum(true[i] == 0 and anomaly_scores[i] >= threshold for i in range(len(true)))\r\n    fn = sum(true[i] == 1 and anomaly_scores[i] < threshold for i in range(len(true)))\r\n    tn = sum(true[i] == 0 and anomaly_scores[i] < threshold for i in range(len(true)))\r\n\r\n    # Calculate true positive rate (sensitivity/recall)\r\n    sen = tp / (tp + fn)\r\n    # Calculate true negative rate (specificity)\r\n    spe = tn / (tn + fp)\r\n    # Calculate accuracy\r\n    acc = (tp + tn) / (tp + fp + fn + tn)\r\n    # Calculate F1 score\r\n    f1 = (2 * tp) / (2 * tp + fp + fn)\r\n    # Calculate ROC curve\r\n    fpr, tpr, _ = roc_curve(true, anomaly_scores)\r\n    # Calculate AUC\r\n    auc_score = auc(fpr, tpr)\r\n\r\n    # Store the results\r\n    results['AUC'] = auc_score\r\n    results['ACC'] = acc\r\n    results['SEN'] = sen\r\n    results['SPE'] = spe\r\n    results['F1'] = f1\r\n\r\n    return results\r\n\r\ndef plot_anomaly_score_distribution(true_labels, anomaly_scores):\r\n    normal_scores = np.array([score for label, score in zip(true_labels, anomaly_scores) if label == 0])\r\n    anomaly_scores = np.array([score for label, score in zip(true_labels, anomaly_scores) if label == 1])\r\n\r\n    plt.figure(figsize=(12, 8))\r\n\r\n    # Plot histogram for normal scores\r\n    hist, bins = np.histogram(normal_scores, bins=100, density=True)\r\n    plt.plot(bins[:-1], hist, color='blue', alpha=0.7, label='Normal')\r\n    # Plot histogram for anomaly scores\r\n    hist, bins = np.histogram(anomaly_scores, bins=50, density=True)\r\n    plt.plot(bins[:-1], hist, color='red', alpha=0.7, label='Anomaly')\r\n\r\n    plt.xlabel('Anomaly Score')\r\n    plt.ylabel('Density')\r\n    plt.title('Anomaly Score Distribution')\r\n    plt.legend()\r\n    plt.grid(True, linestyle='--', alpha=0.5)\r\n    plt.tight_layout()\r\n    plt.show()\r\n\r\n\r\ndef train():\r\n    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\r\n    model = ae_flow.AE_FLOW()\r\n    dataset = ChestXrayDataset(root=\"./data\", name='chest_xray', split='train', label='NORMAL')\r\n    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\r\n    optimizer = torch.optim.Adam(model.parameters())\r\n\r\n    for epoch in range(epochs):\r\n        model.train()\r\n        epoch_loss = 0.0\r\n        anomaly_scores = []\r\n        true_labels = []\r\n        # label always is normal(0)\r\n        for i, (img, lbl) in tqdm(enumerate(dataloader)):\r\n            img = img.to(device)\r\n            rec_img, z_hat, jac = model(img)\r\n            recon_loss = torch.nn.functional.mse_loss(rec_img, img)\r\n            flow_loss, log_z = model.flow_loss()\r\n            loss = (1 - alpha) * recon_loss + alpha * flow_loss\r\n\r\n            epoch_loss += loss.item()\r\n            optimizer.zero_grad()\r\n            loss.backward()\r\n            optimizer.step()\r\n\r\n            anomaly_score = model.anomaly_score(beta, log_z, img)\r\n            anomaly_scores.append(anomaly_score)\r\n            true_labels.append(lbl)\r\n\r\n    true = [tensor.cpu().numpy() for tensor in true_labels]\r\n    true = [item for sublist in true for item in sublist]\r\n\r\n    anomaly_scores = [tensor.cpu().numpy() for tensor in anomaly_scores]\r\n    anomaly_scores = [item for sublist in anomaly_scores for item in sublist]\r\n\r\n    # mean_anomaly_score = torch.mean(torch.cat(anomaly_scores))\r\n    # print(f\"Mean anomaly score: {mean_anomaly_score}\")\r\n\r\n    # Plot anomaly score distribution\r\n    plot_anomaly_score_distribution(true_labels, anomaly_scores)\r\n\r\n    # Find optimal threshold\r\n    optimal_threshold = optimize_threshold(anomaly_scores, true_labels)\r\n    results = metrics(true, anomaly_scores, optimal_threshold)\r\n\r\n    print(f\"Optimal threshold: {optimal_threshold}\")\r\n    print(f\"Epoch {epoch}: {results}\")\r\n\r\n    # TODO: best f1 score\r\n\r\n    return results\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    parser = argparse.ArgumentParser()\r\n    args = parser.parse_args()\r\n    train()\r\n", "repo_name": "Urukat/AE-Flow", "sub_path": "src/train_new.py", "file_name": "train_new.py", "file_ext": "py", "file_size_in_byte": 4505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sklearn.metrics.roc_curve", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 68, "usage_type": "attribute"}, {"api_name": "model.ae_flow.AE_FLOW", "line_number": 69, "usage_type": "call"}, {"api_name": "model.ae_flow", "line_number": 69, "usage_type": "name"}, {"api_name": "dataloader.ChestXrayDataset", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 71, "usage_type": "call"}, {"api_name": "HYPERPARAMETER.batch_size", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 72, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 72, "usage_type": "call"}, {"api_name": "HYPERPARAMETER.epochs", "line_number": 74, "usage_type": "argument"}, {"api_name": "model.train", "line_number": 75, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "attribute"}, {"api_name": "model.flow_loss", "line_number": 84, "usage_type": "call"}, {"api_name": "HYPERPARAMETER.alpha", "line_number": 85, "usage_type": "name"}, {"api_name": "model.anomaly_score", "line_number": 92, "usage_type": "call"}, {"api_name": "HYPERPARAMETER.beta", "line_number": 92, "usage_type": "argument"}, {"api_name": "model.utils.optimize_threshold", "line_number": 109, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 121, "usage_type": "call"}]}
{"seq_id": "26004787683", "text": "import logging\nfrom threading import Lock\n\nfrom utilities import hms_time\nfrom dgt.iface import DgtIface\nfrom dgt.util import ClockIcons, ClockSide, DgtClk, DgtCmd\nfrom dgt.translate import DgtTranslate\nfrom dgt.board import DgtBoard\n\n\nclass DgtHw(DgtIface):\n\n    \"\"\"Handle the DgtXL/3000 communication.\"\"\"\n\n    def __init__(self, dgtboard: DgtBoard):\n        super(DgtHw, self).__init__(dgtboard)\n\n        self.lib_lock = Lock()\n\n    def _display_on_dgt_xl(self, text: str, beep=False, left_icons=ClockIcons.NONE, right_icons=ClockIcons.NONE):\n        text = text.ljust(6)\n        if len(text) > 6:\n            logging.warning('(ser) clock message too long [%s]', text)\n        logging.debug('[%s]', text)\n        with self.lib_lock:\n            res = self.dgtboard.set_text_xl(text, 0x03 if beep else 0x00, left_icons, right_icons)\n            if not res:\n                logging.warning('SetText() returned error %i', res)\n            return res\n\n    def _display_on_dgt_3000(self, text: str, beep=False):\n        text = text.ljust(8)\n        if len(text) > 8:\n            logging.warning('(ser) clock message too long [%s]', text)\n        logging.debug('[%s]', text)\n        text = bytes(text, 'utf-8')\n        with self.lib_lock:\n            res = self.dgtboard.set_text_3k(text, 0x03 if beep else 0x00)\n            if not res:\n                logging.warning('SetText() returned error %i', res)\n            return res\n\n    def _display_on_rev2_pi(self, text: str, beep=False):\n        text = text.ljust(11)\n        if len(text) > 11:\n            logging.warning('(rev) clock message too long [%s]', text)\n        logging.debug('[%s]', text)\n        text = bytes(text, 'utf-8')\n        with self.lib_lock:\n            res = self.dgtboard.set_text_rp(text, 0x03 if beep else 0x00)\n            if not res:\n                logging.warning('SetText() returned error %i', res)\n            return res\n\n    def display_text_on_clock(self, message):\n        \"\"\"Display a text on the dgtxl/3k/rev2.\"\"\"\n        is_new_rev2 = self.dgtboard.is_revelation and self.dgtboard.enable_revelation_pi\n        if is_new_rev2:\n            text = message.l\n        else:\n            text = message.m if self.enable_dgt3000 else message.s\n        if self.get_name() not in message.devs:\n            logging.debug('ignored %s - devs: %s', text, message.devs)\n            return True\n\n        if is_new_rev2:\n            return self._display_on_rev2_pi(text, message.beep)\n        else:\n            if self.enable_dgt3000:\n                return self._display_on_dgt_3000(text, message.beep)\n            else:\n                left_icons = message.ld if hasattr(message, 'ld') else ClockIcons.NONE\n                right_icons = message.rd if hasattr(message, 'rd') else ClockIcons.NONE\n                return self._display_on_dgt_xl(text, message.beep, left_icons, right_icons)\n\n    def display_move_on_clock(self, message):\n        \"\"\"Display a move on the dgtxl/3k/rev2.\"\"\"\n        is_new_rev2 = self.dgtboard.is_revelation and self.dgtboard.enable_revelation_pi\n        if self.enable_dgt3000 or is_new_rev2:\n            bit_board, text = self.get_san(message)\n            if is_new_rev2:\n                text = '{:3d}{:s}'.format(bit_board.fullmove_number, text)\n        else:\n            text = message.move.uci()\n            if message.side == ClockSide.RIGHT:\n                text = text[:2].rjust(3) + text[2:].rjust(3)\n            else:\n                text = text[:2].ljust(3) + text[2:].ljust(3)\n        if self.get_name() not in message.devs:\n            logging.debug('ignored %s - devs: %s', text, message.devs)\n            return True\n        if is_new_rev2:\n            return self._display_on_rev2_pi(text, message.beep)\n        else:\n            if self.enable_dgt3000:\n                return self._display_on_dgt_3000(text, message.beep)\n            else:\n                left_icons = message.ld if hasattr(message, 'ld') else ClockIcons.NONE\n                right_icons = message.rd if hasattr(message, 'rd') else ClockIcons.NONE\n                return self._display_on_dgt_xl(text, message.beep, left_icons, right_icons)\n\n    def display_time_on_clock(self, message):\n        \"\"\"Display the time on the dgtxl/3k/rev2.\"\"\"\n        if self.get_name() not in message.devs:\n            logging.debug('ignored endText - devs: %s', message.devs)\n            return True\n        if self.side_running != ClockSide.NONE or message.force:\n            with self.lib_lock:\n                if self.dgtboard.l_time >= 3600 * 10 or self.dgtboard.r_time >= 3600 * 10:\n                    logging.debug('time values not set - abort function')\n                    return False\n                else:\n                    if self.dgtboard.in_settime:\n                        logging.debug('(ser) clock still in set mode - abort function')\n                        return False\n                    return self.dgtboard.end_text()\n        else:\n            logging.debug('(ser) clock isnt running - no need for endText')\n            return True\n\n    def light_squares_on_revelation(self, uci_move: str):\n        \"\"\"Light the Rev2 leds.\"\"\"\n        self.dgtboard.light_squares_on_revelation(uci_move)\n        return True\n\n    def clear_light_on_revelation(self):\n        \"\"\"Clear the Rev2 leds.\"\"\"\n        self.dgtboard.clear_light_on_revelation()\n        return True\n\n    def stop_clock(self, devs: set):\n        \"\"\"Stop the dgtxl/3k.\"\"\"\n        if self.get_name() not in devs:\n            logging.debug('ignored stopClock - devs: %s', devs)\n            return True\n        logging.debug('(%s) clock sending stop time to clock l:%s r:%s', ','.join(devs),\n                      hms_time(self.dgtboard.l_time), hms_time(self.dgtboard.r_time))\n        return self._resume_clock(ClockSide.NONE)\n\n    def _resume_clock(self, side: ClockSide):\n        if self.dgtboard.l_time >= 3600 * 10 or self.dgtboard.r_time >= 3600 * 10:\n            logging.debug('time values not set - abort function')\n            return False\n\n        l_run = r_run = 0\n        if side == ClockSide.LEFT:\n            l_run = 1\n        if side == ClockSide.RIGHT:\n            r_run = 1\n        with self.lib_lock:\n            l_hms = hms_time(self.dgtboard.l_time)\n            r_hms = hms_time(self.dgtboard.r_time)\n            res = self.dgtboard.set_and_run(l_run, l_hms[0], l_hms[1], l_hms[2], r_run, r_hms[0], r_hms[1], r_hms[2])\n            if not res:\n                logging.warning('finally failed %i', res)\n                return False\n            else:\n                self.side_running = side\n            if not self.dgtboard.disable_end:\n                res = self.dgtboard.end_text()  # this is needed for some(!) clocks\n            self.dgtboard.in_settime = False  # @todo should be set on ACK (see: DgtBoard) not here\n            return res\n\n    def start_clock(self, side: ClockSide, devs: set):\n        \"\"\"Start the dgtxl/3k.\"\"\"\n        if self.get_name() not in devs:\n            logging.debug('ignored startClock - devs: %s', devs)\n            return True\n        logging.debug('(%s) clock sending start time to clock l:%s r:%s', ','.join(devs),\n                      hms_time(self.dgtboard.l_time), hms_time(self.dgtboard.r_time))\n        return self._resume_clock(side)\n\n    def set_clock(self, time_left: int, time_right: int, devs: set):\n        \"\"\"Start the dgtxl/3k.\"\"\"\n        if self.get_name() not in devs:\n            logging.debug('ignored setClock - devs: %s', devs)\n            return True\n        logging.debug('(%s) clock received last time from clock l:%s r:%s [ign]', ','.join(devs),\n                      hms_time(self.dgtboard.l_time), hms_time(self.dgtboard.r_time))\n        logging.debug('(%s) clock sending set time to clock l:%s r:%s [use]', ','.join(devs),\n                      hms_time(time_left), hms_time(time_right))\n        self.dgtboard.in_settime = True  # it will return to false as soon SetAndRun ack received\n        self.dgtboard.l_time = time_left\n        self.dgtboard.r_time = time_right\n        return True\n\n    def get_name(self):\n        \"\"\"Get name.\"\"\"\n        return 'ser'\n", "repo_name": "jromang/picochess", "sub_path": "dgt/hw.py", "file_name": "hw.py", "file_ext": "py", "file_size_in_byte": 8079, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 72, "dataset": "github-code", "pt": "40", "api": [{"api_name": "dgt.iface.DgtIface", "line_number": 11, "usage_type": "name"}, {"api_name": "dgt.board.DgtBoard", "line_number": 15, "usage_type": "name"}, {"api_name": "threading.Lock", "line_number": 18, "usage_type": "call"}, {"api_name": "dgt.util.ClockIcons.NONE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockIcons", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 63, "usage_type": "call"}, {"api_name": "dgt.util.ClockIcons.NONE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockIcons", "line_number": 72, "usage_type": "name"}, {"api_name": "dgt.util.ClockIcons.NONE", "line_number": 73, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockIcons", "line_number": 73, "usage_type": "name"}, {"api_name": "dgt.util.ClockSide.RIGHT", "line_number": 85, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockSide", "line_number": 85, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 90, "usage_type": "call"}, {"api_name": "dgt.util.ClockIcons.NONE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockIcons", "line_number": 98, "usage_type": "name"}, {"api_name": "dgt.util.ClockIcons.NONE", "line_number": 99, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockIcons", "line_number": 99, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 105, "usage_type": "call"}, {"api_name": "dgt.util.ClockSide.NONE", "line_number": 107, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockSide", "line_number": 107, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 110, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 114, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 118, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 134, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 136, "usage_type": "call"}, {"api_name": "utilities.hms_time", "line_number": 137, "usage_type": "call"}, {"api_name": "dgt.util.ClockSide.NONE", "line_number": 138, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockSide", "line_number": 138, "usage_type": "name"}, {"api_name": "dgt.util.ClockSide", "line_number": 140, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 142, "usage_type": "call"}, {"api_name": "dgt.util.ClockSide.LEFT", "line_number": 146, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockSide", "line_number": 146, "usage_type": "name"}, {"api_name": "dgt.util.ClockSide.RIGHT", "line_number": 148, "usage_type": "attribute"}, {"api_name": "dgt.util.ClockSide", "line_number": 148, "usage_type": "name"}, {"api_name": "utilities.hms_time", "line_number": 151, "usage_type": "call"}, {"api_name": "utilities.hms_time", "line_number": 152, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 155, "usage_type": "call"}, {"api_name": "dgt.util.ClockSide", "line_number": 164, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 167, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 169, "usage_type": "call"}, {"api_name": "utilities.hms_time", "line_number": 170, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 176, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 178, "usage_type": "call"}, {"api_name": "utilities.hms_time", "line_number": 179, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 180, "usage_type": "call"}, {"api_name": "utilities.hms_time", "line_number": 181, "usage_type": "call"}]}
{"seq_id": "22795881589", "text": "import sys\nimport cv2 as cv\nimport qdarkstyle\n\nfrom PyQt5 import uic, QtWidgets, QtCore\nfrom PyQt5.QtWidgets import QLabel, QApplication, QMainWindow, QFileDialog, QAction, QMessageBox\nfrom PyQt5.QtGui import QPixmap, QImage, QIcon\n\nfrom basicdip import BasicImageProcessing as BIP\n\n#filename = \"\"\nclass UI(QtWidgets.QMainWindow):\n\n    def __init__(self):\n        self.filename = \"\"\n        super(UI, self).__init__()\n        uic.loadUi(\"interface.ui\", self)\n        self.setWindowTitle(\"Image Processing\")\n        self.show()\n        self.Init()\n        self.bip = \"\"\n        self.toolbar = False\n        self.img_exist = False\n        self.x_current = 0\n        self.y_current = 0\n        self.setWindowIcon(QIcon(\"images/icon/mainicon.png\"))\n        self.curImg = False\n        #self.test()\n\n    def Init(self):\n        #bắt các sự kiện action\n        self.actionOpenImage.triggered.connect(self.openimage)\n        self.actionChuong3.triggered.connect(self.chuong3_clicked)\n        self.actionChuong4.triggered.connect(self.chuong4_clicked)\n        self.actionChuong5.triggered.connect(self.chuong5_clicked)\n        self.actionChuong7.triggered.connect(self.chuong7_clicked)\n        self.actionChuong8.triggered.connect(self.chuong8_clicked)\n        self.actionSave.triggered.connect(self.saveImg)\n\n        #bắt sự kiện các input\n        #chương 2\n        self.rotation_inp.valueChanged.connect(self.rotation)\n        self.scaling_inp.valueChanged.connect(self.scaling)\n        self.tranX_inp.valueChanged.connect(self.getTranX)\n        self.tranY_inp.valueChanged.connect(self.getTranY)\n        self.affine_inp.valueChanged.connect(self.affine)\n\n        #Chương 3\n        self.log_inp.valueChanged.connect(self.log)\n        self.gamma_inp.valueChanged.connect(self.gamma)\n\n        #chương 4\n        self.median_inp.valueChanged.connect(self.median)\n        self.gaussian_inp.valueChanged.connect(self.gaussian)\n        self.highboost_inp.valueChanged.connect(self.highBoost)\n        self.average_inp.valueChanged.connect(self.avg)\n\n        #chuongw 7\n        self.morphology_inp.valueChanged.connect(self.morphology)\n\n        #Checkbox\n        self.chuong2Check.stateChanged.connect(self.groupBox2Enable)\n        self.chuong3Check.stateChanged.connect(self.groupBox3Enable)\n        self.chuong4Check.stateChanged.connect(self.groupBox4Enable)\n        self.chuong7Check.stateChanged.connect(self.groupBox7Enable)\n\n\n    def test(self, m):\n        self.median_val.setText(str(m))\n\n    def saveImg(self):\n        img = self.curImg\n        # size đến tên hình =36\n        s = self.filename[0]\n        print(s)\n        photoName = \"\"\n        for i in range(len(s) - 1, 0, -1):\n            if s[i] == '/':\n                photoName = s[i + 1:len(s) - 4]\n                photoName = photoName + \"_Changed\" + s[len(s) - 4:]\n                break\n\n        q = QMessageBox.question(self, \"Xác nhận\", \"Lưu ảnh hiện tại?\", QMessageBox.Yes | QMessageBox.No,\n                                 QMessageBox.No)\n        if (q == QMessageBox.Yes):\n            cv.imwrite(photoName, img)\n            QMessageBox.about(self, \"Thông báo\", \"Lưu ảnh thành công.\")\n            #self.close()\n\n\n    def groupBox2Enable(self):\n        if (self.img_exist == True):\n            if self.chuong2Check.isChecked():\n                self.chuong2Group.setEnabled(True)\n                self.scaling_inp.setEnabled(True)\n                self.scaling_val.setEnabled(True)\n            else:\n                self.chuong2Group.setEnabled(False)\n        else:\n            self.chuong2Check.setChecked(False)\n            QMessageBox.about(self, \"Thông Báo\", \"Chưa chọn hình ?!!\")\n\n    def groupBox3Enable(self):\n        if (self.img_exist == True):\n            if self.chuong3Check.isChecked():\n                self.chuong3Group.setEnabled(True)\n            else:\n                self.chuong3Group.setEnabled(False)\n        else:\n            self.chuong3Check.setChecked(False)\n            QMessageBox.about(self, \"Thông Báo\", \"Chưa chọn hình ?!!\")\n\n    def groupBox4Enable(self):\n        if (self.img_exist == True):\n            if self.chuong4Check.isChecked():\n                self.chuong4Group.setEnabled(True)\n            else:\n                self.chuong4Group.setEnabled(False)\n        else:\n            self.chuong4Check.setChecked(False)\n            QMessageBox.about(self, \"Thông Báo\", \"Chưa chọn hình ?!!\")\n\n    def groupBox7Enable(self):\n        if (self.img_exist == True):\n            if self.chuong7Check.isChecked():\n                self.chuong7Group.setEnabled(True)\n            else:\n                self.chuong7Group.setEnabled(False)\n        else:\n            self.chuong7Check.setChecked(False)\n            QMessageBox.about(self, \"Thông Báo\", \"Chưa chọn hình ?!!\")\n\n    def openimage(self):\n        self.filename = QFileDialog.getOpenFileName(self, \"Choose Image\", \"\",\"Images File(*.jpg; *.jpeg; *.png);;Python Files (*.py)\")\n        #print (self.filename[0])\n        if(self.filename[0] != ''):\n            width = self.labelOriginal.width()\n            height = self.labelOriginal.height()\n            self.labelOriginal.setPixmap(QPixmap(self.filename[0]).scaled(width, height))\n            self.labelChanged.setPixmap(QPixmap(self.filename[0]).scaled(width, height))\n            width = self.labelOriginal.width()\n            height = self.labelOriginal.height()\n            self.bip = BIP(self.filename[0], width, height)\n            self.img_exist = True\n\n    def bindingToLabel(self, changed_img):\n        qformat = QImage.Format_Indexed8\n        self.curImg=changed_img\n\n        if len(changed_img.shape) == 3:\n            if (changed_img.shape[2]) == 4:\n                qformat = QImage.Format_RGBA8888\n            else:\n                qformat = QImage.Format_RGB888\n        img = QImage(changed_img, changed_img.shape[1], changed_img.shape[0], changed_img.strides[0], qformat)\n        # BGR > RGB\n        img = img.rgbSwapped()\n        self.labelChanged.setPixmap(QPixmap.fromImage(img))\n        self.labelChanged.setAlignment(QtCore.Qt.AlignHCenter | QtCore.Qt.AlignVCenter)\n\n    def negative(self):\n        res = self.bip.negative()\n        self.bindingToLabel(res)\n\n    def histogram(self):\n        res = self.bip.histogram()\n        self.bindingToLabel(res)\n\n    def orignal(self):\n        res = self.bip.original()\n        self.bindingToLabel(res)\n\n    def log(self, m):\n        self.log_val.setText(str(m))\n        res = self.bip.log(m)\n        self.bindingToLabel(res)\n\n    def gamma(self, m):\n        self.gamma_val.setText(str(\"{0:.1f}\".format(round(m*0.1, 1))))\n        res = self.bip.gamma(m*0.1)\n        self.bindingToLabel(res)\n\n    def getTranX(self, x):\n        self.tranX_val.setText(str(x))\n        self.translation()\n\n    def getTranY(self, y):\n        self.tranY_val.setText(str(y))\n        self.translation()\n\n    def translation(self):\n        x = int(self.tranX_val.text())\n        y = int(self.tranY_val.text())\n        res = self.bip.translation(x, y)\n        self.bindingToLabel(res)\n\n    def scaling(self, size):\n        res = self.bip.scaling(size)\n        self.scaling_val.setText(str(size) + \"%\")\n        self.bindingToLabel(res)\n\n    def rotation(self, angle):\n        self.rotation_val.setText(str(angle)+\"°\")\n        res = self.bip.rotation(angle)\n        self.bindingToLabel(res)\n\n    def affine(self, m):\n        self.affine_val.setText(str(m))\n        res = self.bip.affine(m)\n        self.bindingToLabel(res)\n\n    #Chuong4\n\n    def avg(self, m):\n        self.average_val.setText(str(m * 2 - 1))\n        size = int(self.average_val.text())\n        res = self.bip.avg(size)\n        self.bindingToLabel(res)\n\n    def gaussian(self, m):\n        self.gaussian_val.setText(str(m * 2 - 1))\n        size = int(self.gaussian_val.text())\n        res = self.bip.gaussian(size)\n        self.bindingToLabel(res)\n\n    def median(self, m):\n        self.median_val.setText(str(m*2-1))\n        size = int(self.median_val.text())\n        res = self.bip.median(size)\n        self.bindingToLabel(res)\n\n    def unMark(self):\n        res=self.bip.unMark()\n        self.bindingToLabel(res)\n\n    def laplacian(self):\n        res=self.bip.laplacian()\n        self.bindingToLabel(res)\n\n    def compositeLaplacian(self):\n        res=self.bip.compositeLaplacian()\n        self.bindingToLabel(res)\n\n    def highBoost(self, m):\n        self.highboost_val.setText(str(m))\n        A = int(self.highboost_val.text())\n        res = self.bip.highBoost(A*1.0)\n        self.bindingToLabel(res)\n\n    def fourier(self):\n        res=self.bip.fourier()\n        #self.bindingToLabel(res)\n\n    def highPass(self):\n        res=self.bip.highPassGaussian()\n        self.bindingToLabel(res)\n\n    def canny(self):\n        res=self.bip.Canny()\n        self.bindingToLabel(res)\n\n    #chương 7\n    def morphology(self, m):\n        self.morphology_val.setText(str(m))\n        res = self.bip.morphology(m)\n        self.bindingToLabel(res)\n    def open(self):\n        res = self.bip.open()\n        self.bindingToLabel(res)\n    def close(self):\n        res = self.bip.close()\n        self.bindingToLabel(res)\n    def gradient(self):\n        res = self.bip.gradient()\n        self.bindingToLabel(res)\n    def morboundary(self):\n        res = self.bip.morboundary()\n        self.bindingToLabel(res)\n    def convex(self):\n        res = self.bip.convex()\n        self.bindingToLabel(res)\n\n    #chương 8\n    def sobelx(self):\n        res = self.bip.sobelX()\n        self.bindingToLabel(res)\n    def sobely(self):\n        res = self.bip.sobelY()\n        self.bindingToLabel(res)\n    def lapcian(self):\n        res = self.bip.lapcian()\n        self.bindingToLabel(res)\n\n    #triggered event\n    def chuong3_clicked(self):\n        if (self.img_exist == True):\n            self.chuong3Check.setChecked(True)\n            if (self.toolbar == False):\n                self.toolbar = self.addToolBar('Toolbar')\n            else:\n                self.toolbar.clear()\n            actionOriginal = QAction(QIcon('images/icon/original.png'), 'Original', self)\n            actionOriginal.triggered.connect(self.orignal)\n\n\n            actionNegative = QAction(QIcon('images/icon/negative.png'), 'Negative', self)\n            actionNegative.triggered.connect(self.negative)\n\n\n            actionHistogram = QAction(QIcon('images/icon/histogram.png'), 'Histogram', self)\n            actionHistogram.triggered.connect(self.histogram)\n\n            self.toolbar.clear()\n            self.toolbar.addAction(actionOriginal)\n            self.toolbar.addAction(actionNegative)\n            self.toolbar.addAction(actionHistogram)\n            self.chuong3Group.setEnabled(True)\n        else:\n            QMessageBox.about(self, \"Thông Báo\", \"Chưa chọn hình ?!!\")\n\n    def chuong4_clicked(self):\n        if (self.img_exist == True):\n            self.chuong4Check.setChecked(True)\n            if (self.toolbar == False):\n                self.toolbar = self.addToolBar('Toolbar')\n            else:\n                self.toolbar.clear()\n\n            actionUnSharp = QAction(QIcon('images/icon/unsharp.png'), 'Unsharp', self)\n            actionUnSharp.triggered.connect(self.unMark)\n\n            actionLaplacian = QAction('Laplacian', self)\n            actionLaplacian.triggered.connect(self.laplacian)\n\n            actionCLaplacian = QAction('Composite Laplacian', self)\n            actionCLaplacian.triggered.connect(self.compositeLaplacian)\n            self.toolbar.addAction(actionUnSharp)\n            self.toolbar.addAction(actionLaplacian)\n            self.toolbar.addAction(actionCLaplacian)\n\n            self.chuong4Group.setEnabled(True)\n\n        else:\n            QMessageBox.about(self, \"Thông Báo\", \"Chưa chọn hình ?!!\")\n\n    #Chương 5\n    def chuong5_clicked(self):\n        if (self.img_exist == True):\n            if (self.toolbar == False):\n                self.toolbar = self.addToolBar('Toolbar')\n            else:\n                self.toolbar.clear()\n            actionFourier = QAction('Fourier', self)\n            actionFourier.triggered.connect(self.fourier)\n\n            actionHighPass = QAction('High-Pass Filter', self)\n            actionHighPass.triggered.connect(self.highPass)\n\n            #self.toolbar.addAction(actionFourier)\n            self.toolbar.addAction(actionHighPass)\n        else:\n            QMessageBox.about(self, \"Thông Báo\", \"Chưa chọn hình ?!!\")\n\n    #chương 7\n    def chuong7_clicked(self):\n        if (self.img_exist == True):\n            self.chuong7Check.setChecked(True)\n            if (self.toolbar == False):\n                self.toolbar = self.addToolBar('Toolbar')\n            else:\n                self.toolbar.clear()\n\n            actionOpen = QAction(QIcon('images/icon/open.png'), 'Open', self)\n            actionOpen.triggered.connect(self.open)\n            self.toolbar.addAction(actionOpen)\n\n            actionClose = QAction(QIcon('images/icon/close.png'), 'Close', self)\n            actionClose.triggered.connect(self.close)\n            self.toolbar.addAction(actionClose)\n\n            actionGradient = QAction(QIcon('images/icon/gradient.png'), 'Gradient', self)\n            actionGradient.triggered.connect(self.gradient)\n            self.toolbar.addAction(actionGradient)\n\n            actionMorboundary = QAction(QIcon('images/icon/morboundary.png'), 'Morboundary', self)\n            actionMorboundary.triggered.connect(self.morboundary)\n            self.toolbar.addAction(actionMorboundary)\n\n            actionConvex = QAction(QIcon('images/icon/convex.png'), 'Convex', self)\n            actionConvex.triggered.connect(self.convex)\n            self.toolbar.addAction(actionConvex)\n\n            self.chuong7Group.setEnabled(True)\n\n        else:\n            QMessageBox.about(self, \"Thông Báo\", \"Chưa chọn hình ?!!\")\n\n    def chuong8_clicked(self):\n        if (self.img_exist == True):\n            if (self.toolbar == False):\n                self.toolbar = self.addToolBar('Toolbar')\n            else:\n                self.toolbar.clear()\n\n            actionSobelX = QAction(QIcon('images/icon/sobelx.png'), 'SobelX', self)\n            actionSobelX.triggered.connect(self.sobelx)\n            self.toolbar.addAction(actionSobelX)\n\n            actionSobelY = QAction(QIcon('images/icon/sobely.png'), 'SobelY', self)\n            actionSobelY.triggered.connect(self.sobely)\n            self.toolbar.addAction(actionSobelY)\n\n            actionLapcian = QAction(QIcon('images/icon/laplacian.png'), 'Lapcian', self)\n            actionLapcian.triggered.connect(self.lapcian)\n            self.toolbar.addAction(actionLapcian)\n\n            actionCanny = QAction(QIcon('images/icon/canny.png'), 'Canny', self)\n            actionCanny.triggered.connect(self.canny)\n            self.toolbar.addAction(actionCanny)\n        else:\n            QMessageBox.about(self, \"Thông Báo\", \"Chưa chọn hình ?!!\")\n\nif __name__ == \"__main__\":\n    a = QtWidgets.QApplication(sys.argv)\n    a.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())\n    win = UI()\n    sys.exit(a.exec_())\n\n", "repo_name": "anhquoc4062/Image-Processing", "sub_path": "maindip.py", "file_name": "maindip.py", "file_ext": "py", "file_size_in_byte": 15056, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 12, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.uic.loadUi", "line_number": 17, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 17, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.question", "line_number": 83, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 83, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 83, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 83, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 84, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 84, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 85, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 85, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 87, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 87, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 101, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 101, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 111, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 111, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 121, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 121, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 131, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 131, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 134, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 134, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 139, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 140, "usage_type": "call"}, {"api_name": "basicdip.BasicImageProcessing", "line_number": 143, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage.Format_Indexed8", "line_number": 147, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 147, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QImage.Format_RGBA8888", "line_number": 152, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 152, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QImage.Format_RGB888", "line_number": 154, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 154, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 155, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap.fromImage", "line_number": 158, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 158, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 159, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 159, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 302, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 302, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 306, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 306, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 310, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 310, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 319, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 319, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 329, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 329, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 332, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 335, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 344, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 344, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 353, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 356, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 362, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 362, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 373, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 373, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 377, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 377, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 381, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 381, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 385, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 385, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 389, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 389, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 396, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 396, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 405, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 405, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 409, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 409, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 413, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 413, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 417, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 417, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 421, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 421, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 424, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 424, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 424, "usage_type": "attribute"}, {"api_name": "qdarkstyle.load_stylesheet_pyqt5", "line_number": 425, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 427, "usage_type": "call"}]}
{"seq_id": "945751271", "text": "\"\"\"\nModule responsible for all Business Logic related to marking and assessing the solution.\nUses filecmp to compare desired outputs with actual outputs to assign specific marks.\n\n@TeamAlpha 2018\nCodeMarker\nsubmission_processor.py\n\"\"\"\n\nimport filecmp\nimport json\nimport logging\nimport os\nfrom decimal import Decimal\n\nfrom django.core import serializers\n\nfrom app.docker_processor import generate_input, run_dynamic, run_static\nfrom app.models import Assessment, Resource, Submission\nfrom codemarker.settings import MEDIA_ROOT\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef run_submission(submission_id):\n    \"\"\"Run submission in a docker container\n\n    Arguments:\n        submission_id - ID of the submission that we're running\n\n    Returns:\n        JSON dump, whether it was successful or not\n    \"\"\"\n\n    # First get submission\n    submission = Submission.objects.get(pk=submission_id)\n    submission.status = \"in_progress\"\n    submission.save()\n    assessment = Assessment.objects.get(id=submission.assessment_id)\n\n    expected_output_dir = os.path.join(\n        MEDIA_ROOT, str(submission.assessment_id), \"expected_outputs\"\n    )\n    expected_static_output_dir = os.path.join(\n        MEDIA_ROOT, str(submission.assessment_id), \"expected_static_outputs\"\n    )\n    output_dir = os.path.join(\n        MEDIA_ROOT,\n        str(str(submission.assessment_id)),\n        \"submissions\",\n        str(submission_id),\n        \"outputs\",\n    )\n    static_output_dir = os.path.join(\n        MEDIA_ROOT,\n        str(str(submission.assessment_id)),\n        \"submissions\",\n        str(submission_id),\n        \"static_outputs\",\n    )\n    print(assessment)\n\n    if assessment.static_input:\n        submission.info = submission.info + \"\\nRUNNING STATIC INPUT. \\n\"\n        run_static(submission, assessment)\n        test_outputs(\n            expected_static_output_dir,\n            static_output_dir,\n            submission,\n            assessment.max_time,\n        )\n    if assessment.dynamic_input:\n        submission.info = submission.info + \"\\nRUNNING DYNAMIC INPUT. \\n\"\n        resource_language = Resource.objects.get(\n            assessment_id=submission.assessment_id\n        ).language\n        generate_input(submission, resource_language)\n        run_dynamic(submission)\n        test_outputs(expected_output_dir, output_dir, submission, assessment.max_time)\n\n    # Produce JSON output\n    data = serializers.serialize(\"json\", [submission,])\n    struct = json.loads(data)\n    data = json.dumps(struct[0])\n\n    return data\n\n\ndef test_outputs(expected_output_dir, output_dir, submission, max_time):\n    \"\"\"Method testing whether the output matches the expected output\n    \"\"\"\n    if not filecmp.dircmp(expected_output_dir, output_dir).diff_files:\n        submission.result = \"pass\"\n        submission.marks = 100\n        submission.info = submission.info + \" All tests cleared!\\n\"\n    else:\n        LOGGER.error(filecmp.dircmp(expected_output_dir, output_dir).diff_files)\n        failed_output = \"\"\n        for failed in filecmp.dircmp(expected_output_dir, output_dir).diff_files:\n            file = open(os.path.join(output_dir, failed), \"r\")\n            failed_output += file.read()\n            file.close()\n\n        submission.result = \"fail\"\n        submission.marks = 0\n        submission.info = submission.info + failed_output\n\n    total = []\n    for filename in os.listdir(output_dir):\n        if filename.startswith(\"t_\"):\n            file = open(os.path.join(output_dir, filename), \"r\")\n            total.append(Decimal(file.read()))\n            file.close()\n\n    submission.timeTaken = round(max(total), 3)\n    submission.status = \"complete\"\n\n    if (max_time > 0 and submission.timeTaken > max_time):\n        submission.marks = 0\n        submission.info = (\n            submission.info\n            + f\"\\n Runtime of your program exceeded maximum allowance of {max_time}\\n\"\n        )\n        submission.result = \"overtime\"\n\n    submission.save()\n", "repo_name": "StefanRudvin/CodeMarkerBack", "sub_path": "app/submission_processor.py", "file_name": "submission_processor.py", "file_ext": "py", "file_size_in_byte": 3943, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "45", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "app.models.Submission.objects.get", "line_number": 36, "usage_type": "call"}, {"api_name": "app.models.Submission.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "app.models.Submission", "line_number": 36, "usage_type": "name"}, {"api_name": "app.models.Assessment.objects.get", "line_number": 39, "usage_type": "call"}, {"api_name": "app.models.Assessment.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "app.models.Assessment", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "codemarker.settings.MEDIA_ROOT", "line_number": 42, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "codemarker.settings.MEDIA_ROOT", "line_number": 45, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "codemarker.settings.MEDIA_ROOT", "line_number": 48, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "codemarker.settings.MEDIA_ROOT", "line_number": 55, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "app.docker_processor.run_static", "line_number": 65, "usage_type": "call"}, {"api_name": "app.models.Resource.objects.get", "line_number": 74, "usage_type": "call"}, {"api_name": "app.models.Resource.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "app.models.Resource", "line_number": 74, "usage_type": "name"}, {"api_name": "app.docker_processor.generate_input", "line_number": 77, "usage_type": "call"}, {"api_name": "app.docker_processor.run_dynamic", "line_number": 78, "usage_type": "call"}, {"api_name": "django.core.serializers.serialize", "line_number": 82, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 82, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 83, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 84, "usage_type": "call"}, {"api_name": "filecmp.dircmp", "line_number": 92, "usage_type": "call"}, {"api_name": "filecmp.dircmp", "line_number": 97, "usage_type": "call"}, {"api_name": "filecmp.dircmp", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 112, "usage_type": "call"}]}
{"seq_id": "2958046265", "text": "from collections import namedtuple\nimport csv\nimport os\nimport requests\nimport simplejson as json\n\nfrom scrapy.exceptions import DropItem\n\n\nACTIVE_PLAYERS_URL = 'http://www.nba.com/players/active_players.json'\nOUTPUTS_DIR = 'outputs'\n\n\nclass PlayerWriter(namedtuple('PlayerWriter', ['fp', 'writer'])):\n    @staticmethod\n    def get_filepath(player_position):\n        filename = '{}.csv'.format(player_position)\n        cur_dir = os.path.dirname(__file__)\n        return os.path.join(cur_dir, './{outputs_dir}/{filename}'.format(\n            outputs_dir=OUTPUTS_DIR,\n            filename=filename,\n        ))\n\n    @classmethod\n    def from_item(cls, item):\n        fp = open(cls.get_filepath(item['player_position']), 'w')\n        writer = csv.DictWriter(fp, fieldnames=list(item.keys()))\n        writer.writeheader()\n        return cls(fp=fp, writer=writer)\n\n\nclass ScraperPipeline(object):\n    def __init__(self):\n        active_players_json = json.loads(requests.get(ACTIVE_PLAYERS_URL).content)\n        self.active_players = {\n            int(player['personId']): player\n            for player in active_players_json\n        }\n        self.player_writers_map = {}\n\n    def process_item(self, item, spider):\n        player_id = int(item['player_id'])\n        player = self.active_players.get(player_id)\n        if not player:\n            print('GOT HERE')\n            raise DropItem('Player {} is no longer active.'.format(player_id))\n\n        position = player['pos']\n        item['player_position'] = position\n        player_writer = self.player_writers_map.get(position)\n        if not player_writer:\n            self.player_writers_map[position] = PlayerWriter.from_item(item)\n\n        player_writer.writer.writerow(dict(item))\n        return item\n\n    def close_spider(self, spider):\n        for _, player_writer in self.player_writers_map.items():\n            player_writer.fp.close()\n", "repo_name": "ayc92/nba_shotchart_scraper", "sub_path": "shotchart_scraper/shotchart_scraper/pipelines.py", "file_name": "pipelines.py", "file_ext": "py", "file_size_in_byte": 1893, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "collections.namedtuple", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 27, "usage_type": "call"}, {"api_name": "simplejson.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "scrapy.exceptions.DropItem", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "23942300337", "text": "#!/usr/bin/env python\n\nimport logging\nimport os\nimport socket\nimport urllib\n\nimport tornado.gen\nimport tornado.httpclient\nimport tornado.ioloop\nimport tornado.options\nimport tornado.web\n\n\nclass BaseHandler(tornado.web.RequestHandler):\n\n    @property\n    def httpclient(self):\n        if not hasattr(self, '_httpclient'):\n            self._httpclient = tornado.httpclient.AsyncHTTPClient()\n        return self._httpclient\n\n\nclass IndexHandler(BaseHandler):\n\n    def get(self):\n        self.finish(open('client/index.html').read())\n\n\nclass EmbedlyProxyHandler(BaseHandler):\n\n    @tornado.gen.coroutine\n    def get(self):\n        params = {\n            'url': _utf8(self.get_argument('url')),\n            'key': self.settings['embedly_api_key'],\n            'colors': 'true',\n        }\n        url = 'http://api.embed.ly/1/preview?' + urllib.urlencode(params)\n        resp = yield self.httpclient.fetch(url)\n        self.set_header('Content-Type', resp.headers['Content-Type'])\n        self.set_status(resp.code)\n        self.finish(resp.body)\n\n\nclass BitlyProxyHandler(BaseHandler):\n\n    @tornado.gen.coroutine\n    def get(self, path):\n        params = dict((k, self.get_query_arguments(k))\n                      for k in self.request.query_arguments)\n        params['access_token'] = self.settings['bitly_access_token']\n        base_url = 'https://api-ssl.bitly.com' + path\n        url = base_url + '?' + urllib.urlencode(params, doseq=1)\n        logging.debug('Bitly proxy: %s %r', url, params)\n        resp = yield self.httpclient.fetch(url)\n        logging.debug('Proxy resp: %s %s', resp.code, url)\n        self.set_header('Content-Type', resp.headers['Content-Type'])\n        self.set_status(resp.code)\n        self.finish(resp.body)\n\n\ndef _utf8(s):\n    if not s or isinstance(s, str):\n        return s\n    return s.encode('utf8')\n\n\nsettings = {\n    'embedly_api_key': os.environ['EMBEDLY_API_KEY'],\n    'bitly_access_token': os.environ['BITLY_ACCESS_TOKEN'],\n    'static_path': 'client',\n    'debug': socket.gethostname() == 'barbacoa.local',\n}\n\n\napplication = tornado.web.Application([\n    (r'/', IndexHandler),\n    (r'/bitly(/[\\w/]+)', BitlyProxyHandler),\n    (r'/embedly', EmbedlyProxyHandler),\n], **settings)\n\n\nif __name__ == '__main__':\n    tornado.options.parse_command_line()\n    port = int(os.environ.get('PORT', 5000))\n    application.listen(port)\n    tornado.ioloop.IOLoop.instance().start()\n", "repo_name": "mccutchen/colorbursts", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2407, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "tornado.gen.web", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 15, "usage_type": "name"}, {"api_name": "tornado.gen.httpclient.AsyncHTTPClient", "line_number": 20, "usage_type": "call"}, {"api_name": "tornado.gen.httpclient", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 20, "usage_type": "name"}, {"api_name": "urllib.urlencode", "line_number": 39, "usage_type": "call"}, {"api_name": "tornado.gen.gen", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 32, "usage_type": "name"}, {"api_name": "urllib.urlencode", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 57, "usage_type": "call"}, {"api_name": "tornado.gen.gen", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 48, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 71, "usage_type": "attribute"}, {"api_name": "socket.gethostname", "line_number": 73, "usage_type": "call"}, {"api_name": "tornado.gen.web.Application", "line_number": 77, "usage_type": "call"}, {"api_name": "tornado.gen.web", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 77, "usage_type": "name"}, {"api_name": "tornado.gen.options.parse_command_line", "line_number": 85, "usage_type": "call"}, {"api_name": "tornado.gen.options", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 85, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 86, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tornado.gen.ioloop.IOLoop.instance", "line_number": 88, "usage_type": "call"}, {"api_name": "tornado.gen.ioloop", "line_number": 88, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 88, "usage_type": "name"}]}
{"seq_id": "4886781297", "text": "#! /usr/bin/env python\n\n\n\"\"\"This is a log analysis tool for the news database\"\"\"\n\n\nimport psycopg2\nfrom datetime import datetime\n\n\ndef get_article_leaderboard():\n    \"\"\"What are the most popular three articles of all time? \"\"\"\n    db = psycopg2.connect(\"dbname=news\")\n    c = db.cursor()\n    columns_descr = c.description\n    c.execute(\"select * from article_leaderboard\")\n    result_articles = c.fetchall()\n    for row in result_articles:\n        x, y = row\n        print('Article: ' + '%s' % (x) + ' - Views: ' + '%s' % (y))\n    db.close()\n\n\ndef get_author_leaderboard():\n    \"\"\"Who are the most popular article authors of all time?\"\"\"\n    db = psycopg2.connect(\"dbname=news\")\n    c = db.cursor()\n    columns_descr = c.description\n    c.execute(\"select * from author_leaderboard;\")\n    result_authors = c.fetchall()\n    for row in result_authors:\n        x, y = row\n        print('Author: ' + '%s' % (x) + ' - Views: ' + '%s' % (y))\n    db.close()\n\n\ndef get_bad_days():\n    \"\"\"On which days did more than 1% of requests lead to errors?\"\"\"\n    db = psycopg2.connect(\"dbname=news\")\n    c = db.cursor()\n    columns_descr = c.description\n    c.execute(\"select * from bad_days;\")\n    result_days = c.fetchall()\n    for row in result_days:\n        x, y = row\n        oldformat = str(x)\n        datetimeobject = datetime.strptime(oldformat, '%Y-%m-%d')\n        newformat = datetimeobject.strftime('%B %d, %Y')\n        x = newformat\n        print('Date: ' + '%s' % (x) + ' - Error Percentage: ' + '%s' % (y))\n    db.close()\n\n# Print the output of the database analysis\nprint(\"What are the most popular three articles of all time?\" + '\\n')\nget_article_leaderboard()\nprint('\\n')\nprint(\"Who are the most popular article authors of all time?\" + '\\n')\nget_author_leaderboard()\nprint('\\n')\nprint(\"On which days did more than 1% of requests lead to errors?\" + '\\n')\nget_bad_days()\n", "repo_name": "maxvanberk/NewsLogAnalysis", "sub_path": "newslog.py", "file_name": "newslog.py", "file_ext": "py", "file_size_in_byte": 1868, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "psycopg2.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 26, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}]}
{"seq_id": "17112715979", "text": "import asyncio\nimport keep_alive\nfrom discord.ext import commands\nfrom os import environ, listdir\nfrom discord_slash import SlashCommand\n\nclient = commands.Bot(command_prefix=\"[\")\nslash = SlashCommand(client, sync_commands=True, sync_on_cog_reload=True)\n\n\n@client.command()\nasync def reload(ctx):\n    if ctx.author.id in [455259347107446794, 499233545693298698]:\n        await ctx.message.delete()\n        m = \"```Start reloading...```\"\n        msg = await ctx.send(m)\n        for file in listdir(\"./cogs\"):\n            if file in [\"commands\", \"events\"]:\n                for fileName in listdir(f\"./cogs/{file}\"):\n                    if not fileName.startswith(\"__\"):\n                        try:\n                            client.unload_extension(f\"cogs.{file}.{fileName[:-3]}\")\n                            m = m[:-3] + f\"\\nStart reloading {fileName}...```\"\n                            await msg.edit(content=m)\n                        except:\n                            m = m[:-3] + f\"\\nStart loading {fileName}...```\"\n                            await msg.edit(content=m)\n                        client.load_extension(f\"cogs.{file}.{fileName[:-3]}\")\n                        await asyncio.sleep(1)\n                        m = m[:-3] + f\"Done```\"\n                        await msg.edit(content=m)\n        m = m[:-3] + f\"\\nReloading Done```\"\n        await msg.edit(content=m)\n\n\nfor file in listdir(\"./cogs\"):\n    if file in [\"commands\", \"events\"]:\n        for fileName in listdir(f\"./cogs/{file}\"):\n            if not fileName.startswith(\"__\"):\n                client.load_extension(f\"cogs.{file}.{fileName[:-3]}\")\n\nif __name__ == \"__main__\":\n    keep_alive.keep_alive()\n    client.run(environ[\"token\"])\n", "repo_name": "WhiteNightAWA/Atlantis-Map-Group-Bot", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1706, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 7, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 7, "usage_type": "name"}, {"api_name": "discord_slash.SlashCommand", "line_number": 8, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 19, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 29, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 36, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}, {"api_name": "keep_alive.keep_alive", "line_number": 43, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 44, "usage_type": "name"}]}
{"seq_id": "71127348602", "text": "import requests\nfrom Base.base_dao import BaseDAO, BASE_API_URL\nfrom DTO.statistic import StatsCostRevenueTour, StatsCostRevenueGroup, StatsToursOfStaff\nfrom DAO.tour import TourCharacteristicDAO, TourTypeDAO, TourPriceDAO, LocationDAO\nfrom Base.error import Error\n\nclass StatsCostRevenueTourDAO(BaseDAO):\n    DTO_CLASS = StatsCostRevenueTour\n    API_URL = {\n        'read':         f'{BASE_API_URL}/stats/stats_cost_revenue_tour',\n        'read_detail':  f'{BASE_API_URL}/stats/stats_cost_revenue_tour/{{}}'\n    }\n    tour_characteristic_dao = TourCharacteristicDAO()\n    tour_type_dao = TourTypeDAO()\n    tour_price_dao = TourPriceDAO()\n    location_dao = LocationDAO()\n    \n    def get_object(self, data):\n        _, data['characteristic'] = self.tour_characteristic_dao.read_detail(data['characteristic'])\n        _, data['type'] = self.tour_type_dao.read_detail(data['type'])\n        _, data['price'] = self.tour_price_dao.read_detail(data['price'])\n        _, data['location'] = self.location_dao.read_detail(data['location'])\n        print(data['location'])\n        return self.DTO_CLASS(**data)\n    \n    def to_create_request_data(self, data):\n        request_data = {\n            'name': data.name,\n            'description': data.description,\n            'characteristic': data.characteristic.id,\n            'type': data.type.id,\n            'price': data.price.id,\n            'location': data.location.id,\n            'cost': data.cost,\n            'revenue': data.revenue\n        }\n        return request_data\n\nclass StatsCostRevenueGroupDAO(BaseDAO):\n    DTO_CLASS = StatsCostRevenueGroup\n    API_URL = {\n        'read':         f'{BASE_API_URL}/stats/stats_cost_revenue_group/{{}}',\n        'read_detail':  f'{BASE_API_URL}/stats/stats_cost_revenue_group/{{}}'\n    }\n\n    def read(self, tour_id) -> tuple[Error, list[DTO_CLASS]]:\n        \"\"\"\n        Read method in DAO, equivalent to GET method (get list action)\n        :return: tuple[Error, list[DTO_CLASS]]\n        \"\"\"\n        request = requests.get(self.API_URL['read'].format(tour_id))\n    \n        if request.status_code == 200:\n            response = request.json()\n            result = []\n            print(response)\n            for data in response:\n                result.append(self.get_object(data))\n            print(result)\n            return (Error(False, None), result)\n        else:\n            return (Error(True, 'Get the tour list that have an error'), None)\n    \nclass StatsToursOfStaffDAO(BaseDAO):\n    DTO_CLASS = StatsToursOfStaff\n    API_URL = {\n        'read':         f'{BASE_API_URL}/stats/tour_of_staff',\n        'read_detail':  f'{BASE_API_URL}/stats/tour_of_staff/{{}}'\n    }\n\nclass StatsToursOfStaffDAO(BaseDAO):\n    DTO_CLASS = StatsToursOfStaff\n    API_URL = {\n        'read':         f'{BASE_API_URL}/stats/tour_of_staff',\n    }\n    \n    # def to_create_request_data(self, data):\n    #     request_data = {\n    #         'name': data.name\n    #     }\n    #     return request_data\n    \n", "repo_name": "doTruongGiang-IT/QuanLyTourDuLich", "sub_path": "desktop/DAO/statistic.py", "file_name": "statistic.py", "file_ext": "py", "file_size_in_byte": 2987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "40", "api": [{"api_name": "Base.base_dao.BaseDAO", "line_number": 7, "usage_type": "name"}, {"api_name": "DTO.statistic.StatsCostRevenueTour", "line_number": 8, "usage_type": "name"}, {"api_name": "Base.base_dao.BASE_API_URL", "line_number": 10, "usage_type": "name"}, {"api_name": "Base.base_dao.BASE_API_URL", "line_number": 11, "usage_type": "name"}, {"api_name": "DAO.tour.TourCharacteristicDAO", "line_number": 13, "usage_type": "call"}, {"api_name": "DAO.tour.TourTypeDAO", "line_number": 14, "usage_type": "call"}, {"api_name": "DAO.tour.TourPriceDAO", "line_number": 15, "usage_type": "call"}, {"api_name": "DAO.tour.LocationDAO", "line_number": 16, "usage_type": "call"}, {"api_name": "Base.base_dao.BaseDAO", "line_number": 39, "usage_type": "name"}, {"api_name": "DTO.statistic.StatsCostRevenueGroup", "line_number": 40, "usage_type": "name"}, {"api_name": "Base.base_dao.BASE_API_URL", "line_number": 42, "usage_type": "name"}, {"api_name": "Base.base_dao.BASE_API_URL", "line_number": 43, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 51, "usage_type": "call"}, {"api_name": "Base.error.Error", "line_number": 60, "usage_type": "call"}, {"api_name": "Base.error.Error", "line_number": 62, "usage_type": "call"}, {"api_name": "Base.error.Error", "line_number": 46, "usage_type": "name"}, {"api_name": "Base.base_dao.BaseDAO", "line_number": 64, "usage_type": "name"}, {"api_name": "DTO.statistic.StatsToursOfStaff", "line_number": 65, "usage_type": "name"}, {"api_name": "Base.base_dao.BASE_API_URL", "line_number": 67, "usage_type": "name"}, {"api_name": "Base.base_dao.BASE_API_URL", "line_number": 68, "usage_type": "name"}, {"api_name": "Base.base_dao.BaseDAO", "line_number": 71, "usage_type": "name"}, {"api_name": "DTO.statistic.StatsToursOfStaff", "line_number": 72, "usage_type": "name"}, {"api_name": "Base.base_dao.BASE_API_URL", "line_number": 74, "usage_type": "name"}]}
{"seq_id": "19843044203", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# why.py \n\nimport os\nimport sys\n\nimport who\nimport what\nimport where\n\nscenarios = [\n    {'name':'memory loss'},\n    {'name':'lack of movement'},\n    {'name':'mild confusion'},\n    ]\n            \n\npreferred_outcome = [\n    'cognitive ability',\n    'physical freedom',\n    'financial independance',\n    ]\n    \n    \npath_to_solve = []\n \n\n\n \n            \ndef main():\n    \"\"\"\n    main function used for testing \n    \"\"\"\n    print(why_did_that_happen(scenarios[0]))\n \n\ndef why_did_that_happen(scenario):\n    \"\"\"\n    uses the other modules with sensors to try \n    and identify why the 'scenario' occurred.\n    \"\"\"\n    print('calculating best response why ' + scenario['name'] + ' occurred')\n    \n    loc = where.which_room_am_i_in()\n    \n    print('loc = ' + loc)\n    \n    if loc == 'Bedroom':\n        return 'You just woke up'\n    \n    \n    return 'No idea'\n   \n    \n    \n    \n    \n\n    \n    \nmain()\n", "repo_name": "acutesoftware/acute-experiments", "sub_path": "ITNOBAM/z_prototype/why.py", "file_name": "why.py", "file_ext": "py", "file_size_in_byte": 938, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "where.which_room_am_i_in", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "22884247908", "text": "from rest_framework import serializers\n\nfrom api_v1.users.serializers import UserSerializer\nfrom expenses.models import Category, Expense\n\n\nclass CategorySerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Category\n        fields = [\"id\", \"name\"]\n\n\nclass ExpenseSerializer(serializers.ModelSerializer):\n    user = UserSerializer(read_only=True)\n    category = CategorySerializer(read_only=True)\n\n    date_str = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Expense\n        fields = [\n            \"id\",\n            \"user\",\n            \"category\",\n            \"description\",\n            \"value\",\n            \"date\",\n            \"date_str\",\n            \"is_settled\",\n        ]\n\n    def get_date_str(self, obj):\n        return obj.date.strftime(\"%b %d, %Y %H:%M\")\n", "repo_name": "arineto/home_expenses", "sub_path": "backend/api_v1/expenses/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 804, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "expenses.models.Category", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 13, "usage_type": "name"}, {"api_name": "api_v1.users.serializers.UserSerializer", "line_number": 14, "usage_type": "call"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 17, "usage_type": "name"}, {"api_name": "expenses.models.Expense", "line_number": 20, "usage_type": "name"}]}
{"seq_id": "4817928326", "text": "#!/usr/bin/env python3\n# encoding: utf-8\nimport requests\nimport hashlib\nimport hmac\nimport email\nfrom datetime import datetime, date\n\nfrom cortexutils.analyzer import Analyzer\n\n\nclass APIRequestHandler(object):\n    def __init__(self, public_key, private_key):\n        self.URL = 'https://api.isightpartners.com'\n        self.public_key = public_key\n        self.private_key = private_key\n        self.accept_version = '2.5'\n\n    def exec_query(self, endpoint):\n        time_stamp = email.utils.formatdate(localtime=True)\n        accept_header = 'application/json'\n        new_data = endpoint + self.accept_version + accept_header + time_stamp\n\n        key = bytearray()\n        key.extend(map(ord, self.private_key))\n        hashed = hmac.new(key, new_data.encode('utf-8'), hashlib.sha256)\n\n        headers = {\n            'Accept': accept_header,\n            'Accept-Version': self.accept_version,\n            'X-Auth': self.public_key,\n            'X-Auth-Hash': hashed.hexdigest(),\n            'Date': time_stamp,\n        }\n\n        r = requests.get(self.URL + endpoint, headers=headers)\n\n        if r.status_code == 200:\n            return r.json()\n        else:\n            return -1\n\n\nclass FireEyeiSightAnalyzer(Analyzer):\n    def __init__(self):\n        Analyzer.__init__(self)\n        self.service = self.get_param(\n            'config.service', None, 'Service parameter is missing')\n        self.key = self.get_param('config.key', None, 'Missing API key')\n        self.pwd = self.get_param('config.pwd', None, 'Missing API password')\n        self.request_handler = APIRequestHandler(self.key, self.pwd)\n\n    def cleanup(self, data_info=[]):\n        response = {\n            'level': 'no-info',\n            'score': 0,\n            'domain': [],\n            'ip': [],\n            'md5': [],\n            'sha1': [],\n            'sha256': [],\n            'intelligenceType': [],\n            'webLink': [],\n            'identifier': [],\n            'observationTime': [],\n            'ThreatScape': [],\n            'title': [],\n            'size': [],\n            'fileName': [],\n            'packer': [],\n            'actor': [],\n            'report_data': []\n        }\n\n        json_fields = ['domain', 'intelligenceType', 'ip', 'title', 'webLink', 'observationTime', 'ThreatScape', 'sha1',\n                       'sha256', 'md5', 'size', 'fileName', 'packer', 'actor']\n        json_unique_fields = ['domain', 'ip', 'sha1', 'sha256', 'md5', 'size', 'fileName', 'packer', 'actor']\n\n        for report in data_info:\n\n            identifier = report.get('fileIdentifier', None) if self.data_type == 'hash' else report.get(\n                'networkIdentifier', None)\n            if identifier and identifier not in response['identifier']:\n                response['identifier'].append(identifier)\n\n            for field in json_fields:\n                field_value = report.get(field, None)\n                if field == 'observationTime':\n                    field_value = datetime.fromtimestamp(field_value).strftime('%Y-%m-%d %H:%M:%S')\n                if field_value and field in json_unique_fields and field_value not in response[field]:\n                    response[field].append(field_value)\n                elif field_value and field not in json_unique_fields:\n                    response[field].append(field_value)\n\n        for identifier in response['identifier']:\n            if identifier in ['Attacker', 'Compromised']:\n                response['level'] = 'malicious'\n            elif identifier in ['Victim', 'Related'] and response['level'] != 'malicious':\n                response['level'] = 'safe'\n\n        for i in range(len(data_info)):\n            response['report_data'].append({'title': response['title'][i],\n                                            'threatScape': response['ThreatScape'][i],\n                                            'intelligenceType': response['intelligenceType'][i],\n                                            'webLink': response['webLink'][i],\n                                            'observationTime': response['observationTime'][i]})\n\n        response['score'] = len(data_info)\n\n        del response['title']\n        del response['ThreatScape']\n        del response['webLink']\n        del response['observationTime']\n        del response['intelligenceType']\n\n        return response\n\n    def query(self, data):\n        results = dict()\n        try:\n            r = self.request_handler.exec_query('/search/text?text=%s' % data)\n            report_ids = [x.get('reportId', None) for x in r['message']] if r != -1 else []\n            data_info = []\n            for report in report_ids:\n                r = self.request_handler.exec_query('/pivot/report/%s/indicator' % report)\n                if self.data_type == 'domain':\n                    tmp_info = [x for x in r['message'].get('publishedIndicators', [])\n                                if x.get('domain', None) == data] if r != -1 else []\n                elif self.data_type == 'hash':\n                    tmp_info = [x for x in r['message'].get('publishedIndicators', [])\n                                if x.get('md5', None) == data or x.get('sha1', None) == data\n                                or x.get('sha256', None) == data] if r != -1 else []\n                elif self.data_type == 'ip':\n                    tmp_info = [x for x in r['message'].get('publishedIndicators', [])\n                                if x.get('ip', None) == data] if r != -1 else []\n                if tmp_info:\n                    for x in tmp_info:\n                        tmp_dict = {k: v for k, v in x.items() if v}\n                        if tmp_dict not in data_info:\n                            data_info.append(tmp_dict)\n            return self.cleanup(data_info)\n        except Exception as e:\n            self.error(\"OS error: {0}\".format(e))\n        return results\n\n    def summary(self, raw):\n        taxonomies = []\n        level = \"info\"\n        namespace = \"FireEyeiSight\"\n        predicate = \"Report\"\n        score = raw['score']\n        level = raw['level']\n        if score > 0:\n            taxonomies.append(self.build_taxonomy(level, namespace, predicate, score))\n        return {\"taxonomies\": taxonomies}\n\n    def run(self):\n\n        if self.service == 'query':\n            data = self.get_param('data', None, 'Data is missing')\n            if self.data_type in ['domain', 'url', 'hash', 'ip']:\n                rep = self.query(data)\n                self.report(rep)\n            else:\n                self.error('Invalid data type')\n        else:\n            self.error('Invalid service')\n\n\nif __name__ == '__main__':\n    FireEyeiSightAnalyzer().run()\n", "repo_name": "TheHive-Project/Cortex-Analyzers", "sub_path": "analyzers/FireEyeiSight/fireeyeisight_lookup.py", "file_name": "fireeyeisight_lookup.py", "file_ext": "py", "file_size_in_byte": 6667, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 393, "dataset": "github-code", "pt": "45", "api": [{"api_name": "email.utils.formatdate", "line_number": 20, "usage_type": "call"}, {"api_name": "email.utils", "line_number": 20, "usage_type": "attribute"}, {"api_name": "hmac.new", "line_number": 26, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 26, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "cortexutils.analyzer.Analyzer", "line_number": 44, "usage_type": "name"}, {"api_name": "cortexutils.analyzer.Analyzer.__init__", "line_number": 46, "usage_type": "call"}, {"api_name": "cortexutils.analyzer.Analyzer", "line_number": 46, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "name"}]}
{"seq_id": "4187970671", "text": "import os\nimport cv2\nimport time\nimport ctypes\nimport threading\nimport numpy as np\nfrom config.config import cfg\nfrom utils import utils\nfrom utils import vis_tools\nfrom utils import timer\nfrom utils import transform\nfrom data import loader\nfrom data import labels\nfrom data import preprocess\n\n\nclass Dataset(object):\n    def __init__(self, preprocessor, dataset_type):\n        if dataset_type == 'train':\n            self.anno_path     = cfg.CONTFUSE.TRAIN_DATA\n            self.batch_size    = cfg.TRAIN.BATCH_SIZE\n            self.is_data_aug   = cfg.TRAIN.IS_DATA_AUG\n        if dataset_type == 'val':\n            self.anno_path     = cfg.CONTFUSE.VAL_DATA \n            self.batch_size    = cfg.EVAL.BATCH_SIZE\n            self.is_data_aug   = False\n        if dataset_type == 'test':\n            self.anno_path     = cfg.CONTFUSE.TEST_DATA\n            self.batch_size    = cfg.EVAL.BATCH_SIZE\n            self.is_data_aug   = False\n\n\n        self.img_anchors       = loader.load_anchors(cfg.IMAGE.ANCHORS)\n        self.bev_anchors       = loader.load_anchors(cfg.BEV.ANCHORS)\n        self.annotations       = loader.load_annotations(self.anno_path)\n        self.num_samples       = len(self.annotations)\n        self.num_batchs        = int(np.ceil(self.num_samples / self.batch_size))\n        self.batch_count       = 0\n        self.is_use_thread     = cfg.CONTFUSE.IS_USE_THREAD\n\n        self.cuda_preprocessor = preprocessor.preprocessor\n  \n\n        self.loader_need_exit = 0\n        self.timer = timer.Timer()\n        \n        if self.is_use_thread:\n            self.prepr_data = []\n            self.max_cache_size = 10\n            self.lodaer_processing =  threading.Thread(target=self.loader)\n            self.lodaer_processing.start()\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.loader_need_exit = True\n        print('set loader_need_exit True')\n        self.lodaer_processing.join()\n        print('exit lodaer_processing')\n\n    def __len__(self):\n        return len(self.annotations)\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        if self.batch_count < self.num_batchs:\n            self.batch_count += 1\n            return self.load() \n        else:\n            self.batch_count = 0\n            np.random.shuffle(self.annotations)\n            raise StopIteration\n\n\n\n    def preprocess_data(self):\n        batch_bev = np.zeros((self.batch_size, cfg.BEV.INPUT_X, cfg.BEV.INPUT_Y, cfg.BEV.INPUT_Z), dtype=np.float32)\n        batch_img = np.zeros((self.batch_size, cfg.IMAGE.INPUT_H, cfg.IMAGE.INPUT_W, 3), dtype=np.float32)\n        batch_mapping1x = np.zeros((self.batch_size, cfg.BEV.INPUT_X, cfg.BEV.INPUT_Y, 2), dtype=np.int32)\n        batch_mapping2x = np.zeros((self.batch_size, int(cfg.BEV.INPUT_X/2), int(cfg.BEV.INPUT_Y/2), 2), dtype=np.int32)\n        batch_mapping4x = np.zeros((self.batch_size, int(cfg.BEV.INPUT_X/4), int(cfg.BEV.INPUT_Y/4), 2), dtype=np.int32)\n        batch_mapping8x = np.zeros((self.batch_size, int(cfg.BEV.INPUT_X/8), int(cfg.BEV.INPUT_Y/8), 2), dtype=np.int32)\n        batch_bev_label = np.zeros((self.batch_size, cfg.BEV.OUTPUT_X, cfg.BEV.OUTPUT_Y, cfg.BEV.LABEL_Z), dtype=np.float32)\n        batch_img_label = np.zeros((self.batch_size, cfg.IMAGE.OUTPUT_H, cfg.IMAGE.OUTPUT_W, cfg.IMAGE.LABEL_Z), dtype=np.float32)\n        batch_tr = np.zeros((self.batch_size, 3, 4), dtype=np.float32)\n\n        batch_frame_id = []\n        num = 0 \n        while num < self.batch_size:\n            index = self.batch_count * self.batch_size + num\n            if index == self.num_samples: index=0\n            annotation = self.annotations[index]\n            if not annotation: continue\n            lidar_file , image_file, label_file, calib_file  = annotation.split()\n            frame_id = lidar_file[-10:-4]\n            p20, r0, tr_lidar2cam = loader.load_calib(calib_file)\n            img = loader.load_image(image_file)\n            point_cloud = loader.load_lidar(lidar_file)\n            bev, mapping1x, mapping2x, mapping4x, mapping8x = preprocess.lidar_preprocess(point_cloud,\n                                                              p20, r0, tr_lidar2cam, self.cuda_preprocessor)\n            types, dimensions, box2d_corners, locations, rzs = loader.load_label(label_file)\n            bev_label = labels.create_bev_label(locations, dimensions, rzs, types, tr_lidar2cam, self.bev_anchors)\n            img_label = labels.create_img_label(types, box2d_corners, self.img_anchors)\n            # vis_tools.imshow_image(img)\n            batch_bev[num, ...] = bev\n            batch_img[num, ...] = img / 255.0\n            batch_mapping1x[num, ...] = mapping1x\n            batch_mapping2x[num, ...] = mapping2x\n            batch_mapping4x[num, ...] = mapping4x\n            batch_mapping8x[num, ...] = mapping8x\n            batch_bev_label[num, ...] = bev_label\n            batch_img_label[num, ...] = img_label\n            batch_tr[num, ...] = tr_lidar2cam\n            batch_frame_id.append(frame_id)\n            num += 1\n        return (batch_bev, batch_img, batch_mapping1x, batch_mapping2x, batch_mapping4x, \n                batch_mapping8x, batch_bev_label, batch_img_label, batch_tr, batch_frame_id)\n\n\n    def loader(self):\n        while(not self.loader_need_exit):\n            if len(self.prepr_data) < self.max_cache_size: \n                self.prepr_data = self.preprocess_data() + self.prepr_data\n            else:\n                time.sleep(0.1)\n                self.loader_need_exit = False\n\n    def load(self):\n        if self.is_use_thread:\n            while len(self.prepr_data) == 0:\n                time.sleep(0.1)\n            data_ori = self.prepr_data.pop()\n        else:\n            data_ori = self.preprocess_data()\n        return data_ori\n\n                                                          \n\nif __name__ == \"__main__\":\n    pass", "repo_name": "JaHorL/Contfuse", "sub_path": "src/data/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 5839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "45", "api": [{"api_name": "config.config.cfg.CONTFUSE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 20, "usage_type": "name"}, {"api_name": "config.config.cfg.TRAIN", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 21, "usage_type": "name"}, {"api_name": "config.config.cfg.TRAIN", "line_number": 22, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 22, "usage_type": "name"}, {"api_name": "config.config.cfg.CONTFUSE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 24, "usage_type": "name"}, {"api_name": "config.config.cfg.EVAL", "line_number": 25, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 25, "usage_type": "name"}, {"api_name": "config.config.cfg.CONTFUSE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 28, "usage_type": "name"}, {"api_name": "config.config.cfg.EVAL", "line_number": 29, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 29, "usage_type": "name"}, {"api_name": "data.loader.load_anchors", "line_number": 33, "usage_type": "call"}, {"api_name": "data.loader", "line_number": 33, "usage_type": "name"}, {"api_name": "config.config.cfg.IMAGE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 33, "usage_type": "name"}, {"api_name": "data.loader.load_anchors", "line_number": 34, "usage_type": "call"}, {"api_name": "data.loader", "line_number": 34, "usage_type": "name"}, {"api_name": "config.config.cfg.BEV", "line_number": 34, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 34, "usage_type": "name"}, {"api_name": "data.loader.load_annotations", "line_number": 35, "usage_type": "call"}, {"api_name": "data.loader", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 37, "usage_type": "call"}, {"api_name": "config.config.cfg.CONTFUSE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 39, "usage_type": "name"}, {"api_name": "utils.timer.Timer", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.timer", "line_number": 45, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "config.config.cfg.BEV", "line_number": 77, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "config.config.cfg.IMAGE", "line_number": 78, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "config.config.cfg.BEV", "line_number": 79, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "config.config.cfg.BEV", "line_number": 80, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 80, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "config.config.cfg.BEV", "line_number": 81, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "config.config.cfg.BEV", "line_number": 82, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "config.config.cfg.BEV", "line_number": 83, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "config.config.cfg.IMAGE", "line_number": 84, "usage_type": "attribute"}, {"api_name": "config.config.cfg", "line_number": 84, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 85, "usage_type": "attribute"}, {"api_name": "data.loader.load_calib", "line_number": 96, "usage_type": "call"}, {"api_name": "data.loader", "line_number": 96, "usage_type": "name"}, {"api_name": "data.loader.load_image", "line_number": 97, "usage_type": "call"}, {"api_name": "data.loader", "line_number": 97, "usage_type": "name"}, {"api_name": "data.loader.load_lidar", "line_number": 98, "usage_type": "call"}, {"api_name": "data.loader", "line_number": 98, "usage_type": "name"}, {"api_name": "data.preprocess.lidar_preprocess", "line_number": 99, "usage_type": "call"}, {"api_name": "data.preprocess", "line_number": 99, "usage_type": "name"}, {"api_name": "data.loader.load_label", "line_number": 101, "usage_type": "call"}, {"api_name": "data.loader", "line_number": 101, "usage_type": "name"}, {"api_name": "data.labels.create_bev_label", "line_number": 102, "usage_type": "call"}, {"api_name": "data.labels", "line_number": 102, "usage_type": "name"}, {"api_name": "data.labels.create_img_label", "line_number": 103, "usage_type": "call"}, {"api_name": "data.labels", "line_number": 103, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 125, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 131, "usage_type": "call"}]}
{"seq_id": "41367243196", "text": "import numpy as np\nimport multiprocessing\n\nmanager = multiprocessing.Manager()\n\naa = []\nbb = []\naa = manager.list()\n\n\ndef exec_test(a, b):\n    \n    print(bb)\n    aa[1].append(a)\n    print(str(aa))\n\n\nclass ClockProcess(multiprocessing.Process):\n\n    def __init__(self, bb):\n        multiprocessing.Process.__init__(self)\n        self.bb = bb\n        aa.append(self.bb)\n\n    def run(self):\n        print(str(aa))\n\n\nif __name__ == '__main__':\n    aa.append(np.array([1, 2, 3]))\n    aa.append([0, 0, 0])\n    bb = aa[1]\n    p = multiprocessing.Pool()\n    for i in range(4):\n        p.apply_async(exec_test, args=(i, 1))\n\n        # p = ClockProcess(i)\n        # p = multiprocessing.Process(target=exec_test, args=(i, 1))\n        # p.start()\n    # for i in range(4):\n    #     argcv = ['python3', 'test_exec.py', str(i)]\n    #     pid = os.fork()\n    #     if pid == 0:\n    #         print(argcv)\n    #         os.execvp('python3', argcv)\n    #     print(\"a\")\n    p.close()\n    p.join()\n", "repo_name": "fengpanhe/pre", "sub_path": "test/test_fork_exec.py", "file_name": "test_fork_exec.py", "file_ext": "py", "file_size_in_byte": 980, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "multiprocessing.Manager", "line_number": 4, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 18, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process.__init__", "line_number": 21, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 33, "usage_type": "call"}]}
{"seq_id": "44770125478", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nimport variables\nfrom Helper import importDataHelper\n\n\ndef plot_Evaluation(dataset):\n    N = 0\n    filterlistall = list(importDataHelper.readcsvdata(variables.evaluationpresultpath))\n    tplist = []\n    fplist = []\n    filterlistdataset = []\n\n    fig, ax = plt.subplots(figsize=(10, 10))\n\n    num = 0\n    pnum = 0\n    nnum = 0\n    maxnum = 0\n    temp = 0\n    gotdata = False\n    i = 0\n    for filter in filterlistall:\n        if dataset in filter[\"Dataset\"]:\n            i += 1\n            if filter[\"Variable\"] not in \"None\":\n                filterlistdataset.append(str(i) + \": \" + filter[\"Filter\"] + \": \" + filter[\"Variable\"])\n            else:\n                filterlistdataset.append(str(i) + \": \" + filter[\"Filter\"])\n            if not gotdata:\n                num = filter[\"population\"]\n                pnum = filter[\"P\"]\n                nnum = filter[\"N\"]\n                gotdata = True\n            N += 1\n            if filter[\"TP\"] in '':\n                tplist.append(0)\n            else:\n                temp = int(filter[\"TP\"])\n                tplist.append(int(filter[\"TP\"]))\n            if filter[\"FP\"] in '':\n                fplist.append(0)\n            else:\n                temp += int(filter[\"FP\"])\n                fplist.append(int(filter[\"FP\"]))\n            if temp > maxnum:\n                maxnum = temp\n    ind = np.arange(N)\n    p1 = plt.bar(ind, tplist)\n    p2 = plt.bar(ind, fplist, bottom=tplist)\n    plt.ylabel('Amount')\n    plt.title(('Filter evaluation for '+ dataset + ' with '+ num + ' ideas (' + pnum + ' positives and ' + nnum + ' negatives)'))\n    plt.xticks(ind, range(1, len(filterlistdataset)))\n    plt.yticks(np.arange(0, maxnum, 20))\n    plt.legend((p1[0], p2[0]), ('TP', 'FP'))\n    print(filterlistdataset)\n    plt.show()\n", "repo_name": "MoogD/spam-detection-crowdsourced-ideation", "sub_path": "Visualization/plotResults.py", "file_name": "plotResults.py", "file_ext": "py", "file_size_in_byte": 1816, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "Helper.importDataHelper.readcsvdata", "line_number": 10, "usage_type": "call"}, {"api_name": "Helper.importDataHelper", "line_number": 10, "usage_type": "name"}, {"api_name": "variables.evaluationpresultpath", "line_number": 10, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}]}
{"seq_id": "70034415163", "text": "from config import Base,engine,session,Band\n\ndef UpdateStats(MAC,HeartRate,Temperature,Saturation):\n    band_update = session.query(Band).filter_by(MAC=MAC).first()\n    if band_update is None:\n        print(\"Nie ma takiej opaski\")\n        session.bind.dispose()\n    else:\n        band_update.HeartRate = HeartRate\n        band_update.Temperature = Temperature\n        band_update.Saturation = Saturation\n        session.commit()\n        session.bind.dispose()\n        print = \"Baza danych zaktualizowana\"\n\n", "repo_name": "Bronsun/MoyoBand_Network", "sub_path": "DataBase/New DataBase/Raspberry Pi Zero Update/CRUD.py", "file_name": "CRUD.py", "file_ext": "py", "file_size_in_byte": 506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "config.session.query", "line_number": 4, "usage_type": "call"}, {"api_name": "config.Band", "line_number": 4, "usage_type": "argument"}, {"api_name": "config.session", "line_number": 4, "usage_type": "name"}, {"api_name": "config.session.bind.dispose", "line_number": 7, "usage_type": "call"}, {"api_name": "config.session.bind", "line_number": 7, "usage_type": "attribute"}, {"api_name": "config.session", "line_number": 7, "usage_type": "name"}, {"api_name": "config.session.commit", "line_number": 12, "usage_type": "call"}, {"api_name": "config.session", "line_number": 12, "usage_type": "name"}, {"api_name": "config.session.bind.dispose", "line_number": 13, "usage_type": "call"}, {"api_name": "config.session.bind", "line_number": 13, "usage_type": "attribute"}, {"api_name": "config.session", "line_number": 13, "usage_type": "name"}]}
{"seq_id": "15971676803", "text": "#Looks like this file was just used to clean up a folder called \"unclean/***.json and print out all the keys used in the file? - not sure, but it doesn't look like this script has already served it's purpose.\"\r\nimport json\r\n\r\n\r\noriginal_file = \"production_allll.json\"\r\n\r\ninfile = open(original_file)\r\n\r\ncounts = {}\r\noutfiles = {}\r\nall_types = {}\r\n\r\nwhitelist = [\"P22\",\"P3\",\"P4\",\"P5\",\"P6\",\"P7\"]\r\n\r\nfor line in infile:\r\n    parsed = json.loads(line)\r\n    if \"participant_tag\" in parsed:\r\n        print (parsed)\r\n        ptag = parsed[\"participant_tag\"]\r\n        if ptag in counts:\r\n            counts[ptag] += 1\r\n        else:\r\n            counts[ptag] = 1\r\n            if ptag in whitelist:\r\n                outfiles[ptag] = open(\"unclean/\"+ptag+\".json\",\"w\")\r\n        if ptag in whitelist:\r\n            all_types[parsed[\"type\"]] = 1\r\n            outfiles[ptag].write(line)\r\n\r\nfor ptag in outfiles.keys():\r\n    outfiles[ptag].close()\r\n\r\nprint (\"done\",json.dumps(counts,indent=2))\r\nprint (json.dumps(all_types.keys()))\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "jeremy-block/Document-Exploration-Space_files", "sub_path": "explorer/data/json_splitter.py", "file_name": "json_splitter.py", "file_ext": "py", "file_size_in_byte": 1029, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "json.loads", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 33, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}]}
{"seq_id": "73961304123", "text": "from django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.urls import reverse_lazy\nfrom django.views import generic\n\nfrom agents.mixins import OrganisorAndLoginRequiredMixin\nfrom leads.models import Lead, Category\n\nfrom .forms import CategoryModelForm\n\n\nclass CategoryListView(LoginRequiredMixin, generic.ListView):\n    context_object_name = \"categories\"\n    template_name = \"categorys/category_list.html\"\n\n    def get_context_data(self, **kwargs):\n        context = super(CategoryListView, self).get_context_data(**kwargs)\n        user = self.request.user\n        if user.is_organisor:\n            queryset = Lead.objects.filter(organisation=user.userprofile)\n            queryset = Lead.objects.filter(organisation=user.agent.organisation)\n        context.update(\n            {\n                \"unassigned_lead_count\": queryset.filter(category__isnull=True).count(),\n            }\n        )\n        return context\n\n    def get_queryset(self):\n        user = self.request.user\n\n        # Return leads associated to the organisation.\n        if user.is_organisor:\n            queryset = Category.objects.filter(organisation=user.userprofile)\n        else:  # is_agent\n            # Return leads associated to the agent organisation.\n            queryset = Category.objects.filter(organisation=user.agent.organisation)\n        return queryset\n\n\nclass CategoryDetailView(LoginRequiredMixin, generic.DetailView):\n    context_object_name = \"category\"\n    template_name = \"categorys/category_detail.html\"\n\n    def get_context_data(self, **kwargs):\n        context = super(CategoryDetailView, self).get_context_data(**kwargs)\n        # leads = related_name attribute from Lead model\n        leads = self.get_object().leads.all()\n\n        context.update(\n            {\n                \"leads\": leads,\n            }\n        )\n        return context\n\n    def get_queryset(self):\n        user = self.request.user\n\n        if user.is_organisor:\n            queryset = Category.objects.filter(organisation=user.userprofile)\n        else:\n            queryset = Category.objects.filter(organisation=user.agent.organisation)\n        return queryset\n\n\nclass CategoryCreateView(LoginRequiredMixin, generic.CreateView):\n    form_class = CategoryModelForm\n    success_url = reverse_lazy(\"categorys:category_list\")\n    template_name = \"categorys/category_create.html\"\n\n    def form_valid(self, form):\n        category = form.save(commit=False)\n        category.organisation = self.request.user.userprofile\n        category.save()\n        return super(CategoryCreateView, self).form_valid(form)\n\n\nclass CategoryUpdateView(OrganisorAndLoginRequiredMixin, generic.UpdateView):\n    form_class = CategoryModelForm\n    success_url = reverse_lazy(\"categorys:category_list\")\n    template_name = \"categorys/category_update.html\"\n\n    def get_queryset(self):\n        user = self.request.user\n\n        if user.is_organisor:\n            queryset = Lead.objects.filter(organisation=user.userprofile)\n        else:\n            queryset = Lead.objects.filter(organisation=user.agent.organisation)\n        return queryset\n\n\nclass CategoryDeleteView(OrganisorAndLoginRequiredMixin, generic.DeleteView):\n    success_url = reverse_lazy(\"categorys:category_list\")\n    template_name = \"categorys/category_delete.html\"\n\n    def get_queryset(self):\n        user = self.request.user\n        if user.is_organisor:\n            queryset = Category.objects.filter(organisation=user.userprofile)\n        else:\n            queryset = Category.objects.filter(organisation=user.agent.organisation)\n        return queryset\n", "repo_name": "ArpanIng/CRM", "sub_path": "categorys/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3583, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 11, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 11, "usage_type": "name"}, {"api_name": "leads.models.Lead.objects.filter", "line_number": 19, "usage_type": "call"}, {"api_name": "leads.models.Lead.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "leads.models.Lead", "line_number": 19, "usage_type": "name"}, {"api_name": "leads.models.Lead.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "leads.models.Lead.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "leads.models.Lead", "line_number": 20, "usage_type": "name"}, {"api_name": "leads.models.Category.objects.filter", "line_number": 33, "usage_type": "call"}, {"api_name": "leads.models.Category.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "leads.models.Category", "line_number": 33, "usage_type": "name"}, {"api_name": "leads.models.Category.objects.filter", "line_number": 36, "usage_type": "call"}, {"api_name": "leads.models.Category.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "leads.models.Category", "line_number": 36, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 40, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 40, "usage_type": "name"}, {"api_name": "leads.models", "line_number": 47, "usage_type": "name"}, {"api_name": "leads.models", "line_number": 51, "usage_type": "name"}, {"api_name": "leads.models.Category.objects.filter", "line_number": 60, "usage_type": "call"}, {"api_name": "leads.models.Category.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "leads.models.Category", "line_number": 60, "usage_type": "name"}, {"api_name": "leads.models.Category.objects.filter", "line_number": 62, "usage_type": "call"}, {"api_name": "leads.models.Category.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "leads.models.Category", "line_number": 62, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 66, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 66, "usage_type": "name"}, {"api_name": "forms.CategoryModelForm", "line_number": 67, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 68, "usage_type": "call"}, {"api_name": "agents.mixins.OrganisorAndLoginRequiredMixin", "line_number": 78, "usage_type": "name"}, {"api_name": "django.views.generic.UpdateView", "line_number": 78, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 78, "usage_type": "name"}, {"api_name": "forms.CategoryModelForm", "line_number": 79, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 80, "usage_type": "call"}, {"api_name": "leads.models.Lead.objects.filter", "line_number": 87, "usage_type": "call"}, {"api_name": "leads.models.Lead.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "leads.models.Lead", "line_number": 87, "usage_type": "name"}, {"api_name": "leads.models.Lead.objects.filter", "line_number": 89, "usage_type": "call"}, {"api_name": "leads.models.Lead.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "leads.models.Lead", "line_number": 89, "usage_type": "name"}, {"api_name": "agents.mixins.OrganisorAndLoginRequiredMixin", "line_number": 93, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView", "line_number": 93, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 93, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 94, "usage_type": "call"}, {"api_name": "leads.models.Category.objects.filter", "line_number": 100, "usage_type": "call"}, {"api_name": "leads.models.Category.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "leads.models.Category", "line_number": 100, "usage_type": "name"}, {"api_name": "leads.models.Category.objects.filter", "line_number": 102, "usage_type": "call"}, {"api_name": "leads.models.Category.objects", "line_number": 102, "usage_type": "attribute"}, {"api_name": "leads.models.Category", "line_number": 102, "usage_type": "name"}]}
{"seq_id": "32223653434", "text": "#you need the nltk library to run this. In case you don't have it, install it:\n#pip install --user -U nltk\n\nimport nltk\nnltk.download('words')\n\nimport random\nimport itertools\n\nfrom nltk.corpus import words\n\nword_list = words.words()\n\nletter_count = set([])\nfor l in word_list:\n    letter_count.add(len(l))\n\nwhile True:\n    selected_number = (input(\"Please enter the nuber of letters you'd like to guess: \"))\n    if  selected_number.isnumeric():\n        selected_number = int(selected_number)\n\n        if selected_number <= max(letter_count) and selected_number >= min(letter_count):\n            print(\"Cool, you are going to guess a word with \" + str(selected_number) + \" letters\")\n            break\n\n        elif selected_number > max(letter_count):\n            print(\"There is no word with that many letters\")\n\n        elif selected_number < min(letter_count):\n            print(\"You are stupid, try again\")\n\n    else:\n        print(\"You can only enter a number\")\n\nwords_right_count = [word.lower() for word in word_list if len(word) == selected_number]\n\n\n\n\nmystring = ''\nfor x in words_right_count:\n    mystring += ' ' + x\n# mystring = mystring.lower()\n\nnew_list = list(mystring)\n\nseparated_letters = [list(v) for k,v in itertools.groupby(new_list,key=str.isspace) if not k]\n\nselected_word = random.choice(separated_letters)\ndisplay_selected_word = ''\nfor x in selected_word:\n    display_selected_word += ' ' + x\n\ncount = 0\n\nwhile True:\n    user_input = (input(\"Please enter a \" + str(selected_number) + \" letter word: \"))\n    user_input_list = list(user_input)\n    user_output = [len(set(i)) == 1 for i in zip(selected_word, user_input_list)]\n    user_output_new = []\n\n    if len(user_input) == selected_number and user_input in words_right_count:\n        count += 1\n        if user_output.count(True) == len(user_output):\n            print(\"Congratulations!!! You needed \" + str(count) + \" attempts\")\n            break\n        elif count > 5:\n            print(\"FAAAAAAAIIILLL! The correct word would have been \" + display_selected_word)\n            break\n        elif count <= 5:\n            for x, y, z in zip(selected_word, user_output, user_input_list):\n                if y == True:\n                    user_output_new.append(\"True\")\n                else:\n                    if z in selected_word:\n                        user_output_new.append(\"Existing\")\n                    else:\n                        user_output_new.append(\"False\")\n            print(user_output_new)\n\n    elif len(user_input) != selected_number:\n        print(\"You idiot, are you not able to count?! You must enter exactly \" + str(selected_number) + \" letters\")\n\n    elif user_input not in words_right_count:\n        print(\"You must enter an existing word\")", "repo_name": "Pia-Github/woerdl_game", "sub_path": "woerdl_game.py", "file_name": "woerdl_game.py", "file_ext": "py", "file_size_in_byte": 2742, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "nltk.download", "line_number": 5, "usage_type": "call"}, {"api_name": "nltk.corpus.words.words", "line_number": 12, "usage_type": "call"}, {"api_name": "nltk.corpus.words", "line_number": 12, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 48, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 50, "usage_type": "call"}]}
{"seq_id": "19978921479", "text": "import logging\nimport json\nimport os\nfrom google.cloud import secretmanager\n\nlogger = logging.getLogger(__name__)\n\n\nclass SecretManager:\n    def __init__(self):\n        self.client = secretmanager.SecretManagerServiceClient()\n        self._client_id = None\n        self._client_secret = None\n        self._refresh_token = None\n        self._access_token = None\n        self._expires_at = None\n\n    def _get_secret(self, name):\n        _name = f\"projects/{os.getenv('GCP_PROJECT')}/secrets/{name}/\" \\\n                f\"versions/latest\"\n        return self.client.access_secret_version(request={\n            'name': _name\n        }).payload.data.decode('utf-8')\n\n    @property\n    def client_id(self):\n        if not self._client_id:\n            self._client_id = self._get_secret('STRAVA_CLIENT_ID')\n        return self._client_id\n\n    @property\n    def client_secret(self):\n        if not self._client_secret:\n            self._client_secret = self._get_secret('STRAVA_CLIENT_SECRET')\n        return self._client_secret\n\n    def _get_auth_dict(self):\n        auth_secret = self._get_secret('STRAVA_ACCESS_TOKEN')\n        logger.debug(auth_secret)\n        auth_dict = json.loads(auth_secret)\n        self._access_token = auth_dict['access_token']\n        self._refresh_token = auth_dict['refresh_token']\n        self._expires_at = auth_dict['expires_at']\n\n    @property\n    def refresh_token(self):\n        if not self._refresh_token:\n            self._get_auth_dict()\n        return self._refresh_token\n\n    @property\n    def access_token(self):\n        if not self._access_token:\n            self._get_auth_dict()\n        return self._access_token\n\n    @property\n    def expires_at(self):\n        if not self._expires_at:\n            self._get_auth_dict()\n        return self._expires_at\n\n    def set_auth_dict(self, value: dict):\n        self._access_token = value['access_token']\n        self._refresh_token = value['refresh_token']\n        self._expires_at = value['expires_at']\n\n        parent = self.client.secret_path(os.getenv('GCP_PROJECT'), 'STRAVA_ACCESS_TOKEN')\n        self.client.add_secret_version(request={\n            \"parent\": parent,\n            \"payload\": {\"data\": json.dumps(value).encode('utf-8')}\n        })\n", "repo_name": "reevery/strava-gcp-pull", "sub_path": "secretmanager.py", "file_name": "secretmanager.py", "file_ext": "py", "file_size_in_byte": 2231, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "google.cloud.secretmanager.SecretManagerServiceClient", "line_number": 11, "usage_type": "call"}, {"api_name": "google.cloud.secretmanager", "line_number": 11, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 19, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 68, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}]}
{"seq_id": "38469908851", "text": "import pennylane as qml\r\nfrom qiskit import QuantumCircuit\r\nfrom qiskit.circuit import Parameter\r\nfrom pennylane import numpy as np\r\n\r\ndev1 = qml.device(\"default.qubit\",\r\n\t\t\t\t wires=2,\r\n\t\t\t\t shots=5000)\r\n\r\ndev2 = qml.device(\"default.qubit\",\r\n\t\t\t\t wires=\"wire1\",\r\n\t\t\t\t shots=1000)\r\n\r\ndev3 = qml.device(\"default.qubit\",\r\n\t\t\t\t wires=\"wire2\",\r\n\t\t\t\t shots=1000)\r\n\r\n@qml.qnode(dev1)\r\ndef circuit0(phi, theta):\r\n\tqml.RX(phi[0], wires=0)\r\n\tqml.RY(phi[1], wires=1)\r\n\tqml.CNOT(wires=[0, 1])\r\n\tqml.PhaseShift(theta, wires=0)\r\n\treturn qml.expval(qml.PauliZ(0))\r\n\r\nphi = np.array([.5,.2,.4])\r\ntheta = .2\r\ndcircuit = qml.grad(circuit0)\r\ndcircuit(phi, theta)\r\nprint(dir(dcircuit))\r\nprint(dcircuit._grad_with_forward)\r\n\r\n@qml.qnode(dev2)\r\ndef ciruit1(params, theta):\r\n\tqml.RX(params[0], wires=0)\r\n\tqml.CNOT(wires=[0, 1])\r\n\tqml.PhaseShift(theta, wires=0)\r\n\treturn qml.expval(qml._PauliZ(0))\r\n\r\nphi = np.array([.8,.15,.5])\r\ntheta = .1\r\ndcircuit = qml.grad(circuit1)\r\ndcircuit(phi, theta)\r\nprint(dir(dcircuit))\r\nprint(dcircuit._grad_with_forward)\r\n\r\n@qml.qnode(dev3)\r\ndef ciruit2(params, theta):\r\n\tqml.RX(params[0], wires=0)\r\n\tqml.CNOT(wires=[0, 1])\r\n\tqml.PhaseShift(theta, wires=0)\r\n\treturn qml.expval(qml._PauliZ(0))\r\n\r\nphi = np.array([.95,.05,.25])\r\ntheta = .2\r\ndcircuit = qml.grad(circuit0)\r\ndcircuit(phi, theta)\r\nprint(dir(dcircuit))\r\nprint(dcircuit._grad_with_forward)", "repo_name": "dennisbiber/quantium", "sub_path": "qubit_manip.py", "file_name": "qubit_manip.py", "file_ext": "py", "file_size_in_byte": 1355, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pennylane.device", "line_number": 6, "usage_type": "call"}, {"api_name": "pennylane.device", "line_number": 10, "usage_type": "call"}, {"api_name": "pennylane.device", "line_number": 14, "usage_type": "call"}, {"api_name": "pennylane.RX", "line_number": 20, "usage_type": "call"}, {"api_name": "pennylane.RY", "line_number": 21, "usage_type": "call"}, {"api_name": "pennylane.CNOT", "line_number": 22, "usage_type": "call"}, {"api_name": "pennylane.PhaseShift", "line_number": 23, "usage_type": "call"}, {"api_name": "pennylane.expval", "line_number": 24, "usage_type": "call"}, {"api_name": "pennylane.PauliZ", "line_number": 24, "usage_type": "call"}, {"api_name": "pennylane.qnode", "line_number": 18, "usage_type": "call"}, {"api_name": "pennylane.numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "pennylane.numpy", "line_number": 26, "usage_type": "name"}, {"api_name": "pennylane.grad", "line_number": 28, "usage_type": "call"}, {"api_name": "pennylane.RX", "line_number": 35, "usage_type": "call"}, {"api_name": "pennylane.CNOT", "line_number": 36, "usage_type": "call"}, {"api_name": "pennylane.PhaseShift", "line_number": 37, "usage_type": "call"}, {"api_name": "pennylane.expval", "line_number": 38, "usage_type": "call"}, {"api_name": "pennylane._PauliZ", "line_number": 38, "usage_type": "call"}, {"api_name": "pennylane.qnode", "line_number": 33, "usage_type": "call"}, {"api_name": "pennylane.numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "pennylane.numpy", "line_number": 40, "usage_type": "name"}, {"api_name": "pennylane.grad", "line_number": 42, "usage_type": "call"}, {"api_name": "pennylane.RX", "line_number": 49, "usage_type": "call"}, {"api_name": "pennylane.CNOT", "line_number": 50, "usage_type": "call"}, {"api_name": "pennylane.PhaseShift", "line_number": 51, "usage_type": "call"}, {"api_name": "pennylane.expval", "line_number": 52, "usage_type": "call"}, {"api_name": "pennylane._PauliZ", "line_number": 52, "usage_type": "call"}, {"api_name": "pennylane.qnode", "line_number": 47, "usage_type": "call"}, {"api_name": "pennylane.numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "pennylane.numpy", "line_number": 54, "usage_type": "name"}, {"api_name": "pennylane.grad", "line_number": 56, "usage_type": "call"}]}
{"seq_id": "22091147140", "text": "import cv2\nimport numpy as np\n\ndef exstract_points(binary_img,size,start_point,num_points=0,resolution=0.5):\n    closed_curve=has_closed_curve(binary_img)\n  \n    if closed_curve:\n          X_arr , Y_arr = split_close_curve(binary_img,num_points=num_points,resolution=resolution)\n    else: \n          X_arr , Y_arr = split_open_curve(binary_img,num_points=num_points,resolution=resolution)\n    idx=np.where(X_arr==min(X_arr))\n    idx=idx[0][0]\n    new_X=np.zeros_like(X_arr)\n    new_Y=np.zeros_like(X_arr)\n    for i in range(len(X_arr)):\n        new_X[i]=X_arr[idx]\n        new_Y[i]=Y_arr[idx]\n        idx=(idx+1)%len(X_arr)\n    new_X=new_X-new_X[0]\n    new_Y=new_Y[0]-new_Y\n    maxi = np.max(new_X) if np.max(new_X) > np.max(new_Y)-np.min(new_Y) else np.max(new_Y)-np.min(new_Y)\n    points = []\n    for i in range(len(new_X)):\n        x = size*(new_X[i]/maxi)+start_point[0]\n        y = size*(new_Y[i]/maxi)+start_point[1]  \n        points.append([x,y])\n    return points\n\n\ndef has_closed_curve(binary_img):\n    \n    binary_img=binary_img.astype(np.uint8)\n    contours, hierarchy = cv2.findContours(binary_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n    \n    for hir in hierarchy[0]:\n        if hir[2]!=-1:\n            return True\n    \n    return False\n\ndef split_open_curve(binary_image, num_points=0,resolution=0.5):\n    num_point=num_points\n    curve = np.where(binary_image == 1)\n    start_y = int(curve[0][0])\n    start_x = int(curve[1][0])\n\n    distance_map = spread_img(binary_image, start_x, start_y)\n    end_y, end_x = np.where(distance_map == np.max(distance_map))\n    end_y, end_x = end_y[0], end_x[0]\n\n    max_distance = np.max(spread_img(binary_image, end_x, end_y))\n    if ((num_point==0) or (num_point==1)):\n        num_point = int((max_distance-2)*resolution)\n    distances = np.linspace(2, max_distance, num_point)\n    distances = np.round(distances).astype(int)\n\n    x_coords = np.zeros(num_point)\n    y_coords = np.zeros(num_point)\n    for i, distance in enumerate(distances):\n        y_coords[i], x_coords[i] = np.mean(np.where(spread_img(binary_image, end_x, end_y) == distance), axis=1)\n\n    return  x_coords,y_coords\n\ndef split_close_curve(binary_image, num_points=0,resolution=0.5):\n    \n    curve = np.where(binary_image == 1)\n    start_y = int(curve[0][0])\n    start_x = int(curve[1][0])\n\n    distance_map = spread_img(binary_image, start_x, start_y)\n    max_distance = np.max(distance_map)\n    delete=np.zeros_like(binary_image)\n    delete[np.where(distance_map==(int((2+max_distance)/2)))]=1\n    delete=delete.astype(np.uint8)  \n    contours, hierarchy = cv2.findContours(delete, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n    cnt = contours[0]\n    cv2.drawContours(delete, [cnt], 0, (0,255,0), 3)\n\n    binary_image[np.where(delete==1)]=0\n    X,Y=split_open_curve(binary_image, num_points+1,resolution=resolution)\n    return X[1:], Y[1:]\n\ndef spread_img(binary_img1, thisX, thisY):\n    binary_img = np.copy(binary_img1)\n    spread_img = np.zeros_like(binary_img)\n    spread_img[thisY, thisX] = 2\n    binary_img[thisY, thisX] = 0\n    queue = [(thisY, thisX)]\n    while queue:\n        y, x = queue.pop(0)\n        for dy, dx in ((-1, 0), (0, 1), (1, 0), (0, -1)):\n            ny, nx = y + dy, x + dx\n            if (0 <= ny < binary_img.shape[0] and 0 <= nx < binary_img.shape[1] and binary_img[ny][nx]):\n                queue.append((ny, nx))\n                binary_img[ny][nx] = 0\n                spread_img[ny][nx] = spread_img[y][x] + 1\n    return spread_img\n\n\ndef img_to_binary_img(img):\n    \n    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    threshold = 0.5\n    binary_img = gray_img > threshold\n    binary_img = binary_img.astype(int)\n    return (binary_img)\n\n\n", "repo_name": "chesskiss/swarm_drones", "sub_path": "swarm_drone/exstract_point.py", "file_name": "exstract_point.py", "file_ext": "py", "file_size_in_byte": 3698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.where", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 74, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 102, "usage_type": "attribute"}]}
{"seq_id": "33259760918", "text": "\"\"\"\nEntry point of the execution in this application.\n\"\"\"\nfrom csv import DictWriter\nfrom datetime import datetime, timedelta\nfrom json import loads\nfrom shutil import which\n\nfrom requests import get\n\nfrom core.print_log import print_log\nfrom core.process_command import process_execution\n\nif __name__ == '__main__':\n    show_log = True\n    show_log_details = False\n\n    print_log('# Sending request of the mirror status in JSON format.', show_log)\n    response = get('https://archlinux.org/mirrors/status/json/')\n    print_log(str(response.content), show_log_details)\n\n    print_log('# Load the response to a JSON object.', show_log)\n    json_object: dict = loads(response.content)\n\n    print_log('# Check if the field \"urls\" exists.', show_log)\n    if 'urls' not in json_object.keys():\n        print(response.content)\n        print('\\nERROR: The field URLS, not exists in the JSON response.\\n')\n        exit(-1)\n\n    print_log('# Check if exists at least one server.', show_log)\n    if len(json_object['urls']) < 1:\n        print(json_object['urls'])\n        print('\\nERROR: It needs at least one server.\\n')\n        exit(-1)\n\n    print_log('# Start to filter the servers.', show_log)\n    mirror_list = []\n    utc_now = datetime.utcnow()\n    utc_before = utc_now - timedelta(days=5)\n    for server in json_object['urls']:\n        if server['last_sync'] is None:\n            continue\n\n        last_sync = datetime.strptime(server['last_sync'], '%Y-%m-%dT%H:%M:%SZ')\n        if utc_before >= last_sync:\n            continue\n\n        if (\n                server['active'] is True\n                and server['protocol'] == 'https'\n                and server['delay'] is not None\n                and server['delay'] <= 259200\n                # and server['completion_pct'] == 1.0\n        ):\n            mirror_list.append(server)\n\n    print_log('# List of the filtered servers.', show_log)\n    print_log(str(mirror_list), show_log)\n\n    print_log(f'# Total of filtered servers: {len(mirror_list)}.', show_log)\n\n    with open('filtered-mirror-list.csv', 'w') as output_file:\n        csv_headers = mirror_list[0].keys()\n        dict_writer = DictWriter(output_file, csv_headers)\n        dict_writer.writeheader()\n        dict_writer.writerows(mirror_list)\n        print_log('# The filtered servers are stored in the file: ./filtered-mirror-list.json', show_log)\n\n    print_log('# Shows the original server in the mirror list.', show_log)\n    command = ['cat', '/etc/pacman.d/mirrorlist']\n    process_execution(command)\n\n    print_log('# Backup the mirror list.', show_log)\n    command = ['sudo', 'cp', '/etc/pacman.d/mirrorlist', '/etc/pacman.d/mirrorlist.primitive-backup']\n    process_execution(command, display_output=False)\n\n    test_packages = {'62.4 MB': 'blobwars-data', '135.5 MB': 'fillets-ng-data', '268.1 MB': 'megaglest-data',\n                     '437.5 MB': 'frogatto-data', '872.4 MB': 'xonotic-data', '1.3 GB': '0ad-data', }\n    download_at = '437.5 MB'\n    package = test_packages[download_at]\n    server_information = []\n    print_log(f'# The package is {package} with {download_at}', show_log_details)\n\n    error_log_file = 'primitive-error.log'\n    open(error_log_file, 'w').close()\n\n    csv_headers = [\n        'Server',\n        'Sync Time',\n        'Sync CPU',\n        'Install Time',\n        'Install CPU',\n        'Registered UTC Time',\n        'Last Sync',\n        'Completion PCT',\n        'Delay',\n        'Duration Avg',\n        'Duration Std Dev',\n        'Score',\n        'Country',\n        'ISOs',\n        'IPv4',\n        'IPv6',\n        'Details',\n    ]\n\n    with open('primitive-mirror-list.csv', 'w') as output_file:\n        dict_writer = DictWriter(output_file, csv_headers)\n        dict_writer.writeheader()\n        print_log('# The information is started to stored in the file: ./primitive-mirror-list.csv', show_log)\n\n    for server in mirror_list:\n        url = server['url']\n\n        print_log(f'# Set the server to the mirror list:\\n{url}', show_log)\n        command = ['sudo', 'bash', '-c', f'echo \"Server = {url}\\$repo/os/\\$arch\" > /etc/pacman.d/mirrorlist']\n        process_execution(command, display_output=False)\n\n        print_log('# Shows the server in the mirror list.', show_log)\n        command = ['cat', '/etc/pacman.d/mirrorlist']\n        process_execution(command)\n\n        print_log(f'# Check if the package {package} is installed.', show_log)\n        pacman_bin = which('pacman')\n        command = [pacman_bin, '--noconfirm', '-Ss', package]\n        output = process_execution(command, display_output=False)\n\n        if '[installed]' in output['stdout']:\n            print_log(f'# Remove the package {package}.', show_log)\n            command = ['sudo', pacman_bin, '--noconfirm', '-Rns', package]\n            process_execution(command, display_output=False)\n\n        print_log('# Clean the pacman\\'s cache.', show_log)\n        command = ['sudo', pacman_bin, '--noconfirm', '-Sc']\n        process_execution(command, display_output=False)\n\n        print_log('# Update the package databases.', show_log)\n        command = ['sudo', pacman_bin, '-Syy']\n        synchronization = process_execution(command, display_output=False, terminate_on_error=False)\n        if synchronization['error']:\n            with open(error_log_file, 'a') as output_file:\n                output_file.write('# --------------------------------------\\n\\n')\n                output_file.write(f'Server: {url}\\n')\n                output_file.write('Process: Synchronization\\n')\n                output_file.write(f'Error:\\n{synchronization[\"stderr\"]}\\n')\n                output_file.write('\\n')\n            continue\n        else:\n            print_log(f'Finished in {synchronization[\"finish_time\"]}', show_log)\n\n        print_log(f'# Download the package: {package}.', show_log)\n        command = ['sudo', pacman_bin, '--noconfirm', '--downloadonly', '-S', package]\n        installation = process_execution(command, display_output=False, terminate_on_error=False)\n        if installation['error']:\n            with open(error_log_file, 'a') as output_file:\n                output_file.write('# --------------------------------------\\n\\n')\n                output_file.write(f'Server: {url}\\n')\n                output_file.write('Process: Installation\\n')\n                output_file.write(f'Error:\\n{installation[\"stderr\"]}\\n')\n                output_file.write('\\n')\n            continue\n        else:\n            print_log(f'Finished in {installation[\"finish_time\"]}', show_log)\n\n        information = {\n            'Server': server['url'],\n            'Sync Time': synchronization['finish_time'],\n            'Sync CPU': synchronization['finish_time_cpu'],\n            'Install Time': installation['finish_time'],\n            'Install CPU': installation['finish_time_cpu'],\n            'Registered UTC Time': datetime.utcnow().isoformat(),\n            'Last Sync': server['last_sync'],\n            'Completion PCT': server['completion_pct'],\n            'Delay': server['delay'],\n            'Duration Avg': server['duration_avg'],\n            'Duration Std Dev': server['duration_stddev'],\n            'Score': server['score'],\n            'Country': server['country'],\n            'ISOs': server['isos'],\n            'IPv4': server['ipv4'],\n            'IPv6': server['ipv6'],\n            'Details': server['details'],\n        }\n        server_information.append(information)\n\n        with open('primitive-mirror-list.csv', 'a', newline='') as output_file:\n            dict_writer = DictWriter(output_file, csv_headers)\n            dict_writer.writerow(information)\n            print_log('# The information is stored in the file: ./primitive-mirror-list.csv', show_log)\n\n        # if url == 'https://mirror.umd.edu/archlinux/':\n        #     break\n\n        process_execution(['sleep', '5'], display_output=False)\n\n    print_log(f'# Servers Information:\\n{server_information}', show_log)\n", "repo_name": "airvzxf/primitive-mirror-updater", "sub_path": "src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7909, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "core.print_log.print_log", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 20, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 22, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 23, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 25, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 31, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "name"}, {"api_name": "core.print_log.print_log", "line_number": 58, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 59, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 61, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 65, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 68, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 70, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 72, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 74, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 76, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 83, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 109, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 111, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 116, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 118, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 120, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 122, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 124, "usage_type": "call"}, {"api_name": "shutil.which", "line_number": 125, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 127, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 130, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 132, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 134, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 136, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 138, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 140, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 150, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 152, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 154, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 172, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 172, "usage_type": "name"}, {"api_name": "csv.DictWriter", "line_number": 188, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 190, "usage_type": "call"}, {"api_name": "core.process_command.process_execution", "line_number": 195, "usage_type": "call"}, {"api_name": "core.print_log.print_log", "line_number": 197, "usage_type": "call"}]}
{"seq_id": "30893819746", "text": "import logging\n\nfrom dash import dcc, html, dash_table as dt, Input, Output, callback\nimport dash_bootstrap_components as dbc\n\nfrom .. import utils\nfrom . import data\nfrom ....dictionary import COLUMNS\n\n\nlogger = logging.getLogger('dashboard.reports')\n\n\ndef get_content():\n    #columns = utils.make_columns(COLUMNS.get('reports'))\n    columns = utils.make_columns([\n        'PROJECT',\n        'TYPE',\n        'NAME',\n        'VIEW',\n        'DATE',\n        'PDF',\n        'DATA',\n    ])\n\n    # Format columns with links as markdown text\n    for i, c in enumerate(columns):\n        if c['name'] in ['VIEW']:\n            columns[i]['type'] = 'text'\n            columns[i]['presentation'] = 'markdown'\n\n    content = [\n        dbc.Row([\n            dbc.Col(\n                dbc.Button(\n                    'Refresh Data',\n                    id='button-reports-refresh',\n                    outline=True,\n                    color='primary',\n                    size='sm',\n                ),\n            ),\n            dbc.Col(\n                dcc.Dropdown(\n                    id='dropdown-reports-proj',\n                    multi=True,\n                    placeholder='Select Project(s)',\n                ),\n                width=3,\n            ),\n            dbc.Col(\n                dcc.Dropdown(\n                    id='dropdown-reports-type',\n                    multi=True,\n                    placeholder='Select Types(s)',\n                ),\n                width=3,\n            ),\n            dbc.Col(\n                dcc.Dropdown(\n                    value='Current',\n                    id='dropdown-reports-time',\n                    multi=False,\n                    placeholder='Select Time',\n                ),\n                width=3,\n            ),\n        ]),\n        dbc.Spinner(id=\"loading-reports-table\", children=[\n            dbc.Label('Loading...', id='label-reports-rowcount1'),\n        ]),\n        dt.DataTable(\n            columns=columns,\n            data=[],\n            page_action='none',\n            sort_action='native',\n            id='datatable-reports',\n            style_cell={\n                'textAlign': 'center',\n                'padding': '15px 5px 15px 5px',\n                'height': 'auto',\n            },\n            style_header={\n                'fontWeight': 'bold',\n            },\n            style_cell_conditional=[\n                {'if': {'column_id': 'NAME'}, 'textAlign': 'left'},\n            ],\n            # Aligns the markdown cells, both vertical and horizontal\n            css=[dict(selector=\"p\", rule=\"margin: 0; text-align: center\")],\n        ),\n        html.Label('0', id='label-reports-rowcount2')]\n\n    return content\n\n\ndef load_reports(refresh=False):\n    return data.load_data(refresh)\n\n\n@callback(\n    [\n     Output('dropdown-reports-proj', 'options'),\n     Output('dropdown-reports-type', 'options'),\n     Output('dropdown-reports-time', 'options'),\n     Output('datatable-reports', 'data'),\n     Output('label-reports-rowcount1', 'children'),\n     Output('label-reports-rowcount2', 'children'),\n    ],\n    [\n     Input('dropdown-reports-proj', 'value'),\n     Input('dropdown-reports-type', 'value'),\n     Input('dropdown-reports-time', 'value'),\n     Input('button-reports-refresh', 'n_clicks'),\n    ])\ndef update_reports(\n    selected_proj,\n    selected_type,\n    selected_time,\n    n_clicks\n):\n    refresh = False\n\n    logger.debug('update_all')\n\n    # Load selected data with refresh if requested\n\n    if utils.was_triggered('button-reports-refresh'):\n        logger.debug(f'reports refresh:clicks={n_clicks}')\n        refresh = True\n\n    df = load_reports(refresh)\n\n    # Get options\n    projects, types, times = data.load_options(df)\n    projects = utils.make_options(projects)\n    types = utils.make_options(types)\n    times = utils.make_options(times)\n\n    logger.debug(f'loaded options:{projects}')\n    logger.debug(f'loaded options:{types}')\n    logger.debug(f'loaded options:{times}')\n\n    # Apply filters\n    df = data.filter_data(df, selected_proj, selected_type, selected_time)\n\n    # Get the table data as one row per assessor\n    records = df.reset_index().to_dict('records')\n\n    # Format records\n    for r in records:\n        # Make view a link\n        _link = r['VIEW']\n        _text = 'view'\n        r['VIEW'] = f'[{_text}]({_link})'\n\n    # Count how many rows are in the table\n    rowcount = '{} rows'.format(len(records))\n\n    return [projects, types, times, records, rowcount, rowcount]\n", "repo_name": "ccmvumc/garjus", "sub_path": "garjus/dashboard/pages/reports/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4476, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Row", "line_number": 33, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 34, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Button", "line_number": 35, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 43, "usage_type": "call"}, {"api_name": "dash.dcc.Dropdown", "line_number": 44, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 44, "usage_type": "name"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 51, "usage_type": "call"}, {"api_name": "dash.dcc.Dropdown", "line_number": 52, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 52, "usage_type": "name"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 59, "usage_type": "call"}, {"api_name": "dash.dcc.Dropdown", "line_number": 60, "usage_type": "call"}, {"api_name": "dash.dcc", "line_number": 60, "usage_type": "name"}, {"api_name": "dash_bootstrap_components.Spinner", "line_number": 69, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Label", "line_number": 70, "usage_type": "call"}, {"api_name": "dash.dash_table.DataTable", "line_number": 72, "usage_type": "call"}, {"api_name": "dash.dash_table", "line_number": 72, "usage_type": "name"}, {"api_name": "dash.html.Label", "line_number": 92, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 92, "usage_type": "name"}, {"api_name": "dash.callback", "line_number": 101, "usage_type": "call"}, {"api_name": "dash.Output", "line_number": 103, "usage_type": "call"}, {"api_name": "dash.Output", "line_number": 104, "usage_type": "call"}, {"api_name": "dash.Output", "line_number": 105, "usage_type": "call"}, {"api_name": "dash.Output", "line_number": 106, "usage_type": "call"}, {"api_name": "dash.Output", "line_number": 107, "usage_type": "call"}, {"api_name": "dash.Output", "line_number": 108, "usage_type": "call"}, {"api_name": "dash.Input", "line_number": 111, "usage_type": "call"}, {"api_name": "dash.Input", "line_number": 112, "usage_type": "call"}, {"api_name": "dash.Input", "line_number": 113, "usage_type": "call"}, {"api_name": "dash.Input", "line_number": 114, "usage_type": "call"}]}
{"seq_id": "16636728197", "text": "from __future__ import absolute_import\nimport fluent.syntax.ast as FTL\nfrom fluent.migrate.helpers import transforms_from\nfrom fluent.migrate.helpers import VARIABLE_REFERENCE, TERM_REFERENCE\nfrom fluent.migrate import REPLACE, COPY\n\nswitch = \"firefox/switch.lang\"\n\n\ndef migrate(ctx):\n    \"\"\"Migrate bedrock/firefox/templates/firefox/switch.html, part {index}.\"\"\"\n\n    ctx.add_transforms(\n        \"firefox/switch.ftl\",\n        \"firefox/switch.ftl\",\n        [\n            FTL.Message(\n                id=FTL.Identifier(\"switch-switch-from-chrome\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"Switch from Chrome to Firefox in just a few minutes\",\n                    {\n                        \"Firefox\": TERM_REFERENCE(\"brand-name-firefox\"),\n                        \"Chrome\": TERM_REFERENCE(\"brand-name-chrome\"),\n                    },\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-switching-to-firefox-is-fast\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"Switching to Firefox is fast, easy and risk-free, because Firefox imports your bookmarks, autofills, passwords and preferences from Chrome.\",\n                    {\n                        \"Firefox\": TERM_REFERENCE(\"brand-name-firefox\"),\n                        \"Chrome\": TERM_REFERENCE(\"brand-name-chrome\"),\n                    },\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-switching-to-firefox-page-description\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"Switching to Firefox is fast, easy and risk-free. Firefox imports your bookmarks, autofills, passwords and preferences from Chrome.\",\n                    {\n                        \"Firefox\": TERM_REFERENCE(\"brand-name-firefox\"),\n                        \"Chrome\": TERM_REFERENCE(\"brand-name-chrome\"),\n                    },\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-select-what-to-take\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"Select what to take from Chrome.\",\n                    {\"Chrome\": TERM_REFERENCE(\"brand-name-chrome\")},\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-let-firefox-do-the-rest\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"Let Firefox do the rest.\",\n                    {\"Firefox\": TERM_REFERENCE(\"brand-name-firefox\")},\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-use-firefox-and-still-chrome\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"You can use Firefox and still have Chrome. Chrome won’t change on your machine one bit.\",\n                    {\n                        \"Firefox\": TERM_REFERENCE(\"brand-name-firefox\"),\n                        \"Chrome\": TERM_REFERENCE(\"brand-name-chrome\"),\n                    },\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-share-with-your-friends\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"Share with your friends how to switch to Firefox\",\n                    {\"Firefox\": TERM_REFERENCE(\"brand-name-firefox\")},\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-firefox-makes-switching-fast-tweet\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"🔥 Firefox makes switching from Chrome really fast. Try it out!\",\n                    {\n                        \"Firefox\": TERM_REFERENCE(\"brand-name-firefox\"),\n                        \"Chrome\": TERM_REFERENCE(\"brand-name-chrome\"),\n                    },\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-switch-to-firefox\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"Switch to Firefox\",\n                    {\"Firefox\": TERM_REFERENCE(\"brand-name-firefox\")},\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-firefox-makes-switching-fast-email\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"Firefox makes switching from Chrome really fast. I like it a lot, and you should try it.\",\n                    {\n                        \"Firefox\": TERM_REFERENCE(\"brand-name-firefox\"),\n                        \"Chrome\": TERM_REFERENCE(\"brand-name-chrome\"),\n                    },\n                ),\n            ),\n            FTL.Message(\n                id=FTL.Identifier(\"switch-still-not-convinced\"),\n                value=REPLACE(\n                    \"firefox/switch.lang\",\n                    \"Still not convinced that switching to Firefox is easy?\",\n                    {\"Firefox\": TERM_REFERENCE(\"brand-name-firefox\")},\n                ),\n            ),\n        ],\n    )\n\n    ctx.add_transforms(\n        \"firefox/switch.ftl\",\n        \"firefox/switch.ftl\",\n        transforms_from(\n            \"\"\"\nswitch-enjoy-the-web-faster = {COPY(switch, \"Enjoy the web faster, all set up for you.\",)}\nswitch-download-and-switch = {COPY(switch, \"Download and switch\",)}\nswitch-share-to-facebook = {COPY(switch, \"Share to Facebook\",)}\nswitch-send-a-tweet = {COPY(switch, \"Send a tweet\",)}\nswitch-hey = {COPY(switch, \"Hey,\",)}\nswitch-check-it-out = {COPY(switch, \"Check it out and let me know what you think:\",)}\nswitch-send-an-email = {COPY(switch, \"Send an email\",)}\n\"\"\",\n            switch=switch,\n        ),\n    )\n", "repo_name": "academic-resources/stared-repos", "sub_path": "bedrock/lib/fluent_migrations/firefox/switch.py", "file_name": "switch.py", "file_ext": "py", "file_size_in_byte": 5845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "41", "api": [{"api_name": "fluent.syntax.ast.Message", "line_number": 17, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 17, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 18, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 18, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 19, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 23, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 24, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 28, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 28, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 29, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 29, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 30, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 34, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 35, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 39, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 39, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 40, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 40, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 41, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 45, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 46, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 50, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 50, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 51, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 51, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 52, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 55, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 58, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 58, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 59, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 59, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 60, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 63, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 66, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 66, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 67, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 67, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 68, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 72, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 73, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 77, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 77, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 78, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 78, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 79, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 82, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 85, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 85, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 86, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 86, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 87, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 91, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 92, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 96, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 96, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 97, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 97, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 98, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 101, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 104, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 104, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 105, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 105, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 106, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 110, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 111, "usage_type": "call"}, {"api_name": "fluent.syntax.ast.Message", "line_number": 115, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 115, "usage_type": "name"}, {"api_name": "fluent.syntax.ast.Identifier", "line_number": 116, "usage_type": "call"}, {"api_name": "fluent.syntax.ast", "line_number": 116, "usage_type": "name"}, {"api_name": "fluent.migrate.REPLACE", "line_number": 117, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.TERM_REFERENCE", "line_number": 120, "usage_type": "call"}, {"api_name": "fluent.migrate.helpers.transforms_from", "line_number": 129, "usage_type": "call"}]}
{"seq_id": "11338627939", "text": "# function_import --------------------\n\nfrom transformers import pipeline\n\n# function_code --------------------\n\ndef classify_spoken_command(audio_file_path):\n    \"\"\"\n    Classify a spoken command into specific keywords using an audio classification model.\n\n    Args:\n        audio_file_path (str): The path to the audio file containing the spoken command.\n\n    Returns:\n        dict: The classified keyword with its corresponding score.\n    \"\"\"\n    audio_classifier = pipeline('audio-classification', model='superb/wav2vec2-base-superb-ks')\n    keyword = audio_classifier(audio_file_path, top_k=1)\n    return keyword\n\n# test_function_code --------------------\n\ndef test_classify_spoken_command():\n    \"\"\"\n    Test the classify_spoken_command function.\n    \"\"\"\n    test_audio_file_path = 'path_to_test_audio_file.wav'\n    # replace 'path_to_test_audio_file.wav' with the path to a test audio file\n    result = classify_spoken_command(test_audio_file_path)\n    assert isinstance(result, dict), 'The result should be a dictionary.'\n    assert 'label' in result, 'The result should contain a label.'\n    assert 'score' in result, 'The result should contain a score.'\n\n# call_test_function_code --------------------\n\ntest_classify_spoken_command()", "repo_name": "vixuowis/Research-2309", "sub_path": "Exp-2/output/hf-eval-data-v2/f00538_classify_spoken_command.py", "file_name": "f00538_classify_spoken_command.py", "file_ext": "py", "file_size_in_byte": 1243, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "transformers.pipeline", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "33148752684", "text": "import time\nimport os\nimport shutil\nfrom watchdog.observers import Observer\nfrom watchdog.events import FileSystemEventHandler\n\nclass Watcher:\n    DIRECTORY_TO_WATCH = \"./watch_dir/src/\"\n    def __init__(self):\n        self.observer = Observer()\n\n    def run(self):\n        event_handler = Handler()\n        self.observer.schedule(event_handler, self.DIRECTORY_TO_WATCH, recursive=True)\n        self.observer.start()\n        try:\n            while True:\n                time.sleep(5)\n        except:\n            self.observer.stop()\n            print (\"Error\")\n\n        self.observer.join()\n\nclass Handler(FileSystemEventHandler):  \n    @staticmethod\n    def on_any_event(event):\n        src_1 = \"./watch_dir/src/\"\n        src_2 = \"./watch_dir/src/\"\n        dst_path = \"./watch_dir/dst/\"\n        if event.is_directory:\n            return None\n        elif event.event_type == 'created':            \n            print(\"File is created - %s.\" %event.src_path)\n\n            str1 = event.src_path\n            if src_2 in str1:\n                src_2 = str1.replace(src_2,'')\n            filename, extension = os.path.splitext(src_2) \n            values = {'file_name':filename}\n            src_path = event.src_path\n            print(\"Source: \", src_1 + filename)\n            \n            print(\"Destination: \", dst_path)\n            shutil.copy(src_path, dst_path)\n            try:\n                shutil.copy(src_path, dst_path)\n            except:\n                print(\"Data format is not valid !!\")\n        \n        elif event.event_type == 'modified': \n            print(\"Data modified - %s.\" % event.src_path) \n            str1 = event.src_path\n            if src_2 in str1:\n                src_2 = str1.replace(src_2,'')\n            filename, extension = os.path.splitext(src_2) \n            values = {'file_name':filename}\n            src_path = event.src_path\n            print(\"Source: \", src_2 + filename)\n            try:\n                shutil.copy(src_path, dst_path)\n                print(\"File is modifie successfully.\")\n            except:\n                print(\"Error to modified files\")\n        \n        elif event.event_type == \"deleted\":\n            print(\"Data deleted - %s.\" % event.src_path)\n            str1 = event.src_path\n            if src_2 in str1:\n                src_2 = str1.replace(src_2,'')\n            filename, extension = os.path.splitext(src_2) \n            try:\n                os.remove(dst_path+src_2)\n                print(\"File removed successfully\")\n            except:\n                print(\"Error to remove files\")\n        \n        elif  event.event_type == \"moved\":\n            print(\"Data moved - %s.\" % event.src_path)\n            str1 = event.src_path\n            if src_2 in str1:\n                src_2 = str1.replace(src_2,'')\n            filename, extension = os.path.splitext(src_2) \n            try:\n                os.remove(dst_path+src_2)\n                print(\"File moved successfully\")\n            except:\n                print(\"Error to moved files\")\n\n        elif   event.event_type == \"closed\":\n            print(\"Data is closed - %s.\" % event.src_path)   \n\nif __name__ == '__main__':\n    w = Watcher()\n    w.run()", "repo_name": "beshprakash11/serverwatch", "sub_path": "server_watcher.py", "file_name": "server_watcher.py", "file_ext": "py", "file_size_in_byte": 3175, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "watchdog.observers.Observer", "line_number": 10, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}, {"api_name": "watchdog.events.FileSystemEventHandler", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 45, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 85, "usage_type": "call"}]}
{"seq_id": "22774148802", "text": "import os\nimport pickle\nfrom typing import List, Tuple\n\nimport faiss\nimport gdown\nimport numpy as np\n\nfrom .search import Base\n\n\nclass SearchSolution(Base):\n    def __init__(\n        self,\n        data_file=\"./data/train_data.pickle\",\n        data_url=\"https://drive.google.com/uc?id=1D_jPx7uIaCJiPb3pkxcrkbeFcEogdg2R\",\n        nlist=2048,  # the number of cells\n        nprobe=256,  # the number of cells (out of nlist) that are visited to perform a search\n        top_k=8,  # nearest neighbors\n        dim=512,\n    ) -> None:\n        self.data_file = data_file\n        self.data_url = data_url\n        self.top_k = top_k\n        self.nlist = nlist\n        self.dim = dim\n        self.quantizer = faiss.IndexFlatL2(self.dim)\n        self.index = faiss.IndexIVFFlat(\n            self.quantizer, self.dim, self.nlist, faiss.METRIC_INNER_PRODUCT\n        )\n        self.index.nprobe = nprobe\n\n    def add_vectors2index(self, vectors: np.array) -> None:\n        self.index.add(vectors)\n\n    def set_base_from_pickle(self):\n        \"\"\"\n        Downloads the data, if it does not exist.\n        Sets reg_matrix and pass_dict\n\n        reg_matrix : np.array(N, 512)\n        pass_dict : dict -> dict[idx] = [np.array[1, 512]]\n        \"\"\"\n        if not os.path.isfile(self.data_file):\n            if not os.path.isdir(\"./data\"):\n                os.mkdir(\"./data\")\n            gdown.download(self.data_url, self.data_file, quiet=False)\n\n        with open(self.data_file, \"rb\") as f:\n            data = pickle.load(f)\n\n        self.reg_matrix = [None] * len(data[\"reg\"])\n        self.ids = {}\n        for i, key in enumerate(data[\"reg\"]):\n            self.reg_matrix[i] = data[\"reg\"][key][0][None]\n            self.ids[i] = key\n\n        self.reg_matrix = np.concatenate(self.reg_matrix, axis=0).astype(\"float32\")\n        faiss.normalize_L2(self.reg_matrix)\n        self.index.train(self.reg_matrix)\n        self.add_vectors2index(self.reg_matrix)\n        self.pass_dict = data[\"pass\"]\n\n    def search(self, query: np.array) -> List[Tuple]:\n        if query.ndim == 1:\n            query = np.expand_dims(query, axis=0)\n        query = query.astype(\"float32\")\n        D, I = self.index.search(query, self.top_k)\n        idxs = [self.ids[id] for id in I[0]]\n        return list(zip(idxs, D[0]))\n\n    def insert(self, feature: np.array) -> None:\n        if feature.ndim == 1:\n            feature = np.expand_dims(feature, axis=0)\n        feature = feature.astype(\"float32\")\n        faiss.normalize_L2(feature)\n        self.add_vectors2index(feature)\n        self.ids[len(self.ids)] = feature\n        self.reg_matrix = np.concatenate((self.reg_matrix, feature), axis=0)\n\n    def cos_sim(self, query: np.array) -> np.array:\n        pass\n", "repo_name": "olegkorshunov/Hackaton_Search_Base", "sub_path": "search/search_solution.py", "file_name": "search_solution.py", "file_ext": "py", "file_size_in_byte": 2720, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "search.Base", "line_number": 12, "usage_type": "name"}, {"api_name": "faiss.IndexFlatL2", "line_number": 27, "usage_type": "call"}, {"api_name": "faiss.IndexIVFFlat", "line_number": 28, "usage_type": "call"}, {"api_name": "faiss.METRIC_INNER_PRODUCT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 46, "usage_type": "call"}, {"api_name": "gdown.download", "line_number": 47, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 58, "usage_type": "call"}, {"api_name": "faiss.normalize_L2", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 74, "usage_type": "call"}, {"api_name": "faiss.normalize_L2", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "attribute"}]}
{"seq_id": "9623279673", "text": "from typing import Type\n\nfrom django.http.request import HttpRequest\nfrom django.utils import timezone\nfrom django.utils.translation import gettext as _\nfrom qfieldcloud.core.models import User\nfrom rest_framework.authentication import (\n    TokenAuthentication as DjangoRestFrameworkTokenAuthentication,\n)\n\nfrom ..core.exceptions import AuthenticationViaTokenFailedError\nfrom .models import AuthToken\n\n\ndef invalidate_all_tokens(user: User) -> int:\n    now = timezone.now()\n    return AuthToken.objects.filter(user=user, expires_at__gt=now).update(\n        expires_at=now\n    )\n\n\ndef create_token(\n    token_model: Type[AuthToken],\n    user: User,\n    _serializer=None,\n    request: HttpRequest = None,\n) -> AuthToken:\n    user_agent = \"\"\n    client_type = AuthToken.ClientType.UNKNOWN\n\n    if request:\n        user_agent = request.META.get(\"HTTP_USER_AGENT\", \"\")\n        client_type = AuthToken.guess_client_type(user_agent)\n\n    token = token_model.objects.create(\n        user=user, client_type=client_type, user_agent=user_agent\n    )\n\n    return token\n\n\nclass TokenAuthentication(DjangoRestFrameworkTokenAuthentication):\n    \"\"\"\n    Multi token authentication based on simple token based authentication.\n    Clients should authenticate by passing the token key in the \"Authorization\"\n    HTTP header, prepended with the string \"Token \".  For example:\n        Authorization: Token 401f7ac837da42b97f613d789819ff93537bee6a\n    \"\"\"\n\n    model = AuthToken\n\n    def authenticate_credentials(self, key):\n        model = self.get_model()\n        try:\n            token = model.objects.get(key=key)\n        except model.DoesNotExist:\n            raise AuthenticationViaTokenFailedError(_(\"Invalid token.\"))\n\n        if not token.is_active:\n            raise AuthenticationViaTokenFailedError(_(\"Token has expired.\"))\n\n        if not token.user.is_active:\n            raise AuthenticationViaTokenFailedError(_(\"User inactive or deleted.\"))\n\n        # update the token last used time\n        # NOTE the UPDATE may be performed already on the `token = model.objects.get(key=key)`, but we lose \"token has expired\" exception.\n        token.last_used_at = timezone.now()\n        token.save()\n\n        return (token.user, token)\n", "repo_name": "opengisch/qfieldcloud", "sub_path": "docker-app/qfieldcloud/authentication/authentication.py", "file_name": "authentication.py", "file_ext": "py", "file_size_in_byte": 2220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 78, "dataset": "github-code", "pt": "41", "api": [{"api_name": "qfieldcloud.core.models.User", "line_number": 15, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 16, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 16, "usage_type": "name"}, {"api_name": "models.AuthToken.objects.filter", "line_number": 17, "usage_type": "call"}, {"api_name": "models.AuthToken.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.AuthToken", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 23, "usage_type": "name"}, {"api_name": "models.AuthToken", "line_number": 23, "usage_type": "name"}, {"api_name": "qfieldcloud.core.models.User", "line_number": 24, "usage_type": "name"}, {"api_name": "django.http.request.HttpRequest", "line_number": 26, "usage_type": "name"}, {"api_name": "models.AuthToken.ClientType", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.AuthToken", "line_number": 29, "usage_type": "name"}, {"api_name": "models.AuthToken.guess_client_type", "line_number": 33, "usage_type": "call"}, {"api_name": "models.AuthToken", "line_number": 33, "usage_type": "name"}, {"api_name": "models.AuthToken", "line_number": 27, "usage_type": "name"}, {"api_name": "rest_framework.authentication.TokenAuthentication", "line_number": 42, "usage_type": "name"}, {"api_name": "models.AuthToken", "line_number": 50, "usage_type": "name"}, {"api_name": "core.exceptions.AuthenticationViaTokenFailedError", "line_number": 57, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 57, "usage_type": "call"}, {"api_name": "core.exceptions.AuthenticationViaTokenFailedError", "line_number": 60, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 60, "usage_type": "call"}, {"api_name": "core.exceptions.AuthenticationViaTokenFailedError", "line_number": 63, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 63, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 67, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 67, "usage_type": "name"}]}
{"seq_id": "2334894149", "text": "import torch\nimport torch.nn as nn\n# import numpy as np\n\nINIT_PRELU_SLOPE = 0.1\n\ndef Conv2dInit(*args, rectified=True, **kwargs):\n    result = nn.Conv2d(*args, **kwargs)\n    if rectified:\n        nn.init.kaiming_normal(result.weight.data, a=INIT_PRELU_SLOPE)\n    else:\n        nn.init.xavier_normal(result.weight.data)\n    result.bias.data.zero_()\n    return result\n\nclass CTSRCNN(nn.Module):\n    def __init__(self):\n        nn.Module.__init__(self)\n        # self.lrelu = nn.LeakyReLU(negative_slope=0.2)\n        self.stack = nn.Sequential(\n            Conv2dInit(1, 64, (9, 9), padding=4, rectified=False),\n            nn.PReLU(num_parameters=64, init=INIT_PRELU_SLOPE),\n            Conv2dInit(64, 32, (5, 5), padding=2),\n            nn.PReLU(num_parameters=32, init=INIT_PRELU_SLOPE),\n            Conv2dInit(32, 1, (5, 5), padding=2),\n        )\n\n    def forward(self, inp):\n        return self.stack(inp)\n\n    def modules(self):\n        return [self.stack[i] for i in range(len(self.stack))]\n\n    def add_layers(self):\n        modules = self.modules()\n        modules = modules[:-1] + [\n            Conv2dInit(32, 32, (3, 3), padding=1), nn.PReLU(num_parameters=32, init=INIT_PRELU_SLOPE),\n            Conv2dInit(32, 32, (3, 3), padding=1), nn.PReLU(num_parameters=32, init=INIT_PRELU_SLOPE),\n        ] + modules[-1:]\n        self.stack = nn.Sequential(*modules)\n        if modules[-1].weight.data.is_cuda:\n            self.stack = self.stack.cuda()\n\n    # Count (from back) how many layers have been trimmed.\n    # 0 if untrimmed; 2 if double-trimmed once.\n    def trim_count(self):\n        modules = self.modules()\n        for i, module in enumerate(modules[-3::-2]):\n            assert isinstance(module, nn.Conv2d)\n            if module.in_channels == 1 and module.out_channels == 64:\n                return i\n            elif module.out_channels == 32:\n                return i\n        return self.depth()\n\n    # trim layer's outgoing connection\n    def trim_layer(self, i):\n        modules = self.modules()\n        layer_idx = 2 * i if i >= 0 else 2 * i - 1  # 2 -> (4, 6); -1 -> (-3, -5)\n        current = modules[layer_idx]\n        prelu = modules[layer_idx + 1]\n        next = modules[layer_idx + 2]\n\n        assert isinstance(current, nn.Conv2d)\n        assert isinstance(prelu, nn.PReLU)\n        assert isinstance(next, nn.Conv2d)\n        assert current.out_channels == next.in_channels\n        channels = current.out_channels\n        selected = torch.randperm(channels)[:channels // 2]\n        new_current = nn.Conv2d(current.in_channels, channels // 2,\n                                kernel_size=current.kernel_size,\n                                stride=current.stride,\n                                padding=current.padding,\n                                dilation=current.dilation,\n                                bias=current.bias is not None)\n        new_prelu = nn.PReLU(num_parameters=1 if prelu.num_parameters == 1 else prelu.num_parameters // 2)\n        new_next = nn.Conv2d(channels // 2, next.out_channels,\n                             kernel_size=next.kernel_size,\n                             stride=next.stride,\n                             padding=next.padding,\n                             dilation=next.dilation,\n                             bias=next.bias is not None)\n\n        if current.weight.data.is_cuda:\n            new_current, new_prelu, new_next = new_current.cuda(), new_prelu.cuda(), new_next.cuda()\n            selected = selected.cuda()\n\n        torch.index_select(current.weight.data, 0, selected, out=new_current.weight.data)\n        if current.bias is not None:\n            torch.index_select(current.bias.data, 0, selected, out=new_current.bias.data)\n        torch.index_select(prelu.weight.data, 0, selected, out=new_prelu.weight.data)\n        torch.index_select(next.weight.data, 1, selected, out=new_next.weight.data)\n\n        modules[layer_idx] = new_current\n        modules[layer_idx + 1] = new_prelu\n        modules[layer_idx + 2] = new_next\n        self.stack = nn.Sequential(*modules)\n\n    def trim(self):\n        trim_count = self.trim_count()\n        self.trim_layer(-trim_count - 1)\n        self.trim_layer(-trim_count - 2)\n\n    def depth(self):\n        return len(self.stack) // 2 + 1\n", "repo_name": "phulin/srnn", "sub_path": "srcnn.py", "file_name": "srcnn.py", "file_ext": "py", "file_size_in_byte": 4252, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "torch.nn.Conv2d", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_normal", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Module.__init__", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.randperm", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.index_select", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}]}
{"seq_id": "23811192403", "text": "from accounts.models import Token\nfrom django.contrib import auth, messages\nfrom django.core.mail import send_mail\nfrom django.shortcuts import redirect\nfrom django.urls import reverse\n\nSEND_MAIL_SUCCESS_MESSAGE = \"Check your email, you'll find a message with a link that will log you into the site.\"\nSEND_MAIL_SUBJECT = \"Your login link for Superlists\"\n\n\ndef send_login_email(request):\n    email = request.POST[\"email\"]\n    token = Token.objects.create(email=email)\n    url = request.build_absolute_uri(reverse(\"login\", kwargs={\"token\": token.uid}))\n\n    send_mail(\n        subject=SEND_MAIL_SUBJECT,\n        message=f\"Use this link to log in:\\n\\n{url}\",\n        from_email=\"noreply@superlists\",\n        recipient_list=[email],\n    )\n\n    messages.success(\n        request=request,\n        message=SEND_MAIL_SUCCESS_MESSAGE,\n    )\n    return redirect(\"/\")\n\n\ndef login(request, token):\n    user = auth.authenticate(uid=token)\n    if user:\n        auth.login(request, user)\n    return redirect(\"/\")\n\n\ndef logout(request):\n    auth.logout(request)\n    return redirect(\"/\")\n", "repo_name": "VolodymyrVdovyn/TDD_book_django_project", "sub_path": "src/apps/accounts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1071, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "accounts.models.Token.objects.create", "line_number": 13, "usage_type": "call"}, {"api_name": "accounts.models.Token.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "accounts.models.Token", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 14, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 23, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 31, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 31, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 38, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 38, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "35168461086", "text": "import logging\nimport re\n\nfrom urwid import Text, connect_signal\n\nfrom subiquity.models.filesystem import (\n    get_raid_size,\n    humanize_size,\n    raidlevels,\n    raidlevels_by_value,\n)\nfrom subiquity.ui.views.filesystem.compound import (\n    CompoundDiskForm,\n    MultiDeviceField,\n    get_possible_components,\n)\nfrom subiquitycore.ui.container import Pile\nfrom subiquitycore.ui.form import (\n    ChoiceField,\n    ReadOnlyField,\n    WantsToKnowFormField,\n    simple_field,\n)\nfrom subiquitycore.ui.interactive import StringEditor\nfrom subiquitycore.ui.selector import Option\nfrom subiquitycore.ui.stretchy import Stretchy\n\nlog = logging.getLogger(\"subiquity.ui.views.filesystem.raid\")\n\n\nraidlevel_choices = [\n    Option((_(level.name), True, level))\n    for level in raidlevels\n    if level.value != \"container\"\n]\n\n\nclass RaidnameEditor(StringEditor, WantsToKnowFormField):\n    def valid_char(self, ch):\n        if len(ch) == 1 and ch == \"/\":\n            self.bff.in_error = True\n            self.bff.show_extra(\n                (\"info_error\", _(\"/ is not permitted in the name of a RAID device\"))\n            )\n            return False\n        elif len(ch) == 1 and ch.isspace():\n            self.bff.in_error = True\n            self.bff.show_extra(\n                (\n                    \"info_error\",\n                    _(\"Whitespace is not permitted in the name of a RAID device\"),\n                )\n            )\n            return False\n        else:\n            return super().valid_char(ch)\n\n\nRaidnameField = simple_field(RaidnameEditor)\n\n\nclass RaidForm(CompoundDiskForm):\n    def __init__(self, model, possible_components, initial, raid_names):\n        self.raid_names = raid_names\n        super().__init__(model, possible_components, initial)\n        self.size.enabled = False\n\n    name = RaidnameField(_(\"Name:\"))\n    level = ChoiceField(_(\"RAID Level:\"), choices=raidlevel_choices)\n    devices = MultiDeviceField(_(\"Devices:\"))\n    size = ReadOnlyField(_(\"Size:\"))\n\n    def clean_name(self, val):\n        if not val:\n            raise ValueError(\"The name cannot be empty\")\n        if not re.match(\"md[0-9]+\", val):\n            val = \"md/\" + val\n        return val\n\n    def validate_name(self):\n        if self.name.value in self.raid_names:\n            return _(\"There is already a RAID named '{name}'\").format(\n                name=self.name.value\n            )\n        if self.name.value in (\"/dev/md/.\", \"/dev/md/..\"):\n            return _(\". and .. are not valid names for RAID devices\")\n\n    def validate_devices(self):\n        active_device_count = len(self.devices.widget.active_devices)\n        if active_device_count < self.level.value.min_devices:\n            return _(\n                'RAID Level \"{level}\" requires at least {min_active} active devices'\n            ).format(\n                level=self.level.value.name, min_active=self.level.value.min_devices\n            )\n        return super().validate_devices()\n\n\nclass RaidStretchy(Stretchy):\n    def __init__(self, parent, existing=None):\n        self.parent = parent\n        self.existing = existing\n        raid_names = {raid.name for raid in parent.model.all_raids()}\n        if existing is None:\n            title = _('Create software RAID (\"MD\") disk')\n            label = _(\"Create\")\n            x = 0\n            while True:\n                name = \"md{}\".format(x)\n                if name not in raid_names:\n                    break\n                x += 1\n            initial = {\n                \"devices\": {},\n                \"name\": name,\n                \"level\": raidlevels_by_value[\"raid1\"],\n                \"size\": \"-\",\n            }\n        else:\n            raid_names.remove(existing.name)\n            title = _('Edit software RAID disk \"{name}\"').format(name=existing.name)\n            label = _(\"Save\")\n            name = existing.name\n            if name.startswith(\"md/\"):\n                name = name[3:]\n            devices = {}\n            for d in existing.devices:\n                devices[d] = \"active\"\n            for d in existing.spare_devices:\n                devices[d] = \"spare\"\n            initial = {\n                \"devices\": devices,\n                \"name\": name,\n                \"level\": raidlevels_by_value[existing.raidlevel],\n            }\n\n        possible_components = get_possible_components(\n            self.parent.model, existing, initial[\"devices\"], lambda dev: dev.ok_for_raid\n        )\n\n        form = self.form = RaidForm(\n            self.parent.model, possible_components, initial, raid_names\n        )\n\n        form.devices.widget.set_supports_spares(initial[\"level\"].supports_spares)\n        form.buttons.base_widget[0].set_label(label)\n\n        connect_signal(form.level.widget, \"select\", self._select_level)\n        connect_signal(form.devices.widget, \"change\", self._change_devices)\n        connect_signal(form, \"submit\", self.done)\n        connect_signal(form, \"cancel\", self.cancel)\n\n        rows = form.as_rows()\n\n        super().__init__(title, [Pile(rows), Text(\"\"), self.form.buttons], 0, 0)\n\n    def _select_level(self, sender, new_level):\n        active_device_count = len(self.form.devices.widget.active_devices)\n        if active_device_count >= new_level.min_devices:\n            self.form.size.value = humanize_size(\n                get_raid_size(new_level.value, self.form.devices.value)\n            )\n        else:\n            self.form.size.value = \"-\"\n        self.form.devices.widget.set_supports_spares(new_level.supports_spares)\n        self.form.level.value = new_level\n        self.form.devices.showing_extra = False\n        self.form.devices.validate()\n\n    def _change_devices(self, sender, new_devices):\n        if len(sender.active_devices) >= self.form.level.value.min_devices:\n            self.form.size.value = humanize_size(\n                get_raid_size(self.form.level.value.value, new_devices)\n            )\n        else:\n            self.form.size.value = \"-\"\n\n    def done(self, sender):\n        result = self.form.as_data()\n        mdc = self.form.devices.widget\n        result[\"devices\"] = mdc.active_devices\n        result[\"spare_devices\"] = mdc.spare_devices\n        log.debug(\"raid_done: result = {}\".format(result))\n        self.parent.controller.raid_handler(self.existing, result)\n        self.parent.refresh_model_inputs()\n        self.parent.remove_overlay()\n\n    def cancel(self, sender):\n        self.parent.remove_overlay()\n", "repo_name": "canonical/subiquity", "sub_path": "subiquity/ui/views/filesystem/raid.py", "file_name": "raid.py", "file_ext": "py", "file_size_in_byte": 6412, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 420, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 28, "usage_type": "call"}, {"api_name": "subiquitycore.ui.selector.Option", "line_number": 32, "usage_type": "call"}, {"api_name": "subiquity.models.filesystem.raidlevels", "line_number": 33, "usage_type": "name"}, {"api_name": "subiquitycore.ui.interactive.StringEditor", "line_number": 38, "usage_type": "name"}, {"api_name": "subiquitycore.ui.form.WantsToKnowFormField", "line_number": 38, "usage_type": "name"}, {"api_name": "subiquitycore.ui.form.simple_field", "line_number": 59, "usage_type": "call"}, {"api_name": "subiquity.ui.views.filesystem.compound.CompoundDiskForm", "line_number": 62, "usage_type": "name"}, {"api_name": "subiquitycore.ui.form.ChoiceField", "line_number": 69, "usage_type": "call"}, {"api_name": "subiquity.ui.views.filesystem.compound.MultiDeviceField", "line_number": 70, "usage_type": "call"}, {"api_name": "subiquitycore.ui.form.ReadOnlyField", "line_number": 71, "usage_type": "call"}, {"api_name": "re.match", "line_number": 76, "usage_type": "call"}, {"api_name": "subiquitycore.ui.stretchy.Stretchy", "line_number": 99, "usage_type": "name"}, {"api_name": "subiquity.models.filesystem.raidlevels_by_value", "line_number": 116, "usage_type": "name"}, {"api_name": "subiquity.models.filesystem.raidlevels_by_value", "line_number": 134, "usage_type": "name"}, {"api_name": "subiquity.ui.views.filesystem.compound.get_possible_components", "line_number": 137, "usage_type": "call"}, {"api_name": "urwid.connect_signal", "line_number": 148, "usage_type": "call"}, {"api_name": "urwid.connect_signal", "line_number": 149, "usage_type": "call"}, {"api_name": "urwid.connect_signal", "line_number": 150, "usage_type": "call"}, {"api_name": "urwid.connect_signal", "line_number": 151, "usage_type": "call"}, {"api_name": "subiquitycore.ui.container.Pile", "line_number": 155, "usage_type": "call"}, {"api_name": "urwid.Text", "line_number": 155, "usage_type": "call"}, {"api_name": "subiquity.models.filesystem.humanize_size", "line_number": 160, "usage_type": "call"}, {"api_name": "subiquity.models.filesystem.get_raid_size", "line_number": 161, "usage_type": "call"}, {"api_name": "subiquity.models.filesystem.humanize_size", "line_number": 172, "usage_type": "call"}, {"api_name": "subiquity.models.filesystem.get_raid_size", "line_number": 173, "usage_type": "call"}]}
{"seq_id": "14798866576", "text": "'''\ncalcule fibonacci de um inteiro n\ndef fib(n): #fib(4) = 3\n'''\n\n'''\nf = {}\ndef fib(n):\n    if n <= 2: return 1\n    if n not in f:\n        f[n] = fib(n-1) + fib(n-2)\n    return f[n]\n'''\n\nfrom functools import lru_cache\n@lru_cache(maxsize=None)\ndef fib(n):\n    if n <= 2: return 1\n    return fib(n-1) + fib(n-2)\n\n\nprint(fib(100))", "repo_name": "mrocha98/ed-ads", "sub_path": "2020-02-10/fibonacci.py", "file_name": "fibonacci.py", "file_ext": "py", "file_size_in_byte": 330, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "functools.lru_cache", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "26897714461", "text": "import os\nfrom celery import Celery\nfrom celery.schedules import crontab\n\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Skyenkins_django.settings')\n\napp = Celery('Skyenkins_django')\napp.config_from_object('django.conf:settings', namespace='CELERY')\napp.autodiscover_tasks()\n\n\napp.conf.beat_schedule = {\n    'verify-file-every-five-minutes': {\n        'task': 'files.tasks.verify_file',\n        'schedule': crontab(minute='*/5'),\n    },\n\n}", "repo_name": "ann74/Skyenkins_django", "sub_path": "Skyenkins_django/celery.py", "file_name": "celery.py", "file_ext": "py", "file_size_in_byte": 441, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.environ.setdefault", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "celery.Celery", "line_number": 7, "usage_type": "call"}, {"api_name": "celery.schedules.crontab", "line_number": 15, "usage_type": "call"}]}
{"seq_id": "10652600023", "text": "# Python Imports\nimport uuid\nimport os\nimport json\nimport time\nimport re\nimport numpy as np\n\nnow = time.time # shortcut\n\n# Twisted Imports\nfrom twisted.internet import defer, threads, task\nfrom twisted.python import log\nfrom twisted.python.filepath import FilePath\nfrom twisted.logger import Logger\n\n# Octopus Imports\nfrom octopus.sequence.error import AlreadyRunning, NotRunning\nfrom octopus.events import EventEmitter\n\n# Package Imports\nfrom .database.dbutil import makeFinder\n\n\nclass Experiment (EventEmitter):\n\t\"\"\" This object is the representation of a running experiment,\n\tstored in the database and in the event store. \"\"\"\n\n\tdb = None\n\tdataDir = None\n\tlog = Logger()\n\n\t@classmethod\n\tdef exists (cls, id):\n\t\td = cls.db.runQuery(\"SELECT guid FROM experiments WHERE guid = ?\", (id,))\n\t\td.addCallback(lambda r: len(r) > 0)\n\n\t\treturn d\n\n\t@classmethod\n\tdef delete (cls, id):\n\t\treturn cls.db.runOperation(\"UPDATE experiments SET deleted = 1 WHERE guid = ?\", (id, ))\n\n\t@classmethod\n\tdef restore (cls, id):\n\t\treturn cls.db.runOperation(\"UPDATE experiments SET deleted = 0 WHERE guid = ?\", (id, ))\n\n\tdef __init__ (self, sketch):\n\t\tid = str(uuid.uuid4())\n\n\t\tself.id = id\n\t\tself.short_id = id.split('-')[0]\n\t\tself.sketch = sketch\n\t\tself.logMessages = []\n\n\t\tself.log.debug(\n\t\t\t\"Creating experiment {log_source.short_id!s} for Sketch {log_source.sketch.id!s}\"\n\t\t)\n\n\t@defer.inlineCallbacks\n\tdef run (self):\n\t\t\"\"\" Run the experiment.\n\n\t\tMain method to run an experiment. Returns a Deferred which\n\t\tcalls back when the experiment is complete, or errs back\n\t\tif there is an error in the experiment.\n\n\t\tWhilst the experiment is running, the pause(), resume() and\n\t\tstop() methods can be used to interact with it. (Note: stop()\n\t\twill cause the Deferred returned by run() to errback).\n\n\t\tThis method:\n\n\t\t1. Inserts an entry for the experiment into the database.\n\t\t2. Creates a directory for the experiment data.\n\t\t3. Takes a snapshot of the sketch and stores it in the directory.\n\t\t4. Sets event listeners to record any sketch changes during the\n\t\t   experiment.\n\t\t5. Records all changes to variables in the workspace\n\t\t   during the experiment.\n\n\n\t\t\"\"\"\n\n\t\tid = self.id\n\t\tsketch = self.sketch\n\t\tsketch_id = sketch.id\n\t\tworkspace = sketch.workspace\n\n\t\tself.log.debug(\n\t\t\t\"Running experiment {log_source.id!s}\",\n\t\t)\n\n\t\t# If the workspace is already running, we can't run another\n\t\t# experiment on top of it. No experiment entry in the database\n\t\t# will be created.\n\t\ttry:\n\t\t\tyield workspace.reset()\n\t\texcept AlreadyRunning:\n\t\t\tself.log.debug(\"Experiment {log_source.short_id!s} was already running. Abort & reset...\")\n\t\t\tyield workspace.abort()\n\t\t\tyield workspace.reset()\n\n\t\tself.startTime = now()\n\n\t\t# Insert the new experiment into the DB.\n\t\tyield self.db.runOperation(\"\"\"\n\t\t\t\tINSERT INTO experiments\n\t\t\t\t(guid, sketch_guid, title, user_id, started_date)\n\t\t\t\tVALUES (?, ?, ?, ?, ?)\n\t\t\t\"\"\",\n\t\t\t(id, sketch_id, sketch.title, 1, self.startTime)\n\t\t)\n\t\tself.log.debug(\"Experiment {log_source.short_id!s} inserted into database.\")\n\n\t\t# Create a directory to store the experiment logs and data.\n\t\tstime = time.gmtime(self.startTime)\n\n\t\tself._experimentDir = FilePath(self.dataDir)\n\t\tfor segment in [stime.tm_year, stime.tm_mon, stime.tm_mday, id]:\n\t\t\tself._experimentDir = self._experimentDir.child(str(segment))\n\t\t\tif not self._experimentDir.exists():\n\t\t\t\tself._experimentDir.createDirectory()\n\t\t\n\t\tself.log.debug(\n\t\t\t\"Experiment {log_source.short_id!s} directory {dir!s} created.\",\n\t\t\tdir = self._experimentDir\n\t\t)\n\n\t\t# Create files for the sketch logs, snapshot, variables etc.\n\t\teventFile = self._experimentDir.child(\"events.log\").create()\n\t\tsketchFile = self._experimentDir.child(\"sketch.log\").create()\n\t\tsnapFile = self._experimentDir.child(\"sketch.snapshot.log\")\n\t\tvarsFile = self._experimentDir.child(\"variables\")\n\t\topenFiles = { \"_events\": eventFile, \"_sketch\": sketchFile }\n\t\tusedFiles = {}\n\n\t\tself.log.debug(\n\t\t\t\"Experiment {log_source.short_id!s} log files created.\"\n\t\t)\n\n\t\t# Write a snapshot of the sketch.\n\t\twith snapFile.create() as fp:\n\t\t\tfp.write(\"\\n\".join(map(json.dumps, workspace.toEvents())).encode('utf-8'))\n\t\t\n\t\tself.log.debug(\n\t\t\t\"Experiment {log_source.short_id!s} snapshot created in {file!s}.\",\n\t\t\tfile = snapFile\n\t\t)\n\n\t\t# Log events emitted by the sketch (block changes, etc.)\n\t\t# The idea is that with the snapshot and change log, the\n\t\t# layout of the sketch could be replayed over the period\n\t\t# of the experiment.\n\t\tdef onSketchEvent (protocol, topic, data):\n\t\t\tself.log.debug(\n\t\t\t\t\"Experiment {log_source.short_id!s}: sketch event: {protocol}, {topic}, {data}\",\n\t\t\t\tprotocol = protocol,\n\t\t\t\ttopic = topic,\n\t\t\t\tdata = data\n\t\t\t)\n\n\t\t\t# Don't log block state events to the sketch log\n\t\t\t# (there will be lots, and they are not relevant to the\n\t\t\t# structure of the sketch)\n\t\t\tif protocol == \"block\" and topic == \"state\":\n\t\t\t\treturn\n\n\t\t\t# If the sketch is renamed whilst the experiment is\n\t\t\t# running, update the experiment title.\n\t\t\telif protocol == \"sketch\" and topic == \"renamed\":\n\t\t\t\tself.db.runOperation(\"\"\"\n\t\t\t\t\tUPDATE experiments SET title = ? WHERE guid = ?\n\t\t\t\t\"\"\", (data['title'], id)).addErrback(log.err)\n\n\t\t\twriteEvent(sketchFile, protocol, topic, data)\n\n\t\t# Helper function to format an event and write it to the file.\n\t\tdef writeEvent (file, protocol, topic, data):\n\t\t\ttime = now()\n\n\t\t\tevent = {\n\t\t\t\t\"time\": time,\n\t\t\t\t\"relative\": time - self.startTime,\n\t\t\t\t\"protocol\": protocol,\n\t\t\t\t\"topic\": topic,\n\t\t\t\t\"data\": data\n\t\t\t}\n\n\t\t\tfile.write((json.dumps(event) + \"\\n\").encode('utf-8'))\n\n\t\tsketch.subscribe(self, onSketchEvent)\n\n\t\t# Subscribe to workspace events. Block states are written to\n\t\t# the events log.\n\t\t@workspace.on(\"block-state\")\n\t\tdef onBlockStateChange (data):\n\t\t\tself.log.debug(\n\t\t\t\t\"Experiment {log_source.short_id!s}: block {block_id} state -> {state}\",\n\t\t\t\tblock_id = data['block'],\n\t\t\t\tstate = data['state']\n\t\t\t)\n\n\t\t\twriteEvent(eventFile, \"block\", \"state\", data)\n\n\t\t\tdata['sketch'] = sketch_id\n\t\t\tdata['experiment'] = id\n\t\t\tsketch.notifySubscribers(\"block\", \"state\", data, self)\n\n\t\t# Log messages are written to the events log, and also\n\t\t# broadcast to subscribers. The experiment keeps a record\n\t\t# of events so that new clients can get a historical log.\n\t\t@workspace.on(\"log-message\")\n\t\tdef onLogMessage (data):\n\t\t\tself.log.debug(\n\t\t\t\t\"Experiment {log_source.short_id!s}: log message: {message}\",\n\t\t\t\tmessage = data['message']\n\t\t\t)\n\n\t\t\twriteEvent(eventFile, \"experiment\", \"log\", data)\n\n\t\t\tdata['sketch'] = sketch_id\n\t\t\tdata['experiment'] = id\n\t\t\tdata['time'] = round(now(), 2)\n\t\t\tself.logMessages.append(data)\n\t\t\tsketch.notifySubscribers(\"experiment\", \"log\", data, self)\n\n\t\t# Log changes to variable data.\n\t\t#\n\t\t# Note: files are only created when a variable actually gets data,\n\t\t# not necessarily when the variable is created.\n\t\t#\n\t\t# The relative time is written, to save filesize. The absolute\n\t\t# time can be calculated using the start time at the top of the file.\n\t\t@workspace.variables.on(\"variable-changed\")\n\t\tdef onVarChanged (data):\n\t\t\tself.log.debug(\n\t\t\t\t\"Experiment {log_source.short_id!s}: variable {variable_name} value -> {value}\",\n\t\t\t\tvariable_name = data['name'],\n\t\t\t\tvalue = data['value']\n\t\t\t)\n\n\t\t\ttry:\n\t\t\t\tlogFile = openFiles[data['name']]\n\t\t\texcept KeyError:\n\t\t\t\tvarName = unusedVarName(data['name'])\n\t\t\t\tfileName = fileNameFor(varName)\n\t\t\t\tlogFile = self._experimentDir.child(fileName).create()\n\t\t\t\topenFiles[varName] = logFile\n\t\t\t\taddUsedFile(varName, fileName, workspace.variables.get(data['name']))\n\n\t\t\t\tlogFile.write(\n\t\t\t\t\tf\"# name:{data['name']}\\n# type:{type(data['value']).__name__} \\n# start:{self.startTime:.2f}\\n\".encode('utf-8')\n\t\t\t\t)\n\n\t\t\tlogFile.write(f\"{data['time'] - self.startTime:.2f}, {data['value']}\\n\".encode('utf-8'))\n\n\t\t# Update the open files list if a variable is renamed.\n\t\t#\n\t\t# TODO: Variable renaming during experiment run is a bit dodgy.\n\t\t# Which variable to display in the results? It might be better to\n\t\t# disallow renaming during runtime.\n\t\t@workspace.variables.on(\"variable-renamed\")\n\t\tdef onVarRenamed (data):\n\t\t\tself.log.debug(\n\t\t\t\t\"Experiment {log_source.short_id!s}: variable {old_name} renamed -> {new_name}\",\n\t\t\t\told_name = data['oldName'],\n\t\t\t\tnew_name = data['newName']\n\t\t\t)\n\n\t\t\topenFiles[data['newName']] = openFiles[data['oldName']]\n\t\t\tdel openFiles[data['oldName']]\n\t\t\taddUsedFile(data['newName'], \"\", data['variable'])\n\n\t\t# Ensure that renaming vars doesn't lead to any overwriting.\n\t\t# (see TODO above).\n\t\tdef unusedVarName (varName):\n\t\t\tif varName in usedFiles:\n\t\t\t\treturn unusedVarName(varName + \"_\")\n\t\t\treturn varName\n\n\t\t# Format a variable name into a file name\n\t\tdef fileNameFor (varName):\n\t\t\treturn re.sub(r'[^a-z0-9\\.]', '_', varName) + '.csv'\n\n\t\t# Build a list of files and variables to be written to the variables\n\t\t# list file, which is used to generate the var list\n\t\t# when the experiment results are being displayed.\n\t\tdef addUsedFile (varName, fileName, variable):\n\t\t\ttry:\n\t\t\t\tunit = str(variable.unit)\n\t\t\texcept AttributeError:\n\t\t\t\tunit = \"\"\n\n\t\t\tif fileName != \"\":\n\t\t\t\tusedFiles[varName] = {\n\t\t\t\t\t\"name\": varName,\n\t\t\t\t\t\"type\": variable.type.__name__,\n\t\t\t\t\t\"unit\": unit,\n\t\t\t\t\t\"file\": fileName\n\t\t\t\t}\n\t\t\telse:\n\t\t\t\tusedFiles[varName] = {}\n\n\t\t# Write all file data. This is called periodically during the\n\t\t# experiment so that data variables that do not change very\n\t\t# often is still written to disk, and will not be lost if the\n\t\t# program crashes.\n\t\tdef flushFiles ():\n\t\t\ttry:\n\t\t\t\tfor file in openFiles.values():\n\t\t\t\t\tfile.flush()\n\t\t\t\t\tos.fsync(file.fileno())\n\t\t\texcept:\n\t\t\t\tlog.err()\n\n\t\tflushFilesLoop = task.LoopingCall(flushFiles)\n\t\tflushFilesLoop.start(5 * 60, False).addErrback(log.err)\n\n\t\t# Attempt to run the experiment. Make sure that eveything is\n\t\t# cleaned up after the experiment, even in the event of an error.\n\t\ttry:\n\t\t\tself.log.debug(\"Experiment {log_source.short_id!s}: Running workspace\")\n\t\t\tyield workspace.run()\n\t\t\tself.log.debug(\"Experiment {log_source.short_id!s}: Workspace completed\")\n\t\tfinally:\n\t\t\t# Remove event handlers\n\t\t\tsketch.unsubscribe(self)\n\t\t\tworkspace.off(\"block-state\", onBlockStateChange)\n\t\t\tworkspace.off(\"log-message\", onLogMessage)\n\t\t\tworkspace.variables.off(\"variable-changed\", onVarChanged)\n\t\t\tworkspace.variables.off(\"variable-renamed\", onVarRenamed)\n\n\t\t\t# Close file pointers\n\t\t\twith varsFile.create() as fp:\n\t\t\t\tfp.write(json.dumps(usedFiles).encode('utf-8'))\n\n\t\t\ttry:\n\t\t\t\tflushFilesLoop.stop()\n\t\t\texcept:\n\t\t\t\tlog.err()\n\n\t\t\tfor file in openFiles.values():\n\t\t\t\tfile.close()\n\n\t\t\t# Store completed time for experiment.\n\t\t\tself.db.runOperation(\"\"\"\n\t\t\t\tUPDATE experiments SET finished_date = ? WHERE guid = ?\n\t\t\t\"\"\", (now(), id)).addErrback(log.err)\n\n\t\t\tself.log.debug(\"Experiment {log_source.short_id!s}: Set completed in database\")\n\n\tdef pause (self):\n\t\t\"\"\"Pause the experiment if it is running.\n\n\t\tThrows an error if called when the experiment is not running.\n\t\t\"\"\"\n\t\treturn self.sketch.workspace.pause()\n\n\tdef resume (self):\n\t\t\"\"\"Resume the experiment if it is paused.\n\n\t\tThrows an error if called when the experiment is not paused.\n\t\t\"\"\"\n\t\treturn self.sketch.workspace.resume()\n\n\tdef stop (self):\n\t\t\"\"\"Abort the experiment.\n\n\t\tCauses the Deferred returned from run() to errback.\n\n\t\tThrows an error if called when the experiment is not running.\n\t\t\"\"\"\n\t\treturn self.sketch.workspace.abort()\n\n\t#\n\t# Subscribers\n\t#\n\n\tdef variables (self):\n\t\tfrom octopus.machine import Component\n\t\tfrom octopus.data.data import BaseVariable\n\n\t\tvariables = {}\n\n\t\tfor name, var in self.sketch.workspace.variables.items():\n\t\t\tif isinstance(var, Component):\n\t\t\t\tvariables.update(var.variables)\n\t\t\telif isinstance(var, BaseVariable):\n\t\t\t\tvariables[name] = var\n\n\t\treturn variables\n\n\nfind = makeFinder(\n\tExperiment,\n\t'experiments',\n\t{\n\t\t'guid': { 'type': str },\n\t\t'sketch_guid': { 'type': str },\n\t\t'title': {\n\t\t\t'type': str,\n\t\t\t'modifier': lambda x: '%' + x + '%',\n\t\t\t'operator': ' LIKE ?'\n\t\t},\n\t\t'user_id': { 'type': int },\n\t\t'started_date': { 'type': int },\n\t\t'finished_date': { 'type': int },\n\t\t'duration': {\n\t\t\t'type': int,\n\t\t\t'sql': '(finished_date - started_date) AS duration'\n\t\t},\n\t\t'deleted': { 'type': bool }\n\t}\n)\n\n\nclass CompletedExperiment (object):\n\tdef __init__ (self, id):\n\t\tself.id = id\n\n\t@defer.inlineCallbacks\n\tdef load (self):\n\t\texpt = yield self._fetchFromDb(self.id)\n\t\texperimentDir = self._getExperimentDir(self.id, expt['started_date'])\n\n\t\tself.title = expt['sketch_title']\n\t\tself.date = expt['started_date']\n\t\tself.started_date = expt['started_date']\n\t\tself.finished_date = expt['finished_date']\n\t\tself.sketch_id = expt['sketch_guid']\n\n\t\tdef _varName (name):\n\t\t\tif '::' in name:\n\t\t\t\treturn '.'.join(name.split('::')[1:])\n\t\t\telse:\n\t\t\t\treturn name\n\n\t\tvariables = yield self._getVariables(experimentDir)\n\t\tself.variables = [\n\t\t\t{\n\t\t\t\t\"key\": v[\"name\"],\n\t\t\t\t\"name\": _varName(v[\"name\"]),\n\t\t\t\t\"type\": v[\"type\"],\n\t\t\t\t\"unit\": v[\"unit\"]\n\t\t\t}\n\t\t\tfor v in variables.values()\n\t\t\tif \"name\" in v\n\t\t]\n\n\t@defer.inlineCallbacks\n\tdef loadData (self, variables, start, end):\n\t\tdate = yield self._fetchDateFromDb(self.id)\n\t\texperimentDir = self._getExperimentDir(self.id, date)\n\t\tstoredVariablesData = yield self._getVariables(experimentDir)\n\n\t\tdata = yield defer.gatherResults(map(\n\t\t\tlambda variable: self._getData(\n\t\t\t\texperimentDir.child(variable[\"file\"]),\n\t\t\t\tvariable[\"name\"],\n\t\t\t\tvariable[\"type\"],\n\t\t\t\tstart,\n\t\t\t\tend\n\t\t\t),\n\t\t\tmap(lambda name: storedVariablesData[name], variables)\n\t\t))\n\n\t\tdefer.returnValue(data)\n\n\t@defer.inlineCallbacks\n\tdef buildExcelFile (self, variables, time_divisor, time_dp):\n\t\timport pandas as pd\n\t\tfrom io import BytesIO\n\n\t\tdate = yield self._fetchDateFromDb(self.id)\n\t\texperimentDir = self._getExperimentDir(self.id, date)\n\t\tstoredVariablesData = yield self._getVariables(experimentDir)\n\t\tbio = BytesIO()\n\n\t\t# https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#writing-excel-files-to-memory\n\t\twriter = pd.ExcelWriter(bio, engine='xlsxwriter')\n\n\t\tdef varName (variable):\n\t\t\t\"\"\" Generates a column title from a variable name \"\"\"\n\n\t\t\tif variable[\"unit\"] != '':\n\t\t\t\tunit = ' (' + variable[\"unit\"] + ')'\n\t\t\telse:\n\t\t\t\tunit = ''\n\n\t\t\tif '::' in variable[\"name\"]:\n\t\t\t\tname = '.'.join(variable[\"name\"].split('::')[1:])\n\t\t\telse:\n\t\t\t\tname = variable[\"name\"]\n\n\t\t\treturn name + unit\n\n\t\t# Read data for each requested variable\n\t\tcols = yield defer.gatherResults(map(\n\t\t\tlambda variable: threads.deferToThread(\n\t\t\t\tpd.read_csv,\n\t\t\t\texperimentDir.child(variable[\"file\"]).path,\n\t\t\t\tcomment = '#',\n\t\t\t\tindex_col = 0,\n\t\t\t\tusecols = [0, 1],\n\t\t\t\tnames = [\"Time\", varName(variable)]\n\t\t\t),\n\t\t\tmap(lambda name: storedVariablesData[name.decode('ascii')], variables)\n\t\t))\n\n\t\t# Convert the columns into a single DataFrame\n\t\tdataframe = pd.concat(cols, axis = 1)\n\n\t\t# Ensure there is a datapoint at each time point. Fill rather than\n\t\t# interpolate to maintain greatest data fidelity.\n\t\tdataframe.fillna(method = 'pad', inplace = True)\n\n\t\t# Reduce the number of datapoints according to time_divisor / time_dp\n\t\t# This is done over the entire dataframe, after filling empty values,\n\t\t# so that all property values are retained.\n\t\tdef format_time (x):\n\t\t\tif x != \"\":\n\t\t\t\treturn round(float(x) / time_divisor, time_dp)\n\n\t\tdataframe = dataframe.groupby(format_time).first()\n\n\t\t# Remove invalid chars from expt title for Excel sheet title\n\t\tsheet_title = re.sub(r'[\\[\\]\\*\\/\\\\\\?]+', '', self.title)[0:30]\n\n\t\t# Generate excel file\n\t\tdataframe.to_excel(writer, sheet_name = sheet_title)\n\t\twriter.save()\n\n\t\t# Seek to the beginning and read to copy the workbook to a variable in memory\n\t\tbio.seek(0)\n\t\tdefer.returnValue(bio.read())\n\n\tdef _fetchFromDb (self, id):\n\t\tdef _done (rows):\n\t\t\ttry:\n\t\t\t\trow = rows[0]\n\t\t\texcept KeyError:\n\t\t\t\treturn None\n\n\t\t\treturn {\n\t\t\t\t'guid': str(row[0]),\n\t\t\t\t'sketch_guid': str(row[1]),\n\t\t\t\t'user_id': int(row[2]),\n\t\t\t\t'started_date': int(row[3]),\n\t\t\t\t'finished_date': int(row[4]),\n\t\t\t\t'sketch_title': str(row[5])\n\t\t\t}\n\n\t\treturn Experiment.db.runQuery(\"\"\"\n\t\t\tSELECT guid, sketch_guid, user_id, started_date, finished_date, title\n\t\t\tFROM experiments\n\t\t\tWHERE guid = ?\n\t\t\"\"\", (id, )).addCallback(_done)\n\n\tdef _fetchDateFromDb (self, id):\n\t\tdef _done (rows):\n\t\t\ttry:\n\t\t\t\treturn int(rows[0][0])\n\t\t\texcept KeyError:\n\t\t\t\treturn None\n\n\t\treturn Experiment.db.runQuery(\"\"\"\n\t\t\tSELECT started_date\n\t\t\tFROM experiments\n\t\t\tWHERE guid = ?\n\t\t\"\"\", (id, )).addCallback(_done)\n\n\tdef _getExperimentDir (self, id, startTime):\n\t\tstime = time.gmtime(startTime)\n\n\t\texperimentDir = FilePath(Experiment.dataDir)\n\t\tfor segment in [stime.tm_year, stime.tm_mon, stime.tm_mday, id]:\n\t\t\texperimentDir = experimentDir.child(str(segment))\n\t\t\tif not experimentDir.exists():\n\t\t\t\treturn None\n\n\t\treturn experimentDir\n\n\t@defer.inlineCallbacks\n\tdef _getVariables (self, experimentDir):\n\t\tvarsFile = experimentDir.child(\"variables\")\n\t\ttry:\n\t\t\tcontent = yield threads.deferToThread(varsFile.getContent)\n\t\t\tvariables = json.loads(content)\n\t\texcept:\n\t\t\tlog.err()\n\t\t\tvariables = {}\n\n\t\tdefer.returnValue(variables)\n\n\t@defer.inlineCallbacks\n\tdef _getData (self, dataFile, name, var_type, start = None, end = None):\n\n\t\tif var_type == \"int\":\n\t\t\tcast = int\n\t\telif var_type == \"float\":\n\t\t\tcast = float\n\t\telse:\n\t\t\tcast = str\n\n\t\tif end is None:\n\t\t\tstart = None\n\n\t\tdef _readFile ():\n\t\t\tdata = []\n\t\t\twith dataFile.open() as fp:\n\t\t\t\tfor line in fp:\n\t\t\t\t\t# Skip comments\n\t\t\t\t\tif line[0] == 35:  \n\t\t\t\t\t\t# b'#' == 35\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\ttime, value = line.split(b',')\n\t\t\t\t\ttime = float(time)\n\n\t\t\t\t\tif start is not None:\n\t\t\t\t\t\tif time < start:\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\tif time > end:\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\tdata.append((time, cast(value.decode())))\n\n\t\t\treturn data\n\n\t\ttry:\n\t\t\tdata = yield threads.deferToThread(_readFile)\n\t\texcept:\n\t\t\tlog.err()\n\t\t\tdefer.returnValue({})\n\n\t\t# Make a readable variable name\n\t\t#var_name = '.'.join(name.split('::')[1:])\n\n\t\tif len(data) > 400 and cast in (int, float):\n\t\t\tif end is None:\n\t\t\t\ttry:\n\t\t\t\t\tinterval = data[-1][0] - data[0][0]\n\t\t\t\texcept IndexError:\n\t\t\t\t\tinterval = 0\n\t\t\telse:\n\t\t\t\tinterval = end - start\n\n\t\t\tspread = max(data, key = lambda x: x[1])[1] - min(data, key = lambda x: x[1])[1]\n\t\t\tprint (\"Simplifying data with interval \" + str(interval) + \" (currently %s points)\" % len(data))\n\t\t\tprint (\"Spread: %s\" % spread)\n\t\t\tprint (\"Epsilon: %s\" % min(interval / 200., spread / 50.))\n\t\t\tdata = rdp(data, epsilon = min(interval / 200., spread / 50.))\n\n\t\tprint (\" -> %s points\" % len(data))\n\n\t\tdefer.returnValue({\n\t\t\t'name': name,\n\t\t\t'type': var_type,\n\t\t\t'data': data\n\t\t})\n\n\nfrom math import sqrt\n\ndef distance(a, b):\n\treturn  sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2)\n\ndef point_line_distance(point, start, end):\n\tif (start == end):\n\t\treturn distance(point, start)\n\telse:\n\t\tn = abs(\n\t\t\t(end[0] - start[0]) * (start[1] - point[1]) - (start[0] - point[0]) * (end[1] - start[1])\n\t\t)\n\t\td = sqrt(\n\t\t\t(end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2\n\t\t)\n\t\treturn n / d\n\ndef rdp(points, epsilon):\n\t\"\"\"\n\tReduces a series of points to a simplified version that loses detail, but\n\tmaintains the general shape of the series.\n\t\"\"\"\n\tdmax = 0.0\n\tindex = 0\n\tfor i in range(1, len(points) - 1):\n\t\td = point_line_distance(points[i], points[0], points[-1])\n\t\tif d > dmax:\n\t\t\tindex = i\n\t\t\tdmax = d\n\tif dmax >= epsilon:\n\t\tresults = rdp(points[:index+1], epsilon)[:-1] + rdp(points[index:], epsilon)\n\telse:\n\t\tresults = [points[0], points[-1]]\n\treturn results\n", "repo_name": "richardingham/octopus", "sub_path": "octopus/blocktopus/experiment.py", "file_name": "experiment.py", "file_ext": "py", "file_size_in_byte": 19197, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "41", "api": [{"api_name": "time.time", "line_number": 9, "usage_type": "attribute"}, {"api_name": "octopus.events.EventEmitter", "line_number": 25, "usage_type": "name"}, {"api_name": "twisted.python.log", "line_number": 31, "usage_type": "name"}, {"api_name": "twisted.logger.Logger", "line_number": 31, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 49, "usage_type": "call"}, {"api_name": "octopus.sequence.error.AlreadyRunning", "line_number": 99, "usage_type": "name"}, {"api_name": "time.gmtime", "line_number": 117, "usage_type": "call"}, {"api_name": "twisted.python.filepath.FilePath", "line_number": 119, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 144, "usage_type": "attribute"}, {"api_name": "twisted.python.log.err", "line_number": 174, "usage_type": "attribute"}, {"api_name": "twisted.python.log", "line_number": 174, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 190, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 284, "usage_type": "call"}, {"api_name": "os.fsync", "line_number": 313, "usage_type": "call"}, {"api_name": "twisted.python.log.err", "line_number": 315, "usage_type": "call"}, {"api_name": "twisted.python.log", "line_number": 315, "usage_type": "name"}, {"api_name": "twisted.internet.task.LoopingCall", "line_number": 317, "usage_type": "call"}, {"api_name": "twisted.internet.task", "line_number": 317, "usage_type": "name"}, {"api_name": "twisted.python.log.err", "line_number": 318, "usage_type": "attribute"}, {"api_name": "twisted.python.log", "line_number": 318, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 336, "usage_type": "call"}, {"api_name": "twisted.python.log.err", "line_number": 341, "usage_type": "call"}, {"api_name": "twisted.python.log", "line_number": 341, "usage_type": "name"}, {"api_name": "twisted.python.log.err", "line_number": 349, "usage_type": "attribute"}, {"api_name": "twisted.python.log", "line_number": 349, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 60, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 60, "usage_type": "name"}, {"api_name": "octopus.machine.Component", "line_number": 387, "usage_type": "name"}, {"api_name": "octopus.data.data.BaseVariable", "line_number": 389, "usage_type": "name"}, {"api_name": "database.dbutil.makeFinder", "line_number": 395, "usage_type": "call"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 422, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 422, "usage_type": "name"}, {"api_name": "twisted.internet.defer.gatherResults", "line_number": 457, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 457, "usage_type": "name"}, {"api_name": "twisted.internet.defer.returnValue", "line_number": 468, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 468, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 451, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 451, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 478, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 481, "usage_type": "call"}, {"api_name": "twisted.internet.defer.gatherResults", "line_number": 499, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 499, "usage_type": "name"}, {"api_name": "twisted.internet.threads.deferToThread", "line_number": 500, "usage_type": "call"}, {"api_name": "twisted.internet.threads", "line_number": 500, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 501, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 512, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 528, "usage_type": "call"}, {"api_name": "twisted.internet.defer.returnValue", "line_number": 536, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 536, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 470, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 470, "usage_type": "name"}, {"api_name": "{'Component': 'octopus.machine.Component', 'BaseVariable': 'octopus.data.data.BaseVariable'}.db.runQuery", "line_number": 554, "usage_type": "call"}, {"api_name": "{'Component': 'octopus.machine.Component', 'BaseVariable': 'octopus.data.data.BaseVariable'}.db", "line_number": 554, "usage_type": "attribute"}, {"api_name": "{'Component': 'octopus.machine.Component', 'BaseVariable': 'octopus.data.data.BaseVariable'}.db.runQuery", "line_number": 567, "usage_type": "call"}, {"api_name": "{'Component': 'octopus.machine.Component', 'BaseVariable': 'octopus.data.data.BaseVariable'}.db", "line_number": 567, "usage_type": "attribute"}, {"api_name": "time.gmtime", "line_number": 574, "usage_type": "call"}, {"api_name": "twisted.python.filepath.FilePath", "line_number": 576, "usage_type": "call"}, {"api_name": "{'Component': 'octopus.machine.Component', 'BaseVariable': 'octopus.data.data.BaseVariable'}.dataDir", "line_number": 576, "usage_type": "attribute"}, {"api_name": "twisted.internet.threads.deferToThread", "line_number": 588, "usage_type": "call"}, {"api_name": "twisted.internet.threads", "line_number": 588, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 589, "usage_type": "call"}, {"api_name": "twisted.python.log.err", "line_number": 591, "usage_type": "call"}, {"api_name": "twisted.python.log", "line_number": 591, "usage_type": "name"}, {"api_name": "twisted.internet.defer.returnValue", "line_number": 594, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 594, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 584, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 584, "usage_type": "name"}, {"api_name": "twisted.internet.threads.deferToThread", "line_number": 632, "usage_type": "call"}, {"api_name": "twisted.internet.threads", "line_number": 632, "usage_type": "name"}, {"api_name": "twisted.python.log.err", "line_number": 634, "usage_type": "call"}, {"api_name": "twisted.python.log", "line_number": 634, "usage_type": "name"}, {"api_name": "twisted.internet.defer.returnValue", "line_number": 635, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 635, "usage_type": "name"}, {"api_name": "twisted.internet.defer.returnValue", "line_number": 657, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 657, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 596, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 596, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 667, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 676, "usage_type": "call"}]}
{"seq_id": "1826537764", "text": "import argparse\nimport pickle\nfrom pathlib import Path\nimport glob\nimport os\nimport sys\nfrom storages import log_store\nfrom storages.log_store import LogStore\nimport log_model\nfrom log_model import logModel\nfrom log_model.logModel import LogModel\nimport numpy as np\nimport pandas as pd\nfrom sklearn import metrics\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser(description='Compute outlier factors and evaluate them')\n    parser.add_argument('model', metavar='Modelile', help='Model file')\n    parser.add_argument('logfile', metavar='Target', help='Log to be analyzed')\n    parser.add_argument('output', metavar='Output', help='Output')\n    parser.add_argument('--gpu', '-g', type=int, default=-1,\n                        help='GPU ID (negative value indicates CPU)')\n    parser.add_argument('--n_units', '-n', type=int, default=0,\n                        help='Number of n_units')\n    parser.add_argument('--lstm', '-s', type=int, default=0,\n                    help='the height of stacked LSTMs')\n    parser.add_argument('--activation', '-a', default='sigmoid', help='activation function')\n    args = parser.parse_args()\n\n    print('Creating output directory...')\n    if not Path('output').exists():\n        os.mkdir('output')\n    else:\n        if not Path('output').is_dir():\n            sys.exit('output is not a directory.')\n\n    logname = Path(args.logfile).stem\n    logstore = (Path('output') / logname).with_suffix('.pickle')\n\n    print(\"loading log file...\")\n    if logstore.exists():\n        with logstore.open(mode='rb') as logstorefile:\n            log_store = pickle.load(logstorefile)\n    else:\n        sys.exit('no logfile')\n\n    print('loading model file...')\n    if not Path(args.model).exists():\n        sys.exit('model does not exists.')\n    else:\n        log_model = LogModel(log_store, lstm_num=args.lstm, n_units=args.n_units, gpu=args.gpu, directory='output/', logLSTM_file=args.model, activation=args.activation)\n\n    print(\"start evaluating...\")\n    scores = list(log_model.eval(log_store))\n    scores = np.array(scores, np.float32)\n    scores = pd.Series(scores, log_store.test_i_seq[1:])\n\n    scores.to_csv(args.output)\n", "repo_name": "terminiter/DeepSentinel", "sub_path": "evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 2166, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "41", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 33, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 36, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 38, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "call"}, {"api_name": "storages.log_store", "line_number": 44, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 46, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 50, "usage_type": "call"}, {"api_name": "log_model.logModel.LogModel", "line_number": 52, "usage_type": "call"}, {"api_name": "storages.log_store", "line_number": 52, "usage_type": "argument"}, {"api_name": "log_model.eval", "line_number": 55, "usage_type": "call"}, {"api_name": "storages.log_store", "line_number": 55, "usage_type": "argument"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 57, "usage_type": "call"}, {"api_name": "storages.log_store.test_i_seq", "line_number": 57, "usage_type": "attribute"}, {"api_name": "storages.log_store", "line_number": 57, "usage_type": "name"}]}
{"seq_id": "42284011516", "text": "# 3장 데이터 시각화\r\n# matplotlib.pyplot\r\n\r\nfrom matplotlib import pyplot as plt\r\nfrom matplotlib import lines\r\n\r\nyears = [1950, 1960, 1970, 1980, 1990, 2000, 2010]\r\ngdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]\r\n\r\n# x축에 연도, y축에 GDP가 있는 선 그래프를 만들자.\r\nplt.plot(years, gdp,color='green', marker='o', linestyle = 'solid')\r\n\r\n# 제목을 더하자\r\nplt.title(\"Nominal GDP\")\r\n\r\n# y축에 레이블을 추가하자.\r\nplt.ylabel(\"Billions of $\")\r\nplt.show()\r\n\r\n# 막대 그래프\r\n\r\n'''\r\n막대 그래프(bar chart)는 이산적인(discrete) 항목들에 대한 변화르 보여 줄 때 사용하면 좋다.\r\n'''\r\n\r\nmovies = [\"Annie Hallk\", \"Ben-Hur\", \"Casablanca\", \"Gandhi\", \"West Side Story\"]\r\nnum_oscars = [5, 11, 3, 8, 10]\r\n\r\n# 막대의 x 좌표는 [0, 1, 2, 3, 4] y 좌표는 [num_oscars]로 설정\r\nplt.bar(range(len(movies)), num_oscars)\r\n\r\nplt.title(\"My Favorite Movies\")     # 제목을 추가\r\nplt.ylabel(\"# of Academy Awards\")   # y축에 레이블을 추가하자\r\n\r\n# x축 각 막대의 중앙에 영화 제목을 레이블로 추가하자,\r\nplt.xticks(range(len(movies)), movies)\r\nplt.show()\r\n\r\n'''\r\n막대 그래프를 이용하면 히스토그램(histogram)도 그릴 수 있다. 히스토그램이란 정해진 구간에서 \r\n해당되는 항목의 개수를 보여줌으로써 값의 분포를 관찰할 수 있는 그래프의 형태이다.\r\n'''\r\n\r\nfrom collections import Counter\r\ngrades = [83, 95, 91, 87, 70, 0, 85, 82, 100, 67, 73, 77, 0]\r\n\r\n# 점수는 10점 단위로 그룹화한다. 100점은 90점대에 속한다.\r\nhistogram = Counter(min(grade // 10*10, 90) for grade in grades)\r\n\r\nplt.bar([x + 5 for x in histogram.keys()],         # 각 막대를 오른쪽으로 5만큼 옮기고\r\n        histogram.values(),                         # 각 막대의 높이를 정해주고\r\n        10,                                         # 너비는 10으로 하자\r\n        edgecolor = (0, 0, 0))                      # 각 막대의 테두리는 검은새긍로 설정하자.\r\n\r\nplt.axis([-5, 105, 0, 5])                           # x축은 -5부터 105      y축은 0부터 5\r\n\r\n\r\nplt.xticks([10 *i for i in range(11)])              # x축의 레이블은 0,10, .... , 100\r\nplt.xlabel(\"Decile\")                                # x축에 레이블을 추가하자\r\nplt.ylabel(\"# of Students\")                         # y축에 레이블을 추가하자\r\nplt.title(\"Distribution of Exam 1 Grades\")\r\nplt.show()\r\n\r\n'''\r\nplt.bar의 세 번째 인자(argument)는 막대의 너비를 정한다. 여기서는 각 구간의 너비가 10이므로\r\n막대의 너비 또한 10으로 설정(width = 10으로 해도 무관) 또, 막대들을 오른쪽으로 5씩 이동해서\r\n(예를 들어)'10'에 해당하는 막대의 중점이 15가 되게 했다. 막대 간 구분이 되도록 각 막대의 \r\n테두리를 검은색으로 설정, plt.axis x축 범위를 -5에서 105 (막대가 잘리지 않도록 그리기 위해서)\r\ny축의 범위 0-5로 정했다. plt.xticks =>  x축의 레이블은 0,10, .... , 100\r\nplt.axis를 사용할 때는 특히 신중해야함 y축을 0부터 시작하지 않으면 데이터를 정확하게 확인하기 힘듬\r\n'''\r\n\r\n# 선 그래프\r\n\r\n'''\r\nplt.plot()을 이용하면 선 그래프(line chart)를 그릴 수 있다. \r\n'''\r\n\r\nvariance = [1, 2, 4, 8, 16, 32, 64, 128, 256]\r\nbias_squared = [256, 128, 64, 32, 16, 8, 4, 2, 1]\r\ntotal_error = [x + y for x, y in zip(variance, bias_squared)]\r\nxs = [i for i, _ in enumerate(variance)]\r\n\r\n# 한 차트에 여러 개의 선을 그리기위해 plt.plot을 여러 번 호출할 수 있다.\r\nplt.plot(xs, variance,          'g-',    label = 'variance')            # 실선\r\nplt.plot(xs, bias_squared,      'r-.',   label = 'bias^2')              # 일점쇄선\r\nplt.plot(xs, total_error,       'b:',    label = 'total error')         # 점선\r\n\r\n# 각 선에 레이블을 미리 달아 놨기 때문에\r\n# 범례(legend)를 쉽게 그릴 수 있다.\r\n\r\nplt.legend(loc=9)\r\nplt.xlabel(\"model complexity\")\r\nplt.xticks([])\r\nplt.title(\"The Bias-Variance Tradeoff\")\r\nplt.show()\r\n\r\n", "repo_name": "ChaeMyungSeock/Study", "sub_path": "HomeWork/datasciencebook/datascience_p50~p54.py", "file_name": "datascience_p50~p54.py", "file_ext": "py", "file_size_in_byte": 4115, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}]}
{"seq_id": "3371934697", "text": "from celery import Celery\nfrom celery import bootsteps\nfrom kombu import Consumer, Exchange, Queue\n\nmy_queue = Queue('images', Exchange('autoteile'), 'images')\n\napp = Celery(broker='amqp://localhost')\n\n\nclass MyConsumerStep(bootsteps.ConsumerStep):\n\n    def get_consumers(self, channel):\n        return [Consumer(channel,\n                         queues=[my_queue],\n                         callbacks=[self.handle_message],\n                         accept=['json'])]\n\n    def handle_message(self, body, message):\n        print('Received message: {0!r}'.format(body))\n        message.ack()\n\n\napp.steps['consumer'].add(MyConsumerStep)\n\n\ndef send_me_a_message(who, producer=None):\n    with app.producer_or_acquire(producer) as producer:\n        producer.publish(\n            {'hello': who},\n            serializer='json',\n            exchange=my_queue.exchange,\n            routing_key='routing_key',\n            declare=[my_queue],\n            # retry=True,\n        )\n\n\nif __name__ == '__main__':\n    send_me_a_message('world!')\n", "repo_name": "birobotond/rabbitmq-tasking", "sub_path": "demo/worker.py", "file_name": "worker.py", "file_ext": "py", "file_size_in_byte": 1027, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "kombu.Queue", "line_number": 5, "usage_type": "call"}, {"api_name": "kombu.Exchange", "line_number": 5, "usage_type": "call"}, {"api_name": "celery.Celery", "line_number": 7, "usage_type": "call"}, {"api_name": "celery.bootsteps.ConsumerStep", "line_number": 10, "usage_type": "attribute"}, {"api_name": "celery.bootsteps", "line_number": 10, "usage_type": "name"}, {"api_name": "kombu.Consumer", "line_number": 13, "usage_type": "call"}]}
{"seq_id": "34611138729", "text": "from flask import request, render_template, session, abort\nfrom flask import send_from_directory, url_for\nfrom models.reseña import Reseña\nfrom controllers.BookController import BookController\n\nclass ReseñaController():\n\n\tdef __init__(self):\n\t\tpass\n\n\t# Llama al indice de bookRoutes\n\tdef index(self, id_libro):\n\t\treturn BookController.libro(self, id_libro)\n\n\tdef new(self, id_libro):\n\t\tif request.method == 'GET':\n\t\t\treturn render_template ('reseñas/new.html')\n\t\telif request.method == 'POST':\n\t\t\tperfil_id = session['perfil_id']\n\t\t\tcalificacion = request.form['calificacion']\n\t\t\tcomentario = request.form['comentario']\n\t\t\t# https://stackoverflow.com/questions/1602934/check-if-a-given-key-already-exists-in-a-dictionary\n\t\t\t# 0 (for false) or 1 (for true)\n\t\t\tspoiler = 0\n\t\t\tfor campo in request.form:\n\t\t\t\tif campo == \"spoiler\":\n\t\t\t\t\tspoiler = 1\n\n\t\t\tReseña.new(perfil_id, id_libro, calificacion, comentario, spoiler)\n\t\t\treturn self.index(id_libro)\n\n\tdef edit(self, reseña_id):\n\t\treseña = Reseña.encontrar_por_id(reseña_id)\n\t\tif request.method == 'GET':\n\t\t\treturn render_template ('reseñas/edit.html', reseña=reseña)\n\t\telif request.method == 'POST':\n\t\t\tperfil_id = session['perfil_id']\n\t\t\tcalificacion = request.form['calificacion']\n\t\t\tcomentario = request.form['comentario']\n\t\t\t\n\t\t\t# 0 (for false) or 1 (for true)\n\t\t\tspoiler = 0\n\t\t\tfor campo in request.form:\n\t\t\t\tif campo == \"spoiler\":\n\t\t\t\t\tspoiler = 1\n\t\t\tReseña.edit(reseña_id, calificacion, comentario, spoiler)\n\t\t\treturn self.index(request.form['libro_id'])\n\n\tdef delete(self, id_libro, reseña_id, perfil_id):\n\t\tReseña.delete(id_libro, reseña_id, perfil_id)\n\t\treturn self.index(id_libro)\n\nreseñaController = ReseñaController()", "repo_name": "ArmandoTercero/Bookflix", "sub_path": "bookflix/controllers/ReseñaController.py", "file_name": "ReseñaController.py", "file_ext": "py", "file_size_in_byte": 1697, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "controllers.BookController.BookController.libro", "line_number": 13, "usage_type": "call"}, {"api_name": "controllers.BookController.BookController", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "models.reseña.Reseña.new", "line_number": 29, "usage_type": "call"}, {"api_name": "models.reseña.Reseña", "line_number": 29, "usage_type": "name"}, {"api_name": "models.reseña.Reseña.encontrar_por_id", "line_number": 33, "usage_type": "call"}, {"api_name": "models.reseña.Reseña", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "models.reseña.Reseña.edit", "line_number": 46, "usage_type": "call"}, {"api_name": "models.reseña.Reseña", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "name"}, {"api_name": "models.reseña.Reseña.delete", "line_number": 50, "usage_type": "call"}, {"api_name": "models.reseña.Reseña", "line_number": 50, "usage_type": "name"}]}
{"seq_id": "3358073222", "text": "from django.http.response import HttpResponseRedirect\nfrom django.shortcuts import get_object_or_404, render,redirect\nfrom django.http  import HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom smartapp.forms import *\nfrom .models import *\nimport requests\n\nAPI_KEY = 'f9612b287f7246c9910557c118324602'\n\ndef register(request):\n    if request.user.is_authenticated:\n    #redirect user to the profile page\n        return redirect('home')\n    if request.method==\"POST\":\n        form = UserRegisterForm(request.POST)\n        if form.is_valid():\n            form.save()\n            username = form.cleaned_data.get('username')\n            return redirect('login')\n            \n    else:\n        form = UserRegisterForm()\n    return render(request,\"registration/register.html\",{'form':form})\n\n# Create your views here.\n@login_required(login_url='/accounts/login/')\ndef index(request):\n    return render(request, 'index.html')\n\ndef home(request):\n    return render(request, 'registration/home.html')\n\n@login_required(login_url='/accounts/login/')\ndef create_profile(request):\n    current_user = request.user\n    title = \"Create Profile\"\n    if request.method == 'POST':\n        form = ProfileForm(request.POST, request.FILES)\n        if form.is_valid():\n            profile = form.save(commit=False)\n            profile.user = current_user\n            profile.save()\n        return HttpResponseRedirect('/')\n\n    else:\n        form = ProfileForm()\n    return render(request, 'create_profile.html', {\"form\": form, \"title\": title})\n\n@login_required(login_url=\"/accounts/login/\")\ndef profile(request):\n    current_user = request.user\n    profile = Profile.objects.filter(user_id=current_user.id).first()\n    return render(request, \"profile.html\", {\"profile\": profile})\n\n\n@login_required(login_url='/accounts/login/')\ndef update_profile(request,id):\n    user = User.objects.get(id=id)\n    profile = Profile.objects.get(user_id = user)\n    form = UpdateProfileForm(instance=profile)\n    if request.method == \"POST\":\n            form = UpdateProfileForm(request.POST,request.FILES,instance=profile)\n            if form.is_valid():  \n                \n                profile = form.save(commit=False)\n                profile.save()\n                return redirect('profile') \n            \n    return render(request, 'update_profile.html', {\"form\":form})\n\n\n@login_required(login_url='/accounts/login/')\ndef finance(request):\n    country = request.GET.get('country')\n    category = request.GET.get('category')\n\n    if country:\n        url = f'https://newsapi.org/v2/top-headlines?language=en&country={country}&apiKey={API_KEY}'\n        response = requests.get(url)\n        data = response.json()\n        articles = data['articles']\n    else:\n        url = f'https://newsapi.org/v2/top-headlines?language=en&?category=Business&apiKey={API_KEY}'\n        response = requests.get(url)\n        data = response.json()\n        articles = data['articles']\n\n\n\n    context = {\n        'articles' : articles\n    }\n\n    return render(request, 'news_api/news.html', context)\n\n", "repo_name": "lizgi/Smart-Budget", "sub_path": "smartapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3115, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.shortcuts.redirect", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 21, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponseRedirect", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 55, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 60, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 69, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 81, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 86, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 96, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 74, "usage_type": "call"}]}
{"seq_id": "1494371183", "text": "# encoding:utf-8\nimport yaml\nimport os\nfrom string import Template\n\n\ndef read_yaml(YamlPath):\n    if not os.path.isfile(YamlPath):\n        raise FileNotFoundError(\"文件路径错误，请检查\")\n    with open(YamlPath, 'r', encoding='utf-8') as f:\n        cfg = f.read()\n    YamlData = yaml.load(cfg, Loader=yaml.FullLoader)\n    return YamlData\n\n\ndef yaml_template(YamlPath, Change_data):\n    if not os.path.isfile(YamlPath):\n        raise FileNotFoundError(\"文件路径错误，请检查\")\n    if not isinstance(Change_data, dict):\n        raise TypeError('传入的数据类型错误，必须为字典，请检查：{}'.format(Change_data))\n    with open(YamlPath, 'r') as f:\n        cfg = f.read()\n        YamlData = Template(cfg).safe_substitute(Change_data)\n        return yaml.load(YamlData, Loader=yaml.FullLoader)\n\n\ndef write_yaml(YamlPath, data):\n    with open(YamlPath, 'w') as f:\n        yaml.dump(data=data, stream=f, allow_unicode=True)\n\n\n# yaml里引用yaml文件\nclass Loader(yaml.Loader):\n    def __init__(self, stream):\n        self._root = os.path.split(stream.name)[0]\n        super(Loader, self).__init__(stream)\n\n    def include(self, node):\n        filename = os.path.join(self._root, self.construct_scalar(node))\n        with open(filename, 'r') as f:\n            return yaml.load(f, Loader)\n\n\nLoader.add_constructor('!include', Loader.include)\n\n\ndef yaml_read_yaml(yaml_path):\n    \"\"\"\n    yaml文件里引用yaml文件\n    格式：  c: !include 需要引用的yaml文件路径\n    :param yaml_path: yaml文件路径\n    :return: 读取的数据\n    \"\"\"\n    with open(yaml_path, 'r') as f:\n        return yaml.load(f, Loader)\n\n\nif __name__ == '__main__':\n    print(yaml_read_yaml(r'E:\\PyCharm2017\\TestFramework_demo\\a.yaml'))\n    # with open(r'E:\\PyCharm2017\\TestFramework_demo\\a.yaml', 'r') as f:\n    #     data = yaml.load(f)\n    #     print(data)\n", "repo_name": "lta123456/Lta-test", "sub_path": "Base/baseYaml.py", "file_name": "baseYaml.py", "file_ext": "py", "file_size_in_byte": 1877, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.isfile", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 12, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "string.Template", "line_number": 23, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 24, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 24, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 29, "usage_type": "call"}, {"api_name": "yaml.Loader", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 41, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 55, "usage_type": "call"}]}
{"seq_id": "19971965228", "text": "import os\nimport datetime\nimport shutil\n\nimport logging \nfrom logging import Formatter\nfrom logging.handlers import RotatingFileHandler\n\nimport json \n# import demjson\nfrom pprint import pprint\nfrom glob import glob \n\nfrom utils.dirs import create_dirs\nfrom utils.easydict import EasyDict\n\ndef setup_logging(log_dir):\n    log_file_format = \"[%(levelname)s] - %(asctime)s - %(name)s - : %(message)s in %(pathname)s:%(lineno)d\"\n    log_console_format = \"[%(levelname)s]: %(message)s\"\n\n    # Main logger\n    main_logger = logging.getLogger()\n    main_logger.setLevel(logging.INFO)\n    \n    console_handler = logging.StreamHandler()\n    console_handler.setLevel(logging.INFO)\n    console_handler.setFormatter(Formatter(log_console_format))\n\n    exp_file_handler = RotatingFileHandler('{}exp_debug.log'.format(log_dir), maxBytes=10**7, backupCount=5)\n    exp_file_handler.setLevel(logging.DEBUG)\n    exp_file_handler.setFormatter(Formatter(log_file_format))\n\n    exp_error_file_handler = RotatingFileHandler('{}exp_error.log'.format(log_dir), maxBytes=10**6, backupCount=5)\n    exp_error_file_handler.setLevel(logging.WARNING)\n    exp_error_file_handler.setFormatter(Formatter(log_file_format))\n\n    main_logger.addHandler(console_handler)\n    main_logger.addHandler(exp_file_handler)\n    main_logger.addHandler(exp_error_file_handler)\n\ndef get_config_from_json(json_file):\n    '''\n    Get config from a json file.\n    :param json_file: the path of config file\n    :return: config(namespace), config(dictionary)\n    '''\n\n    # parse the configuration\n    with open(json_file, 'r') as f:\n        try:\n            # import pdb; pdb.set_trace()\n            config_dict = json.load(f)\n            config = EasyDict(config_dict)\n            return config, config_dict\n        except:\n            print('INVALID JSON file format.. Please check.')\n            exit(-1)\n\n\ndef process_config(json_file):\n    '''\n    Get the json file. Processing it to be accessible.\n    ...\n\n    :param json_file: the path of the config file\n    :return: config, object(namespace)\n    '''\n    config, _ = get_config_from_json(json_file)\n    print('The Configuration of the experiments .. ')\n    # pprint(config)\n\n    # making sure that you have provided the exp_name.\n    try:\n        print(\" ****************************************************** \")\n        print(\" ******** The experiment name is {} !~~\".format(config.exp_name))\n        print(\" ****************************************************** \")\n    except:\n        print(\"ERROR!!!...Please provide the exp_name in json file: {}\"\n              .format(json_file))\n        exit(-1)\n\n    results_root = config.results_root\n    agent = config.agent\n    # create some important directories to be used for that experiments. \n    try:\n        # results_root = config.results_root\n        # cur_exp_root = os.path.join(\n        #     results_root, config.agent, config.exp_name \n        #     # '_' + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n        # )\n        # import pdb; pdb.set_trace()\n        exps_names = glob(os.path.join(\n            results_root, agent, config.exp_name.format('*')))\n        finish_len = len(exps_names)\n        cur_exp_root = os.path.join(\n            results_root, agent, config.exp_name.format(finish_len + 1))\n\n        print('ROOT DIR: {}'.format(cur_exp_root))\n    except:\n        print('ERROR!!..Lack of some attributes!')\n        exit(-1)\n\n\n    config.summary_dir = os.path.join(cur_exp_root, 'summaries/')\n    config.checkpoint_dir = os.path.join(cur_exp_root, 'checkpoints/')\n    config.out_dir = os.path.join(cur_exp_root, 'out/')\n    config.log_dir = os.path.join(cur_exp_root, 'logs/')\n    create_dirs([config.summary_dir, config.checkpoint_dir, config.out_dir, config.log_dir])\n\n    # save config parameters to result folder\n    shutil.copyfile(json_file, os.path.join(cur_exp_root, 'params.json'))\n\n    # setup logging in the project\n    setup_logging(config.log_dir)\n\n    logging.getLogger().info('Hi, This is root.')\n    logging.getLogger().info('After the configurations are successfully processed and dirs are created!')\n    logging.getLogger().info('The pipeline of the project will begin now.')\n\n    return config\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "lyricsyee/MLSL-Net", "sub_path": "utils/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 4195, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 23, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 30, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 34, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 35, "usage_type": "call"}, {"api_name": "json.load", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.easydict.EasyDict", "line_number": 53, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "utils.dirs.create_dirs", "line_number": 108, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 116, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 117, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 118, "usage_type": "call"}]}
{"seq_id": "4404260034", "text": "from typing import Callable, Optional, Union\nimport sys\nimport numpy as np\n\nimport pandas as pd\n\nfrom mitosheet.public.v3.errors import handle_sheet_function_errors\nfrom mitosheet.public.v3.sheet_functions.utils import get_final_result_series_or_primitive, get_index_from_series, get_series_from_primitive_or_series\nfrom mitosheet.public.v3.types.decorators import cast_values_in_all_args_to_type, cast_values_in_arg_to_type\nfrom mitosheet.public.v3.types.sheet_function_types import StringInputType, StringRestrictedInputType, StringFunctionReturnType, IntRestrictedInputType, IntFunctionReturnType\n\n\n@cast_values_in_arg_to_type('arg', 'str')\n@handle_sheet_function_errors\ndef CLEAN(arg: StringRestrictedInputType) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"CLEAN\",\n        \"description\": \"Returns the text with the non-printable ASCII characters removed.\",\n        \"search_terms\": [\"clean\", \"trim\", \"remove\"],\n        \"examples\": [\n            \"CLEAN(A)\"\n        ],\n        \"syntax\": \"CLEAN(string)\",\n        \"syntax_elements\": [{\n                \"element\": \"string\",\n                \"description\": \"The string or series whose non-printable characters are to be removed.\"\n            }\n        ]\n    }\n    \"\"\"\n    clean_helper: Callable[[str], str] = lambda x:''.join([i if 32 <= ord(i) < 126 else \"\" for i in x])\n\n    if isinstance(arg, str):\n        return clean_helper(arg)\n    \n    return arg.fillna('').apply(clean_helper)\n\n\n@cast_values_in_all_args_to_type('str')\n@handle_sheet_function_errors\ndef CONCAT(*argv: StringInputType) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"CONCAT\",\n        \"description\": \"Returns the passed strings and series appended together.\",\n        \"search_terms\": [\"&\", \"concatenate\", \"append\", \"combine\"],\n        \"examples\": [\n            \"CONCAT('Bite', 'the bullet')\",\n            \"CONCAT(A, B)\"\n        ],\n        \"syntax\": \"CONCAT(string1, [string2, ...])\",\n        \"syntax_elements\": [{\n                \"element\": \"string1\",\n                \"description\": \"The first string or series.\"\n            },\n            {\n                \"element\": \"string2, ... [OPTIONAL]\",\n                \"description\": \"Additional strings or series to append in sequence.\"\n            }\n        ]\n    }\n    \"\"\"\n\n    return get_final_result_series_or_primitive(\n        '',\n        argv,\n        lambda df: df.sum().sum(), # Concats in the same order as Excel - yay!\n        lambda previous_value, new_value: previous_value + new_value,\n        lambda previous_series, new_series: previous_series + new_series\n    )\n\n@cast_values_in_arg_to_type('string', 'str')\n@cast_values_in_arg_to_type('substrings', 'str')\n@handle_sheet_function_errors\ndef FIND(string: StringRestrictedInputType, substrings: StringRestrictedInputType) -> IntFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"FIND\",\n        \"description\": \"Returns the position at which a string is first found within text, case-sensitive. Returns 0 if not found.\",\n        \"search_terms\": [\"find\", \"search\"],\n        \"examples\": [\n            \"FIND(A, 'Jack')\",\n            \"FIND('Ben has a friend Jack', 'Jack')\"\n        ],\n        \"syntax\": \"FIND(text_to_search, search_for)\",\n        \"syntax_elements\": [{\n                \"element\": \"text_to_search\",\n                \"description\": \"The text or series to search for the first occurrence of search_for.\"\n            },\n            {\n                \"element\": \"search_for\",\n                \"description\": \"The string to look for within text_to_search.\"\n            }\n        ]\n    }\n    \"\"\"\n\n    if isinstance(string, str) and isinstance(substrings, str):\n        if substrings == '':\n            return 1\n        \n        return string.find(substrings) + 1\n\n    # otherwise, turn them into series\n    index = get_index_from_series(string, substrings)\n    string = get_series_from_primitive_or_series(string, index).fillna('')\n    substrings = get_series_from_primitive_or_series(substrings, index).fillna('XTREME_MITOSHEET_NULL_VALUE_1234567890_0987654321_NULL_VALUE_MITOSHEET_XTREME') # This is a hack to make sure that the find function returns 0 for null values\n    \n    return pd.Series(\n        [s.find(ss) + 1 for s, ss in zip(string, substrings)],\n        index=index\n    )\n\n\n@cast_values_in_arg_to_type('string', 'str')\n@cast_values_in_arg_to_type('num_chars', 'int')\n@handle_sheet_function_errors\ndef LEFT(string: StringRestrictedInputType, num_chars: Optional[IntRestrictedInputType]=None) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"LEFT\",\n        \"description\": \"Returns a substring from the beginning of a specified string.\",\n        \"search_terms\": [\"left\"],\n        \"examples\": [\n            \"LEFT(A, 2)\",\n            \"LEFT('The first character!')\"\n        ],\n        \"syntax\": \"LEFT(string, [number_of_characters])\",\n        \"syntax_elements\": [{\n                \"element\": \"string\",\n                \"description\": \"The string or series from which the left portion will be returned.\"\n            },\n            {\n                \"element\": \"number_of_characters [OPTIONAL, 1 by default]\",\n                \"description\": \"The number of characters to return from the start of string.\"\n            }\n        ]\n    }\n    \"\"\"\n\n    left_helper: Callable[[str, int], str] = lambda s, nc: s[:min(nc, len(s))]\n\n    if num_chars is None:\n        num_chars = 1\n\n    if isinstance(string, str) and isinstance(num_chars, int):\n        return left_helper(string, num_chars)\n\n    # otherwise, turn them into series for simplicity\n    index = get_index_from_series(string, num_chars)\n    string = get_series_from_primitive_or_series(string, index).fillna('')\n    num_chars = get_series_from_primitive_or_series(num_chars, index).fillna(0)\n\n    return pd.Series(\n        [left_helper(s, nc) for s, nc in zip(string, num_chars)],\n        index=string.index\n    )\n\n\n\n@cast_values_in_arg_to_type('arg', 'str')\n@handle_sheet_function_errors\ndef LEN(arg: StringRestrictedInputType) -> IntFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"LEN\",\n        \"description\": \"Returns the length of a string.\",\n        \"search_terms\": [\"length\", \"size\"],\n        \"examples\": [\n            \"LEN(A)\",\n            \"LEN('This is 21 characters')\"\n        ],\n        \"syntax\": \"LEN(string)\",\n        \"syntax_elements\": [{\n                \"element\": \"string\",\n                \"description\": \"The string or series whose length will be returned.\"\n            }\n        ]\n    }\n    \"\"\"\n    if isinstance(arg, str):\n        return len(arg)\n    \n    return arg.fillna('').str.len()\n\n\n@cast_values_in_arg_to_type('arg', 'str')\n@handle_sheet_function_errors\ndef LOWER(arg: StringRestrictedInputType) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"LOWER\",\n        \"description\": \"Converts a given string to lowercase.\",\n        \"search_terms\": [\"lowercase\", \"uppercase\"],\n        \"examples\": [\n            \"=LOWER('ABC')\",\n            \"=LOWER(A)\",\n            \"=LOWER('Nate Rush')\"\n        ],\n        \"syntax\": \"LOWER(string)\",\n        \"syntax_elements\": [{\n                \"element\": \"string\",\n                \"description\": \"The string or series to convert to lowercase.\"\n            }\n        ]\n    }\n    \"\"\"\n    if isinstance(arg, str):\n        return arg.lower()\n\n    return arg.fillna('').str.lower()\n\n\n\n@cast_values_in_arg_to_type('string', 'str')\n@cast_values_in_arg_to_type('start_loc', 'int')\n@cast_values_in_arg_to_type('num_chars', 'int')\n@handle_sheet_function_errors\ndef MID(string: StringRestrictedInputType, start_loc: IntRestrictedInputType, num_chars: IntRestrictedInputType) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"MID\",\n        \"description\": \"Returns a segment of a string.\",\n        \"search_terms\": [\"middle\"],\n        \"examples\": [\n            \"MID(A, 2, 2)\",\n            \"MID('Some middle characters!', 3, 4)\"\n        ],\n        \"syntax\": \"MID(string, starting_at, extract_length)\",\n        \"syntax_elements\": [{\n                \"element\": \"string\",\n                \"description\": \"The string or series to extract the segment from.\"\n            },\n            {\n                \"element\": \"starting_at\",\n                \"description\": \"The index from the left of string from which to begin extracting.\"\n            },\n            {\n                \"element\": \"extract_length\",\n                \"description\": \"The length of the segment to extract.\"\n            }\n        ]\n    }\n    \"\"\"\n\n    if isinstance(string, str) and isinstance(start_loc, int) and isinstance(num_chars, int):\n        return string[start_loc - 1:start_loc + num_chars - 1]\n\n    # Turn all of them into a series to simplify things\n    index = get_index_from_series(string, start_loc, num_chars)\n    string = get_series_from_primitive_or_series(string, index).fillna('')\n    start_loc = get_series_from_primitive_or_series(start_loc, index).fillna(0)\n    num_chars = get_series_from_primitive_or_series(num_chars, index).fillna(0)\n    \n    return pd.Series(\n        [s[sl-1:sl+nc-1] for s, sl, nc in zip(string, start_loc, num_chars)],\n        index=index\n    )\n\n\n@cast_values_in_arg_to_type('arg', 'str')\n@handle_sheet_function_errors\ndef PROPER(arg: StringRestrictedInputType) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"PROPER\",\n        \"description\": \"Capitalizes the first letter of each word in a specified string.\",\n        \"search_terms\": [\"proper\", \"capitalize\"],\n        \"examples\": [\n            \"=PROPER('nate nush')\",\n            \"=PROPER(A)\"\n        ],\n        \"syntax\": \"PROPER(string)\",\n        \"syntax_elements\": [{\n                \"element\": \"string\",\n                \"description\": \"The value or series to convert to convert to proper case.\"\n            }\n        ]\n    }\n    \"\"\"\n    if isinstance(arg, str):\n        return arg.title()\n\n    return arg.fillna('').str.title()\n\n\n@cast_values_in_arg_to_type('string', 'str')\n@cast_values_in_arg_to_type('num_chars', 'int')\n@handle_sheet_function_errors\ndef RIGHT(string: StringRestrictedInputType, num_chars: Optional[IntRestrictedInputType]=None) -> StringFunctionReturnType:\n\n    \"\"\"\n    {\n        \"function\": \"RIGHT\",\n        \"description\": \"Returns a substring from the beginning of a specified string.\",\n        \"search_terms\": [],\n        \"examples\": [\n            \"RIGHT(A, 2)\",\n            \"RIGHT('The last character!')\"\n        ],\n        \"syntax\": \"RIGHT(string, [number_of_characters])\",\n        \"syntax_elements\": [{\n                \"element\": \"string\",\n                \"description\": \"The string or series from which the right portion will be returned.\"\n            },\n            {\n                \"element\": \"number_of_characters [OPTIONAL, 1 by default]\",\n                \"description\": \"The number of characters to return from the end of string.\"\n            }\n        ]\n    }\n    \"\"\"\n    right_helper: Callable[[str, int], str] = lambda s, nc: s[-min(nc, len(s)):] if nc != 0 else ''\n\n    if num_chars is None:\n        num_chars = 1\n\n    if isinstance(string, str) and isinstance(num_chars, int):\n        return right_helper(string, num_chars)\n    \n    index = get_index_from_series(string, num_chars)\n    string = get_series_from_primitive_or_series(string, index).fillna('')\n    num_chars = get_series_from_primitive_or_series(num_chars, index).fillna(0)\n\n    return pd.Series(\n        [right_helper(s, nc) for s, nc in zip(string, num_chars)],\n        index=string.index\n    )\n\n\n@cast_values_in_arg_to_type('string', 'str')\n@cast_values_in_arg_to_type('old_text', 'str')\n@cast_values_in_arg_to_type('new_text', 'str')\n@cast_values_in_arg_to_type('count', 'int')\n@handle_sheet_function_errors\ndef SUBSTITUTE(string: StringRestrictedInputType, old_text: StringRestrictedInputType, new_text: StringRestrictedInputType, count: Optional[IntRestrictedInputType]=None) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"SUBSTITUTE\",\n        \"description\": \"Replaces existing text with new text in a string.\",\n        \"search_terms\": [\"replace\", \"find and replace\"],\n        \"examples\": [\n            \"SUBSTITUTE('Better great than never', 'great', 'late')\",\n            \"SUBSTITUTE(A, 'dog', 'cat')\"\n        ],\n        \"syntax\": \"SUBSTITUTE(text_to_search, search_for, replace_with, [count])\",\n        \"syntax_elements\": [{\n                \"element\": \"text_to_search\",\n                \"description\": \"The text within which to search and replace.\"\n            },\n            {\n                \"element\": \"search_for\",\n                \"description\": \" The string to search for within text_to_search.\"\n            },\n            {\n                \"element\": \"replace_with\",\n                \"description\": \"The string that will replace search_for.\"\n            },\n            {\n                \"element\": \"count\",\n                \"description\": \"The number of times to perform the substitute. Default is all.\"\n            }\n        ]\n    }\n    \"\"\"\n\n    if count is None:\n        count = -1\n\n    if isinstance(string, str) and isinstance(old_text, str) and isinstance(new_text, str) and isinstance(count, int):\n        return string.replace(old_text, new_text, count)\n\n    index = get_index_from_series(string, old_text, new_text, count)\n    string = get_series_from_primitive_or_series(string, index).fillna('')\n    old_text = get_series_from_primitive_or_series(old_text, index).fillna('')\n    new_text = get_series_from_primitive_or_series(new_text, index).fillna('')\n    count = get_series_from_primitive_or_series(count, index).fillna(0)\n\n    return pd.Series(\n        [s.replace(ot, nt, c) for s, ot, nt, c in zip(string, old_text, new_text, count)],\n        index=index\n    )\n\n\n@cast_values_in_arg_to_type('arg', 'str')\n@handle_sheet_function_errors\ndef TEXT(arg: StringRestrictedInputType) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"TEXT\",\n        \"description\": \"Turns the passed series into a string.\",\n        \"search_terms\": [\"string\", \"dtype\"],\n        \"examples\": [\n            \"=TEXT(Product_Number)\",\n            \"=TEXT(Start_Date)\"\n        ],\n        \"syntax\": \"TEXT(series)\",\n        \"syntax_elements\": [{\n                \"element\": \"series\",\n                \"description\": \"The series to convert to a string.\"\n            }\n        ]\n    }\n    \"\"\"\n    if isinstance(arg, str):\n        return arg\n    \n    return arg.fillna('')\n\n@cast_values_in_arg_to_type('arg', 'str')\n@handle_sheet_function_errors\ndef TRIM(arg: StringRestrictedInputType) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"TRIM\",\n        \"description\": \"Returns a string with the leading and trailing whitespace removed.\",\n        \"search_terms\": [\"trim\", \"whitespace\", \"spaces\"],\n        \"examples\": [\n            \"=TRIM('  ABC')\",\n            \"=TRIM('  ABC  ')\",\n            \"=TRIM(A)\"\n        ],\n        \"syntax\": \"TRIM(string)\",\n        \"syntax_elements\": [{\n                \"element\": \"string\",\n                \"description\": \"The value or series to remove the leading and trailing whitespace from.\"\n            }\n        ]\n    }\n    \"\"\"\n    if isinstance(arg, str):\n        return arg.strip()\n\n    return arg.fillna('').str.strip()\n\n\n@cast_values_in_arg_to_type('arg', 'str')\n@handle_sheet_function_errors\ndef UPPER(arg: StringRestrictedInputType) -> StringFunctionReturnType:\n    \"\"\"\n    {\n        \"function\": \"UPPER\",\n        \"description\": \"Converts a given string to uppercase.\",\n        \"search_terms\": [\"uppercase\", \"capitalize\"],\n        \"examples\": [\n            \"=UPPER('abc')\",\n            \"=UPPER(A)\",\n            \"=UPPER('Nate Rush')\"\n        ],\n        \"syntax\": \"UPPER(string)\",\n        \"syntax_elements\": [{\n                \"element\": \"string\",\n                \"description\": \"The string or series to convert to uppercase.\"\n            }\n        ]\n    }\n    \"\"\"\n    if isinstance(arg, str):\n        return arg.upper()\n    \n    return arg.fillna('').str.upper()\n\n\n\n# TODO: we should see if we can list these automatically!\nSTRING_FUNCTIONS = {\n    'CLEAN': CLEAN,\n    'CONCAT': CONCAT,\n    'FIND': FIND,\n    'LEFT': LEFT,\n    'LEN': LEN,\n    'LOWER': LOWER,\n    'MID': MID,\n    'PROPER': PROPER,\n    'RIGHT': RIGHT,\n    'SUBSTITUTE': SUBSTITUTE,\n    'TEXT': TEXT,\n    'TRIM': TRIM,\n    'UPPER': UPPER,\n}\n", "repo_name": "mito-ds/monorepo", "sub_path": "mitosheet/mitosheet/public/v3/sheet_functions/string_functions.py", "file_name": "string_functions.py", "file_ext": "py", "file_size_in_byte": 16157, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1800, "dataset": "github-code", "pt": "41", "api": [{"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 32, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 13, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 14, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 15, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringInputType", "line_number": 42, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_final_result_series_or_primitive", "line_number": 65, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_all_args_to_type", "line_number": 40, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 41, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 42, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 76, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_index_from_series", "line_number": 106, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 107, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 108, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 110, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 73, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 74, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 75, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.IntFunctionReturnType", "line_number": 76, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 119, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 119, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.IntRestrictedInputType", "line_number": 119, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 142, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_index_from_series", "line_number": 151, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 152, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 153, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 155, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 116, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 117, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 118, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 119, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 164, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 162, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 163, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.IntFunctionReturnType", "line_number": 164, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 190, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 188, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 189, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 190, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 220, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.IntRestrictedInputType", "line_number": 220, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_index_from_series", "line_number": 251, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 252, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 253, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 254, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 256, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 216, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 217, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 218, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 219, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 220, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 264, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 262, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 263, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 264, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 291, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 291, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.IntRestrictedInputType", "line_number": 291, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 314, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_index_from_series", "line_number": 322, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 323, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 324, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 326, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 288, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 289, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 290, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 291, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 337, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 337, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.IntRestrictedInputType", "line_number": 337, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_index_from_series", "line_number": 374, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 375, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 376, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 377, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.sheet_functions.utils.get_series_from_primitive_or_series", "line_number": 378, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 380, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 332, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 333, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 334, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 335, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 336, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 337, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 388, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 386, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 387, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 388, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 413, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 411, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 412, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 413, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringRestrictedInputType", "line_number": 440, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.decorators.cast_values_in_arg_to_type", "line_number": 438, "usage_type": "call"}, {"api_name": "mitosheet.public.v3.errors.handle_sheet_function_errors", "line_number": 439, "usage_type": "name"}, {"api_name": "mitosheet.public.v3.types.sheet_function_types.StringFunctionReturnType", "line_number": 440, "usage_type": "name"}]}
{"seq_id": "5481241886", "text": "from collections import deque, defaultdict\n\ndef solution(record):\n    answer = []\n    arr = []\n    users = defaultdict(list)\n    for i in record:\n      arr.append(i.split())\n    dq = deque(arr)\n    while dq:\n      i = dq.popleft()\n      if i[0] == 'Enter' or i[0] == 'Change':\n        if i[2]:\n          users[i[1]] = i[2]\n\n    dq = deque(arr)\n    while dq:\n      i = dq.popleft()\n      if i[0] == 'Enter':\n        answer.append(users[i[1]] + '님이 들어왔습니다.')\n      elif i[0] == 'Leave':\n        answer.append(users[i[1]] + '님이 나갔습니다.')\n    return answer", "repo_name": "phk9436/studyAlgorithm", "sub_path": "KAKAO_BLIND_RECRUITMENT/lv2/오픈채팅방.py", "file_name": "오픈채팅방.py", "file_ext": "py", "file_size_in_byte": 581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.defaultdict", "line_number": 6, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "32464650540", "text": "import settings\n\nfrom django.conf.urls import url, include\nfrom newqc import views as newqc_views\n\nfrom django.views.generic import RedirectView, TemplateView\nfrom django.db.models import Q, F\n\n\nfrom access.views import SubListView\nfrom newqc.views import NextUnresolvedLotView, ConfigView, RetainViewSet, LotViewSet, TestCardViewSet, STATUS_BUTTONS, retain_list_info, rm_retain_list_info, lot_list_info, retain_month_list, lot_list_attn, batchsheet_list_info, receiving_log_list_info\n\nfrom rest_framework import routers\nfrom rest_framework.authtoken import views\nrouter = routers.DefaultRouter()#trailing_slash=False)\n\nrouter.register(r'nextLot', NextUnresolvedLotView, 'nextLot')\nrouter.register(r'constants', ConfigView, 'constants')\nrouter.register(r'lots', LotViewSet, 'lot-list')\nrouter.register(r'retains', RetainViewSet, 'retain-list')\nrouter.register(r'testcards', TestCardViewSet, 'testcard-list')\n\n\n#retain_view = SubListView.as_view(retain_list_info)\n\nurlpatterns = (\n    # Example:\n    # url(r'^fd/', include('fd.foo.urls')),\n\n    # Uncomment the admin/doc line below and add 'django.contrib.admindocs' \n    # to INSTALLED_APPS to enable admin documentation:\n    # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n    # Uncomment the next line to enable the admin:\n#     url(r'^$', redirect_to, {'url': '/qc/retains/'}),\n    url(r'^$', RedirectView.as_view(url='/qc/retains/')),\n    url(r'^review/$', newqc_views.review),\n    \n    # url(r'^new_coa/(?P<lss_pk>\\d+)/$', newqc_views.new_coa),\n    url(r'^get_next_r_number/$', newqc_views.get_next_r_number),\n    #url(r'^edit_coa/(?P<lss_pk>\\d+)/$', newqc_views.edit_coa),\n    url(r'^coa/(?P<coa_pk>\\d+)/$', newqc_views.coa),\n    url(r'^add_retains/$', newqc_views.add_retains),\n    url(r'^batch_print/$', newqc_views.batch_print),\n    url(r'^scrape_testcards/$', newqc_views.scrape_testcards),\n    url(r'^analyze_scanned_cards/$', newqc_views.analyze_scanned_cards),\n    url(r'^barcode/rm/(?P<retain_pk>\\d+)/$', newqc_views.get_rm_barcode),\n    url(r'^barcode/(?P<retain_pk>\\d+)/$', newqc_views.get_barcode),\n    url(r'^batchsheets/$', SubListView.as_view(**batchsheet_list_info)),\n    url(r'^batchsheets/(?P<lot_pk>\\d+)/$$', newqc_views.batchsheet_detail),\n    url(r'^lots/$', newqc_views.lot_list),\n    url(r'^lots/paginate(?P<paginate_by>\\d+)/$', newqc_views.lot_list),\n    url(r'^lots_requiring_attention/$', SubListView.as_view(**lot_list_attn)),\n    url(r'^lots/(?P<year>\\d{4})/(?P<month>\\d{2})/(?P<day>\\d{2})/$', newqc_views.lots_by_day),\n    url(r'^lots/(\\d{4})/(\\d{2})/$', newqc_views.lots_by_month),\n    url(r'^lots/(?P<lot_pk>\\d+)/$', newqc_views.lot_detail),\n    url(r'^lots/(?P<lot_pk>\\d+)/update/(?P<update>\\w+)/$', newqc_views.lot_detail),\n    url(r'^lots/(?P<lot_pk>\\d+)/test_results/$', newqc_views.edit_test_results),\n    url(r'^lots/(?P<status>\\w+)/$', newqc_views.lots_by_status),\n    \n    #url(r'^complex_lot_list/$', newqc_views.complex_lot_list),\n    #url(r'^resolve_complex_lot/(?P<lot_pk>\\d+)/$', newqc_views.resolve_complex_lot),\n    \n    url(r'^qc_resolver/$', newqc_views.qc_resolver),\n    \n    url(r'^qc_resolver/api/', include(router.urls)),\n    # url(r'^api_auth/', include('rest_framework.urls', namespace='rest_framework')),\n    url(r'^api-token-auth/', views.obtain_auth_token),\n    \n    \n    url(r'^retains/$', SubListView.as_view(**retain_list_info)),\n    url(r'^retains/(\\d{4})/(\\d{2})/$', newqc_views.retains_by_month),\n    url(r'^retains/(\\d{4})/(\\d{2})/(\\d{2})/$', newqc_views.retains_by_day),\n    url(r'^retains/(\\w+)/$', newqc_views.retains_by_status),\n#     url(r'^retains/today/$', ListView.as_view(), {'queryset':Retain.objects.filter(date=datetime.date.today()),\n#                                                                     'extra_context': dict({\n#                                                                         'page_title': 'QC Retains',\n#                                                                         'print_link': 'javascript:document.forms[\"retain_selections\"].submit()',\n#                                                                         'month_list': retain_month_list,\n#                                                                     }, **STATUS_BUTTONS),}),\n\n\n\n    url(r'^add_rm_retains/$', newqc_views.add_rm_retains),\n    url(r'^rm_retains/$', SubListView.as_view(**rm_retain_list_info)),\n    url(r'^rm_retains/(\\d{4})/(\\d{2})/$', newqc_views.rm_retains_by_month),\n    url(r'^rm_retains/(\\d{4})/(\\d{2})/(\\d{2})/$', newqc_views.rm_retains_by_day),\n    url(r'^rm_retains/supplier/(\\w+)/$', newqc_views.rm_retains_by_supplier),\n    url(r'^rm_retains/(\\w+)/$', newqc_views.rm_retains_by_status),\n    \n    \n    url(r'^ajax_retain_status_change/$', newqc_views.ajax_retain_status_change),\n    \n\n    url(r'^flavors/(?P<flavor_number>\\d+)/print/$', newqc_views.flavor_history_print),\n    \n    url(r'^resolve_retains/$', newqc_views.resolve_retains_any),\n    #url(r'^resolve_retains/(?P<retain_pk>\\d+)/$', newqc_views.resolve_retains_specific),\n    \n    url(r'^resolve_testcards/$', newqc_views.resolve_testcards_any),\n    url(r'^resolve_testcards_ajax_post/$', newqc_views.resolve_testcards_ajax_post),\n    url(r'^resolve_testcards/(?P<testcard_pk>\\d+)/$', newqc_views.resolve_testcards_specific),\n    url(r'^testcard_list/$', newqc_views.testcard_list),\n    url(r'^passed_finder/$', newqc_views.passed_finder),\n#     url(r'^no_testcards_left/$', direct_to_template, {'template':'qc/no_testcards_left.html'}),\n    url(r'^no_testcards_left/$', TemplateView.as_view(template_name='about.html')),\n    \n    url(r'^receiving_log/$', SubListView.as_view(**receiving_log_list_info)),\n    url(r'^receiving_log_print/$', newqc_views.receiving_log_print),\n    url(r'^add_receiving_log/$', newqc_views.add_receiving_log),\n    \n    url(r'^scanned_docs/$', newqc_views.scanned_docs),\n    url(r'^scanned_docs/paginate(?P<paginate_by>\\d+)/$', newqc_views.scanned_docs),\n    \n    url(r'^migrate_scanned_docs/$', newqc_views.migrate_scanned_docs),\n    url(r'^last_chance_retains/$', newqc_views.last_chance_retains),\n    url(r'^last_chance_rms/$', newqc_views.last_chance_rms),\n)\n\n# if settings.DEBUG:\n#     import debug_toolbar\n#     urlpatterns += (\n#         url(r'^__debug__/', include(debug_toolbar.urls)),\n#     )\n\n\n", "repo_name": "FDI-IT/fd", "sub_path": "newqc/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 6270, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.routers", "line_number": 15, "usage_type": "name"}, {"api_name": "newqc.views.NextUnresolvedLotView", "line_number": 17, "usage_type": "argument"}, {"api_name": "newqc.views.ConfigView", "line_number": 18, "usage_type": "argument"}, {"api_name": "newqc.views.LotViewSet", "line_number": 19, "usage_type": "argument"}, {"api_name": "newqc.views.RetainViewSet", "line_number": 20, "usage_type": "argument"}, {"api_name": "newqc.views.TestCardViewSet", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 36, "usage_type": "call"}, {"api_name": "django.views.generic.RedirectView.as_view", "line_number": 36, "usage_type": "call"}, {"api_name": "django.views.generic.RedirectView", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 37, "usage_type": "call"}, {"api_name": "newqc.views.review", "line_number": 37, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 37, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 40, "usage_type": "call"}, {"api_name": "newqc.views.get_next_r_number", "line_number": 40, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 40, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 42, "usage_type": "call"}, {"api_name": "newqc.views.coa", "line_number": 42, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 42, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 43, "usage_type": "call"}, {"api_name": "newqc.views.add_retains", "line_number": 43, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 43, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 44, "usage_type": "call"}, {"api_name": "newqc.views.batch_print", "line_number": 44, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 44, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 45, "usage_type": "call"}, {"api_name": "newqc.views.scrape_testcards", "line_number": 45, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 45, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "newqc.views.analyze_scanned_cards", "line_number": 46, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 46, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 47, "usage_type": "call"}, {"api_name": "newqc.views.get_rm_barcode", "line_number": 47, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 47, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "newqc.views.get_barcode", "line_number": 48, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 48, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 49, "usage_type": "call"}, {"api_name": "access.views.SubListView.as_view", "line_number": 49, "usage_type": "call"}, {"api_name": "access.views.SubListView", "line_number": 49, "usage_type": "name"}, {"api_name": "newqc.views.batchsheet_list_info", "line_number": 49, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 50, "usage_type": "call"}, {"api_name": "newqc.views.batchsheet_detail", "line_number": 50, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 50, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 51, "usage_type": "call"}, {"api_name": "newqc.views.lot_list", "line_number": 51, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 51, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 52, "usage_type": "call"}, {"api_name": "newqc.views.lot_list", "line_number": 52, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 52, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 53, "usage_type": "call"}, {"api_name": "access.views.SubListView.as_view", "line_number": 53, "usage_type": "call"}, {"api_name": "access.views.SubListView", "line_number": 53, "usage_type": "name"}, {"api_name": "newqc.views.lot_list_attn", "line_number": 53, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 54, "usage_type": "call"}, {"api_name": "newqc.views.lots_by_day", "line_number": 54, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 54, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 55, "usage_type": "call"}, {"api_name": "newqc.views.lots_by_month", "line_number": 55, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 55, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 56, "usage_type": "call"}, {"api_name": "newqc.views.lot_detail", "line_number": 56, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 56, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 57, "usage_type": "call"}, {"api_name": "newqc.views.lot_detail", "line_number": 57, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 57, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 58, "usage_type": "call"}, {"api_name": "newqc.views.edit_test_results", "line_number": 58, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 58, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 59, "usage_type": "call"}, {"api_name": "newqc.views.lots_by_status", "line_number": 59, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 59, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 64, "usage_type": "call"}, {"api_name": "newqc.views.qc_resolver", "line_number": 64, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 64, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 66, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 66, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 68, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.views.obtain_auth_token", "line_number": 68, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.views", "line_number": 68, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 71, "usage_type": "call"}, {"api_name": "access.views.SubListView.as_view", "line_number": 71, "usage_type": "call"}, {"api_name": "access.views.SubListView", "line_number": 71, "usage_type": "name"}, {"api_name": "newqc.views.retain_list_info", "line_number": 71, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 72, "usage_type": "call"}, {"api_name": "newqc.views.retains_by_month", "line_number": 72, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 72, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 73, "usage_type": "call"}, {"api_name": "newqc.views.retains_by_day", "line_number": 73, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 73, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 74, "usage_type": "call"}, {"api_name": "newqc.views.retains_by_status", "line_number": 74, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 74, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 84, "usage_type": "call"}, {"api_name": "newqc.views.add_rm_retains", "line_number": 84, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 84, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 85, "usage_type": "call"}, {"api_name": "access.views.SubListView.as_view", "line_number": 85, "usage_type": "call"}, {"api_name": "access.views.SubListView", "line_number": 85, "usage_type": "name"}, {"api_name": "newqc.views.rm_retain_list_info", "line_number": 85, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 86, "usage_type": "call"}, {"api_name": "newqc.views.rm_retains_by_month", "line_number": 86, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 86, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 87, "usage_type": "call"}, {"api_name": "newqc.views.rm_retains_by_day", "line_number": 87, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 87, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 88, "usage_type": "call"}, {"api_name": "newqc.views.rm_retains_by_supplier", "line_number": 88, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 88, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 89, "usage_type": "call"}, {"api_name": "newqc.views.rm_retains_by_status", "line_number": 89, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 89, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 92, "usage_type": "call"}, {"api_name": "newqc.views.ajax_retain_status_change", "line_number": 92, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 92, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 95, "usage_type": "call"}, {"api_name": "newqc.views.flavor_history_print", "line_number": 95, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 95, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 97, "usage_type": "call"}, {"api_name": "newqc.views.resolve_retains_any", "line_number": 97, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 97, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 100, "usage_type": "call"}, {"api_name": "newqc.views.resolve_testcards_any", "line_number": 100, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 100, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 101, "usage_type": "call"}, {"api_name": "newqc.views.resolve_testcards_ajax_post", "line_number": 101, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 101, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 102, "usage_type": "call"}, {"api_name": "newqc.views.resolve_testcards_specific", "line_number": 102, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 102, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 103, "usage_type": "call"}, {"api_name": "newqc.views.testcard_list", "line_number": 103, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 103, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 104, "usage_type": "call"}, {"api_name": "newqc.views.passed_finder", "line_number": 104, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 104, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 106, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView.as_view", "line_number": 106, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 106, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 108, "usage_type": "call"}, {"api_name": "access.views.SubListView.as_view", "line_number": 108, "usage_type": "call"}, {"api_name": "access.views.SubListView", "line_number": 108, "usage_type": "name"}, {"api_name": "newqc.views.receiving_log_list_info", "line_number": 108, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 109, "usage_type": "call"}, {"api_name": "newqc.views.receiving_log_print", "line_number": 109, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 109, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 110, "usage_type": "call"}, {"api_name": "newqc.views.add_receiving_log", "line_number": 110, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 110, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 112, "usage_type": "call"}, {"api_name": "newqc.views.scanned_docs", "line_number": 112, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 112, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 113, "usage_type": "call"}, {"api_name": "newqc.views.scanned_docs", "line_number": 113, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 113, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 115, "usage_type": "call"}, {"api_name": "newqc.views.migrate_scanned_docs", "line_number": 115, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 115, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 116, "usage_type": "call"}, {"api_name": "newqc.views.last_chance_retains", "line_number": 116, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 116, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 117, "usage_type": "call"}, {"api_name": "newqc.views.last_chance_rms", "line_number": 117, "usage_type": "attribute"}, {"api_name": "newqc.views", "line_number": 117, "usage_type": "name"}]}
{"seq_id": "27302901203", "text": "import hashlib\nimport time\n\nfrom django.core.cache import caches, DEFAULT_CACHE_ALIAS\n\nfrom corehq.project_limits.rate_counter.interfaces import AbstractRateCounter\n\nREDIS = caches[DEFAULT_CACHE_ALIAS]\nLOCMEM = caches['locmem']\n\n\nclass SlidingWindowRateCounter(AbstractRateCounter):\n    \"\"\"\n    A \"Sliding Window Over Fixed Grains\" approach that approximates perfect sliding window\n\n    A perfect sliding window approach would require keeping the timestamp of every event\n    and count the number that fall between now and now - window_duration;\n    for lower memory and performance overhead we instead approximate that\n    by dividing time up into fixed \"grains\", i.e. sub-windows,\n    over which we slide the larger window.\n    For the earliest grain which is sliding out of the window,\n    we assume linear distribution of events over time, and thus compute its contribution\n    to the total as (% overlap of grain with our window) * (events in grain).\n\n    See a description of this approach (with grains_per_window=1) here:\n    https://konghq.com/blog/how-to-design-a-scalable-rate-limiting-algorithm/\n\n    \"\"\"\n    def __init__(self, key, window_duration, window_offset=0, grains_per_window=1,\n                 memoize_timeout=15.0, _FixedWindowRateCounter=None):\n        \"\"\"\n\n        :param key: short description of the window e.g. \"week\"\n        :param window_duration: length of the window in seconds\n        :param window_offset: offset from epoch of window boundary\n        :param grains_per_window: How many grains the window should be divided into\n            (higher number = more accuracy)\n        :param memoize_timeout: how long to memoize the information in memory in seconds\n            This is the upper limit on how long a `get` could return a stale value.\n        \"\"\"\n        _FixedWindowRateCounter = _FixedWindowRateCounter or FixedWindowRateCounter\n        self.key = key\n        self.window_duration = window_duration\n        self.window_offset = window_offset\n        self.grains_per_window = grains_per_window\n        self.memoize_timeout = memoize_timeout\n        self.grain_duration = window_duration / grains_per_window\n        self.grain_counter = _FixedWindowRateCounter(\n            key=key,\n            window_duration=self.grain_duration,\n            window_offset=window_offset,\n            keep_windows=grains_per_window + 1,\n            memoize_timeout=memoize_timeout\n        )\n\n    def get(self, scope, timestamp=None):\n        if timestamp is None:\n            timestamp = time.time()\n        counts = [\n            self.grain_counter.get(scope, timestamp - i * self.grain_duration,\n                                   key_is_active=(i == 0))\n            for i in range(self.grains_per_window + 1)\n        ]\n        earliest_grain_count = counts.pop()\n        # This is the percentage of the way through the current grain we are\n        progress_in_current_grain = (timestamp % self.grain_duration) / self.grain_duration\n        # This is the count from the percentage of the earliest grain that should count\n        contribution_from_earliest = earliest_grain_count * (1 - progress_in_current_grain)\n        return sum(counts) + contribution_from_earliest\n\n    def retry_after(self):\n        \"\"\"Calculates the time (in seconds) left in the current grain\"\"\"\n        timestamp = time.time()\n        progress_in_current_grain = (timestamp % self.grain_duration) / self.grain_duration\n        progress_left_in_grain = 1 - progress_in_current_grain\n        return progress_left_in_grain * self.grain_duration\n\n    def increment(self, scope, delta=1, timestamp=None):\n        # this intentionally doesn't return because this is the active grain count,\n        # not the total that would be returned by get\n        self.grain_counter.increment(scope, delta, timestamp=timestamp)\n\n    def increment_and_get(self, scope, delta=1, timestamp=None):\n        self.increment(scope, delta, timestamp=timestamp)\n        return self.get(scope, timestamp=timestamp)\n\n\nclass FixedWindowRateCounter(AbstractRateCounter):\n    def __init__(self, key, window_duration, window_offset=0, keep_windows=1,\n                 memoize_timeout=15.0, _CounterCache=None):\n        \"\"\"\n        :param key: short description of the window e.g. \"week\"\n        :param window_duration: length of the window in seconds\n        :param window_offset: offset of window boundary in seconds from the epoch\n        :param keep_windows: number of windows to retain (including current one)\n        :param memoize_timeout: how long to memoize the information in memory in seconds\n            This is the upper limit on how long a `get` could return a stale value.\n        \"\"\"\n        _CounterCache = _CounterCache or CounterCache\n        assert keep_windows >= 1\n        self.key = key\n        self.window_duration = window_duration\n        self.window_offset = window_offset\n        self.counter = _CounterCache(memoize_timeout, timeout=keep_windows * window_duration)\n\n    @staticmethod\n    def _digest(string):\n        return hashlib.sha1(string.encode('utf-8')).hexdigest()\n\n    def _cache_key(self, scope, timestamp=None):\n        if timestamp is None:\n            timestamp = time.time()\n        if isinstance(scope, str):\n            scope = (scope,)\n        return self._digest('fwrc-{}-{}-{}'.format(\n            self.key,\n            ':'.join(map(self._digest, scope)),\n            int((timestamp - self.window_offset) / self.window_duration)\n        ))\n\n    def get(self, scope, timestamp=None, key_is_active=True):\n        return self.counter.get(self._cache_key(scope, timestamp=timestamp), key_is_active=key_is_active)\n\n    def increment_and_get(self, scope, delta=1, timestamp=None):\n        return self.counter.incr(self._cache_key(scope, timestamp=timestamp), delta)\n\n    def increment(self, scope, delta=1, timestamp=None):\n        self.increment_and_get(scope, delta, timestamp=timestamp)\n\n\nclass CounterCache(object):\n    def __init__(self, memoized_timeout, timeout, local_cache=LOCMEM, shared_cache=REDIS):\n        self.memoized_timeout = memoized_timeout\n        self.timeout = int(timeout)\n        self.local_cache = local_cache\n        self.shared_cache = shared_cache\n\n    def incr(self, key, delta=1):\n        value = self.shared_cache.incr(key, delta, ignore_key_check=True)\n        if value == 1:\n            self.shared_cache.expire(key, timeout=self.timeout)\n        self.local_cache.set(key, value, timeout=self.memoized_timeout)\n        return value\n\n    def get(self, key, key_is_active=True):\n        \"\"\"\n        :param key: Cache key\n        :param key_is_active: Whether you believe the key is being actively updated\n            If not, then use the longer timeout for local memory cache as well.\n        \"\"\"\n        local_timeout = self.memoized_timeout if key_is_active else self.timeout\n        value = self.local_cache.get(key, default=None)\n        if value is None:\n            value = self.shared_cache.get(key, default=0)\n            self.local_cache.set(key, value, timeout=local_timeout)\n        assert value is not None\n        return value\n", "repo_name": "dimagi/commcare-hq", "sub_path": "corehq/project_limits/rate_counter/rate_counter.py", "file_name": "rate_counter.py", "file_ext": "py", "file_size_in_byte": 7082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 472, "dataset": "github-code", "pt": "45", "api": [{"api_name": "django.core.cache.caches", "line_number": 8, "usage_type": "name"}, {"api_name": "django.core.cache.DEFAULT_CACHE_ALIAS", "line_number": 8, "usage_type": "name"}, {"api_name": "django.core.cache.caches", "line_number": 9, "usage_type": "name"}, {"api_name": "corehq.project_limits.rate_counter.interfaces.AbstractRateCounter", "line_number": 12, "usage_type": "name"}, {"api_name": "time.time", "line_number": 58, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "corehq.project_limits.rate_counter.interfaces.AbstractRateCounter", "line_number": 88, "usage_type": "name"}, {"api_name": "hashlib.sha1", "line_number": 108, "usage_type": "call"}, {"api_name": "time.time", "line_number": 112, "usage_type": "call"}]}
{"seq_id": "42537837044", "text": "# chat/urls.py\nfrom django.urls import path, include\nfrom rest_framework import routers\nfrom . import views\n\nrouter = routers.DefaultRouter()\nrouter.register(r'sessions', views.EditSessionView, 'Sessions')\nrouter.register(r'collaborate', views.EditSessionAllowedView, 'ListSessions')\n\nurlpatterns = [\n    path('', include(router.urls)),\n    path('', views.index, name='index'),\n    path('<str:room_name>/', views.room, name='room'),\n    path('account/register', views.UserCreate.as_view())\n]", "repo_name": "elskater98/origen-challenge-v2", "sub_path": "origen/chat/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 491, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 6, "usage_type": "call"}, {"api_name": "rest_framework.routers", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "73061750843", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun  4 13:35:18 2021\n\n@author: mike_ubuntu\n\"\"\"\n\nfrom typing import Union, Callable\nimport numpy as np\nfrom sklearn.linear_model import Lasso\n\nimport epde.globals as global_var\nfrom epde.operators.utils.template import CompoundOperator\nfrom epde.structure.main_structures import Equation\n\n\nclass LASSOSparsity(CompoundOperator):\n    \"\"\"\n    The operator, which applies LASSO regression to the equation object to detect the \n    valuable term coefficients.\n    \n    Notable attributes:\n    -------------------\n        \n    params : dict\n        Inhereted from the ``CompoundOperator`` class. \n        Parameters of the operator; main parameters: \n            \n            sparsity - value of the sparsity constant in the LASSO operator;\n            \n    g_fun : np.ndarray or None:\n        values of the function, used during the weak derivatives estimations. \n            \n    Methods:\n    -----------\n    apply(equation)\n        calculate the coefficients of the equation, that will be stored in the equation.weights np.ndarray.    \n        \n    \"\"\"\n    key = 'LASSOBasedSparsity'\n    \n    def apply(self, objective : Equation, arguments : dict):\n        \"\"\"\n        Apply the operator, to fit the LASSO regression to the equation object to detect the \n        valueable terms. In the Equation class, a term is selected to represent the right part of\n        the equation, and its values are used here as the target, and the values of the other \n        terms are utilizd as the features. The method does not return the vector of coefficients, \n        but rather assigns the result to the equation attribute ``equation.weights_internal``\n        \n        Parameters:\n        ------------\n        equation : Equation object\n            the equation object, to that the coefficients are obtained.\n            \n        Returns:\n        ------------\n        None\n        \"\"\"\n        # print(f'Metaparameter: {objective.metaparameters}, objective.metaparameters[(\"sparsity\", objective.main_var_to_explain)]')\n        self_args, subop_args = self.parse_suboperator_args(arguments = arguments)\n\n        estimator = Lasso(alpha = objective.metaparameters[('sparsity', objective.main_var_to_explain)]['value'], \n                          copy_X=True, fit_intercept=True, max_iter=1000, \n                          positive=False, precompute=False, random_state=None,\n                          selection='cyclic', tol=0.0001, warm_start=False)\n        _, target, features = objective.evaluate(normalize = True, return_val = False)\n        self.g_fun_vals = global_var.grid_cache.g_func.reshape(-1)\n\n        estimator.fit(features, target, sample_weight = self.g_fun_vals)\n        objective.weights_internal = estimator.coef_\n\n    def use_default_tags(self):\n        self._tags = {'sparsity', 'gene level', 'no suboperators', 'inplace'}\n", "repo_name": "ITMO-NSS-team/EPDE", "sub_path": "epde/operators/common/sparsity.py", "file_name": "sparsity.py", "file_ext": "py", "file_size_in_byte": 2892, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 47, "dataset": "github-code", "pt": "41", "api": [{"api_name": "epde.operators.utils.template.CompoundOperator", "line_number": 18, "usage_type": "name"}, {"api_name": "epde.structure.main_structures.Equation", "line_number": 43, "usage_type": "name"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 63, "usage_type": "call"}, {"api_name": "epde.globals.grid_cache.g_func.reshape", "line_number": 68, "usage_type": "call"}, {"api_name": "epde.globals.grid_cache", "line_number": 68, "usage_type": "attribute"}, {"api_name": "epde.globals", "line_number": 68, "usage_type": "name"}]}
{"seq_id": "31517727768", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse, HttpResponseBadRequest, HttpResponseForbidden\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.conf import settings\n\nfrom linebot import LineBotApi, WebhookParser\nfrom linebot.exceptions import InvalidSignatureError, LineBotApiError\nfrom linebot.models import MessageEvent, TextSendMessage, JoinEvent, FollowEvent, MemberJoinedEvent, TextSendMessage, TemplateSendMessage, ButtonsTemplate, URITemplateAction\n\nfrom leetcodelinebot.models import ReportLog, Users\nfrom leetcodelinebot.lineNotify import line_notify_send_message\nfrom leetcodelinebot.myself import myself\nfrom datetime import datetime\nfrom pytz import timezone\nimport re\n\nline_bot_api = LineBotApi(settings.LINE_CHANNEL_ACCESS_TOKEN)\nparser = WebhookParser(settings.LINE_CHANNEL_SECRET)\n\n@csrf_exempt\ndef callback(request):\n    if request.method == 'POST':\n        signature = request.META['HTTP_X_LINE_SIGNATURE']\n        body = request.body.decode('utf-8')\n \n        try:\n            events = parser.parse(body, signature)\n        except InvalidSignatureError:\n            return HttpResponseForbidden()\n        except LineBotApiError:\n            return HttpResponseBadRequest()\n \n        for event in events:\n            if isinstance(event, JoinEvent):  # 如果有加入聊天室的事件\n                line_bot_api.reply_message(  # 回復傳入的訊息文字\n                    event.reply_token,\n                    TemplateSendMessage(\n                        alt_text='哈囉 哈囉',\n                        template=ButtonsTemplate(\n                            thumbnail_image_url='https://picx.zhimg.com/v2-e1425095196ac03e4c781a42be0cdc26_r.jpg',  # 替換成你要顯示的圖片網址\n                            title='大家好我是Tasktrackbot',\n                            text='請先將我加入好友才可以為你服務',\n                            actions=[\n                                URITemplateAction(\n                                    label='加入好友',\n                                    uri='https://liff.line.me/1645278921-kWRPP32q/?accountId=615veimk'\n                                )\n                            ]\n                        )\n                    )\n                )\n                        \n            elif isinstance(event, FollowEvent):  # 如果是加好友事件\n                user_id = event.source.user_id\n                profile = line_bot_api.get_profile(user_id)\n\n                user = Users.objects(user_id=user_id).first()\n                if user:\n                    user.display_name = profile.display_name\n                    user.status_message = profile.status_message\n                    user.picture_url = profile.picture_url\n                    user.save()\n                else:\n                    taiwan_tz = timezone('Asia/Taipei')\n                    taiwan_time = datetime.now(taiwan_tz)\n                    users = Users(\n                        user_id = user_id,\n                        name = profile.display_name,\n                        status_message = profile.status_message,\n                        picture_url = profile.picture_url,\n                        punish = 0,\n                        created_at=taiwan_time\n                    )\n                    users.save()\n                    \n                line_bot_api.reply_message(\n                    event.reply_token,\n                    TextSendMessage(text='哈囉~我是Tasktrackbot\\n很高興能為你服務')  # 回覆未加入好友訊息\n                )\n\n            elif isinstance(event, MemberJoinedEvent):  # 如果是新的使用者加入群組事件\n                line_bot_api.reply_message(  # 回復傳入的訊息文字\n                    event.reply_token,\n                    TemplateSendMessage(\n                        alt_text='歡迎 歡迎',\n                        template=ButtonsTemplate(\n                            thumbnail_image_url='https://picx.zhimg.com/v2-e1425095196ac03e4c781a42be0cdc26_r.jpg',  # 替換成你要顯示的圖片網址\n                            title='歡迎新朋友~我是Tasktrackbot',\n                            text='請先將我加入好友才可以為你服務',\n                            actions=[\n                                URITemplateAction(\n                                    label='加入好友',\n                                    uri='https://liff.line.me/1645278921-kWRPP32q/?accountId=615veimk'\n                                )\n                            ]\n                        )\n                    )\n                )\n            elif isinstance(event, MessageEvent):  # 如果有訊息事件\n                try:\n                    user_id = event.source.user_id\n                    profile = line_bot_api.get_profile(user_id)\n                    user = Users.objects(user_id=user_id).first()\n                    if user:\n                        user.display_name = profile.display_name\n                        user.status_message = profile.status_message\n                        user.picture_url = profile.picture_url\n                        user.save()\n                    else:\n                        taiwan_tz = timezone('Asia/Taipei')\n                        taiwan_time = datetime.now(taiwan_tz)\n                        users = Users(\n                            user_id = user_id,\n                            name = profile.display_name,\n                            status_message = profile.status_message,\n                            picture_url = profile.picture_url,\n                            punish = 0,\n                            created_at=taiwan_time\n                        )\n                        users.save()\n                    if event.message.text == '查詢紀錄':\n                        reply_text = get_report_stats(user_id)  # 呼叫函式取得 ReportLog 統計數據\n                        line_bot_api.reply_message(\n                            event.reply_token,\n                            TextSendMessage(text=profile.display_name + reply_text)  # 構建回覆訊息\n                        )\n                    elif event.message.text == '回報進度':\n                        line_bot_api.reply_message(\n                            event.reply_token,\n                            TextSendMessage(text=\"輸入格式：「完成 1」\")  # 構建回覆訊息\n                        )\n                    elif event.message.text is not None and event.message.text.startswith('完成'):\n                        topic = extract_topic_from_message(event.message.text)\n                        if topic is not None:\n                            reply_text = write_to_report_log(user_id=user_id, name=profile.display_name, topic=topic, done=True)\n                            line_bot_api.reply_message(\n                                event.reply_token,\n                                TextSendMessage(text=reply_text)  # 回覆新增成功訊息\n                            )\n                        else:\n                            line_bot_api.reply_message(\n                                event.reply_token,\n                                TextSendMessage(text='未提取到數字，舉例:[完成 1]')  # 回覆未提取到數字訊息\n                            )\n                    elif event.message.text is not None and event.message.text.startswith('：'):\n                        isMe = myself(user_id)\n                        if isMe:\n                            line_notify_send_message(event.message.text.replace('：', ''))\n                except LineBotApiError:\n                    line_bot_api.reply_message(\n                        event.reply_token,\n                        TextSendMessage(text='加我好友再說話！')  # 回覆未加入好友訊息\n                    )\n        return HttpResponse()\n    else:\n        return HttpResponseBadRequest()\n\ndef write_to_report_log(user_id, name, topic, done):\n    # 检查是否已存在相同用户和相同topic的记录\n    existing_log = ReportLog.objects(user_id=user_id, topic=topic).first()\n\n    if existing_log:\n        reply_text = f\"第{topic}題您已經完成了，請更換題目\"\n        return reply_text\n\n    taiwan_tz = timezone('Asia/Taipei')\n    taiwan_time = datetime.now(taiwan_tz)\n    formatted_time = taiwan_time.strftime('%Y/%m/%d %H:%M')\n\n    report_log = ReportLog(\n        user_id=user_id,\n        name=name,\n        topic=topic,\n        done=done,\n        created_at=taiwan_time\n    )\n\n    report_log.save()\n    \n    user = Users.objects(user_id=user_id).first()\n    \n    user.punish +=1 \n    user.save()\n    \n    reply_text = f\"成功新增紀錄 {formatted_time}\"\n    return reply_text\n\ndef get_report_stats(user_id):\n    taiwan_tz = timezone('Asia/Taipei')\n    now = datetime.now(taiwan_tz)\n    today = now.date()\n    start_of_day = datetime.combine(today, datetime.min.time())\n    end_of_day = datetime.combine(today, datetime.max.time())\n\n    all_topics = ReportLog.objects(user_id=user_id).distinct(\"topic\")\n    total_count = len(all_topics)\n    \n    today_topics = ReportLog.objects(user_id=user_id, created_at__gte=start_of_day, created_at__lte=end_of_day).distinct(\"topic\")\n    today_count = len(today_topics)\n\n    reply_text = f\"過去總共完成了{total_count}題測驗，今日已完成{today_count}題\"\n\n    return reply_text\n\ndef extract_topic_from_message(message):\n    # 刪除所有空格\n    message = message.replace(\" \", \"\")\n    # 使用正則表達式提取數字部分\n    match = re.search(r'完成(\\d+)|(\\d+)完成', message)\n    if match:\n        # 提取到數字部分，回傳作為 topic\n        topic = match.group(1) or match.group(2)\n        return topic\n    # 若未提取到數字部分，回傳 None\n    return None", "repo_name": "uliang1024/Linebot-Django", "sub_path": "leetcodelinebot/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9793, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "linebot.LineBotApi", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.settings.LINE_CHANNEL_ACCESS_TOKEN", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 17, "usage_type": "name"}, {"api_name": "linebot.WebhookParser", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.settings.LINE_CHANNEL_SECRET", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 18, "usage_type": "name"}, {"api_name": "linebot.exceptions.InvalidSignatureError", "line_number": 28, "usage_type": "name"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 29, "usage_type": "call"}, {"api_name": "linebot.exceptions.LineBotApiError", "line_number": 30, "usage_type": "name"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 31, "usage_type": "call"}, {"api_name": "linebot.models.JoinEvent", "line_number": 34, "usage_type": "argument"}, {"api_name": "linebot.models.TemplateSendMessage", "line_number": 37, "usage_type": "call"}, {"api_name": "linebot.models.ButtonsTemplate", "line_number": 39, "usage_type": "call"}, {"api_name": "linebot.models.URITemplateAction", "line_number": 44, "usage_type": "call"}, {"api_name": "linebot.models.FollowEvent", "line_number": 53, "usage_type": "argument"}, {"api_name": "leetcodelinebot.models.Users.objects", "line_number": 57, "usage_type": "call"}, {"api_name": "leetcodelinebot.models.Users", "line_number": 57, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "leetcodelinebot.models.Users", "line_number": 66, "usage_type": "call"}, {"api_name": "linebot.models.TextSendMessage", "line_number": 78, "usage_type": "call"}, {"api_name": "linebot.models.MemberJoinedEvent", "line_number": 81, "usage_type": "argument"}, {"api_name": "linebot.models.TemplateSendMessage", "line_number": 84, "usage_type": "call"}, {"api_name": "linebot.models.ButtonsTemplate", "line_number": 86, "usage_type": "call"}, {"api_name": "linebot.models.URITemplateAction", "line_number": 91, "usage_type": "call"}, {"api_name": "linebot.models.MessageEvent", "line_number": 99, "usage_type": "argument"}, {"api_name": "leetcodelinebot.models.Users.objects", "line_number": 103, "usage_type": "call"}, {"api_name": "leetcodelinebot.models.Users", "line_number": 103, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 110, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 111, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 111, "usage_type": "name"}, {"api_name": "leetcodelinebot.models.Users", "line_number": 112, "usage_type": "call"}, {"api_name": "linebot.models.TextSendMessage", "line_number": 125, "usage_type": "call"}, {"api_name": "linebot.models.TextSendMessage", "line_number": 130, "usage_type": "call"}, {"api_name": "linebot.models.TextSendMessage", "line_number": 138, "usage_type": "call"}, {"api_name": "linebot.models.TextSendMessage", "line_number": 143, "usage_type": "call"}, {"api_name": "leetcodelinebot.myself.myself", "line_number": 146, "usage_type": "call"}, {"api_name": "leetcodelinebot.lineNotify.line_notify_send_message", "line_number": 148, "usage_type": "call"}, {"api_name": "linebot.exceptions.LineBotApiError", "line_number": 149, "usage_type": "name"}, {"api_name": "linebot.models.TextSendMessage", "line_number": 152, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 154, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 156, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 20, "usage_type": "name"}, {"api_name": "leetcodelinebot.models.ReportLog.objects", "line_number": 160, "usage_type": "call"}, {"api_name": "leetcodelinebot.models.ReportLog", "line_number": 160, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 166, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 167, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 167, "usage_type": "name"}, {"api_name": "leetcodelinebot.models.ReportLog", "line_number": 170, "usage_type": "call"}, {"api_name": "leetcodelinebot.models.Users.objects", "line_number": 180, "usage_type": "call"}, {"api_name": "leetcodelinebot.models.Users", "line_number": 180, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 189, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 190, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 190, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 192, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 192, "usage_type": "name"}, {"api_name": "datetime.datetime.min.time", "line_number": 192, "usage_type": "call"}, {"api_name": "datetime.datetime.min", "line_number": 192, "usage_type": "attribute"}, {"api_name": "datetime.datetime.combine", "line_number": 193, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 193, "usage_type": "name"}, {"api_name": "datetime.datetime.max.time", "line_number": 193, "usage_type": "call"}, {"api_name": "datetime.datetime.max", "line_number": 193, "usage_type": "attribute"}, {"api_name": "leetcodelinebot.models.ReportLog.objects", "line_number": 195, "usage_type": "call"}, {"api_name": "leetcodelinebot.models.ReportLog", "line_number": 195, "usage_type": "name"}, {"api_name": "leetcodelinebot.models.ReportLog.objects", "line_number": 198, "usage_type": "call"}, {"api_name": "leetcodelinebot.models.ReportLog", "line_number": 198, "usage_type": "name"}, {"api_name": "re.search", "line_number": 209, "usage_type": "call"}]}
{"seq_id": "14282547896", "text": "import pygame\nimport math\n\nfrom physx import PhysX_Object\n\nclass Planet(PhysX_Object):\n\n\tdef __init__(self, location, category = 2):\n\t\tPhysX_Object.__init__(self, location, (0, 0), (0, 0), (200, 200), 1e10, 1)\n\n\t\tsource = \"../img/planet_\" + str(category) + \".png\"\n\t\tself.load_image(pygame.image.load(source))\n\n\tdef update(self, time_elapsed, events):\n\t\tPhysX_Object.update(self, time_elapsed, events)\n\n\tdef gravitate(self, other):\n\t\t# range-scaling grav\n\t\t'''d_x = math.fabs(self.x_loc) - math.fabs(other.x_loc)\n\t\td_y = math.fabs(self.y_loc) - math.fabs(other.y_loc)\n\t\tr = ((d_x ** 2) + (d_y ** 2)) ** 0.5\n\n\t\tG = 6.67e-5\n\n\t\tacc = G * self.mass / (r ** 1)\n\n\t\tother.x_acc += acc * (d_x / r)\n\t\tother.y_acc += acc * (d_y / r)'''\n\n\t\t# constant range-restricted grav\n\t\td_x = math.fabs(self.x_loc) - math.fabs(other.x_loc)\n\t\td_y = math.fabs(self.y_loc) - math.fabs(other.y_loc)\n\t\tr = ((d_x ** 2) + (d_y ** 2)) ** 0.5\n\n\t\tG = 6.67e-9\n\t\tf_g = G * self.mass\n\n\t\tif (r < 200):\n\t\t\tf_x = (d_x / r) * f_g\n\t\t\tf_y = (d_y / r) * f_g\n\n\t\t\tother.x_acc += f_x\n\t\t\tother.y_acc += f_y\n\n\n\n\n\n", "repo_name": "Dirker27/Icarus", "sub_path": "Python/src/planet.py", "file_name": "planet.py", "file_ext": "py", "file_size_in_byte": 1064, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "physx.PhysX_Object", "line_number": 6, "usage_type": "name"}, {"api_name": "physx.PhysX_Object.__init__", "line_number": 9, "usage_type": "call"}, {"api_name": "physx.PhysX_Object", "line_number": 9, "usage_type": "name"}, {"api_name": "pygame.image.load", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 12, "usage_type": "attribute"}, {"api_name": "physx.PhysX_Object.update", "line_number": 15, "usage_type": "call"}, {"api_name": "physx.PhysX_Object", "line_number": 15, "usage_type": "name"}, {"api_name": "math.fabs", "line_number": 31, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 32, "usage_type": "call"}]}
{"seq_id": "32721042599", "text": "import pandas as pd\nimport pickle\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.model_selection import train_test_split\n\n\ndef fit(extractor: str):\n\n    '''\n    This function trains and save the stroke classification model using features previously extracted from the MGABT {pixel, percent} extractors.\n\n    :param extractor: Type of extractor used: *percent* referring to MGABTD_percent and *pixel* referring to MGABTD_pixel\n    :type extractor: str\n    '''\n\n    df = pd.read_csv(f'train/dataset_{extractor}.csv', header=0)\n\n    X_train, _, y_train, _ = train_test_split(\n            df.drop(['target'], axis=1),\n            df['target'],\n            train_size=0.7,\n            stratify=df['target']\n    )\n    clf = ExtraTreesClassifier()\n    clf.fit(X_train, y_train)\n\n    with open(f'model/extratree_{extractor}.pkl', 'wb') as outp:\n        pickle.dump(clf, outp)\n\n    return clf", "repo_name": "WillianaLeite/stroke-classification-api", "sub_path": "train/extratree_classifier.py", "file_name": "extratree_classifier.py", "file_ext": "py", "file_size_in_byte": 899, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.ensemble.ExtraTreesClassifier", "line_number": 24, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "134737686", "text": "\"\"\"\nRegister error handler in flask framework\n\n\"\"\"\nfrom flask import jsonify, Blueprint\nfrom exception import RequestDataFormatException\nfrom exception import CaptchaValidException\nfrom exception import GetCaptchaException\n\n\nerrorHandler = Blueprint(\"errorHandler\", __name__)\n\n\n@errorHandler.app_errorhandler(RequestDataFormatException)\ndef handleRequestDataFormatException(error):\n    response = jsonify(error.toDict())\n    response.statusCode = error.statusCode\n    return response\n\n@errorHandler.app_errorhandler(CaptchaValidException)\ndef handleCaptchaValidException(error):\n    response = jsonify(error.toDict())\n    response.statusCode = error.statusCode\n    return response\n\n@errorHandler.app_errorhandler(GetCaptchaException)\ndef handleGetCaptchaException(error):\n    response = jsonify(error.toDict())\n    response.statusCode = error.statusCode\n    return response\n\n", "repo_name": "wudinaonao/FlaskMark12306Captcha", "sub_path": "controller/ErrorHandler.py", "file_name": "ErrorHandler.py", "file_ext": "py", "file_size_in_byte": 875, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "flask.Blueprint", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 16, "usage_type": "call"}, {"api_name": "exception.RequestDataFormatException", "line_number": 14, "usage_type": "argument"}, {"api_name": "flask.jsonify", "line_number": 22, "usage_type": "call"}, {"api_name": "exception.CaptchaValidException", "line_number": 20, "usage_type": "argument"}, {"api_name": "flask.jsonify", "line_number": 28, "usage_type": "call"}, {"api_name": "exception.GetCaptchaException", "line_number": 26, "usage_type": "argument"}]}
{"seq_id": "31856767029", "text": "from sklearn.base import is_classifier, is_regressor\n\nfrom ._estimator import classifier, regressor\n\n__all__ = ['validate_config', 'validate_estimator']\n\n\ndef validate_config(config):\n    if config is None or len(config) == 0:\n        raise ValueError(\n            \"Invalid 'config', 'config' should be a list\"\n            \" of (estimator, params) tuples.\"\n        )\n\n    estimators, params = zip(*config)\n    return estimators, params\n\n\ndef validate_estimator(estimator, estimator_type=None):\n    assert estimator_type in {'classifier', 'regressor', None}\n    _estimator = classifier if estimator_type == 'classifier' else regressor\n\n    if estimator in _estimator:\n        return _estimator[estimator]()\n\n    elif isinstance(estimator, type):\n        return estimator()\n\n    elif is_classifier(estimator) or is_regressor(estimator):\n        return estimator\n\n    raise ValueError\n", "repo_name": "yoiqsram/scikit-trainer", "sub_path": "sktrainer/_utils.py", "file_name": "_utils.py", "file_ext": "py", "file_size_in_byte": 882, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "_estimator.classifier", "line_number": 21, "usage_type": "name"}, {"api_name": "_estimator.regressor", "line_number": 21, "usage_type": "name"}, {"api_name": "sklearn.base.is_classifier", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.base.is_regressor", "line_number": 29, "usage_type": "call"}]}
{"seq_id": "8350766928", "text": "import json\n\nfrom django.core.serializers.json import DjangoJSONEncoder\nfrom django.forms import Media, widgets\nfrom django.forms.renderers import get_default_renderer\nfrom django.utils.encoding import force_str\nfrom django.utils.functional import Promise, cached_property\nfrom django.utils.html import conditional_escape\nfrom django.utils.safestring import mark_safe\n\nfrom .config import CONFIG_TOOLS, PLUGINS, PLUGINS_KEYS, VERSION\n\n\nclass LazyEncoder(DjangoJSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, Promise):\n            return force_str(obj)\n        return super().default(obj)\n\n\njson_encode = LazyEncoder().encode\n\n\nclass EditorJsWidget(widgets.Textarea):\n    def __init__(self, plugins=None, tools=None, config=None, **kwargs):\n        self.plugins = plugins\n        self.tools = tools\n        self.config = config\n\n        # Fix \"__init__() got an unexpected keyword argument 'widget'\"\n        widget = kwargs.pop('widget', None)\n        if widget:\n            self.plugins = widget.plugins\n            self.tools = widget.tools\n            self.config = widget.config\n\n        super().__init__(**kwargs)\n\n    def configuration(self):\n        tools = {}\n        config = self.config or {}\n\n        if self.plugins or self.tools:\n            custom_tools = self.tools or {}\n            # get name packages without version\n            plugins = ['@'.join(p.split('@')[:2])\n                       for p in self.plugins or PLUGINS]\n\n            for plugin in plugins:\n                plugin_key = PLUGINS_KEYS.get(plugin)\n\n                if not plugin_key:\n                    continue\n\n                plugin_tools = custom_tools.get(\n                    plugin_key) or CONFIG_TOOLS.get(plugin_key) or {}\n                plugin_class = plugin_tools.get('class')\n\n                if plugin_class:\n\n                    tools[plugin_key] = custom_tools.get(\n                        plugin_key, CONFIG_TOOLS.get(plugin_key)\n                    )\n\n                    tools[plugin_key]['class'] = plugin_class\n\n                    custom_tools.pop(plugin_key, None)\n\n            if custom_tools:\n                tools.update(custom_tools)\n        else:  # default\n            tools.update(CONFIG_TOOLS)\n\n        config.update(tools=tools)\n        return config\n\n    @cached_property\n    def media(self):\n        js_list = [\n            '//cdn.jsdelivr.net/npm/@editorjs/editorjs@' + VERSION  # lib\n        ]\n\n        plugins = self.plugins or PLUGINS\n\n        if plugins:\n            js_list += ['//cdn.jsdelivr.net/npm/' + p for p in plugins]\n\n        js_list.append('django-editorjs-fields/js/django-editorjs-fields.js')\n\n        return Media(\n            js=js_list,\n            css={\n                'all': [\n                    'django-editorjs-fields/css/django-editorjs-fields.css'\n                ]\n            },\n        )\n\n    def render(self, name, value, attrs=None, renderer=None):\n        if value is None:\n            value = ''\n\n        if renderer is None:\n            renderer = get_default_renderer()\n\n        return mark_safe(renderer.render(\"django-editorjs-fields/widget.html\", {\n            'widget': {\n                'name': name,\n                'value': conditional_escape(force_str(value)),\n                'attrs': self.build_attrs(self.attrs, attrs),\n                'config': json_encode(self.configuration()),\n            }\n        }))\n", "repo_name": "2ik/django-editorjs-fields", "sub_path": "django_editorjs_fields/widgets.py", "file_name": "widgets.py", "file_ext": "py", "file_size_in_byte": 3395, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 118, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.core.serializers.json.DjangoJSONEncoder", "line_number": 14, "usage_type": "name"}, {"api_name": "django.utils.functional.Promise", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.utils.encoding.force_str", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms.widgets.Textarea", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.forms.widgets", "line_number": 24, "usage_type": "name"}, {"api_name": "config.PLUGINS", "line_number": 47, "usage_type": "name"}, {"api_name": "config.PLUGINS_KEYS.get", "line_number": 50, "usage_type": "call"}, {"api_name": "config.PLUGINS_KEYS", "line_number": 50, "usage_type": "name"}, {"api_name": "config.CONFIG_TOOLS.get", "line_number": 56, "usage_type": "call"}, {"api_name": "config.CONFIG_TOOLS", "line_number": 56, "usage_type": "name"}, {"api_name": "config.CONFIG_TOOLS.get", "line_number": 62, "usage_type": "call"}, {"api_name": "config.CONFIG_TOOLS", "line_number": 62, "usage_type": "name"}, {"api_name": "config.CONFIG_TOOLS", "line_number": 72, "usage_type": "argument"}, {"api_name": "config.update", "line_number": 74, "usage_type": "call"}, {"api_name": "config.VERSION", "line_number": 80, "usage_type": "name"}, {"api_name": "config.PLUGINS", "line_number": 83, "usage_type": "name"}, {"api_name": "django.forms.Media", "line_number": 90, "usage_type": "call"}, {"api_name": "django.utils.functional.cached_property", "line_number": 77, "usage_type": "name"}, {"api_name": "django.forms.renderers.get_default_renderer", "line_number": 104, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 106, "usage_type": "call"}, {"api_name": "django.utils.html.conditional_escape", "line_number": 109, "usage_type": "call"}, {"api_name": "django.utils.encoding.force_str", "line_number": 109, "usage_type": "call"}]}
{"seq_id": "28982273473", "text": "import csv\nfrom pathlib import Path\n\nimport numpy as np\n\nfrom src.experiments.vlc_simulator_validation.analysis.tek_reader.tek_result import TekResult\n\n\nclass TekReader:\n    @staticmethod\n    def read(csv_path: Path) -> TekResult:\n        # Initialize TekCsv instance\n        tek_result = TekResult()\n\n        # Initialize lists to store data\n        time_values = []\n        signal_values = []\n        tek_result.metadata = {}\n\n        try:\n            with open(csv_path, 'r') as csv_file:\n                # Create a CSV reader\n                csv_reader = csv.reader(csv_file)\n\n                for row in csv_reader:\n                    if len(row) >= 5:  # Ensure there are at least 5 columns in each row\n                        # Extract data from columns 4 and 5 (0-based indexing)\n                        time_value = float(row[3])\n                        signal_value = float(row[4])\n\n                        time_values.append(time_value)\n                        signal_values.append(signal_value)\n\n                    if len(row) >= 2:  # Ensure there are at least 2 columns in each row\n                        # Extract data from columns 1 and 2 and build a dictionary\n                        column1_value = row[0]\n                        column2_value = row[1]\n\n                        tek_result.metadata[column1_value] = column2_value\n\n            # Convert the lists to NumPy arrays\n            tek_result.time = np.array(time_values)\n            tek_result.signal = np.array(signal_values)\n\n            return tek_result\n\n        except Exception as e:\n            raise e\n", "repo_name": "OptMA-VLC/pyblocks", "sub_path": "src/experiments/vlc_simulator_validation/analysis/tek_reader/tek_reader.py", "file_name": "tek_reader.py", "file_ext": "py", "file_size_in_byte": 1590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pathlib.Path", "line_number": 11, "usage_type": "name"}, {"api_name": "src.experiments.vlc_simulator_validation.analysis.tek_reader.tek_result.TekResult", "line_number": 13, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "src.experiments.vlc_simulator_validation.analysis.tek_reader.tek_result.TekResult", "line_number": 11, "usage_type": "name"}]}
{"seq_id": "73932769722", "text": "import scrapy\nfrom pathlib import Path\n\nimport os, sys\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\nfrom helpers import leagues, seasons\n\nclass StadiumsSpider(scrapy.Spider):\n    name = \"stadiums\"\n\n    def start_requests(self):\n        urls = []    \n        for league in leagues:\n            for season in seasons:\n                url = f\"https://www.transfermarkt.com/laliga/besucherzahlen/wettbewerb/{league}/plus/1?saison_id={season}\"\n                urls.append(url) \n                \n    \n        for url in urls:\n            yield scrapy.Request(url=url, callback=self.parse)\n\n    def parse(self, response):\n        season = response.url[-4:]\n        league = response.url.split(\"/\")[-3]\n        \n        base_path = Path(__file__).parent\n        file_path = (base_path / f\"../stadium-pages/stadiums-{league}-{season}.html\").resolve()\n\n        with open(file_path, 'wb') as f:\n            f.write(response.body)\n        self.log(f'Saved file at {file_path}')", "repo_name": "derek-watson14/transfermarkt_scraper", "sub_path": "webscrapers/spiders/stadium_spider.py", "file_name": "stadium_spider.py", "file_ext": "py", "file_size_in_byte": 998, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "scrapy.Spider", "line_number": 9, "usage_type": "attribute"}, {"api_name": "helpers.leagues", "line_number": 14, "usage_type": "name"}, {"api_name": "helpers.seasons", "line_number": 15, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 21, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 27, "usage_type": "call"}]}
{"seq_id": "31438410247", "text": "from pathlib import Path\n\nimport pytest\nfrom airflow.models import DagBag, Variable\n\nDAGS_FOLDER = f\"{Path(__file__).resolve().parent.parent}/dags\"\n\n\n@pytest.fixture(autouse=True)\ndef env_vars_setup(monkeypatch):\n    mock_env_vars = {\n        \"AIRFLOW_CONN_GOOGLE_CLOUD_DEFAULT\": \"google-cloud-platform://\",\n        \"DAGS_FOLDER\": DAGS_FOLDER,\n    }\n    for k, v in mock_env_vars.items():\n        monkeypatch.setenv(k, v)\n\n\n@pytest.fixture(autouse=True)\ndef airflow_vars_setup(monkeypatch):\n    mock_airflow_vars = {\n        \"ethereum_destination_dataset_project_id\": \"test\",\n        \"ethereum_load_all_partitions\": False,\n        \"ethereum_output_bucket\": \"test\",\n        \"ethereum_parse_destination_dataset_project_id\": \"test\",\n        \"ethereum_provider_uris\": \"test\",\n    }\n    monkeypatch.setattr(Variable, \"get\", mock_airflow_vars.get)\n\n\n@pytest.fixture\ndef dag_bag():\n    yield DagBag(dag_folder=DAGS_FOLDER, include_examples=False)\n\n\ndef test_no_import_errors(dag_bag):\n    assert len(dag_bag.import_errors) == 0, \"No Import Failures\"\n", "repo_name": "blockchain-etl/ethereum-etl-airflow", "sub_path": "tests/test_dag_validation.py", "file_name": "test_dag_validation.py", "file_ext": "py", "file_size_in_byte": 1043, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 376, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pathlib.Path", "line_number": 6, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "call"}, {"api_name": "airflow.models.Variable", "line_number": 28, "usage_type": "argument"}, {"api_name": "pytest.fixture", "line_number": 19, "usage_type": "call"}, {"api_name": "airflow.models.DagBag", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 31, "usage_type": "attribute"}]}
{"seq_id": "15128837936", "text": "from PreProcess import getProcessedConcepts\nfrom nltk.corpus import wordnet as wn\nimport time\n\ncurrent_milli_time = lambda: int(round(time.time() * 1000))\n\nfile = open(\"C:\\\\Users\\\\myste\\\\Google Drive\\\\CMU\\\\Sem1\\\\Research\\\\Concepts\\\\concepts.txt\", \"r\", encoding='utf8')\nlines = []\n\nlines = concepts = getProcessedConcepts()\n\n# Filling up synsets for each concept\nsynsets = []\nfor line in lines:\n    sublist = []\n    for word in line.split(\" \"):\n        for synset in wn.synsets(word):\n            sublist.append(synset)\n            break;\n    synsets.append(sublist)\nprint(synsets)\n\n\ndef wupBestMatch(query):\n    query = query.lower()\n    print(\"Finding best match for \" + query)\n    st = current_milli_time()\n    querySyns = []\n    for queryWord in query.split(\" \"):\n        for querySyn in wn.synsets(queryWord):\n            querySyns.append(querySyn)\n            break;\n\n    # Finding similar concept\n    maxScoreWup = 0\n    maxIdxWup = 0\n    idx = 0\n    for synList in synsets:\n        scoreWup = 0\n        noMatchWup = 0\n        for querySyn in querySyns:\n            for syn in synList:\n                indScore = wn.wup_similarity(querySyn, syn);\n                if indScore is not None:\n                    scoreWup += indScore\n                else:\n                    noMatchWup += 1\n        if len(querySyns) * len(synList) - noMatchWup > 0:\n            scoreWup /= len(querySyns) * len(synList) - noMatchWup\n            if maxScoreWup < scoreWup:\n                maxScoreWup = scoreWup\n                maxIdxWup = idx\n        idx += 1\n    print(\"Best match Wup similarity: \" + lines[maxIdxWup] + \" :: \", maxScoreWup)\n    print(\"Found in %d millisecs\" % (current_milli_time() - st))\n\n\ndef lchBestMatch(query):\n    query = query.lower()\n    print(\"Finding best match for \" + query)\n    st = current_milli_time()\n    querySyns = []\n    for queryWord in query.split(\" \"):\n        for querySyn in wn.synsets(queryWord):\n            querySyns.append(querySyn)\n            break;\n\n    # Finding similar concept\n    maxScoreLch = 0\n    maxIdxLch = 0\n    idx = 0\n    for synList in synsets:\n        scoreLch = 0\n        noMatchLch = 0\n        for querySyn in querySyns:\n            for syn in synList:\n                indScore = 0\n                if querySyn._pos == syn._pos:\n                    indScore = wn.lch_similarity(querySyn, syn)\n                if (indScore is not None):\n                    scoreLch += indScore\n                else:\n                    noMatchLch += 1\n        if len(querySyns)*len(synList) - noMatchLch > 0:\n            scoreLch /= len(querySyns)*len(synList) - noMatchLch\n            if maxScoreLch < scoreLch:\n                maxScoreLch = scoreLch\n                maxIdxLch = idx\n        idx += 1\n    print(\"Best match Lch similarity: \" + lines[maxIdxLch] + \" :: \",maxScoreLch)\n    print(\"Found in %d millisecs\" % (current_milli_time() - st))\n\n# Testing\nquery1 = \"hgfdgfkhuflkdflu\"\nquery2 = \"No white trains\"\nquery3 = \"Regular soccer match\"\nquery4 = \"chicken breasts\"\nquery5 = \"buildings and trees\"\nquery6 = \"bikes\"\nwupBestMatch(query1)\nlchBestMatch(query1)\nprint('----------------------------')\nwupBestMatch(query2)\nlchBestMatch(query2)\nprint('----------------------------')\nwupBestMatch(query3)\nlchBestMatch(query3)\nprint('----------------------------')\nwupBestMatch(query4)\nlchBestMatch(query4)\nprint('----------------------------')\nwupBestMatch(query5)\nlchBestMatch(query5)\nprint('----------------------------')\nwupBestMatch(query6)\nlchBestMatch(query6)", "repo_name": "MysteryVaibhav/AdHocVideoSearch", "sub_path": "wordnet.py", "file_name": "wordnet.py", "file_ext": "py", "file_size_in_byte": 3494, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "time.time", "line_number": 5, "usage_type": "call"}, {"api_name": "PreProcess.getProcessedConcepts", "line_number": 10, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet.synsets", "line_number": 17, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 17, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.synsets", "line_number": 30, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 30, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.wup_similarity", "line_number": 43, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 43, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.synsets", "line_number": 64, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 64, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.lch_similarity", "line_number": 79, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 79, "usage_type": "name"}]}
{"seq_id": "8365927946", "text": "import argparse\r\nfrom collections import OrderedDict\r\nfrom typing import Dict, List, Optional, Tuple\r\n\r\nimport numpy as np\r\nimport torch\r\nfrom pytorch_lightning import LightningModule, Trainer, seed_everything\r\nfrom pytorch_lightning.callbacks import ModelCheckpoint\r\nfrom pytorch_lightning.plugins import DataParallelPlugin, DDP2Plugin\r\nfrom torch import Tensor, optim\r\nfrom torch.optim.optimizer import Optimizer\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom zenquant.agents.common.experience_source import Experience, ExperienceSourceDataset\r\nfrom zenquant.agents.common.loss import dqn_loss\r\nfrom zenquant.agents.common.agent import ValueAgent\r\nfrom zenquant.agents.common.memory import MultiStepBuffer\r\nfrom zenquant.agents.common.networks import CNN\r\nfrom vnpy.trader.constant import (\r\n    Status,\r\n    Direction,\r\n    Offset,\r\n    Exchange\r\n)\r\n\r\n\r\nfrom zenquant.env.backtest_env import BacktestEnv \r\n\r\nclass DQN(LightningModule):\r\n    def __init__(\r\n        self,\r\n        env: 'BacktestEnv',\r\n        test_env: Optional['BacktestEnv'] = None,\r\n        **kwargs,\r\n    ):\r\n        self.eps_start = kwargs.get(\"eps_start\",1.0)\r\n        self.eps_end = kwargs.get(\"eps_end\",0.02)\r\n        self.eps_last_frame = kwargs.get(\"eps_last_frame\",150000) \r\n        self.sync_rate = kwargs.get(\"sync_rate\",1000)\r\n        self.gamma = kwargs.get(\"gamma\",0.99)\r\n        self.learning_rate = kwargs.get(\"learning_rate\",0.001) \r\n        self.batch_size = kwargs.get(\"batch_size\",32) \r\n        self.replay_size = kwargs.get(\"replay_size\",10000) \r\n        self.warm_start_size =  kwargs.get(\"warm_start_size\",1000) ##large than batch size \r\n        self.avg_reward_len = kwargs.get(\"avg_reward_len\",100) \r\n        self.min_episode_reward =  kwargs.get(\"min_episode_reward\",-21)\r\n        self.seed = kwargs.get(\"seed\",42)\r\n        self.batches_per_epoch = kwargs.get(\"batches_per_epoch\",1000) \r\n        self.n_steps = kwargs.get(\"n_steps\",1) \r\n\r\n        super().__init__()\r\n        self.env = env \r\n        self.env.seed(self.seed)\r\n        self.test_env = test_env \r\n        self.obs_shape = self.env.observation_space.shape\r\n        self.n_actions = self.env.action_space.n\r\n        # Model Attributes\r\n        self.buffer = None\r\n        self.dataset = None\r\n\r\n        self.net = None\r\n        self.target_net = None\r\n\r\n        self.build_networks()\r\n\r\n        self.agent = ValueAgent(\r\n            self.net,\r\n            eps_start=self.eps_start,\r\n            eps_end=self.eps_end,\r\n            eps_frames=self.eps_last_frame,\r\n        )\r\n\r\n        self.save_hyperparameters()\r\n        # Metrics\r\n        self.total_episode_steps = [0]\r\n        self.total_rewards = [0]\r\n        self.done_episodes = 0\r\n        self.total_steps = 0\r\n\r\n        for _ in range(self.avg_reward_len):\r\n            self.total_rewards.append(torch.tensor(self.min_episode_reward, device=self.device))\r\n\r\n        self.avg_rewards = float(np.mean(self.total_rewards[-self.avg_reward_len :]))\r\n        ##初始状态\r\n        self.state = self.env.reset()\r\n\r\n        self.action_idx_list = self.get_valid_action_space()\r\n    def get_valid_action_space(self):\r\n        actions = self.env.action.actions\r\n        #pos_list=[[True,True],[True,False],[False,True],[False,False],no open]\r\n        n= self.n_actions\r\n        action_idx_list = [list(range(n)),list(range(n)),list(range(n)),list(range(n)),list(range(n))]\r\n        for idx,item in enumerate(actions):\r\n            ##无空不可平空\r\n            if item and item[0] == Direction.LONG and item[1] == Offset.CLOSE:\r\n                action_idx_list[1].remove(idx)\r\n                action_idx_list[3].remove(idx)\r\n            ##无多不可平多\r\n            if item and item[0] == Direction.SHORT and item[1] == Offset.CLOSE:\r\n                action_idx_list[2].remove(idx)\r\n                action_idx_list[3].remove(idx)\r\n            if item and item[1] == Offset.OPEN:\r\n                action_idx_list[4].remove(idx)\r\n        return action_idx_list\r\n    def get_current_action_space(self):\r\n        if self.env.portfolio.occupy_rate > self.env.action.limit_order_margin_rate:\r\n            return self.action_idx_list[4]\r\n        elif self.env.portfolio.long_pos > self.env.min_volume:\r\n            if self.env.portfolio.short_pos > self.env.min_volume:\r\n                return self.action_idx_list[0]\r\n            else:\r\n                return self.action_idx_list[1]\r\n        else:\r\n            if self.env.portfolio.short_pos > self.env.min_volume:\r\n                return self.action_idx_list[2]\r\n            else:\r\n                return self.action_idx_list[3]\r\n\r\n    def run_n_episodes(self, env, n_epsiodes: int = 1, epsilon: float = 1.0) -> List[int]:\r\n        \"\"\"Carries out N episodes of the environment with the current agent.\r\n        Args:\r\n            env: environment to use, either train environment or test environment\r\n            n_epsiodes: number of episodes to run\r\n            epsilon: epsilon value for DQN agent\r\n        \"\"\"\r\n        total_rewards = []\r\n\r\n        for _ in range(n_epsiodes):\r\n            episode_state = env.reset()\r\n            done = False\r\n            episode_reward = 0\r\n\r\n            while not done:\r\n                self.agent.epsilon = epsilon\r\n                action_space = self.get_current_action_space()\r\n                action = self.agent(episode_state,action_space, self.device)\r\n                next_state, reward, done, _ = env.step(action[0])\r\n                episode_state = next_state\r\n                episode_reward += reward\r\n\r\n            total_rewards.append(episode_reward)\r\n\r\n        return total_rewards\r\n    def populate(self, warm_start: int) -> None:\r\n        \"\"\"Populates the buffer with initial experience.\"\"\"\r\n        if warm_start > 0:\r\n            self.state = self.env.reset()\r\n\r\n            for _ in range(warm_start):\r\n                self.agent.epsilon = 1.0\r\n                action_space = self.get_current_action_space()\r\n                action = self.agent(self.state,action_space, self.device)\r\n                next_state, reward, done, _ = self.env.step(action[0])\r\n                exp = Experience(state=self.state, action=action[0], reward=reward, done=done, new_state=next_state)\r\n                self.buffer.append(exp)\r\n                self.state = next_state\r\n\r\n                if done:\r\n                    self.state = self.env.reset()\r\n    def build_networks(self) -> None:\r\n        \"\"\"Initializes the DQN train and target networks.\"\"\"\r\n        ##input feature_dim = input_channel,size\r\n        self.net = CNN(self.obs_shape[1],self.obs_shape[0],(self.obs_shape[1]+self.n_actions)//2, self.n_actions)\r\n        self.target_net = CNN(self.obs_shape[1],self.obs_shape[0],(self.obs_shape[1]+self.n_actions)//2, self.n_actions)\r\n    def forward(self, x: Tensor) -> Tensor:\r\n        \"\"\"Passes in a state x through the network and gets the q_values of each action as an output.\r\n        Args:\r\n            x: environment state\r\n        Returns:\r\n            q values\r\n        \"\"\"\r\n        output = self.net(x)\r\n        return output\r\n    def train_batch(\r\n        self,\r\n    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:\r\n        \"\"\"Contains the logic for generating a new batch of data to be passed to the DataLoader.\r\n        Returns:\r\n            yields a Experience tuple containing the state, action, reward, done and next_state.\r\n        \"\"\"\r\n        episode_reward = 0\r\n        episode_steps = 0\r\n\r\n        while True:\r\n            self.total_steps += 1\r\n            action_space = self.get_current_action_space()\r\n            action = self.agent(self.state, action_space, self.device)\r\n\r\n            next_state, r, is_done, _ = self.env.step(action[0])\r\n\r\n            episode_reward += r\r\n            episode_steps += 1\r\n\r\n            exp = Experience(state=self.state, action=action[0], reward=r, done=is_done, new_state=next_state)\r\n\r\n            self.agent.update_epsilon(self.global_step)\r\n            self.buffer.append(exp)\r\n            self.state = next_state\r\n\r\n            if is_done:\r\n                print(\"epsiode end\")\r\n                print(self.env.portfolio.net_capital)\r\n                self.done_episodes += 1\r\n                self.total_rewards.append(episode_reward)\r\n                self.total_episode_steps.append(episode_steps)\r\n                self.avg_rewards = float(np.mean(self.total_rewards[-self.avg_reward_len :]))\r\n                self.state = self.env.reset()\r\n                episode_steps = 0\r\n                episode_reward = 0\r\n\r\n            states, actions, rewards, dones, new_states = self.buffer.sample(self.batch_size)\r\n\r\n            for idx, _ in enumerate(dones):\r\n                yield states[idx], actions[idx], rewards[idx], dones[idx], new_states[idx]\r\n\r\n            # Simulates epochs\r\n            if self.total_steps % self.batches_per_epoch == 0:\r\n                break\r\n\r\n    def training_step(self, batch: Tuple[Tensor, Tensor], _) -> OrderedDict:\r\n        \"\"\"Carries out a single step through the environment to update the replay buffer. Then calculates loss\r\n        based on the minibatch recieved.\r\n        Args:\r\n            batch: current mini batch of replay data\r\n            _: batch number, not used\r\n        Returns:\r\n            Training loss and log metrics\r\n        \"\"\"\r\n\r\n        # calculates training loss\r\n        loss = dqn_loss(batch, self.net, self.target_net, self.gamma,torch.nn.SmoothL1Loss())\r\n\r\n        if self._use_dp_or_ddp2(self.trainer):\r\n            loss = loss.unsqueeze(0)\r\n\r\n        # Soft update of target network\r\n        if self.global_step % self.sync_rate == 0:\r\n            self.target_net.load_state_dict(self.net.state_dict())\r\n\r\n        self.log_dict(\r\n            {\r\n                \"total_reward\": self.total_rewards[-1],\r\n                \"avg_reward\": self.avg_rewards,\r\n                \"train_loss\": loss,\r\n                \"episodes\": self.done_episodes,\r\n                \"episode_steps\": self.total_episode_steps[-1],\r\n            }\r\n        )\r\n\r\n        return OrderedDict(\r\n            {\r\n                \"loss\": loss,\r\n                \"avg_reward\": self.avg_rewards,\r\n            }\r\n        )\r\n\r\n    def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:\r\n        \"\"\"Evaluate the agent for 10 episodes.\"\"\"\r\n        test_reward = self.run_n_episodes(self.test_env, 1, 0)\r\n        avg_reward = sum(test_reward) / len(test_reward)\r\n        return {\"test_reward\": avg_reward}\r\n\r\n    def test_epoch_end(self, outputs) -> Dict[str, Tensor]:\r\n        \"\"\"Log the avg of the test results.\"\"\"\r\n        rewards = [x[\"test_reward\"] for x in outputs]\r\n        avg_reward = sum(rewards) / len(rewards)\r\n        self.log(\"avg_test_reward\", avg_reward)\r\n        return {\"avg_test_reward\": avg_reward}\r\n\r\n    def configure_optimizers(self) -> List[Optimizer]:\r\n        \"\"\"Initialize Adam optimizer.\"\"\"\r\n        optimizer = optim.Adam(self.net.parameters(), lr=self.learning_rate)\r\n        return [optimizer]\r\n\r\n    def _dataloader(self) -> DataLoader:\r\n        \"\"\"Initialize the Replay Buffer dataset used for retrieving experiences.\"\"\"\r\n        self.buffer = MultiStepBuffer(self.replay_size, self.n_steps)\r\n        self.populate(self.warm_start_size)\r\n\r\n        self.dataset = ExperienceSourceDataset(self.train_batch)\r\n        return DataLoader(dataset=self.dataset, batch_size=self.batch_size)\r\n\r\n    def train_dataloader(self) -> DataLoader:\r\n        \"\"\"Get train loader.\"\"\"\r\n        return self._dataloader()\r\n\r\n    def test_dataloader(self) -> DataLoader:\r\n        \"\"\"Get test loader.\"\"\"\r\n        return self._dataloader()\r\n    @staticmethod\r\n    def _use_dp_or_ddp2(trainer: Trainer) -> bool:\r\n        return isinstance(trainer.training_type_plugin, (DataParallelPlugin, DDP2Plugin))\r\n", "repo_name": "graceyangfan/rubbishcode", "sub_path": "agents/dqn.py", "file_name": "dqn.py", "file_ext": "py", "file_size_in_byte": 11713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pytorch_lightning.LightningModule", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 33, "usage_type": "name"}, {"api_name": "zenquant.agents.common.agent.ValueAgent", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 83, "usage_type": "call"}, {"api_name": "vnpy.trader.constant.Direction.LONG", "line_number": 95, "usage_type": "attribute"}, {"api_name": "vnpy.trader.constant.Direction", "line_number": 95, "usage_type": "name"}, {"api_name": "vnpy.trader.constant.Offset.CLOSE", "line_number": 95, "usage_type": "attribute"}, {"api_name": "vnpy.trader.constant.Offset", "line_number": 95, "usage_type": "name"}, {"api_name": "vnpy.trader.constant.Direction.SHORT", "line_number": 99, "usage_type": "attribute"}, {"api_name": "vnpy.trader.constant.Direction", "line_number": 99, "usage_type": "name"}, {"api_name": "vnpy.trader.constant.Offset.CLOSE", "line_number": 99, "usage_type": "attribute"}, {"api_name": "vnpy.trader.constant.Offset", "line_number": 99, "usage_type": "name"}, {"api_name": "vnpy.trader.constant.Offset.OPEN", "line_number": 102, "usage_type": "attribute"}, {"api_name": "vnpy.trader.constant.Offset", "line_number": 102, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 119, "usage_type": "name"}, {"api_name": "zenquant.agents.common.experience_source.Experience", "line_number": 154, "usage_type": "call"}, {"api_name": "zenquant.agents.common.networks.CNN", "line_number": 163, "usage_type": "call"}, {"api_name": "zenquant.agents.common.networks.CNN", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 165, "usage_type": "name"}, {"api_name": "zenquant.agents.common.experience_source.Experience", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 206, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 220, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 220, "usage_type": "name"}, {"api_name": "zenquant.agents.common.loss.dqn_loss", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.nn.SmoothL1Loss", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 231, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 250, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 220, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 257, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 257, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 263, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 263, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 272, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 270, "usage_type": "name"}, {"api_name": "torch.optim.optimizer.Optimizer", "line_number": 270, "usage_type": "name"}, {"api_name": "zenquant.agents.common.memory.MultiStepBuffer", "line_number": 277, "usage_type": "call"}, {"api_name": "zenquant.agents.common.experience_source.ExperienceSourceDataset", "line_number": 280, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 281, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 275, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 283, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 287, "usage_type": "name"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 291, "usage_type": "name"}, {"api_name": "pytorch_lightning.plugins.DataParallelPlugin", "line_number": 292, "usage_type": "name"}, {"api_name": "pytorch_lightning.plugins.DDP2Plugin", "line_number": 292, "usage_type": "name"}]}
{"seq_id": "4404373014", "text": "\nimport pandas as pd\nfrom time import perf_counter\nfrom typing import Any, Dict, List, Optional, Set, Tuple\nfrom mitosheet.ai.ai_utils import fix_final_dataframe_name, fix_up_missing_imports\nfrom mitosheet.code_chunks.code_chunk import CodeChunk\nfrom mitosheet.code_chunks.ai_transformation_code_chunk import AITransformationCodeChunk\n\nfrom mitosheet.state import State\nfrom mitosheet.step_performers.step_performer import StepPerformer\nfrom mitosheet.step_performers.utils.utils import get_param\nfrom mitosheet.ai.recon import exec_and_get_new_state_and_result\n\nclass AITransformationStepPerformer(StepPerformer):\n    \"\"\"\n    Allows you to execute an arbitrary chunk of code, generated by an AI.\n    \"\"\"\n\n    @classmethod\n    def step_version(cls) -> int:\n        return 1\n\n    @classmethod\n    def step_type(cls) -> str:\n        return 'ai_transformation'\n\n    @classmethod\n    def execute(cls, prev_state: State, params: Dict[str, Any]) -> Tuple[State, Optional[Dict[str, Any]]]:\n        # We don't use any of these parameters, but we keep them for clarity, and we don't want to remove them accidently\n        user_input: str = get_param(params, 'user_input')\n        prompt_version: str = get_param(params, 'prompt_version')\n        prompt: str = get_param(params, 'prompt')\n        completion: str = get_param(params, 'completion')\n        edited_completion: str = get_param(params, 'edited_completion')\n\n        pandas_start_time = perf_counter()\n        post_state, last_line_value, frontend_result = exec_and_get_new_state_and_result(prev_state, edited_completion)\n        pandas_processing_time = perf_counter() - pandas_start_time\n\n        if isinstance(last_line_value, pd.DataFrame) or isinstance(last_line_value, pd.Series):\n            final_code = fix_final_dataframe_name(edited_completion, post_state.df_names[-1], isinstance(last_line_value, pd.Series))\n        else:\n            final_code = edited_completion\n\n        return post_state, {\n            'pandas_processing_time': pandas_processing_time,\n            'result': frontend_result,\n            'final_code': final_code\n        }\n\n    @classmethod\n    def transpile(\n        cls,\n        prev_state: State,\n        post_state: State,\n        params: Dict[str, Any],\n        execution_data: Optional[Dict[str, Any]],\n    ) -> List[CodeChunk]:\n        return [\n            AITransformationCodeChunk(\n                prev_state, \n                post_state, \n                get_param(params, 'user_input'),\n                get_param(execution_data if execution_data is not None else {}, 'final_code')\n            )\n        ]\n\n    @classmethod\n    def get_modified_dataframe_indexes(cls, params: Dict[str, Any]) -> Set[int]:\n        return set() # NOTE: We act as though we modify all sheet indexes, as we can't easily figure out what we do modify\n\n    @classmethod\n    def get_created_dataframe_indexes(cls, params: Dict[str, Any]) -> Set[int]:\n        return {-1} # NOTE: We act as though we create all sheet indexes, as we can't easily figure out what we do modify", "repo_name": "mito-ds/monorepo", "sub_path": "mitosheet/mitosheet/step_performers/ai_transformation.py", "file_name": "ai_transformation.py", "file_ext": "py", "file_size_in_byte": 3041, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1800, "dataset": "github-code", "pt": "41", "api": [{"api_name": "mitosheet.step_performers.step_performer.StepPerformer", "line_number": 14, "usage_type": "name"}, {"api_name": "mitosheet.state.State", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 28, "usage_type": "name"}, {"api_name": "mitosheet.step_performers.utils.utils.get_param", "line_number": 30, "usage_type": "call"}, {"api_name": "mitosheet.step_performers.utils.utils.get_param", "line_number": 31, "usage_type": "call"}, {"api_name": "mitosheet.step_performers.utils.utils.get_param", "line_number": 32, "usage_type": "call"}, {"api_name": "mitosheet.step_performers.utils.utils.get_param", "line_number": 33, "usage_type": "call"}, {"api_name": "mitosheet.step_performers.utils.utils.get_param", "line_number": 34, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 36, "usage_type": "call"}, {"api_name": "mitosheet.ai.recon.exec_and_get_new_state_and_result", "line_number": 37, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 40, "usage_type": "attribute"}, {"api_name": "mitosheet.ai.ai_utils.fix_final_dataframe_name", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 41, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 28, "usage_type": "name"}, {"api_name": "mitosheet.state.State", "line_number": 54, "usage_type": "name"}, {"api_name": "mitosheet.state.State", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 57, "usage_type": "name"}, {"api_name": "mitosheet.code_chunks.ai_transformation_code_chunk.AITransformationCodeChunk", "line_number": 60, "usage_type": "call"}, {"api_name": "mitosheet.step_performers.utils.utils.get_param", "line_number": 63, "usage_type": "call"}, {"api_name": "mitosheet.step_performers.utils.utils.get_param", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 58, "usage_type": "name"}, {"api_name": "mitosheet.code_chunks.code_chunk.CodeChunk", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 73, "usage_type": "name"}]}
{"seq_id": "30366197775", "text": "from nba_api.stats.endpoints import teamyearbyyearstats, teamplayerdashboard, teamdashboardbylastngames, \\\n    teamdashboardbyshootingsplits\nfrom nba_api.stats.static import teams\n\n\ndef get_team_id(teamName):\n    nba_teams = teams.get_teams()\n\n    selected_team = [team for team in nba_teams\n                     if team['full_name'] == teamName][0]\n\n    teamInfo = teamyearbyyearstats.TeamYearByYearStats(team_id=str(selected_team.get('id')))\n\n    return teamInfo.get_data_frames()[0].TEAM_ID[0]\n\n\ndef get_team_info(teamName):\n    team_id = get_team_id(teamName)\n\n    team_stats = teamyearbyyearstats.TeamYearByYearStats(team_id=team_id).get_data_frames()[0]\n\n    season_played = []\n    for season in team_stats['YEAR']:\n        season_played.append(season)\n\n    game_played = []\n    for gp in team_stats['GP']:\n        game_played.append(gp)\n\n    wins = []\n    for win in team_stats['WINS']:\n        wins.append(win)\n\n    losses = []\n    for loss in team_stats['LOSSES']:\n        losses.append(loss)\n\n    win_percentage = []\n    for win_pct in team_stats['WIN_PCT']:\n        win_percentage.append(win_pct)\n\n    conf_rank = []\n    for _ in team_stats['CONF_RANK']:\n        conf_rank.append(_)\n\n    po_wins = []\n    for powins in team_stats['PO_WINS']:\n        po_wins.append(powins)\n\n    po_losses = []\n    for polosses in team_stats['PO_LOSSES']:\n        po_losses.append(polosses)\n\n    # N/A - not in final, FINALS APPEARANCE - lost in final, LEAGUE CHAMPION - won in final.\n    final_appearance = []\n    for _ in team_stats['NBA_FINALS_APPEARANCE']:\n        final_appearance.append(_)\n\n    po_gp = [[] for _ in po_wins]\n    for i in range(len(po_gp)):\n        po_gp[i] = po_wins[i] + po_losses[i]\n\n    latest_season = season_played[-1]\n    team_dashboard = teamplayerdashboard.TeamPlayerDashboard(team_id=team_id, season=latest_season).get_data_frames()[1]\n\n    player_name = []\n    for player in team_dashboard['PLAYER_NAME']:\n        player_name.append(player)\n\n    team_lastgame = teamdashboardbylastngames.TeamDashboardByLastNGames(\n        team_id=team_id, season=latest_season).get_data_frames()[1]\n\n    last_5_games = ['Last 5 games']\n    w = []\n    for win in team_lastgame['W']:\n        w.append(win)\n\n    l = []\n    for lose in team_lastgame['L']:\n        l.append(lose)\n\n    w_pct = []\n    for win_pct in team_lastgame['W_PCT']:\n        w_pct.append(win_pct)\n\n    # field goal attempt\n    fga = []\n    for FGA in team_lastgame['FGA']:\n        fga.append(FGA)\n    # field goal made\n    fgm = []\n    for FGM in team_lastgame['FGM']:\n        fgm.append(FGM)\n\n    fg_pct = []\n    for FG_pct in team_lastgame['FG_PCT']:\n        fg_pct.append(FG_pct)\n\n    reb = []\n    for rebound in team_lastgame['REB']:\n        reb.append(rebound)\n\n    ast = []\n    for assist in team_lastgame['AST']:\n        ast.append(assist)\n    # turnovers\n    tov = []\n    for turnover in team_lastgame['TOV']:\n        tov.append(turnover)\n\n    stl = []\n    for steal in team_lastgame['STL']:\n        stl.append(steal)\n\n    blk = []\n    for block in team_lastgame['BLK']:\n        blk.append(block)\n\n    pts = []\n    for point in team_lastgame['PTS']:\n        pts.append(point)\n    # team efficiency\n    plus_minus = []\n    for plusandminus in team_lastgame['PLUS_MINUS']:\n        plus_minus.append(plusandminus)\n\n    team_shooting = teamdashboardbyshootingsplits.TeamDashboardByShootingSplits(\n        team_id=team_id, season=latest_season).get_data_frames()[3]\n\n    shot_area = []\n    for values in team_shooting['GROUP_VALUE']:\n        shot_area.append(values)\n\n    # field goal attempt\n    fga_area = []\n    for FGA in team_shooting['FGA']:\n        fga_area.append(FGA)\n    # field goal made\n    fgm_area = []\n    for FGM in team_shooting['FGM']:\n        fgm_area.append(FGM)\n\n    fg_pct_area = []\n    for FG_pct in team_shooting['FG_PCT']:\n        fg_pct_area.append(FG_pct)\n\n    return [season_played, game_played, wins, losses, win_percentage, conf_rank, po_gp, po_wins, po_losses,\n        final_appearance], \\\n        [player_name], \\\n        [last_5_games, w, l, w_pct, fga, fgm, fg_pct, pts, ast, reb, stl, blk, tov, plus_minus], \\\n        [shot_area, fga_area, fgm_area, fg_pct_area]\n\n\ndef get_team_info_using_teamid(teamID):\n    team_stats = teamyearbyyearstats.TeamYearByYearStats(teamID).get_data_frames()[0]\n\n    season_played = []\n    for season in team_stats['YEAR']:\n        season_played.append(season)\n\n    game_played = []\n    for gp in team_stats['GP']:\n        game_played.append(gp)\n\n    wins = []\n    for win in team_stats['WINS']:\n        wins.append(win)\n\n    losses = []\n    for loss in team_stats['LOSSES']:\n        losses.append(loss)\n\n    win_percentage = []\n    for win_pct in team_stats['WIN_PCT']:\n        win_percentage.append(win_pct)\n\n    conf_rank = []\n    for _ in team_stats['CONF_RANK']:\n        conf_rank.append(_)\n\n    po_wins = []\n    for powins in team_stats['PO_WINS']:\n        po_wins.append(powins)\n\n    po_losses = []\n    for polosses in team_stats['PO_LOSSES']:\n        po_losses.append(polosses)\n\n    # N/A - not in final, FINALS APPEARANCE - lost in final, LEAGUE CHAMPION - won in final.\n    final_appearance = []\n    for _ in team_stats['NBA_FINALS_APPEARANCE']:\n        final_appearance.append(_)\n\n    po_gp = [[] for _ in po_wins]\n    for i in range(len(po_gp)):\n        po_gp[i] = po_wins[i] + po_losses[i]\n\n    return [season_played, game_played, wins, losses, win_percentage, conf_rank, po_gp, po_wins, po_losses,\n            final_appearance]\n", "repo_name": "akanel15/NBADashboard", "sub_path": "teamStats.py", "file_name": "teamStats.py", "file_ext": "py", "file_size_in_byte": 5486, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "nba_api.stats.static.teams.get_teams", "line_number": 7, "usage_type": "call"}, {"api_name": "nba_api.stats.static.teams", "line_number": 7, "usage_type": "name"}, {"api_name": "nba_api.stats.endpoints.teamyearbyyearstats.TeamYearByYearStats", "line_number": 12, "usage_type": "call"}, {"api_name": "nba_api.stats.endpoints.teamyearbyyearstats", "line_number": 12, "usage_type": "name"}, {"api_name": "nba_api.stats.endpoints.teamyearbyyearstats.TeamYearByYearStats", "line_number": 20, "usage_type": "call"}, {"api_name": "nba_api.stats.endpoints.teamyearbyyearstats", "line_number": 20, "usage_type": "name"}, {"api_name": "nba_api.stats.endpoints.teamplayerdashboard.TeamPlayerDashboard", "line_number": 64, "usage_type": "call"}, {"api_name": "nba_api.stats.endpoints.teamplayerdashboard", "line_number": 64, "usage_type": "name"}, {"api_name": "nba_api.stats.endpoints.teamdashboardbylastngames.TeamDashboardByLastNGames", "line_number": 70, "usage_type": "call"}, {"api_name": "nba_api.stats.endpoints.teamdashboardbylastngames", "line_number": 70, "usage_type": "name"}, {"api_name": "nba_api.stats.endpoints.teamdashboardbyshootingsplits.TeamDashboardByShootingSplits", "line_number": 127, "usage_type": "call"}, {"api_name": "nba_api.stats.endpoints.teamdashboardbyshootingsplits", "line_number": 127, "usage_type": "name"}, {"api_name": "nba_api.stats.endpoints.teamyearbyyearstats.TeamYearByYearStats", "line_number": 155, "usage_type": "call"}, {"api_name": "nba_api.stats.endpoints.teamyearbyyearstats", "line_number": 155, "usage_type": "name"}]}
{"seq_id": "27708251713", "text": "from flask import Flask, request\n\napp = Flask(__name__)\n\n\n@app.route('/index', methods=['get', 'post'])\ndef index():\n    return 'index page'\n\n\n@app.route('/upload', methods=['post'])\ndef upload():\n    '''接收文件'''\n    file_obj = request.files.get('pic')\n    if not file_obj:\n        return '未上传文件'\n\n    # 第一种：保存文件\n    # f = open('./demo.jpg', 'wb')\n    # data = file_obj.read()\n    # f.write(data)\n    # f.close()\n    # 第二种保存文件\n    file_obj.save('./demo1.png')\n\n    return '上传成功'\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\n\n\n", "repo_name": "llf-1996/base_demo_flask", "sub_path": "08_request对象.py", "file_name": "08_request对象.py", "file_ext": "py", "file_size_in_byte": 590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.request.files.get", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}]}
{"seq_id": "35168689736", "text": "import logging\n\nfrom urwid import BoxAdapter, LineBox, Text, connect_signal\n\nfrom subiquitycore.models.network import WLANConfig\nfrom subiquitycore.ui.buttons import cancel_btn, menu_btn\nfrom subiquitycore.ui.container import ListBox, Pile, WidgetWrap\nfrom subiquitycore.ui.form import Form, PasswordField, StringField\nfrom subiquitycore.ui.stretchy import Stretchy\nfrom subiquitycore.ui.utils import Color, Padding, disabled\n\nlog = logging.getLogger(\"subiquitycore.ui.views.network_configure_wlan_interface\")\n\n\nclass NetworkList(WidgetWrap):\n    def __init__(self, parent, ssids):\n        self.parent = parent\n        button = cancel_btn(_(\"Cancel\"), on_press=self.do_cancel)\n        ssid_list = [\n            menu_btn(label=ssid, on_press=self.do_network) for ssid in ssids if ssid\n        ]\n        p = Pile([BoxAdapter(ListBox(ssid_list), height=10), Padding.fixed_10(button)])\n        box = LineBox(p, title=_(\"Select a network\"))\n        super().__init__(box)\n\n    def do_network(self, sender):\n        self.parent.form.ssid.value = sender.label\n        self.parent.parent.remove_overlay()\n\n    def do_cancel(self, sender):\n        self.parent.parent.remove_overlay()\n\n\nclass WLANForm(Form):\n    ok_label = _(\"Save\")\n\n    ssid = StringField(caption=\"Network Name:\")\n    psk = PasswordField(caption=\"Password:\")\n\n    def validate_psk(self):\n        psk = self.psk.value\n        if len(psk) == 0:\n            return\n        elif len(psk) < 8:\n            return \"Password must be at least 8 characters long if present\"\n        elif len(psk) > 63:\n            return \"Password must be less than 63 characters long\"\n\n\nclass NetworkConfigureWLANStretchy(Stretchy):\n    def __init__(self, parent, dev_info):\n        self.parent = parent\n        self.dev_info = dev_info\n        title = _(\"Network interface {nic} WIFI configuration\").format(\n            nic=dev_info.name\n        )\n\n        self.form = WLANForm()\n\n        connect_signal(self.form, \"submit\", self.done)\n        connect_signal(self.form, \"cancel\", self.cancel)\n\n        if self.dev_info.wlan.config.ssid:\n            self.form.ssid.value = self.dev_info.wlan.config.ssid\n        if self.dev_info.wlan.config.psk:\n            self.form.psk.value = self.dev_info.wlan.config.psk\n\n        self.ssid_row = self.form.ssid._table\n        self.psk_row = self.form.psk._table\n        self.ssid_row.bind(self.psk_row)\n\n        self.error = Text(\"\")\n\n        self.inputs = Pile(self._build_iface_inputs())\n\n        widgets = [\n            self.inputs,\n            Padding.center_79(Color.info_error(self.error)),\n            self.form.buttons,\n        ]\n        super().__init__(title, widgets, 0, 0)\n\n    def show_ssid_list(self, sender):\n        self.parent.show_overlay(\n            NetworkList(self, self.dev_info.wlan.visible_ssids), width=60\n        )\n\n    def start_scan(self, sender):\n        fp = self.inputs.focus_position - 1\n        while not self.inputs.contents[fp][0].selectable():\n            fp -= 1\n        self.inputs.focus_position = fp\n        self.parent.controller.start_scan(self.dev_info.name)\n\n    def _build_iface_inputs(self):\n        visible_ssids = self.dev_info.wlan.visible_ssids\n        if len(visible_ssids) > 0:\n            networks_btn = menu_btn(\n                \"Choose a visible network\", on_press=self.show_ssid_list\n            )\n        else:\n            networks_btn = disabled(menu_btn(\"No visible networks\"))\n\n        scan_state = self.dev_info.wlan.scan_state\n        if not scan_state:\n            scan_btn = menu_btn(\"Scan for networks\", on_press=self.start_scan)\n        elif scan_state.startswith(\"error\"):\n            self.error.set_text(\"scan failed: %s\" % (scan_state,))\n            scan_btn = disabled(menu_btn(\"Scanning for networks failed\"))\n        else:\n            scan_btn = disabled(menu_btn(\"Scanning for networks\"))\n\n        warning = \"Only open or WPA2/PSK networks are supported at this time.\"\n        col = [\n            Text(warning),\n            Text(\"\"),\n            self.ssid_row,\n            Text(\"\"),\n            Padding.fixed_32(networks_btn),\n            Padding.fixed_32(scan_btn),\n            Text(\"\"),\n            self.psk_row,\n        ]\n        return col\n\n    def update_link(self, dev_info):\n        if dev_info.name != self.dev_info.name:\n            return\n        self.dev_info = dev_info\n        self.inputs.contents = [\n            (obj, (\"pack\", None)) for obj in self._build_iface_inputs()\n        ]\n\n    def done(self, sender):\n        if self.form.ssid.value:\n            ssid = self.form.ssid.value\n        else:\n            ssid = None\n        if self.form.psk.value:\n            psk = self.form.psk.value\n        else:\n            psk = None\n        self.parent.controller.set_wlan(\n            self.dev_info.name, WLANConfig(ssid=ssid, psk=psk)\n        )\n        self.parent.update_link(self.dev_info)\n        self.parent.remove_overlay()\n\n    def cancel(self, sender=None):\n        self.parent.remove_overlay()\n", "repo_name": "canonical/subiquity", "sub_path": "subiquitycore/ui/views/network_configure_wlan_interface.py", "file_name": "network_configure_wlan_interface.py", "file_ext": "py", "file_size_in_byte": 4961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 420, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "subiquitycore.ui.container.WidgetWrap", "line_number": 15, "usage_type": "name"}, {"api_name": "subiquitycore.ui.buttons.cancel_btn", "line_number": 18, "usage_type": "call"}, {"api_name": "subiquitycore.ui.buttons.menu_btn", "line_number": 20, "usage_type": "call"}, {"api_name": "subiquitycore.ui.container.Pile", "line_number": 22, "usage_type": "call"}, {"api_name": "urwid.BoxAdapter", "line_number": 22, "usage_type": "call"}, {"api_name": "subiquitycore.ui.container.ListBox", "line_number": 22, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.Padding.fixed_10", "line_number": 22, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.Padding", "line_number": 22, "usage_type": "name"}, {"api_name": "urwid.LineBox", "line_number": 23, "usage_type": "call"}, {"api_name": "subiquitycore.ui.form.Form", "line_number": 34, "usage_type": "name"}, {"api_name": "subiquitycore.ui.form.StringField", "line_number": 37, "usage_type": "call"}, {"api_name": "subiquitycore.ui.form.PasswordField", "line_number": 38, "usage_type": "call"}, {"api_name": "subiquitycore.ui.stretchy.Stretchy", "line_number": 50, "usage_type": "name"}, {"api_name": "urwid.connect_signal", "line_number": 60, "usage_type": "call"}, {"api_name": "urwid.connect_signal", "line_number": 61, "usage_type": "call"}, {"api_name": "urwid.Text", "line_number": 72, "usage_type": "call"}, {"api_name": "subiquitycore.ui.container.Pile", "line_number": 74, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.Padding.center_79", "line_number": 78, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.Padding", "line_number": 78, "usage_type": "name"}, {"api_name": "subiquitycore.ui.utils.Color.info_error", "line_number": 78, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.Color", "line_number": 78, "usage_type": "name"}, {"api_name": "subiquitycore.ui.buttons.menu_btn", "line_number": 98, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.disabled", "line_number": 102, "usage_type": "call"}, {"api_name": "subiquitycore.ui.buttons.menu_btn", "line_number": 102, "usage_type": "call"}, {"api_name": "subiquitycore.ui.buttons.menu_btn", "line_number": 106, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.disabled", "line_number": 109, "usage_type": "call"}, {"api_name": "subiquitycore.ui.buttons.menu_btn", "line_number": 109, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.disabled", "line_number": 111, "usage_type": "call"}, {"api_name": "subiquitycore.ui.buttons.menu_btn", "line_number": 111, "usage_type": "call"}, {"api_name": "urwid.Text", "line_number": 115, "usage_type": "call"}, {"api_name": "urwid.Text", "line_number": 116, "usage_type": "call"}, {"api_name": "urwid.Text", "line_number": 118, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.Padding.fixed_32", "line_number": 119, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.Padding", "line_number": 119, "usage_type": "name"}, {"api_name": "subiquitycore.ui.utils.Padding.fixed_32", "line_number": 120, "usage_type": "call"}, {"api_name": "subiquitycore.ui.utils.Padding", "line_number": 120, "usage_type": "name"}, {"api_name": "urwid.Text", "line_number": 121, "usage_type": "call"}, {"api_name": "subiquitycore.models.network.WLANConfig", "line_number": 144, "usage_type": "call"}]}
{"seq_id": "6757307764", "text": "import numpy as numpy\nimport csv\nfrom sklearn.neighbors import KNeighborsClassifier\nimport pandas\nfrom sklearn.preprocessing import StandardScaler\n\nfile_name = 'student-mat-nograde3.csv'\n\ndata_frame = pandas.read_csv(file_name)\ndata_frame = pandas.get_dummies(data_frame)\nprint(data_frame)\n#raw_data = open(file_name, 'rt')\nnumpy_array = data_frame.values\n\n#using scaler\nscaler = StandardScaler()\nscaler.fit(numpy_array)\nnumpy_array = scaler.transform(numpy_array)\n\nprint(numpy_array)\n\nfile_name = 'student-math.csv'\none_row = []\nraw_data = open(file_name, 'rt')\n\nwith open(file_name) as f:\n    reader = csv.reader(raw_data, delimiter=',')\n    next(reader)\n    data_y = []\n    for i, row in enumerate(reader):\n        if int(row[len(row) - 1]) > 15:\n            data_y.append(1)\n        else:\n            data_y.append(0)\n        #data_y.append(row[len(row) - 1])\n    y = list(data_y)\n    data_y = numpy.array(y).astype('int')\n\nneigh = KNeighborsClassifier(n_neighbors=4)\nprint(data_y)\nprint(data_y.shape)\nneigh.fit(numpy_array, data_y)\nprint(data_y)\nprint(neigh.score(numpy_array, data_y))", "repo_name": "aboonyanit/CS221FinalProject", "sub_path": "readFileNotClean.py", "file_name": "readFileNotClean.py", "file_ext": "py", "file_size_in_byte": 1090, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 16, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "43365786863", "text": "import stripe\nimport frappe\nimport datetime\n\n\n# price IDS\n@frappe.whitelist(allow_guest=True)\ndef hasActiveSubscription(*args, **kwargs):\n    invalidate_cache = True if \"invalidate_cache\" in kwargs else False\n    site = kwargs.get(\"site\", frappe.local.site)\n    if not site:\n        return False\n    frappe.utils.execute_in_shell(\"bench --site {} clear-cache\".format(site))\n    frappe.utils.execute_in_shell(\"bench --site {} clear-website-cache\".format(site))\n    country = frappe.get_site_config(site_path=site)[\"country\"]\n    customer_id = frappe.get_site_config(site_path=site)[\"customer_id\"]\n    stripe_manager = StripeSubscriptionManager(country=country)\n\n    if invalidate_cache or (not frappe.conf.has_subscription):\n        hasSubscription = (\n            \"yes\" if stripe_manager.has_valid_site_subscription(customer_id) else \"no\"\n        )\n        # save site expiry date in site_config\n\n        command = \"bench --site {} set-config has_subscription {}\".format(\n            site, hasSubscription\n        )\n        frappe.utils.execute_in_shell(command)\n    result = True if frappe.conf.has_subscription == \"yes\" else False\n    return result\n\n\ndef get_room(customer_id):\n    try:\n        site = frappe.db.get_list(\n            \"SaaS sites\",\n            filters={\"cus_id\": customer_id},\n            fields=[\"site_name\"],\n            ignore_permissions=True,\n        )[0][\"site_name\"]\n        return f\"{site}:website\"\n    except Exception as e:\n        print(\"error\", e)\n\n\ndef get_site(customer_id):\n    site = frappe.db.get_list(\n        \"SaaS sites\",\n        filters={\"cus_id\": customer_id},\n        fields=[\"site_name\"],\n        ignore_permissions=True,\n    )\n    return site[0][\"site_name\"] if len(site) > 0 else None\n\n\nclass StripeSubscriptionManager:\n    def __init__(self, country=\"\"):\n        self.api_key = frappe.conf.STRIPE_SECRET_KEY\n        self.endpoint_secret = frappe.conf.STRIPE_ENDPOINT_SECRET\n        if country:\n            self.region = country\n        else:\n            self.region = frappe.conf.country or \"US\"\n        if self.region == \"IN\":\n            self.api_key = frappe.conf.STRIPE_SECRET_KEY_IN\n\n            self.endpoint_secret = frappe.conf.STRIPE_ENDPOINT_SECRET_IN\n        self.plan_to_product_id = frappe.conf.stripe_prices[\"US\"][\"products\"]\n\n        self.trial_price_id = frappe.conf.stripe_prices[\"US\"][\"trial_price_id\"]\n        self.trial_product = \"ONEHASH_PRO\"\n        print(\"region\", self.region)\n        if self.region == \"IN\":\n            print(\"setting plan to product id for IN\")\n            self.plan_to_product_id = frappe.conf.stripe_prices[\"IN\"][\"products\"]\n            self.trial_price_id = frappe.conf.stripe_prices[\"IN\"][\"trial_price_id\"]\n        print(\"plan to product id\", self.plan_to_product_id)\n        self.onehas_subscription_product_ids = [\n            x for x in self.plan_to_product_id.values()\n        ]\n        print(\"stripe api key\", self.api_key)\n        stripe.api_key = self.api_key\n\n    def plan_id_to_product(self):\n        return {v: k for k, v in self.plan_to_product_id.items()}\n\n    def get_subscriptions(self, customer_id):\n        return stripe.Subscription.list(customer=customer_id)\n\n    def getSession(self, session_id, expand=[]):\n        return stripe.checkout.Session.retrieve(session_id, expand=expand)\n\n    def create_customer(self, site_name, email, fname, lname, phone):\n        return stripe.Customer.create(\n            email=email,\n            metadata={\"full name\": fname + \" \" + lname},\n            name=site_name,\n            phone=phone,\n        )\n\n    def get_current_onehash_price(self, customer_id):\n        subscription = self.get_onehash_subscription(frappe.conf.customer_id)\n        return subscription[\"plan\"][\"id\"]\n\n    def end_trial(self, customer_id):\n        alls = stripe.Customer.list()\n        subscriptions = stripe.Subscription.list(customer=customer_id)\n        for subscription in subscriptions:\n            if subscription[\"status\"] == \"trialing\":\n                stripe.Subscription.delete(subscription[\"id\"])\n\n    def start_free_trial_of_site(self, customer_id):\n        return stripe.Subscription.create(\n            customer=customer_id,\n            items=[{\"price\": self.trial_price_id}],\n            payment_settings={\"save_default_payment_method\": \"on_subscription\"},\n            trial_settings={\"end_behavior\": {\"missing_payment_method\": \"pause\"}},\n            metadata={\"plan\": \"ONEHASH_PRO\"},\n            trial_period_days=14,\n        )\n\n    def has_valid_site_subscription(self, cus_id):\n        return self.get_onehash_subscription(cus_id) != \"NONE\"\n\n    def create_new_purchase_session(self, customer_id, price_id, subdomain):\n        success_url = (\n            \"http://\"\n            + subdomain\n            + \".\"\n            + (\n                frappe.conf.domain\n                if frappe.conf.domain != \"localhost\"\n                else frappe.conf.domain + \":8000\"\n            )\n            + \"/plans?payment_success=True\"\n        )\n        session = stripe.checkout.Session.create(\n            success_url=success_url,\n            mode=\"subscription\",\n            line_items=[\n                {\n                    \"price\": price_id,\n                    \"quantity\": 1,\n                }\n            ],\n            client_reference_id=subdomain,\n            customer=customer_id,\n            metadata={\"del_trial\": True},\n        )\n        return session.url\n\n    # def create_checkout_session(self,customer_id,plan,success_url,cancel_url):\n    def get_customer_details(self, customer_id):\n        customer = stripe.Customer.retrieve(customer_id, expand=[\"subscriptions\"])\n        return customer\n\n    def get_current_onehash_product_id(self, customer_id):\n        customer = self.get_customer_details(customer_id)\n        product_id = None\n        for subscription in customer[\"subscriptions\"][\"data\"]:\n            if (\n                subscription[\"status\"] in [\"active\", \"trialing\"]\n                and subscription[\"plan\"][\"product\"]\n                and subscription[\"plan\"][\"product\"]\n                in self.onehas_subscription_product_ids\n            ):\n                product_id = subscription[\"plan\"][\"product\"]\n                break\n        return product_id\n\n    def get_current_onehash_product(self, customer_id):\n        subscriptions = stripe.Subscription.list(customer=customer_id)\n        product = None\n        for subscription in subscriptions[\"data\"]:\n            if (\n                subscription[\"status\"] in [\"active\", \"trialing\"]\n                and subscription[\"plan\"][\"product\"]\n                and subscription[\"plan\"][\"product\"]\n                in self.onehas_subscription_product_ids\n            ):\n                product = subscription[\"plan\"][\"product\"]\n                break\n        return stripe.Product.retrieve(product)\n\n    def hadle_subscription_deleted(self, event):\n        hasActiveSubscription(\n            invalidate_cache=True, site=get_site(event[\"data\"][\"object\"][\"customer\"])\n        )\n\n    def has_valid_subscription_v2(self, customer_id, plan):\n        # if plan == \"ONEHASH\"\n        # check if the customer has any product in subscription which product id starts with ONEHASH\n        # if yes then return true else false\n        if not customer_id:\n            return False\n        customer = self.get_customer_details(customer_id)\n        if \"subscriptions\" not in customer:\n            return False\n        if plan == \"ONEHASH\":\n            for subscription in customer[\"subscriptions\"][\"data\"]:\n                if (\n                    subscription[\"status\"] in [\"active\", \"trialing\"]\n                    and subscription[\"plan\"][\"product\"]\n                    and subscription[\"plan\"][\"product\"]\n                    in self.onehas_subscription_product_ids\n                ):\n                    return True\n            return False\n        else:\n            for subscription in customer[\"subscriptions\"][\"data\"]:\n                if (\n                    subscription[\"status\"] in [\"active\", \"trialing\"]\n                    and subscription[\"plan\"][\"product\"]\n                    and subscription[\"plan\"][\"product\"] == self.plan_to_product_id[plan]\n                ):\n                    return True\n            return False\n\n    def get_onehash_subscription(self, customer_id):\n        print(\"getting onehash subscription for customer\", customer_id)\n        if not customer_id:\n            return \"NONE\"\n        subscriptions = stripe.Subscription.list(customer=customer_id)\n        for subscription in subscriptions[\"data\"]:\n            print(\"subscription status\", subscription[\"status\"])\n            print(\"subscription plan\", subscription[\"plan\"][\"product\"])\n            print(\"product ids\", self.onehas_subscription_product_ids)\n            print(\n                \"subscription in onehash\",\n                subscription[\"plan\"][\"product\"] in self.onehas_subscription_product_ids,\n            )\n            if (\n                subscription[\"status\"] in [\"active\", \"trialing\"]\n                and subscription[\"plan\"][\"product\"]\n                and (\n                    subscription[\"plan\"][\"product\"]\n                    in self.onehas_subscription_product_ids\n                )\n            ):\n                return subscription\n        return \"NONE\"\n\n    def cancel_onehash_subscription(self, customer_id):\n        current_sub = self.get_onehash_subscription(customer_id)\n        if current_sub != \"NONE\":\n            stripe.Subscription.delete(current_sub[\"id\"])\n\n    def upgrade_subscription(self, customer_id, new_price_id, subdomain):\n        # this will upgrade the subscription to new price id and try to charge the customer immediately\n        subscription = self.get_onehash_subscription(customer_id)\n        # check is customer has any pending invoice if yes then return \"PENDING_INVOICE\"\n        # check is customer has any pending update if yes then return \"PENDING_UPDATE\"\n        # check is customer has any unpaid invoice if yes then return \"UNPAID_INVOICE\"\n        # check is customer has no payment method if yes then return \"NO_PAYMENT_METHOD\"\n        customer = self.get_customer_details(customer_id)\n        invoices = stripe.Invoice.list(customer=customer_id)\n        for invoice in invoices[\"data\"]:\n            if invoice[\"status\"] != \"paid\" and invoice[\"status\"] != \"void\":\n                return \"PENDING_INVOICE\"\n        customer_payment_methods = stripe.PaymentMethod.list(\n            customer=customer_id, type=\"card\"\n        )\n\n        if len(customer_payment_methods[\"data\"]) == 0:\n            return \"NO_PAYMENT_METHOD\"\n        # remove any pending update\n        if customer[\"subscriptions\"][\"data\"][0][\"pending_update\"]:\n            return \"PENDING_UPDATE\"\n\n        try:\n            response = stripe.Subscription.modify(\n                subscription.id,\n                payment_behavior=\"pending_if_incomplete\",\n                proration_behavior=\"always_invoice\",\n                items=[\n                    {\n                        \"id\": subscription[\"items\"][\"data\"][0].id,\n                        \"price\": new_price_id,\n                    }\n                ],\n            )\n\n        except Exception as e:\n            return \"TECHNICAL_ERROR\"\n        if \"pending_update\" in response and response[\"pending_update\"]:\n            return \"PENDING_UPDATE\"\n\n        else:\n            # success\n            return \"SUCCESS\"\n\n    def handle_payment_intent_failed(self, event):\n        customer_id = event[\"data\"][\"object\"][\"customer\"]\n        hasActiveSubscription(\n            invalidate_cache=True, site=get_site(event[\"data\"][\"object\"][\"customer\"])\n        )\n        frappe.publish_realtime(\"payment_failed\", room=get_room(customer_id))\n\n    def handle_payment_intent_action_required(self, event):\n        #   payment_intent_id = \"pi_1NUkv1CwmuPVDwVySsT5gUDX\"\n        payment_intent_id = event[\"data\"][\"object\"][\"payment_intent\"]\n        payment_intent = stripe.PaymentIntent.retrieve(payment_intent_id)\n        client_secret = payment_intent[\"client_secret\"]\n        frappe.publish_realtime(\n            \"requires_payment_action\",\n            message={\"client_secret\": client_secret},\n            room=get_room(event[\"data\"][\"object\"][\"customer\"]),\n        )\n        # after 3 minute check if payment is done , if not dome then void this transaction\n        from threading import Timer\n\n        invoice_id = event[\"data\"][\"object\"][\"id\"]\n\n        def void_payment(invoice_id):\n            payment_intent = stripe.PaymentIntent.retrieve(invoice_id)\n            if payment_intent[\"status\"] == \"requires_payment_action\":\n                stripe.Invoice.void_invoice(invoice_id)\n\n        t = Timer(30 * 60, void_payment, (invoice_id))\n        t.start()\n\n    def handle_subscription_updated(self, event):\n        hasActiveSubscription(\n            invalidate_cache=True, site=get_site(event[\"data\"][\"object\"][\"customer\"])\n        )\n\n    def handle_checkout_session_completed(self, event):\n        session_metadata = event[\"data\"][\"object\"][\"metadata\"]\n        customer_id = event[\"data\"][\"object\"][\"customer\"]\n        if \"del_trial\" in session_metadata and session_metadata[\"del_trial\"] == \"True\":\n            self.end_trial(customer_id)\n\n    def get_product(self, product_id):\n        return stripe.Product.retrieve(product_id)\n\n    def handle_invoice_failed(self, event):\n        hasActiveSubscription(\n            invalidate_cache=True, site=get_site(event[\"data\"][\"object\"][\"customer\"])\n        )\n\n    def handle_invoice_paid(self, event):\n        customer_id = event[\"data\"][\"object\"][\"customer\"]\n        sub_id = event[\"data\"][\"object\"][\"subscription\"]\n        subscription = stripe.Subscription.retrieve(sub_id, expand=[\"latest_invoice\"])\n        product_id = subscription[\"items\"][\"data\"][0][\"price\"][\"product\"]\n        price_id = subscription[\"items\"][\"data\"][0][\"price\"][\"id\"]\n        # invoice paid\n        if product_id in self.onehas_subscription_product_ids:\n            site_name = get_site(customer_id)\n            if not site_name:\n                return\n            if price_id != frappe.conf.price_id:\n                # end tria\n                # fetch the site_name from the database - saved payment intent id\n                print(\"updating onehash subscription for site\", site_name)\n                fulfilOneHashUpdate(\n                    self.onehas_subscription_product_ids,\n                    product_id,\n                    price_id,\n                    site_name,\n                )\n                # call payment success on that site\n            command = \"bench --site {} set-config has_subscription {}\".format(\n                site_name, \"yes\"\n            )\n            frappe.utils.execute_in_shell(command)\n            frappe.publish_realtime(\"payment_success\", room=get_room(customer_id))\n\n\ndef test2():\n    hasActiveSubscription(invalidate_cache=True, site=\"dlwkef.localhost\")\n\n\ndef fulfilOneHashUpdate(pids, product_id, price_id, site_name):\n    # pass the checkout session\n    if product_id in pids:\n        # handle onehash subscription\n        product = stripe.Product.retrieve(product_id)\n        user_limit = 10\n        plan_name = \"\"\n        if product.name == \"OneHash Pro\":\n            user_limit = 100000\n            plan_name = \"ONEHASH_PRO\"\n        elif product.name == \"OneHash Starter\":\n            user_limit = 10\n            plan_name = \"ONEHASH_STARTER\"\n        else:\n            user_limit = 30\n            plan_name = \"ONEHASH_PLUS\"\n        command_to_set_limit = (\n            \"bench --site {site_name} set-config  max_users {user_limit}\".format(\n                site_name=site_name, user_limit=user_limit\n            )\n        )\n        command_to_set_plan = \"bench --site {site_name} set-config  plan {plan}\".format(\n            site_name=site_name, plan=plan_name\n        )\n        command_to_set_price_id = (\n            \"bench --site {site_name} set-config  price_id {price_id}\".format(\n                site_name=site_name, price_id=price_id\n            )\n        )\n        frappe.utils.execute_in_shell(command_to_set_limit)\n        frappe.utils.execute_in_shell(command_to_set_plan)\n        frappe.utils.execute_in_shell(command_to_set_price_id)\n\n\n# unit testing to do.\n\n\n# test if new signups are being made - site has a trial subscription and  correspnding user limits are being setpayment_intent = stripe.PaymentIntent.retrieve(payment_intent_id)\n\n\ndef checkCorrectEmailAddressWithRegex(email):\n    if email == \"\":\n        return False\n    import re\n\n    return re.match(r\"[^@]+@[^@]+\\.[^@]+\", email)\n\n\ndef updateDoctypeWithDocs(new_doc, doctype_name):\n    doc = frappe.get_doc(doctype_name, new_doc.name)\n    doc.update(new_doc)\n    doc.save()\n    return doc\n", "repo_name": "onehashai/clientside", "sub_path": "clientside/stripe.py", "file_name": "stripe.py", "file_ext": "py", "file_size_in_byte": 16655, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "frappe.local", "line_number": 10, "usage_type": "attribute"}, {"api_name": "frappe.utils.execute_in_shell", "line_number": 13, "usage_type": "call"}, {"api_name": "frappe.utils", "line_number": 13, "usage_type": "attribute"}, {"api_name": "frappe.utils.execute_in_shell", "line_number": 14, "usage_type": "call"}, {"api_name": "frappe.utils", "line_number": 14, "usage_type": "attribute"}, {"api_name": "frappe.get_site_config", "line_number": 15, "usage_type": "call"}, {"api_name": "frappe.get_site_config", "line_number": 16, "usage_type": "call"}, {"api_name": "frappe.conf", "line_number": 19, "usage_type": "attribute"}, {"api_name": "frappe.utils.execute_in_shell", "line_number": 28, "usage_type": "call"}, {"api_name": "frappe.utils", "line_number": 28, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 29, "usage_type": "attribute"}, {"api_name": "frappe.whitelist", "line_number": 7, "usage_type": "call"}, {"api_name": "frappe.db.get_list", "line_number": 35, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 35, "usage_type": "attribute"}, {"api_name": "frappe.db.get_list", "line_number": 47, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 47, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 58, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 59, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 63, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 65, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 67, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 68, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 70, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 75, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 76, "usage_type": "attribute"}, {"api_name": "stripe.api_key", "line_number": 82, "usage_type": "attribute"}, {"api_name": "stripe.Subscription.list", "line_number": 88, "usage_type": "call"}, {"api_name": "stripe.Subscription", "line_number": 88, "usage_type": "attribute"}, {"api_name": "stripe.checkout.Session.retrieve", "line_number": 91, "usage_type": "call"}, {"api_name": "stripe.checkout", "line_number": 91, "usage_type": "attribute"}, {"api_name": "stripe.Customer.create", "line_number": 94, "usage_type": "call"}, {"api_name": "stripe.Customer", "line_number": 94, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 102, "usage_type": "attribute"}, {"api_name": "stripe.Customer.list", "line_number": 106, "usage_type": "call"}, {"api_name": "stripe.Customer", "line_number": 106, "usage_type": "attribute"}, {"api_name": "stripe.Subscription.list", "line_number": 107, "usage_type": "call"}, {"api_name": "stripe.Subscription", "line_number": 107, "usage_type": "attribute"}, {"api_name": "stripe.Subscription.delete", "line_number": 110, "usage_type": "call"}, {"api_name": "stripe.Subscription", "line_number": 110, "usage_type": "attribute"}, {"api_name": "stripe.Subscription.create", "line_number": 113, "usage_type": "call"}, {"api_name": "stripe.Subscription", "line_number": 113, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 132, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 131, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 133, "usage_type": "attribute"}, {"api_name": "stripe.checkout.Session.create", "line_number": 137, "usage_type": "call"}, {"api_name": "stripe.checkout", "line_number": 137, "usage_type": "attribute"}, {"api_name": "stripe.Customer.retrieve", "line_number": 154, "usage_type": "call"}, {"api_name": "stripe.Customer", "line_number": 154, "usage_type": "attribute"}, {"api_name": "stripe.Subscription.list", "line_number": 172, "usage_type": "call"}, {"api_name": "stripe.Subscription", "line_number": 172, "usage_type": "attribute"}, {"api_name": "stripe.Product.retrieve", "line_number": 183, "usage_type": "call"}, {"api_name": "stripe.Product", "line_number": 183, "usage_type": "attribute"}, {"api_name": "stripe.Subscription.list", "line_number": 223, "usage_type": "call"}, {"api_name": "stripe.Subscription", "line_number": 223, "usage_type": "attribute"}, {"api_name": "stripe.Subscription.delete", "line_number": 246, "usage_type": "call"}, {"api_name": "stripe.Subscription", "line_number": 246, "usage_type": "attribute"}, {"api_name": "stripe.Invoice.list", "line_number": 256, "usage_type": "call"}, {"api_name": "stripe.Invoice", "line_number": 256, "usage_type": "attribute"}, {"api_name": "stripe.PaymentMethod.list", "line_number": 260, "usage_type": "call"}, {"api_name": "stripe.PaymentMethod", "line_number": 260, "usage_type": "attribute"}, {"api_name": "stripe.Subscription.modify", "line_number": 271, "usage_type": "call"}, {"api_name": "stripe.Subscription", "line_number": 271, "usage_type": "attribute"}, {"api_name": "frappe.publish_realtime", "line_number": 297, "usage_type": "call"}, {"api_name": "stripe.PaymentIntent.retrieve", "line_number": 302, "usage_type": "call"}, {"api_name": "stripe.PaymentIntent", "line_number": 302, "usage_type": "attribute"}, {"api_name": "frappe.publish_realtime", "line_number": 304, "usage_type": "call"}, {"api_name": "stripe.PaymentIntent.retrieve", "line_number": 315, "usage_type": "call"}, {"api_name": "stripe.PaymentIntent", "line_number": 315, "usage_type": "attribute"}, {"api_name": "stripe.Invoice.void_invoice", "line_number": 317, "usage_type": "call"}, {"api_name": "stripe.Invoice", "line_number": 317, "usage_type": "attribute"}, {"api_name": "threading.Timer", "line_number": 319, "usage_type": "call"}, {"api_name": "stripe.Product.retrieve", "line_number": 334, "usage_type": "call"}, {"api_name": "stripe.Product", "line_number": 334, "usage_type": "attribute"}, {"api_name": "stripe.Subscription.retrieve", "line_number": 344, "usage_type": "call"}, {"api_name": "stripe.Subscription", "line_number": 344, "usage_type": "attribute"}, {"api_name": "frappe.conf", "line_number": 352, "usage_type": "attribute"}, {"api_name": "frappe.utils.execute_in_shell", "line_number": 366, "usage_type": "call"}, {"api_name": "frappe.utils", "line_number": 366, "usage_type": "attribute"}, {"api_name": "frappe.publish_realtime", "line_number": 367, "usage_type": "call"}, {"api_name": "stripe.Product.retrieve", "line_number": 378, "usage_type": "call"}, {"api_name": "stripe.Product", "line_number": 378, "usage_type": "attribute"}, {"api_name": "frappe.utils.execute_in_shell", "line_number": 403, "usage_type": "call"}, {"api_name": "frappe.utils", "line_number": 403, "usage_type": "attribute"}, {"api_name": "frappe.utils.execute_in_shell", "line_number": 404, "usage_type": "call"}, {"api_name": "frappe.utils", "line_number": 404, "usage_type": "attribute"}, {"api_name": "frappe.utils.execute_in_shell", "line_number": 405, "usage_type": "call"}, {"api_name": "frappe.utils", "line_number": 405, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 419, "usage_type": "call"}, {"api_name": "frappe.get_doc", "line_number": 423, "usage_type": "call"}]}
{"seq_id": "27331347837", "text": "from keras import callbacks\n\ndef callback_for_training(tf_log_dir_name='rfrl-network',patience_lr=10,snapshot_name=None):\n    cb = [None] * 3\n    \"\"\"\n    Tensorboard log callback\n    \"\"\"\n    tb = callbacks.TensorBoard(log_dir=tf_log_dir_name, histogram_freq=0)\n    cb[0]= tb\n   \n   \n    \n    \"\"\"\n    Model Checkpointer\n    \"\"\"\n    checkpointer = callbacks.ModelCheckpoint(filepath=snapshot_name+\".{epoch:02d}-{val_classifier_accuracy:.2f}.hdf5\",\n                            verbose=0,\n                            monitor='val_classifier_accuracy')\n    cb[1] = checkpointer\n    \n    \"\"\"\n    Reduce Learning Rate\n    \"\"\"\n    reduce_lr_loss = callbacks.ReduceLROnPlateau(monitor='val_classifier_loss', factor=0.1, patience=8, verbose=1, min_lr=1e-8, mode='auto')\n    cb[2] = reduce_lr_loss\n    \n    return cb\n", "repo_name": "SharifAmit/RFRL-Net", "sub_path": "src/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 806, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "keras.callbacks.TensorBoard", "line_number": 8, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 8, "usage_type": "name"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 16, "usage_type": "name"}, {"api_name": "keras.callbacks.ReduceLROnPlateau", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 24, "usage_type": "name"}]}
{"seq_id": "25889871074", "text": "\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow.python.keras.backend import dtype\nfrom util import fid as FID\nfrom util import restrict_gpu, PreprocessModel\nfrom data import celeba_local as CELEBA\n\nfrom util import restrict_gpu, PreprocessModel\nfrom networks.STARGAN import StarGAN as SG_BASE\nfrom networks.STARGAN_multicycle import StarGAN_MultiCycle as SG_MC\nfrom networks.STARGAN_multitrain import StarGAN_MultiCycle as SG_MT\n\nimport os\n\n\ndef main(use_lpips=False):\n\n    #restrict_gpu.chooseOneGPU(int(input(\"Input the ID of the GPU you'd Like to choose: \")))\n    AUTOTUNE = tf.data.AUTOTUNE\n\n    load_dir = \"D:/celeba/\"\n    #load_dir = '/media/Data1/CELEBA/'\n    labels = [\"Blond_Hair\", \"Brown_Hair\", \"Black_Hair\", \"Male\", \"Young\"]\n    label_mask = tf.constant([1.0 if 'hair' in label.lower() else 0.0 for label in labels])\n\n    dataset, test_dataset, val_dataset = CELEBA.load_celeba(load_dir, labels, tt_split=True)\n\n    BATCH_SIZE = 16\n    IMG_WIDTH = 178\n    IMG_HEIGHT = 218\n    \n    RESCALE_DIM = 128\n    EPOCHS=20\n\n    preprocess_model = PreprocessModel.get_preprocess_model(IMG_WIDTH, RESCALE_DIM, random_flip=False)\n    fid_model = FID.get_model([RESCALE_DIM, RESCALE_DIM, 3])\n    model_idx = int(input(\"Input the Num of the model you want to eval on (1 for Stargan, 2 for SG_MC, 3 for SG_MT): \"))\n\n    if model_idx < 0:\n        dataset = dataset.shard(80, 1)\n        val_dataset = val_dataset.shard(80, 1)\n        test_dataset= test_dataset.shard(80, 1)\n        model_idx = -model_idx\n\n    if model_idx == 1:\n        model_date = \"12_06/16/\"\n        model_folder = './FINAL_OUTPUT/starGAN_celeba/'\n        model = SG_BASE(\n            [RESCALE_DIM, RESCALE_DIM, 3], len(labels),\n            model_folder,\n            time_created=model_date,\n            preprocess_model=preprocess_model,\n            restore_model_from_checkpoint=False\n            )\n    elif model_idx == 2:\n        model_date = \"12_08/18/\"\n        model_folder = './FINAL_OUTPUT/sg_multi_celeba/'\n        model = SG_MC(\n            [RESCALE_DIM, RESCALE_DIM, 3], len(labels),\n            model_folder,\n            time_created=model_date,\n            preprocess_model=preprocess_model,\n            restore_model_from_checkpoint=False\n            )\n    elif model_idx == 3:\n        model_date = \"12_11/22/\"\n        model_folder = './FINAL_OUTPUT/sg_multitrain_celeba/'\n        model = SG_MT(\n            [RESCALE_DIM, RESCALE_DIM, 3], len(labels),\n            model_folder,\n            time_created=model_date,\n            preprocess_model=preprocess_model,\n            restore_model_from_checkpoint=False\n            )\n    else:\n        raise(ValueError(\"Model ID out of Range!\"))\n\n    if use_lpips:\n        import util.lpips as lpips\n        lpips_model = lpips.get_lpips_model(RESCALE_DIM)\n\n    def get_fids(base_dataset, n_cycles=0, test_lpips=True, save_imgs = False, save_to=model_folder, trailing_zeros=5):\n\n        if save_imgs and not os.path.exists(save_to):\n            os.makedirs(save_to)\n\n        base_dataset = base_dataset.batch(BATCH_SIZE)\n\n        if test_lpips:\n            lpips_avg = tf.keras.metrics.Mean(\"lpips average\", dtype=tf.float32)\n            lpips_recon = tf.keras.metrics.Mean(\"lpips reconstructive average\", dtype=tf.float32)\n\n        base_acts = []\n        predicted_acts = []\n        reconstructed_acts = []\n        label_accuracy = tf.keras.metrics.BinaryAccuracy(\"label_acc\", dtype=tf.float32)\n        recon_accuracy = tf.keras.metrics.BinaryAccuracy('recon_acc', dtype=tf.float32)\n        for i, batch in base_dataset.enumerate():\n            print(f\"\\rCalculating Activations for images {i*BATCH_SIZE} through {((i+1)*BATCH_SIZE ) - 1}\", end=\"\")\n            images = batch['image']\n            labels = batch['label']\n            preprocessed = preprocess_model.predict(images)\n            base_acts.append(fid_model.predict(preprocessed))\n            predict_on = preprocessed\n            for n in range(n_cycles):\n                #print(i)\n                cycle_labels = model.gen_target(labels)\n                predict_on = model.gen.model.predict([predict_on, cycle_labels])\n            inv_labels = model.gen_inv_target(labels, label_mask)\n            predicted = tf.clip_by_value(model.gen.model.predict([predict_on, inv_labels]), -1.0, 1.0)\n            predicted_acts.append(fid_model.predict(predicted))\n            reconstructed = tf.clip_by_value(model.gen.model.predict([predict_on, labels]), -1.0, 1.0)\n            reconstructed_acts.append(fid_model.predict(reconstructed))\n            inv_pred_labels = tf.math.sigmoid(model.disc.model.predict(predicted)[1])\n            recon_pred_labels = tf.math.sigmoid(model.disc.model.predict(reconstructed)[1])\n            label_accuracy(inv_labels, inv_pred_labels)\n            recon_accuracy(labels, recon_pred_labels)\n            if save_imgs or save_imgs is tuple:\n                if save_imgs is not tuple:\n                    save_predicted = save_imgs\n                    save_base = save_imgs\n                else:\n                    save_base, save_predicted = save_imgs\n                for j, (image, predicted_image, reconstructed_image) in enumerate(zip(preprocessed*0.5+0.5, predicted*0.5+0.5, reconstructed*0.5+0.5)):\n                    if save_base:\n                        image = tf.clip_by_value(image, 0.0, 1.0)\n                        image = tf.image.convert_image_dtype(image, tf.uint8)\n                        encoded = tf.io.encode_jpeg(image, format='rgb')\n                        tf.io.write_file(os.path.join(save_to, 'images/', (\"image{:0\"+str(trailing_zeros)+\"d}.jpg\").format(i*BATCH_SIZE+j)), encoded)\n                    if save_predicted:\n                        predicted_image = tf.clip_by_value(predicted_image, 0.0, 1.0)\n                        predicted_image = tf.image.convert_image_dtype(predicted_image, tf.uint8)\n                        predicted_encoded = tf.io.encode_jpeg(predicted_image, format='rgb')\n                        tf.io.write_file(os.path.join(save_to, f'images_cycled_{n_cycles}/', (\"image{:0\"+str(trailing_zeros)+\"d}.jpg\").format(i*BATCH_SIZE+j)), predicted_encoded)\n                        reconstructed_image = tf.clip_by_value(reconstructed_image, 0.0, 1.0)\n                        reconstructed_image = tf.image.convert_image_dtype(reconstructed_image, tf.uint8)\n                        reconstructed_encoded = tf.io.encode_jpeg(reconstructed_image, format='rgb')\n                        tf.io.write_file(os.path.join(save_to, f'images_cycled_{n_cycles}_reconstructed/', (\"image{:0\"+str(trailing_zeros)+\"d}.jpg\").format(i*BATCH_SIZE+j)), reconstructed_encoded)\n            if test_lpips:\n                lpips_img = tf.cast(tf.image.convert_image_dtype((preprocessed*0.5+0.5), tf.uint8), tf.float32)\n                lpips_predicted = tf.cast(tf.image.convert_image_dtype((predicted*0.5+0.5), tf.uint8), tf.float32)\n                lpips_reconstructed = tf.cast(tf.image.convert_image_dtype((reconstructed*0.5+0.5), tf.uint8), tf.float32)\n                lpips_batch = lpips_model([lpips_img, lpips_predicted])\n                lpips_rec_batch = lpips_model([lpips_img, lpips_reconstructed])\n\n                lpips_batch = tf.reduce_mean(lpips_batch[~np.isnan(lpips_batch)])\n                lpips_avg(lpips_batch)\n\n                lpips_rec_batch = tf.reduce_mean(lpips_rec_batch[~np.isnan(lpips_rec_batch)])\n                lpips_recon(lpips_rec_batch)\n        base_acts = tf.concat(base_acts, axis=0)\n        predicted_acts = tf.concat(predicted_acts, axis=0)\n        reconstructed_acts = tf.concat(reconstructed_acts, axis=0)\n\n        base_moments = FID.calc_mu_sigma(base_acts)\n        predicted_moments = FID.calc_mu_sigma(predicted_acts)\n        reconstructed_moments = FID.calc_mu_sigma(reconstructed_acts)\n\n        fid = FID.calc_fid_activations(base_moments, predicted_moments, calc_mu_sigma=False)\n        fid_recon = FID.calc_fid_activations(base_moments, reconstructed_moments, calc_mu_sigma=False)\n        print(f\"\\n\\nFID for inverse with {n_cycles} cycles is: {fid}\")\n        print(f\"FID for reconstructed with {n_cycles} cycles is: {fid_recon}\")\n        if test_lpips:\n            print(f\"LPIPS Score for inverse with {n_cycles} cycles is: {lpips_avg.result()}\")\n            print(f\"LPIPS Score for reconstructed with {n_cycles} cycles is: {lpips_recon.result()}\")\n            lpips_avg.reset_states()\n            lpips_recon.reset_states()\n        print(f\"Label Prediction Accuracy for inverse with {n_cycles} cycles is: {label_accuracy.result()}\")\n        print(f\"Labal Preciction Accuracy for reconstructed with {n_cycles} cycles is: {recon_accuracy.result()}\")\n        label_accuracy.reset_states()\n        recon_accuracy.reset_states()\n\n    \n    get_fids(test_dataset, n_cycles=0, test_lpips=use_lpips, save_imgs=(True, True), save_to=os.path.join(model_folder, 'test_imgs/'), trailing_zeros=5)\n    get_fids(test_dataset, n_cycles=1, test_lpips=use_lpips, save_imgs=(False, True), save_to=os.path.join(model_folder, 'test_imgs/'), trailing_zeros=5)\n    get_fids(test_dataset, n_cycles=2, test_lpips=use_lpips, save_imgs=(False, True), save_to=os.path.join(model_folder, 'test_imgs/'), trailing_zeros=5)\n    get_fids(test_dataset, n_cycles=3, test_lpips=use_lpips, save_imgs=(False, True), save_to=os.path.join(model_folder, 'test_imgs/'), trailing_zeros=5)\n\n    model.save_models()\n\n        \nif __name__ =='__main__':\n    main()", "repo_name": "plaidScience/SchneiderMastersProject2021", "sub_path": "run_scripts/evaluation/celeba_evaluate.py", "file_name": "celeba_evaluate.py", "file_ext": "py", "file_size_in_byte": 9400, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "tensorflow.data", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 25, "usage_type": "call"}, {"api_name": "data.celeba_local.load_celeba", "line_number": 27, "usage_type": "call"}, {"api_name": "data.celeba_local", "line_number": 27, "usage_type": "name"}, {"api_name": "util.PreprocessModel.get_preprocess_model", "line_number": 36, "usage_type": "call"}, {"api_name": "util.PreprocessModel", "line_number": 36, "usage_type": "name"}, {"api_name": "util.fid.get_model", "line_number": 37, "usage_type": "call"}, {"api_name": "util.fid", "line_number": 37, "usage_type": "name"}, {"api_name": "networks.STARGAN.StarGAN", "line_number": 49, "usage_type": "call"}, {"api_name": "networks.STARGAN_multicycle.StarGAN_MultiCycle", "line_number": 59, "usage_type": "call"}, {"api_name": "networks.STARGAN_multitrain.StarGAN_MultiCycle", "line_number": 69, "usage_type": "call"}, {"api_name": "util.lpips.get_lpips_model", "line_number": 81, "usage_type": "call"}, {"api_name": "util.lpips", "line_number": 81, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.BinaryAccuracy", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.BinaryAccuracy", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.clip_by_value", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.math.sigmoid", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tensorflow.math.sigmoid", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.clip_by_value", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.image.convert_image_dtype", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tensorflow.uint8", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tensorflow.io.encode_jpeg", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 129, "usage_type": "attribute"}, {"api_name": "tensorflow.io.write_file", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "tensorflow.clip_by_value", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.image.convert_image_dtype", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 133, "usage_type": "attribute"}, {"api_name": "tensorflow.uint8", "line_number": 133, "usage_type": "attribute"}, {"api_name": "tensorflow.io.encode_jpeg", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 134, "usage_type": "attribute"}, {"api_name": "tensorflow.io.write_file", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "tensorflow.clip_by_value", "line_number": 136, "usage_type": "call"}, {"api_name": "tensorflow.image.convert_image_dtype", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tensorflow.uint8", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tensorflow.io.encode_jpeg", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 138, "usage_type": "attribute"}, {"api_name": "tensorflow.io.write_file", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.image.convert_image_dtype", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tensorflow.uint8", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.image.convert_image_dtype", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tensorflow.uint8", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.image.convert_image_dtype", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tensorflow.uint8", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 154, "usage_type": "call"}, {"api_name": "util.fid.calc_mu_sigma", "line_number": 156, "usage_type": "call"}, {"api_name": "util.fid", "line_number": 156, "usage_type": "name"}, {"api_name": "util.fid.calc_mu_sigma", "line_number": 157, "usage_type": "call"}, {"api_name": "util.fid", "line_number": 157, "usage_type": "name"}, {"api_name": "util.fid.calc_mu_sigma", "line_number": 158, "usage_type": "call"}, {"api_name": "util.fid", "line_number": 158, "usage_type": "name"}, {"api_name": "util.fid.calc_fid_activations", "line_number": 160, "usage_type": "call"}, {"api_name": "util.fid", "line_number": 160, "usage_type": "name"}, {"api_name": "util.fid.calc_fid_activations", "line_number": 161, "usage_type": "call"}, {"api_name": "util.fid", "line_number": 161, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path", "line_number": 177, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}]}
{"seq_id": "13015501398", "text": "import csv\r\nfrom os import path\r\nimport logging\r\n\r\nclass FileLoader:\r\n\r\n    def __init__(self, paramDict):\r\n        self.paramDict=paramDict\r\n    \r\n    def __str__(self):\r\n        return 'FileLoader->{}'.format(self.paramFile)\r\n\r\n    def __getColumns(self):\r\n        '''Returns the list of columns'''\r\n        \r\n        if path.exists(self.paramDict['filename']):\r\n            with open(self.paramDict['filename'],mode=\"r\") as paramVals:\r\n                reader = csv.reader(paramVals,delimiter=self.paramDict['delimiter'])\r\n                headers = next(reader, None)\r\n                return headers\r\n        else:\r\n            logging.error(\"Data file {} does not exists\".format(self.paramDict['filename']))\r\n            return None\r\n\r\n\r\n    def ddlCheck(self):\r\n        ''' Return true if the columns defined in the parameter file exists in the data file'''\r\n\r\n        fileColumns=self.__getColumns()\r\n        paramColumns= [v for v in self.paramDict['outColumns']]\r\n        if not self.paramDict['keyColumn'] in paramColumns: paramColumns.append(self.paramDict['keyColumn'])\r\n        # If no headers exits then return false\r\n        if fileColumns==None:\r\n            # logging.error(\"Data file {} is empty\".format(self.paramDict['filename']))\r\n            return False\r\n        # Checking if all the parameter columns exists in the file or not\r\n        elif all(x in fileColumns for x in paramColumns):\r\n            logging.info(\"Syntax check for {} passed sucessfully\".format(self.paramDict['filename']))\r\n            return True\r\n        else:\r\n            logging.error(\"DDL Check failed : Mismatch between parameter columns and the columns present in the file '{}'\".format(self.paramDict['filename']))\r\n            return False\r\n\r\n   \r\n    def getData(self):\r\n        '''Extracts and returns the filtered data'''\r\n\r\n        paramDict=self.paramDict\r\n        mainDict=dict()\r\n        if self.ddlCheck():\r\n            with open(paramDict['filename'], mode=\"r\", newline='' ) as psvfile:\r\n                rawData = csv.DictReader(psvfile,delimiter='|') \r\n                for row in rawData:\r\n                    # Filter rows on basis of filterValue passed in the parameter file\r\n                    if not row[paramDict['keyColumn']]==paramDict['filterValue'] : continue\r\n                    # Create the subset dictionary with the required columns for filtered data\r\n                    mainDict[row[paramDict['keyColumn']]]={key : row[key] for key in paramDict['outColumns']}\r\n            if mainDict=={}:\r\n                logging.warning(\"No data available for filter value : {}\".format(paramDict['filterValue']))\r\n            else:\r\n                logging.info(\"Data ready to be consumed\")\r\n            return mainDict\r\n        else:\r\n            logging.error(\"No data extracted from file : {}\".format(self.paramDict['filename']))\r\n            return None", "repo_name": "anieshaz/RestFile", "sub_path": "fileloader.py", "file_name": "fileloader.py", "file_ext": "py", "file_size_in_byte": 2868, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 41, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 61, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 64, "usage_type": "call"}]}
{"seq_id": "35919912492", "text": "\"\"\"\nAbstract Syntax Notation One (ASN.1) parser.\n\nTechnical informations:\n* PER standard\n  http://www.tu.int/ITU-T/studygroups/com17/languages/X.691-0207.pdf\n* Python library\n  http://pyasn1.sourceforge.net/\n* Specification of Abstract Syntax Notation One (ASN.1)\n  ISO/IEC 8824:1990 Information Technology\n* Specification of Basic Encoding Rules (BER) for ASN.1\n  ISO/IEC 8825:1990 Information Technology\n* OpenSSL asn1parser, use command:\n  openssl asn1parse -i -inform DER -in file.der\n* ITU-U recommendations:\n  http://www.itu.int/rec/T-REC-X/en\n  (X.680, X.681, X.682, X.683, X.690, X.691, X.692, X.693, X.694)\n* dumpasn1\n  http://www.cs.auckland.ac.nz/~pgut001/dumpasn1.c\n\nGeneral information:\n* Wikipedia (english) article\n  http://en.wikipedia.org/wiki/Abstract_Syntax_Notation_One\n* ASN.1 information site\n  http://asn1.elibel.tm.fr/en/\n* ASN.1 consortium\n  http://www.asn1.org/\n\nEncodings:\n* Basic Encoding Rules (BER)\n* Canonical Encoding Rules (CER) -- DER derivative that is not widely used\n* Distinguished Encoding Rules (DER) -- used for encrypted applications\n* XML Encoding Rules (XER)\n* Packed Encoding Rules (PER) -- result in the fewest number of bytes\n* Generic String Encoding Rules (GSER)\n=> Are encodings compatibles? Which encodings are supported??\n\nAuthor: Victor Stinner\nCreation date: 24 september 2006\n\"\"\"\n\nfrom lib.hachoir_parser import Parser\nfrom lib.hachoir_core.field import (FieldSet,\n    FieldError, ParserError,\n    Bit, Bits, Bytes, UInt8, GenericInteger, String,\n    Field, Enum, RawBytes)\nfrom lib.hachoir_core.endian import BIG_ENDIAN\nfrom lib.hachoir_core.tools import createDict, humanDatetime\nfrom lib.hachoir_core.stream import InputStreamError\nfrom lib.hachoir_core.text_handler import textHandler\n\n# --- Field parser ---\n\nclass ASNInteger(Field):\n    \"\"\"\n    Integer: two cases:\n    - first byte in 0..127: it's the value\n    - first byte in 128..255: byte & 127 is the number of bytes,\n      next bytes are the value\n    \"\"\"\n    def __init__(self, parent, name, description=None):\n        Field.__init__(self, parent, name, 8, description)\n        stream = self._parent.stream\n        addr = self.absolute_address\n        value = stream.readBits(addr, 8, BIG_ENDIAN)\n        if 128 <= value:\n            nbits = (value & 127) * 8\n            if not nbits:\n                raise ParserError(\"ASN.1: invalid ASN integer size (zero)\")\n            if 64 < nbits:\n                # Arbitrary limit to catch errors\n                raise ParserError(\"ASN.1: ASN integer is limited to 64 bits\")\n            self._size = 8 + nbits\n            value = stream.readBits(addr+8, nbits, BIG_ENDIAN)\n        self.createValue = lambda: value\n\nclass OID_Integer(Bits):\n    def __init__(self, parent, name, description=None):\n        Bits.__init__(self, parent, name, 8, description)\n        stream = self._parent.stream\n        addr = self.absolute_address\n        size = 8\n        value = 0\n        byte = stream.readBits(addr, 8, BIG_ENDIAN)\n        value = byte & 127\n        while 128 <= byte:\n            addr += 8\n            size += 8\n            if 64 < size:\n                # Arbitrary limit to catch errors\n                raise ParserError(\"ASN.1: Object identifier is limited 64 bits\")\n            byte = stream.readBits(addr, 8, BIG_ENDIAN)\n            value = (value << 7) + (byte & 127)\n        self._size = size\n        self.createValue = lambda: value\n\ndef readSequence(self, content_size):\n    while self.current_size < self.size:\n        yield Object(self, \"item[]\")\n\ndef readSet(self, content_size):\n    yield Object(self, \"value\", size=content_size*8)\n\ndef readASCIIString(self, content_size):\n    yield String(self, \"value\", content_size, charset=\"ASCII\")\n\ndef readUTF8String(self, content_size):\n    yield String(self, \"value\", content_size, charset=\"UTF-8\")\n\ndef readBMPString(self, content_size):\n    yield String(self, \"value\", content_size, charset=\"UTF-16\")\n\ndef readBitString(self, content_size):\n    yield UInt8(self, \"padding_size\", description=\"Number of unused bits\")\n    if content_size > 1:\n        yield Bytes(self, \"value\", content_size-1)\n\ndef readOctetString(self, content_size):\n    yield Bytes(self, \"value\", content_size)\n\ndef formatObjectID(fieldset):\n    text = [ fieldset[\"first\"].display ]\n    items = [ field for field in fieldset if field.name.startswith(\"item[\") ]\n    text.extend( str(field.value) for field in items )\n    return \".\".join(text)\n\ndef readObjectID(self, content_size):\n    yield textHandler(UInt8(self, \"first\"), formatFirstObjectID)\n    while self.current_size < self.size:\n        yield OID_Integer(self, \"item[]\")\n\ndef readBoolean(self, content_size):\n    if content_size != 1:\n        raise ParserError(\"Overlong boolean: got %s bytes, expected 1 byte\"%content_size)\n    yield textHandler(UInt8(self, \"value\"), lambda field:str(bool(field.value)))\n\ndef readInteger(self, content_size):\n    # Always signed?\n    yield GenericInteger(self, \"value\", True, content_size*8)\n\n# --- Format ---\n\ndef formatFirstObjectID(field):\n    value = field.value\n    return \"%u.%u\" % (value // 40, value % 40)\n\ndef formatValue(fieldset):\n    return fieldset[\"value\"].display\n\ndef formatUTCTime(fieldset):\n    import datetime\n    value = fieldset[\"value\"].value\n    year = int(value[0:2])\n    if year < 50:\n        year += 2000\n    else:\n        year += 1900\n    month = int(value[2:4])\n    day = int(value[4:6])\n    hour = int(value[6:8])\n    minute = int(value[8:10])\n    if value[-1] == \"Z\":\n        second = int(value[10:12])\n        dt = datetime.datetime(year, month, day, hour, minute, second)\n    else:\n        # Skip timezone...\n        dt = datetime.datetime(year, month, day, hour, minute)\n    return humanDatetime(dt)\n\n# --- Object parser ---\n\nclass Object(FieldSet):\n    TYPE_INFO = {\n        0: (\"end[]\", None, \"End (reserved for BER, None)\", None), # TODO: Write parser\n        1: (\"boolean[]\", readBoolean, \"Boolean\", None),\n        2: (\"integer[]\", readInteger, \"Integer\", None),\n        3: (\"bit_str[]\", readBitString, \"Bit string\", None),\n        4: (\"octet_str[]\", readOctetString, \"Octet string\", None),\n        5: (\"null[]\", None, \"NULL (empty, None)\", None),\n        6: (\"obj_id[]\", readObjectID, \"Object identifier\", formatObjectID),\n        7: (\"obj_desc[]\", None, \"Object descriptor\", None), # TODO: Write parser\n        8: (\"external[]\", None, \"External, instance of\", None), # TODO: Write parser # External?\n        9: (\"real[]\", readASCIIString, \"Real number\", None), # TODO: Write parser\n        10: (\"enum[]\", readInteger, \"Enumerated\", None),\n        11: (\"embedded[]\", None, \"Embedded PDV\", None), # TODO: Write parser\n        12: (\"utf8_str[]\", readUTF8String, \"Printable string\", None),\n        13: (\"rel_obj_id[]\", None, \"Relative object identifier\", None), # TODO: Write parser\n        14: (\"time[]\", None, \"Time\", None), # TODO: Write parser\n      # 15: invalid??? sequence of???\n        16: (\"seq[]\", readSequence, \"Sequence\", None),\n        17: (\"set[]\", readSet, \"Set\", None),\n        18: (\"num_str[]\", readASCIIString, \"Numeric string\", None),\n        19: (\"print_str[]\", readASCIIString, \"Printable string\", formatValue),\n        20: (\"teletex_str[]\", readASCIIString, \"Teletex (T61, None) string\", None),\n        21: (\"videotex_str[]\", readASCIIString, \"Videotex string\", None),\n        22: (\"ia5_str[]\", readASCIIString, \"IA5 string\", formatValue),\n        23: (\"utc_time[]\", readASCIIString, \"UTC time\", formatUTCTime),\n        24: (\"general_time[]\", readASCIIString, \"Generalized time\", None),\n        25: (\"graphic_str[]\", readASCIIString, \"Graphic string\", None),\n        26: (\"visible_str[]\", readASCIIString, \"Visible (ISO64, None) string\", None),\n        27: (\"general_str[]\", readASCIIString, \"General string\", None),\n        28: (\"universal_str[]\", readASCIIString, \"Universal string\", None),\n        29: (\"unrestricted_str[]\", readASCIIString, \"Unrestricted string\", None),\n        30: (\"bmp_str[]\", readBMPString, \"BMP string\", None),\n      # 31: multiple octet tag number, TODO: not supported\n\n      # Extended tag values:\n      #   31: Date\n      #   32: Time of day\n      #   33: Date-time\n      #   34: Duration\n    }\n    TYPE_DESC = createDict(TYPE_INFO, 2)\n\n    CLASS_DESC = {0: \"universal\", 1: \"application\", 2: \"context\", 3: \"private\"}\n    FORM_DESC = {False: \"primitive\", True: \"constructed\"}\n\n    def __init__(self, *args, **kw):\n        FieldSet.__init__(self, *args, **kw)\n        key = self[\"type\"].value & 31\n        if self['class'].value == 0:\n            # universal object\n            if key in self.TYPE_INFO:\n                self._name, self._handler, self._description, create_desc = self.TYPE_INFO[key]\n                if create_desc:\n                    self.createDescription = lambda: \"%s: %s\" % (self.TYPE_INFO[key][2], create_desc(self))\n                    self._description = None\n            elif key == 31:\n                raise ParserError(\"ASN.1 Object: tag bigger than 30 are not supported\")\n            else:\n                self._handler = None\n        elif self['form'].value:\n            # constructed: treat as sequence\n            self._name = 'seq[]'\n            self._handler = readSequence\n            self._description = 'constructed object type %i' % key\n        else:\n            # primitive, context/private\n            self._name = 'raw[]'\n            self._handler = readASCIIString\n            self._description = '%s object type %i' % (self['class'].display, key)\n        field = self[\"size\"]\n        self._size = field.address + field.size + field.value*8\n\n    def createFields(self):\n        yield Enum(Bits(self, \"class\", 2), self.CLASS_DESC)\n        yield Enum(Bit(self, \"form\"), self.FORM_DESC)\n        if self['class'].value == 0:\n            yield Enum(Bits(self, \"type\", 5), self.TYPE_DESC)\n        else:\n            yield Bits(self, \"type\", 5)\n        yield ASNInteger(self, \"size\", \"Size in bytes\")\n        size = self[\"size\"].value\n        if size:\n            if self._handler:\n                for field in self._handler(self, size):\n                    yield field\n            else:\n                yield RawBytes(self, \"raw\", size)\n\nclass ASN1File(Parser):\n    PARSER_TAGS = {\n        \"id\": \"asn1\",\n        \"category\": \"container\",\n        \"file_ext\": (\"der\",),\n        \"min_size\": 16,\n        \"description\": \"Abstract Syntax Notation One (ASN.1)\"\n    }\n    endian = BIG_ENDIAN\n\n    def validate(self):\n        try:\n            root = self[0]\n        except (InputStreamError, FieldError):\n            return \"Unable to create root object\"\n        if root.size != self.size:\n            return \"Invalid root object size\"\n        return True\n\n    def createFields(self):\n        yield Object(self, \"root\")\n\n", "repo_name": "midgetspy/Sick-Beard", "sub_path": "lib/hachoir_parser/container/asn1.py", "file_name": "asn1.py", "file_ext": "py", "file_size_in_byte": 10708, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2905, "dataset": "github-code", "pt": "41", "api": [{"api_name": "lib.hachoir_core.field.Field", "line_number": 54, "usage_type": "name"}, {"api_name": "lib.hachoir_core.field.Field.__init__", "line_number": 62, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Field", "line_number": 62, "usage_type": "name"}, {"api_name": "lib.hachoir_core.endian.BIG_ENDIAN", "line_number": 65, "usage_type": "argument"}, {"api_name": "lib.hachoir_core.field.ParserError", "line_number": 69, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.ParserError", "line_number": 72, "usage_type": "call"}, {"api_name": "lib.hachoir_core.endian.BIG_ENDIAN", "line_number": 74, "usage_type": "argument"}, {"api_name": "lib.hachoir_core.field.Bits", "line_number": 77, "usage_type": "name"}, {"api_name": "lib.hachoir_core.field.Bits.__init__", "line_number": 79, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Bits", "line_number": 79, "usage_type": "name"}, {"api_name": "lib.hachoir_core.endian.BIG_ENDIAN", "line_number": 84, "usage_type": "argument"}, {"api_name": "lib.hachoir_core.field.ParserError", "line_number": 91, "usage_type": "call"}, {"api_name": "lib.hachoir_core.endian.BIG_ENDIAN", "line_number": 92, "usage_type": "argument"}, {"api_name": "lib.hachoir_core.field.String", "line_number": 105, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.String", "line_number": 108, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.String", "line_number": 111, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.UInt8", "line_number": 114, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Bytes", "line_number": 116, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Bytes", "line_number": 119, "usage_type": "call"}, {"api_name": "lib.hachoir_core.text_handler.textHandler", "line_number": 128, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.UInt8", "line_number": 128, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.ParserError", "line_number": 134, "usage_type": "call"}, {"api_name": "lib.hachoir_core.text_handler.textHandler", "line_number": 135, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.UInt8", "line_number": 135, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.GenericInteger", "line_number": 139, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 167, "usage_type": "call"}, {"api_name": "lib.hachoir_core.tools.humanDatetime", "line_number": 168, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.FieldSet", "line_number": 172, "usage_type": "name"}, {"api_name": "lib.hachoir_core.tools.createDict", "line_number": 213, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.FieldSet.__init__", "line_number": 219, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.FieldSet", "line_number": 219, "usage_type": "name"}, {"api_name": "lib.hachoir_core.field.ParserError", "line_number": 229, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Enum", "line_number": 246, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Bits", "line_number": 246, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Enum", "line_number": 247, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Bit", "line_number": 247, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Enum", "line_number": 249, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Bits", "line_number": 249, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.Bits", "line_number": 251, "usage_type": "call"}, {"api_name": "lib.hachoir_core.field.RawBytes", "line_number": 259, "usage_type": "call"}, {"api_name": "lib.hachoir_parser.Parser", "line_number": 261, "usage_type": "name"}, {"api_name": "lib.hachoir_core.endian.BIG_ENDIAN", "line_number": 269, "usage_type": "name"}, {"api_name": "lib.hachoir_core.stream.InputStreamError", "line_number": 274, "usage_type": "name"}, {"api_name": "lib.hachoir_core.field.FieldError", "line_number": 274, "usage_type": "name"}]}
{"seq_id": "1530909549", "text": "import random\nimport os\nfrom colorama import Fore, Style\n\nclass heroes:\n\tdef __init__(self, name, health, power, armor):\n\t\tself.name = name\n\t\tself.health = health\n\t\tself.power = power\n\t\tself.armor = armor\n\n\tdef attack(self, enemy):\n\t\tprint(self.name, 'attack', enemy.name)\n\t\tenemy.gotAttacked(self)\n\n\tdef gotAttacked(self, enemy):\n\t\tdodge = random.randint(0,1)\n\t\tif dodge == 0:\n\t\t\tprint(self.name, Fore.BLUE + 'Dodge the attack' + Fore.WHITE)\n\t\telse:\n\t\t\tcrit = random.randint(0,1)\n\t\t\tattackedDamage = random.randint(2, enemy.power//self.armor)\n\t\t\tif crit == 1:\n\t\t\t\tprint(Fore.RED + \"!!!CRITICA: HIT!!!\" + Fore.WHITE)\n\t\t\t\tattackedDamage = attackedDamage * random.randint(2,5)\n\t\t\tprint(self.name, 'got attacked by', enemy.name, 'by', Fore.RED + str(attackedDamage) + Fore.WHITE, 'Dmg')\n\t\t\tself.health = self.health - attackedDamage\n\t\t\t# print(self.name, \"health is \", self.health)\n\n\tdef healUp(self):\n\t\theal = random.randint(1,10)\n\t\tself.health = self.health + heal\n\t\tprint(self.name, 'Heals and got +' + Fore.GREEN + str(heal), 'HP' + Fore.WHITE)\n\t\t# print(self.name , 'health is', self.health)\n\t\t\n\nlistHeroes = [heroes(\"Naruto\", 120, 16, 2), heroes(\"Sasuke\", 95, 22, 5)]\n\nenemyHero = 0\nuserhero = 0\ndef chooseChar(enemyHero, userhero):\n\tn = 1\n\tprint(Fore.YELLOW + 'Choose your hero!' + Fore. WHITE)\n\tfor x in listHeroes:\n\t\tprint('({}) {} Health {} Power {} Armor {}'.format(Fore.YELLOW + str(n) + Fore.WHITE, x.name, x.health, x.power, x.armor))\n\t\tn += 1\n\n\tuserhero = int(input('Choose the Hero number! '))\n\twhile userhero > n:\n\t\tprint(\"Wrong input\")\n\t\tuserhero = int(input('Choose the Hero number! '))\n\n\tif userhero == 1:\n\t\tenemyHero = 2\n\telse:\n\t\tenemyHero = 1\n\tenemyHero = listHeroes[enemyHero-1]\n\tuserhero = listHeroes[userhero-1]\n\n\treturn enemyHero, userhero\n\ndef enemyTurn(enemyHero, userhero):\n\tprint(Fore.YELLOW + '|| Enemy Turn' + Fore.WHITE)\n\tmove = random.randint(0,1)\n\tif move == 1:\n\t\tenemyHero.attack(userhero)\n\telse:\n\t\tenemyHero.healUp()\n\n\treturn enemyHero, userhero\n\ndef userTurn(enemyHero, userhero):\n\tos.system('clear')\n\tprint(\"you've choose\", userhero.name, 'as your character')\n\tprint(\"Your enemy will be\", enemyHero.name)\n\tprint(\"\")\n\n\twhile enemyHero.health >= 0 and userhero.health >= 0:\n\t\tinputChoose = input(Fore.YELLOW + '|| Attack enemy? [y/n]' + Fore.WHITE)\n\t\twhile inputChoose != 'y' and inputChoose != 'n':\n\t\t\tprint('Type correctly')\n\t\t\tinputChoose = input('|| Attack enemy? [y/n]')\n\t\tif inputChoose == 'n':\n\t\t\tuserhero.healUp()\n\t\telse:\n\t\t\tuserhero.attack(enemyHero)\n\n\t\tenemyHero, userhero = enemyTurn(enemyHero, userhero)\n\t\tprint('\\n')\n\n\t\tprint(enemyHero.name, enemyHero.health, 'HP')\n\t\tprint(userhero.name, userhero.health, 'HP')\n\t\n\tif enemyHero.health < 0:\n\t\tos.system('clear')\n\t\tprint(enemyHero.name, enemyHero.health, 'HP')\n\t\tprint(userhero.name, userhero.health, 'HP\\n')\n\t\tprint(\"|| Congratulation! You win the game\")\n\t\tprint('|| - Game Over -')\n\telse:\n\t\tos.system('clear')\n\t\tprint(enemyHero.name, enemyHero.health, 'HP')\n\t\tprint(userhero.name, userhero.health, 'HP\\n')\n\t\tprint('|| You Lose!')\n\t\tprint('|| - Game Over -')\ndef openingtxt():\n\tos.system('clear')\n\tprint(\"  _____                 _                                _   _                  _        \")\n\tprint(\" / ____|               | |                              | \\ | |                | |       \")\n\tprint(\"| (___   __ _ ___ _   _| | _____ ________   _____ ______|  \\| | __ _ _ __ _   _| |_ ___  \")\n\tprint(\" \\___ \\ / _` / __| | | | |/ / _ \\______\\ \\ / / __|______| . ` |/ _` | '__| | | | __/ _ \\ \")\n\tprint(\" ____) | (_| \\__ \\ |_| |   <  __/       \\ V /\\__ \\      | |\\  | (_| | |  | |_| | || (_) |\")\n\tprint(\"|_____/ \\__,_|___/\\__,_|_|\\_\\___|        \\_/ |___/      |_| \\_|\\__,_|_|   \\__,_|\\__\\___/ \")\n\nopeningtxt()\nenemyHero, userhero = chooseChar(enemyHero, userhero)\nuserhero.name = Fore.CYAN + userhero.name + Fore.WHITE\n# enemyHero.name = Fore.RED + enemyHero.name + Fore.WHITE\nuserTurn(enemyHero, userhero)\n", "repo_name": "riziry/MeLearningHelloWorld", "sub_path": "Py/OOP/oop5SimpleGame.py", "file_name": "oop5SimpleGame.py", "file_ext": "py", "file_size_in_byte": 3904, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "colorama.Fore.BLUE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 19, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 22, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 24, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 24, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 25, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 26, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 26, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 31, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 33, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 33, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 43, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 43, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 45, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 45, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 63, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 63, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 64, "usage_type": "call"}, {"api_name": "os.system", "line_number": 73, "usage_type": "call"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 79, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 79, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 95, "usage_type": "call"}, {"api_name": "os.system", "line_number": 101, "usage_type": "call"}, {"api_name": "os.system", "line_number": 107, "usage_type": "call"}, {"api_name": "colorama.Fore.CYAN", "line_number": 117, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 117, "usage_type": "name"}, {"api_name": "colorama.Fore.WHITE", "line_number": 117, "usage_type": "attribute"}]}
{"seq_id": "20812670838", "text": "import argparse\nimport os\nfrom pathlib import Path\nimport shutil\n\nimport pandas as pd\n\nfrom ._tabular import TABLE_EXTENSIONS\nfrom .utils import (\n    create_minimal_data_dirs,\n    find_dataset_csvs,\n    find_metadata_csvs,\n)\n\n\ndef parse_arguments():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        '--fraction', type=float, required=True,\n        help='Approximate fraction of samples to keep, ex. 0.1',\n    )\n    parser.add_argument(\n        '--min-per-dataset', type=int, required=False, default=10,\n        help='Keep at least this many samples from each dataset',\n    )\n    parser.add_argument(\n        '--input-data-root', type=Path, required=False, default=Path('data/'),\n        help='Input dataset',\n    )\n    parser.add_argument(\n        '--output-data-root', type=Path, required=True,\n        help='Where to store resulting dataset',\n    )\n    return parser.parse_args()\n\n\ndef downsample_dataset(df: pd.DataFrame, fraction: float, min_count: int) -> pd.DataFrame:\n    count_by_fraction = len(df) * fraction\n    if count_by_fraction < min_count:\n        return df.sample(n=min_count)\n    else:\n        return df.sample(frac=fraction)\n\n\ndef main():  # pylint: disable=too-many-locals\n    args = parse_arguments()\n\n    if args.output_data_root.exists():\n        raise RuntimeError('Output directory must not exist')\n    create_minimal_data_dirs(args.output_data_root)\n\n    print('Sampling datasets...')\n    remaining_object_names = set()\n    for dataset_csv_path in find_dataset_csvs(args.input_data_root / 'datasets'):\n        input_df = pd.read_csv(dataset_csv_path, header=None)\n        downsampled_df = downsample_dataset(input_df, args.fraction, args.min_per_dataset)\n\n        relative_path = dataset_csv_path.relative_to(args.input_data_root)\n        downsampled_path = args.output_data_root / relative_path\n        downsampled_df.to_csv(downsampled_path, header=None, index=False)\n\n        remaining_object_names.update(downsampled_df[0])\n\n    print('Copying metadata...')\n    for metadata_csv_path in find_metadata_csvs(args.input_data_root / 'source/metadata'):\n        df = pd.read_csv(metadata_csv_path, header=None)\n        downsampled_df = df[df[0].isin(remaining_object_names)]\n\n        relative_path = metadata_csv_path.relative_to(args.input_data_root)\n        downsampled_path = args.output_data_root / relative_path\n        downsampled_df.to_csv(downsampled_path, header=None, index=False)\n\n    print('Copying source data...')\n    for dirpath, _dirnames, filenames in os.walk(args.input_data_root / 'source/'):\n        if Path(dirpath).relative_to(args.input_data_root) == Path('source/metadata/'):\n            continue\n\n        for filename in filenames:\n            file_path = Path(dirpath) / filename\n            file_name = file_path.relative_to(args.input_data_root)\n            new_file_path = args.output_data_root / file_name\n\n            if (\n                file_path.suffix.lower() in TABLE_EXTENSIONS\n                or str(file_name) in remaining_object_names\n            ):\n                shutil.copy(file_path, new_file_path)\n\n    print('Done')\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "swarmer/mlstarterpack", "sub_path": "src/mlstarterpack/ecdata/sample.py", "file_name": "sample.py", "file_ext": "py", "file_size_in_byte": 3152, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 27, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 37, "usage_type": "attribute"}, {"api_name": "utils.create_minimal_data_dirs", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.find_dataset_csvs", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.find_metadata_csvs", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 66, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 74, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 75, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 79, "usage_type": "call"}, {"api_name": "_tabular.TABLE_EXTENSIONS", "line_number": 84, "usage_type": "name"}, {"api_name": "shutil.copy", "line_number": 87, "usage_type": "call"}]}
{"seq_id": "16536041592", "text": "import time\r\nfrom selenium.webdriver.common.by import By\r\n\r\n\r\ndef test_find_add_to_card_btn(browser):\r\n    browser.get('http://selenium1py.pythonanywhere.com/catalogue/coders-at-work_207/')\r\n    message = 'Кнопка добавления товара в корзину отсутсвует'\r\n    time.sleep(30)\r\n    assert browser.find_element(By.CSS_SELECTOR, '.btn.btn-lg.btn-primary.btn-add-to-basket').is_displayed(), message\r\n\r\n", "repo_name": "DoomCV/pytest_homework_stepik", "sub_path": "test_items.py", "file_name": "test_items.py", "file_ext": "py", "file_size_in_byte": 435, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "time.sleep", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 9, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 9, "usage_type": "name"}]}
{"seq_id": "37876858004", "text": "import logging\nimport ssl\nfrom typing import List\nfrom urllib.parse import urljoin\n\nimport certifi\nfrom aiohttp import ClientSession, web\nfrom aiohttp.web import Request, Response\n\nfrom .common import ServerResponse, UrlReplace\nfrom .utils import processing_text\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef get_target_url(request: Request) -> str:\n    return urljoin(request.app.config.TARGET_URL, request.path_qs)\n\n\ndef get_urls_replace(request: Request) -> List[UrlReplace]:\n    urls = []\n    for url in request.app.config.REPLACE_URLS:\n        urls.append(UrlReplace(src=url, dst=f\"{request.url.scheme}://{request.host}/\"))\n    return urls\n\n\nasync def fetch_response(request: Request) -> ServerResponse:\n    target_url = get_target_url(request)\n    request_body = await request.read()\n\n    ssl_context = ssl.create_default_context(cafile=certifi.where())\n    timeout = request.app.config.HTTP_TIMEOUT\n\n    async with ClientSession() as session:\n        async with session.request(\n            method=request.method,\n            url=target_url,\n            data=request_body,\n            timeout=timeout,\n            ssl=ssl_context,\n        ) as resp:\n            data = await resp.read()\n            return ServerResponse(\n                data=data,\n                headers=resp.headers,\n                status=resp.status,\n                content_type=resp.content_type,\n            )\n\n\nasync def proxy(request: Request) -> Response:\n    \"\"\"\n    Данный handler проксирует поступающие запросы на url указанный в config.TARGET_URL.\n    Полученный ответ от сервера преобразуется к следующему виду:\n     - к словам из шести букв добавляется символ «™»;\n     - urls из списка config.REPLACE_URLS, заменяются на url proxy-сервера.\n\n    \"\"\"\n    resp = await fetch_response(request)\n\n    exclude_headers = (\"Transfer-Encoding\", \"Content-Encoding\")\n    headers = {\n        key: val for key, val in resp.headers.items() if key not in exclude_headers\n    }\n    if resp.content_type == \"text/html\":\n        data = processing_text(data=resp.data.decode(), urls=get_urls_replace(request))\n        return web.Response(text=data, status=resp.status, headers=headers)\n\n    return web.Response(body=resp.data, status=resp.status, headers=headers)\n", "repo_name": "zaharinea/habra_proxy", "sub_path": "proxy/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 2398, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "aiohttp.web.Request", "line_number": 16, "usage_type": "name"}, {"api_name": "urllib.parse.urljoin", "line_number": 17, "usage_type": "call"}, {"api_name": "aiohttp.web.Request", "line_number": 20, "usage_type": "name"}, {"api_name": "common.UrlReplace", "line_number": 23, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "common.UrlReplace", "line_number": 20, "usage_type": "name"}, {"api_name": "aiohttp.web.Request", "line_number": 27, "usage_type": "name"}, {"api_name": "ssl.create_default_context", "line_number": 31, "usage_type": "call"}, {"api_name": "certifi.where", "line_number": 31, "usage_type": "call"}, {"api_name": "aiohttp.ClientSession", "line_number": 34, "usage_type": "call"}, {"api_name": "common.ServerResponse", "line_number": 43, "usage_type": "call"}, {"api_name": "common.ServerResponse", "line_number": 27, "usage_type": "name"}, {"api_name": "aiohttp.web.Request", "line_number": 51, "usage_type": "name"}, {"api_name": "utils.processing_text", "line_number": 66, "usage_type": "call"}, {"api_name": "aiohttp.web.Response", "line_number": 67, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 67, "usage_type": "name"}, {"api_name": "aiohttp.web.Response", "line_number": 69, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 69, "usage_type": "name"}, {"api_name": "aiohttp.web.Response", "line_number": 51, "usage_type": "name"}]}
{"seq_id": "29945107765", "text": "\"\"\"\nReplay Buffer\n\"\"\"\nfrom collections import namedtuple, deque\nimport random\nimport mindspore as ms\n\n\nclass Replay_Buffer:\n    \"\"\"Replay buffer to store experiences that the agent can then use for training data\"\"\"\n\n    def __init__(self, buffer_size, batch_size, seed):\n\n        self.memory = deque(maxlen=buffer_size)\n        self.batch_size = batch_size\n        self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n        # 因为random.seed不返回任何值\n        # self.seed = random.seed(seed)\n        random.seed(seed)\n\n    def add_experience(self, states, actions, rewards, next_states, dones):\n        \"\"\"Adds experience(s) into the replay buffer\"\"\"\n        # if type(dones) == list:\n        if isinstance(dones, list):\n            # assert type(dones[0]) != list, \"A done shouldn't be a list\"\n            assert not isinstance(dones[0], list), \"A done shouldn't be a list\"\n            experiences = [\n                self.experience(state, action, reward, next_state, done)\n                for state, action, reward, next_state, done in zip(states, actions, rewards, next_states, dones)\n            ]\n            self.memory.extend(experiences)\n        else:\n            experience = self.experience(states, actions, rewards, next_states, dones)\n            self.memory.append(experience)\n\n    def sample(self, num_experiences=None, separate_out_data_types=True):\n        \"\"\"Draws a random sample of experience from the replay buffer\"\"\"\n        result = None\n\n        experiences = self.pick_experiences(num_experiences)\n        if separate_out_data_types:\n            states, actions, rewards, next_states, dones = self.separate_out_data_types(experiences)\n            result = states, actions, rewards, next_states, dones\n        else:\n            result = experiences\n\n        return result\n\n    def separate_out_data_types(self, experiences):\n        \"\"\"separate_out_data_types\"\"\"\n        states, actions, rewards, next_states, dones = [], [], [], [], []\n        states_append, actions_append, rewards_append, next_states_append, dones_append \\\n            = states.append, actions.append, rewards.append, next_states.append, dones.append\n        for e in experiences:\n            if e is not None:\n                states_append(e.state)\n                actions_append(e.action)\n                rewards_append(e.reward)\n                next_states_append(e.next_state)\n                dones_append(int(e.done))\n\n        states, actions = ms.Tensor(states, dtype=ms.float32), ms.Tensor(actions).unsqueeze(-1)\n        rewards = ms.Tensor(rewards).unsqueeze(-1)\n        next_states, dones = ms.Tensor(next_states, dtype=ms.float32), ms.Tensor(dones).unsqueeze(-1)\n        if len(states.shape) == 1:\n            states = states.unsqueeze(-1)\n        if len(next_states.shape) == 1:\n            next_states = next_states.unsqueeze(-1)\n\n        actions = actions.squeeze(-1) if len(actions.shape) == 3 else actions\n\n        return (\n            states,\n            actions,\n            rewards,\n            next_states,\n            dones\n        )\n\n    def pick_experiences(self, num_experiences=None):\n        \"\"\"\n        pick experience\n        \"\"\"\n        if num_experiences is not None:\n            batch_size = num_experiences\n        else:\n            batch_size = self.batch_size\n        return random.sample(self.memory, k=batch_size)\n\n    def __len__(self):\n        return len(self.memory)\n", "repo_name": "mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore", "sub_path": "utilities/data_structures/Replay_Buffer.py", "file_name": "Replay_Buffer.py", "file_ext": "py", "file_size_in_byte": 3466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.deque", "line_number": 14, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 16, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 19, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 62, "usage_type": "call"}, {"api_name": "mindspore.float32", "line_number": 62, "usage_type": "attribute"}, {"api_name": "mindspore.Tensor", "line_number": 63, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 64, "usage_type": "call"}, {"api_name": "mindspore.float32", "line_number": 64, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 88, "usage_type": "call"}]}
{"seq_id": "28747764556", "text": "import json\nimport math\nimport numpy as np\nfrom pprint import pprint\nfrom queue import Queue \n\nimport matrix_utils as mu\n\nclass CreasePattern():\n\n\tdef __init__(self, path_to_json, scale=1.0):\n\t\t'''Constructs a new crease pattern\n\n\t\tArgs:\n\t\t\tpath_to_json (str): the path to the file containing all of the required\n\t\t\t\tcrease pattern data\n\n\t\t'''\n\t\twith open(path_to_json) as f:\n\t\t\tdata = json.load(f)\n\n\t\t# Used to pad arrays that would otherwise be \"jagged\" because of differing\n\t\t# numbers of folds, faces, etc.\n\t\tself.filler_index = -1 \n\n\t\t# A 2D array containing all of the coordinates the points in the \n\t\t# reference (starting) configuration: each of these should lie in \n\t\t# span(e1, e2), i.e. on the xy-plane\n\t\tself.reference_points = np.array(data['reference_points'], dtype=np.float)\n\t\tself.reference_points *= scale \n\t\t\n\t\t# A 2D array containing the indices corresponding to each pair of \n\t\t# reference points that form the start & end vertices of each fold \n\t\t# vector\n\t\t#\n\t\t# For example, an entry [0, 1] would correspond to the fold vector \n\t\t# pointing *from* point 0 *towards* point 1\n\t\tself.fold_vector_points = np.array(data['fold_vector_points'], dtype=np.int32)\n\n\t\t# A 2D array containing the fold indices associated with each interior fold\n\t\t# intersection: these are expected to be in counter-clockwise (CCW) order\n\t\t#\n\t\t# In the Matlab implementation, these indices are signed (positive or\n\t\t# negative), where the sign corresponded to:\n\t\t# \n\t\t# (+): the corresponding fold vector points away from the intersection \n\t\t# (-): the corresponding fold vector points towards the intersection\n\t\t#\n\t\t# Since Matlab uses 1-based indexing, this works - however, it leads to \n\t\t# issues in Python, which uses 0-based indexing (since 0 can't be negative)\n\t\t#\n\t\t# Indices:\n\t\t#\n\t\t# i -> index of the interior fold intersection\n\t\t# j -> index of the j-th fold surrounding the i-th interior fold intersection\n\t\tself.intersection_fold_indices = np.array(data['intersection_fold_indices'], dtype=np.int32)\n\n\t\t# To avoid the use of negative indices (mentioned above), we use a second array\n\t\t# below:\n\t\t#\n\t\t# `True`: the j-th fold vector points away (emanates) from the i-th interior fold intersection\n\t\t# `False`: the j-th fold vector points towards the i-th interior fold intersection\n\t\tself.sign_intersection_fold_indices = np.array(data['sign_intersection_fold_indices'], dtype=np.bool)\n\n\t\t# A 2D array containing the indices of all of the folds (and reference points) that  \n\t\t# form the boundary of each face (again, in CCW order)\n\t\t#\n\t\t# Because there may be faces with differing numbers of boundary folds (i.e. triangular\n\t\t# vs. quadrilateral faces), we introduce the \"filler\" index to right-fill \"jagged\" arrays\n\t\t# so that each sub-array has the same number of entries\n\t\tself.face_boundary = np.array(data['face_boundary'], dtype=np.int32)\n\n\t\t# A 2D array specifying the \"sign\" of each fold in the `face_boundary` array:\n\t\t#\n\t\t# `True`: the fold vector points CCW around the face\n\t\t# `False`: the fold vector points CW around the face\n\t\tself.sign_face_boundary = np.array(data['sign_face_boundary'], dtype=np.bool)\n\n\t\t# The index of the face that will remain fixed throughout the simulation\n\t\tself.fixed_face = data['fixed_face']\n\n\t\t# A 1D array that specifies an upper bound on the range of values that each fold\n\t\t# angle can take on\n\t\tself.fold_angle_upper_bound = np.array(data['fold_angle_upper_bound'], dtype=np.float)\n\n\t\t# A 1D array that specifies a lower bound on the range of values that each fold\n\t\t# angle can take on\n\t\tself.fold_angle_lower_bound = np.array(data['fold_angle_lower_bound'], dtype=np.float)\n\n\t\t# A 1D array that specifies each of the fold angles in the reference configuration\n\t\tself.fold_angle_initial_value = np.array(data['fold_angle_initial_value'], dtype=np.float)\n\n\t\t# A 1D array that specifies the target fold angles\n\t\tself.fold_angle_target = np.array(data['fold_angle_target'], dtype=np.float)\n\n\t\tself.compute_properties()\n\n\tdef compute_properties(self):\n\t\t'''Based on the initial pattern data provided by the user, compute a number of other\n\t\tproperties that are needed throughout the simulation\n\n\t\t'''\n\t\tdebug = False\n\t\tdef print_debug(msg):\n\t\t\tif debug: \n\t\t\t\tprint(msg)\n\n\t\t# The total number of reference points (i.e. vertices)\n\t\tself.num_reference_ponts = self.reference_points.shape[0]\n\n\t\t# The total number of folds (i.e. creases) across the ENTIRE pattern\n\t\tself.num_folds = self.fold_vector_points.shape[0]\n\n\t\t# The total number of interior fold intersections (i.e. interior vertices)\n\t\tself.num_fold_intersections = self.intersection_fold_indices.shape[0]\n\n\t\t# The total number of faces\n\t\tself.num_faces = self.face_boundary.shape[0]\n\n\t\t# Quick sanity check...\n\t\tassert self.fold_angle_upper_bound.shape == (self.num_folds,) \n\t\tassert self.fold_angle_lower_bound.shape == (self.num_folds,) \n\t\tassert self.fold_angle_initial_value.shape == (self.num_folds,) \n\n\t\t# A 2D array containing the number of folds emanating from each interior fold intersection\n\t\tself.num_intersection_folds = np.zeros(self.num_fold_intersections, dtype=np.int32)\n\n\t\tfor i in range(self.num_fold_intersections):\n\t\t\tcount = np.count_nonzero(self.intersection_fold_indices[i] != self.filler_index)\n\t\t\tself.num_intersection_folds[i] = count\n\n\t\t# A 2D array containing the coordinates of the starting point of each fold vector\n\t\tself.p1 = np.array([self.reference_points[i] for i, _ in self.fold_vector_points])\n\n\t\t# A 2D array containing the coordinates of the ending point of each fold vector\n\t\tself.p2 = np.array([self.reference_points[j] for _, j in self.fold_vector_points])\n\n\t\t# A 2D array containing the direction vector along each fold \n\t\tself.fold_vector = self.p2 - self.p1\n\n\t\t# Additional sanity checks...\n\t\tassert self.p1.shape == (self.num_folds, 2)\n\t\tassert self.p2.shape == (self.num_folds, 2)\n\t\tassert self.fold_vector.shape == (self.num_folds, 2)\n\n\t\t# A 1D array containing the angle that each fold vector makes w.r.t. e1\n\t\tself.fold_ref_angle_wrt_e1 = np.zeros(self.num_folds)\n\n\t\tfor i, v in enumerate(self.fold_vector):\n\t\t\t# Extract the xy-coordinates of this vector and calculate its length (L2 norm)\n\t\t\tx, y = v\n\t\t\tnorm_of_v = math.sqrt(x*x + y*y)\n\n\t\t\t# Equation `2.13`\n\t\t\t# \n\t\t\t# Note: we can avoid doing a cross-product (as in the original formula) and\n\t\t\t# simply check the sign of the vector's y-coordinate\n\t\t\t# \n\t\t\t# The angles returned by this formula are always positive in the range 0..2π\n\t\t\tif y >= 0.0: \n\t\t\t\tself.fold_ref_angle_wrt_e1[i] = math.acos(x / norm_of_v)\n\t\t\telse:\n\t\t\t\tself.fold_ref_angle_wrt_e1[i] = 2.0 * math.pi - math.acos(x / norm_of_v)\n\n\t\tif self.num_fold_intersections == 0:\n\t\t\tself.has_interior_fold_intersections = False \n\t\telse:\n\t\t\tself.has_interior_fold_intersections = True\n\n\t\tif self.has_interior_fold_intersections:\n\t\t\t# A 3D array containing the direction vectors along each of the folds that emanate \n\t\t\t# from each of the interior fold intersections\n\t\t\t#\n\t\t\t# The direction vectors should always be oriented *outwards* from the interior fold \n\t\t\t# intersection\n\t\t\t#\n\t\t\t# Indices:\n\t\t\t#\n\t\t\t# i -> index of the interior fold intersection \n\t\t\t# j -> index of the j-th fold surrounding the i-th interior fold intersection\n\t\t\t# k -> index of the coordinate (x or y): thus, k should always be 0 or 1\n\t\t\t# \n\t\t\t# When indexing into the second dimension of this array, j, one should always use a \n\t\t\t# loop like:\n\t\t\t# \n\t\t\t# \tfor j in range(num_intersection_folds[i])\n\t\t\t#\t\t...\n\t\t\t#\n\t\t\t# This is because different interior fold intersections might have different numbers\n\t\t\t# of surrounding folds, so we want to avoid referencing any \"extra\" / \"filler\" folds\n\t\t\t# that implicitly \"pad\" the i-th interior fold intersection\n\t\t\tself.fold_direction_i = np.zeros((self.num_fold_intersections, np.max(self.num_intersection_folds), 2))\n\n\t\t\t# The angle that each interior fold makes with e1\n\t\t\tself.fold_ref_angle_wrt_e1_i = np.zeros((self.num_fold_intersections, np.max(self.num_intersection_folds)))\n\n\t\t\tfor i in range(self.num_fold_intersections):\n\n\t\t\t\tprint_debug(f'Processing interior fold intersection #{i}')\n\n\t\t\t\t# See notes above...\n\t\t\t\tfor j in range(self.num_intersection_folds[i]):\n\t\t\t\t\t# The index of the j-th fold surrounding the i-th interior fold intersection\n\t\t\t\t\tglobal_fold_index = self.intersection_fold_indices[i][j]\n\t\t\t\t\toutward_facing_fold_vector = self.fold_vector[global_fold_index].copy()\n\n\t\t\t\t\tdid_flip = False \n\n\t\t\t\t\t# We might need to flip the vector along this fold\n\t\t\t\t\tif not self.sign_intersection_fold_indices[i][j]:\n\t\t\t\t\t\toutward_facing_fold_vector *= -1.0\n\t\t\t\t\t\tdid_flip = True\n\n\t\t\t\t\t# Store this vector\n\t\t\t\t\tself.fold_direction_i[i][j] = outward_facing_fold_vector\n\n\t\t\t\t\t# Calculate the angle that it makes with e1\n\t\t\t\t\tx, y = outward_facing_fold_vector\n\t\t\t\t\tnorm_of_v = math.sqrt(x*x + y*y)\n\n\t\t\t\t\tif y >= 0.0: \n\t\t\t\t\t\tself.fold_ref_angle_wrt_e1_i[i][j] = math.acos(x / norm_of_v)\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.fold_ref_angle_wrt_e1_i[i][j] = 2.0 * math.pi - math.acos(x / norm_of_v)\n\n\t\t\t\t\tprint_debug(f'\\tFold {global_fold_index}: outward facing fold vector = <{x}, {y}>, did flip = {did_flip}, norm = {norm_of_v}, angle = {self.fold_ref_angle_wrt_e1_i[i][j]}')\n\n\n\t\t\tfor i in range(self.num_fold_intersections):\n\t\t\t\tprint_debug(f'Interior fold intersection #{i} angles w.r.t. +x-axis:')\n\t\t\t\tfor j in range(self.num_intersection_folds[i]):\n\t\t\t\t\t# Retrieve the *global* fold index of the fold that corresponds to the j-th fold\n\t\t\t\t\t# emanating from the i-th interior fold intersection\n\t\t\t\t\tglobal_fold_index = self.intersection_fold_indices[i][j]\n\t\t\t\t\tstart_index, end_index = self.fold_vector_points[global_fold_index]\n\t\t\t\t\ttheta = self.fold_ref_angle_wrt_e1_i[i][j]\n\t\t\t\t\tprint_debug(f'\\tFold #{j} (corresponding to fold index {global_fold_index}, with reference point indices [{start_index}-{end_index}]) forms angle {math.degrees(theta)} w.r.t. +x-axis')\n\n\t\t\t# A 2D array containing the face corner angles surrounding each interior fold intersection (in CCW order)\n\t\t\tself.face_corner_angles = np.zeros((self.num_fold_intersections, max(self.num_intersection_folds)))\n\n\t\t\tfor i in range(self.num_fold_intersections):\n\n\t\t\t\tfor j in range(self.num_intersection_folds[i]):\n\t\t\t\t\t# If this is the last fold that emanates from the i-th interior fold intersection,\n\t\t\t\t\t# add 2π - for example, if the first fold makes a 45-degree angle w.r.t. e1 and the\n\t\t\t\t\t# last fold makes a 315-degree angle w.r.t. e1, we want to return 90, not 45 - 315\n\t\t\t\t\t# (which would be -270): 360 + (45 - 315) = 90\n\t\t\t\t\t# \n\t\t\t\t\t# Otherwise, the face corner angle is simply the difference between the next\n\t\t\t\t\t# fold angle and the current fold angle\n\t\t\t\t\tif j == self.num_intersection_folds[i] - 1:\n\t\t\t\t\t\tself.face_corner_angles[i][j] = 2.0 * math.pi + (self.fold_ref_angle_wrt_e1_i[i][0] - self.fold_ref_angle_wrt_e1_i[i][j])\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.face_corner_angles[i][j] = self.fold_ref_angle_wrt_e1_i[i][j + 1] - self.fold_ref_angle_wrt_e1_i[i][j]\n\n\t\t\t\t\t# Prevent face corner angles from ever being negative or greater than 2π - is there\n\t\t\t\t\t# a better way to do this? I don't think this should ever happen...\n\t\t\t\t\t# if self.face_corner_angles[i][j] >= (2.0 * math.pi):\n\t\t\t\t\t# \tself.face_corner_angles[i][j] -= 2.0 * math.pi\n\t\t\t\t\t# elif self.face_corner_angles[i][j] < 0.0:\n\t\t\t\t\t# \tself.face_corner_angles[i][j] += 2.0 * math.pi\n\n\t\t\tfor i in range(self.num_fold_intersections):\n\t\t\t\tprint_debug(f'Interior fold intersection #{i} corner angles:')\n\t\t\t\tfor j in range(self.num_intersection_folds[i]):\n\t\t\t\t\tglobal_fold_index = self.intersection_fold_indices[i][j]\n\t\t\t\t\ttheta = self.face_corner_angles[i][j]\n\t\t\t\t\tprint_debug(f'\\tCorner angle #{j} (corresponding to fold index {global_fold_index}) has angle: {math.degrees(theta)}')\n\n\t\t# A 3D array containing all of the corner points of each polygonal face, which may or \n\t\t# may not be triangular\n\t\t#\n\t\t# Indices:\n\t\t#\n\t\t# i -> index of the face\n\t\t# j -> index of the j-th corner point bounding the i-th face\n\t\t# k -> index of the coordinate (x or y): thus, k should always be 0 or 1\n\t\t#\n\t\t# The maximum number of boundary folds for a given face is dictated by the \n\t\t# size of the 2nd dimension of the `face_boundary` array, which will be \n\t\t# right-filled to accomodate any faces that have less than `max_boundary_folds_per_face`\n\t\t# boundary folds\n\t\tmax_boundary_folds_per_face = self.face_boundary.shape[1]\n\t\tself.face_corner_points = np.zeros((self.num_faces, 2 * max_boundary_folds_per_face, 2))\n\n\t\t# A 1D array containing the number of corner points per face\n\t\tself.num_face_corner_points = np.zeros(self.num_faces, dtype=np.int32)\n\n\t\t# Figure out which reference points form this polygonal face, in CCW order\n\t\tfor i in range(self.num_faces):\n\n\t\t\tcount = 0\n\n\t\t\tfor j in range(len(self.face_boundary[i])):\n\n\t\t\t\t# The index of the j-th fold that bounds the i-th face\n\t\t\t\tk = self.face_boundary[i][j]\n\n\t\t\t\t# Have we reached the end of this face?\n\t\t\t\tif k == self.filler_index:\n\t\t\t\t\tbreak\n\n\t\t\t\t# If individual reference points are used to define corners of a face \n\t\t\t\t# at the boundary, the points should be referred as # of folds +\n\t\t\t\t# reference point index #\n\t\t\t\tif k >= self.num_folds:\n\t\t\t\t\tself.face_corner_points[i][count] = self.reference_points[k - self.num_folds]\n\t\t\t\t\tcount += 1\n\t\t\t\t\tcontinue\n\n\t\t\t\tif self.sign_face_boundary[i][j]:\n\t\t\t\t\t# Connect to fold in \"positive\" direction - the fold already goes CCW\n\t\t\t\t\tself.face_corner_points[i][count] = self.p1[k]\n\t\t\t\t\tcount += 1\n\t\t\t\t\tself.face_corner_points[i][count] = self.p2[k]\n\t\t\t\t\tcount += 1\n\t\t\t\telse:\n\t\t\t\t\t# Connect to fold in \"negative\" direction\n\t\t\t\t\tself.face_corner_points[i][count] = self.p2[k] \n\t\t\t\t\tcount += 1\n\t\t\t\t\tself.face_corner_points[i][count] = self.p1[k]\n\t\t\t\t\tcount += 1\n\t\t    \n\t\t\tself.num_face_corner_points[i] = count\n\n\n\t\tfor i in range(self.num_faces):\n\t\t\tprint_debug('Face {} corner points:'.format(i))\n\t\t\tfor j in range(self.num_face_corner_points[i]):\n\t\t\t\tprint_debug('\\tPoint {}: {}'.format(j, self.face_corner_points[i][j]))\n\n\t\t# A 2D array containing the center point of each face (for labeling purposes) - note that this is\n\t\t# not required for simulation\n\t\tself.face_centers = np.zeros((self.num_faces, 2)) \n\n\t\t# TODO: is there a better way to do this like:\n\t\t# `np.average(self.face_corner_points, axis=1)`\n\n\t\tfor i in range(self.num_faces):\n\t\t\t# Running sum...\n\t\t\tcenter = [0.0, 0.0]\n\t\t\tcount = 0\n\n\t\t\tfor j in self.face_boundary[i]:\n\n\t\t\t\tif j != self.filler_index:\n\n\t\t\t\t\tif j >= self.num_folds:\n\t\t\t\t\t\tcenter += self.reference_points[j - self.num_folds]\n\t\t\t\t\t\tcount += 1\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t# Get the start and end points of this fold, compute its midpoint,\n\t\t\t\t\t# and accumulate\n\t\t\t\t\ts = self.p1[j]\n\t\t\t\t\te = self.p2[j]\n\t\t\t\t\tmidpoint = (s + e) * 0.5\n\t\t\t\t\tcenter += midpoint\n\t\t\t\t\tcount += 1\n\n\t\t\tcenter /= float(count)\n\n\t\t\tself.face_centers[i] = center\n\n\t\tassert self.face_centers.shape == (self.num_faces, 2)\n\n\t\t# A 2D array containing the indices of the folds crossed by each path connecting the fixed face to \n\t\t# every other face\n\t\t#\n\t\t# Indices:\n\t\t# i -> index of the \"target\" face\n\t\t# j -> index of the j-th fold crossed en-route to the i-th from the fixed face \n\t\t# \n\t\t# For example, an entry at index 0 of the form [3, 2, 1] would indicate that in order to \n\t\t# get from the fixed face (say, the 3rd face) to the 0-th face, we would need to cross the \n\t\t# folds at indices 3, 2, and 1 (in that order)\n\t\t# \n\t\t# Note that these are *global* fold indices (not indices in reference to any particular interior  \n\t\t# fold intersection)\n\t\t#\n\t\t# NOTE: can this be done via Hamiltonian refinement (see page 233 of \"Geometric Folding Algorithms\")?\n\t\tself.fold_paths = np.full((self.num_faces, self.num_folds), self.filler_index, dtype=np.int32)\n\t\tself.sign_fold_paths = np.full((self.num_faces, self.num_folds), True, dtype=np.bool)\n\t\t\n\t\t# Construct a dictionary that maps each face index to all of its neighboring faces\n\t\t#\n\t\t# Each entry in the `set()` will be a tuple, where the first entry is the index\n\t\t# of the neighboring face and the second entry is the index of the fold that is\n\t\t# shared by the two faces\n\t\tface_neighbors = { i: set() for i in range(self.num_faces) }\n\n\t\tfor i in range(self.num_faces): \n\n\t\t\tfor fold_index in self.face_boundary[i]:\n\n\t\t\t\t# Don't consider boundary folds that are filler indices: again, these exist\n\t\t\t\t# so that the second dimension of the `face_boundary` array is the same\n\t\t\t\t# across all faces\n\t\t\t\tif fold_index == self.filler_index:\n\t\t\t\t\tbreak\n\n\t\t\t\t# TODO: just added this - skip reference points used for face boundary: \n\t\t\t\t# does this always work?\n\t\t\t\tif fold_index >= self.num_folds:\n\t\t\t\t\tcontinue\n\n\t\t\t\t# Is there another face that shares this same fold?\n\t\t\t\tfor j in range(self.num_faces):\n\n\t\t\t\t\tif i != j and fold_index in self.face_boundary[j]:\n\t\t\t\t\t\tface_neighbors[i].add((j, fold_index))\n\n\t\tprint_debug('Face neighbors:')\n\t\tprint_debug(face_neighbors)\n\n\t\tdef breadth_first_search(root):\n\t\t\t'''A helper function for performing a breadth first search from the fold\n\t\t\tat index `root` to all other reachable folds\n\n\t\t\t'''\n\t\t\tfrontier = Queue()\n\t\t\tfrontier.put(root)\n\n\t\t\t# Keep track of where each node \"came from\" (i.e. its parent in the BFS algorithm)\n\t\t\tcame_from = {}\n\t\t\tcame_from[root] = None\n\n\t\t\twhile not frontier.empty():\n\t\t\t\tcurrent = frontier.get()\n\n\t\t\t\t# Parse out face indices, ignoring the shared fold indicies (for now)\n\t\t\t\tindices_of_neighboring_faces = [neighbor[0] for neighbor in face_neighbors[current]]\n\n\t\t\t\tfor next in indices_of_neighboring_faces:\n\t\t\t\t\tif next not in came_from:\n\t\t\t\t\t\tfrontier.put(next)\n\t\t\t\t\t\tcame_from[next] = current\n\n\t\t\treturn came_from\n\n\t\t# Create a data structure that maps each face index to a dictionary\n\t\t#\n\t\t# The dictionary contains the results of a breadth first search from \n\t\t# each face index, outwards\n\t\t#\n\t\t# Entries are of the form:\n\t\t#\n\t\t# i: { a:b, c:d, e:f, ... }\n\t\t#\n\t\t# Which means that during the breadth first search starting at the i-th\n\t\t# face, face \"a\" came from face \"b\", face \"c\" came from face \"d\", etc.\n\t\t#\n\t\t# We can then use this information below to work backwards from the \n\t\t# fixed face to the i-th face, constructing a \"path\" between the two\n\t\t#\n\t\t# However, what we are *actually* interested in is, the indices of the \n\t\t# folds (and their corresponding signs) crossed along such a path\n\t\t# \n\t\t# This is where the `face_neighbors` data structure comes in: it helps\n\t\t# us determine the index of the shared fold between any two neighboring\n\t\t# faces\n\t\tcame_from = {i: breadth_first_search(i) for i in range(self.num_faces)} \n\t\tprint_debug('Results of BFS:')\n\t\tprint_debug(came_from)\n\n\t\tfor i in range(self.num_faces):\n\n\t\t\ttree = came_from[i]\n\t\t\t\n\t\t\t# We always start at the fixed face and work backwards towards\n\t\t\t# the i-th face\n\t\t\tparent = self.fixed_face\n\t\t\tpath = []\n\t\t\tsign_path = []\n\n\t\t\twhile parent is not None:\n\t\t\t\t# The index of the face that the current parent face \"came from\"\n\t\t\t\tchild = tree[parent]\n\n\t\t\t\t# This should only happen when we reach the i-th face itself\n\t\t\t\t# (whose corresponding entry in \"came from\" is necessarily `None`)\n\t\t\t\tif child is None:\n\t\t\t\t\tbreak\n\n\t\t\t\t# Find the index of the shared fold between `parent` and `child`\n\t\t\t\tshared_fold = next((neighbor[1] for neighbor in face_neighbors[parent] if neighbor[0] == child), None)\n\t\t\t\tif shared_fold is None:\n\t\t\t\t\traise Error('No shared fold found between faces - this should never happen')\n\n\t\t\t\t# Find the index of the shared fold in the parent's list of \n\t\t\t\t# boundary folds: use this index to look up the corresponding\n\t\t\t\t# sign of the shared fold at the parent face\n\t\t\t\tindex = self.face_boundary[parent].tolist().index(shared_fold)\n\t\t\t\tsign = not self.sign_face_boundary[parent][index]\n\n\t\t\t\tpath.append(shared_fold)\n\t\t\t\tsign_path.append(sign)\n\n\t\t\t\t# Recurse\n\t\t\t\tparent = child\t\n\n\t\t\t# Expand each sub-list so that it matches the required length - pad as necessary\n\t\t\tpath = path[:self.num_folds] + [self.filler_index] * (self.num_folds - len(path))\n\t\t\tsign_path = sign_path[:self.num_folds] + [True] * (self.num_folds - len(sign_path))\n\n\t\t\tself.fold_paths[i] = path\n\t\t\tself.sign_fold_paths[i] = sign_path\n\n\t\t\tprint_debug(f'Face {i}: \\n{path}\\n{sign_path}\\n')\n\n\t\tassert self.fold_paths.shape == (self.num_faces, self.num_folds)\n\n\tdef compute_folding_map(self, fold_angles):\n\t\t'''Computes a folding map\n\n\t\tArgs:\n\t\t\tfold_angles (numpy.ndarray): a 1D array containing the fold angles of the desired \n\t\t\t\tconfiguration\n\n\t\tReturns:\n\t\t\tnumpy.ndarray: a 3D array containing a 4x4 transformation matrix that maps a point\n\t\t\t\tin the fixed face to a target point in the i-th face, taking into account all\n\t\t\t\tof the folds that you would encounter on such a path\n\n\t\t'''\n\t\tfolding_map = np.zeros((self.num_faces, 4, 4))\n\n\t\tfor face_index in range(self.num_faces):\n\n\t\t\t# Create a 4x4 identity matrix\n\t\t\tcomposite = np.eye(4, 4)\n\n\t\t\t# Traverse the fold path and accumulate transformation matrices\n\t\t\tfor fold_index, fold_sign in zip(self.fold_paths[face_index], self.sign_fold_paths[face_index]):\n\n\t\t\t\t# There are no more \"actual\" folds along this path, so terminate\n\t\t\t\tif fold_index == self.filler_index:\n\t\t\t\t\tbreak\n\n\t\t\t\talpha = self.fold_ref_angle_wrt_e1[fold_index]\n\t\t\t\tphi = fold_angles[fold_index]\n\n\t\t\t\t# If the path crosses this fold in the \"negative\" direction, add π \n\t\t\t\tif not fold_sign:\n\t\t\t\t\talpha += math.pi\n\n\t\t\t\t# `b` is the starting reference point along this fold - note that\n\t\t\t\t# we convert `b` to a 3-element vector with z implicitly set to 0\n\t\t\t\t# before continuing\n\t\t\t\tb = self.p1[fold_index]\n\t\t\t\tb = np.append(b, 0.0)\n\n\t\t\t\tfold_transformation = mu.get_fold_transform(alpha, phi, b)\n\n\t\t\t\t# Accumulate transformations\n\t\t\t\tcomposite = np.matmul(composite, fold_transformation)\n\n\t\t\tfolding_map[face_index] = composite\n\n\t\treturn folding_map\n\n\tdef compute_folded_positions(self, fold_angles):\n\t\t# Compute the face map based on the provided fold angles\n\t\tface_map = self.compute_folding_map(fold_angles)\n\t\t\n\t\tfixed_face_center = np.append(self.face_centers[self.fixed_face], 0.0)\n\n\t\t# Add all face polygons to one array (so that depth testing works)\n\t\ttransformed = np.zeros((self.num_faces, np.max(self.num_face_corner_points), 3))\n\n\t\tfor i in range(self.num_faces):\n\t\t\t# Grab all of the 2D corner points that form this face\n\t\t\tpoints_2d = self.face_corner_points[i][:self.num_face_corner_points[i]]\n\t\t\textra_0 = np.zeros((np.shape(points_2d)[0], 1))\n\t\t\textra_1 = np.ones((np.shape(points_2d)[0], 1))\n\n\t\t\t# \"Expand\" the 2D coordinates into 4D by adding zeros and ones\n\t\t\tpoints_3d = np.hstack((points_2d, extra_0))\n\t\t\tpoints_4d = np.hstack((points_3d, extra_1))\n\n\t\t\t# Grab the 4x4 transformation matrix\n\t\t\tcomposite = face_map[i]\n\n\t\t\t# Transform each corner point by the composite matrix\n\t\t\tfor j in range(self.num_face_corner_points[i]):\n\t\t\t\tpoints_4d[j] = np.dot(composite, points_4d[j])\n\n\t\t\t# Add a new polygon (drop the w-coordinate)\n\t\t\ttransformed[i,:self.num_face_corner_points[i]] = points_4d[:,:3] - fixed_face_center\n\n\t\treturn transformed\n\nif __name__ == \"__main__\":\n\tcp = CreasePattern('patterns/simple.json')\n\n", "repo_name": "mwalczyk/kinematic-origami", "sub_path": "crease_pattern.py", "file_name": "crease_pattern.py", "file_ext": "py", "file_size_in_byte": 22951, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "41", "api": [{"api_name": "json.load", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.count_nonzero", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 147, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 152, "usage_type": "call"}, {"api_name": "math.acos", "line_number": 161, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 163, "usage_type": "attribute"}, {"api_name": "math.acos", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 195, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 219, "usage_type": "call"}, {"api_name": "math.acos", "line_number": 222, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 224, "usage_type": "attribute"}, {"api_name": "math.acos", "line_number": 224, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 240, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 253, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 288, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 383, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 383, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 384, "usage_type": "attribute"}, {"api_name": "queue.Queue", "line_number": 422, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 527, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 532, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 546, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 552, "usage_type": "call"}, {"api_name": "matrix_utils.get_fold_transform", "line_number": 554, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 557, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 567, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 570, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 570, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 575, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 575, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 576, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 576, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 579, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 580, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 587, "usage_type": "call"}]}
{"seq_id": "6755658887", "text": "#!/usr/bin/env python\nimport cffi\nfrom enum import Enum\n\n\"\"\"\nThis example will print 10 HOTP codes from just written HOTP#2 slot.\nFor more examples of use please refer to unittest/test_bindings.py file.\n\"\"\"\n\nffi = cffi.FFI()\nget_string = ffi.string\n\nclass DeviceErrorCode(Enum):\n    STATUS_OK = 0\n    NOT_PROGRAMMED = 3\n    WRONG_PASSWORD = 4\n    STATUS_NOT_AUTHORIZED = 5\n    STATUS_AES_DEC_FAILED = 0xa\n\n\ndef get_library():\n    fp = 'NK_C_API.h'  # path to C API header\n\n    declarations = []\n    with open(fp, 'r') as f:\n        declarations = f.readlines()\n\n    a = iter(declarations)\n    for declaration in a:\n        if declaration.startswith('extern') and not '\"C\"' in declaration:\n            declaration = declaration.replace('extern', '').strip()\n            while not ';' in declaration:\n                declaration += (next(a)).strip()\n            # print(declaration)\n            ffi.cdef(declaration)\n\n    C = ffi.dlopen(\"build/libnitrokey.so\")  # path to built library\n    return C\n\n\ndef get_hotp_code(lib, i):\n    return lib.NK_get_hotp_code(i)\n\nprint('Warning!')\nprint('This example will change your configuration on inserted stick and overwrite your HOTP#2 slot.')\nprint('Please write \"continue\" to continue or any other string to quit')\na = raw_input()\n\nif not a == 'continue':\n    exit()\n\nADMIN = raw_input('Please enter your admin PIN (empty string uses 12345678)')\nADMIN = ADMIN or '12345678'  # use default if empty string\n\nlibnitrokey = get_library()\nlibnitrokey.NK_set_debug(False)  # do not show debug messages\n\nADMIN_TEMP = '123123123'\nRFC_SECRET = '12345678901234567890'\n\n# libnitrokey.NK_login('S')  # connect only to Nitrokey Storage device\n# libnitrokey.NK_login('P')  # connect only to Nitrokey Pro device\ndevice_connected = libnitrokey.NK_login_auto()  # connect to any Nitrokey Stick\nif device_connected:\n    print('Connected to Nitrokey device!')\nelse:\n    print('Could not connect to Nitrokey device!')\n    exit()\nuse_8_digits = True\npin_correct = libnitrokey.NK_first_authenticate(ADMIN, ADMIN_TEMP) == DeviceErrorCode.STATUS_OK\nif pin_correct:\n    print('Your PIN is correct!')\nelse:\n    print('Your PIN is not correct! Please try again. Please be careful to not lock your stick!')\n    retry_count_left = libnitrokey.NK_get_admin_retry_count()\n    print('Retry count left: %d' % retry_count_left )\n    exit()\n\n# For function parameters documentation please check NK_C_API.h\nassert libnitrokey.NK_write_config(255, 255, 255, False, True, ADMIN_TEMP) == DeviceErrorCode.STATUS_OK\nlibnitrokey.NK_first_authenticate(ADMIN, ADMIN_TEMP)\nlibnitrokey.NK_write_hotp_slot(1, 'python_test', RFC_SECRET, 0, use_8_digits, False, False, \"\",\n                            ADMIN_TEMP)\n# RFC test according to: https://tools.ietf.org/html/rfc4226#page-32\ntest_data = [\n    1284755224, 1094287082, 137359152, 1726969429, 1640338314, 868254676, 1918287922, 82162583, 673399871,\n    645520489,\n]\nprint('Getting HOTP code from Nitrokey Pro (RFC test, 8 digits): ')\nfor i in range(10):\n    hotp_slot_1_code = get_hotp_code(libnitrokey, 1)\n    print('%d: %d, should be %s' % (i, hotp_slot_1_code, str(test_data[i])[-8:] ))\nlibnitrokey.NK_logout()  # disconnect device\n", "repo_name": "vinaebizs/libnitrokey", "sub_path": "python_bindings_example.py", "file_name": "python_bindings_example.py", "file_ext": "py", "file_size_in_byte": 3181, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "cffi.FFI", "line_number": 10, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 13, "usage_type": "name"}]}
{"seq_id": "40613876509", "text": "#!/usr/bin/env python3\n# coding=utf-8\n# -*- coding: UTF-8 -*-\nfrom fastapi import FastAPI, Form\nfrom fastapi.responses import HTMLResponse\nimport MySQLdb\n\napp = FastAPI()\n\n\n@app.get('/', response_class=HTMLResponse)\ndef index():\n    form = \"\"\"\n    <form method=\"post\" action=\"/action\" >\n        文字輸出欄位：<input name=\"my_head\">\n        <input type=\"submit\" value=\"送出\">\n    </form>\n    \"\"\"\n    return form\n\n\n@app.post('/action', response_class=HTMLResponse)\ndef action(my_head: str = Form(default='')):\n    # 建立資料庫連線\n    conn = MySQLdb.connect(host=\"127.0.0.1\",\n                           user=\"hj\",\n                           passwd=\"test1234\",\n                           db=\"testdb\")\n    # 欲查詢的 query 指令\n    query = \"SELECT description FROM people where name LIKE '{}%';\".format(\n        my_head)\n    # 執行查詢\n    cursor = conn.cursor()\n    cursor.execute(query)\n\n    results = \"\"\"\n    <p><a href=\"/\">Back to Query Interface</a></p>\n    \"\"\"\n    # 取得並列出所有查詢結果\n    for (description, ) in cursor.fetchall():\n        results += \"<p>{}</p>\".format(description)\n    return results\n", "repo_name": "hjhsu/105DBSys_ProjectExample", "sub_path": "python_fastapi_example/python_fastapi_example.py", "file_name": "python_fastapi_example.py", "file_ext": "py", "file_size_in_byte": 1145, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "41", "api": [{"api_name": "fastapi.FastAPI", "line_number": 8, "usage_type": "call"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 11, "usage_type": "name"}, {"api_name": "fastapi.Form", "line_number": 23, "usage_type": "call"}, {"api_name": "MySQLdb.connect", "line_number": 25, "usage_type": "call"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 22, "usage_type": "name"}]}
{"seq_id": "41826775987", "text": "import numpy as np\r\nimport skimage.measure as measure\r\n\r\n\r\ndef get_three_period(mask):\r\n    np.set_printoptions(threshold=np.inf)\r\n    labeled_mask = measure.label(mask)\r\n    # print(labeled_mask)\r\n    max_label = np.max(labeled_mask)\r\n    p_on_list = []\r\n    p_off_list = []\r\n    r_on_list = []\r\n    r_off_list = []\r\n    t_on_list = []\r\n    t_off_list = []\r\n    for label in range(1, max_label + 1):\r\n        bool_array = np.where(labeled_mask == label, 1, 0)\r\n        # print(bool_array)\r\n        # print(np.where(bool_array))\r\n        start, end = np.where(bool_array)[0][[0, -1]]\r\n        # print(start, end)\r\n        if mask[start] == 1:\r\n            if end - start > 10:\r\n                p_on_list.append(start)\r\n                p_off_list.append(end)\r\n        elif mask[start] == 2:\r\n            if end - start > 10:\r\n                r_on_list.append(start)\r\n                r_off_list.append(end)\r\n        elif mask[start] == 3:\r\n            if end - start > 50:\r\n                t_on_list.append(start)\r\n                t_off_list.append(end)\r\n    point_dict = {}\r\n    point_dict['p_on_list'] = p_on_list\r\n    point_dict['p_off_list'] = p_off_list\r\n    point_dict['r_on_list'] = r_on_list\r\n    point_dict['r_off_list'] = r_off_list\r\n    point_dict['t_on_list'] = t_on_list\r\n    point_dict['t_off_list'] = t_off_list\r\n    # print(pr_period, qrs_period, t_period)\r\n    return point_dict\r\n\r\n\r\nif __name__ == '__main__':\r\n    x = np.zeros((200, ))\r\n    x[10:30] = 1\r\n    x[40:55] = 2\r\n    x[66:90] = 3\r\n    x[110:140] = 1\r\n    x[150:160] = 2\r\n    x[180:190] = 3\r\n    a, b, c = get_three_period(x)", "repo_name": "Jiangsiqing/ECG-", "sub_path": "utils/post_process.py", "file_name": "post_process.py", "file_ext": "py", "file_size_in_byte": 1601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.set_printoptions", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 6, "usage_type": "attribute"}, {"api_name": "skimage.measure.label", "line_number": 7, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "36632412792", "text": "import pandas as pd\nimport shapefile\nimport shapely.geometry\nfrom shapely.geometry.polygon import Polygon\nfrom simpledbf import Dbf5\n\n#functions\n\ndef FindFalsePos(lijst1, lijst2, code):\n    \n\n    preselector = []\n    for j in lijst1:\n        if j not in lijst2 and j.startswith(code):\n            preselector.append(j)\n\n    return preselector\n\ndef FindCentroid(i, dataobject, code, shapes):\n\n\n    ranginbestand = dataobject[dataobject[code] == i].index.values.astype(int)[0]\n    ishape = shapes.shape(ranginbestand).points\n    posn = 0\n    loncounter = 0\n    latcounter = 0\n    for position in ishape:\n        posn += 1\n        loncounter += position[0]\n        latcounter += position[1]\n    centroid = shapely.geometry.Point([loncounter / posn, latcounter / posn])\n\n    return centroid\n\ndef FindShape(j, dataobject, code, shapes):\n\n    ranginbestand = dataobject[dataobject[code] == j].index.values.astype(int)[0]\n    shapeid = shapely.geometry.asShape(shapes.shape(ranginbestand))\n    \n    return shapeid\n\n\n#data invoer\ngem2018 = Dbf5('gem_2018.dbf').to_dataframe()\nwijk2018 = Dbf5('wijk_2018.dbf').to_dataframe()\nbuurt2018 = Dbf5('buurt2018.dbf').to_dataframe()\n\ngem2018s = shapefile.Reader('gem_2018')\nwijk2018s = shapefile.Reader('wijk_2018')\nbuurt2018s = shapefile.Reader('buurt2018')\n\ngem2017 = Dbf5('gem_2017.dbf').to_dataframe()\nwijk2017 = Dbf5('wijk_2017.dbf').to_dataframe()\nbuurt2017 = Dbf5('buurt_2017.dbf').to_dataframe()\n\ngem2017s = shapefile.Reader('gem_2017')\nwijk2017s = shapefile.Reader('wijk_2017')\nbuurt2017s = shapefile.Reader('buurt_2017')\n\n#in één lijst\nlijst2018 = []\nfor gem in gem2018.iloc[:,0]:\n    if gem not in lijst2018:\n        lijst2018.append(gem)\n\nfor wijk in wijk2018.iloc[:,0]:\n    if wijk not in lijst2018:\n        lijst2018.append(wijk)\n\nfor buurt in buurt2018.iloc[:,0]:\n    if buurt not in lijst2018:\n        lijst2018.append(buurt)\n\nlijst2017 = []\nfor gem in gem2017.iloc[:,0]:\n    if gem not in lijst2017:\n        lijst2017.append(gem)\n\nfor wijk in wijk2017.iloc[:,0]:\n    if wijk not in lijst2017:\n        lijst2017.append(wijk)\n\nfor buurt in buurt2017.iloc[:,0]:\n    if buurt not in lijst2017:\n        lijst2017.append(buurt)\n\nselectgm = FindFalsePos(lijst2017, lijst2018, 'GM')\nselectwk = FindFalsePos(lijst2017, lijst2018, 'WK')\nselectbu = FindFalsePos(lijst2017, lijst2018, 'BU')\n\nprint(\"data verzameld\")\n\n#bewerking\njointlijst = []\nfor i in lijst2018:\n    print(i)\n    if i in lijst2017:\n        jointlijst.append([i, i])\n    else:\n        if i.startswith(\"GM\"):\n            gmmatches = []\n            ishape = FindShape(i, gem2018, 'GM_CODE', gem2018s)\n\n            for gm in selectgm:\n                gmshape = FindShape(gm, gem2017, 'GM_CODE', gem2017s)\n                if gmshape.intersects(ishape):\n                    gmmatches.append(gm)\n\n            jointlijst.append([i] + gmmatches)        \n            \n\n        elif i.startswith(\"WK\"):\n            wkmatches = []\n            ishape = FindShape(i, wijk2018, 'WK_CODE', wijk2018s)\n\n            for wk in selectwk:\n                wkshape = FindShape(wk, wijk2017, 'WK_CODE', wijk2017s)\n                if wkshape.intersects(ishape):\n                    wkmatches.append(wk)\n\n            if not wkmatches:\n                for gm in gem2017.iloc[:,0]:\n                    print(\"no match %s\" % gm)\n                    gmshape = FindShape(gm, gem2017, 'GM_CODE', gem2017s)\n                    if gmshape.intersects(ishape):\n                        wkmatches.append(gm)\n\n            jointlijst.append([i] + wkmatches)\n            \n\n        elif i.startswith(\"BU\"):\n            bumatches = []\n            ishape = FindShape(i, buurt2018, 'BU_CODE', buurt2018s)\n\n            for bu in selectbu:\n                bushape = FindShape(bu, buurt2017, 'BU_CODE', buurt2017s)\n                if bushape.intersects(ishape):\n                    bumatches.append(bu)\n\n            if not bumatches:\n                for wk in wijk2017.iloc[:,0]:\n                    wkshape = FindShape(wk, wijk2017, 'WK_CODE', wijk2017s)\n                    if wkshape.intersects(ishape):\n                        bumatches.append(gm)\n\n            jointlijst.append([i] + bumatches)\n\n\ndf = pd.DataFrame.from_records(jointlijst)\ndf.to_csv(\"matchlist.csv\")", "repo_name": "michielsd/hoodassembly", "sub_path": "Uitvoer_shape/mergecodering.py", "file_name": "mergecodering.py", "file_ext": "py", "file_size_in_byte": 4230, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "shapely.geometry.geometry.Point", "line_number": 31, "usage_type": "call"}, {"api_name": "shapely.geometry.geometry", "line_number": 31, "usage_type": "attribute"}, {"api_name": "shapely.geometry", "line_number": 31, "usage_type": "name"}, {"api_name": "shapely.geometry.geometry.asShape", "line_number": 38, "usage_type": "call"}, {"api_name": "shapely.geometry.geometry", "line_number": 38, "usage_type": "attribute"}, {"api_name": "shapely.geometry", "line_number": 38, "usage_type": "name"}, {"api_name": "simpledbf.Dbf5", "line_number": 44, "usage_type": "call"}, {"api_name": "simpledbf.Dbf5", "line_number": 45, "usage_type": "call"}, {"api_name": "simpledbf.Dbf5", "line_number": 46, "usage_type": "call"}, {"api_name": "shapefile.Reader", "line_number": 48, "usage_type": "call"}, {"api_name": "shapefile.Reader", "line_number": 49, "usage_type": "call"}, {"api_name": "shapefile.Reader", "line_number": 50, "usage_type": "call"}, {"api_name": "simpledbf.Dbf5", "line_number": 52, "usage_type": "call"}, {"api_name": "simpledbf.Dbf5", "line_number": 53, "usage_type": "call"}, {"api_name": "simpledbf.Dbf5", "line_number": 54, "usage_type": "call"}, {"api_name": "shapefile.Reader", "line_number": 56, "usage_type": "call"}, {"api_name": "shapefile.Reader", "line_number": 57, "usage_type": "call"}, {"api_name": "shapefile.Reader", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 149, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 149, "usage_type": "attribute"}]}
{"seq_id": "39364559712", "text": "from typing import IO, Tuple\n\n\nclass Expr:\n    \"\"\"\n    a * x + b\n    \"\"\"\n    def __init__(self, a: float, b: float):\n        self.a = a\n        self.b = b\n\n    def __sub__(self, other):\n        return Expr(self.a - other.a, self.b - other.b)\n\n    def __mul__(self, other):\n        assert self.a == 0 or other.a == 0\n        return Expr(self.a * other.b + self.b * other.a, self.b * other.b)\n\n    def __invert__(self):\n        new_a = 1.0 / self.a if self.a != 0 else 0\n        new_b = 1.0 / self.b if self.b != 0 else 0\n        return Expr(new_a, new_b)\n\n    def __add__(self, other):\n        return Expr(self.a + other.a, self.b + other.b)\n\n\nclass Monkey:\n    def __init__(self, monkeys=None, value=None, op=None, params=None):\n        self.monkeys = monkeys\n        self.value = Expr(0, value) if value is not None else None\n        self.op = op\n        self.params = params\n\n    def eval(self):\n        if self.value is not None:\n            return self.value\n\n        a = self.monkeys[self.params[0]].eval()\n        b = self.monkeys[self.params[1]].eval()\n        if self.op == '+':\n            return a + b\n        elif self.op == '-':\n            return a - b\n        elif self.op == '*':\n            return a * b\n        elif self.op == '/':\n            return a * (~b)\n        else:\n            raise Exception\n\n\ndef solution(f: IO) -> Tuple[int, int]:\n    monkeys = {}\n    for line1 in f:\n        line = line1.strip().split(' ')\n        name = line[0][:-1]\n        if len(line) == 2:\n            monkeys[name] = Monkey(value=int(line[1]))\n        else:\n            monkeys[name] = Monkey(monkeys=monkeys, op=line[2], params=[line[1], line[3]])\n    rv1 = int(monkeys['root'].eval().b)\n\n    monkeys['humn'].value = Expr(1, 0)\n    a = monkeys[monkeys['root'].params[0]].eval()\n    b = monkeys[monkeys['root'].params[1]].eval()\n    if a.a == 0:\n        a, b = b, a\n    return rv1, int((b.b - a.b) / a.a)\n\n\ndef solve_sample():\n    with open('../sample/day21.txt') as f:\n        part1, part2 = solution(f)\n        assert part1 == 152\n        assert part2 == 301\n\n\ndef solve():\n    with open('../input/day21.txt') as f:\n        print(solution(f))\n\n\nsolve_sample()\nsolve()\n", "repo_name": "gavinhar/Advent2022-py", "sub_path": "days/day21.py", "file_name": "day21.py", "file_ext": "py", "file_size_in_byte": 2174, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "typing.IO", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 53, "usage_type": "name"}]}
{"seq_id": "40330129835", "text": "from __future__ import print_function\nimport numpy as np\nimport pandas as pd\nfrom warnings import warn\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib import cm\nfrom matplotlib.ticker import FormatStrFormatter\nfrom matplotlib.colors import Normalize, LogNorm\nfrom matplotlib.cm import ScalarMappable\nimport matplotlib.patheffects as pe\n\nfrom scipy.interpolate import interpn\nfrom statsmodels.nonparametric.smoothers_lowess import lowess\ntry:\n\tfrom adjustText import adjust_text\n\tadjust_text_present = True\nexcept:\n\n\tadjust_text_present = False\n\n\ndef lowess_trend(x, y, frac=.25, max_points=300, min_points=20, delta_frac=.005, **kwargs):\n\t'''\n\tA wrapper for statsmodel's lowess with a somewhat more useful parameterization\n\tParameters:\n\t\t`x`, `y`: the points. `y` will be smoothed as a function of `x`.\n\t\t`frac`: `float` in [0, 1]. The fraction of the points used for each linear regression.\n\t\t`min_points`: `int`. The maximum number of points to be used for each linear regression.\n\t\t\t\t\tOverrides `frac` when larger.\n\t\t`max_points`: `int`. The maximum number of points to be used for each linear regression.\n\t\t\t\t\tOverrides `frac` when smaller.\n\t\t`delta_frac`: the fraction of the range of `x` within which to use linear interpolation\n\t\t`              instead of a new regression.\n\t\tOther args passed to lowess.\n\tReturns:\n\t\tThe unsorted smoothed y values.\n\t'''\n\tfrac = min(max_points/len(x), frac)\n\tfrac = max(frac, min_points/len(x))\n\trng = x.max() - x.min()\n\tdelta = min(delta_frac * rng, 50/len(x)*rng)\n\tdelta = min(delta, rng)\n\treturn lowess(y, x, frac, delta=delta, is_sorted=False, return_sorted=False, **kwargs)\n\n\ndef identify_outliers_by_trend(x, y, n_outliers, y_trend=None, min_outlier_std=3, **kwargs):\n\t'''\n\tGet the `n_outliers` farthest from the smoothed trend. y_trend is not supplied, it will be estimated\n\tusing `lowess_trend`.\n\tParameters:\n\t\t`x`, `y`: the points. `y` will be smoothed as a function of `x`.\n\t\t`n_outliers`: how many outliers to return.\n\t\t`y_trend`: The trend of y(x). If not provided, will be estimated by lowess.\n\t\t`min_outlier_std` (`float`): `y` must be at least this many standard deviations from `y_trend`\n\t\t\t\t(standard deviation measured as deviation from the trend) to be an outlier.\n\t\tOther args are passed to `lowess_trend`, if used.\n\tReturns:\n\t\tthe numerical index of the outliers\n\t'''\n\tif y_trend is None:\n\t\ty_trend = lowess_trend(x, y, **kwargs)\n\n\tdiff = y-y_trend\n\tsd = np.std(diff)\n\tnormed = np.abs(diff/sd)\n\tindex = np.arange(len(normed)).astype(int)\n\tcandidates = normed[normed > min_outlier_std]\n\tindex = index[normed > min_outlier_std]\n\torder = np.argsort(candidates)\n\treturn index[order[-n_outliers:]]\n\t\n\ndef identify_outliers_by_density(x, y, density, n_outliers, candidate_density_quantile=.05, high_density_quantile=.5,\n\t\tmax_candidates=500, max_high=10000):\n\t'''\n\tIdentify outliers in 2D space by point density. This is done by first identifying a set of candidate points of lowest\n\tdensity, then a set of points with high density, then choosing candidates that have the greatest minimum distance\n\tto any point with high density.\n\tParameters:\n\t\t`x`, `y`, `density`: 1D arrays giving the position of each point and the estimated density of points at that position\n\t\t`n_outliers`: how many outliers to return. If fewer candidates are found than the number of requested outliers,\n\t\t\t\t all candidates will be returned.\n\t\t`candidate_density_quantile`: the fraction of points to choose as possible outliers based on density\n\t\t`high_density_quantile`: the fraction of points to be treated as dense\n\t`   max_candidates`: overrides `candidate_density_quantile` if too many candidates are considered. Useful for very large datasets.\n\t`   max_high`: overrides `high_density_quantile` if too many high density points are considered. Useful for very large datasets.\n\tReturns:\n\t\tthe numerical index of the outliers\n\t'''\n\tif not (len(x)==len(y)==len(density)):\n\t\traise ValueError(\"`x`, `y`, and `density` must have the same length\")\n\tif candidate_density_quantile > high_density_quantile:\n\t\traise ValueError(\"`candidate_density_quantile` must be less than `high_density_quantile`\")\n\tcandidate_density_quantile = min(candidate_density_quantile, max_candidates/len(x))\n\thigh_density_quantile = max(high_density_quantile, 1-max_high/len(x))\n\n\tcandidates = np.arange(len(density)).astype(int)[density < np.quantile(density, candidate_density_quantile)]\n\thigh_density = density > np.quantile(density, high_density_quantile)\n\tx_diff = np.subtract.outer(x[candidates], x[high_density])\n\ty_diff = np.subtract.outer(y[candidates], y[high_density])\n\tr2 = np.square(x_diff) + np.square(y_diff)\n\tr2_min = r2.min(axis=1)\n\tfarthest = np.argsort(r2_min)[-n_outliers:]\n\treturn candidates[farthest]\n\n\ndef identify_outliers_by_diagonal(x, y, n_outliers):\n\t'''\n\tIdentify points in 2D space as outliers by distance from the diagonal x==y, i.e. the points with the greatest difference\n\tbetween x and y.\n\tParameters:\n\t\t`x`, `y` : 1D arrays giving the position of each point\n\t\t`n_outliers`: how many outliers to return. If fewer candidates are found than the number of requested outliers,\n\t\t\t\t all candidates will be returned.\n\tReturns:\n\t\tthe numerical index of the outliers\n\t'''\n\tdiff = np.abs(x - y)\n\tdiff[pd.isnull(diff)] = 0\n\torder = np.argsort(diff)\n\treturn order[-n_outliers:]\n\n\ndef identify_outliers_by_zscore(x, y, n_outliers):\n\t'''\n\tIdentify points in 2D space as outliers by zscore. `x` and `y` are first zscored, then combined into a scaled Euclidian distance\n\tfrom the mean (`x**2 + y**2`). Those with the greatest distance are returned as outliers.\n\tParameters:\n\t\t`x`, `y`: 1D arrays giving the position of each point\n\t\t`n_outliers`: how many outliers to return. If fewer candidates are found than the number of requested outliers,\n\t\t\t\t all candidates will be returned.\n\tReturns:\n\t\tthe numerical index of the outliers\n\t'''\n\tzx = np.abs(x - np.mean(x))/np.std(x)\n\tzy = np.abs(y - np.mean(y))/np.std(y)\n\tr = zx**2 + zy**2\n\tr[pd.isnull(r)] = 0\n\torder = np.argsort(r)\n\treturn order[-n_outliers:]\n\n\ndef identify_outliers_1d(x, n_outliers):\n\t'''\n\tIdentify points in 1D space as outliers by distance from median. \n\tParameters:\n\t\t`x`: 1D array\n\t\t`n_outliers`: how many outliers to return. If fewer candidates are found than the number of requested outliers,\n\t\t\t\t all candidates will be returned.\n\tReturns:\n\t\tthe numerical index of the outliers\n\t'''\n\tzx = np.abs(x - np.median(x))\n\torder = np.argsort(zx)\n\treturn order[-n_outliers:]\n\n\ndef dict_plot(dictionary, plot_func, figure_width=7.5, min_subplot_width=3.74,\n\t\t\t  aspect_ratio=.8, aliases={}, xlabel=None, ylabel=None, *args, **kwargs):\n\t'''\n\tA utility for generating a figure with a subplot for each entry in `dictionary`. \n\tParameters:\n\t\t`dictionary` (`dict`): The data to be plotted. The keys of the dictionary will be used\n\t\t\tas subplot titles.\n\t\t`plot_func` (callable): will be called as `plot_func(value, *args, **kwargs)` for each value in `\n\t\t\tdictionary`.\n\t\t`figure_width` (`float`: total width of the figure\n\t\t`min_subplot_width` (`float`): when laying out subplots, how narrow they are allowed to be.\n\t\t`aspect_ratio` (`float`): the ration of subplot height to width - not the same as matplotlib's\n\t\t\tdefinition\n\t\t`aliases` (`dict`):  optional alternative names to use as plot titles\n\t\t`xlabel`, `ylabel` (`str`): optional axis labels for the subplots\n\t\tOther args and kwargs passed to `plot_func`\n\tReturns:\n\t\tfig, axes: the matplotlib figure and subplots\n\t'''\n\tnplots = len(dictionary)\n\tplots_per_row = min(nplots, int(figure_width//min_subplot_width))\n\tnrows = int(np.ceil(nplots/plots_per_row))\n\tpanel_width = figure_width/plots_per_row\n\tpanel_height = panel_width * aspect_ratio\n\tfigure_height = panel_height * nrows\n\tfig, axes = plt.subplots(nrows, plots_per_row, figsize=(figure_width, figure_height))\n\tif nrows > 1:\n\t\taxes = [a for ax in axes for a in ax]\n\telif nplots == 1:\n\t\taxes = [axes]\n\tfor key, ax in zip(dictionary.keys(), axes):\n\t\tplt.sca(ax)\n\t\tplot_func(dictionary[key], *args, **kwargs)\n\t\tif not xlabel is None:\n\t\t\tplt.xlabel(xlabel)\n\t\tif not ylabel is None:\n\t\t\tplt.ylabel(ylabel)\n\t\tif key in aliases:\n\t\t\tkey = aliases[key]\n\t\tplt.title(key)\n\tplt.tight_layout()\n\treturn fig, axes\n\n\ndef density_scatter(x, y, ax=None, sort=True, bins=50, trend_line=True, trend_line_args=dict(color='r'),\n\tlowess_args={}, diagonal=False, diagonal_kws=dict(color='black', lw=.3, linestyle='--'), \n\tlabel_specific=[], label_outliers=0, outliers_from='trend', label_kws=dict(\n\t\t\t\t\tfontsize=8, color=(.3, 0, 0), \n\t\t\t\t\tpath_effects=[pe.withStroke(linewidth=-2, foreground=(1, 1, 1))]\n\t\t\t\t\t), \n\toutlier_scatter_kws=dict(color=(.8, .2, .1), s=10, linewidth=.6, edgecolor=[0, 0, 0]), **kwargs ):\n\t\"\"\"\n\tAdapted from Guillaume's answer at\n\t https://stackoverflow.com/questions/20105364/how-can-i-make-a-scatter-plot-colored-by-density-in-matplotlib\n\tScatter plot colored by 2d histogram, with optional trend_line, diagonal, and outlier labeling\n\tParameters:\n\t\t`x`, `y`: `pandas.Series` with overlapping indices or iterables of the same length. Values to plot on each axis.\n\t\t`ax` (`matplotlib.Axis`): if provided, draw plot to this\n\t\t`sort` (`bool`): if `True` (default), the densest points are plotted last. \n\t\t`bins` (`int`): How many bins to use in np.histogram2d for estimating density. Default 50.\n\t\t`trend_line` (`bool`): Whether to draw a lowess trend_line line\n\t\t`lowess_args` (`dict`): passed to `lowess_trend` for the trend_line line\n\t\t`trend_line_args` (`dict`): passed to `pyplot.plot` for the trend_line line\n\t\t`diagonal` (`bool`): If true, draw a line on the diagonal\n\t\t`diagonal_kws` (`dict`): Passed to `pyplot.plot`. By default, colors diagonal line red\n\t\t`label_outliers` (`int`): if > 0, the number of outliers to label with their index. \n\t\t\t\tIf `trend_line`, the outliers will be identified by deviation from the trend.\n\t\t'outliers_from':\n\t\t\t'trend': outliers identified by distance from trend line\n\t\t\t'diagonal': outliers identified by difference between `x` and `y`\n\t\t\t'density': outliers identified by minimum distance to plot region of high density\n\t\t\t'xy_zscore': outliers identified by euclidian distance from zero in z-score space\n\t\t`label_kws` (`dict`): passed to `pyplot.text` for the labels\n\t\t'outlier_scatter_kws': passed to `pyplot.scatter` to plot over outliers\n\t\t**kwargs: additional arguments passed to `pyplot.scatter`.\n\t\"\"\"\n\tif ax is None :\n\t\tfig = plt.gcf()\n\t\tax = plt.gca()\n\telse:\n\t\tfig = ax.figure\n\tindex = None\n\tif isinstance(x, pd.Series) and isinstance(y, pd.Series):\n\t\tx, y = x.align(y, join=\"inner\")\n\t\tindex = x.index\n\tif len(x) != len(y):\n\t\traise ValueError(\"If not pd.Series, x and y must be the same length\")\n\tmask = pd.notnull(x) & pd.notnull(y)\n\tx = np.array(x[mask]).astype(float)\n\ty = np.array(y[mask]).astype(float)\n\tif not index is None:\n\t\tindex = index[mask]\n\ttry:\n\t\tdata , x_e, y_e = np.histogram2d( x, y, bins = bins, density = True )\n\texcept ValueError as e:\n\t\tprint(x)\n\t\tprint(y)\n\t\tprint(bins)\n\t\traise e\n\tz =  interpn( ( 0.5*(x_e[1:] + x_e[:-1]) , 0.5*(y_e[1:]+y_e[:-1]) ) , \n\t\t\tdata , np.vstack([x,y]).T , \n\t\t\tmethod = \"splinef2d\", bounds_error = False\n\t)\n\n\t#To be sure to plot all data\n\tz[np.where(np.isnan(z))] = 0.0\n\tz[z < 0] = 0\n\tz = np.sqrt(z)\n\n\t# Sort the points by density, so that the densest points are plotted last\n\tif sort :\n\t\tidx = z.argsort()\n\t\tx, y, z = x[idx], y[idx], z[idx]\n\t\tif not index is None:\n\t\t\tindex = index[idx]\n\n\tax.scatter( x, y, c=z, **kwargs )\n\n\tnorm = Normalize(vmin = np.min(z), vmax = np.max(z))\n\tcolormap = cm.ScalarMappable(norm = norm)\n\tcolormap.set_array([])\n\tcbar = fig.colorbar(colormap, ax=ax)\n\tcbar.ax.set_ylabel('Density (sqrt)')\n\n\tsmoothed=None\n\tif trend_line:\n\t\tsmoothed = lowess_trend(x, y, **lowess_args)\n\t\txsort = np.argsort(x)\n\t\tax.plot(x[xsort], smoothed[xsort], **trend_line_args)\n\n\toutliers = None\n\tif label_outliers:\n\t\tif outliers_from == 'trend':\n\t\t\toutliers = identify_outliers_by_trend(x, y, label_outliers, smoothed)\n\t\telif outliers_from =='density':\n\t\t\toutliers = identify_outliers_by_density(x, y, z, label_outliers)\n\t\telif outliers_from == 'diagonal':\n\t\t\toutliers = identify_outliers_by_diagonal(x, y, label_outliers)\n\t\telif outliers_from == 'xy_zscore':\n\t\t\toutliers = identify_outliers_by_zscore(x, y, label_outliers)\n\t\telif outliers_from == 'x':\n\t\t\toutliers = identify_outliers_1d(x, label_outliers)\n\t\telif outliers_from == 'y':\n\t\t\toutliers = identify_outliers_1d(y, label_outliers)\n\t\telse:\n\t\t\traise ValueError(\"`outliers_from` must be one of 'trend', 'density', 'diagonal', 'xy_zscore', 'x', or 'y'\")\n\n\tif len(label_specific) and not index is None:\n\t\tlabel_specific = [index.get_loc(v) for v in label_specific]\n\n\tif not outliers is None:\n\t\tlabel_specific = sorted(set(label_specific) | set(outliers))\n\n\tif not index is None:\n\t\tlabels = index[label_specific]\n\telse:\n\t\tlabels = label_specific\n\tif len(label_specific):\n\t\ttexts = [plt.text(s=labels[i], x=x[val], y=y[val], **label_kws)\n\t\t\t\tfor i, val in enumerate(label_specific)]\n\t\tlabel_x = np.array([x[label] for label in label_specific])\n\t\tlabel_y = np.array([y[label] for label in label_specific])\n\t\tplt.scatter(label_x, label_y, **outlier_scatter_kws)\n\t\tif adjust_text_present and len(texts) > 0:\n\t\t\tadjust_text(texts, expand_text=(1.1, 1.5),\n\t\t\t\tlim=500,\n\t\t\t\tarrowprops=dict(arrowstyle=\"-\", color=[.7, .5, .5]),\n\t\t\t\t#x=outlier_x, y=outlier_y\n\t\t\t\t)\n\t\telif len(texts) > 0:\n\t\t\twarn(\"adjustText not found. Install to have labels moved off points.\")\n\n\tif not diagonal:\n\t\treturn ax\n\n\tminmin = min(ax.get_xlim()[0], ax.get_ylim()[0])\n\tmaxmax = max(ax.get_xlim()[1], ax.get_ylim()[1])\n\tax.plot([minmin, maxmax], [minmin, maxmax], **diagonal_kws)\n\n\treturn ax\n\n\ndef binplot(x, y, binned_axis='x', nbins=10, endpoints=None, right=False, ax=None, colors=None, cbar_label='Number Samples', **kwargs):\n\t'''\n\tcreates a plot with values binned into boxes along one axis. \n\tParams:\n\t\tx: iterable of numbers indicating position on x axis\n\t\ty: iterable of numbers indicating position on y axis. \n\t\tbinned_axis (str): 'x' or 'y', the axis to bin ('x' default)\n\t\tnbins (int): number of discrete bins that will be created\n\t\tendpoints (None or tuple of two numbers): The right/top edge of the first bin and the left/bottom edge of the last bin. If provided,\n\t\t\t\tthe first and last bins will include points in [-infinity, endpoints[0]] and [endpoints[1], +infinity] respectively. Other bins\n\t\t\t\twill be evenly spaced between them. If endpoints is None (default), bins will be evenly spaced between the minimum and maximum\n\t\t\t\tdata points.\n\t\tright (bool): whether points falling on an edge are included in the left or right bin.\n\t\taxis (None or pyplot.Axis): axis to draw plot on (default or None draws to current axis)\n\t\tcolors (None or str or iterable of RGBA values): color palette used to color the bins\n\tAdditional keyword arguments are passed to pyplot.boxplot.\n\t'''\n\tif isinstance(x, pd.Series) and isinstance(y, pd.Series):\n\t\tx, y = x.align(y, join=\"inner\")\n\t\tindex = x.index\n\tmask = pd.notnull(x) & pd.notnull(y)\n\tx = np.array(x)[mask]\n\ty = np.array(y)[mask]\n\tif colors is None:\n\t\tcolors = \"viridis\"\n\t\t\n\tif binned_axis == 'x':\n\t\tunbinned = 'y'\n\t\tvert = True\n\telif binned_axis == 'y':\n\t\tunbinned = 'x'\n\t\tvert = False\n\telse:\n\t\traise ValueError(\"binned_axis must be 'x' or 'y'\")\n\t\t\n\tif isinstance(x, pd.Series) and isinstance(y, pd.Series):\n\t\tassert len(set(x.index) & set(y.index)) > 2, \"x and y lack common indices\"\n\telse:\n\t\tassert len(x) == len(y), \"if x and y are not Series, they must be the same length\"\n\t\t\n\tdf = pd.DataFrame({'x': x, 'y': y})\n\t\n\tif endpoints is None:\n\t\tbins = np.linspace(df[binned_axis].min()-1e-12, df[binned_axis].max()+1e-12, nbins+1)\n\t\tspace = bins[2] - bins[1]\n\t\tmedians = .5*(bins[1:] + bins[:-1])\n\telse:\n\t\tbins = [-np.inf] + list(np.linspace(endpoints[0], endpoints[1], nbins-1)) + [np.inf]\n\t\tspace = bins[2] - bins[1]\n\t\tmedians = np.array(\n\t\t\t[bins[1] - .5*space] + list(.5*np.array(bins[2:-1]) + .5*np.array(bins[1:-2])) + [bins[-2] + .5*space]\n\t\t)\n\t\t\n\tdigits = np.digitize(df[binned_axis], bins, right=right).astype(int)\n\n\tif any(digits > nbins):\n\t\tprint(df[binned_axis][digits > nbins])\n\t\tassert False\n\tdf[binned_axis] = medians[digits-1]\n\t\n\tif ax is None:\n\t\tax=plt.gca()\n\telse:\n\t\tplt.sca(ax)\n\tvals = sorted([val for val in sorted(medians) if (df[binned_axis] == val).sum() > 0])\n\t\n\tboxes = plt.boxplot(x=[df[df[binned_axis] == val][unbinned]\n\t\t\t\t   for val in vals\n\t\t\t\t  ], \n\t\t\t\tpositions=vals, widths=[.9*space]*len(vals), patch_artist=True, vert=vert,\n\n\t\t\t\t**kwargs)\n\n\tcounts = df[binned_axis].value_counts().reindex(index=medians).fillna(0)\n\tnormer = LogNorm(vmin=0)\n\tnormer.autoscale(counts.values)\n\tcvals = normer(counts.values)\n\tif isinstance(colors, str):\n\t\tcmap = cm.get_cmap(colors)\n\t\tcolors = [cmap(v) for v in cvals]\n\tfor box, color in zip(boxes['boxes'], colors):\n\t\tbox.set_facecolor(color)\n\n\t\t\n\tif binned_axis == 'x':\n\t\tax.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n\t\tif not endpoints is None:\n\t\t\tplt.xticks(bins[1:-1])\n\t\telse:\n\t\t\tplt.xticks(bins)\n\telse:\n\t\tax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n\t\tif not endpoints is None:\n\t\t\tplt.yticks(bins[1:-1])\n\t\telse:\n\t\t\tplt.yticks(bins)\n\tif binned_axis == 'x':\n\t\tplt.xlim(bins[1] - 1.2*space, bins[-2] + 1.2 * space)\n\telse:\n\t\tplt.ylim(bins[1] - 1.2*space, bins[-2] + 1.2 * space)\n\n\ttry:\n\t\tmappable=ScalarMappable(norm=normer, cmap=cmap)\n\t\tmappable.set_array(colors)\n\t\tplt.gcf().colorbar(mappable, ax=plt.gca(), label=cbar_label)\n\texcept:\n\t\tpass\n\n\treturn ax", "repo_name": "broadinstitute/chronos", "sub_path": "chronos/plotting.py", "file_name": "plotting.py", "file_ext": "py", "file_size_in_byte": 17413, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "41", "api": [{"api_name": "statsmodels.nonparametric.smoothers_lowess.lowess", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.quantile", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.quantile", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.subtract.outer", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.subtract.outer", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.square", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 119, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 137, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.patheffects.withStroke", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.patheffects", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 241, "usage_type": "attribute"}, {"api_name": "pandas.notnull", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.histogram2d", "line_number": 252, "usage_type": "call"}, {"api_name": "scipy.interpolate.interpn", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 266, "usage_type": "call"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.cm.ScalarMappable", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 278, "usage_type": "name"}, {"api_name": "numpy.argsort", "line_number": 286, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 317, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 317, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "adjustText.adjust_text", "line_number": 323, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 329, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 358, "usage_type": "attribute"}, {"api_name": "pandas.notnull", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 363, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 376, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 388, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 390, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.digitize", "line_number": 394, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 402, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 402, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 404, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 404, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.boxplot", "line_number": 407, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 407, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 415, "usage_type": "call"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 419, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 419, "usage_type": "name"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 426, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 428, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 428, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 430, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 430, "usage_type": "name"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 432, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 434, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 434, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 436, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 436, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 438, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 438, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 440, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 440, "usage_type": "name"}, {"api_name": "matplotlib.cm.ScalarMappable", "line_number": 443, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 445, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 445, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 445, "usage_type": "call"}]}
{"seq_id": "14423296845", "text": "# coding=utf-8\n\n\"\"\"BERT finetuning runner.\"\"\"\n\nfrom __future__ import absolute_import, division, print_function\n\n\nimport torch\nfrom torch.utils.data import DataLoader\nimport math\n\nimport logging\nfrom tqdm import tqdm\nfrom nltk.stem.wordnet import WordNetLemmatizer\n\nfrom similarity_score_new_weight.translation import back_translation\nfrom reader import Reader_lexical\n\nimport sys\n\nsys.path.insert(0, 'similarity_score_new_weight/sentence/sentence-transformers/')\nfrom sentence_transformers_new import SentenceTransformer, LoggingHandler, losses, util, InputExample\n\n# from sentence_transformers import SentenceTransformer, LoggingHandler, losses, util, InputExample\nfrom sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n\nimport re\nimport nltk\nfrom nltk.corpus import wordnet\n\nlogger = logging.getLogger(__name__)\n\n\nclass Similarity_new:\n    def __init__(self, train_file, golden_file, model=\"stsb-roberta-large\"):\n        self.reader = Reader_lexical()\n        self.reader.create_feature(train_file)\n        self.reader.read_labels(golden_file)\n        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        self.model_name = model\n        self.lemmatizer = WordNetLemmatizer()\n\n        self.translation_module = back_translation()\n\n    def clean(self, text_a_raw):\n        pat_is = re.compile(\"(it|he|she|that|this|there|here) \\'s\", re.I)\n        # to find the 's following the letters\n\n        # to find the abbreviation of not\n        pat_not = re.compile(\"(?<=[a-zA-Z]) n\\'t\")\n        # to find the abbreviation of would\n        pat_would = re.compile(\"(?<=[a-zA-Z]) \\'d\")\n        # to find the abbreviation of will\n        pat_will = re.compile(\"(?<=[a-zA-Z]) \\'ll\")\n        # to find the abbreviation of am\n        pat_am = re.compile(\"(?<=[I|i]) \\'m\")\n        # to find the abbreviation of are\n        pat_are = re.compile(\"(?<=[a-zA-Z]) \\'re\")\n        # to find the abbreviation of have\n        pat_ve = re.compile(\"(?<=[a-zA-Z]) \\'ve\")\n        new_text = pat_is.sub(r\"\\1 is\", text_a_raw)\n        # new_text = pat_s.sub(\"\", new_text)\n        # new_text = pat_s2.sub(\"\", new_text)\n        new_text = pat_not.sub(\" not\", new_text)\n        new_text = pat_would.sub(\" would\", new_text)\n        new_text = pat_will.sub(\" will\", new_text)\n        new_text = pat_am.sub(\" am\", new_text)\n        new_text = pat_are.sub(\" are\", new_text)\n        new_text = pat_ve.sub(\" have\", new_text)\n        # text_a_raw = new_text.replace('\\'', ' ')\n        text_a_raw = text_a_raw.split(' ')\n        while '' in text_a_raw:\n            text_a_raw.remove('')\n        text_a_raw = \" \".join(text_a_raw)\n\n        return text_a_raw\n\n    def calculate_similarity_score(self, original_sentences, update_sentence):\n\n        original_embedding = self.get_sentence_embedding(self.clean(original_sentences).lower())\n        update_embedding = self.get_sentence_embedding(self.clean(update_sentence).lower())\n        # what is used in the sentencebert Semantic Textual Similarity\n        cosine = util.pytorch_cos_sim(original_embedding, update_embedding)[0].item()\n\n        return cosine\n\n    def get_sentence_embedding(self, sentences):\n        \"\"\"\n        create sentence embedding from sentence-bert\n        :param sentences:  sentence\n        :param tokenizer: pre-trained tokenizer\n        :param model: pre-trained model\n        :return: embedding\n        \"\"\"\n        # Load AutoModel from huggingface model repository using best model for Semantic Textual Similarity\n\n        # Tokenize sentences\n        sentence_embeddings = self.model_sentence.encode(sentences,  convert_to_tensor=True)\n        return sentence_embeddings\n\n    def train_similarity_create_dataset(self,  noise_gloss, wordnet_gloss,\n                                        finding_gloss):\n        bert_dataset = []\n\n        train_dataset = self.reader.words_candidate\n        labels = self.reader.correct_label\n        weight_pair = self.reader.weight_correct_label\n\n        count1 = 0\n        count2 = 0\n\n        for main_word in tqdm(train_dataset):\n            for instance in train_dataset[main_word]:\n                for context in train_dataset[main_word][instance]:\n                    text_temp = context[1]\n                    try:\n                        word_labels = labels[main_word][instance][0]\n                        weight_labels = labels[main_word][instance][1]\n                    except:\n                        count2 = count2 + 1\n                        continue\n                    context[1] = context[1] + \" \"\n                    text = context[1]\n                    original_text = text\n                    translated_sentence = self.translation_module.get_translate_example(self.clean(original_text))\n\n                    # get the pair of original sentence with the update sentence with the candidate word per weight\n                    final = 1\n                    for word_index in range(0, len(word_labels)):\n                        label = word_labels[word_index]\n                        temp_text = text_temp.split(\" \")\n                        temp_text[int(context[2])] = label\n                        pair_text = \" \".join(temp_text)\n\n                        bert_dataset.append(\n                            [self.clean(original_text).lower(), self.clean(pair_text).lower(), final])\n\n                    synonyms = noise_gloss.adding_synonyms(context[0], int(context[2]), True,\n                                                           original_text,\n                                                           wordnet_gloss, finding_gloss, main_word.split('.')[-1])\n\n\n\n                    for synonym in synonyms:\n                        if synonym not in word_labels:\n                            temp_text = text_temp.split(\" \")\n                            temp_text[int(context[2])] = synonym\n                            pair_text = \" \".join(temp_text)\n                            bert_dataset.append(\n                                [self.clean(original_text).lower(), self.clean(pair_text).lower(), 0.9])\n\n                    # not add the pair original and translate sentence\n                    indices = [index for index, element in enumerate(translated_sentence.split(\" \")) if\n                               element == context[0]]\n                    if len(indices) > 0:\n                        min_value = -1\n                        min_distance = 100\n                        for index in indices:\n                            if abs(index - int(context[2])) < min_distance:\n                                min_value = index\n                                min_distance = abs(index - int(context[2]))\n\n                        final = 1\n                        if min_value != -1:\n                            for label in word_labels:\n                                temp_text = translated_sentence.split(\" \")\n                                temp_text[min_value] = label\n                                pair_text = \" \".join(temp_text)\n                                bert_dataset.append(\n                                    [self.clean(pair_text).lower(), self.clean(translated_sentence).lower(), 1.0])\n\n                            for synonym in synonyms:\n                                if synonym not in word_labels:\n                                    temp_text = translated_sentence.split(\" \")\n                                    temp_text[min_value] = synonym\n                                    pair_text = \" \".join(temp_text)\n                                    bert_dataset.append(\n                                        [self.clean(pair_text).lower(), self.clean(translated_sentence).lower(),\n                                         0.9])\n                        count1 = count1 + 1\n\n\n\n        self.x_train = bert_dataset\n        return\n\n    def train_similarity_model(self, model_name=\"stsb-roberta-large\", batch_size=4,\n                               num_epochs=4, model_save_path=\"checkpoint/similarity_new_bert/\"):\n\n        # Read the dataset\n        model_name = model_name\n        train_batch_size = batch_size\n        num_epochs = num_epochs\n        model_save_path = model_save_path\n\n        model = SentenceTransformer(model_name)\n\n        train_samples = []\n        dev_samples = []\n        # dev_flag = int(0.8 * len(self.x_train))\n\n        i = 0\n        for pair in self.x_train:\n            inp_example = InputExample(texts=[pair[0], pair[1]], label=float(pair[2]))\n            train_samples.append(inp_example)\n\n        train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)\n        train_loss = losses.CosineSimilarityLoss(model=model)\n\n        # Development set: Measure correlation between cosine score and gold labels\n        # evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')\n\n        # Configure the training. We skip evaluation in this example\n        warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1)  # 10% of train data for warm-up\n        # warmup_steps = 0\n\n        # Train the model\n        model.fit(train_objectives=[(train_dataloader, train_loss)],\n                  epochs=num_epochs,\n                  warmup_steps=warmup_steps,\n                  output_path=model_save_path)\n\n    def pos_tag(self, word):\n        candidate_word = word\n        substituability_score = -1\n        if candidate_word == self.target_word or \\\n                self.lemmatizer.lemmatize(candidate_word, self.pos_initial) == self.target_word or \\\n                self.target_word in candidate_word:\n            substituability_score = 0\n        return substituability_score\n\n    def initial_test(self, word):\n        candidate_word = word\n        substituability_score = -1\n        if candidate_word == self.target_word or \\\n                self.lemmatizer.lemmatize(candidate_word, self.pos_initial) == self.target_word or \\\n                self.target_word in candidate_word:\n            substituability_score = 0\n        return substituability_score\n\n    def post_tag_target(self, word):\n        main_word = word\n        if main_word.split('.')[0] == \"\":\n            word_temp = \".\"\n        else:\n            word_temp = main_word.split('.')[0]\n        self.target_word = word_temp\n        target_pos = main_word.split('.')[-1]\n        to_wordnet_pos = {'N': wordnet.NOUN, 'J': wordnet.ADJ, 'V': wordnet.VERB, 'R': wordnet.ADV}\n        from_lst_pos = {'j': 'J', 'a': 'J', 'v': 'V', 'n': 'N', 'r': 'R'}\n        try:\n            self.pos_initial = to_wordnet_pos[from_lst_pos[target_pos]]\n        except:\n            self.pos_initial = to_wordnet_pos[target_pos]\n        return\n\n    def load_model(self, model=\"stsb-roberta-large\"):\n        self.model_sentence = SentenceTransformer(model)\n        self.model_sentence.to(self.device)\n        self.model_sentence.eval()", "repo_name": "gmichalo/LexSubCon", "sub_path": "similarity_score_new_weight/similarity_new_predict.py", "file_name": "similarity_new_predict.py", "file_ext": "py", "file_size_in_byte": 10827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.path.insert", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 31, "usage_type": "call"}, {"api_name": "reader.Reader_lexical", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 39, "usage_type": "attribute"}, {"api_name": "nltk.stem.wordnet.WordNetLemmatizer", "line_number": 41, "usage_type": "call"}, {"api_name": "similarity_score_new_weight.translation.back_translation", "line_number": 43, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 46, "usage_type": "call"}, {"api_name": "re.I", "line_number": 46, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 50, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 52, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 54, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 56, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 58, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 60, "usage_type": "call"}, {"api_name": "sentence_transformers_new.util.pytorch_cos_sim", "line_number": 83, "usage_type": "call"}, {"api_name": "sentence_transformers_new.util", "line_number": 83, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 112, "usage_type": "call"}, {"api_name": "sentence_transformers_new.SentenceTransformer", "line_number": 196, "usage_type": "call"}, {"api_name": "sentence_transformers_new.InputExample", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 207, "usage_type": "call"}, {"api_name": "sentence_transformers_new.losses.CosineSimilarityLoss", "line_number": 208, "usage_type": "call"}, {"api_name": "sentence_transformers_new.losses", "line_number": 208, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 214, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 249, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 249, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADJ", "line_number": 249, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 249, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet.ADV", "line_number": 249, "usage_type": "attribute"}, {"api_name": "sentence_transformers_new.SentenceTransformer", "line_number": 258, "usage_type": "call"}]}
{"seq_id": "74247253563", "text": "import os\nimport random\nimport re\nimport threading\nfrom glob import glob\n\nfrom .collections import StatefulCollection\nfrom .exceptions import (NoAvailableWorkException, SourceNotNamedException)\nfrom .. import events\nfrom .. import logger\nfrom .. import tasks\nfrom ..progress import FileLockedException\nfrom ...astrom import StreamingAstromWriter, Source, SourceReading, StreamingVettingWriter\nfrom ...orbfit import Orbfit\n\n__author__ = \"David Rusk <drusk@uvic.ca>\"\n\n\nclass WorkUnit(object):\n    \"\"\"\n    A unit of work to be processed, associated with the data from a single\n    input file.\n    \"\"\"\n\n    def __init__(self, filename,\n                 parsed_data,\n                 progress_manager,\n                 output_context,\n                 dry_run=False):\n        self.filename = filename\n        self.data = parsed_data\n        self.progress_manager = progress_manager\n        self.output_context = output_context\n        self.dry_run = dry_run\n\n        self.sources = StatefulCollection(parsed_data.get_sources())\n\n        self.readings_by_source = {}\n        for source in self.sources:\n            self.readings_by_source[source] = StatefulCollection(source.get_readings())\n\n        self.processed_items = set()\n        self._mark_previously_processed_items()\n\n        if self.is_current_item_processed():\n            self.next_item()\n\n        self.finished_callbacks = []\n\n        self._unlocked = False\n\n    def register_finished_callback(self, callback):\n        self.finished_callbacks.append(callback)\n\n    def next_source(self):\n        next(self.get_sources())\n\n    def previous_source(self):\n        self.get_sources().previous()\n\n    def next_obs(self):\n        next(self.get_current_source_readings())\n\n    def previous_obs(self):\n        self.get_current_source_readings().previous()\n\n    def next_item(self):\n        raise NotImplementedError()\n\n    def accept_current_item(self):\n        self.process_current_item()\n\n    def reject_current_item(self):\n        self.process_current_item()\n\n    def process_current_item(self):\n        self.processed_items.add(self.get_current_item())\n\n        if self.is_current_source_finished():\n            self.progress_manager.record_index(self.get_filename(),\n                                               self.get_current_source_number())\n            self.get_writer().flush()\n\n        if self.is_finished():\n            self.progress_manager.record_done(self.get_filename())\n            self.unlock()\n\n            for callback in self.finished_callbacks:\n                callback(self.get_results_file_paths())\n\n            self._close_writers()\n\n    def get_current_item(self):\n        raise NotImplementedError()\n\n    def get_current_item_index(self):\n        raise NotImplementedError()\n\n    def is_item_processed(self, item):\n        return item in self.processed_items\n\n    def is_current_item_processed(self):\n        return self.is_item_processed(self.get_current_item())\n\n    def is_source_finished(self, source):\n        raise NotImplementedError()\n\n    def is_current_source_finished(self):\n        return self.is_source_finished(self.get_current_source())\n\n    def get_filename(self):\n        return self.filename\n\n    def get_data(self):\n        return self.data\n\n    def get_sources(self):\n        return self.sources\n\n    def get_unprocessed_sources(self):\n        unprocessed_sources = []\n        for source in self.get_sources():\n            if not self.is_source_finished(source):\n                unprocessed_sources.append(source)\n\n        return unprocessed_sources\n\n    def get_source_count(self):\n        return len(self.get_sources())\n\n    def get_current_source(self):\n        source = self.get_sources().get_current_item()\n        assert isinstance(source, Source)\n        return source\n\n    def get_current_source_number(self):\n        return self.get_sources().get_index()\n\n    def get_current_source_readings(self):\n        return self.readings_by_source[self.get_current_source()]\n\n    def get_obs_count(self):\n        return len(self.get_current_source_readings())\n\n    def get_current_reading(self):\n        \"\"\"\n        :return: SourceReading\n        :rtype: SourceReading\n        \"\"\"\n        return self.get_current_source_readings().get_current_item()\n\n    def get_current_obs_number(self):\n        return self.get_current_source_readings().get_index()\n\n    def get_writer(self):\n        raise NotImplementedError()\n\n    def get_results_file_paths(self):\n        raise NotImplementedError()\n\n    def is_finished(self):\n        return len(self._get_item_set() - self.processed_items) == 0\n\n    def is_apcor_needed(self):\n        raise NotImplementedError()\n\n    def unlock(self):\n        if not self._unlocked:\n            self.progress_manager.unlock(self.get_filename(), do_async=True)\n            self._unlocked = True\n\n    def _get_item_set(self):\n        raise NotImplementedError()\n\n    def _mark_previously_processed_items(self):\n        pass\n\n    def _close_writers(self):\n        pass\n\n\nclass RealsWorkUnit(WorkUnit):\n    \"\"\"\n    A unit of work when performing the process reals task.\n    \"\"\"\n\n    def __init__(self,\n                 filename,\n                 parsed_data,\n                 progress_manager,\n                 output_context,\n                 dry_run=False):\n        super(RealsWorkUnit, self).__init__(\n            filename,\n            parsed_data,\n            progress_manager,\n            output_context,\n            dry_run=dry_run)\n        self._writers = {}\n\n    def next_item(self):\n        assert not self.is_finished()\n\n        self.next_obs()\n        while self.is_current_item_processed():\n            self._next_sequential_item()\n\n    def _next_sequential_item(self):\n        \"\"\"\n        Go to the next item in the 'ideal' processing sequence.\n        \"\"\"\n        if self.get_current_source_readings().is_on_last_item():\n            self.next_source()\n        else:\n            self.next_obs()\n\n    def get_current_item(self):\n        return self.get_current_reading()\n\n    def get_current_item_index(self):\n        return (self.get_sources().get_index() * self.get_obs_count() +\n                self.get_current_source_readings().get_index())\n\n    def is_source_finished(self, source):\n        for reading in source.get_readings():\n            if reading not in self.processed_items:\n                return False\n\n        return True\n\n    def is_apcor_needed(self):\n        return True\n\n    def get_writer(self):\n        filename = self.get_output_filename(self.get_current_source())\n        if filename in self._writers:\n            return self._writers[filename]\n\n        writer = self._create_writer(filename)\n        self._writers[filename] = writer\n        return writer\n\n    def get_results_file_paths(self):\n        return [self.output_context.get_full_path(filename)\n                for filename in self._writers]\n\n    def get_output_filename(self, source):\n        if not source.has_provisional_name():\n            raise SourceNotNamedException(source)\n\n        return \".\".join((os.path.basename(self.filename),\n                         source.get_provisional_name(),\n                         \"mpc\"))\n\n    def _create_writer(self, filename):\n        # NOTE: this import is only here so that we don't load up secondary\n        # dependencies (like astropy) used in MPCWriter when they are not\n        # needed (i.e. cands task).  This is to help reduce the application\n        # startup time.\n        from ...mpc import MPCWriter\n\n        return MPCWriter(self.output_context.open(filename),\n                         auto_flush=False)\n\n    def _get_item_set(self):\n        all_readings = set()\n        for readings in self.readings_by_source.values():\n            all_readings.update(readings)\n        return all_readings\n\n    def _mark_previously_processed_items(self):\n        processed_indices = self.progress_manager.get_processed_indices(self.get_filename())\n        for index in processed_indices:\n            for reading in self.get_sources()[index].get_readings():\n                self.processed_items.add(reading)\n\n    def _close_writers(self):\n        for writer in list(self._writers.values()):\n            writer.close()\n\n\nclass CandidatesWorkUnit(WorkUnit):\n    \"\"\"\n    A unit of work when performing the process candidates task.\n    \"\"\"\n\n    def __init__(self,\n                 filename,\n                 parsed_data,\n                 progress_manager,\n                 output_context,\n                 dry_run=False):\n        super(CandidatesWorkUnit, self).__init__(\n            filename,\n            parsed_data,\n            progress_manager,\n            output_context,\n            dry_run=dry_run)\n\n        self._writer = None\n\n    def next_item(self):\n        assert not self.is_finished()\n\n        self.next_source()\n        while self.is_current_item_processed():\n            self.next_source()\n\n    def get_current_item(self):\n        return self.get_current_source()\n\n    def get_current_item_index(self):\n        return self.get_sources().get_index()\n\n    def is_source_finished(self, source):\n        return source in self.processed_items\n\n    def is_apcor_needed(self):\n        return False\n\n    def get_writer(self):\n        if self._writer is None:\n            self._writer = self._create_writer()\n\n        return self._writer\n\n    def get_results_file_paths(self):\n        if self._writer is None:\n            return []\n\n        return [self.output_context.get_full_path(self._writer.get_filename())]\n\n    def get_output_filename(self):\n        return self.get_filename().replace(tasks.get_suffix(tasks.CANDS_TASK),\n                                           tasks.get_suffix(tasks.REALS_TASK))\n\n    def _create_writer(self):\n        filename = self.get_output_filename()\n        return StreamingAstromWriter(self.output_context.open(filename),\n                                     self.data.sys_header)\n\n    def _get_item_set(self):\n        return set(self.sources)\n\n    def _mark_previously_processed_items(self):\n        processed_indices = self.progress_manager.get_processed_indices(self.get_filename())\n        for index in processed_indices:\n            self.processed_items.add(self.get_sources()[index])\n\n    def _close_writers(self):\n        if self._writer is not None:\n            self._writer.close()\n\n\nclass VettingWorkUnit(CandidatesWorkUnit):\n    \"\"\"\n    A unit of work when performing the process candidates task.\n    \"\"\"\n\n\n    def _create_writer(self):\n        filename = self.get_output_filename()\n        return StreamingVettingWriter(self.output_context.open(filename))\n\n    def get_output_filename(self):\n\n        suffix = tasks.get_suffix(tasks.VETTING_TASK)\n        try:\n            base_name = re.search(\"(?P<base_name>.*?)\\.\\d*{}\".format(suffix), self.filename).group('base_name')\n        except:\n            base_name = os.path.splitext(self.filename)[0]\n        filename_pattern = self.output_context.get_full_path(\n            \"{}.?{}\".format(base_name, suffix))\n        file_count = len(glob(filename_pattern))\n        filename = self.output_context.get_full_path(\n            \"{}.{}{}\".format(base_name, file_count, suffix))\n        return filename\n\n\nclass TracksWorkUnit(WorkUnit):\n    \"\"\"\n    A unit of work when performing the process track task.\n    \"\"\"\n\n    def __init__(self,\n                 builder,\n                 filename,\n                 parsed_data,\n                 progress_manager,\n                 output_context,\n                 dry_run=False):\n        super(TracksWorkUnit, self).__init__(\n            filename,\n            parsed_data,\n            progress_manager,\n            output_context,\n            dry_run=dry_run)\n\n        self.builder = builder\n        self._writer = None\n        self._ssos_queried = False\n        self._comparitors = {}\n\n    def print_orbfit_info(self):\n        # TODO: this should not be here.\n        orb = Orbfit(self.get_writer().get_chronological_buffered_observations())\n        print(orb.residuals)\n        print(orb)\n\n    def query_ssos(self):\n        \"\"\"\n        Use the MPC file that has been built up in processing this work\n        unit to generate another workunit.\n        \"\"\"\n        self._ssos_queried = True\n        mpc_filename = self.save()\n        return self.builder.build_workunit(mpc_filename)\n\n    def save(self):\n        \"\"\"\n        Update the SouceReading information for the currently recorded observations and then flush those to a file.\n        @return: mpc_filename of the resulting save.\n        \"\"\"\n        self.get_writer().flush()\n        mpc_filename = self.get_writer().get_filename()\n        self.get_writer().close()\n        self._writer = None\n        return mpc_filename\n\n    def is_finished(self):\n        return self._ssos_queried or self.get_source_count() == 0 or super(TracksWorkUnit, self).is_finished()\n\n    def next_item(self):\n        assert not self.is_finished()\n\n        self.next_obs()\n        while self.is_current_item_processed():\n            self._next_sequential_item()\n\n    def _next_sequential_item(self):\n        \"\"\"\n        Go to the next item to process.\n        \"\"\"\n        if self.get_current_source_readings().is_on_last_item():\n            self.next_source()\n        else:\n            self.next_obs()\n\n    def get_current_item(self):\n        return self.get_current_reading()\n\n    def get_current_item_index(self):\n        return (self.get_sources().get_index() * self.get_obs_count() +\n                self.get_current_source_readings().get_index())\n\n    def is_source_finished(self, source):\n        for reading in source.get_readings():\n            if reading not in self.processed_items:\n                return False\n\n        return True\n\n    def is_apcor_needed(self):\n        return True\n\n    def get_writer(self):\n        \"\"\"\n        Get a writer.\n\n        This method also makes the output filename be the same as the .track file but with .mpc.\n        (Currently only works on local filesystem)\n        :rtype MPCWriter\n        \"\"\"\n        if self._writer is None:\n            suffix = tasks.get_suffix(tasks.TRACK_TASK)\n            try:\n                base_name = re.search(\"(?P<base_name>.*?)\\.\\d*{}\".format(suffix), self.filename).group('base_name')\n            except:\n                base_name = os.path.splitext(self.filename)[0]\n            mpc_filename_pattern = self.output_context.get_full_path(\n                \"{}.?{}\".format(base_name, suffix))\n            mpc_file_count = len(glob(mpc_filename_pattern))\n            mpc_filename = \"{}.{}{}\".format(base_name, mpc_file_count, suffix)\n            self._writer = self._create_writer(mpc_filename)\n\n        return self._writer\n\n    def get_results_file_paths(self):\n        if self._writer is None:\n            return []\n\n        return [self.output_context.get_full_path(self._writer.get_filename())]\n\n    def _create_writer(self, filename):\n        # NOTE: this import is only here so that we don't load up secondary\n        # dependencies (like astropy) used in MPCWriter when they are not\n        # needed (i.e. cands task).  This is to help reduce the application\n        # startup time.\n        from ...mpc import MPCWriter\n\n        writer = MPCWriter(self.output_context.open(filename),\n                           auto_flush=False, auto_discovery=False)\n\n        for rawname in self.data.mpc_observations:\n            writer.write(self.data.mpc_observations[rawname])\n        \n        return writer\n\n    def _get_item_set(self):\n        all_readings = set()\n        for readings in self.readings_by_source.values():\n            all_readings.update(readings)\n        return all_readings\n\n    def _mark_previously_processed_items(self):\n        processed_indices = self.progress_manager.get_processed_indices(self.get_filename())\n        for index in processed_indices:\n            for reading in self.get_sources()[index].get_readings():\n                self.processed_items.add(reading)\n\n    def _close_writers(self):\n        if self._writer is not None:\n            self._writer.close()\n\n\nclass TargetWorkUnit(TracksWorkUnit):\n\n    def __init__(self,\n                 builder,\n                 filename,\n                 parsed_data,\n                 progress_manager,\n                 output_context,\n                 dry_run=False):\n        super(TracksWorkUnit, self).__init__(\n            filename,\n            parsed_data,\n            progress_manager,\n            output_context,\n            dry_run=dry_run)\n\n\nclass WorkUnitProvider(object):\n    \"\"\"\n    Obtains new units of work for the application.\n    \"\"\"\n\n    def __init__(self,\n                 taskid,\n                 directory_context,\n                 progress_manager,\n                 builder,\n                 randomize=False,\n                 name_filter=None):\n        self.taskid = taskid\n        self.directory_context = directory_context\n        self.progress_manager = progress_manager\n        self.builder = builder\n        self.randomize = randomize\n        self.name_filter = name_filter\n        self._done = []\n        self._already_fetched = []\n\n    @property\n    def directory(self):\n        \"\"\"\n        The directory that workunits are being acquired from.\n        \"\"\"\n        return self.directory_context.directory\n\n    def _filter(self, filename):\n        \"\"\"\n        return 'true' if filename doesn't match name_filter regex and should be filtered out of the list.\n        @param filename:\n        @return:\n        \"\"\"\n        return self.name_filter is not None and re.search(self.name_filter, filename) is None\n\n    def get_workunit(self, ignore_list=None):\n        \"\"\"\n        Gets a new unit of work.\n\n        Args:\n          ignore_list: list(str)\n            A list of filenames which should be ignored.  Defaults to None.\n\n        Returns:\n          new_workunit: WorkUnit\n            A new unit of work that has not yet been processed.  A lock on\n            it has been acquired.\n\n        Raises:\n          NoAvailableWorkException\n            There is no more work available.\n        \"\"\"\n        if ignore_list is None:\n            ignore_list = []\n\n        potential_files = self.get_potential_files(ignore_list)\n\n        while len(potential_files) > 0:\n            potential_file = self.select_potential_file(potential_files)\n            potential_files.remove(potential_file)\n            if self._filter(potential_file):\n                continue\n\n            if self.directory_context.get_file_size(potential_file) == 0:\n                continue\n\n            if self.progress_manager.is_done(potential_file):\n                self._done.append(potential_file)\n                continue\n            else:\n                try:\n                    self.progress_manager.lock(potential_file)\n                except FileLockedException:\n                    continue\n\n                self._already_fetched.append(potential_file)\n                return self.builder.build_workunit(\n                    self.directory_context.get_full_path(potential_file))\n\n        logger.info(\"No eligible workunits remain to be fetched.\")\n\n        raise NoAvailableWorkException()\n\n    def get_potential_files(self, ignore_list):\n        \"\"\"\n        Get a listing of files for the appropriate task which may or may\n        not be locked and/or done.\n        \"\"\"\n        logger.debug(f\"Getting a list of potential files using {self.directory_context}\")\n        exclude_prefix = self.taskid == tasks.suffixes.get(tasks.REALS_TASK, '') and 'fk' or None\n        filenames = [filename for filename in\n                     self.directory_context.get_listing(self.taskid, exclude_prefix=exclude_prefix)\n                     if filename not in ignore_list and\n                     filename not in self._done and\n                     filename not in self._already_fetched]\n\n\n        # if the extension is .mpc. then we look for the largest numbered MPC file.\n        # look for the largest numbered MPC file only.\n        if self.taskid == tasks.suffixes.get(tasks.TRACK_TASK, ''):\n            basenames = {}\n            for filename in filenames:\n                fullname = os.path.splitext(filename)[0]\n                if fullname in basenames:\n                    continue\n                basename = os.path.splitext(fullname)[0]\n                # only do the 'maximum' search when the 2nd extension is an integer value\n                try:\n                    idx = int(filename.split('.')[-2])\n                    if idx > basenames.get(basename, 0):\n                        basenames[basename] = idx\n                except:\n                    # since we failed, just keep the file in the list\n                    basenames[fullname] = ''\n            filenames = []\n            for basename in basenames:\n                # sometimes the version is empty, so no '.' is needed\n                version = basenames[basename]\n                version = len(str(version)) > 0 and \".{}\".format(version) or version\n                filenames.append(\"{}{}{}\".format(basename, version, self.taskid))\n                # print basename, basenames[basename], filenames[-1]\n        logger.debug(f\"Got a list of these files {filenames}\")\n\n        return filenames\n\n    def select_potential_file(self, potential_files):\n        if self.randomize:\n            # Don't want predictable patterns in the order we get work units.\n            return random.choice(potential_files)\n        else:\n            return potential_files[0]\n\n    def shutdown(self):\n        pass\n\n\nclass PreFetchingWorkUnitProvider(object):\n    def __init__(self, workunit_provider, prefetch_quantity, image_manager):\n        self.workunit_provider = workunit_provider\n        self.prefetch_quantity = prefetch_quantity\n        self.image_manager = image_manager\n\n        self.fetched_files = []\n        self.workunits = []\n\n        self._threads = []\n        self._all_fetched = False\n\n    @property\n    def directory(self):\n        \"\"\"\n        The directory that workunits are being acquired from.\n        \"\"\"\n        return self.workunit_provider.directory\n\n    def get_workunit(self):\n        if self._all_fetched and len(self.workunits) == 0:\n            raise NoAvailableWorkException()\n\n        if len(self.workunits) > 0:\n            workunit = self.workunits.pop(0)\n        else:\n            logger.debug(f\"No workunits available, getting some using {self.workunit_provider}\")\n            workunit = self.workunit_provider.get_workunit(\n                ignore_list=self.fetched_files)\n            self.fetched_files.append(workunit.get_filename())\n\n        self.trigger_prefetching()\n        logger.debug(\"Returning {}\".format(workunit))\n        return workunit\n\n    def trigger_prefetching(self):\n        if self._all_fetched:\n            return\n\n        num_to_fetch = self.prefetch_quantity - len(self.workunits)\n        logger.info(f\"Fetching {num_to_fetch} files.\")\n        if num_to_fetch < 0:\n            # Prefetch quantity can be 0\n            num_to_fetch = 0\n\n        while num_to_fetch > 0:\n            if self._all_fetched:\n                return\n\n            self.prefetch_workunit()\n            num_to_fetch -= 1\n\n    def prefetch_workunit(self):\n        thread = threading.Thread(target=self._do_prefetch_workunit)\n        self._threads.append(thread)\n        logger.debug(f\"Starting prefetch thread.\")\n        thread.start()\n        logger.debug(f\"Prefetch started.\")\n\n    def _do_prefetch_workunit(self):\n        try:\n            workunit = self.workunit_provider.get_workunit(\n                ignore_list=self.fetched_files)\n            filename = workunit.get_filename()\n\n            # 2 or more threads created back to back could end up\n            # retrieving the same workunit.  Only keep one of them.\n            if filename not in self.fetched_files:\n                self.fetched_files.append(filename)\n                self.workunits.append(workunit)\n                self.image_manager.download_singlets_for_workunit(workunit)\n\n                logger.info(\"%s was prefetched.\" % filename)\n\n        except NoAvailableWorkException:\n            self._all_fetched = True\n\n    def shutdown(self):\n        # Make sure all threads are finished so that no more locks are\n        # acquired\n        for thread in self._threads:\n            thread.join()\n\n        for workunit in self.workunits:\n            workunit.unlock()\n\n\nclass WorkUnitBuilder(object):\n    \"\"\"\n    Used to construct a WorkUnit with its necessary components.\n    \"\"\"\n\n    def __init__(self, parser, input_context, output_context, progress_manager,\n                 dry_run=False):\n        logger.debug(\"Setting up Work Unit Builder.\")\n        self.parser = parser\n        self.input_context = input_context\n        self.output_context = output_context\n        self.progress_manager = progress_manager\n        self.dry_run = dry_run\n\n    def build_workunit(self, input_fullpath):\n        try:\n            parsed_data = self.parser.parse(input_fullpath)\n            logger.debug(\"Parsed %s (%d sources)\" %\n                         (input_fullpath, parsed_data.get_source_count()))\n\n            input_filename = os.path.split(input_fullpath)[1]\n            return self._do_build_workunit(\n                input_filename,\n                parsed_data,\n                self.progress_manager,\n                self.output_context,\n                self.dry_run)\n\n        except AssertionError as e:\n            logger.critical(str(e))\n            events.send(events.NO_AVAILABLE_WORK)\n\n    def _do_build_workunit(self,\n                           filename,\n                           data,\n                           progress_manager,\n                           output_context,\n                           dry_run):\n        raise NotImplementedError()\n\n\nclass TracksWorkUnitBuilder(WorkUnitBuilder):\n    \"\"\"\n    Used to construct a WorkUnit for doing 'Track'\n    \"\"\"\n\n    def __init__(self, parser, input_context, output_context, progress_manager,\n                 dry_run=False):\n        super(TracksWorkUnitBuilder, self).__init__(\n            parser, input_context, output_context, progress_manager,\n            dry_run=dry_run\n        )\n\n    @staticmethod\n    def get_readings(data):\n        # Note: Track workunits only have 1 source\n        return data.get_sources()[0].get_readings()\n\n    @staticmethod\n    def set_readings(data, readings):\n        # Note: Track workunits only have 1 source\n        data.get_sources()[0].readings = readings\n\n    def get_discovery_index(self, data):\n        # Note: there should only be one reading marked \"discovery\" amongst\n        # the data.\n        for i, reading in enumerate(self.get_readings(data)):\n            if reading.discovery:\n                return i\n        logger.warning(\"No discovery index found in track workunit.\")\n        return None\n\n    def move_discovery_to_front(self, data):\n        \"\"\"\n        Moves the discovery triplet to the front of the reading list.\n        Leaves everything else in the same order.\n        \"\"\"\n        readings = self.get_readings(data)\n        discovery_index = self.get_discovery_index(data)\n\n        reordered_readings = (readings[discovery_index:discovery_index + 3] +\n                              readings[:discovery_index] +\n                              readings[discovery_index + 3:])\n\n        self.set_readings(data, reordered_readings)\n\n    def _do_build_workunit(self,\n                           filename,\n                           data,\n                           progress_manager,\n                           output_context,\n                           dry_run):\n\n        # Doesn't work... TODO: FixMe\n        # self.move_discovery_to_front(data)\n\n        return TracksWorkUnit(self, filename, data, progress_manager, output_context, dry_run=dry_run)\n\n\nclass TargetWorkUnitBuilder(TracksWorkUnitBuilder):\n\n    def __init__(self, parser, input_context, output_context, progress_manager,\n                 dry_run=False):\n        super(TracksWorkUnitBuilder, self).__init__(\n            parser, input_context, output_context, progress_manager,\n            dry_run=dry_run)\n\n    @staticmethod\n    def get_readings(data):\n        # Note: Track workunits only have 1 source\n        return data.get_sources()[0].get_readings()\n\n    @staticmethod\n    def set_readings(data, readings):\n        # Note: Track workunits only have 1 source\n        data.get_sources()[0].readings = readings\n\n    def get_discovery_index(self, data):\n        return None\n\n    def move_discovery_to_front(self, data):\n        pass\n\n    def _do_build_workunit(self,\n                           filename,\n                           data,\n                           progress_manager,\n                           output_context,\n                           dry_run):\n\n        # Doesn't work... TODO: FixMe\n        # self.move_discovery_to_front(data)\n\n        return TracksWorkUnit(self, filename, data, progress_manager, output_context, dry_run=dry_run)\n\n\nclass RealsWorkUnitBuilder(WorkUnitBuilder):\n    \"\"\"\n    Used to construct a WorkUnit with its necessary components.\n    Constructs RealsWorkUnits for the process reals task.\n    \"\"\"\n\n    def __init__(self, parser, input_context, output_context, progress_manager,\n                 dry_run=False):\n        super(RealsWorkUnitBuilder, self).__init__(\n            parser, input_context, output_context, progress_manager,\n            dry_run=dry_run)\n\n    def _do_build_workunit(self,\n                           filename,\n                           data,\n                           progress_manager,\n                           output_context,\n                           dry_run):\n        return RealsWorkUnit(\n            filename, data, progress_manager, output_context,\n            dry_run=dry_run)\n\n\nclass CandidatesWorkUnitBuilder(WorkUnitBuilder):\n    \"\"\"\n    Used to construct a WorkUnit with its necessary components.\n    Constructs CandidatesWorkUnits for the process candidates task.\n    \"\"\"\n\n    def __init__(self, parser, input_context, output_context, progress_manager,\n                 dry_run=False):\n        super(CandidatesWorkUnitBuilder, self).__init__(\n            parser, input_context, output_context, progress_manager,\n            dry_run=dry_run)\n\n    def _do_build_workunit(self,\n                           filename,\n                           data,\n                           progress_manager,\n                           output_context,\n                           dry_run):\n        return CandidatesWorkUnit(\n            filename, data, progress_manager, output_context,\n            dry_run=dry_run)\n\n\nclass VettingWorkUnitBuilder(WorkUnitBuilder):\n    \"\"\"\n    Used to construct a WorkUnit with its necessary components.\n    Constructs CandidatesWorkUnits for the process candidates task.\n    \"\"\"\n\n    def __init__(self, parser, input_context, output_context, progress_manager,\n                 dry_run=False):\n        super(VettingWorkUnitBuilder, self).__init__(\n            parser, input_context, output_context, progress_manager,\n            dry_run=dry_run)\n\n    def _do_build_workunit(self,\n                           filename,\n                           data,\n                           progress_manager,\n                           output_context,\n                           dry_run):\n        return VettingWorkUnit(\n            filename, data, progress_manager, output_context,\n            dry_run=dry_run)\n\n\nclass ExamineWorkUnitBuilder(WorkUnitBuilder):\n    \"\"\"\n    Used to construct a WorkUnit with its necessary components.\n    Constructs CandidatesWorkUnits for the process candidates task.\n    \"\"\"\n\n    def __init__(self, parser, input_context, output_context, progress_manager,\n                 dry_run=False):\n        super(ExamineWorkUnitBuilder, self).__init__(\n            parser, input_context, output_context, progress_manager,\n            dry_run=dry_run)\n\n    def _do_build_workunit(self,\n                           filename,\n                           data,\n                           progress_manager,\n                           output_context,\n                           dry_run):\n        return ExamineWorkUnit(\n            filename, data, progress_manager, output_context,\n            dry_run=dry_run)\n\n\nclass ExamineWorkUnit(RealsWorkUnit):\n    pass\n", "repo_name": "OSSOS/MOP", "sub_path": "src/ossos/core/ossos/gui/models/workload.py", "file_name": "workload.py", "file_ext": "py", "file_size_in_byte": 32136, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.StatefulCollection", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.StatefulCollection", "line_number": 40, "usage_type": "call"}, {"api_name": "astrom.Source", "line_number": 133, "usage_type": "argument"}, {"api_name": "exceptions.SourceNotNamedException", "line_number": 249, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}, {"api_name": "mpc.MPCWriter", "line_number": 262, "usage_type": "call"}, {"api_name": "astrom.StreamingAstromWriter", "line_number": 339, "usage_type": "call"}, {"api_name": "astrom.StreamingVettingWriter", "line_number": 363, "usage_type": "call"}, {"api_name": "re.search", "line_number": 369, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 371, "usage_type": "call"}, {"api_name": "os.path", "line_number": 371, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 374, "usage_type": "call"}, {"api_name": "orbfit.Orbfit", "line_number": 406, "usage_type": "call"}, {"api_name": "re.search", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 479, "usage_type": "call"}, {"api_name": "os.path", "line_number": 479, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 482, "usage_type": "call"}, {"api_name": "mpc.MPCWriter", "line_number": 501, "usage_type": "call"}, {"api_name": "re.search", "line_number": 577, "usage_type": "call"}, {"api_name": "progress.FileLockedException", "line_number": 616, "usage_type": "name"}, {"api_name": "exceptions.NoAvailableWorkException", "line_number": 625, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 646, "usage_type": "call"}, {"api_name": "os.path", "line_number": 646, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 649, "usage_type": "call"}, {"api_name": "os.path", "line_number": 649, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 672, "usage_type": "call"}, {"api_name": "exceptions.NoAvailableWorkException", "line_number": 701, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 733, "usage_type": "call"}, {"api_name": "exceptions.NoAvailableWorkException", "line_number": 754, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 787, "usage_type": "call"}, {"api_name": "os.path", "line_number": 787, "usage_type": "attribute"}, {"api_name": "{'MPCWriter': 'mpc.MPCWriter'}", "line_number": 863, "usage_type": "call"}, {"api_name": "{'MPCWriter': 'mpc.MPCWriter'}", "line_number": 900, "usage_type": "call"}, {"api_name": "{'MPCWriter': 'mpc.MPCWriter'}", "line_number": 921, "usage_type": "call"}]}
{"seq_id": "25991765408", "text": "import os\nfrom dotenv import load_dotenv\nimport requests\nfrom pprint import pprint\n\nload_dotenv()\n\nAPI_KEY = os.getenv(\"API_KEY\")\nAPI_URL = os.getenv(\"API_URL\")\n\nurl = f\"{API_URL}/indexes\"\n\nINDEX_NAME = \"test_index\"\n\nheaders = {\"x-api-key\": API_KEY}\n\ndata = {\n    \"engine_id\": \"marengo2.5\",\n    \"index_options\": [\"visual\", \"conversation\", \"text_in_video\", \"logo\"],\n    \"index_name\": INDEX_NAME,\n}\n\nresponse = requests.post(url, headers=headers, json=data)\nINDEX_ID = response.json().get(\"_id\")\n\nprint(f\"Status code: {response.status_code}\")\npprint(response.json())\n", "repo_name": "ayushkhd/neurolearn", "sub_path": "video-to-highlight/twelvelabs_create_index.py", "file_name": "twelvelabs_create_index.py", "file_ext": "py", "file_size_in_byte": 565, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "41", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 6, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 8, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 23, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 27, "usage_type": "call"}]}
{"seq_id": "15638213824", "text": "#!/usr/bin/python3\n\"\"\"script that lists all State objects that contain the\nletter a from the database\n\"\"\"\n\nimport sys\nfrom model_state import Base, State\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nif __name__ == \"__main__\":\n\n    engine = create_engine('mysql+mysqldb://{}:{}@localhost/{}'.format(\n                           sys.argv[1], sys.argv[2], sys.argv[3]),\n                           pool_pre_ping=True)\n    Session = sessionmaker(bind=engine)\n\n    session = Session()\n    state = session.query(State).filter(State.name.contains('a'))\n\n    if state is not None:\n        for stat in state:\n            print(\"{}: {:s}\".format(stat.id, stat.name))\n", "repo_name": "Fikayomi31/alx-higher_level_programming", "sub_path": "0x0F-python-object_relational_mapping/9-model_state_filter_a.py", "file_name": "9-model_state_filter_a.py", "file_ext": "py", "file_size_in_byte": 688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 16, "usage_type": "call"}, {"api_name": "model_state.State", "line_number": 19, "usage_type": "argument"}, {"api_name": "model_state.State.name.contains", "line_number": 19, "usage_type": "call"}, {"api_name": "model_state.State.name", "line_number": 19, "usage_type": "attribute"}]}
{"seq_id": "15602318464", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.loadMain ,name = \"indexpage\"),\n    path('login/',views.login , name= \"login\"),\n    path('signin/',views.singin , name = \"sign\"),\n    path('dashboard/',views.userDashBroard,name='userdashbord'),\n    path('logout/<int:id>',views.logout,name='logout'),\n    path('selectsubject/',views.userDashBrod_selectedsub,name='selecet_subject'),\n    path('addusersubject/',views.addusersubject,name='adduser_subject'),\n    path('removeusersubject/<int:id>',views.removeusersubject,name='removeuser_subject'),\n    path('exampage/', views.exampage, name='exampage'),\n    path('dashboard/json/', views.json, name='test_json'),\n    path('dashboard/json_subject',views.json_subject,name='json_subject'),\n    path('exampage/sub/<int:id>',views.examsubtopic,name=\"examsubtopic\"),#json\n    path('exampage/sub/subexam/<int:id>',views.examsubtopicpage , name =\"examsubtopicpage\"), # real page\n    path('exampage/check',views.testingPost,name=\"check\"),\n    path('exampage/exam_json',views.getExamResult,name='exam_json'),\n    # path('exampage/exam_chart/<int:id>', views.getSubjectChart, name='exam_chart'),   # delete this code\n    path('exampage/json/<int:id>',views.getSubjectChart , name='exam_sub_json'), # get json informatoin\n    path('exampage/json_chart/<int:id>', views.getChart, name='exam_chart'),\n    path('exampage/fullpage/<int:id>',views.fullpaper,name='fullpapaer'),\n    path('dashboard/profile',views.updateUser,name='userprofile'),\n]\n", "repo_name": "badshasha/finalproject", "sub_path": "mainpage/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1513, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}]}
{"seq_id": "38126299093", "text": "import cv2 as cv\nimport numpy as np\n\nimg = cv.imread('Resources/Photos/park.jpg')\ncv.imshow('Park', img)\n\ngray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\ncv.imshow('Gray', gray)\n\n#simple thresholding\nthreshold,thresh = cv.threshold(gray,100,256,cv.THRESH_BINARY)\ncv.imshow('simpleThresh',thresh)\n\nthreshold,thresh_inv = cv.threshold(gray,100,256,cv.THRESH_BINARY_INV)\ncv.imshow('simpleThresh_inv',thresh_inv)\n\n#adaptive thresh\nadaptive = cv.adaptiveThreshold(gray,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,3)\ncv.imshow('adaptive',adaptive)\n\n\ncv.waitKey(0)", "repo_name": "ankur-raut/openCV", "sub_path": "thresholding.py", "file_name": "thresholding.py", "file_ext": "py", "file_size_in_byte": 558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "cv2.imread", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_MEAN_C", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 22, "usage_type": "call"}]}
{"seq_id": "26582672329", "text": "import os\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\ncdir = os.path.dirname(os.path.abspath(__file__))\nnews = pd.read_table(cdir + '/newsCorpora.csv', header=None)\nnews.columns = ['ID', 'TITLE', 'URL', 'PUBLISHER', 'CATEGORY', 'STORY', 'HOSTNAME', 'TIMESTAMP']\n\n# Reuters”, “Huffington Post”, “Businessweek”, “Contactmusic.com”, “Daily Mail”の5社のみを対象とする\nspecific_news = news[news['PUBLISHER'].isin(\n    ['Reuters', 'Huffington Post', 'Businessweek', 'Contactmusic.com', 'Daily Mail'])]\n\n# TITLEとCATEGORYの列のみを抽出する\nspecific_news = specific_news[['TITLE', 'CATEGORY']]\n\n# データをシャッフルする\n# q: このrandom_stateは何？\n# a: 乱数のシード値。同じ値を指定すると、同じ乱数が生成される。\n# q: このfracは何？\n# a: 0.5なら、半分のデータを返す。1なら、全てのデータを返す。\nspecific_news = specific_news.sample(frac=1, random_state=0)\n\n\n# データを学習用と評価用に分割する\n# stratify: ラベルの比率を維持する\ntrain_data, valid_data = train_test_split(specific_news, test_size=0.2, random_state=0, stratify=specific_news['CATEGORY'])\n\n# 評価用データを評価用とテスト用に分割する\nvalid_data, test_data = train_test_split(valid_data, test_size=0.5, random_state=0, stratify=valid_data['CATEGORY'])\n\n# データの保存\ntrain_data.to_csv(cdir + '/train.txt', sep='\\t', index=False)\nvalid_data.to_csv(cdir + '/valid.txt', sep='\\t', index=False)\ntest_data.to_csv(cdir + '/test.txt', sep='\\t', index=False)\n\n", "repo_name": "n4cl/NLP100-2020", "sub_path": "ch06/50.py", "file_name": "50.py", "file_ext": "py", "file_size_in_byte": 1602, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 6, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 29, "usage_type": "call"}]}
{"seq_id": "40715520753", "text": "import logging\nfrom argparse import ArgumentParser\nfrom pathlib import Path\nfrom typing import List\nimport numpy as np\nfrom tqdm import tqdm\nimport pandas as pd\n\n\ndef stack_features(features_files: List[Path]):\n    data = [np.load(str(fl)) for fl in features_files]\n    sample_length = max([ft.shape[0] for ft in data])\n    for i in range(len(data)):\n        if data[i].shape[0] < sample_length:\n            paddings = np.zeros((sample_length - data[i].shape[0], data[i].shape[1]))\n            data[i] = np.concatenate([data[i], paddings], axis=0)\n\n    return np.concatenate(data, axis=-1)\n\n\nif __name__ == '__main__':\n\n    arg_parser = ArgumentParser()\n    arg_parser.add_argument(\"--motion\", action=\"store\", type=str, nargs='*', help=\"List of folders with motion data\")\n    arg_parser.add_argument(\"--audio\", type=str, nargs='*', help=\"List of folders with audio data\")\n    arg_parser.add_argument(\"--phase\", type=str, nargs='*', help=\"List of folders with phase data\")\n    arg_parser.add_argument(\"--dst\", type=str, help=\"Folder to store resulted dataset\")\n    arg_parser.add_argument(\"--first\", action=\"store_true\")\n    arg_parser.add_argument(\"--metadata_trn_path\", type=str, default=None)\n    args = arg_parser.parse_args()\n\n    dst_folder = Path(args.dst)\n    if not dst_folder.exists():\n        dst_folder.mkdir(parents=True)\n    logging.basicConfig(level=logging.INFO, filename=str(dst_folder / \"log.txt\"))\n\n    motion_folders = [Path(folder) for folder in args.motion] if args.motion is not None else []\n    audio_folders = [Path(folder) for folder in args.audio]\n    phase_folders = [Path(folder) for folder in args.phase] if args.phase is not None else []\n    logging.info(f\"Motion: {motion_folders}\\n\"\n                 f\"Audio: {audio_folders}\\n\"\n                 f\"Phase: {phase_folders}\")\n\n    first_file_names = []\n    if args.first:\n        assert args.metadata_trn_path is not None, \"You must specify the path to the thain metadata\"\n        metadata_trn = pd.read_csv(args.metadata_trn_path)\n        for i in range(len(metadata_trn)):\n            file_name = metadata_trn[\"prefix\"].iloc[i]\n            if metadata_trn[\"main-agent_id\"].iloc[i] == 1:\n                first_file_names.append(file_name)\n\n    for audio_path in tqdm(audio_folders[0].glob('*.npy')):\n        if args.first:\n            cur_name = audio_path.name.replace(\"*.npy\", \"\")\n            if cur_name not in first_file_names:\n                continue\n\n        audio_files = [audio_folder / audio_path.name for audio_folder in audio_folders]\n        audio_data = stack_features(audio_files)\n\n        motion_data = None\n        if len(motion_folders) > 0:\n            motion_files = [motion_folder / audio_path.name for motion_folder in motion_folders]\n            motion_data = stack_features(motion_files)\n        phase_data = None\n        if len(phase_folders) > 0:\n            phase_files = [phase_folder / audio_path.name for phase_folder in phase_folders]\n            phase_data = stack_features(phase_files)\n\n        dst_path = dst_folder / audio_path.name.replace('.npy', '.npz')\n        logging.info(f\"{dst_path}:\\n\"\n                     f\"Motion shape: {motion_data.shape if motion_data is not None else None}\\n\"\n                     f\"Audio shape: {audio_data.shape}\\n\"\n                     f\"Phase shape: {phase_data.shape if phase_data is not None else None}\\n\")\n\n        np.savez(str(dst_path), Audio=audio_data, Motion=motion_data, Phase=phase_data)\n", "repo_name": "FineMotion/GENEA_2023", "sub_path": "make_dataset.py", "file_name": "make_dataset.py", "file_ext": "py", "file_size_in_byte": 3448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "41", "api": [{"api_name": "typing.List", "line_number": 10, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 18, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 37, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 38, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 47, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 53, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 77, "usage_type": "call"}]}
{"seq_id": "185078337", "text": "# -*- coding: utf-8 -*-\n\nimport logging\nimport optparse\nimport StringIO\n\nfrom django.core.management.base import BaseCommand\nfrom django.db.utils import IntegrityError\nfrom django.template.defaultfilters import slugify\n\nfrom easy_thumbnails.exceptions import InvalidImageFormatError\nfrom easy_thumbnails.files import get_thumbnailer\n\nfrom opaque_keys import InvalidKeyError\nfrom opaque_keys.edx.keys import CourseKey\nfrom opaque_keys.edx.locator import CourseLocator\nfrom openedx.core.djangoapps.models.course_details import CourseDetails\nfrom xmodule.contentstore.content import StaticContent\nfrom xmodule.contentstore.django import contentstore\nfrom xmodule.exceptions import NotFoundError\nfrom xmodule.modulestore.django import modulestore\n\nfrom courses import settings as courses_settings\nfrom courses.models import Course, CourseUniversityRelation\nfrom universities.models import University\n\nlogger = logging.getLogger(__name__)\n\n\nclass CourseHandler(object):\n\n    def __init__(self, course):\n        self.key = unicode(course.id)\n        self.course = course\n\n    @property\n    def memory_image_file(self):\n        try:\n            asset_location = StaticContent.get_location_from_path(self.image_url)\n            content = contentstore().find(asset_location, as_stream=True)\n        except (NotFoundError, InvalidKeyError):\n            return None\n        image_file = StringIO.StringIO(content.copy_to_in_mem().data)\n        return image_file\n\n    @property\n    def title(self):\n        title = self.course.display_name_with_default\n        return title or ''\n\n    @property\n    def university_name(self):\n        name = ''\n        if self.course.display_organization:\n            name = unicode(self.course.display_organization)\n        return name\n\n    @property\n    def image_location(self):\n        course_locator = CourseLocator.from_string(self.key)\n        location = StaticContent.compute_location(\n            course_locator, self.course.course_image\n        )\n        return location\n\n    @property\n    def image_url(self):\n        location = self.image_location\n        url = unicode(location)\n        return url\n\n    def make_thumbnail(self, options):\n        if not self.memory_image_file:\n            return None\n        base_filename = slugify(self.key)\n        try:\n            thumbnail = get_thumbnailer(\n                self.memory_image_file,\n                relative_name='courses-thumbnails/{}'.format(base_filename)\n            ).get_thumbnail(options)\n        except (InvalidImageFormatError, OSError) as err:\n            logger.error(err)\n            thumbnail = None\n        return thumbnail\n\n    def get_university(self):\n        try:\n            university = University.objects.get(code=self.course.org)\n        except University.DoesNotExist:\n            university = None\n        return university\n\n    def assign_university(self, course):\n        university = self.get_university()\n        assigned = False\n        if university:\n            try:\n                CourseUniversityRelation.objects.create(\n                    course=course,\n                    university=university\n                )\n                assigned = True\n            except IntegrityError:\n                pass\n        return assigned\n\n\nclass Command(BaseCommand):\n    help = \"Update FUN's course data.\"\n    option_list = BaseCommand.option_list + (\n        optparse.make_option(\n            '--force-universities-assignment',\n            action='store_true',\n            dest='assign_universities',\n            default=False,\n            help='Force university assignment on existing courses.',\n        ),\n        optparse.make_option(\n            '--course-id',\n            action='store',\n            type='string',\n            dest='course_id',\n            default='',\n            help='Update only the given course.',\n        ),\n    )\n\n    def update_all_courses(self, mongo_courses, assign_universities=False):\n        '''\n        For each course, we create or update the corresponding\n        course in SQL Course table.\n        '''\n        for mongo_course in mongo_courses:\n            try:\n                self.update_course(\n                    modulestore().get_course(mongo_course.id),\n                    assign_universities=assign_universities\n                )\n            except InvalidKeyError as err:\n                # Log the error but continue indexing other courses\n                logger.error(err)\n\n        self.stdout.write('Number of courses parsed: {}\\n'.format(len(mongo_courses)))\n        return None\n\n    def update_course(self, mongo_course, assign_universities=False):\n        '''\n        For the given course, we create or update the corresponding\n        course in SQL Course table.\n        '''\n\n        course_handler = CourseHandler(mongo_course)\n        key = course_handler.key\n        self.stdout.write('Updating data for course {}\\n'.format(key))\n        course, was_created = Course.objects.get_or_create(key=key)\n        if was_created or assign_universities:\n            university = course_handler.assign_university(course)\n            if university:\n                self.stdout.write('\\t University assigned to \"{}\"\\n'.format(key))\n            else:\n                self.stdout.write('\\t No university assigned to \"{}\"\\n'.format(key))\n        course.is_active = True\n        course.university_display_name = course_handler.university_name\n        course.title = course_handler.title\n        course.image_url = course_handler.image_url\n        thumbnails_info = {}\n        for thumbnail_alias, thumbnails_options in \\\n                courses_settings.FUN_THUMBNAIL_OPTIONS.items():\n            thumbnail = course_handler.make_thumbnail(thumbnails_options)\n            if thumbnail:\n                thumbnails_info[thumbnail_alias] = thumbnail.url\n        course.thumbnails_info = thumbnails_info\n        course.start_date = mongo_course.start\n        course.enrollment_start_date = mongo_course.enrollment_start\n        course.enrollment_end_date = mongo_course.enrollment_end\n        course.end_date = mongo_course.end\n\n        course_key = CourseKey.from_string(key)\n        course.short_description = CourseDetails.fetch_about_attribute(\n            course_key,\n            'short_description',\n        )\n\n        course.save()\n        del course\n        self.stdout.write('Updated course {}\\n'.format(key))\n        return None\n\n    def deactivate_orphan_courses(self, mongo_courses):\n        '''\n        Orphan courses are entries that exist in SQL Course table while the\n        corresponding Mongo courses are not listed anymore. This method deactivate\n        orphan courses.\n        '''\n        courses_keys = [unicode(c.id) for c in mongo_courses]\n        orphan_courses = Course.objects.exclude(key__in=courses_keys)\n        orphan_courses.update(is_active=False)\n        self.stdout.write('Deactivated {} orphan courses\\n'.format(orphan_courses.count()))\n        return None\n\n    def handle(self, *args, **options):\n        '''\n        This command can handle the update of a single course if the course ID\n        if provided. Otherwise, it will update all courses found in MongoDB.\n        '''\n        assign_universities = options.get('assign_universities')\n        course_id = options.get('course_id')\n        if course_id:\n            # course_key is a CourseKey object and course_id its sting representation\n            course_key = CourseKey.from_string(course_id)\n            course = modulestore().get_course(course_key)\n            self.update_course(\n                mongo_course=course,\n                assign_universities=assign_universities\n            )\n        else:\n            courses = modulestore().get_courses()\n            self.update_all_courses(\n                mongo_courses=courses,\n                assign_universities=assign_universities,\n            )\n            self.deactivate_orphan_courses(courses)\n", "repo_name": "openfun/fun-apps", "sub_path": "courses/management/commands/update_courses.py", "file_name": "update_courses.py", "file_ext": "py", "file_size_in_byte": 7902, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "xmodule.contentstore.content.StaticContent.get_location_from_path", "line_number": 39, "usage_type": "call"}, {"api_name": "xmodule.contentstore.content.StaticContent", "line_number": 39, "usage_type": "name"}, {"api_name": "xmodule.contentstore.django.contentstore", "line_number": 40, "usage_type": "call"}, {"api_name": "xmodule.exceptions.NotFoundError", "line_number": 41, "usage_type": "name"}, {"api_name": "opaque_keys.InvalidKeyError", "line_number": 41, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 43, "usage_type": "call"}, {"api_name": "opaque_keys.edx.locator.CourseLocator.from_string", "line_number": 60, "usage_type": "call"}, {"api_name": "opaque_keys.edx.locator.CourseLocator", "line_number": 60, "usage_type": "name"}, {"api_name": "xmodule.contentstore.content.StaticContent.compute_location", "line_number": 61, "usage_type": "call"}, {"api_name": "xmodule.contentstore.content.StaticContent", "line_number": 61, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.slugify", "line_number": 75, "usage_type": "call"}, {"api_name": "easy_thumbnails.files.get_thumbnailer", "line_number": 77, "usage_type": "call"}, {"api_name": "easy_thumbnails.exceptions.InvalidImageFormatError", "line_number": 81, "usage_type": "name"}, {"api_name": "universities.models.University.objects.get", "line_number": 88, "usage_type": "call"}, {"api_name": "universities.models.University.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "universities.models.University", "line_number": 88, "usage_type": "name"}, {"api_name": "universities.models.University.DoesNotExist", "line_number": 89, "usage_type": "attribute"}, {"api_name": "universities.models.University", "line_number": 89, "usage_type": "name"}, {"api_name": "courses.models.CourseUniversityRelation.objects.create", "line_number": 98, "usage_type": "call"}, {"api_name": "courses.models.CourseUniversityRelation.objects", "line_number": 98, "usage_type": "attribute"}, {"api_name": "courses.models.CourseUniversityRelation", "line_number": 98, "usage_type": "name"}, {"api_name": "django.db.utils.IntegrityError", "line_number": 103, "usage_type": "name"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 108, "usage_type": "name"}, {"api_name": "django.core.management.base.BaseCommand.option_list", "line_number": 110, "usage_type": "attribute"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 110, "usage_type": "name"}, {"api_name": "optparse.make_option", "line_number": 111, "usage_type": "call"}, {"api_name": "optparse.make_option", "line_number": 118, "usage_type": "call"}, {"api_name": "xmodule.modulestore.django.modulestore", "line_number": 136, "usage_type": "call"}, {"api_name": "opaque_keys.InvalidKeyError", "line_number": 139, "usage_type": "name"}, {"api_name": "courses.models.Course.objects.get_or_create", "line_number": 155, "usage_type": "call"}, {"api_name": "courses.models.Course.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "courses.models.Course", "line_number": 155, "usage_type": "name"}, {"api_name": "courses.settings.FUN_THUMBNAIL_OPTIONS.items", "line_number": 168, "usage_type": "call"}, {"api_name": "courses.settings.FUN_THUMBNAIL_OPTIONS", "line_number": 168, "usage_type": "attribute"}, {"api_name": "courses.settings", "line_number": 168, "usage_type": "name"}, {"api_name": "opaque_keys.edx.keys.CourseKey.from_string", "line_number": 178, "usage_type": "call"}, {"api_name": "opaque_keys.edx.keys.CourseKey", "line_number": 178, "usage_type": "name"}, {"api_name": "openedx.core.djangoapps.models.course_details.CourseDetails.fetch_about_attribute", "line_number": 179, "usage_type": "call"}, {"api_name": "openedx.core.djangoapps.models.course_details.CourseDetails", "line_number": 179, "usage_type": "name"}, {"api_name": "courses.models.Course.objects.exclude", "line_number": 196, "usage_type": "call"}, {"api_name": "courses.models.Course.objects", "line_number": 196, "usage_type": "attribute"}, {"api_name": "courses.models.Course", "line_number": 196, "usage_type": "name"}, {"api_name": "opaque_keys.edx.keys.CourseKey.from_string", "line_number": 210, "usage_type": "call"}, {"api_name": "opaque_keys.edx.keys.CourseKey", "line_number": 210, "usage_type": "name"}, {"api_name": "xmodule.modulestore.django.modulestore", "line_number": 211, "usage_type": "call"}, {"api_name": "xmodule.modulestore.django.modulestore", "line_number": 217, "usage_type": "call"}]}
{"seq_id": "34778534731", "text": "#!/usr/bin/python\n\nfrom musicazoo.lib.mzbot import MZBot\nimport time\nimport sqlite3\nimport os\nimport re\n\nclass TrackerBot(MZBot):\n\tdef __init__(self,url):\n\t\tself.luid=None\n\t\tself.db=os.path.join(os.path.dirname(os.path.realpath(__file__)),'mz.db')\n\t\tMZBot.__init__(self,url)\n\n\tdef poll(self):\n\t\treq={'cmd':'cur','args':{'parameters':{'youtube':['url']}}}\n\t\tresult=self.assert_success(self.doCommand(req))\n\t\tif result and result['type']=='youtube':\n\t\t\tuid=result['uid']\n\t\t\tif uid != self.luid:\n\t\t\t\turl=result['parameters']['url']\n\t\t\t\tv=re.search(r'watch\\?v=(.+)$',url)\n\t\t\t\tif v:\n\t\t\t\t\tself.got(v.group(1))\n\t\t\t\tself.luid=uid\n\t\telse:\n\t\t\tself.luid=None\n\n\tdef got(self,url):\n\t\tconn=sqlite3.connect(self.db)\n\t\tc=conn.cursor()\n\t\tc.execute('INSERT INTO youtube_history (url) VALUES (?)',[url])\n\t\tconn.commit()\n\t\tconn.close()\n\nif __name__ == \"__main__\":\n\ttb = TrackerBot(\"http://musicazoo.mit.edu/cmd\")\n\twhile True:\n\t\ttime.sleep(3)\n\t\ttb.poll()\n", "repo_name": "kandouss/musicazoo", "sub_path": "musicazoo/bots/top/tracker.py", "file_name": "tracker.py", "file_ext": "py", "file_size_in_byte": 934, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "41", "api": [{"api_name": "musicazoo.lib.mzbot.MZBot", "line_number": 9, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 12, "usage_type": "call"}, {"api_name": "musicazoo.lib.mzbot.MZBot.__init__", "line_number": 13, "usage_type": "call"}, {"api_name": "musicazoo.lib.mzbot.MZBot", "line_number": 13, "usage_type": "name"}, {"api_name": "re.search", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 30, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "29965893486", "text": "import hdl\nreload(hdl)\n\nfrom hdl.models import SparseSlowModel\nfrom hdl.learners import SGD\n\nwhitenpatches = 150000\n\n#l = SGD(model=SparseSlowModel(patch_sz=48,N=2048,T=48,sparse_cost='subspacel1',slow_cost='dist'),datasource='PLoS09_Cars_Planes',batchsize=48,display_every=20000,save_every=20000,eta_target_maxupdate=.05)\n#l = SGD(model=SparseSlowModel(patch_sz=32,N=1028,T=64,sparse_cost='subspacel1',slow_cost='dist'),datasource='PLoS09_Cars_Planes',batchsize=64,display_every=20000,save_every=20000,eta_target_maxupdate=.05)\nl = SGD(model=SparseSlowModel(patch_sz=20,N=400,T=48,sparse_cost='subspacel1',slow_cost='dist',perc_var=99.9),datasource='PLoS09_Cars_Planes',batchsize=48,display_every=1000,save_every=20000,eta_target_maxupdate=.05)\n\ndatabatch = l.get_databatch(whitenpatches)\nl.model.learn_whitening(databatch)\nl.model.setup()\n\nfrom kairos.data import PLoS09\n#from kairos.metrics.kanalysis import kanalysis\n\nfrom devthor.kanalysis import kanalysis_linear as kanalysis\n\nX, Y = PLoS09.get_testing_selection(l.videos,l.chunk_labels)\n\ndef evaluate_kernel(model,X,Y,output_function='infer_abs'):\n\n    offset_y = (X.shape[1] - model.patch_sz)/2\n    offset_x = (X.shape[2] - model.patch_sz)/2\n    X = X[:,offset_y:offset_y+model.patch_sz,offset_x:offset_x+model.patch_sz]\n\n    batch = X.reshape(X.shape[0],X.shape[1]*X.shape[2]).T\n\n    new_X = model.output(model.preprocess(batch),output_function=output_function).T\n\n    k_curve, k_auc = kanalysis(new_X,Y)\n\n    return k_curve, k_auc\n\ndef plot_kanalysis(l,X,Y):\n\n    from matplotlib import pyplot as plt\n    import os\n    from hdl.config import state_dir\n\n    output_functions = ['infer', 'infer_abs', 'proj', 'proj_abs', 'infer_loga', 'proj_loga']\n\n    all_k_auc = []\n    for output_function in output_functions:\n        k_curve, k_auc = evaluate_kernel(l.model,X,Y,output_function)\n\n        all_k_auc.append(k_auc)\n\n        plt.figure(10)\n        plt.clf()\n        plt.plot(range(len(k_curve)),k_curve)\n        plt.xlabel('Kernel dim')\n        plt.ylabel('loss')\n        plt.title('Kernel Analysis: %.4f'%k_auc)\n\n        savepath = os.path.join(state_dir,l.model.model_name + '_' + l.model.tstring)\n        if not os.path.exists(savepath): os.makedirs(savepath)\n\n        fname = os.path.join(savepath,'KAnalysis_%s_%d.png'%(output_function,l.iter))\n        plt.savefig(fname)\n\n    return l.iter, min(all_k_auc)\n\ndef plot_k_results(k_results):\n\n    from matplotlib import pyplot as plt\n    import os\n    from hdl.config import state_dir\n    import numpy as np\n\n    trials = np.array([k[0] for k in k_results])\n    values = np.array([k[1] for k in k_results])\n\n    plt.figure(10)\n    plt.clf()\n    plt.plot(trials,values)\n    plt.xlabel('Save trial')\n    plt.ylabel('KAnalysis AUC')\n    plt.title('Kernel Analysis AUC')\n\n    savepath = os.path.join(state_dir,l.model.model_name + '_' + l.model.tstring)\n    if not os.path.exists(savepath): os.makedirs(savepath)\n\n    fname = os.path.join(savepath,'KAnalysis_AUC_progress.png')\n    plt.savefig(fname)\n\n\nk_results = []\n\nk_results.append(plot_kanalysis(l,X,Y))\nplot_k_results(k_results)\n\nepochs = 20\nfor epoch in range(epochs):\n    l.learn(iterations=10000)\n    k_results.append(plot_kanalysis(l,X,Y))\n    plot_k_results(k_results)\n\nl.change_target(.1)\nepochs = 10\nfor epoch in range(epochs):\n    l.learn(iterations=10000)\n    k_results.append(plot_kanalysis(l,X,Y))\n    plot_k_results(k_results)\n\nl.change_target(.1)\nepochs = 10\nfor epoch in range(epochs):\n    l.learn(iterations=10000)\n    k_results.append(plot_kanalysis(l,X,Y))\n    plot_k_results(k_results)\n\nfrom hdl.display import display_final\ndisplay_final(l.model)\n\n", "repo_name": "cadieu/hdl", "sub_path": "scripts/learn_kairos.py", "file_name": "learn_kairos.py", "file_ext": "py", "file_size_in_byte": 3629, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "41", "api": [{"api_name": "hdl.learners.SGD", "line_number": 11, "usage_type": "call"}, {"api_name": "hdl.models.SparseSlowModel", "line_number": 11, "usage_type": "call"}, {"api_name": "kairos.data.PLoS09.get_testing_selection", "line_number": 22, "usage_type": "call"}, {"api_name": "kairos.data.PLoS09", "line_number": 22, "usage_type": "name"}, {"api_name": "devthor.kanalysis.kanalysis_linear", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "hdl.config.state_dir", "line_number": 59, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "hdl.config.state_dir", "line_number": 84, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "hdl.display.display_final", "line_number": 117, "usage_type": "call"}]}
{"seq_id": "8685709935", "text": "import math\nimport random\nimport json\nimport copy\nfrom tkinter.constants import COMMAND\n\nfrom zipfile import ZipFile\nimport PIL.Image\nfrom PIL import ImageTk\nimport tkinter as tk\nfrom tkinter import ttk, font, messagebox\nfrom ttkthemes import ThemedStyle\n\nfrom tooltip import *\nfrom event_manager import EventManager\nfrom calc import Calculator\nfrom stat_collector import StatCollector\nfrom quotes import Quote\nfrom target import Target\nfrom condition_info import InfoClass\nfrom dice import DiceRoller\nfrom player_window import PlayerWin\nfrom starter import *\nfrom undo_redo import ActionStack\nfrom light_menu import *\nfrom object_builder import ObjectBuilder\n\n\nmap_win = tk.Tk()\nmap_win.overrideredirect(1)\nmap_win.withdraw()\n\nclass BattleMap():\n    def __init__(self, root):\n        self.root = root\n        self.reg_font = ('Papyrus', '14')\n        self.small_font = (\"Papyrus\", \"9\")\n        self.big_font = (\"Papyrus\", \"16\")\n        self.start_win = StartWindow(self.root)\n        self.start_win.btn_new_file.config(command=lambda: self.start_up_seq('new'))\n        self.start_win.btn_open_existing.config(command=lambda: self.start_up_seq('open'))\n        self.start_win.win_start.protocol('WM_DELETE_WINDOW', lambda: self.root.destroy())\n        \n    def start_up_seq(self, opt):\n        if opt == 'new':\n            self.start_win.new_file()\n            self.start_win.btn_start_game.config(command=lambda: self.new_game_btns('start'))\n            self.start_win.btn_cancel.config(command=lambda: self.new_game_btns('cancel'))\n        elif opt == 'open':\n            open_complete = self.start_win.open_file()\n            if open_complete:\n                self.start_win.win_start.destroy()\n                self.main_window()\n\n    def new_game_btns(self, btn):\n        if btn == 'start':\n            start_complete = self.start_win.start_new_battle()\n            if start_complete:\n                self.start_win.win_start.destroy()\n                self.main_window()\n        elif btn == 'cancel':\n            self.start_win.game_start_win.destroy()\n    \n    def main_window(self):\n        self.root.overrideredirect(0)\n\n        game_title = self.root.game_name\n        if len(game_title) > 32:\n            game_title = game_title[0:32] + \"...\"\n        self.root.title(f\"Battle Map | {game_title}\")\n        style = ThemedStyle(self.root)\n        style.theme_use(\"equilux\")\n        bg = style.lookup('TLabel', 'background')\n        fg = style.lookup('TLabel', 'foreground')\n        self.root.configure(bg=style.lookup('TLabel', 'background'))\n\n        # Window definition\n        with ZipFile(self.root.filename, 'r') as savefile:\n            battle_bytes = savefile.read('battle_info.json')\n            battle_obj = json.loads(battle_bytes.decode('utf-8'))\n            self.map_size = battle_obj['map_size']\n            self.round = battle_obj['round']\n            self.turn = battle_obj['turn']\n\n        self.top_frame = ttk.Frame(master=self.root, borderwidth=2, relief='ridge')\n        self.top_frame.pack(side='top', fill='both')\n        self.top_frame.columnconfigure(1, weight=1)\n        self.top_frame.rowconfigure(0, minsize=100, weight=1)\n        self.quote_frame = ttk.Frame(master=self.root)\n        self.quote_frame.pack(side='top', fill='x')\n        self.quote_frame.columnconfigure(0, minsize=20)\n        self.bottom_frame = ttk.Frame(master=self.root, borderwidth=2, relief='ridge')\n        self.bottom_frame.pack(side='top', fill='both', expand=True)\n        self.bottom_frame.columnconfigure(0, minsize=100)\n        self.bottom_frame.columnconfigure(1, weight=1, minsize=500)\n        self.bottom_frame.columnconfigure(2, minsize=150)\n        self.bottom_frame.columnconfigure(3, minsize=50)\n        self.bottom_frame.rowconfigure(0, weight=1, minsize=350)\n        self.controller_frame = ttk.Frame(master=self.root)\n        self.controller_frame.pack(side='top', fill='x')\n\n        self.em = EventManager(self.root)\n        self.calculator = Calculator(self.root)\n        self.quoter = Quote()\n        self.count_quotes = 0\n        self.target = Target(self.root)\n        self.info = InfoClass(self.root)\n        self.dice_roll = DiceRoller(self.root)\n        self.copy_win = PlayerWin(self.root, self.map_size, game_title)\n        self.go_back = ActionStack(self.root)\n\n        # Board Setup\n        lbl_map = ttk.Label(master=self.top_frame, text=game_title, font=('Papyrus', '16'))\n        lbl_map.grid(row=0, column=1)\n        btn_player_win = ttk.Button(master=self.top_frame, command=self.open_for_players, text=\"Player Window\")\n        btn_player_win.grid(row=0, column=2, sticky='se')\n        btn_save = ttk.Button(master=self.top_frame, command=self.save_game, text=\"Save\")\n        btn_save.grid(row=0, column=3, sticky='se')\n        btn_clear = ttk.Button(master=self.top_frame, command=self.clear_map, text=\"Clear Map\")\n        btn_clear.grid(row=0, column=4, sticky='se')\n        btn_input = ttk.Button(master=self.top_frame, command=self.input_creature_window, text=\"New Creature\")\n        btn_input.grid(row=0, column=5, sticky='se')\n        btn_obj = ttk.Button(master=self.top_frame, command=self.input_object_window, text=\"New Object\")\n        btn_obj.grid(row=0, column=6, sticky='se')\n        btn_reset = ttk.Button(master=self.top_frame, command=lambda: self.refresh_map(reset=True), text=\"Reset Map\")\n        btn_reset.grid(row=0, column=7, sticky='se')\n        btn_restart = ttk.Button(master=self.top_frame, command=self.full_reset, text=\"Reset Battle\")\n        btn_restart.grid(row=0, column=8, sticky='se')\n        btn_close_all = ttk.Button(master=self.top_frame, command=self.root.destroy, text=\"Close All\")\n        btn_close_all.grid(row=0, column=9, sticky='se')\n        self.lbl_quote = ttk.Label(master=self.quote_frame, text=\"\", font=self.reg_font)\n        self.lbl_quote.grid(row=0, column=0, sticky='w', pady=5)\n        self.find_quote()\n\n        self.side_board = ttk.Frame(master=self.bottom_frame)\n        self.side_board.grid(row=0, column=0, padx=5, pady=10, sticky=\"nw\")\n        self.side_count = 0\n\n        canvas_frame = ttk.Frame(master=self.bottom_frame, borderwidth=2, relief='ridge')\n        self.grid_canvas = tk.Canvas(master=canvas_frame, bg='gray28', bd=0, highlightthickness=0)\n        grid_scroll_vert = ttk.Scrollbar(master=canvas_frame, command=self.grid_canvas.yview)\n        grid_scroll_horz = ttk.Scrollbar(master=self.bottom_frame, orient='horizontal', command=self.grid_canvas.xview)\n        self.grid_frame = ttk.Frame(master=self.grid_canvas)\n        canvas_frame.grid(row=0, column=1, sticky=\"nsew\")\n        self.grid_canvas.pack(side='left', fill='both', expand=True)\n        self.grid_canvas.config(yscrollcommand=grid_scroll_vert.set, xscrollcommand=grid_scroll_horz.set)\n        grid_scroll_vert.pack(side='right', fill='y')\n        grid_scroll_horz.grid(row=1, column=1, sticky='ew')\n        self.grid_canvas.create_window((4,4), window=self.grid_frame, anchor='nw', tags='self.grid_frame')\n        self.grid_frame.bind(\"<Configure>\", self._on_config)\n        self.grid_canvas.bind('<Enter>', self._on_enter_canvas)\n        self.grid_canvas.bind('<Leave>', self._on_leave_canvas)\n        self.grid_frame.lower()\n\n        self.round_bar = ttk.Frame(master=self.bottom_frame)\n        self.tool_bar = ttk.Frame(master=self.bottom_frame)\n        self.round_bar.grid(row=0, column=2, padx=5, pady=10, sticky=\"nw\")\n        self.tool_bar.grid(row=0, column=3, padx=5, pady=10, sticky=\"nw\")\n\n        # Image paths\n        undo_icon_path = \"entry\\\\bin\\\\red_undo.png\"\n        undo_icon = ImageTk.PhotoImage(image=PIL.Image.open(undo_icon_path).resize((20,20)))\n        redo_icon_path = \"entry\\\\bin\\\\red_redo.png\"\n        redo_icon = ImageTk.PhotoImage(image=PIL.Image.open(redo_icon_path).resize((20,20)))\n        move_icon_path = \"entry\\\\bin\\\\red_icons8-circled-down-left-32.png\"\n        move_icon = ImageTk.PhotoImage(image=PIL.Image.open(move_icon_path).resize((20,20)))\n        trig_icon_path = \"entry\\\\bin\\\\red_trig.png\"\n        trig_icon = ImageTk.PhotoImage(image=PIL.Image.open(trig_icon_path).resize((20,20)))\n        target_icon_path = \"entry\\\\bin\\\\red_target.png\"\n        target_icon = ImageTk.PhotoImage(image=PIL.Image.open(target_icon_path).resize((20,20)))\n        cond_info_icon_path = \"entry\\\\bin\\\\red_page_icon.png\"\n        cond_info_icon = ImageTk.PhotoImage(image=PIL.Image.open(cond_info_icon_path).resize((20,20)))\n        turn_icon_path = \"entry\\\\bin\\\\swords.png\"\n        self.turn_icon = ImageTk.PhotoImage(image=PIL.Image.open(turn_icon_path).resize((20,20)))\n        d20_icon_path = \"entry\\\\bin\\\\red_role-playing.png\"\n        d20_icon = ImageTk.PhotoImage(image=PIL.Image.open(d20_icon_path).resize((20,20)))\n        highlight_path = \"entry\\\\bin\\\\highlight.png\"\n        highlight_img = ImageTk.PhotoImage(image=PIL.Image.open(highlight_path).resize((20,20)))\n\n        ally_path = \"entry\\\\bin\\\\ally_token.png\"\n        self.ally_img = ImageTk.PhotoImage(image=PIL.Image.open(ally_path).resize((27,27)))\n        enemy_path = \"entry\\\\bin\\\\enemy_token.png\"\n        self.enemy_img = ImageTk.PhotoImage(image=PIL.Image.open(enemy_path).resize((27,27)))\n        bystander_path = \"entry\\\\bin\\\\bystander_token.png\"\n        self.bystander_img = ImageTk.PhotoImage(image=PIL.Image.open(bystander_path).resize((27,27)))\n        dead_path = \"entry\\\\bin\\\\dead_token.png\"\n        self.dead_img = ImageTk.PhotoImage(image=PIL.Image.open(dead_path).resize((27,27)))\n\n        up_btn_path = \"entry\\\\bin\\\\up_button.png\"\n        down_btn_path = \"entry\\\\bin\\\\down_button.png\"\n        left_btn_path = \"entry\\\\bin\\\\left_button.png\"\n        right_btn_path = \"entry\\\\bin\\\\right_button.png\"\n        nw_btn_path = \"entry\\\\bin\\\\nw_button.png\"\n        ne_btn_path = \"entry\\\\bin\\\\ne_button.png\"\n        sw_btn_path = \"entry\\\\bin\\\\sw_button.png\"\n        se_btn_path = \"entry\\\\bin\\\\se_button.png\"\n        z_up_btn_path = \"entry\\\\bin\\\\z_up.png\"\n        undo_move_path = \"entry\\\\bin\\\\undo_move.png\"\n        z_down_btn_path = \"entry\\\\bin\\\\z_down.png\"\n        self.up_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(up_btn_path).resize((40,40)))\n        self.down_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(down_btn_path).resize((40,40)))\n        self.left_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(left_btn_path).resize((40,40)))\n        self.right_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(right_btn_path).resize((40,40)))\n        self.nw_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(nw_btn_path).resize((40,40)))\n        self.ne_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(ne_btn_path).resize((40,40)))\n        self.sw_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(sw_btn_path).resize((40,40)))\n        self.se_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(se_btn_path).resize((40,40)))\n        self.z_up_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(z_up_btn_path).resize((40,40)))\n        self.undo_move_img = ImageTk.PhotoImage(image=PIL.Image.open(undo_move_path).resize((40,40)))\n        self.z_down_btn_img = ImageTk.PhotoImage(image=PIL.Image.open(z_down_btn_path).resize((40,40)))\n        \n        self.map_frames = []\n        self.root.token_list = []\n        self.root.obj_list = []\n        self.root.copy_win_open = False\n        self.root.light_params = {}\n\n        # Grid labels\n        for col_spot in range(self.map_size[1]):\n            lbl_grid_top = ttk.Label(master=self.grid_frame, text=col_spot+1, font=self.small_font)\n            lbl_grid_top.grid(row=0, column=col_spot+1)\n            self.grid_frame.columnconfigure(col_spot+1, minsize=33)#, weight=1)\n\n        for row_spot in range(self.map_size[0]):\n            lbl_grid_side = ttk.Label(master=self.grid_frame, text=row_spot+1, font=self.small_font)\n            lbl_grid_side.grid(row=row_spot+1, column=0)\n            self.grid_frame.rowconfigure(row_spot+1, minsize=33)#, weight=1)\n\n        self.grid_frame.columnconfigure(0, minsize=33)#, weight=1)\n        self.grid_frame.rowconfigure(0, minsize=33)#, weight=1)\n\n        # Space frames\n        self.token_labels = []\n        for i in range(self.map_size[0]):\n            self.map_frames.append([])\n            self.token_labels.append([])\n            for j in range(self.map_size[1]):\n                self.space = tk.Frame(master=self.grid_frame, relief=tk.RAISED, borderwidth=1, bg='gray28')\n                self.space.grid(row=i+1, column=j+1, sticky='nsew')\n                self.space.coord = (j, i)\n                CreateToolTip(self.space, text=f\"{i+1}, {j+1}\")\n                self.map_frames[i].append(self.space)\n                self.token_labels[i].append(None)\n        \n        self.initialize()\n\n        go_back_frame = ttk.Frame(master=self.top_frame)\n        go_back_frame.grid(row=0, column=0, sticky='nw')\n        self.btn_undo = tk.Button(master=go_back_frame, command=lambda: self.time_travel(True), image=undo_icon, bd=0, bg='gray28', activebackground='gray28')\n        self.btn_undo.grid(row=0, column=0, padx=5, pady=5, sticky='nw')\n        self.btn_undo.image = undo_icon\n        self.btn_undo['state'] = 'disabled'\n        self.btn_redo = tk.Button(master=go_back_frame, command=lambda: self.time_travel(False), image=redo_icon, bd=0, bg='gray28', activebackground='gray28')\n        self.btn_redo.grid(row=0, column=1, padx=5, pady=5, sticky='nw')\n        self.btn_redo.image = redo_icon\n        self.btn_redo['state'] = 'disabled'\n\n        # Round bar\n        lbl_round_title = ttk.Label(master=self.round_bar, text=\"Round: \", font=self.big_font)\n        lbl_round_title.grid(row=0, column=0, sticky='e')\n        if self.round == 0:\n            tmp_round = \"S\"\n        else:\n            tmp_round = self.round\n        self.lbl_round = ttk.Label(master=self.round_bar, text=tmp_round, font=self.big_font, borderwidth=1, relief=tk.RAISED, width=3, anchor=tk.CENTER)\n        self.lbl_round.grid(row=0, column=1, sticky='w')\n        self.initiative_frame = ttk.Frame(master=self.round_bar)\n        self.initiative_frame.grid(row=1, column=0, columnspan=2, sticky='ew')\n        self.initiative_frame.columnconfigure([0,1], weight=1)\n        btn_next_turn = ttk.Button(master=self.round_bar, text=\"Turn Complete\", command=self.next_turn, width=18)\n        btn_next_turn.grid(row=2, column=0, columnspan=2)\n        btn_next_round = ttk.Button(master=self.round_bar, text=\"Round Complete\", command=self.next_round, width=18)\n        btn_next_round.grid(row=3, column=0, columnspan=2)\n        btn_reset_rounds = ttk.Button(master=self.round_bar, text=\"Reset Rounds\", command=self.reset_round, width=18)\n        btn_reset_rounds.grid(row=4, column=0, columnspan=2)\n\n        # Tool bar Buttons\n        self.btn_move = ttk.Button(master=self.tool_bar, command=self.move_token, image=move_icon)\n        self.btn_move.grid(row=0, column=0, sticky=\"n\")\n        self.btn_move.image = move_icon\n        CreateToolTip(self.btn_move, text=\"Move Token\", left_disp=True)\n\n        self.btn_trig = ttk.Button(master=self.tool_bar, command=self.open_trig, image=trig_icon)\n        self.btn_trig.grid(row=1, column=0, sticky='n')\n        self.btn_trig.image = trig_icon\n        CreateToolTip(self.btn_trig, text=\"Distance\", left_disp=True)\n\n        self.btn_target = ttk.Button(master=self.tool_bar, command=self.target_item,image=target_icon)\n        self.btn_target.grid(row=2, column=0, sticky='n')\n        self.btn_target.image = target_icon\n        CreateToolTip(self.btn_target, text=\"Target\", left_disp=True)\n\n        self.btn_cond_info = ttk.Button(master=self.tool_bar, command=self.show_cond_info, image=cond_info_icon)\n        self.btn_cond_info.grid(row=3, column=0, sticky='n')\n        self.btn_cond_info.image = cond_info_icon\n        CreateToolTip(self.btn_cond_info, text=\"Condition Info\", left_disp=True)\n\n        self.btn_dice_roller = ttk.Button(master=self.tool_bar, command=self.open_dice_roller, image=d20_icon)\n        self.btn_dice_roller.grid(row=4, column=0, sticky='n')\n        self.btn_dice_roller.image = d20_icon\n        CreateToolTip(self.btn_dice_roller, text=\"Dice Roller\", left_disp=True)\n\n        self.btn_field_light = ttk.Button(master=self.tool_bar, command=self.field_light, image=highlight_img)\n        self.btn_field_light.grid(row=5, column=0, sticky='n')\n        self.btn_field_light.image = highlight_img\n        CreateToolTip(self.btn_field_light, text=\"Field Highlight\", left_disp=True)\n\n        #Controller Pane\n        self.controller_frame.columnconfigure(0, weight=1)\n        dpad_frame = ttk.Frame(master=self.controller_frame)\n        dpad_frame.grid(row=0, column=0, rowspan=4, sticky='w', padx=100)\n        btn_nw = tk.Button(master=dpad_frame, image=self.nw_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.dpad_move('nw'))\n        btn_nw.grid(row=0, column=0)\n        btn_nw.image = self.nw_btn_img\n        btn_nw.name = 'nw'\n        btn_up = tk.Button(master=dpad_frame, image=self.up_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.dpad_move('n'))\n        btn_up.grid(row=0, column=1)\n        btn_up.image = self.up_btn_img\n        btn_up.name = 'up'\n        btn_ne = tk.Button(master=dpad_frame, image=self.ne_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.dpad_move('ne'))\n        btn_ne.grid(row=0, column=2)\n        btn_ne.image = self.ne_btn_img\n        btn_ne.name = 'ne'\n        btn_left = tk.Button(master=dpad_frame, image=self.left_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.dpad_move('w'))\n        btn_left.grid(row=1, column=0)\n        btn_left.image = self.left_btn_img\n        btn_left.name = 'w'\n        btn_right = tk.Button(master=dpad_frame, image=self.right_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.dpad_move('e'))\n        btn_right.grid(row=1, column=2)\n        btn_right.image = self.right_btn_img\n        btn_right.name = 'e'\n        btn_sw = tk.Button(master=dpad_frame, image=self.sw_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.dpad_move('sw'))\n        btn_sw.grid(row=2, column=0)\n        btn_sw.image = self.sw_btn_img\n        btn_sw.name = 'sw'\n        btn_down = tk.Button(master=dpad_frame, image=self.down_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.dpad_move('s'))\n        btn_down.grid(row=2, column=1)\n        btn_down.image = self.down_btn_img\n        btn_down.name = 's'\n        btn_se = tk.Button(master=dpad_frame, image=self.se_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.dpad_move('se'))\n        btn_se.grid(row=2, column=2)\n        btn_se.image = self.se_btn_img\n        btn_se.name = 'se'\n        btn_z_up = tk.Button(master=dpad_frame, image=self.z_up_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.zpad('+'))\n        btn_z_up.grid(row=0, column=3, padx=10)\n        btn_z_up.image = self.z_up_btn_img\n        btn_undo_move = tk.Button(master=dpad_frame, image=self.undo_move_img, bg='gray28', bd=0, activebackground='gray28', command=self.undo_move)\n        btn_undo_move.grid(row=1, column=3, padx=10)\n        btn_undo_move.image = self.undo_move_img\n        btn_undo_move.name = 'undom'\n        btn_z_down = tk.Button(master=dpad_frame, image=self.z_down_btn_img, bg='gray28', bd=0, activebackground='gray28', command=lambda: self.zpad('-'))\n        btn_z_down.grid(row=2, column=3, padx=10)\n        btn_z_down.image = self.z_down_btn_img\n        self.z_frame = tk.Frame(master=dpad_frame, bg='gray28')\n        self.z_frame.grid(row=1, column=1, sticky='nsew')\n        self.z_frame.name = 'zf'\n\n        cont_btn_frame = ttk.Frame(master=self.controller_frame)\n        cont_btn_frame.grid(row=0, column=1, rowspan=4, sticky='e', padx=20)\n        btn_turn_complete = ttk.Button(master=cont_btn_frame, text=\"Turn Complete\", command=self.next_turn, width=19)\n        btn_turn_complete.grid(row=0, column=0, columnspan=2)\n        self.cont_targets = ttk.Combobox(master=cont_btn_frame, width=18, state='readonly')\n        self.cont_targets.grid(row=1, column=0, columnspan=2)\n        self.cont_targets.bind(\"<<ComboboxSelected>>\", self._on_select_target)\n        self.target_names = []\n        self.ent_target_delta = ttk.Entry(master=cont_btn_frame, width=20)\n        self.ent_target_delta.grid(row=2, column=0, columnspan=2, pady=5)\n        self.ent_target_delta.insert(0, '0')\n        self.ent_target_delta.bind(\"<FocusIn>\", lambda e: self._on_delta_focus(event=e, typ='in'))\n        self.ent_target_delta.bind(\"<FocusOut>\", lambda e: self._on_delta_focus(event=e, typ='out'))\n        btn_heal = ttk.Button(master=cont_btn_frame, text='Heal', command=lambda: self.target_hp('heal'), width=8)\n        btn_heal.grid(row=3, column=0, padx=5, pady=5)\n        btn_dmg = ttk.Button(master=cont_btn_frame, text='Damage', command=lambda: self.target_hp('dmg'), width=8)\n        btn_dmg.grid(row=3, column=1, padx=5, pady=5)\n        lbl_ac = ttk.Label(master=cont_btn_frame, text=\"AC: \", font=self.reg_font)\n        lbl_ac.grid(row=0, column=2, sticky='w', pady=5)\n        lbl_max_hp = ttk.Label(master=cont_btn_frame, text=\"Max HP: \", font=self.reg_font)\n        lbl_max_hp.grid(row=1, column=2, sticky='w', pady=5)\n        lbl_curr_hp = ttk.Label(master=cont_btn_frame, text=\"Current HP: \", font=self.reg_font)\n        lbl_curr_hp.grid(row=2, column=2, sticky='w', pady=5)\n        lbl_temp_hp = ttk.Label(master=cont_btn_frame, text=\"Temp HP: \", font=self.reg_font)\n        lbl_temp_hp.grid(row=3, column=2, sticky='w', pady=5)\n        self.lbl_target_ac = ttk.Label(master=cont_btn_frame, text=\"\", font=self.reg_font)\n        self.lbl_target_ac.grid(row=0, column=3, sticky='w', padx=5, pady=5)\n        self.lbl_target_max_hp = ttk.Label(master=cont_btn_frame, text=\"\", font=self.reg_font)\n        self.lbl_target_max_hp.grid(row=1, column=3, sticky='w', padx=5, pady=5)\n        self.lbl_target_hp = ttk.Label(master=cont_btn_frame, text=\"\", font=self.reg_font)\n        self.lbl_target_hp.grid(row=2, column=3, sticky='w', padx=5, pady=5)\n        self.lbl_target_temp_hp = ttk.Label(master=cont_btn_frame, text=\"\", font=self.reg_font)\n        self.lbl_target_temp_hp.grid(row=3, column=3, sticky='w', padx=5, pady=5)\n\n        lbl_title_turn = ttk.Label(master=self.controller_frame, text=\"Current Turn\", font=self.big_font)\n        lbl_title_turn.grid(row=0, column=2, sticky='e', padx=20)\n        self.lbl_current_turn = tk.Label(master=self.controller_frame, text=\"\", font=self.reg_font, bg='gray28')\n        self.lbl_current_turn.grid(row=1, column=2, sticky='e', padx=20)\n        lbl_title_pos = ttk.Label(master=self.controller_frame, text=\"Position\", font=self.big_font)\n        lbl_title_pos.grid(row=2, column=2, sticky='e', padx=20)\n        self.lbl_position = ttk.Label(master=self.controller_frame, text=\"\", font=self.reg_font)\n        self.lbl_position.grid(row=3, column=2, sticky='e', padx=20)\n        lbl_max_move_title = ttk.Label(master=self.controller_frame, text=\"Movement Speed\", font=self.big_font)\n        lbl_max_move_title.grid(row=0, column=3, sticky='e', padx=20)\n        self.lbl_max_move = tk.Label(master=self.controller_frame, text=\"\", font=self.reg_font, bg='gray28', fg='gray70')\n        self.lbl_max_move.grid(row=1, column=3, sticky='e', padx=20)\n        lbl_amount_move_title = ttk.Label(master=self.controller_frame, text=\"Feet Moved\", font=self.big_font)\n        lbl_amount_move_title.grid(row=2, column=3, sticky='e', padx=20)\n        self.lbl_amount_moved = tk.Label(master=self.controller_frame, text=\"\", font=self.reg_font, bg='gray28', fg='gray70')\n        self.lbl_amount_moved.grid(row=3, column=3, sticky='e', padx=20)\n\n        self.z_delta = 0\n\n        self.root.bind(\"<Key>\", self._on_numpad_keys)\n        self.controller_frame.bind(\"<Button-1>\", self._on_defocus)\n\n        self.place_tokens()\n        self.root.deiconify()\n\n    def _on_config(self, event):\n        self.grid_canvas.configure(scrollregion=self.grid_canvas.bbox('all'))\n\n    def _on_enter_canvas(self, event):\n        self.grid_canvas.bind_all('<MouseWheel>', self._on_mousewheel)\n        self.grid_canvas.bind_all('<Shift-MouseWheel>', self._on_shift_mousewheel)\n    \n    def _on_leave_canvas(self, event):\n        self.grid_canvas.unbind_all('<MouseWheel>')\n        self.grid_canvas.unbind_all('<Shift-MouseWheel>')\n\n    def _on_mousewheel(self, event):\n        self.grid_canvas.yview_scroll(int(-1*(event.delta/120)), 'units')\n\n    def _on_shift_mousewheel(self, event):\n        self.grid_canvas.xview_scroll(int(-1*(event.delta/120)), 'units')\n\n    def _on_select_target(self, event):\n        for being in self.root.token_list:\n            if being['name'] == self.cont_targets.get():\n                sel_obj = being\n        self.lbl_target_ac.config(text=sel_obj['ac'])\n        self.lbl_target_max_hp.config(text=sel_obj['max_HP'])\n        self.lbl_target_hp.config(text=sel_obj['current_HP'])\n        self.lbl_target_temp_hp.config(text=sel_obj['temp_HP'])\n\n    def _on_numpad_keys(self, event):\n        # Controller movements\n        if event.keysym == '0' or event.keysym == 'Insert':\n            self.undo_move()\n        elif event.keysym == '1' or event.keysym == 'End':\n            self.dpad_move('sw')\n        elif event.keysym == '2' or event.keysym == 'Down':\n            self.dpad_move('s')\n        elif event.keysym == '3' or event.keysym == 'Next':\n            self.dpad_move('se')\n        elif event.keysym == '4' or event.keysym == 'Left':\n            self.dpad_move('w')\n        elif event.keysym == '5' or event.keysym == 'Clear':\n            if self.z_delta != 0:\n                if self.z_delta == 1:\n                    self.dpad_move('+')\n                elif self.z_delta == -1:\n                    self.dpad_move('-')\n        elif event.keysym == '6' or event.keysym == 'Right':\n            self.dpad_move('e')\n        elif event.keysym == '7' or event.keysym == 'Home':\n            self.dpad_move('nw')\n        elif event.keysym == '8' or event.keysym == 'Up':\n            self.dpad_move('n')\n        elif event.keysym == '9' or event.keysym == 'Prior':\n            self.dpad_move('ne')\n        elif event.keysym == 'minus':\n            self.zpad('-')\n        elif event.keysym == 'plus':\n            self.zpad('+')\n        elif event.keysym == 'Return':\n            self.next_turn()\n        \n        if self.z_delta == 0:\n            self.z_frame.config(bg='gray28')\n            self.root.unbind_all(\"<Button-1>\")\n\n    def _on_delta_focus(self, event, typ):\n        if typ == 'in':\n            self.root.unbind(\"<Key>\")\n        elif typ == 'out':\n            self.root.bind(\"<Key>\", self._on_numpad_keys)\n\n    def _on_defocus(self, event):\n        event.widget.focus_set()\n\n    def initialize(self):\n        self.root.token_list = []\n        self.root.obj_list = []\n        with ZipFile(self.root.filename, \"r\") as savefile:\n            creat_bytes = savefile.read('creatures.json')\n            creat_str = creat_bytes.decode('utf-8')\n            creatures = json.loads(creat_str)\n            obj_bytes = savefile.read('objects.json')\n            obj_str = obj_bytes.decode('utf-8')\n            objects = json.loads(obj_str)\n        for being in creatures.values():\n            self.root.token_list.append(being)\n        for thing in objects.values():\n            self.root.obj_list.append(thing)\n\n    def place_tokens(self):\n        self.initiative_holder = {}\n        spaces_taken = []\n        self.target_names = []\n        for item in self.root.obj_list:\n            occupied = False\n            if item[\"coordinate\"][0] != \"\" and item[\"coordinate\"][1] != \"\":\n                row_pos = int(item[\"coordinate\"][1])\n                col_pos = int(item[\"coordinate\"][0])\n                self.target_names.append(item['name'])\n                for space_tuple in spaces_taken:\n                    if space_tuple[0] == row_pos and space_tuple[1] == col_pos and space_tuple[2] == int(item[\"coordinate\"][2]):\n                        occupied = True\n                if occupied == False:\n                    spaces_taken.append((row_pos, col_pos, int(item[\"coordinate\"][2])))\n                    o_length = item[\"length\"]\n                    o_width = item[\"width\"]\n                    f_len = 5 * round(o_length / 5)\n                    if f_len < 5:\n                        f_len = 5\n                    f_wid = 5 * round(o_width / 5)\n                    if f_wid < 5:\n                        f_wid = 5\n                    o_col = int(f_wid / 5)\n                    o_row = int(f_len / 5)\n                    for x in range(o_col):\n                        col_pos = int(item[\"coordinate\"][0]) + x\n                        for y in range(o_row):\n                            row_pos = int(item[\"coordinate\"][1]) + y\n                            obj_img = ImageTk.PhotoImage(image=PIL.Image.open(item[\"img_ref\"]).resize((30,30)))\n                            lbl_unit = tk.Label(master=self.map_frames[col_pos][row_pos], image=obj_img, bg=\"gray28\", borderwidth=0)\n                            lbl_unit.image = obj_img\n                            lbl_unit.coord = (row_pos, col_pos)\n                            lbl_unit.pack(fill='both', expand=True, padx=2, pady=2)\n                            CreateToolTip(lbl_unit, text=f\"{item['name']}: {row_pos}, {col_pos}\", left_disp=True)\n\n        for being in self.root.token_list:\n            token_type = being[\"type\"]\n            if token_type == \"ally\":\n                token_img = self.ally_img\n            elif token_type == \"enemy\":\n                token_img = self.enemy_img\n            elif token_type == \"bystander\":\n                token_img = self.bystander_img\n            elif token_type == \"dead\":\n                token_img = self.dead_img\n            else:\n                raise NameError(\"Token type not specified.\")\n            \n            occupied = False\n            if being[\"coordinate\"][0] != \"\" and being[\"coordinate\"][1] != \"\":\n                row_pos = int(being[\"coordinate\"][1])\n                col_pos = int(being[\"coordinate\"][0])\n                self.target_names.append(being['name'])\n                for space_tuple in spaces_taken:\n                    if space_tuple[0] == row_pos and space_tuple[1] == col_pos and space_tuple[2] == int(being[\"coordinate\"][2]):\n                        occupied = True\n                if occupied == False:\n                    spaces_taken.append((row_pos, col_pos, int(being[\"coordinate\"][2])))\n                    lbl_unit = tk.Label(master=self.map_frames[col_pos][row_pos], image=token_img, bg=\"gray28\", borderwidth=0)\n                    lbl_unit.image = token_img\n                    lbl_unit.coord = (row_pos, col_pos)\n                    lbl_unit.pack(fill='both', expand=True, padx=2, pady=2)\n                    self.token_labels[col_pos][row_pos] = lbl_unit\n                    CreateToolTip(lbl_unit, text=\"{0}, {1}\".format(being[\"name\"], being[\"coordinate\"][2]), left_disp=True)\n                    if being['initiative'] != math.inf:\n                        self.initiative_holder[being['name']] = being\n                    if being[\"size\"] == \"large\" or being[\"size\"] == \"huge\" or being[\"size\"] == \"gargantuan\":\n                        if being[\"size\"] == \"large\":\n                            space_need = 4\n                        elif being[\"size\"] == \"huge\":\n                            space_need = 9\n                        else:\n                            space_need = 16\n                        row_offset = 0\n                        col_offset = 0\n                        go_to_next_row = math.sqrt(space_need)\n                        for i in range(1, space_need):\n                            if i < space_need:\n                                col_offset += 1\n                                if col_offset == go_to_next_row:\n                                    col_offset = 0\n                                    row_offset += 1\n                                row_pos = int(being[\"coordinate\"][1]) + row_offset\n                                col_pos = int(being[\"coordinate\"][0]) + col_offset\n                                lbl_unit = tk.Label(master=self.map_frames[col_pos][row_pos], image=token_img, bg=\"gray28\", borderwidth=0)\n                                lbl_unit.image = token_img\n                                lbl_unit.coord = (row_pos, col_pos)\n                                lbl_unit.pack(fill='both', expand=True)\n                                CreateToolTip(lbl_unit, text=\"{0}, {1}\".format(being[\"name\"], being[\"coordinate\"][2]), left_disp=True)\n                else:\n                    messagebox.showerror(\"Internal Error\", \"Restart program\\nError 0x006\")\n                    return\n\n            else:\n                self.unused_tokens(being, token_img)\n        self.cont_targets.config(values=self.target_names)\n        self.refresh_initiatives()\n    \n    def unused_tokens(self, creature, token_img):\n        next_row = int(self.side_count / 2)\n        next_col = self.side_count % 2\n        lbl_side_unit = tk.Label(master=self.side_board, image=token_img, bg=\"gray28\", borderwidth=0)\n        lbl_side_unit.grid(row=next_row, column=next_col, padx=5, pady=5, sticky=\"ne\")\n        #lbl_side_unit.bind(\"<Button-3>\", self.em.right_click_menu)\n        lbl_side_unit.image = token_img\n        CreateToolTip(lbl_side_unit, text=creature[\"name\"])\n        self.side_count += 1\n    \n    def post_initiatives(self):\n        init_dict_in_order = {k:v for k, v in sorted(self.initiative_holder.items(), key= lambda item: item[1]['initiative'], reverse=True)}\n        order_count = 0\n        lbl_turn_img = tk.Label(master=self.initiative_frame, image=self.turn_icon, bg=\"gray28\", borderwidth=0)\n        lbl_turn_img.grid(row=self.turn, column=0, sticky='w')\n        lbl_turn_img.image = self.turn_icon\n        self.move_path = []\n\n        for next_up in init_dict_in_order.items():\n            if next_up[1]['initiative'] != math.inf and next_up[1]['type'] != 'dead':\n                lbl_your_turn = ttk.Label(master=self.initiative_frame, text=f\"{next_up[0]}: \", font=self.small_font)\n                lbl_your_turn.grid(row=order_count, column=1, sticky='w')\n                lbl_your_init = ttk.Label(master=self.initiative_frame, text=next_up[1]['initiative'], font=self.small_font)\n                lbl_your_init.grid(row=order_count, column=2, sticky='e')\n                if order_count == self.turn:\n                    self.turn_obj = next_up[1]\n                    curr_pos = (int(self.turn_obj['coordinate'][0]), int(self.turn_obj['coordinate'][1]), int(self.turn_obj['coordinate'][2]))\n                    self.move_path.append(curr_pos)\n                    self.lbl_current_turn.config(text=self.turn_obj['name'])\n                    self.lbl_max_move.config(text=self.turn_obj['speed'])\n                    self.lbl_position.config(text=f\"{curr_pos[0]+1}: {curr_pos[1]+1}: {curr_pos[2]}\")\n                    self.lbl_amount_moved.config(text=\"0\")\n                    self.map_frames[curr_pos[0]][curr_pos[1]].config(bg='orange3')\n                    if self.turn_obj['status'] == 'PC':\n                        self.lbl_current_turn.config(fg='green3')\n                    elif self.turn_obj['status'] == 'Monster':\n                        self.lbl_current_turn.config(fg='orange3')\n                    else:\n                        self.lbl_current_turn.config(fg='DodgerBlue2')\n\n                    if self.root.copy_win_open:\n                        if self.turn_obj['status'] != 'PC':\n                            self.copy_win.set_turn_lbl(\"X\")\n                        else:\n                            self.copy_win.set_turn_lbl(self.turn_obj['name'])\n                order_count += 1\n\n    def refresh_initiatives(self):\n        init_frame_slaves = self.initiative_frame.grid_slaves()\n        if len(init_frame_slaves):\n            for item in init_frame_slaves:\n                item.destroy()\n        for i in range(len(self.map_frames)):\n            for frm in self.map_frames[i]:\n                frm.config(bg='gray28')\n        self.post_initiatives()\n\n    def next_turn(self, not_from_redo=True):\n        self.lbl_amount_moved.config(bg='gray28')\n        if not_from_redo:\n            self.log_action('turn button')\n        on_board_inits = self.initiative_holder\n        inf_exists = True\n        fucked_up = 100\n        while inf_exists and fucked_up > 0:\n            for key, value in on_board_inits.items():\n                if value == math.inf:\n                    del on_board_inits[key]\n                    break\n            if math.inf not in on_board_inits:\n                inf_exists = False\n            fucked_up -= 1\n        self.turn += 1\n        if self.turn > len(self.initiative_holder) - 1:\n            self.next_round()\n        else:\n            for being in self.root.token_list:\n                if being['name'] == self.turn_obj['name']:\n                    being['coordinate'] = [str(self.move_path[-1][0]), str(self.move_path[-1][1]), str(self.move_path[-1][2])]\n            if self.root.copy_win_open:\n                self.copy_win.gray_map()\n            self.refresh_map()\n            #self.refresh_initiatives()\n\n    def next_round(self, not_from_redo=True):\n        if not_from_redo:\n            self.log_action('round button', {'turn': self.turn})\n        self.round += 1\n        self.lbl_round.config(text=self.round)\n        self.turn = 0\n        for being in self.root.token_list:\n            if being['name'] == self.turn_obj['name']:\n                being['coordinate'] = [str(self.move_path[-1][0]), str(self.move_path[-1][1]), str(self.move_path[-1][2])]\n        if self.root.copy_win_open:\n            self.copy_win.gray_map()\n        self.refresh_map()\n        #self.refresh_initiatives()\n\n    def reset_round(self, not_from_redo=True):\n        if not_from_redo:\n            restore_round = {\n                'round': self.round,\n                'turn': self.turn\n            }\n            self.log_action('reset round', restore_round)\n        self.round = 0\n        self.lbl_round.config(text=\"S\")\n        self.turn = 0\n        self.refresh_map()\n        #self.refresh_initiatives()\n\n    def refresh_map(self, reset=False):\n        for row in self.map_frames:\n            for col in row:\n                remove_tokens = col.pack_slaves()\n                if len(remove_tokens) > 0:\n                    for token in remove_tokens:\n                        token.destroy()\n        remove_side_list = self.side_board.grid_slaves()\n        if len(remove_side_list) > 0:\n            for side_token in remove_side_list:\n                side_token.destroy()\n        self.side_count = 0\n\n        if reset:\n            self.initialize()\n        self.place_tokens()\n        if self.root.copy_win_open:\n            self.copy_win.update_players()\n        self.refresh_initiatives()\n\n    def open_for_players(self):\n        self.copy_win.start_win()\n        self.refresh_map()\n\n    def save_game(self):\n        new_token_dict = {}\n        for being in self.root.token_list:\n            name = being[\"name\"]\n            new_token_dict[name] = being\n        new_object_dict = {}\n        for obj in self.root.obj_list:\n            obj_name = obj[\"name\"]\n            new_object_dict[obj_name] = obj\n        battle_dict = {\n            \"map_size\": self.map_size,\n            \"round\": self.round,\n            \"turn\": self.turn\n        }\n        battleJSON = json.dumps(battle_dict, indent=4)\n        with ZipFile(self.root.filename, \"w\") as savefile:\n            creatJSON = json.dumps(new_token_dict, indent=4)\n            objJSON = json.dumps(new_object_dict, indent=4)\n            savefile.writestr('battle_info.json', battleJSON)\n            savefile.writestr('creatures.json', creatJSON)\n            savefile.writestr('objects.json', objJSON)\n        self.go_back.clear_all()\n\n    def clear_map(self):\n        restore_tokens = copy.deepcopy(self.root.token_list)\n        self.log_action('list', restore_tokens)\n        for being in self.root.token_list:\n            being[\"coordinate\"] = ['', '', '']\n        self.refresh_map()\n\n    def dpad_move(self, dir):\n        last_pos = copy.deepcopy(self.move_path[-1])\n        if dir == 'n':\n            curr_pos = (last_pos[0] - 1, last_pos[1], last_pos[2] + self.z_delta)\n        elif dir == 's':\n            curr_pos = (last_pos[0] + 1, last_pos[1], last_pos[2] + self.z_delta)\n        elif dir == 'w':\n            curr_pos = (last_pos[0], last_pos[1] - 1, last_pos[2] + self.z_delta)\n        elif dir == 'e':\n            curr_pos = (last_pos[0], last_pos[1] + 1, last_pos[2] + self.z_delta)\n        elif dir == 'ne':\n            curr_pos = (last_pos[0] - 1, last_pos[1] + 1, last_pos[2] + self.z_delta)\n        elif dir == 'se':\n            curr_pos = (last_pos[0] + 1, last_pos[1] + 1, last_pos[2] + self.z_delta)\n        elif dir == 'sw':\n            curr_pos = (last_pos[0] + 1, last_pos[1] - 1, last_pos[2] + self.z_delta)\n        elif dir == 'nw':\n            curr_pos = (last_pos[0] - 1, last_pos[1] - 1, last_pos[2] + self.z_delta)\n        else:\n            curr_pos = (last_pos[0], last_pos[1], last_pos[2] + self.z_delta)\n        \n        if curr_pos[0] < 0 or curr_pos[0] > self.map_size[0] - 1 or curr_pos[1] < 0 or curr_pos[1] > self.map_size[1] - 1:\n            messagebox.showwarning(\"BattleTracker\", \"Cannot move off map.\")\n            return\n        \n        self.z_delta = 0\n\n        if self.turn_obj['size'] == 'large':\n            space_need = 4\n        elif self.turn_obj['size'] == 'huge':\n            space_need = 9\n        elif self.turn_obj['size'] == 'gargantuan':\n            space_need = 16\n        else:\n            space_need = 1\n        next_row_num = math.sqrt(space_need)\n        row_offset = 0\n        col_offset = 0\n\n        if dir == '+':\n            for i in range(space_need):\n                self.map_frames[curr_pos[0] + col_offset][curr_pos[1] + row_offset].config(bg='orange1')\n                col_offset += 1\n                if col_offset == next_row_num:\n                    col_offset = 0\n                    row_offset += 1\n        elif dir == '-':\n            for i in range(space_need):\n                self.map_frames[curr_pos[0] + col_offset][curr_pos[1] + row_offset].config(bg='DarkOrange4')\n                col_offset += 1\n                if col_offset == next_row_num:\n                    col_offset = 0\n                    row_offset += 1\n        else:\n            for i in range(space_need):\n                self.map_frames[last_pos[0] + col_offset][last_pos[1] + row_offset].config(bg='orange4')\n                col_offset += 1\n                if col_offset == next_row_num:\n                    col_offset = 0\n                    row_offset += 1\n            col_offset = 0\n            row_offset = 0\n            for i in range(space_need):\n                self.map_frames[curr_pos[0] + col_offset][curr_pos[1] + row_offset].config(bg='orange3')\n                col_offset += 1\n                if col_offset == next_row_num:\n                    col_offset = 0\n                    row_offset += 1\n\n        self.move_path.append(curr_pos)\n        if self.root.copy_win_open:\n            self.copy_win.track_moves(self.move_path)\n        feet_moved = int(self.lbl_amount_moved.cget('text'))\n        feet_moved += 5\n        self.lbl_amount_moved.config(text=feet_moved)\n        self.lbl_position.config(text=f\"{curr_pos[0]+1}: {curr_pos[1]+1}: {curr_pos[2]}\")\n        if feet_moved > int(self.lbl_max_move.cget('text')):\n            self.lbl_amount_moved.config(bg='red4')\n        else:\n            self.lbl_amount_moved.config(bg='gray28')\n\n    def zpad(self, dir):\n        if dir == '+':\n            self.z_delta = 1\n        else:\n            self.z_delta = -1\n        self.z_frame.config(bg='green3')\n        self.root.bind_all(\"<Button-1>\", self.green_handle)\n\n    def green_handle(self, event):\n        try:\n            name = event.widget.name\n            if name == 'zf':\n                if self.z_delta == 1:\n                    self.dpad_move('+')\n                elif self.z_delta == -1:\n                    self.dpad_move('-')\n        except:\n            pass\n        self.z_frame.config(bg='gray28')\n        self.root.unbind_all(\"<Button-1>\")\n\n    def target_hp(self, type):\n        sel_target = self.cont_targets.get()\n        tgt_delta = self.ent_target_delta.get()\n        try:\n            tgt_delta = int(tgt_delta)\n            if type == 'dmg':\n                tgt_delta *= -1\n        except ValueError:\n            messagebox.showwarning(\"BattleTracker\", \"HP difference must be a whole number.\")\n            return\n        for being in self.root.token_list:\n            if being['name'] == sel_target:\n                if type == 'dmg' and abs(tgt_delta) > being['temp_HP']:\n                    tgt_delta += being['temp_HP']\n                    being['temp_HP'] = 0\n                else:\n                    being['temp_HP'] += tgt_delta\n                    break\n                being['current_HP'] += tgt_delta\n                if being['current_HP'] > being['max_HP']:\n                    being['current_HP'] = being['max_HP']\n                elif being['current_HP'] <= 0:\n                    being['type'] = 'dead'\n        self._on_select_target(None)\n\n    def input_creature_window(self):\n        self.in_win = StatCollector(self.root, self.map_size, self.round, self.turn)\n        self.in_win.btn_submit.configure(command=lambda arg=['in_win', 'submit']: self.change_token_list(arg))\n\n    def input_object_window(self):\n        self.obj_win = ObjectBuilder(self.root, self.map_size)\n        try:\n            self.obj_win.btn_submit.configure(command=lambda: self.change_obj_list())\n        except AttributeError:\n            self.root.destroy()\n\n    def change_obj_list(self):\n        change_complete = self.obj_win.submit()\n        if change_complete:\n            self.obj_win.obj_win.destroy()\n            self.refresh_map()\n\n    def change_token_list(self, arg):\n        origin = arg[0]\n        select_btn = arg[1]\n        if origin == 'move_win':\n            if select_btn == 'set':\n                old_copy = copy.deepcopy(self.root.token_list)\n                self.log_action('list', old_copy)\n                set_complete = self.em.set_new_coord()\n                if set_complete:\n                    self.em.move_win.destroy()\n                    self.refresh_map()\n            elif select_btn == 'remove':\n                old_copy = copy.deepcopy(self.root.token_list)\n                self.log_action('list', old_copy)\n                rem_complete = self.em.remove_token()\n                if rem_complete:\n                    self.em.move_win.destroy()\n                    self.refresh_map()\n        elif origin == 'target_win':\n            if select_btn == 'submit':\n                old_copy = copy.deepcopy(self.root.token_list)\n                self.log_action('list', old_copy)\n                submit_complete = self.target.on_submit()\n                if submit_complete:\n                    self.target.target_win.destroy()\n                    self.refresh_map()\n            elif select_btn == 'delete':\n                old_copy = copy.deepcopy(self.root.token_list)\n                self.log_action('list', old_copy)\n                delete_complete = self.target.delete_token()\n                if delete_complete:\n                    self.target.target_win.destroy()\n                    self.refresh_map()\n        elif origin == 'in_win':\n            if select_btn == 'submit':\n                old_copy = copy.deepcopy(self.root.token_list)\n                self.log_action('list', old_copy)\n                submit_complete = self.in_win.submit()\n                if submit_complete:\n                    self.in_win.range_win.destroy()\n                    self.refresh_map()\n\n    def move_token(self):\n        self.em.move_token(self.map_size)\n        self.em.btn_set.configure(command=lambda arg=['move_win', 'set']: self.change_token_list(arg))\n        self.em.btn_remove.configure(command=lambda arg=['move_win', 'remove']: self.change_token_list(arg))\n        #self.wait_destroy_move_win()\n\n    def open_trig(self):\n        self.calculator.trig_win()\n\n    def target_item(self):\n        self.target.target_window()\n        self.target.btn_submit.configure(command=lambda arg=['target_win', 'submit']: self.change_token_list(arg))\n        self.target.btn_delete_target.configure(command=lambda arg=['target_win', 'delete']: self.change_token_list(arg))\n        #self.target.target_win.protocol(\"WM_DELETE_WINDOW\", lambda stuff=(self.target.token_list): self.refresh_map(tokens=stuff, origWin='target'))\n\n    def open_dice_roller(self):\n        self.dice_roll.dice_pane()\n\n    def field_light(self):\n        try:\n            self.lighter.escape()\n        except AttributeError:\n            pass\n        self.lighter = GenLightWin(self.root, self.btn_field_light)\n        self.lighter.open_light_win()\n        self.lighter.btn_confirm.config(command=self.get_offsets)\n\n    def get_offsets(self):\n        self.light_list, self.light_shape = self.lighter.collect()\n        self.lighter.escape()\n        if len(self.light_list) == 0:\n            return\n        for sect in range(len(self.map_frames)):\n            for item in self.map_frames[sect]:\n                item.bind(\"<Button-1>\", self.on_light)\n                pieces = item.pack_slaves()\n                for piece in pieces:\n                    piece.bind(\"<Button-1>\", self.on_light)\n        \n    def on_light(self, event):\n        start = list(event.widget.coord)\n        if self.light_shape == 'Square':\n            self.map_frames[start[1]][start[0]].config(bg='SpringGreen3')\n        curr_pos = start\n        for i in range(len(self.light_list)):\n            curr_pos[0] += self.light_list[i][0]\n            curr_pos[1] += self.light_list[i][1]\n            if curr_pos[0] < self.map_size[1] and curr_pos[1] < self.map_size[0] and curr_pos[0] >= 0 and curr_pos[1] >= 0:\n                self.map_frames[curr_pos[1]][curr_pos[0]].config(bg='SpringGreen3')\n        start = list(event.widget.coord)\n        if self.light_shape == 'Line':\n            self.map_frames[start[1]][start[0]].config(bg='gray28')\n        self.root.bind_all(\"<Escape>\", self.clear_light)\n        self.root.bind_all(\"<Button-3>\", self.clear_light)\n\n    def clear_light(self, event):\n        for sect in range(len(self.map_frames)):\n            for item in self.map_frames[sect]:\n                item.config(bg='gray28')\n                item.unbind(\"<Button-1>\")\n                pieces = item.pack_slaves()\n                for piece in pieces:\n                    piece.unbind(\"<Button-1>\")\n                self.root.unbind_all(\"<Escape>\")\n                self.root.unbind_all(\"<Button-3>\")\n\n    def full_reset(self):\n        empty_dict = {}\n        make_sure = messagebox.askokcancel(\"Warning\", \"Confirm request to delete ALL tokens and FULL RESET MAP.\\nIf confirmed, this action cannot be undone.\")\n        if make_sure:\n            battle_dict = {\n                \"map_size\": self.map_size,\n                \"round\": 0,\n                \"turn\": 0\n            }\n            battleJSON = json.dumps(battle_dict, indent=4)\n            with ZipFile(self.root.filename, \"w\") as savefile:\n                creatJSON = json.dumps(empty_dict)\n                objJSON = json.dumps(empty_dict)\n                savefile.writestr('battle_info.json', battleJSON)\n                savefile.writestr('creatures.json', creatJSON)\n                savefile.writestr('objects.json', objJSON)\n            self.refresh_map(reset=True)\n        self.go_back.clear_all()\n\n    def find_quote(self):\n        last_index = len(self.quoter.quote_list) - 1\n        rand_index = random.randint(0, last_index)\n        random_quote = self.quoter.get_quote(rand_index)\n        self.lbl_quote.config(text=random_quote)\n\n    def show_cond_info(self):\n        self.info.explain_conditions()\n\n    def time_travel(self, do_undo):\n        if do_undo:\n            hist_action = self.go_back.undo()\n            if self.go_back.undo_empty():\n                self.btn_undo['state'] = 'disabled'\n            self.btn_redo['state'] = 'normal'\n        else:\n            hist_action = self.go_back.redo()\n            if self.go_back.redo_empty():\n                self.btn_redo['state'] = 'disabled'\n            self.btn_undo['state'] = 'normal'\n\n        action_name = hist_action['origin']\n        \n        if action_name == 'turn button':\n            if do_undo:\n                self.turn -= 1\n                if self.turn < 0:\n                    self.turn = len(self.initiative_holder) - 1\n                    self.round -= 1\n                    if self.round <= 0:\n                        self.round = 0\n                        self.lbl_round.config(text=\"S\")\n                    else:\n                        self.lbl_round.config(text=self.round)\n                    self.refresh_initiatives()\n            else:\n                self.next_turn(False)\n        \n        elif action_name == 'round button':\n            if do_undo:\n                self.round -= 1\n                if self.round <= 0:\n                    self.round = 0\n                    self.lbl_round.config(text=\"S\")\n                else:\n                    self.lbl_round.config(text=self.round)\n                self.turn = hist_action['restore']['turn']\n                self.refresh_initiatives()\n            else:\n                self.next_round(False)\n        \n        elif action_name == 'reset round':\n            if do_undo:\n                self.round = hist_action['restore']['round']\n                self.turn = hist_action['restore']['turn']\n                if self.round <= 0:\n                    self.round = 0\n                    self.lbl_round.config(text=\"S\")\n                else:\n                    self.lbl_round.config(text=self.round)\n                self.refresh_initiatives()\n            else:\n                self.reset_round(False)\n\n        elif action_name == 'list':\n                self.root.token_list = copy.deepcopy(hist_action['restore'])\n                self.refresh_map()\n\n    def undo_move(self):\n        if len(self.move_path) > 1:\n            last_move = self.move_path.pop()\n        else:\n            return\n        if self.turn_obj['size'] == 'large':\n            space_need = 4\n        elif self.turn_obj['size'] == 'huge':\n            space_need = 9\n        elif self.turn_obj['size'] == 'gargantuan':\n            space_need = 16\n        else:\n            space_need = 1\n        next_row_num = math.sqrt(space_need)\n        row_offset = 0\n        col_offset = 0\n\n        for i in range(space_need):\n            self.map_frames[last_move[0] + col_offset][last_move[1] + row_offset].config(bg='gray28')\n            col_offset += 1\n            if col_offset == next_row_num:\n                col_offset = 0\n                row_offset += 1\n\n        feet_moved = int(self.lbl_amount_moved.cget('text'))\n        feet_moved -= 5\n        self.lbl_amount_moved.config(text=feet_moved)\n        if feet_moved > int(self.lbl_max_move.cget('text')):\n            self.lbl_amount_moved.config(bg='red4')\n        else:\n            self.lbl_amount_moved.config(bg='gray28')\n        new_curr_move = self.move_path[-1]\n        self.lbl_position.config(text=f\"{new_curr_move[0]+1}: {new_curr_move[1]+1}: {new_curr_move[2]}\")\n        row_offset = 0\n        col_offset = 0\n        for i in range(space_need):\n            self.map_frames[new_curr_move[0] + col_offset][new_curr_move[1] + row_offset].config(bg='orange3')\n            col_offset += 1\n            if col_offset == next_row_num:\n                col_offset = 0\n                row_offset += 1\n        if self.root.copy_win_open:\n            self.copy_win.gray_map()\n            self.copy_win.track_moves(self.move_path)\n\n    def log_action(self, origin, restore_data=None):\n        if self.btn_undo['state'] == 'disabled':\n            self.btn_undo['state'] = 'normal'\n        self.go_back.add_undo(origin, restore_data)\n        if self.go_back.redo_empty() == False:\n            self.go_back.clear_redo()\n            self.btn_redo['state'] = 'disabled'\n\nbattle = BattleMap(map_win)\n\nif __name__ == '__main__':\n    map_win.mainloop()", "repo_name": "terminus78/BattleTracker", "sub_path": "entry/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 56140, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "45", "api": [{"api_name": "tkinter.Tk", "line_number": 29, "usage_type": "call"}, {"api_name": "ttkthemes.ThemedStyle", "line_number": 71, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 78, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 80, "usage_type": "call"}, {"api_name": "tkinter.ttk.Frame", "line_number": 85, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 85, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 89, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 89, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 92, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 92, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 99, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 99, "usage_type": "name"}, {"api_name": "event_manager.EventManager", "line_number": 102, "usage_type": "call"}, {"api_name": "calc.Calculator", "line_number": 103, "usage_type": "call"}, {"api_name": "quotes.Quote", "line_number": 104, "usage_type": "call"}, {"api_name": "target.Target", "line_number": 106, "usage_type": "call"}, {"api_name": "condition_info.InfoClass", "line_number": 107, "usage_type": "call"}, {"api_name": "dice.DiceRoller", "line_number": 108, "usage_type": "call"}, {"api_name": "player_window.PlayerWin", "line_number": 109, "usage_type": "call"}, {"api_name": "undo_redo.ActionStack", "line_number": 110, "usage_type": "call"}, {"api_name": "tkinter.ttk.Label", "line_number": 113, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 113, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 115, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 115, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 117, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 117, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 119, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 119, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 121, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 121, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 123, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 123, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 125, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 125, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 127, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 127, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 129, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 129, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 131, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 131, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 135, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 135, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 139, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 139, "usage_type": "name"}, {"api_name": "tkinter.Canvas", "line_number": 140, "usage_type": "call"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 141, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 141, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 142, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 142, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 143, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 143, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 155, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 155, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 156, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 156, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 162, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 162, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 162, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 162, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 162, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 164, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 164, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 164, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 164, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 164, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 166, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 166, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 166, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 166, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 166, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 168, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 168, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 168, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 168, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 168, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 170, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 170, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 170, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 170, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 170, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 172, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 172, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 172, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 172, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 172, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 174, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 174, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 174, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 174, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 174, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 176, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 176, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 176, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 176, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 176, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 178, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 178, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 178, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 178, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 178, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 181, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 181, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 181, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 181, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 181, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 183, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 183, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 183, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 183, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 183, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 185, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 185, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 185, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 185, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 185, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 187, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 187, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 187, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 187, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 187, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 200, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 200, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 200, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 200, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 200, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 201, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 201, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 201, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 201, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 201, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 202, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 202, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 202, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 202, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 202, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 203, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 203, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 203, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 203, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 203, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 204, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 204, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 204, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 204, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 204, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 205, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 205, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 205, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 205, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 205, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 206, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 206, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 206, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 206, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 206, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 207, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 207, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 207, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 207, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 207, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 208, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 208, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 208, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 208, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 208, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 209, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 209, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 209, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 209, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 209, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 210, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 210, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 210, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 210, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 210, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 220, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 220, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 225, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 225, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 238, "usage_type": "call"}, {"api_name": "tkinter.RAISED", "line_number": 238, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Frame", "line_number": 247, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 247, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 249, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 253, "usage_type": "call"}, {"api_name": "tkinter.ttk.Label", "line_number": 259, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 259, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 265, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 265, "usage_type": "name"}, {"api_name": "tkinter.RAISED", "line_number": 265, "usage_type": "attribute"}, {"api_name": "tkinter.CENTER", "line_number": 265, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Frame", "line_number": 267, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 267, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 270, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 270, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 272, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 272, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 274, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 274, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 278, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 278, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 283, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 283, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 288, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 288, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 293, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 293, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 298, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 298, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 303, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 303, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 310, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 310, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 312, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 316, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 320, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 324, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 328, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 332, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 336, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 340, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 344, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 347, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 351, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 354, "usage_type": "call"}, {"api_name": "tkinter.ttk.Frame", "line_number": 358, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 358, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 360, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 360, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 362, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 362, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 366, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 366, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 371, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 371, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 373, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 373, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 375, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 375, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 377, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 377, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 379, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 379, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 381, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 381, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 383, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 383, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 385, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 385, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 387, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 387, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 389, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 389, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 392, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 392, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 394, "usage_type": "call"}, {"api_name": "tkinter.ttk.Label", "line_number": 396, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 396, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 398, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 398, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 400, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 400, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 402, "usage_type": "call"}, {"api_name": "tkinter.ttk.Label", "line_number": 404, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 404, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 406, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 492, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 495, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 498, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 533, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 533, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 533, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 533, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 533, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 534, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 563, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 569, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 580, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 589, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 595, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 595, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 606, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 616, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 622, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Label", "line_number": 623, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 623, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 625, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 625, "usage_type": "name"}, {"api_name": "math.inf", "line_number": 669, "usage_type": "attribute"}, {"api_name": "math.inf", "line_number": 672, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 752, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 753, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 754, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 755, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 762, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 769, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 790, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 790, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 803, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 878, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 878, "usage_type": "name"}, {"api_name": "stat_collector.StatCollector", "line_number": 896, "usage_type": "call"}, {"api_name": "object_builder.ObjectBuilder", "line_number": 900, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 917, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 924, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 932, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 939, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 947, "usage_type": "call"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 1022, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1022, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 1029, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 1030, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 1031, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 1032, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 1041, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 1104, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 1120, "usage_type": "call"}]}
{"seq_id": "70208023803", "text": "import argparse\nimport json\nimport os\nimport socket\nimport subprocess\nimport sys\nimport threading\nimport time\nimport webbrowser\nfrom pathlib import Path\nfrom urllib import request\n\nfrom opensafely import utils\n\n\nDESCRIPTION = \"Run a jupyter lab notebook using the OpenSAFELY environment\"\n\n\n# poor mans debugging because debugging threads on windows is hard\nif os.environ.get(\"DEBUG\", False):\n\n    def debug(msg):\n        # threaded output for some reason needs the carriage return or else\n        # it doesn't reset the cursor.\n        sys.stderr.write(\"DEBUG: \" + msg.replace(\"\\n\", \"\\r\\n\") + \"\\r\\n\")\n        sys.stderr.flush()\n\nelse:\n\n    def debug(msg):\n        pass\n\n\ndef add_arguments(parser):\n    parser.add_argument(\n        \"--directory\",\n        \"-d\",\n        default=os.getcwd(),\n        type=Path,\n        help=\"Directory to run the jupyter server in (default is current dir)\",\n    )\n    parser.add_argument(\n        \"--name\",\n        help=\"Name of docker image (defaults to use directory name)\",\n    )\n\n    # we copy a number of standard jupyter lab arguments, and capture them to handle them ourselves\n    parser.add_argument(\n        \"--no-browser\",\n        \"-n\",\n        default=False,\n        action=\"store_true\",\n        help=\"Do not attempt to open a browser\",\n    )\n    parser.add_argument(\n        \"--port\",\n        \"-p\",\n        default=None,\n        help=\"Port to run on\",\n    )\n    parser.add_argument(\n        \"jupyter_args\",\n        nargs=argparse.REMAINDER,\n        metavar=\"...\",\n        help=\"Any additional arguments to pass to the jupyter lab command\",\n    )\n\n\ndef open_browser(name, port):\n    try:\n        metadata = None\n        metadata_path = \"/tmp/.local/share/jupyter/runtime/nbserver-*.json\"\n\n        # wait for jupyter to be set up\n        start = time.time()\n        while metadata is None and time.time() - start < 120.0:\n            ps = subprocess.run(\n                [\"docker\", \"exec\", name, \"bash\", \"-c\", f\"cat {metadata_path}\"],\n                text=True,\n                capture_output=True,\n            )\n            if ps.returncode == 0:\n                debug(ps.stdout)\n                metadata = json.loads(ps.stdout)\n            else:\n                time.sleep(1)\n\n        if metadata is None:\n            debug(\"open_browser: Could not get metadata\")\n            return\n\n        url = f\"http://localhost:{port}/?token={metadata['token']}\"\n        debug(f\"open_browser: url={url}\")\n\n        # wait for port to be open\n        debug(\"open_browser: waiting for port\")\n        start = time.time()\n        while time.time() - start < 60.0:\n            try:\n                response = request.urlopen(url, timeout=1)\n            except (request.URLError, OSError):\n                pass\n            else:\n                break\n\n        if not response:\n            debug(\"open_browser: open_browser: could not get response\")\n            return\n\n        # open a webbrowser pointing to the docker container\n        debug(\"open_browser: open_browser: opening browser window\")\n        webbrowser.open(url, new=2)\n\n    except Exception:\n        # reformat exception printing to work from thread\n        import traceback\n\n        sys.stderr.write(\"Error in open browser thread:\\r\\n\")\n        tb = traceback.format_exc().replace(\"\\n\", \"\\r\\n\")\n        sys.stderr.write(tb)\n        sys.stderr.flush()\n\n\ndef get_free_port():\n    sock = socket.socket()\n    sock.bind((\"\", 0))\n    port = sock.getsockname()[1]\n    sock.close()\n    return port\n\n\ndef main(directory, name, port, no_browser, jupyter_args):\n    if name is None:\n        name = f\"os-jupyter-{directory.name}\"\n\n    if port is None:\n        # this is a race condition, as something else could consume the socket\n        # before docker binds to it, but the chance of that on a user's\n        # personal machine is very small.\n        port = str(get_free_port())\n\n    jupyter_cmd = [\n        \"jupyter\",\n        \"lab\",\n        \"--ip=0.0.0.0\",\n        f\"--port={port}\",\n        \"--allow-root\",\n        \"--no-browser\",\n        # display the url from the hosts perspective\n        f\"--LabApp.custom_display_url=http://localhost:{port}/\",\n        *jupyter_args,\n    ]\n\n    print(f\"Running following jupyter cmd in OpenSAFELY docker container {name}...\")\n    print(\" \".join(jupyter_cmd))\n\n    if not no_browser:\n        # start thread to open web browser\n        thread = threading.Thread(target=open_browser, args=(name, port), daemon=True)\n        thread.name = \"browser thread\"\n        debug(\"starting open_browser thread\")\n        thread.start()\n\n    docker_args = [\n        # we use our port on both sides of the docker port mapping so that\n        # jupyter's logging uses the correct port from the user's perspective\n        f\"-p={port}:{port}\",\n        f\"--name={name}\",\n        f\"--hostname={name}\",\n        \"--env\",\n        \"HOME=/tmp\",\n        # allow importing from the top level\n        \"--env\",\n        \"PYTHONPATH=/workspace\",\n    ]\n\n    debug(\"docker: \" + \" \".join(docker_args))\n    ps = utils.run_docker(docker_args, \"python\", jupyter_cmd, interactive=True)\n    # we want to exit with the same code that jupyter did\n    return ps.returncode\n", "repo_name": "opensafely-core/opensafely-cli", "sub_path": "opensafely/jupyter.py", "file_name": "jupyter.py", "file_ext": "py", "file_size_in_byte": 5148, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.environ.get", "line_number": 20, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.stderr.flush", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 38, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "name"}, {"api_name": "argparse.REMAINDER", "line_number": 63, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 76, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 77, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 84, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 86, "usage_type": "call"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "time.time", "line_number": 98, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 100, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 100, "usage_type": "name"}, {"api_name": "urllib.request.URLError", "line_number": 101, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 101, "usage_type": "name"}, {"api_name": "webbrowser.open", "line_number": 112, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 118, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 118, "usage_type": "attribute"}, {"api_name": "traceback.format_exc", "line_number": 119, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 120, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 120, "usage_type": "attribute"}, {"api_name": "sys.stderr.flush", "line_number": 121, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 121, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 125, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 159, "usage_type": "call"}, {"api_name": "opensafely.utils.run_docker", "line_number": 178, "usage_type": "call"}, {"api_name": "opensafely.utils", "line_number": 178, "usage_type": "name"}]}
{"seq_id": "4707377306", "text": "import math\nfrom operator import indexOf\nfrom tkinter import *\nfrom tkinter.messagebox import showinfo, showwarning #Pop up message box\nfrom tkcalendar import Calendar #Data picker\nfrom datetime import *\nimport controllers.controller as controller\n#Window Settings ------------------------------------------------------------\n#Window Sizes --> Tuple : (x,y) \nLOGIN_SIZE = (350,500) #Login Page\nREGISTER_SIZE = (350,500) #Register Page\nCREATE_SHOW_SIZE = (400,500) #Register Page\nUSER_AREA_SIZE = (700,500) #User Area Page\nRESERVATION_INFO_SIZE = (500,200) #Reservation Info Page\nCONFIRM_CHANGE_SIZE = (1100,500) #Confirm Seat Change Page\nSHOW_AREA_SIZE = (700,500) #Show Area Page \nSHOW_INFO_SIZE = (900,500) #Show Info Page \nORDER_SIZE = (600,200) #Order Page\n#Window Colors\nBG1 = \"#a3a3a3\"\nBG2=\"#d1d1d1\"\n###########################################################################\n###########################################################################\n###########################################################################\n# LOGIN WINDOW ------------------------------------------------------------\ndef autenticate_user(parent,email,password,admin):\n    auth = controller.authenticate_user(email,password,admin)\n    if(auth == 0): \n        Label(parent, text=\"O utilizador não existe\",pady=10,fg=\"red\",bg=BG2,width=35,anchor=W).place(x=10,y=200)\n    elif(auth == -1):\n        Label(parent, text=\"A palavra passe está incorreta\",pady=10,fg=\"red\",bg=BG2,width=35,anchor=W).place(x=10,y=200)\n    elif(auth == -2):\n        Label(parent, text=\"É favor preencher todos os campos\",pady=10,fg=\"red\",bg=BG2,anchor=W).place(x=10,y=200)\n    elif(auth == -3):\n        Label(parent, text=\"Acesso interdito\",pady=10,fg=\"red\",bg=BG2,anchor=W,width=35).place(x=10,y=200)\n    else:\n        user_area(parent,auth)\n\ndef login_page(parent):\n    geometry = controller.calculate_geometry(parent,LOGIN_SIZE)\n    parent.destroy()\n    root = Tk()\n    root.title(\"Show Time! - Iniciar Sessão\")\n    root.geometry(geometry)\n    root.resizable(False,False)\n    root.config(bg=BG2)\n    Label(root,text=\"Bem-vindo ao Show Time!\",bg=BG2,padx=10,pady=10).place(x=10,y=10)\n    #Email\n    user_email = StringVar()\n    Label(root,text=\"Email\",bg=BG2,padx=10,pady=10).place(x=10,y=50)\n    Entry(root,width=30,borderwidth=2,textvariable=user_email).place(x=100,y=50)\n    #Password\n    user_password = StringVar()\n    Label(root,text=\"Password\",bg=BG2,padx=10,pady=10).place(x=10,y=100)\n    Entry(root,width=30,borderwidth=2,textvariable=user_password,show=\"*\").place(x=100,y=100)\n    #Button\n    Button(root,text=\"Entrar\",bg=\"#b2cadb\",width=10,height=2,command=lambda:autenticate_user(root,user_email.get(),user_password.get(),False)).place(x=10,y=150)\n    Button(root,text=\"Registar\",bg=\"#b2cadb\",width=10,height=2,command=lambda:register_page(root)).place(x=265,y=150)\n    Button(root,text=\"Voltar\",bg=\"#a19c9c\",command=lambda:show_area(root), width=10,height=2).place(x=10,y=450)\n    Button(root,text=\"Entrar como Administrador\",bg=\"#fff275\",height=2,command=lambda:login_page_admin(root)).place(x=190,y=450)\n    root.mainloop()\n\ndef login_page_admin(parent):\n    geometry = controller.calculate_geometry(parent,LOGIN_SIZE)\n    parent.destroy()\n    root = Tk()\n    root.title(\"Show Time! - Iniciar Sessão - Administrador\")\n    root.geometry(geometry)\n    root.resizable(False,False)\n    root.config(bg=BG2)\n    Label(root,text=\"Bem-vindo Administrador!\",bg=BG2,padx=10,pady=10).place(x=10,y=10)\n    #Email\n    user_email = StringVar()\n    Label(root,text=\"Email\",bg=BG2,padx=10,pady=10).place(x=10,y=50)\n    Entry(root,width=30,borderwidth=2,textvariable=user_email).place(x=100,y=50)\n    #Password\n    user_password = StringVar()\n    Label(root,text=\"Password\", bg=BG2,padx=10,pady=10).place(x=10,y=100)\n    Entry(root,width=30,borderwidth=2, textvariable=user_password, show=\"*\").place(x=100,y=100)\n    #Button\n    Button(root,text=\"Entrar\",bg=\"#b2cadb\",width=10,height=2,command=lambda:autenticate_user(root,user_email.get(),user_password.get(),True)).place(x=10,y=150)\n    Button(root,text=\"Voltar\",bg=\"#a19c9c\",command=lambda:login_page(root), width=10,height=2).place(x=10,y=450)\n    root.mainloop()\n#------------------------------------------------------------------------------------------------------------------------\n# REGISTER WINDOW ------------------------------------------------------------\ndef registrate_user(parent,email,name,password):\n    control = controller.registrate_user(name,email,password)\n    if(control == 0): \n        Label(parent, text=\"O email já existe\",pady=10,fg=\"red\",bg=BG2,anchor=W).place(x=10,y=250)\n    elif(control == -1):\n        Label(parent, text=\"É favor preencher todos os campos\",pady=10,fg=\"red\",bg=BG2,anchor=W).place(x=10,y=250)\n    else:\n        showinfo(\"Email registado com sucesso\",f\"Bem-vindo {name} ao Show Time!\\n\\nJá pode ter acesso livre à reserva de bilhetes para os seus espetáculos preferidos.\")\n        user_area(parent,control)\n\ndef register_page(parent): \n    geometry = controller.calculate_geometry(parent,REGISTER_SIZE)\n    parent.destroy()\n    root = Tk()\n    root.title(\"Show Time! - Registo\")\n    root.geometry(geometry)\n    root.resizable(False,False)\n    root.config(bg=BG2)\n    Label(root, text=\"Bem-vindo/a ao Show Time!\", bg=BG2, padx=10,pady=10).grid(column=0,row=0, sticky=W)\n    Label(root, text=\"Crie a sua conta!\", bg=BG2, padx=5,pady=6).grid(column=0,row=1, sticky=W)\n    #Full Name\n    regist_name = StringVar()\n    Label(root, text=\"Nome Completo\", padx=10,pady=10, bg=BG2).grid(column=0,row=2, sticky=W)\n    Entry(root,width=30,borderwidth=2,textvariable=regist_name).grid(column=1,row=2)\n    #Email\n    regist_email = StringVar()\n    Label(root, text=\"E-mail\", bg=BG2, padx=10,pady=10).grid(column=0,row=3, sticky=W)\n    Entry(root,width=30,borderwidth=2,textvariable=regist_email).grid(column=1,row=3)\n    #Password\n    regist_password = StringVar()\n    Label(root, text=\"Password\", bg=BG2, padx=10,pady=10).grid(column=0,row=4, sticky=W)\n    Entry(root,width=30,borderwidth=2,textvariable=regist_password,show=\"*\").grid(column=1,row=4)\n    #Button\n    Button(root,text=\"Registo\",bg=\"#b2cadb\",width=10,height=2,command=lambda:registrate_user(root,regist_email.get(),regist_name.get(),regist_password.get())).grid(column=0,row=5)\n    Button(root,text=\"Já tenho conta\",bg=\"#b2cadb\",width=12,height=2,command=lambda:login_page(root)).grid(column=1,row=5)\n    root.mainloop()\n#------------------------------------------------------------------------------------------------------------------------\n# USER AREA -----------------------------------------\ndef admin_panel(parent,type,info): #type is \"d\" or \"m\" or \"y\"\n    if(type==\"d\"):\n        Label(parent, text=f\"{controller.get_day_total_price(info)}€\",fg=\"white\",bg=\"#ff8c42\",font=(\"Verdana\", 12,\"bold\"),width=5).place(x=10,y=120)\n    elif(type==\"m\"):\n        updated_info = datetime.strptime(info,\"%B\").strftime(\"%m\") #Pass e.g January to 01\n        Label(parent, text=f\"{controller.get_month_total_price(updated_info)}€\",fg=\"white\",bg=\"#ff8c42\",font=(\"Verdana\", 12,\"bold\"),width=5).place(x=10,y=215)\n    elif(type==\"y\"):\n        updated_info = datetime.strptime(info,\"%Y\").strftime(\"%y\") #Pass e.g 2022 to 22\n        Label(parent, text=f\"{controller.get_year_total_price(updated_info)}€\",fg=\"white\",bg=\"#ff8c42\",font=(\"Verdana\", 12,\"bold\"),width=5).place(x=10,y=310)\n\ndef user_area(parent,session):\n    geometry = controller.calculate_geometry(parent,USER_AREA_SIZE)\n    parent.destroy()\n    root = Tk()\n    root.title(f\"Show Time! - Área do utilizador - {session.getFullName()}\")\n    root.geometry(geometry)\n    root.resizable(False,False)\n    root.config(bg=BG1)\n    if session.isAdmin() == True:\n        Label(root, text=\"Ferramentas de Administrador\", font=(\"Verdana\", 16), bg=\"#fff275\").place(x=5,y=5)\n        #Admin Panel\n        #Get dates - Day\n        dates_day_list = list()\n        dates_month_list = list()\n        dates_year_list = list()\n        default_date_day = StringVar()\n        default_date_month = StringVar()\n        default_date_year = StringVar()\n        for shows in controller.model.Show_List:\n            if shows.getDate() not in dates_day_list:\n                dates_day_list.append(shows.getDate())\n            if datetime.strptime(shows.getDate()[3:5],\"%m\").strftime(\"%B\") not in dates_month_list:\n                dates_month_list.append(datetime.strptime(shows.getDate()[3:5],\"%m\").strftime(\"%B\")) #To show e.g January instead of 01\n            if datetime.strptime(shows.getDate()[6:10],\"%y\").strftime(\"%Y\") not in dates_year_list:\n                dates_year_list.append(datetime.strptime(shows.getDate()[6:10],\"%y\").strftime(\"%Y\")) #To show e.g 2022 instead of 22\n        Label(root,bg=\"#ff8c42\",height=21,width=31).place(x=5,y=50)\n        Label(root, text=\"Ver total de vendas\",font=(\"Verdana\",12,\"bold\"),bg=\"#ff8c42\",fg=\"white\").place(x=8,y=60)\n        #Sales by Day -----------\n        if(len(dates_day_list) != 0): default_date_day.set(dates_day_list[0])       \n        else: \n            dates_day_list.append(\"Sem datas\")\n            default_date_day.set(\"Sem datas\")    \n        Label(root, text=\"Por Dia\",fg=\"white\",bg=\"#ff8c42\",font=(\"Verdana\",9)).place(x=10,y=100)\n        Label(root, text=\"0.00€\",fg=\"white\",bg=\"#ff8c42\",font=(\"Verdana\", 12,\"bold\"),width=5).place(x=10,y=120)\n        OptionMenu(root,default_date_day,*dates_day_list).place(x=10,y=145)\n        Button(root, text=\"Atualizar\",command=lambda:admin_panel(root,\"d\",default_date_day.get()),width=7,height=1,bg=\"#fff275\").place(x=150,y=145)\n        #--------\n        #Sales by Month -----------\n        if(len(dates_month_list) != 0): default_date_month.set(dates_month_list[0])\n        else: \n            dates_month_list.append(\"Sem datas\")\n            default_date_month.set(\"Sem datas\")    \n        Label(root, text=\"Por Mês\",fg=\"white\",bg=\"#ff8c42\",font=(\"Verdana\",9)).place(x=10,y=195)\n        Label(root, text=\"0.00€\",fg=\"white\",bg=\"#ff8c42\",font=(\"Verdana\", 12,\"bold\"),width=5).place(x=10,y=215)\n        OptionMenu(root,default_date_month,*dates_month_list).place(x=10,y=240)\n        Button(root, text=\"Atualizar\",command=lambda:admin_panel(root,\"m\",default_date_month.get()),width=7,height=1,bg=\"#fff275\").place(x=150,y=240)\n        #--------\n        #Sales by Year -----------\n        if(len(dates_year_list) != 0): default_date_year.set(dates_year_list[0])\n        else: \n            dates_year_list.append(\"Sem datas\")\n            default_date_year.set(\"Sem datas\")  \n        Label(root, text=\"Por Ano\",fg=\"white\",bg=\"#ff8c42\",font=(\"Verdana\",9)).place(x=10,y=290)\n        Label(root, text=\"0.00€\",fg=\"white\",bg=\"#ff8c42\",font=(\"Verdana\", 12,\"bold\"),width=5).place(x=10,y=310)\n        OptionMenu(root,default_date_year,*dates_year_list).place(x=10,y=335)\n        Button(root,text=\"Atualizar\",command=lambda:admin_panel(root,\"y\",default_date_year.get()),width=7,height=1,bg=\"#fff275\").place(x=150,y=335)\n        #--------\n        #User Number -----------\n        Label(root,bg=\"#6c8ead\",height=10,width=31).place(x=250,y=50)\n        Label(root, text=\"Utilizadores Registados\",font=(\"Verdana\",12,\"bold\"),bg=\"#6c8ead\",fg=\"white\").place(x=258,y=60)     \n        Label(root, text=f\"{controller.get_all_users()}\",fg=\"white\",bg=\"#6c8ead\",font=(\"Verdana\", 18)).place(x=260,y=90)\n        #--------\n        #Show Number -----------\n        Label(root,bg=\"#a23e48\",height=10,width=31).place(x=250,y=214)\n        Label(root, text=\"Reservas Registadas\",font=(\"Verdana\",12,\"bold\"),bg=\"#a23e48\",fg=\"white\").place(x=258,y=224)   \n        Label(root, text=f\"{controller.get_all_reservations()}\",fg=\"white\",bg=\"#a23e48\",font=(\"Verdana\", 18)).place(x=260,y=254)\n        #--------\n        #botao para adicionar o espetaculo FAZER A FUNÇAO PARA ADICIONAR OS ESPETACULOS\n        Button(root, text=\"Adicionar Espetáculo\", width=30,height=3,command=lambda:create_show_page(root,session),font=(\"Arial\", 11, \"bold\"), bg=\"#fff275\").place(x=10,y=425)\n    else:\n        #texto\n        Label(root, text=\"A sua lista de reservas\", font=(\"Verdana\", 16), background=\"#7eb6de\").grid(row=0, column=1, sticky=W)\n        #separaçao\n        Label(root, text=\" \", bg=BG1).grid(row=1, column=0)\n        Label(root, text=\" \", bg=BG1).grid(row=1, column=1)\n        #List Box  \n        #Create a scrollbar\n        scrollbar = Scrollbar(root)\n        scrollbar.grid(row=2,column=2)\n        #Create List Box\n        reservation_list_box = Listbox(root,yscrollcommand=scrollbar.set,width=100,height=20)\n        for reservations in controller.model.Reservation_List:\n            if(reservations.getUserID() == session.getID()):\n                reservation_list_box.insert(END, f\"#{reservations.getID()} - {reservations.getShowName()} [{reservations.getSeatNumber()}] - {reservations.getPrice()}€\")\n        reservation_list_box.grid(row=2,column=1,sticky=W)\n        scrollbar.config(command=reservation_list_box.yview) #Join the scrollbar with the listbox\n        #separaçao\n        Label(root, text=\" \", bg=BG1).grid(row=3, column=1)\n        Button(root, text=\"Consultar reserva\", command=lambda:list_box_to_reservation(root,session,reservation_list_box), width=30, height=3, font=(\"Arial\", 11, \"bold\"), bg=\"#3d9adb\").grid(column=1, row=4)\n    #Informação do utilizador\n    #nome do user\n        Label(root, text=f\"Total de reservas: {controller.get_user_total_price(session)}€\", font=(\"Arial\", 9), justify=\"right\", bg=BG1).place(x=400,y=25)\n    Label(root, text=f\"Bem-Vindo {session.getFullName()}\", font=(\"Arial\", 9), justify=\"right\", bg=BG1).place(x=400,y=5)\n    #botao logout\n    Button(root,text=\"Encerrar Sessão\",width=15,height=2, font=(\"Arial\", 9, \"bold\"), background=\"red\", command=lambda:show_area(root)).place(x=570,y=5)\n    #botao pagina principal\n    Button(root, text=\"Ver Espetáculos\", font=(\"Arial\", 9, \"bold\"), width=15, height=2, bg=\"#3f6f91\", command=lambda:show_area(root,session)).place(x=570,y=450)\n    root.mainloop()\n\n#receives a list box and return the selected reservation\ndef list_box_to_reservation(root,session,list_box):\n    if(list_box.curselection() != ()): #Assuming only one reservation belongs to the tuple\n        i = list_box.curselection()[0] #get selected index in list box\n        reservation_id = list_box.get(i)[1:5] #Clean the string and get the show ID\n        for reservations in controller.model.Reservation_List:\n            if(reservations.getID() == reservation_id):\n                curr_reserv = reservations #Get respective show object\n    else:\n        curr_reserv = None #Show Object\n    reservation_info(root,session,curr_reserv)\n\n#pop up da info dos bilhetes - window\ndef reservation_info(parent,session,reservation):\n    if(reservation is not None):\n        info_window = Toplevel(parent) # Criar uma página em cima da página atual\n        curr_show = None #Get Show Object\n        for shows in controller.model.Show_List:\n            if(shows.getID() == reservation.getShowID()):\n                curr_show = shows #get respective show object\n        info_window.title(f\"Informações - #{reservation.getID()}\")\n        geometry = controller.calculate_geometry(parent,RESERVATION_INFO_SIZE)\n        info_window.geometry(geometry)\n        info_window.config(bg=BG1)\n        Label(info_window, text=f\"Nome do Espetáculo: {reservation.getShowName()}\", font=(\"Arial\", 9), bg=BG1).grid(row=0, column=0, sticky=W)\n        Label(info_window, text=f\"Data do Espetáculo: {curr_show.getDate()}\", font=(\"Arial\", 9), bg=BG1).grid(row=1, column=0, sticky=W)\n        Label(info_window, text=f\"Tipo de bilhete: {reservation.getSeatType()}\", font=(\"Arial\", 9), bg=BG1).grid(row=2, column=0, sticky=W)\n        Label(info_window, text=f\"Lugar: {reservation.getSeatNumber()}\", font=(\"Arial\", 9), bg=BG1).grid(row=3, column=0, sticky=W)\n        Label(info_window, text=f\"Preço do Bilhete: {reservation.getPrice()}€\", font=(\"Arial\", 9), bg=BG1).grid(row=4, column=0, sticky=W)\n        Label(info_window, text=\" \", bg=BG1).grid(row=5, column=0)\n        Button(info_window, text=\"Alterar Lugar\", command = lambda: confirm_seat_change(parent,info_window,session,reservation,curr_show,reservation.getSeatNumber()), bg=\"gray\").grid(row=6, column=2)\n        Button(info_window, text=\"Reembolsar\", command = lambda: confirm_refund(parent,info_window,session,reservation,curr_show,reservation.getSeatNumber()), bg=\"gray\").grid(row=6, column=3)\n        Button(info_window, text=\"Cancelar\", command = lambda: choice(info_window,\"cancel\"), bg=\"gray\").grid(row=6, column=4)\n    else:\n        showinfo(\"Informação\",\"Por favor selecione uma reserva, ou reserve um bilhete na área dos espetáculos.\")\n\ndef confirm_refund(parent,toplevel_window,session,reservation,show,seat_number):\n    reservation_id = controller.clear_order(reservation,show,seat_number)\n    showinfo(\"Lugar Reembolsado\",f\"Bilhete #{reservation_id} reembolsado!\\n\\n{session.getFullName()}, o seu bilhete foi reembolsado com sucesso.\",parent=toplevel_window)\n    user_area(parent,session)\n\ndef confirm_seat_change(parent,toplevel_window,session,reservation,show,seat_number): #can be better\n    seat_change_window = Toplevel(toplevel_window)\n    seat_change_window.title(f\"Alterar Reserva\")\n    geometry = controller.calculate_geometry(parent,CONFIRM_CHANGE_SIZE)\n    seat_change_window.geometry(geometry)\n    seat_change_window.config(bg=BG1)\n    Label(seat_change_window, text=f\"{session.getFullName()}, selecione um novo lugar:\", font=(\"Arial\", 12), bg=BG1).grid(row=0, column=0, sticky=W)\n    Label(seat_change_window, text=f\"O seu lugar: {seat_number}\", font=(\"Arial\", 12), bg=BG1).grid(row=1, column=0, sticky=W)\n    Label(seat_change_window, text=f\"Se deseja manter o seu lugar atual, clique no lugar a verde.\", font=(\"Arial\", 12), bg=BG1).grid(row=2, column=0, sticky=W)\n    show_room(parent,session,seat_change_window,show,seat_number)\n    controller.clear_order(reservation,show,seat_number)\n\ndef create_show_page(parent,session,show=None): #show parameter (receives a show object) is not none when the admin creates a show \n                                                #through \"add another date\" button, in show info\n    root = Toplevel(parent)\n    root.title(\"Criar espetáculo\")\n    geometry = controller.calculate_geometry(parent,CREATE_SHOW_SIZE)\n    root.geometry(geometry)\n    root.resizable(False,False)\n    root.config(bg=BG2)\n    Label(root, text=\"Insira as informações do espetáculo\",font=(\"Verdana\", 12),bg=BG2, padx=5,pady=5).place(x=10,y=10)\n    #Show Name\n    Label(root, text=\"Nome do Espetáculo\", bg=BG2, padx=10,pady=10).place(x=10,y=40)\n    label_show_name = Entry(root,width=30,borderwidth=2)\n    label_show_name.place(x=200,y=50)\n    #Date\n    Label(root, text=\"Data do Espetáculo\", bg=BG2, padx=10,pady=10).place(x=10,y=100)\n    calendar = Calendar(root,selectmode='day',year=2022,month=1,day=1) #Create Calendar object\n    calendar.place(x=190,y=100,width=200,height=190)\n    #Show Description\n    Label(root, text=\"Descrição do Espetáculo\", bg=BG2, padx=10,pady=10).place(x=10,y=310)\n    label_show_description = Text(root,height=5)\n    label_show_description.place(x=200,y=320,width=190)\n    #Populate data if show is not none\n    if(show is not None):\n        label_show_name.insert(0,show.getShowName())\n        label_show_description.delete(1.0,\"end\") #Good practice -> erase all in Text Widget before insert new text\n        label_show_description.insert(1.0,show.getDescription())\n    #Button\n    Button(root,text=\"Adicionar Espetáculo\",bg=\"#b2cadb\",width=20,height=2,command=lambda:confirm_show_creation(parent,root,session,label_show_name.get(),calendar.get_date(),label_show_description.get(\"1.0\",END))).place(x=10,y=450)\n    Button(root,text=\"Cancelar\",bg=\"#b2cadb\",width=10,height=2,command=lambda:choice(root,\"cancel\")).place(x=200,y=450)\n    root.mainloop()\n\ndef confirm_show_creation(parent,toplevel_window,session,show_name,show_date,show_description):\n    controll = controller.create_show(show_name,show_date,show_description) #Create show and add it to the show list\n    if(controll == -1):\n        showwarning(\"Erro!\",\"Dê o nome ao espetáculo!\",parent=toplevel_window)\n    elif(controll != -1):\n        showinfo(\"Espetáculo inserido!\",f'O espetáculo \"{show_name}\" foi inserido!',parent=toplevel_window)\n        show_area(parent,session)\n\ndef confirm_show_deletion(parent,toplevel_window,session,show):\n    show_id = controller.clear_show(show)\n    controller.clear_order_by_show_id(show_id) #delete all related reservations\n    showinfo(\"Espetáculo removido!\",f'O espetáculo \"{show.getShowName()}\" foi removido!',parent=toplevel_window)\n    show_area(parent,session)\n\n#---------------------------------------------------\n#Opçoes na Window de info de bilhetes\ndef choice(parent,option):\n    if option == \"cancel\":\n        parent.destroy()\n#------------------------------------------------------------------------------------------------------------------------\n# SHOW AREA -----------------------------------------\ndef show_area(parent=None,session=None):\n    root = Tk()\n    if(parent is not None):\n        geometry = controller.calculate_geometry(parent,SHOW_AREA_SIZE)\n        parent.destroy()\n    else:\n        ws = root.winfo_screenwidth() # width of the screen\n        hs = root.winfo_screenheight() # height of the screen\n        #Calculate the x and y to pop the window in the middle of the screen\n        x = int((ws/2) - (SHOW_AREA_SIZE[0]/2))\n        y = int((hs/2) - (SHOW_AREA_SIZE[1]/2))\n        geometry = str(SHOW_AREA_SIZE[0])+\"x\"+str(SHOW_AREA_SIZE[1])+\"+\"+str(x)+\"+\"+str(y)\n    root.title(f\"Show Time! - Ver Espetáculo\")\n    root.geometry(geometry)\n    root.resizable(False,False)\n    root.config(bg=BG1)\n    #texto\n    Label(root, text=\"Espetáculos Disponíveis\", font=(\"Verdana\", 16), background=\"#7dd1bf\").grid(row=0, column=1, sticky=W)\n    #separaçao\n    Label(root, text=\" \", bg=BG1).grid(row=1, column=1)\n    Label(root, text=\"\", bg=BG1).grid(row=2, column=0)\n    #List Box  \n    #Create a scrollbar\n    scrollbar = Scrollbar(root)\n    scrollbar.grid(row=2,column=2)\n    #Create List Box\n    show_list_box = Listbox(root,yscrollcommand=scrollbar.set,width=100,height=20)\n    for shows in controller.model.Show_List:\n        show_list_box.insert(END, f\"#{shows.getID()} - {shows.getShowName()} na data {shows.getDate()} com {math.ceil((shows.getSeatCount()/shows.getFullCapacity())*100)}% de ocupação\")\n    show_list_box.grid(row=2,column=1,sticky=W)\n    scrollbar.config(command=show_list_box.yview) #Join the scrollbar with the listbox\n    #separaçao\n    Label(root, text=\" \", bg=BG1).grid(row=3, column=1)\n    #Botão para redirecionar\n    Button(root, text=\"Consultar espetáculo\", command=lambda:list_box_to_show(root,session,show_list_box),width=30, height=3, font=(\"Arial\", 11, \"bold\"), bg=\"#2a9c83\").grid(column=1, row=4)\n    #Info do user\n    if(session == None):\n        Label(root,text=\"Sem sessão iniciada\", font=(\"Arial\", 9), justify=\"right\", bg=BG1).place(x=570,y=430)\n        Button(root,text=\"Iniciar Sessão\",width=15,height=2, font=(\"Arial\", 9, \"bold\"), background=\"#5cedce\", command=lambda:login_page(root)).place(x=580,y=453)\n    else:\n        Label(root, text=f\"Bem-Vindo {session.getFullName()}\", font=(\"Arial\", 9), justify=\"right\", bg=BG1).place(x=400,y=5)\n        Label(root, text=f\"Data de hoje: {datetime.now():%d/%m/%y}\", font=(\"Arial\", 9), justify=\"right\", bg=BG1).place(x=400,y=25)\n        #botao espaço utilizador\n        Button(root,text=\"Espaço do Utilizador\",width=16,height=2,wraplength=100,font=(\"Arial\", 9, \"bold\"), background=\"#5cedce\", command=lambda:user_area(root,session)).place(x=560,y=450)\n        #encerrar sessao\n        Button(root,text=\"Encerrar Sessão\",width=15,height=2, font=(\"Arial\", 9, \"bold\"), background=\"red\", command=lambda:show_area(root)).place(x=570,y=5)\n    root.mainloop()\n\n#receives a list box and return the selected show\ndef list_box_to_show(root,session,list_box):\n    if(list_box.curselection() != ()): #Assuming only one show belongs to the tuple\n        i = list_box.curselection()[0] #get selected index in list box\n        show_id = int(list_box.get(i)[1:(indexOf(list_box.get(i),\"-\")-1)]) #Clean the string and get the show ID\n        for shows in controller.model.Show_List:\n            if(shows.getID() == show_id):\n                curr_show = shows #Get respective show object\n    else:\n        curr_show = None #Show Object\n    show_info(root,session,curr_show)\n\n#pop up da info de cada espetáculo - window\ndef show_info(parent,session,show):\n    if(show is not None):\n        info_window = Toplevel(parent) # Criar uma página em cima da página atual\n        info_window.title(f\"Informação do Espetáculo - #{show.getID()}\")\n        geometry = controller.calculate_geometry(parent,SHOW_INFO_SIZE)\n        info_window.geometry(geometry)\n        info_window.config(bg=BG1)\n        Label(info_window, text=f\"Nome do Espetáculo: {show.getShowName()}\", font=(\"Arial\", 9), bg=BG1).grid(row=0, column=0, sticky=W)\n        Label(info_window, text=f\"Data do Espetáculo: {show.getDate()}\", font=(\"Arial\", 9), bg=BG1).grid(row=1, column=0, sticky=W)\n        Label(info_window, text=f\"Descrição: {show.getDescription()}\", font=(\"Arial\", 9), bg=BG1).grid(row=2, column=0, sticky=W)\n        # Tabela de Preços ---\n        Label(info_window, text=f\"----------------------------------------------\", font=(\"Arial\", 9), bg=BG1).grid(row=3, column=0, sticky=W)\n        Label(info_window, text=f\"Tabela de Preços:\", font=(\"Arial\", 9), bg=BG1).grid(row=4, column=0, sticky=W)\n        Label(info_window, text=f\"Preço NORMAL - 4,00€\", font=(\"Arial\", 9), bg=BG1).grid(row=5, column=0, sticky=W)\n        Label(info_window, text=f\"Preço VIP - 12,00€\", font=(\"Arial\", 9), bg=BG1).grid(row=6, column=0, sticky=W)\n        Label(info_window, text=f\"----------------------------------------------\", font=(\"Arial\", 9), bg=BG1).grid(row=7, column=0, sticky=W)\n        # -------\n        Label(info_window, text=\" \", bg=BG1).grid(row=8, column=0)\n        if(session == None):\n            Label(info_window, text=\"Inicie sessão para reservar bilhetes\", font=(\"Arial\", 9), bg=BG1).grid(row=9, column=0, sticky=W)\n        elif(session.isAdmin() == True):\n            show_room(parent,session,info_window,show)\n            Button(info_window,wraplength=150,height=2,text=\"Adicionar nova data para este espetáculo\",bg=\"gray\",command=lambda:create_show_page(parent,session,show)).place(x=10,y=300)\n            Button(info_window,height=2,text=\"Remover este espetáculo\", bg=\"gray\",command=lambda:confirm_show_deletion(parent,info_window,session,show)).place(x=10,y=350)\n        else:\n            show_room(parent,session,info_window,show)\n        Button(info_window,text=\"Voltar\",width=10,height=2,command=lambda:choice(info_window,\"cancel\"),bg=\"gray\").place(x=10,y=450)\n    else:\n        showinfo(\"Informação\",\"Por favor selecione um espetáculo, ou aguarde por novos espetáculos.\")\n\ndef show_room(parent,session,info_window,show,seat_change=None): #Print the room in form of buttons\n    room = show.getRoom()\n    for l in range(len(room)):\n        Label(info_window, text=show.getSeatNumber((l,0))[0], font=(\"Arial\", 9), bg=BG1).grid(row=l+5, column=4)\n        for c in range(len(room[l])):\n            if(l == 0): # Print seat numbers\n                Label(info_window, text=show.getSeatNumber((l,c))[1:], font=(\"Arial\", 9), bg=BG1).grid(row=l+4, column=c+5)\n            seat_number = show.getSeatNumber((l,c))\n            if(room[l][c] == \"N0\"):\n                if(session.isAdmin() == True):\n                    Button(info_window,text=\" \",padx=16,pady=2,state=DISABLED).grid(column=c+5,row=l+5)\n                else:\n                    Button(info_window,text=\" \",padx=16,pady=2,command=lambda seat_number=seat_number:order(parent,session,info_window,seat_number,show)).grid(column=c+5,row=l+5)\n            elif(room[l][c] == \"N1\"):\n                if(seat_change is not None and show.getSeatNumber((l,c)) == seat_change): \n                    Button(info_window,text=\" \",bg='green',padx=16,pady=2,command=lambda seat_number=seat_number:order(parent,session,info_window,seat_number,show)).grid(column=c+5,row=l+5)\n                else:\n                    Button(info_window,text=\" \",bg='red',padx=16,pady=2,state=DISABLED).grid(column=c+5,row=l+5)                \n            elif(room[l][c] == \"V0\"):\n                if(session.isAdmin() == True):\n                    Button(info_window,text=\"VIP\",padx=10,pady=2,state=DISABLED).grid(column=c+5,row=l+5)\n                else:\n                    Button(info_window,text=\"VIP\",padx=10,pady=2,command=lambda seat_number=seat_number:order(parent,session,info_window,seat_number,show)).grid(column=c+5,row=l+5)\n            elif(room[l][c] == \"V1\"):\n                if(seat_change is not None and show.getSeatNumber((l,c)) == seat_change): \n                    Button(info_window,text=\"VIP\",bg='green',padx=10,pady=2,command=lambda seat_number=seat_number:order(parent,session,info_window,seat_number,show)).grid(column=c+5,row=l+5)\n                else:\n                    Button(info_window,text=\"VIP\",bg='red',padx=10,pady=2,state=DISABLED).grid(column=c+5,row=l+5)  \n            elif(room[l][c] == \"NA\"):\n                pass\n\ndef order(parent,session,info_window,seat_number,show): #Order Confirmation Window\n    order_window = Toplevel(info_window)\n    order_window.title(f\"Confirmar lugar {seat_number} - {show.getShowName()}\")\n    geometry = controller.calculate_geometry(info_window,ORDER_SIZE)\n    order_window.geometry(geometry)\n    order_window.config(bg=BG1)\n    Label(order_window, text=\"Por Favor confirme a reserva deste bilhete:\", font=(\"Arial\", 12), bg=BG1).grid(row=0, column=0, sticky=W)\n    Label(order_window, text=f\"Reserva para o espetáculo |{show.getShowName()}| em nome de {session.getFullName()}\", font=(\"Arial\", 9), bg=BG1).grid(row=1, column=0, sticky=W)\n    Label(order_window, text=f\"Data do Espetáculo: {show.getDate()}\", font=(\"Arial\", 9), bg=BG1).grid(row=2, column=0, sticky=W)\n    Label(order_window, text=f\"Lugar: {seat_number}\", font=(\"Arial\", 9), bg=BG1).grid(row=3, column=0, sticky=W)\n    Label(order_window, text=f\"Tipo de Reserva: {show.getSeatType(seat_number)}\", font=(\"Arial\", 9), bg=BG1).grid(row=4, column=0, sticky=W)\n    Label(order_window, text=f\"Preço: {show.getPriceFromSeat(seat_number)}€\", font=(\"Arial\", 9), bg=BG1).grid(row=5, column=0, sticky=W)\n    Label(order_window, text=\" \", bg=BG1).grid(row=6, column=0)\n    Button(order_window, text=\"Reservar\", command = lambda:confirm_order(parent,order_window,session,seat_number,show), bg=\"gray\").grid(row=20, column=1)\n    Button(order_window, text=\"Cancelar\", command = order_window.destroy, bg=\"gray\").grid(row=20, column=3)\n\ndef confirm_order(parent,toplevel_window,session,seat_number,show):\n    reservation_id = controller.order_ticket(session,show,seat_number)\n    showinfo(\"Lugar Reservado!\",f\"Bilhete #{reservation_id} foi reservado!\",parent=toplevel_window)\n    user_area(parent,session)\n#------------------------------------------------------------------------------------------------------------------------\ndef main():\n    controller.start()\n    show_area()\n    controller.save()", "repo_name": "psytins/projeto-final-AED", "sub_path": "views/view.py", "file_name": "view.py", "file_ext": "py", "file_size_in_byte": 31235, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "controllers.controller.authenticate_user", "line_number": 27, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 27, "usage_type": "name"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 40, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 40, "usage_type": "name"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 64, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 64, "usage_type": "name"}, {"api_name": "controllers.controller.registrate_user", "line_number": 87, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 87, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 93, "usage_type": "call"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 97, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 97, "usage_type": "name"}, {"api_name": "controllers.controller.get_day_total_price", "line_number": 126, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 126, "usage_type": "name"}, {"api_name": "datetime.strptime", "line_number": 128, "usage_type": "call"}, {"api_name": "controllers.controller.get_month_total_price", "line_number": 129, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 129, "usage_type": "name"}, {"api_name": "datetime.strptime", "line_number": 131, "usage_type": "call"}, {"api_name": "controllers.controller.get_year_total_price", "line_number": 132, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 132, "usage_type": "name"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 135, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 135, "usage_type": "name"}, {"api_name": "controllers.controller.model", "line_number": 152, "usage_type": "attribute"}, {"api_name": "controllers.controller", "line_number": 152, "usage_type": "name"}, {"api_name": "datetime.strptime", "line_number": 155, "usage_type": "call"}, {"api_name": "datetime.strptime", "line_number": 156, "usage_type": "call"}, {"api_name": "datetime.strptime", "line_number": 157, "usage_type": "call"}, {"api_name": "datetime.strptime", "line_number": 158, "usage_type": "call"}, {"api_name": "controllers.controller.get_all_users", "line_number": 194, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 194, "usage_type": "name"}, {"api_name": "controllers.controller.get_all_reservations", "line_number": 199, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 199, "usage_type": "name"}, {"api_name": "controllers.controller.model", "line_number": 215, "usage_type": "attribute"}, {"api_name": "controllers.controller", "line_number": 215, "usage_type": "name"}, {"api_name": "controllers.controller.get_user_total_price", "line_number": 225, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 225, "usage_type": "name"}, {"api_name": "controllers.controller.model", "line_number": 238, "usage_type": "attribute"}, {"api_name": "controllers.controller", "line_number": 238, "usage_type": "name"}, {"api_name": "controllers.controller.model", "line_number": 250, "usage_type": "attribute"}, {"api_name": "controllers.controller", "line_number": 250, "usage_type": "name"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 254, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 254, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 267, "usage_type": "call"}, {"api_name": "controllers.controller.clear_order", "line_number": 270, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 270, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 271, "usage_type": "call"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 277, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 277, "usage_type": "name"}, {"api_name": "controllers.controller.clear_order", "line_number": 284, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 284, "usage_type": "name"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 290, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 290, "usage_type": "name"}, {"api_name": "tkcalendar.Calendar", "line_number": 301, "usage_type": "call"}, {"api_name": "controllers.controller.create_show", "line_number": 318, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 318, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 320, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 322, "usage_type": "call"}, {"api_name": "controllers.controller.clear_show", "line_number": 326, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 326, "usage_type": "name"}, {"api_name": "controllers.controller.clear_order_by_show_id", "line_number": 327, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 327, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 328, "usage_type": "call"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 341, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 341, "usage_type": "name"}, {"api_name": "controllers.controller.model", "line_number": 365, "usage_type": "attribute"}, {"api_name": "controllers.controller", "line_number": 365, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 366, "usage_type": "call"}, {"api_name": "datetime.now", "line_number": 379, "usage_type": "call"}, {"api_name": "operator.indexOf", "line_number": 390, "usage_type": "call"}, {"api_name": "controllers.controller.model", "line_number": 391, "usage_type": "attribute"}, {"api_name": "controllers.controller", "line_number": 391, "usage_type": "name"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 403, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 403, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 427, "usage_type": "call"}, {"api_name": "controllers.controller.calculate_geometry", "line_number": 463, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 463, "usage_type": "name"}, {"api_name": "controllers.controller.order_ticket", "line_number": 477, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 477, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 478, "usage_type": "call"}, {"api_name": "controllers.controller.start", "line_number": 482, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 482, "usage_type": "name"}, {"api_name": "controllers.controller.save", "line_number": 484, "usage_type": "call"}, {"api_name": "controllers.controller", "line_number": 484, "usage_type": "name"}]}
{"seq_id": "40314492865", "text": "\nfrom OpenGL.GL import *\nfrom OpenGL.GLUT import *\nfrom OpenGL.GLU import *\n\nimport pygame\nfrom pygame.locals import *\n\n\nfrom utils import *\nfrom letras import *\n\ndef inicio():\n    width= -60\n    height= 0\n    L_S(width,height,1)\n    L_T(width+5,height,1)\n    L_A(width+10,height,1)\n    L_R(width+15,height,1)\n    L_T(width+20,height,1)\n\ndef salida():\n    b1=40\n    b2=0\n    L_E(b1,b2,1)\n    L_X(b1+5,b2,1)\n    L_I(b1+10,b2,1)\n    L_T(b1+15,b2,1)\n\ndef marco(vertices):\n    size=5\n    r,g,b=1, 0.7, 1\n    DrawPolygon(vertices, r, g, b, size)\n\ndef clearCanvas():\n    glClearColor(0.0, 0.0, 0.0, 1.0)\n    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n\ndef Choose(sx,sy):\n    vertices=[[-20,-10,1],\n              [ 20,-10,1],\n              [ 20, 10,1],\n              [-20, 10,1]]\n    marco(vertices)\n    v=traslate(vertices,sx,sy)\n    clearCanvas()\n    marco(v)\n    puntero(sx,sy-21)\n    inicio()\n    salida()\n\ndef start():\n    scale = 1\n    width, height = scale * 600, scale * 300\n\n    pygame.init()\n    pygame.display.set_caption('BomberTony')\n\n    display_openGL(width, height, scale)\n\n    pygame.mixer.music.load('Sonidos/musica2.mp3')\n    pygame.mixer.music.play(100)\n    marcador=-1\n    inicio()\n    salida()\n\n    glFlush()\n    pygame.display.flip()\n    cicle=True\n\n    while cicle==True:\n        \n        for event in pygame.event.get():\n            if event.type == QUIT:\n                cicle==False\n                return\n            if event.type==pygame.KEYDOWN:\n                if event.key==pygame.K_LEFT:\n                    sx=-50\n                    sy=0\n                    Choose(sx,sy)\n                    marcador=0\n                    glFlush()\n            \n                elif event.key==pygame.K_RIGHT:\n                    sx=50\n                    sy=0\n                    Choose(sx,sy)\n                    marcador=1\n                    glFlush()\n        \n                elif event.key==pygame.K_SPACE:\n                    if marcador==0:\n                        cicle=False\n                    elif marcador==1:\n                        cicle=False\n\n    return marcador", "repo_name": "tonyccm2/Bombertony", "sub_path": "start.py", "file_name": "start.py", "file_ext": "py", "file_size_in_byte": 2099, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pygame.init", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 68, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 92, "usage_type": "attribute"}]}
{"seq_id": "70440244924", "text": "from uuid import uuid4\n\nimport requests\nfrom flask import Flask, jsonify, url_for, request\n\nfrom blockchain import BlockChain\n\n\napp = Flask(__name__)\n\nblockchain = BlockChain()\n\nnode_address = uuid4().hex  # Unique address for current node\n\n\n@app.route('/create-transaction', methods=['POST'])\ndef create_transaction():\n    \"\"\"\n    Input Payload:\n    {\n        \"sender\": \"address_1\"\n        \"recipient\": \"address_2\",\n        \"amount\": 3\n    }\n    \"\"\"\n    transaction_data = request.get_json()\n\n    index = blockchain.create_new_transaction(**transaction_data)\n\n    response = {\n        'message': 'Transaction has been submitted successfully',\n        'block_index': index\n    }\n\n    return jsonify(response), 201\n\n\n@app.route('/mine', methods=['GET'])\ndef mine():\n    block = blockchain.mine_block(node_address)\n\n    response = {\n        'message': 'Successfully Mined the new Block',\n        'block_data': block\n    }\n    return jsonify(response)\n\n\n@app.route('/chain', methods=['GET'])\ndef get_full_chain():\n    response = {\n        'chain': blockchain.get_serialized_chain\n    }\n    return jsonify(response)\n\n\n@app.route('/register-node', methods=['POST'])\ndef register_node():\n\n    node_data = request.get_json()\n\n    blockchain.create_node(node_data.get('address'))\n\n    response = {\n        'message': 'New node has been added',\n        'node_count': len(blockchain.nodes),\n        'nodes': list(blockchain.nodes),\n    }\n    return jsonify(response), 201\n\n\n@app.route('/sync-chain', methods=['GET'])\ndef consensus():\n\n    def get_neighbour_chains():\n        neighbour_chains = []\n        for node_address in blockchain.nodes:\n            resp = requests.get(node_address + url_for('get_full_chain')).json()\n            chain = resp['chain']\n            neighbour_chains.append(chain)\n        return neighbour_chains\n\n    neighbour_chains = get_neighbour_chains()\n    if not neighbour_chains:\n        return jsonify({'message': 'No neighbour chain is available'})\n\n    longest_chain = max(neighbour_chains, key=len)  # Get the longest chain\n\n    if len(blockchain.chain) >= len(longest_chain):  # If our chain is longest, then do nothing\n        response = {\n            'message': 'Chain is already up to date',\n            'chain': blockchain.get_serialized_chain\n        }\n    else:  # If our chain isn't longest, then we store the longest chain\n        blockchain.chain = [blockchain.get_block_object_from_block_data(block) for block in longest_chain]\n        response = {\n            'message': 'Chain was replaced',\n            'chain': blockchain.get_serialized_chain\n        }\n\n    return jsonify(response)\n\n\nif __name__ == '__main__':\n\n    from argparse import ArgumentParser\n    parser = ArgumentParser()\n    parser.add_argument('-H', '--host', default='127.0.0.1')\n    parser.add_argument('-p', '--port', default=5000, type=int)\n    args = parser.parse_args()\n\n    app.run(host=args.host, port=args.port, debug=True)\n", "repo_name": "gauravvjn/blockchain-python", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 36, "dataset": "github-code", "pt": "41", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "blockchain.BlockChain", "line_number": 11, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "blockchain.create_new_transaction", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 35, "usage_type": "call"}, {"api_name": "blockchain.mine_block", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 46, "usage_type": "call"}, {"api_name": "blockchain.get_serialized_chain", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "blockchain.create_node", "line_number": 62, "usage_type": "call"}, {"api_name": "blockchain.nodes", "line_number": 66, "usage_type": "attribute"}, {"api_name": "blockchain.nodes", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 69, "usage_type": "call"}, {"api_name": "blockchain.nodes", "line_number": 77, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 85, "usage_type": "call"}, {"api_name": "blockchain.chain", "line_number": 89, "usage_type": "attribute"}, {"api_name": "blockchain.get_serialized_chain", "line_number": 92, "usage_type": "attribute"}, {"api_name": "blockchain.chain", "line_number": 95, "usage_type": "attribute"}, {"api_name": "blockchain.get_block_object_from_block_data", "line_number": 95, "usage_type": "call"}, {"api_name": "blockchain.get_serialized_chain", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 101, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 107, "usage_type": "call"}]}
{"seq_id": "4562130668", "text": "import logging\nimport re\nfrom datetime import datetime\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional, Tuple, Type, Union\n\nimport numpy as np\nimport pandas as pd\nimport typer\nimport xarray as xr\nfrom numpy.typing import NDArray\nfrom pydantic import BaseModel, Extra, Field\n\nfrom tsdat.tstring import Template\n\n__all__ = [\n    \"ParameterizedClass\",\n    \"decode_cf\",\n    \"record_corrections_applied\",\n    \"assign_data\",\n    \"get_start_time\",\n    \"get_start_date_and_time_str\",\n    \"get_filename\",\n    \"get_datastream\",\n    \"get_fields_from_datastream\",\n    \"DATASTREAM_TEMPLATE\",\n    \"FILENAME_TEMPLATE\",\n    \"generate_schema\",\n]\n\nlogger = logging.getLogger(__name__)\n\nDATASTREAM_TEMPLATE = Template(\n    \"{location_id}.{dataset_name}[-{qualifier}][-{temporal}].{data_level}\"\n)\n\nFILENAME_TEMPLATE = Template(\n    \"{datastream}.{start_date}.{start_time}[.{title}].{extension}\"\n)\n\n\ndef datetime_substitutions(\n    time: Union[datetime, np.datetime64, None]\n) -> Dict[str, str]:\n    substitutions: Dict[str, str] = {}\n    if time is not None:\n        t = pd.to_datetime(time)\n        substitutions.update(\n            year=t.strftime(\"%Y\"),\n            month=t.strftime(\"%m\"),\n            day=t.strftime(\"%d\"),\n            hour=t.strftime(\"%H\"),\n            minute=t.strftime(\"%M\"),\n            second=t.strftime(\"%S\"),\n            date_time=t.strftime(\"%Y%m%d.%H%M%S\"),\n            date=t.strftime(\"%Y%m%d\"),\n            time=t.strftime(\"%H%M%S\"),\n            start_date=t.strftime(\"%Y%m%d\"),  # included for backwards compatibility\n            start_time=t.strftime(\"%H%M%S\"),  # included for backwards compatibility\n        )\n    return substitutions\n\n\nclass ParameterizedClass(BaseModel, extra=Extra.forbid):\n    \"\"\"------------------------------------------------------------------------------------\n    Base class for any class that accepts 'parameters' as an argument.\n\n    Sets the default 'parameters' to {}. Subclasses of ParameterizedClass should override\n    the 'parameters' properties to support custom required or optional arguments from\n    configuration files.\n\n    ------------------------------------------------------------------------------------\n    \"\"\"\n\n    parameters: Any = Field(default_factory=dict)\n\n\ndef _nested_union(dict1: Dict[Any, Any], dict2: Dict[Any, Any]) -> Dict[Any, Any]:\n    for k, v in dict1.items():\n        if isinstance(v, dict):\n            node = dict2.setdefault(k, {})\n            _nested_union(v, node)  # type: ignore\n        else:\n            dict2[k] = v\n    return dict2\n\n\n# Brilliant solution seen here https://stackoverflow.com/a/65363852/15641512\ndef model_to_dict(model: BaseModel, by_alias: bool = True) -> Dict[Any, Any]:\n    \"\"\"---------------------------------------------------------------------------------\n    Converts the model to a dict with unset optional properties excluded.\n\n    Performs a nested union on the dictionaries produced by setting the `exclude_unset`\n    and `exclude_none` options to True for the `model.dict()` method. This allows for\n    the preservation of explicit `None` values in the yaml, while still filtering out\n    values that default to `None`.\n\n    Borrowed approximately from https://stackoverflow.com/a/65363852/15641512.\n\n\n    Args:\n        model (BaseModel): The pydantic model to dict-ify.\n\n    Returns:\n        Dict[Any, Any]: The model as a dictionary.\n\n    ---------------------------------------------------------------------------------\"\"\"\n    return _nested_union(\n        model.dict(exclude_unset=True, by_alias=by_alias),\n        model.dict(exclude_none=True, by_alias=by_alias),\n    )\n\n\ndef decode_cf(dataset: xr.Dataset) -> xr.Dataset:\n    \"\"\"---------------------------------------------------------------------------------\n    Wrapper around `xarray.decode_cf()` which handles additional edge cases.\n\n    This helps ensure that the dataset is formatted and encoded correctly after it has\n    been constructed or modified. Handles edge cases for units and data type encodings\n    on datetime variables.\n\n    Args:\n        dataset (xr.Dataset): The dataset to decode.\n\n    Returns:\n        xr.Dataset: The decoded dataset.\n\n    ---------------------------------------------------------------------------------\"\"\"\n    # We have to make sure that time variables do not have units set as attrs, and\n    # instead have units set on the encoding or else xarray will crash when trying\n    # to save: https://github.com/pydata/xarray/issues/3739\n    for variable in dataset.variables.values():\n        if (\n            np.issubdtype(variable.data.dtype, np.datetime64)  # type: ignore\n            and \"units\" in variable.attrs\n        ):\n            units = variable.attrs[\"units\"]\n            del variable.attrs[\"units\"]\n            variable.encoding[\"units\"] = units  # type: ignore\n\n        # If the _FillValue is already encoded, remove it since it can't be overwritten per xarray\n        if \"_FillValue\" in variable.encoding:  # type: ignore\n            del variable.encoding[\"_FillValue\"]  # type: ignore\n\n    # Leaving the \"dtype\" entry in the encoding for datetime64 variables causes a crash\n    # when saving the dataset. Not fixed by: https://github.com/pydata/xarray/pull/4684\n    ds: xr.Dataset = xr.decode_cf(dataset)  # type: ignore\n    for variable in ds.variables.values():\n        if variable.data.dtype.type == np.datetime64:  # type: ignore\n            if \"dtype\" in variable.encoding:  # type: ignore\n                del variable.encoding[\"dtype\"]  # type: ignore\n    return ds\n\n\ndef record_corrections_applied(\n    dataset: xr.Dataset, variable_name: str, message: str\n) -> None:\n    \"\"\"---------------------------------------------------------------------------------\n    Records the message on the 'corrections_applied' attribute.\n\n    Args:\n        dataset (xr.Dataset): The corrected dataset.\n        variable_name (str): The name of the variable in the dataset.\n        message (str): The message to record.\n\n    ---------------------------------------------------------------------------------\"\"\"\n    variable_attrs = dataset[variable_name].attrs\n    corrections: List[str] = variable_attrs.get(\"corrections_applied\", [])\n    corrections.append(message)\n    variable_attrs[\"corrections_applied\"] = corrections\n\n\ndef assign_data(\n    dataset: xr.Dataset, data: NDArray[Any], variable_name: str\n) -> xr.Dataset:\n    \"\"\"---------------------------------------------------------------------------------\n    Assigns the data to the specified variable in the dataset.\n\n    If the variable exists and it is a data variable, then the DataArray for the\n    specified variable in the dataset will simply have its data replaced with the new\n    numpy array. If the variable exists and it is a coordinate variable, then the data\n    will replace the coordinate data. If the variable does not exist in the dataset then\n    a KeyError will be raised.\n\n\n    Args:\n        dataset (xr.Dataset): The dataset where the data should be assigned.\n        data (NDArray[Any]): The data to assign.\n        variable_name (str): The name of the variable in the dataset to assign data to.\n\n    Raises:\n        KeyError: Raises a KeyError if the specified variable is not in the dataset's\n            coords or data_vars dictionary.\n\n    Returns:\n        xr.Dataset: The dataset with data assigned to it.\n\n    ---------------------------------------------------------------------------------\"\"\"\n    if variable_name in dataset.data_vars:\n        dataset[variable_name].data = data\n    elif variable_name in dataset.coords:\n        tmp_name = f\"__{variable_name}__\"\n        dataset = dataset.rename_vars({variable_name: tmp_name})\n\n        # TODO: ensure attrs are copied over too\n        dataset[variable_name] = xr.zeros_like(dataset[tmp_name], dtype=data.dtype)  # type: ignore\n        dataset[variable_name].data[:] = data[:]\n        # dataset = dataset.swap_dims({tmp_name: variable_name})  # type: ignore\n        dataset = dataset.drop_vars(tmp_name)\n        # dataset = dataset.rename_dims(\n        #     {tmp_name: variable_name}\n        # )  # FIXME: This might drop all dimensions other than the one that was just renamed\n    else:\n        raise KeyError(\n            f\"'{variable_name}' must be a coord or a data_var in the dataset to assign\"\n            \" data to it.\"\n        )\n    return dataset\n\n\ndef get_start_time(dataset: xr.Dataset) -> pd.Timestamp:\n    \"\"\"---------------------------------------------------------------------------------\n    Gets the earliest 'time' value and returns it as a pandas Timestamp.\n\n    Args:\n        dataset (xr.Dataset): The dataset whose start time should be retrieved.\n\n    Returns:\n        pd.Timestamp: The timestamp of the earliest time value in the dataset.\n\n    ---------------------------------------------------------------------------------\"\"\"\n    time64: np.datetime64 = np.min(dataset[\"time\"].data)  # type: ignore\n    datetime: pd.Timestamp = pd.to_datetime(time64)  # type: ignore\n    return datetime\n\n\ndef get_start_date_and_time_str(dataset: xr.Dataset) -> Tuple[str, str]:\n    \"\"\"---------------------------------------------------------------------------------\n    Gets the start date and start time strings from a Dataset.\n\n    The strings are formatted using strftime and the following formats:\n        - date: \"%Y%m%d\"\n        - time: \"\"%H%M%S\"\n\n    Args:\n        dataset (xr.Dataset): The dataset whose start date and time should be retrieved.\n\n    Returns:\n        Tuple[str, str]: The start date and time as strings like \"YYYYmmdd\", \"HHMMSS\".\n\n    ---------------------------------------------------------------------------------\"\"\"\n    timestamp = get_start_time(dataset)\n    return timestamp.strftime(\"%Y%m%d\"), timestamp.strftime(\"%H%M%S\")\n\n\ndef get_file_datetime_str(file: Union[Path, str]) -> str:\n    datetime_match = re.match(r\".*(\\d{8}\\.\\d{6}).*\", Path(file).name)\n    if datetime_match is not None:\n        return datetime_match.groups()[0]\n    logger.error(f\"File {file} does not contain a recognized date string\")\n    return \"\"\n\n\ndef get_datastream(**global_attrs: str) -> str:\n    return DATASTREAM_TEMPLATE.substitute(global_attrs)\n\n\ndef get_fields_from_datastream(datastream: str) -> Dict[str, str]:\n    \"\"\"Extracts fields from the datastream.\n\n    WARNING: this only works for the default datastream template.\n    \"\"\"\n    fields = DATASTREAM_TEMPLATE.extract_substitutions(datastream)\n    if fields is None:\n        return {}\n    return {k: v for k, v in fields.items() if v is not None}\n\n\ndef get_filename(\n    dataset: xr.Dataset, extension: str, title: Optional[str] = None\n) -> str:\n    \"\"\"---------------------------------------------------------------------------------\n    Returns the standardized filename for the provided dataset.\n\n    Returns a key consisting of the dataset's datastream, starting date/time, the\n    extension, and an optional title. For file-based storage systems this method may be\n    used to generate the basename of the output data file by providing extension as\n    '.nc', '.csv', or some other file ending type. For ancillary plot files this can be\n    used in the same way by specifying extension as '.png', '.jpeg', etc and by\n    specifying the title, resulting in files named like\n    '<datastream>.20220424.165314.plot_title.png'.\n\n    Args:\n        dataset (xr.Dataset): The dataset (used to extract the datastream and starting /\n            ending times).\n        extension (str): The file extension that should be used.\n        title (Optional[str]): An optional title that will be placed between the start\n            time and the extension in the generated filename.\n\n    Returns:\n        str: The filename constructed from provided parameters.\n\n    ---------------------------------------------------------------------------------\"\"\"\n    extension = extension.lstrip(\".\")\n\n    start_date, start_time = get_start_date_and_time_str(dataset)\n    return FILENAME_TEMPLATE.substitute(\n        dataset.attrs,  # type: ignore\n        extension=extension,\n        title=title,\n        start_date=start_date,\n        start_time=start_time,\n    )\n\n\ndef get_fields_from_dataset(dataset: xr.Dataset) -> Dict[str, Any]:\n    return {\n        **dict(dataset.attrs),\n        **datetime_substitutions(dataset.time.values[0]),\n    }\n\n\nclass StandardsType(str, Enum):\n    tsdat = \"tsdat\"\n    acdd = \"acdd\"\n    ioos = \"ioos\"\n\n\ndef generate_schema(\n    dir: Path = typer.Option(\n        Path(\".vscode/schema/\"),\n        file_okay=False,\n        dir_okay=True,\n    ),\n    standards: StandardsType = typer.Option(StandardsType.tsdat),\n):\n    from tsdat import (\n        ACDDDatasetConfig,\n        DatasetConfig,\n        IOOSDatasetConfig,\n        PipelineConfig,\n        QualityConfig,\n        RetrieverConfig,\n        StorageConfig,\n        YamlModel,\n    )\n\n    dir.mkdir(exist_ok=True)\n\n    dataset_config_cls: Union[\n        Type[ACDDDatasetConfig], Type[IOOSDatasetConfig], Type[DatasetConfig]\n    ]\n    if standards == StandardsType.acdd:\n        dataset_config_cls = ACDDDatasetConfig\n    elif standards == StandardsType.ioos:\n        dataset_config_cls = IOOSDatasetConfig\n    else:\n        dataset_config_cls = DatasetConfig\n    print(f\"Using {standards} dataset standards\")\n\n    cls_mapping: Dict[str, Type[YamlModel]] = {\n        \"retriever\": RetrieverConfig,\n        \"dataset\": dataset_config_cls,\n        \"quality\": QualityConfig,\n        \"storage\": StorageConfig,\n        \"pipeline\": PipelineConfig,\n    }\n\n    for cfg_type, cfg_cls in cls_mapping.items():\n        path = dir / f\"{cfg_type}-schema.json\"\n        cfg_cls.generate_schema(path)\n        print(f\"Wrote {cfg_type} schema files to {path}\")\n    print(\"Done!\")\n\n\n# IDEA: Method to print a summary of the list of problems with the data\n", "repo_name": "tsdat/tsdat", "sub_path": "tsdat/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 13763, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "tsdat.tstring.Template", "line_number": 34, "usage_type": "call"}, {"api_name": "tsdat.tstring.Template", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.datetime64", "line_number": 44, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 46, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 48, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 45, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 65, "usage_type": "name"}, {"api_name": "pydantic.Extra.forbid", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pydantic.Extra", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 76, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 76, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 79, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 90, "usage_type": "name"}, {"api_name": "xarray.Dataset", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.issubdtype", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.datetime64", "line_number": 135, "usage_type": "attribute"}, {"api_name": "xarray.Dataset", "line_number": 148, "usage_type": "attribute"}, {"api_name": "xarray.decode_cf", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.datetime64", "line_number": 150, "usage_type": "attribute"}, {"api_name": "xarray.Dataset", "line_number": 157, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 169, "usage_type": "name"}, {"api_name": "xarray.Dataset", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.typing.NDArray", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 175, "usage_type": "name"}, {"api_name": "xarray.zeros_like", "line_number": 207, "usage_type": "call"}, {"api_name": "xarray.Dataset", "line_number": 176, "usage_type": "attribute"}, {"api_name": "xarray.Dataset", "line_number": 222, "usage_type": "attribute"}, {"api_name": "numpy.datetime64", "line_number": 233, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 233, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 234, "usage_type": "name"}, {"api_name": "pandas.Timestamp", "line_number": 234, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 234, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 235, "usage_type": "name"}, {"api_name": "pandas.Timestamp", "line_number": 222, "usage_type": "attribute"}, {"api_name": "xarray.Dataset", "line_number": 238, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 238, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 257, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 257, "usage_type": "name"}, {"api_name": "re.match", "line_number": 258, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 258, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 269, "usage_type": "name"}, {"api_name": "xarray.Dataset", "line_number": 281, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 281, "usage_type": "name"}, {"api_name": "xarray.Dataset", "line_number": 317, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 317, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 317, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 324, "usage_type": "name"}, {"api_name": "tsdat.tstring", "line_number": 325, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 331, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 331, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 332, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 336, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 351, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 352, "usage_type": "name"}, {"api_name": "tsdat.ACDDDatasetConfig", "line_number": 352, "usage_type": "name"}, {"api_name": "tsdat.IOOSDatasetConfig", "line_number": 352, "usage_type": "name"}, {"api_name": "tsdat.DatasetConfig", "line_number": 352, "usage_type": "name"}, {"api_name": "tsdat.ACDDDatasetConfig", "line_number": 355, "usage_type": "name"}, {"api_name": "tsdat.IOOSDatasetConfig", "line_number": 357, "usage_type": "name"}, {"api_name": "tsdat.DatasetConfig", "line_number": 359, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 362, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 362, "usage_type": "name"}, {"api_name": "tsdat.YamlModel", "line_number": 362, "usage_type": "name"}, {"api_name": "tsdat.RetrieverConfig", "line_number": 363, "usage_type": "name"}, {"api_name": "tsdat.QualityConfig", "line_number": 365, "usage_type": "name"}, {"api_name": "tsdat.StorageConfig", "line_number": 366, "usage_type": "name"}, {"api_name": "tsdat.PipelineConfig", "line_number": 367, "usage_type": "name"}]}
{"seq_id": "14962219552", "text": "from collections import deque\n\n#상어의 현재 위치와 이동 거리 초기화\ndef init_s():\n    for x, row in enumerate(graph):\n        for y, col in enumerate(row):\n            if graph[x][y] == 9:\n                q = deque([[0, x, y]])\n    return [q[0][1], q[0][2]], q\n\ndef bfs(q):\n    eat = []\n    while q:\n        cost, x, y = q.popleft()\n        visited[x][y] = True\n        for i in range(4):\n            nx = x + dx[i]\n            ny = y + dy[i]\n            if 0 <= nx < n and 0 <= ny < n:\n                if not visited[nx][ny] and graph[nx][ny] <= size:\n                    visited[nx][ny] = True\n                    q.append([cost+1, nx, ny])\n                    if 0< graph[nx][ny] < size:\n                        eat.append([cost+1, nx, ny])\n    eat.sort(key = lambda x : (x[0], x[1], x[2]))\n    return eat\n\nn = int(input())\ngraph = []\nfor _ in range(n):\n    graph.append(list(map(int, input().split())))\n\ndx = [-1, 1, 0, 0]\ndy = [0, 0, 1, -1]\n\ncloc, q = init_s()\nsize = 2\ntotal_cost = 0\ncount = 0\nwhile True:\n    visited = [[False]*n for _ in range(n)]\n    eat = bfs(q)\n    if len(eat) > 0:\n        count += 1\n        total_cost += eat[0][0]\n        graph[cloc[0]][cloc[1]] = 0\n        graph[eat[0][1]][eat[0][2]] = 9\n        cloc, q = init_s()\n        if count == size:\n            count = 0\n            size += 1\n    else:\n        break\nprint(total_cost)", "repo_name": "ShinWon-Chul/AlgorithmWithPython", "sub_path": "BOJ/Breadth-first_Search/BOJ_16236_아기 상어_211220.py", "file_name": "BOJ_16236_아기 상어_211220.py", "file_ext": "py", "file_size_in_byte": 1374, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "collections.deque", "line_number": 8, "usage_type": "call"}]}
{"seq_id": "41348071923", "text": "'''\r\n# \r\n# Description:\r\n#       深度回归预测模型（Pytorch）\r\n# Author:\r\n#       Xinghua.He\r\n# History:\r\n#       2020.12.27\r\n# \r\n'''\r\n# Directory package.\r\nfrom utils.dataset.EnergyDataset import *\r\nfrom utils.models.RegressionModel import *\r\nfrom utils.processing import *\r\n\r\n# Sys packages.\r\nimport os\r\nimport sys\r\nimport argparse\r\nimport time\r\nimport datetime\r\n\r\n# External packages.\r\nimport tqdm\r\nimport scipy.io as scio\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn import preprocessing\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nfrom torch.utils.data import DataLoader\r\nfrom torch.autograd import Variable\r\nfrom torch.utils.tensorboard import SummaryWriter\r\nfrom torchvision.transforms import transforms\r\nimport torchvision.models as models\r\n\r\nif __name__ == \"__main__\":\r\n    parser = argparse.ArgumentParser()\r\n    parser.add_argument(\"--epochs\", type=int, default=2000, help=\"number of epochs\")\r\n    parser.add_argument(\"--batch_size\", type=int, default=4, help=\"size of each image batch\")\r\n    parser.add_argument(\"--data_path\", type=str, default=None, help=\"data path of dataset\")\r\n    parser.add_argument(\"--pretraining\", type=bool, default=False, help=\"pretraining model path\")\r\n    parser.add_argument(\"--pretraining_path\", type=str, default=\"./config/resnet50-19c8e357.pth\", help=\"pretraining model path\")\r\n    parser.add_argument(\"--lr\", type=float, default=0.001, help=\"learning rate\")\r\n    opt = parser.parse_args()\r\n    print(opt)\r\n\r\n    # Detetced device type (cpu or cudn).\r\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n    print(device)\r\n\r\n    # Make directory.\r\n    os.makedirs('checkpoints', exist_ok=True)\r\n    os.makedirs('output', exist_ok=True)\r\n\r\n    if opt.data_path is not None:\r\n        # Setting data path according args.\r\n        DATAPATH = opt.data_path\r\n    else:\r\n        # Current directory path.\r\n        ROOTPATH = os.path.dirname(os.path.abspath(__file__))\r\n        # Data path.\r\n        DATAPATH = os.path.join(ROOTPATH, \"..\", \"DatDatas\", \"features.mat\")\r\n\r\n    # Obtain features.\r\n    datas = scio.loadmat(DATAPATH)\r\n    samples = datas['features']\r\n    features = samples[:, 0:10]\r\n    # Obtain content.\r\n    standard_content = samples[:, 10:13]\r\n\r\n    # Standar features.\r\n    xScale = preprocessing.StandardScaler()\r\n    features = xScale.fit_transform(features, standard_content)\r\n    # Standar contents.\r\n    yScale = preprocessing.StandardScaler()\r\n    standard_content = yScale.fit_transform(standard_content)\r\n\r\n    # Split dataset.\r\n    x_train_dataset, x_test_dataset, y_train_dataset, y_test_dataset = train_test_split(features, standard_content, test_size=0.2, random_state=3, shuffle=True)\r\n\r\n    train_dataset = list()\r\n    test_dataset = list()\r\n    for i in range(len(x_train_dataset)):\r\n        train_dataset.append((np.array(x_train_dataset[i]), np.array(y_train_dataset[i])))\r\n    for i in range(len(x_test_dataset)):\r\n        test_dataset.append((np.array(x_test_dataset[i]), np.array(y_test_dataset[i])))\r\n\r\n    # Constructed dataset.\r\n    train_dataset = EnergyDataset(data_dir=DATAPATH, samples=train_dataset, transform=None)\r\n    test_dataset = EnergyDataset(data_dir=DATAPATH, samples=test_dataset, transform=None)\r\n\r\n    # Constructed dataloader.\r\n    train_dataloader = DataLoader(dataset=train_dataset, batch_size=opt.batch_size, shuffle=False)\r\n    valid_dataloader = DataLoader(dataset=test_dataset, batch_size=1)\r\n\r\n    # Defined model.\r\n    model = RegressionModel(in_features=10, num_features=20).to(device)\r\n    model.initialize_weights()\r\n\r\n    # Defined optimizer.\r\n    optimizer = optim.SGD(params=model.parameters(), lr=0.0001)\r\n    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1)\r\n\r\n    # Main training.\r\n    for epoch in tqdm.tqdm(range(opt.epochs), ncols=100):\r\n        model.train()\r\n        optimizer.zero_grad()\r\n\r\n        # Training.\r\n        loss_mean = 0.\r\n        for i, datas in enumerate(train_dataloader):\r\n            samples, content = datas\r\n            # to device.\r\n            samples = Variable(samples.to(device))\r\n            content = Variable(content.to(device), requires_grad=False)\r\n            # Forward.\r\n            outputs1, outputs2, outputs3, loss = model(samples, content)\r\n            # Backward.\r\n            loss.backward()\r\n            # Update weights\r\n            optimizer.step()\r\n            optimizer.zero_grad()\r\n\r\n        # Evaluate.\r\n        if (epoch + 1) % 50  == 0:\r\n            # Setting evaluated model.\r\n            model.eval()\r\n            target = np.zeros_like(y_test_dataset)\r\n            predicted1 = list()\r\n            predicted2 = list()\r\n            predicted3 = list()\r\n            Cr = list()\r\n            Mn = list()\r\n            Cu = list()\r\n            for i, data in enumerate(tqdm.tqdm(valid_dataloader, ncols=100)):\r\n                sample, content = data\r\n                sample = Variable(sample.to(device), requires_grad=False)\r\n                # content = Variable(content.to(device), requires_grad=False)\r\n                # forward.\r\n                pre1, pre2, pre3 = model(sample)\r\n\r\n                predicted1.append(yScale.inverse_transform(pre1.data.to('cpu').numpy())) # <class 'list'>\r\n                predicted2.append(yScale.inverse_transform(pre2.data.to('cpu').numpy())) # <class 'list'>\r\n                predicted3.append(yScale.inverse_transform(pre3.data.to('cpu').numpy())) # <class 'list'>\r\n                target[i] = yScale.inverse_transform(content.data.numpy()) # <class 'numpy.ndarray'>\r\n            # Eventral output.\r\n            for i in range(len(y_test_dataset)):\r\n                Cr.append(predicted1[i][0][0] * 0.3 + predicted2[i][0][0] * 0.3 + predicted3[i][0][0] * 0.4)\r\n            for i in range(len(y_test_dataset)):\r\n                Mn.append(predicted1[i][0][1] * 0.3 + predicted2[i][0][1] * 0.3 + predicted3[i][0][1] * 0.4)\r\n            for i in range(len(y_test_dataset)):\r\n                Cu.append(predicted1[i][0][2] * 0.3 + predicted2[i][0][2] * 0.3 + predicted3[i][0][2] * 0.4)\r\n\r\n            # Plot figures.\r\n            _, ax = plt.subplots()\r\n            ax.plot([i for i in range(len(y_test_dataset))], Cr)\r\n            ax.plot([i for i in range(len(y_test_dataset))], Mn)\r\n            ax.plot([i for i in range(len(y_test_dataset))], Cu)\r\n            ax.plot([i for i in range(len(y_test_dataset))], target[:,0], \"--\")\r\n            ax.plot([i for i in range(len(y_test_dataset))], target[:,1], \"--\")\r\n            ax.plot([i for i in range(len(y_test_dataset))], target[:,2], \"--\")\r\n            plt.savefig(f\"./output/{epoch+1}.jpg\")\r\n            # Save data.\r\n\r\n            # Release storage.\r\n            del predicted1\r\n            del predicted2\r\n            del predicted3\r\n            del target\r\n            del Cr\r\n            del Cu\r\n            del Mn\r\n            model.train()\r\n    \r\n", "repo_name": "XingHuaHe/Energy-Analysis", "sub_path": "Energy Analysis/Python/quantitative_analysis/DeepRegression_Pytorch.py", "file_name": "DeepRegression_Pytorch.py", "file_ext": "py", "file_size_in_byte": 6991, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 56, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "scipy.io.loadmat", "line_number": 69, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 69, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 76, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 79, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 79, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 132, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}]}
{"seq_id": "11774228065", "text": "import os, sys, logging, ConfigParser\r\nfrom PySide import QtGui, QtWebKit\r\nfrom jinja2 import Template\r\nfrom riotwatcher import LoLException\r\nfrom friendship import *\r\n\r\nscriptPath = os.path.realpath(os.path.dirname(__file__))\r\nlog = logging.getLogger('log')\r\nlog.setLevel(logging.DEBUG)\r\nch = logging.StreamHandler()\r\nch.setLevel(logging.DEBUG)\r\nlog.addHandler(ch)\r\n\r\nconfigFilePath = os.path.join(scriptPath, 'config.cfg')\r\nconfig = ConfigParser.ConfigParser()\r\nconfig.read(configFilePath)\r\napiKey = config.get('SummonerData', 'api_key')\r\nsummonerName = config.get('SummonerData', 'summoner_name')\r\npast10 = config.getboolean('Algorithm', 'include_past_10_normal_matches')\r\n\r\nif not config.getboolean('Algorithm', 'debug'):\r\n\tlog.setLevel(logging.CRITICAL)\r\n\r\npremades = None\r\ntry:\r\n\tpremades = getPremades(summonerName, apiKey, past10)\r\n\tpass\r\nexcept LoLException as e:\r\n\tapp = QtGui.QApplication(sys.argv)\r\n\tmsgbox = QtGui.QMessageBox(QtGui.QMessageBox.Critical, u\"Error\", str(e).decode('utf8'))\r\n\tmsgbox.exec_()\r\n\t\r\nif premades != None:\r\n\tapp = QtGui.QApplication(sys.argv)\r\n\tgeo = QtGui.QDesktopWidget().screenGeometry()\r\n\t\r\n\ttemplateString = open(os.path.join(scriptPath,'templates','main.html')).read().decode('utf8')\r\n\ttemplate = Template(templateString)\r\n\t\r\n\twebwindow = QtWebKit.QWebView()\r\n\twebwindow.move(int(geo.width()/2)-int(300/2), int(geo.height()/2)-int(200/2))\r\n\twebwindow.setWindowTitle(u'DenBaum ist der coolste')\r\n\twebwindow.resize(300,200)\r\n\twebwindow.setHtml(template.render(premades=premades))\r\n\twebwindow.show()\r\n\t\r\n\tapp.exec_()", "repo_name": "DenBaum/lolm8guesser", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "os.path.realpath", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "ConfigParser.ConfigParser", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 22, "usage_type": "attribute"}, {"api_name": "riotwatcher.LoLException", "line_number": 28, "usage_type": "name"}, {"api_name": "PySide.QtGui.QApplication", "line_number": 29, "usage_type": "call"}, {"api_name": "PySide.QtGui", "line_number": 29, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "PySide.QtGui.QMessageBox", "line_number": 30, "usage_type": "call"}, {"api_name": "PySide.QtGui", "line_number": 30, "usage_type": "name"}, {"api_name": "PySide.QtGui.QApplication", "line_number": 34, "usage_type": "call"}, {"api_name": "PySide.QtGui", "line_number": 34, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PySide.QtGui.QDesktopWidget", "line_number": 35, "usage_type": "call"}, {"api_name": "PySide.QtGui", "line_number": 35, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "jinja2.Template", "line_number": 38, "usage_type": "call"}, {"api_name": "PySide.QtWebKit.QWebView", "line_number": 40, "usage_type": "call"}, {"api_name": "PySide.QtWebKit", "line_number": 40, "usage_type": "name"}]}
{"seq_id": "23079827319", "text": "#matplotlib inline\nimport matplotlib.pyplot as plt\n\ndef readHeaderFromFile(filename):\n    #datafile = open(filename, \"r\")\n    #headers=[]\n    #for line in datafile:\n    #    entry = line.strip().split(\";\")\n    #    #print(entry);\n    #    if entry[0] == \"Anz_Kunden\":\n    #        headers = entry\n    #        return headers;\n    return [\"Anz_Kunden\", \"Gesamtkosten\"]\n\ndef readDatasetsFromFile(filename):\n    dataset = []\n    datafile = open(filename, \"r\")\n    for line in datafile:\n        entry = line.strip().split(\";\")\n        #print(entry);\n        result = []\n        if entry[0] == \"Anz_Kunden\":\n            continue\n        else:\n            result.append(int(entry[0]))\n            #result.append(float(entry[1].replace(\",\",\".\")))\n            #result.append(int(entry[2]))\n            #result.append(float(entry[3].replace(\",\",\".\")))\n            #result.append(int(entry[4]))\n            #result.append(float(entry[5].replace(\",\",\".\")))\n            result.append(float(entry[6].replace(\",\",\".\")))\n            dataset.append(result)\n    return dataset\n\ndef findEntry(dataset, i):\n    for e in dataset:\n        if e[0] == i:\n            return e\n    return \"not found\"\n\ndef getCost(dataset, i):\n    e = findEntry(dataset, i)\n    return e[1]\n\nFILENAME = \"data.csv\"\nheaders = readHeaderFromFile(FILENAME)\ndataset = readDatasetsFromFile(FILENAME)\n\n\nprint(headers)\n#print(findEntry(dataset, 36))\n#print(dataset)\n#for i in dataset:\n#    s = \"\"\n#    for e in i:\n#        s = s + str(e) + \"\\t\"\n#    print(s)\n\nsx = range(1,94)\nsy = []\nfor i in sx:\n    sy.append(getCost(dataset, i))\n\nplt.plot(sx,sy)\nplt.show()", "repo_name": "nebulx29/LearnPython", "sub_path": "language_basics/read_file_datasets.py", "file_name": "read_file_datasets.py", "file_ext": "py", "file_size_in_byte": 1609, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}]}
{"seq_id": "37941245743", "text": "from collections import deque\n\n\ndef change_zero(start):\n    queue = deque()\n    queue.append(start)\n    visited_for_zero[start[0]][start[1]] = 1\n    sign = False\n    temp = []\n\n    while queue:\n        cur_row, cur_col = queue.popleft()\n        temp.append((cur_row, cur_col))\n\n        for i in range(6):\n            if cur_row % 2:\n                nxt_row = cur_row + dr_odd[i]\n                nxt_col = cur_col + dc_odd[i]\n            else:\n                nxt_row = cur_row + dr_even[i]\n                nxt_col = cur_col + dc_even[i]\n\n            if 0 <= nxt_row < H and 0 <= nxt_col < W:\n                if not visited_for_zero[nxt_row][nxt_col] and not data[nxt_row][nxt_col]:\n                    queue.append((nxt_row, nxt_col))\n                    visited_for_zero[nxt_row][nxt_col] = 1\n            else:\n                sign = True\n        if sign:\n            break\n    else:\n        for tp in temp:\n            data[tp[0]][tp[1]] = 1\n\n\ndef cnt(start):\n    global ans\n    queue = deque()\n    queue.append(start)\n    visited[start[0]][start[1]] = 1\n\n    while queue:\n        cur_row, cur_col = queue.popleft()\n        temp = 6\n        for i in range(6):\n            if cur_row % 2:\n                nxt_row = cur_row + dr_odd[i]\n                nxt_col = cur_col + dc_odd[i]\n            else:\n                nxt_row = cur_row + dr_even[i]\n                nxt_col = cur_col + dc_even[i]\n\n            if 0 <= nxt_row < H and 0 <= nxt_col < W:\n                if data[nxt_row][nxt_col]:\n                    temp -= 1\n                    if not visited[nxt_row][nxt_col]:\n                        queue.append((nxt_row, nxt_col))\n                        visited[nxt_row][nxt_col] = 1\n        ans += temp\n\n\nW, H = map(int, input().split())\ndata = [list(map(int, input().split())) for _ in range(H)]\n\ndr_even = [-1, -1, 0, 0, 1, 1]\ndc_even = [0, 1, 1, -1, 0, 1]\ndr_odd = [-1, -1, 0, 0, 1, 1]\ndc_odd = [-1, 0, 1, -1, -1, 0]\n\nvisited_for_zero = [[0 for _ in range(W)] for _ in range(H)]\nfor row in range(H):\n    for col in range(W):\n        if not data[row][col] and not visited_for_zero[row][col]:\n            change_zero((row, col))\n\nvisited = [[0 for _ in range(W)] for _ in range(H)]\nans = 0\n\nfor row in range(H):\n    for col in range(W):\n        if data[row][col] and not visited[row][col]:\n            cnt((row, col))\n\nprint(ans)\n", "repo_name": "algoitssm/algoritm_sungryul", "sub_path": "Baekjoon/Graph/5547_Graph_일루미네이션_미해결.py", "file_name": "5547_Graph_일루미네이션_미해결.py", "file_ext": "py", "file_size_in_byte": 2335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.deque", "line_number": 5, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 38, "usage_type": "call"}]}
{"seq_id": "42309069050", "text": "\"\"\"\nhttp://incompleteideas.net/sutton/MountainCar/MountainCar1.cp\npermalink: https://perma.cc/6Z2N-PFWC\n\"\"\"\n\nimport math\n\nimport numpy as np\n\nimport gym\nfrom gym import spaces\nfrom gym.utils import seeding\n\n\n\nfrom gym.envs.registration import register\n\n#woot\nregister(\n    id='MountTest-v0',\n    entry_point='mountain_car:MountainCarEnv',\n)\n\n\n\n\n\nclass MountainCarEnv(gym.Env):\n    metadata = {\n        'render.modes': ['human', 'rgb_array'],\n        'video.frames_per_second': 30\n    }\n\n    def __init__(self):\n        self.min_position = -1.2\n        self.max_position = 0.6\n        self.max_speed = 0.07\n        self.goal_position = 0.5\n        self.h=1\n        \n        #Make them into boxes later         \n        self.initial_conditions=np.random.random(9)\n        self.default_conditions=np.asarray([0]*8+[1])\n\n        \n        \n        self.force=0.001*0.3\n        self.gravity=0.0025*0.3\n\n        self.low = np.array([-self.max_speed]+[-2.5]*12)\n        self.high = np.array([self.max_speed]+[2.5]*12)\n        print(self.low,self.high)\n        self.viewer = None\n\n        self.action_space = spaces.Discrete(3)\n        self.observation_space = spaces.Box(self.low, self.high, dtype=np.float32)\n        self.seed()\n        self.reset()\n        \n        \n    def hill(self,k):\n        \"\"\"Defines the height of the hill at position k\n        This is just for rendering and should be an integral of slope\n\"\"\"\n        x = self.initial_conditions\n        k1=k+1.5\n        sum_= -x[8]*np.exp(-k1**2)+x[8]\n        for i in range(4):\n            k2=(k+3-x[i+4]*3)\n            sum_+= (x[i]-0.5)*np.exp(-(i+1)*k2**2)\n        return sum_\n\n    def slope(self,k):\n        \"\"\"Defines the slope of the hill at position k\"\"\"\n        x = self.initial_conditions\n        k1=k+1.5\n        sum_= 2*k1*x[8]*np.exp(-k1**2)\n        for i in range(4):\n            k2=(k+3-x[i+4]*3)\n            sum_+= -2*(i+1)*k2*(x[i]-0.5)*np.exp(-(i+1)*k2**2)\n        return sum_\n    \n    def nearby(self):\n        xs=np.linspace(self.pos-0.4,self.pos+0.4,12)\n        ys = self._height(xs)\n        return xs,ys\n    def set_initials(self,action):\n        self.initial_conditions=action\n    def seed(self, seed=None):\n        self.np_random, seed = seeding.np_random(seed)\n        return [seed]\n\n    def step(self, action):\n        assert self.action_space.contains(action), \"%r (%s) invalid\" % (action, type(action))\n        velocity = self.state[0]\n        #print((action-1)*self.force,self.slope(3*position)*(-self.gravity))\n        velocity += (action-1)*self.force + self.slope(3*self.pos)*(-self.gravity)\n        velocity = np.clip(velocity, -self.max_speed, self.max_speed)\n        self.pos += velocity\n        self.pos = np.clip(self.pos, self.min_position, self.max_position)\n        if (self.pos==self.min_position and velocity<0): velocity = 0\n\n        self.timesteps+=1\n        done = bool(self.pos >= self.goal_position or self.timesteps>199)\n        reward = -1.0\n\n        self.state[0] = velocity\n        #subtract current height to get relative position\n        self.state[1:13]=self.nearby()[1]-self._height(self.pos)\n        return np.array(self.state), reward, done, {}\n    def defaults(self):\n        self.initial_conditions = self.default_conditions.copy()\n        self.reset()\n    \n    def reset(self):\n        #self.initial_conditions=np.random.random(4)\n        self.state = np.zeros(13)\n        self.pos=self.np_random.uniform(low=-0.6, high=-0.4)\n        self.dft=np.fft.fft(self.initial_conditions)\n        self.timesteps=0\n        self.state[1:13]=self.nearby()[1]\n        return np.array(self.state)\n\n    def _height(self, xs):\n        return self.hill(3 * xs)#*.45*self.h+.55\n\n    def render(self, mode='human'):\n        screen_width = 600\n        screen_height = 400\n\n        world_width = self.max_position - self.min_position\n        scale = screen_width/world_width*0.6\n        carwidth=40\n        carheight=20\n        off=[10,150]\n        \n        xs = np.linspace(self.min_position, self.max_position, 100)\n        ys = self._height(xs)\n        off[1]=-min(ys)*scale+10\n        xys = list(zip((xs-self.min_position)*scale+off[0], ys*scale+off[1]))\n\n        if self.viewer is None:\n            from gym.envs.classic_control import rendering\n            self.r=rendering\n            self.viewer = rendering.Viewer(screen_width, screen_height)\n\n\n            clearance = 10\n\n            l,r,t,b = -carwidth/2, carwidth/2, carheight, 0\n            car = rendering.FilledPolygon([(l,b), (l,t), (r,t), (r,b)])\n            car.add_attr(rendering.Transform(translation=(0, clearance)))\n            self.cartrans = rendering.Transform()\n            car.add_attr(self.cartrans)\n            self.viewer.add_geom(car)\n            frontwheel = rendering.make_circle(carheight/2.5)\n            frontwheel.set_color(.5, .5, .5)\n            frontwheel.add_attr(rendering.Transform(translation=(carwidth/4,clearance)))\n            frontwheel.add_attr(self.cartrans)\n            self.viewer.add_geom(frontwheel)\n            backwheel = rendering.make_circle(carheight/2.5)\n            backwheel.add_attr(rendering.Transform(translation=(-carwidth/4,clearance)))\n            backwheel.add_attr(self.cartrans)\n            backwheel.set_color(.5, .5, .5)\n            self.viewer.add_geom(backwheel)\n\n\n\n        self.track = self.r.make_polyline(xys)\n        self.track.set_linewidth(2)\n        self.viewer.add_onetime(self.track)\n\n        \n        flagx = (self.goal_position-self.min_position)*scale+off[0]\n        flagy1 = self._height(self.goal_position)*scale+off[1]\n        flagy2 = flagy1 + 50\n        flagpole = self.r.Line((flagx, flagy1), (flagx, flagy2))\n        self.viewer.add_onetime(flagpole)\n        flag = self.r.FilledPolygon([(flagx, flagy2), (flagx, flagy2-10), (flagx+25, flagy2-5)])\n        flag.set_color(0,.8,0)\n        self.viewer.add_onetime(flag)\n\n        xs,ys=self.nearby()\n        ys-=0.01\n        #print(ys)\n        xys = list(zip((xs-self.min_position)*scale+off[0], ys*scale+off[1]))\n\n        self.track = self.r.make_polyline(xys)\n        self.track.set_linewidth(6)\n        self.track.set_color(1.0,0.75,0)\n        self.viewer.add_onetime(self.track)\n        pos = self.pos\n        self.cartrans.set_translation((pos-self.min_position)*scale+off[0], self._height(pos)*scale+off[1])\n        #This will visually show if the slope function is correct.\n        self.cartrans.set_rotation(np.arctan(self.slope(3 * pos)*3))#self.slope(3 * pos)*0.8)\n\n        return self.viewer.render(return_rgb_array = mode=='rgb_array')\n    \n    def get_keys_to_action(self):\n        return {():1,(276,):0,(275,):2,(275,276):1} #control with left and right arrow keys \n    \n    def close(self):\n        if self.viewer:\n            self.viewer.close()\n            self.viewer = None\n\n##    def hill(self,k):\n##        k-=0.5\n##        x=self.dft\n##        N=x.size\n##        sum_=0\n##        for n in range(N):\n##            sum_+=x[n]*np.e**(-2j*np.pi*(N-k)*n/N)\n##        #returning only the real bit\n##        #return sum_/N\n##        return sum_.real/N\n##\n##    def slope(self,k):\n##        k-=0.5\n##        x=self.dft\n##        N=x.size\n##        sum_=0\n##        for n in range(N):\n##            #not making it as steep. . . JK\n##            sum_+=(2j*np.pi*n/N)*x[n]*np.e**(-2j*np.pi*(N-k)*n/N)\n##            #sum_+=(1j*n)*x[n]*np.e**(-2j*np.pi*(N-k)*n/N)\n##        #returning only the real bit\n##        #return sum_/N\n##        return sum_.real/N\n\n", "repo_name": "ksprague1/Teacher", "sub_path": "mountain_car.py", "file_name": "mountain_car.py", "file_ext": "py", "file_size_in_byte": 7448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "gym.envs.registration.register", "line_number": 19, "usage_type": "call"}, {"api_name": "gym.Env", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "gym.spaces.Discrete", "line_number": 55, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 55, "usage_type": "name"}, {"api_name": "gym.spaces.Box", "line_number": 56, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 84, "usage_type": "call"}, {"api_name": "gym.utils.seeding.np_random", "line_number": 90, "usage_type": "call"}, {"api_name": "gym.utils.seeding", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.fft.fft", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 137, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 144, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.Viewer", "line_number": 145, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 145, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.FilledPolygon", "line_number": 151, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 151, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.Transform", "line_number": 152, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 152, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.Transform", "line_number": 153, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 153, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.make_circle", "line_number": 156, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 156, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.Transform", "line_number": 158, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 158, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.make_circle", "line_number": 161, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 161, "usage_type": "name"}, {"api_name": "gym.envs.classic_control.rendering.Transform", "line_number": 162, "usage_type": "call"}, {"api_name": "gym.envs.classic_control.rendering", "line_number": 162, "usage_type": "name"}, {"api_name": "numpy.arctan", "line_number": 195, "usage_type": "call"}]}
{"seq_id": "72380671500", "text": "from sgfmill.sgf import Sgf_game, Tree_node\nimport datetime\nimport argparse\nimport os\nfrom typing import List, Dict\nimport logging\n\nVERSION = 0.1\n\n\nclass SGFJoiner:\n    \"\"\"\n    Joins multiple boards to SGF by board size.\n    Boards become children of empty board root.\n    All methods return a dictionary by board size:\n        join_games - joins a list of sgfmill Sgf_games\n        join_files - joins a list of SGF files\n        join_dir - joins all sgf files in a directory\n        serialise - returns a dict of serialised sgf files\n    \"\"\"\n    def __init__(self):\n        self.boards = {}\n\n    def serialise(self) -> Dict[int, bytes]:\n        return {size: game.serialise() for size, game in self.boards.items()}\n\n    def join_games(self, games: List[Sgf_game]) -> Dict[int, Sgf_game]:\n        for game in games:\n            board_size = game.get_size()\n            master_board = self.boards.setdefault(board_size, Sgf_game(size=board_size))\n            self._cross_game_node_reparent(game.get_root(), master_board.get_root())\n        # set date and application properties\n        for board in self.boards.values():\n            root_node = board.get_root()\n            root_node.set('DT', datetime.datetime.now().strftime('%Y-%m-%d'))\n            root_node.set('AP', ('SGF joiner', '{}'.format(VERSION)))\n        return self.boards\n\n    def join_files(self, sgf_files: List[str]) -> Dict[int, Sgf_game]:\n        games = []\n        for path in sgf_files:\n            try:\n                with open(path) as sgf_file:\n                    games.append(Sgf_game.from_string(sgf_file.read()))\n            except FileNotFoundError:\n                logging.error(\"Skipping, no file found: {}\".format(path))\n            except ValueError:\n                logging.error(\"Skipping, not a valid sgf: {}\".format(path))\n\n        logging.info(\"Read {} boards for joining\".format(len(games)))\n        return self.join_games(games)\n\n    def join_dir(self, dirpath: str):\n        files = [os.path.join(dirpath, f) for f in os.listdir(dirpath) if f.endswith(\".sgf\")]\n        return self.join_files(files)\n\n    @classmethod\n    def _cross_game_node_reparent(cls, node: Tree_node, new_parent_node: Tree_node):\n        new_node = new_parent_node.new_child()\n        for property_name in node.properties():\n            # do not copy properties which must be defined at root level only\n            if property_name in ['FF', 'CA', 'SZ', 'GM', 'DT', 'KM', 'AP']:\n                continue\n            new_node.set(property_name, node.get(property_name))\n        colour, point = node.get_move()\n        if colour:\n            new_node.set_move(colour, point)\n        for child in node:\n            cls._cross_game_node_reparent(child, new_node)\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    group = parser.add_mutually_exclusive_group(required=True)\n    group.add_argument(\"--dir\", dest=\"dir\", help=\"Path to directory with sgf files\")\n    group.add_argument(\"--files\", dest=\"files\", action=\"append\", nargs=\"*\", help=\"Path to files\")\n    parser.add_argument(\"--outdir\", dest=\"outdir\", help=\"Path to directory to store results or printed to stdout\")\n\n    args = parser.parse_args()\n    logging.info(\"Running with arguments: {}\".format(args))\n    joiner = SGFJoiner()\n    joiner.join_dir(args.dir) if args.dir else joiner.join_files(args.files)\n    result = joiner.serialise()\n    if args.outdir:\n        for s, c in result.items():\n            with open(os.path.join(args.outdir, \"joined_{}x{}.sgf\".format(s, s)), \"wb\") as fh:\n                fh.write(c)\n    else:\n        print(result)\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "gle8098/diagram-recognition", "sub_path": "src/godr/sgf_joiner.py", "file_name": "sgf_joiner.py", "file_ext": "py", "file_size_in_byte": 3614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "46", "api": [{"api_name": "typing.Dict", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "sgfmill.sgf.Sgf_game", "line_number": 27, "usage_type": "name"}, {"api_name": "sgfmill.sgf.Sgf_game", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 39, "usage_type": "name"}, {"api_name": "sgfmill.sgf.Sgf_game.from_string", "line_number": 44, "usage_type": "call"}, {"api_name": "sgfmill.sgf.Sgf_game", "line_number": 44, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 39, "usage_type": "name"}, {"api_name": "sgfmill.sgf.Sgf_game", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 54, "usage_type": "call"}, {"api_name": "sgfmill.sgf.Tree_node", "line_number": 58, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}]}
{"seq_id": "10121091112", "text": "from flask import Flask, jsonify,abort, make_response\nfrom flask import request,Response\nfrom CorV_token import create_token,verify_token\nimport json\nimport redis\nimport mysql_sql\nimport os\nimport time\nimport mysql_sql\n\napp = Flask(__name__)\nMY_URL = '/api/'\n\n\"\"\"通用接口\"\"\"\n# 404 处理\n@app.errorhandler(404)\ndef not_found(error):\n    return make_response(jsonify({'error': 'Not found'}), 404)\n\n\"\"\"登陆注册接口\"\"\"\n@app.route(MY_URL + 'login/', methods=['POST'])\ndef login():\n    data = request.get_data()\n    data = json.loads(data.decode(\"utf-8\"))\n    try:\n        username = data.get('username')\n        password = data.get('password')\n    except Exception:\n        abort(404)\n\n    # 检查匹配\n    TorF = mysql_sql.USER_INFO_match(username, password)\n    if TorF == str(True):\n        # token生成\n        token = create_token(username)\n        # json生成\n        data = {\n            \"code\": 200,\n            \"message\": \"Success\",\n            \"token\": token\n        }\n        ret_json = json.dumps(data)\n    elif TorF == str(False):\n        data = {\n            \"code\": 400,\n            \"message\": \"False\"\n        }\n        ret_json = json.dumps(data)\n\n    return ret_json\n\n\"\"\"请求发布接口\"\"\"\n@app.route(MY_URL + 'home_source/', methods=['GET','POST'])\ndef home_source():\n    data = request.get_data()\n    #据说要改\n    # token = request.values.get('token')\n    data = json.loads(data.decode(\"utf-8\"))\n    token = data.get('token')\n\n    try:\n        data = verify_token(token)\n    except Exception:\n        abort(404)\n    # 检查匹配\n    TorF = mysql_sql.USER_INFO_find(data['username'])['flag']\n    if TorF == str(True):\n        username = data[\"username\"]\n        # json生成\n        data = {\n            \"code\": 200,\n            \"message\": \"Success\",\n            \"username\": username\n        }\n    elif TorF == str(False):\n        data = {\n            \"code\": 200,\n            \"message\": \"False\"\n        }\n\n    ret_json = json.dumps(data)\n    return ret_json\n\n@app.route(MY_URL + 'service_visualize/', methods=['GET', 'POST'])\ndef service_visualize():\n    data = request.get_data()\n    data = json.loads(data.decode(\"utf-8\"))\n    token = data.get('token')\n\n    try:\n        data = verify_token(token)\n    except Exception:\n        abort(404)\n    # 检查匹配\n\n    TorF = mysql_sql.USER_INFO_find(data['username'])['flag']\n\n    if TorF == str(True):\n        service_info = mysql_sql.SERVICE_INFO_get()\n        length = len(service_info)\n\n        def get_data(servicename: str, servicebrief: str, servicedetail: str, next: str, nextdata):\n            return {\n                \"servicename\": servicename,\n                \"servicebrief\": servicebrief,\n                \"servicedetail\": servicedetail,\n                \"next\": next,\n                \"nextdata\": nextdata\n            }\n\n        data = get_data(service_info[length - 1][0], service_info[length - 1][1], service_info[length - 1][2], \"0\",\n                        \"null\")\n        for i in range(length - 1):\n            data = get_data(service_info[length - i - 2][0], service_info[length - i - 2][1],\n                            service_info[length - i - 2][2], \"1\", data)\n\n        # json生成\n        data = {\n            \"code\": 200,\n            \"message\": \"Success\",\n            \"data\": data\n        }\n    elif TorF == str(False):\n        data = {\n            \"code\": 200,\n            \"message\": \"False\"\n        }\n\n    ret_json = json.dumps(data)\n    return ret_json\n\n# 用以校验token 并发布流信息\n@app.route(MY_URL + 'service_request/', methods=['GET','POST'])\ndef service_request():\n\n    data = request.get_data()\n    data = json.loads(data.decode(\"utf-8\"))\n\n    # 获取 token servicename 并校验 token\n    try:\n        token = data.get('token')\n        servicename = data.get('servicename')\n        data = verify_token(token)\n    except Exception:\n        abort(404)\n    # 检查匹配\n    TorF = mysql_sql.USER_INFO_find(data['username'])['flag']\n    TorF1 = mysql_sql.SERVICE_INFO_find(servicename)['flag']\n    TorF = TorF & TorF1\n    if TorF == str(True):\n        # json生成\n        data = {\n            \"code\": 200,\n            \"message\": \"Publish Success\",\n        }\n    elif TorF == str(False):\n        data = {\n            \"code\": 200,\n            \"message\": \"Publish False\"\n        }\n\n    # 流信息发布 通道为'event'\n    red = redis.StrictRedis(host='localhost', port=6379, db=6)\n    red.publish(str(servicename), u'[Code:200 Message:Publish Begin]')\n    time.sleep(10)\n    red.publish(str(servicename), u'[Code:200 Message:Success] [Userid]:%s [Event]:%s [Data]:%s [Retry]:%s' % (\"id\",'event',\"None\",'None'))\n\n    ret_json = json.dumps(data)\n    #Response(ret_json, content_type='application/json')\n    return ret_json\n# 实时监听事件(yield)\ndef event_stream():\n    red = redis.StrictRedis(host='localhost', port=6379, db=6)\n    pubsub = red.pubsub()\n    pubsub.subscribe('event')\n    for message in pubsub.listen():\n        yield 'data: %s\\n\\n' % message['data']\n# sse数据接收接口\n@app.route(MY_URL + 'sse_message/', methods=['GET', 'POST'])\ndef sse_message():\n    # 流信息\n    data = event_stream()\n    print(data)\n    return Response(data, mimetype=\"text/event-stream\")\n\n# 返回流式文件\n@app.route(MY_URL + '/model_download/<filename>', methods=['POST', 'GET'])\ndef model_download(filename):\n    # json 处理\n    data = request.get_data()\n    data = json.loads(data.decode(\"utf-8\"))\n    token = data.get('token')\n    servicename = data.get('servicename')\n\n    # 判断数据库中同时有存在的username和servicename再返回图片\n    data = verify_token(token)\n    print(data)\n    flag1 = mysql_sql.USER_INFO_find(data['username'])['flag']\n    flag2 = mysql_sql.SERVICE_INFO_find(servicename=servicename)['flag']\n\n    if flag1 and flag2:\n        TorF = True\n\n    if TorF:\n        # 存放模型的文件夹名字 “download”\n        DOWNLOAD_PATH = os.path.join(os.path.dirname(__file__), 'download')\n        DOWNLOAD_PATH = os.path.join(DOWNLOAD_PATH,filename)\n        print(DOWNLOAD_PATH)\n        # json生成\n        data = {\n            \"code\": 200,\n            \"message\": \"Success\",\n        }\n\n        # 流式读取\n        def send_chunk():\n            with open(DOWNLOAD_PATH, 'rb') as target_file:\n                while True:\n                    chunk = target_file.read(2 * 1024 * 1024)  # 每次读取2mb大小\n                    if not chunk:\n                        break\n                    yield chunk\n    else:\n        data = {\n            \"code\": 200,\n            \"message\": \"False\"\n        }\n\n    ret_json = json.dumps(data)\n    # 现在只返回图片\n    return Response(send_chunk(), content_type='application/octet-stream')\n\n\"\"\"策略设置与训练调用接口\"\"\"\n@app.route(MY_URL + 'strategy_set/',methods=['GET','POST'])\ndef strategy_set():\n    data = request.get_data()\n    data = json.loads(data.decode(\"utf-8\"))\n\n    token = data.get('token')\n    servicename = data.get('servicename')\n    strategies = data.get('strategies') # 字典\n\n    # 获取 token servicename 并校验 token\n    try:\n        data = verify_token(token)\n    except Exception:\n        abort(404)\n\n    # 检查匹配\n    TorF = mysql_sql.USER_INFO_find(data['username'])['flag']\n    if TorF == str(True):\n        # json生成\n        data = {\n            \"code\": 200,\n            \"message\": \"Token Verify = Success\",\n        }\n    elif TorF == str(False):\n        data = {\n            \"code\": 400,\n            \"message\": \"Token Verify = False\"\n        }\n    print(strategies)\n\n    # GLOBAL_MODEL_INFO表更新 版本自己设置未更新\n    mysql_sql.GLOBAL_MODEL_INFO_update1(servicename,\"0.1\",strategies['maxround'],strategies['aggregationtiming'],\n                                  strategies['epsilon'],strategies['batchsize'],strategies['lr'])\n\n    # 流信息发布 通道为'event'\n    red = redis.StrictRedis(host='localhost', port=6379, db=6)\n    red.publish('event', u'[Code:200 Message:Publish Begin]')\n    time.sleep(10)\n    red.publish('event', u'[Code:200 Message:Success] [Userid]:%s [Event]:%s [Data]:%s [Retry]:%s' % (\"id\",'event',\"None\",'None'))\n\n    ret_json = json.dumps(data)\n    #Response(ret_json, content_type='application/json')\n    return ret_json\n\n@app.route(MY_URL + 'task_visualize/', methods=['GET', 'POST'])\ndef task_visualize():\n    data = request.get_data()\n    data = json.loads(data.decode(\"utf-8\"))\n    token = data.get('token')\n\n    try:\n        data = verify_token(token)\n    except Exception:\n        abort(404)\n    # 检查匹配\n\n    TorF = mysql_sql.USER_INFO_find(data['username'])['flag']\n\n    if TorF == str(True):\n        service_info = mysql_sql.SERVICE_INFO_get()\n        print(service_info)\n        global_model_info = mysql_sql.GLOBAL_MODEL_INFO_get()\n        print(global_model_info)\n        length = len(service_info)\n\n        def get_data(servicename: str, servicebrief: str, servicedetail: str, next: str, nextdata):\n            # 下面几个参数分别是第3、4、6、7项\n            maxround = mysql_sql.GLOBAL_MODEL_INFO_find(servicename)['data'][0][2]\n            aggregationtiming = mysql_sql.GLOBAL_MODEL_INFO_find(servicename)['data'][0][3]\n            batchsize= mysql_sql.GLOBAL_MODEL_INFO_find(servicename)['data'][0][5]\n            lr= mysql_sql.GLOBAL_MODEL_INFO_find(servicename)['data'][0][6]\n            return {\n                \"servicename\": servicename,\n                \"servicebrief\": servicebrief,\n                \"servicedetail\": servicedetail,\n                \"strategies\":{\n                                \"maxround\":maxround,\n                                \"aggregationtiming\":aggregationtiming,\n                                \"batchsize\":batchsize,\n                                \"lr\":lr\n                },\n                \"next\": next,\n                \"nextdata\": nextdata\n            }\n\n        data = get_data(service_info[length - 1][0], service_info[length - 1][1], service_info[length - 1][2], \"0\",\n                        \"null\")\n        for i in range(length - 1):\n            data = get_data(service_info[length - i - 2][0], service_info[length - i - 2][1],\n                            service_info[length - i - 2][2], \"1\", data)\n\n        # json生成\n        data = {\n            \"code\": 200,\n            \"message\": \"Success\",\n            \"data\": data\n        }\n    elif TorF == str(False):\n        data = {\n            \"code\": 200,\n            \"message\": \"False\"\n        }\n\n    ret_json = json.dumps(data)\n    return ret_json\n\n\"\"\"信息列表相关功能\"\"\"\n@app.route(MY_URL + 'info_visualize/', methods=['GET','POST'])\ndef info_visualize():\n\n    data = request.get_data()\n    data = json.loads(data.decode(\"utf-8\"))\n\n    # 获取 token 并校验 token\n    try:\n        token = data.get('token')\n        data = verify_token(token)\n    except Exception:\n        abort(404)\n\n    # 检查匹配\n    TorF = mysql_sql.USER_INFO_find(data['username'])['flag']\n    if TorF == str(True):\n        # json生成\n        data = {\n            \"code\": 200,\n            \"message\": \"Publish Success\",\n        }\n    elif TorF == str(False):\n        data = {\n            \"code\": 200,\n            \"message\": \"Publish False\"\n        }\n\n    ret_json = json.dumps(data)\n    return ret_json\n\n@app.route(MY_URL + 'userpage_source/', methods=['GET','POST'])\ndef userpage_source():\n\n    data = request.get_data()\n    data = json.loads(data.decode(\"utf-8\"))\n\n    # 获取 token servicename 并校验 token\n    try:\n        token = data.get('token')\n        data = verify_token(token)\n    except Exception:\n        abort(404)\n\n    # 检查匹配\n    TorF = mysql_sql.USER_INFO_find(data['username'])['flag']\n    if TorF == str(True):\n        # json生成\n        data = {\n            \"code\": 200,\n            \"message\": \"Publish Success\",\n            \"username\":str(data['username'])\n        }\n    elif TorF == str(False):\n        data = {\n            \"code\": 200,\n            \"message\": \"Publish False\"\n        }\n\n    ret_json = json.dumps(data)\n    return ret_json", "repo_name": "sysu19351138/WebObject", "sub_path": "server_api.py", "file_name": "server_api.py", "file_ext": "py", "file_size_in_byte": 12002, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 29, "usage_type": "call"}, {"api_name": "mysql_sql.USER_INFO_match", "line_number": 32, "usage_type": "call"}, {"api_name": "CorV_token.create_token", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 42, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 58, "usage_type": "call"}, {"api_name": "CorV_token.verify_token", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 64, "usage_type": "call"}, {"api_name": "mysql_sql.USER_INFO_find", "line_number": 66, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 87, "usage_type": "call"}, {"api_name": "CorV_token.verify_token", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 93, "usage_type": "call"}, {"api_name": "mysql_sql.USER_INFO_find", "line_number": 96, "usage_type": "call"}, {"api_name": "mysql_sql.SERVICE_INFO_get", "line_number": 99, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 136, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 137, "usage_type": "call"}, {"api_name": "CorV_token.verify_token", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 145, "usage_type": "call"}, {"api_name": "mysql_sql.USER_INFO_find", "line_number": 147, "usage_type": "call"}, {"api_name": "mysql_sql.SERVICE_INFO_find", "line_number": 148, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 163, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 165, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 168, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 173, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 184, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 190, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 190, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 191, "usage_type": "call"}, {"api_name": "CorV_token.verify_token", "line_number": 196, "usage_type": "call"}, {"api_name": "mysql_sql.USER_INFO_find", "line_number": 198, "usage_type": "call"}, {"api_name": "mysql_sql.SERVICE_INFO_find", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path", "line_number": 206, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 229, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 231, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 236, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 236, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 237, "usage_type": "call"}, {"api_name": "CorV_token.verify_token", "line_number": 245, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 247, "usage_type": "call"}, {"api_name": "mysql_sql.USER_INFO_find", "line_number": 250, "usage_type": "call"}, {"api_name": "mysql_sql.GLOBAL_MODEL_INFO_update1", "line_number": 265, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 269, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 271, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 274, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 280, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 280, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 281, "usage_type": "call"}, {"api_name": "CorV_token.verify_token", "line_number": 285, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 287, "usage_type": "call"}, {"api_name": "mysql_sql.USER_INFO_find", "line_number": 290, "usage_type": "call"}, {"api_name": "mysql_sql.SERVICE_INFO_get", "line_number": 293, "usage_type": "call"}, {"api_name": "mysql_sql.GLOBAL_MODEL_INFO_get", "line_number": 295, "usage_type": "call"}, {"api_name": "mysql_sql.GLOBAL_MODEL_INFO_find", "line_number": 301, "usage_type": "call"}, {"api_name": "mysql_sql.GLOBAL_MODEL_INFO_find", "line_number": 302, "usage_type": "call"}, {"api_name": "mysql_sql.GLOBAL_MODEL_INFO_find", "line_number": 303, "usage_type": "call"}, {"api_name": "mysql_sql.GLOBAL_MODEL_INFO_find", "line_number": 304, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 337, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 344, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 344, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 345, "usage_type": "call"}, {"api_name": "CorV_token.verify_token", "line_number": 350, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 352, "usage_type": "call"}, {"api_name": "mysql_sql.USER_INFO_find", "line_number": 355, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 368, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 374, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 374, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 375, "usage_type": "call"}, {"api_name": "CorV_token.verify_token", "line_number": 380, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 382, "usage_type": "call"}, {"api_name": "mysql_sql.USER_INFO_find", "line_number": 385, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 399, "usage_type": "call"}]}
{"seq_id": "39455495382", "text": "# -*- coding: utf-8 -*-\nimport os\nimport json\nimport logging\nfrom urllib.parse import urlencode\n\nfrom ast import literal_eval as make_tuple\nfrom calendar import month_name\nfrom functools import reduce\n\nfrom celery import shared_task\n\nimport requests\n\nfrom django.conf import settings\nfrom django.contrib.gis.geos import GEOSGeometry, MultiPolygon\nfrom django.core.cache import cache\n\nfrom mmw.settings import layer_classmaps\n\nfrom apps.modeling.geoprocessing import multi, parse\nfrom apps.modeling.tr55.utils import aoi_resolution\n\nfrom apps.modeling.tasks import run_gwlfe\n\nfrom apps.modeling.mapshed.calcs import streams\nfrom apps.modeling.mapshed.tasks import (NOCACHE,\n                                         collect_data,\n                                         convert_data,\n                                         nlcd_streams,\n                                         )\n\nfrom apps.geoprocessing_api.calcs import (animal_population,\n                                          point_source_pollution,\n                                          catchment_water_quality,\n                                          stream_data,\n                                          streams_for_huc12s,\n                                          huc12s_with_aois,\n                                          drexel_fast_zonal,\n                                          )\n\nlogger = logging.getLogger(__name__)\n\nDRB = 'drb'\n\nRWD_HOST = os.environ.get('RWD_HOST', 'localhost')\nRWD_PORT = os.environ.get('RWD_PORT', '5000')\n\nACRES_PER_SQM = 0.000247105\nCM_PER_MM = 0.1\nM_PER_CM = 0.01\n\n\n@shared_task\ndef start_rwd_job(location, snapping, simplify, data_source):\n    \"\"\"\n    Calls the Rapid Watershed Delineation endpoint\n    that is running in the Docker container, and returns\n    the response unless there is an out-of-watershed error\n    which raises an exception.\n    \"\"\"\n    lat, lng = location\n    end_point = 'rwd' if data_source == DRB else 'rwd-nhd'\n    rwd_url = 'http://%s:%s/%s/%f/%f' % (RWD_HOST, RWD_PORT, end_point,\n                                         lat, lng)\n\n    params = {}\n\n    # The Webserver defaults to enable snapping, uses 1 (true) 0 (false)\n    if not snapping:\n        params['snapping'] = 0\n\n    # RWD also defaults to simplify the shape according to a tolerance.\n    # Passing it `?simplify=0` returns the unsimplified result.\n    if simplify is not False:\n        params['simplify'] = simplify\n\n    query_string = urlencode(params)\n\n    if query_string:\n        rwd_url += ('?%s' % query_string)\n\n    logger.debug('rwd request: %s' % rwd_url)\n\n    response_json = requests.get(rwd_url).json()\n    if 'error' in response_json:\n        raise Exception(response_json['error'])\n\n    return response_json\n\n\n@shared_task\ndef analyze_streams(results, area_of_interest, datasource='nhdhr', wkaoi=None):\n    \"\"\"\n    Given geoprocessing results with stream data and an area of interest,\n    returns the streams and stream order within it.\n\n    If a wkaoi is specified and caching is enabled, the results will be\n    cached and reused.\n    \"\"\"\n    key = None\n\n    if wkaoi and settings.GEOP['cache']:\n        key = f'db_{wkaoi}__{datasource}__stream_data'\n        cached = cache.get(key)\n        if cached:\n            return {'survey': cached}\n\n    survey = stream_data(results, area_of_interest, datasource)\n\n    if key:\n        cache.set(key, survey, None)\n\n    return {'survey': survey}\n\n\n@shared_task\ndef analyze_animals(area_of_interest):\n    \"\"\"\n    Given an area of interest, returns the animal population within it.\n    \"\"\"\n    return {'survey': animal_population(area_of_interest)}\n\n\n@shared_task\ndef analyze_pointsource(area_of_interest):\n    \"\"\"\n    Given an area of interest, returns point sources of pollution within it.\n    \"\"\"\n    return {'survey': point_source_pollution(area_of_interest)}\n\n\n@shared_task\ndef analyze_catchment_water_quality(area_of_interest):\n    \"\"\"\n    Given an area of interest in the DRB, returns catchment water quality data\n    within it.\n    \"\"\"\n    return {'survey': catchment_water_quality(area_of_interest)}\n\n\n@shared_task(throws=Exception)\ndef analyze_nlcd(result, area_of_interest=None, nlcd_year='2011_2011'):\n    if 'error' in result:\n        raise Exception(f'[analyze_nlcd_{nlcd_year}] {result[\"error\"]}')\n\n    pixel_width = aoi_resolution(area_of_interest) if area_of_interest else 1\n\n    result = parse(result)\n    histogram = {}\n    total_ara = 0\n    total_count = 0\n    categories = []\n\n    def area(dictionary, key, default=0):\n        return dictionary.get(key, default) * pixel_width * pixel_width\n\n    # Convert results to histogram, calculate total\n    for key, count in result.items():\n        nlcd, ara = key\n        total_count += count\n        total_ara += count if ara == 1 else 0\n        histogram[nlcd] = count + histogram.get(nlcd, 0)\n\n    has_ara = total_ara > 0\n\n    for nlcd, (code, name) in layer_classmaps.NLCD.items():\n        categories.append({\n            'area': area(histogram, nlcd),\n            'active_river_area': area(result, (nlcd, 1)) if has_ara else None,\n            'code': code,\n            'coverage': float(histogram.get(nlcd, 0)) / total_count,\n            'nlcd': nlcd,\n            'type': name,\n        })\n\n    return {\n        'survey': {\n            'name': f'land_{nlcd_year}',\n            'displayName':\n                f'Land Use/Cover {nlcd_year[5:]} (NLCD{nlcd_year[2:4]})',\n            'categories': categories,\n        }\n    }\n\n\n@shared_task(throws=Exception)\ndef analyze_soil(result, area_of_interest=None):\n    if 'error' in result:\n        raise Exception(f'[analyze_soil] {result[\"error\"]}')\n\n    pixel_width = aoi_resolution(area_of_interest) if area_of_interest else 1\n\n    histogram = {}\n    total_count = 0\n    categories = []\n\n    # Convert results to histogram, calculate total\n    for key, count in result.items():\n        total_count += count\n        s = make_tuple(key[4:])  # Change {\"List(1)\":5} to {1:5}\n        s = s if s != settings.NODATA else 3  # Map NODATA to 3\n        histogram[s] = count + histogram.get(s, 0)\n\n    for soil, (code, name) in layer_classmaps.SOIL.items():\n        categories.append({\n            'area': histogram.get(soil, 0) * pixel_width * pixel_width,\n            'code': code,\n            'coverage': float(histogram.get(soil, 0)) / total_count,\n            'type': name,\n        })\n\n    return {\n        'survey': {\n            'name': 'soil',\n            'displayName': 'Soil',\n            'categories': categories,\n        }\n    }\n\n\n@shared_task(throws=Exception)\ndef analyze_climate(result, wkaoi):\n    \"\"\"\n    Given the result of multigeoprocessing call for climate rasters,\n    combines them so that the 'ppt' values are grouped together and\n    'tmean' together. Each group is a dictionary where the keys are strings\n    of the month '1', '2', ..., '12', and the values the average in the\n    area of interest.\n\n    Then, transforms these dictionaries into a final result of the format used\n    for all other Analyze operations. The 'categories' contain twelve objects,\n    one for each month, with a 'month' field containing the name of the month,\n    and 'ppt' and 'tmean' fields with corresponding values. The 'index' can be\n    used for sorting purposes on the client side.\n    \"\"\"\n    if 'error' in result:\n        raise Exception(f'[analyze_climate] {result[\"error\"]}')\n\n    ppt = {k[5:]: v['List(0)']\n           for k, v in result[wkaoi].items() if 'ppt' in k}\n    tmean = {k[7:]: v['List(0)']\n             for k, v in result[wkaoi].items() if 'tmean' in k}\n\n    categories = [{\n        'monthidx': i,\n        'month': month_name[i],\n        'ppt': ppt[str(i)] * CM_PER_MM,\n        'tmean': tmean[str(i)],\n    } for i in range(1, 13)]\n\n    return {\n        'survey': {\n            'name': 'climate',\n            'displayName': 'Climate',\n            'categories': categories\n        }\n    }\n\n\n@shared_task\ndef analyze_terrain(result):\n    \"\"\"\n    Given a geoprocessing result in the shape of:\n\n        [\n          {\n            \"avg\": 2503.116786250801,\n            \"max\": 10501.0,\n            \"min\": -84.0\n          },\n          {\n            \"avg\": 2.708598957407307,\n            \"max\": 44.52286911010742,\n            \"min\": 0.0\n          }\n        ]\n\n    Assumes the first result is for Elevation in cm and the second for Slope\n    in %, and transforms it into a dictionary of the shape:\n\n        [\n          {\n            \"elevation\": 25.03116786250801,\n            \"slope\": 2.708598957407307,\n            \"type\": \"average\"\n          },\n          {\n            \"elevation\": -0.84,\n            \"slope\": 0.0,\n            \"type\": \"minimum\"\n          },\n          {\n            \"elevation\": 105.01,\n            \"slope\": 44.52286911010742,\n            \"type\": \"maximum\"\n          }\n        ]\n\n    which has Elevation in m and keeps Slope in %.\n    \"\"\"\n    if 'error' in result:\n        raise Exception(f'[analyze_terrain] {result[\"error\"]}')\n\n    [elevation, slope] = result\n\n    def cm_to_m(x):\n        return x * M_PER_CM if x else None\n\n    categories = [\n        dict(type='average',\n             elevation=cm_to_m(elevation['avg']),\n             slope=slope['avg']),\n        dict(type='minimum',\n             elevation=cm_to_m(elevation['min']),\n             slope=slope['min']),\n        dict(type='maximum',\n             elevation=cm_to_m(elevation['max']),\n             slope=slope['max'])\n    ]\n\n    return {\n        'survey': {\n            'name': 'terrain',\n            'displayName': 'Terrain',\n            'categories': categories\n        }\n    }\n\n\n@shared_task\ndef analyze_protected_lands(result, area_of_interest=None):\n    if 'error' in result:\n        raise Exception(f'[analyze_protected_lands] {result[\"error\"]}')\n\n    pixel_width = aoi_resolution(area_of_interest) if area_of_interest else 1\n\n    result = parse(result)\n    histogram = {}\n    total_count = 0\n    categories = []\n\n    for key, count in result.items():\n        total_count += count\n        histogram[key] = count + histogram.get(key, 0)\n\n    for class_id, (code, name) in layer_classmaps.PROTECTED_LANDS.items():\n        categories.append({\n            'area': histogram.get(class_id, 0) * pixel_width * pixel_width,\n            'class_id': class_id,\n            'code': code,\n            'coverage': float(histogram.get(class_id, 0)) / total_count,\n            'type': name,\n        })\n\n    return {\n        'survey': {\n            'name': 'protected_lands',\n            'displayName': 'Protected Lands',\n            'categories': categories,\n        }\n    }\n\n\n@shared_task\ndef analyze_drb_2100_land(area_of_interest, key):\n    result = drexel_fast_zonal(area_of_interest, key)\n    histogram = {}\n    total_count = 0\n    categories = []\n\n    for nlcd, count in result.items():\n        total_count += count\n        histogram[nlcd] = count + histogram.get(nlcd, 0)\n\n    for nlcd, (code, name) in layer_classmaps.NLCD.items():\n        categories.append({\n            'area': histogram.get(nlcd, 0),\n            'code': code,\n            'coverage': float(histogram.get(nlcd, 0)) / total_count,\n            'nlcd': nlcd,\n            'type': name,\n        })\n\n    return {\n        'survey': {\n            'name': f'drb_2100_land_{key}',\n            'displayName': f'DRB 2100 land forecast ({key})',\n            'categories': categories,\n        }\n    }\n\n\ndef collect_nlcd(histogram, geojson=None):\n    \"\"\"\n    Convert raw NLCD geoprocessing result to area dictionary\n    \"\"\"\n    pixel_width = aoi_resolution(geojson) if geojson else 1\n\n    categories = [{\n        'area': histogram.get(nlcd, 0) * pixel_width * pixel_width,\n        'code': code,\n        'nlcd': nlcd,\n        'type': name,\n    } for nlcd, (code, name) in layer_classmaps.NLCD.items()]\n\n    return {'categories': categories}\n\n\n@shared_task\ndef collect_worksheet_aois(result, shapes):\n    \"\"\"\n    Given a geoprocessing result of NLCD and NLCD+Streams for every\n    area of interest within every HUC-12, processes the raw results\n    and returns a dictionary a area of interest IDs corresponding to\n    their processed results.\n    \"\"\"\n    if 'error' in result:\n        raise Exception(f'[collect_worksheet_aois] {result[\"error\"]}')\n\n    NULL_RESULT = {'nlcd_streams': {}, 'nlcd': {}}\n    collection = {}\n\n    for shape in shapes:\n        output = result.get(shape['id'], NULL_RESULT)\n        nlcd = collect_nlcd(parse(output['nlcd']),\n                            shape['shape'])\n        streams = stream_data(nlcd_streams(output['nlcd_streams']),\n                              shape['shape'])\n        collection[shape['id']] = {'nlcd': nlcd, 'streams': streams}\n\n    return collection\n\n\n@shared_task\ndef collect_worksheet_wkaois(result, shapes):\n    \"\"\"\n    Given a geoprocessing result of MapShed and a list of HUC-12s, processes\n    the raw results through GWLFE and returns a dictionary of WKAOIs to the\n    modeled results, and also the processed NLCD and NLCD+Streams.\n    \"\"\"\n    if 'error' in result:\n        raise Exception(f'[collect_worksheet_wkaois] {result[\"error\"]}')\n\n    collection = {}\n\n    for shape in shapes:\n        wkaoi = shape['id']\n        geojson = shape['shape']\n\n        converted = convert_data(result, wkaoi)\n\n        histogram = converted[0]['n_count']\n\n        collected = collect_data(converted, geojson)\n        modeled = run_gwlfe(collected, None, None)\n\n        collection[wkaoi] = {\n            'mapshed': collected,\n            'gwlfe': modeled,\n            'nlcd': collect_nlcd(histogram, geojson),\n            'streams': stream_data(nlcd_streams(result[wkaoi]['nlcd_streams']),\n                                   geojson)\n        }\n\n    return collection\n\n\n@shared_task(time_limit=300)\ndef collect_worksheet(area_of_interest):\n    \"\"\"\n    Given an area of interest, matches it to HUC-12s and generates a dictionary\n    containing land and stream analysis for the matched AoIs, land and stream\n    analysis for the matched HUC-12s, and GWLF-E results for the HUC-12s.\n\n    This dictionary can be POSTed to /export/worksheet to generate an Excel\n    worksheet containing these values, which can be used for further modeling.\n    \"\"\"\n    def to_aoi_id(m):\n        return f'{NOCACHE}-{m[\"wkaoi\"]}'\n\n    matches = huc12s_with_aois(area_of_interest)\n\n    huc12_ids = [m['huc12'] for m in matches]\n    streams = streams_for_huc12s(huc12_ids)\n\n    aoi_shapes = [{\n        'id': to_aoi_id(m),\n        'shape': m['aoi_geom'],\n    } for m in matches]\n    aoi_results = collect_worksheet_aois(\n        multi('worksheet_aoi', aoi_shapes, streams),\n        aoi_shapes)\n\n    wkaoi_shapes = [{\n        'id': m['wkaoi'],\n        'shape': m['huc12_geom']\n    } for m in matches]\n    wkaoi_results = collect_worksheet_wkaois(\n        multi('mapshed', wkaoi_shapes, streams),\n        wkaoi_shapes)\n\n    collection = {}\n\n    for m in matches:\n        filename = f'{m[\"huc12\"]}__{m[\"name\"].replace(\" \", \"_\")}'\n        collection[filename] = {\n            'name': m['name'],\n            'aoi': aoi_results.get(to_aoi_id(m), {}),\n            'huc12': wkaoi_results.get(m['wkaoi'], {}),\n            'geojson': m['aoi_geom'],\n        }\n\n    return collection\n\n\n@shared_task(time_limit=300)\ndef draw_drainage_area_point(input):\n    \"\"\"\n    Given a JSON geometry of a point, makes a request to the Drexel / ANS\n    Drainage Area API and returns the area of interest and it's corresponding\n    Land Use Summary.\n    \"\"\"\n    # Parse the input and ensure it is a valid point, raising errors\n\n    # Send a request to the Drexel / ANS API to get the drainage area,\n    # raising errors if the API cannot be reached or errors out\n\n    # Validate the response and raise errors, if any\n\n    # Return the valid response\n\n    # TODO Replace this test code with actual implementation described above\n    point = GEOSGeometry(json.dumps(input['geometry']))\n    box = json.loads(MultiPolygon(point.buffer(0.01), srid=4326).geojson)\n    area_of_interest = {\n        'type': 'Feature',\n        'properties': {\n            'drainage_area': True,\n        },\n        'geometry': box,\n    }\n\n    return {\n        'area_of_interest': area_of_interest,\n        'point': json.loads(point.geojson),\n    }\n\n\n@shared_task(time_limit=300)\ndef draw_drainage_area_stream(input):\n    # Parse the input and ensure it is a valid polygon, raising errors if not\n\n    # Send a request to the Drexel / ANS API to get the drainage area,\n    # raising errors if the API cannot be reached or errors out\n\n    # Validate the response and raise errors, if any\n\n    # Return the valid response\n\n    # TODO Replace this test code with actual implementation described above\n    def union(collection, line):\n        return collection.union(line)\n\n    polygon = GEOSGeometry(json.dumps(input['geometry']))\n\n    # Ensure it intersects with a stream section, raising errors if not\n    stream_lines = [GEOSGeometry(s) for s in streams(polygon.geojson)]\n    if not stream_lines:\n        raise Exception('No streams within given shape.')\n\n    segment = reduce(union, stream_lines[1:], stream_lines[0])\n\n    box = json.loads(MultiPolygon(segment.buffer(0.01), srid=4326).geojson)\n    area_of_interest = {\n        'type': 'Feature',\n        'properties': {\n            'drainage_area': True,\n        },\n        'geometry': box,\n    }\n\n    return {\n        'area_of_interest': area_of_interest,\n        'stream_segment': json.loads(segment.geojson),\n    }\n", "repo_name": "ben-norton/model-my-watershed", "sub_path": "src/mmw/apps/geoprocessing_api/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 17327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "46", "api": [{"api_name": "logging.getLogger", "line_number": 42, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 46, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 47, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 47, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlencode", "line_number": 78, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 85, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 54, "usage_type": "name"}, {"api_name": "django.conf.settings.GEOP", "line_number": 103, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 103, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 105, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 105, "usage_type": "name"}, {"api_name": "apps.geoprocessing_api.calcs.stream_data", "line_number": 109, "usage_type": "call"}, {"api_name": "django.core.cache.cache.set", "line_number": 112, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 112, "usage_type": "name"}, {"api_name": "celery.shared_task", "line_number": 92, "usage_type": "name"}, {"api_name": "apps.geoprocessing_api.calcs.animal_population", "line_number": 122, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 117, "usage_type": "name"}, {"api_name": "apps.geoprocessing_api.calcs.point_source_pollution", "line_number": 130, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 125, "usage_type": "name"}, {"api_name": "apps.geoprocessing_api.calcs.catchment_water_quality", "line_number": 139, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 133, "usage_type": "name"}, {"api_name": "apps.modeling.tr55.utils.aoi_resolution", "line_number": 147, "usage_type": "call"}, {"api_name": "apps.modeling.geoprocessing.parse", "line_number": 149, "usage_type": "call"}, {"api_name": "mmw.settings.layer_classmaps.NLCD.items", "line_number": 167, "usage_type": "call"}, {"api_name": "mmw.settings.layer_classmaps.NLCD", "line_number": 167, "usage_type": "attribute"}, {"api_name": "mmw.settings.layer_classmaps", "line_number": 167, "usage_type": "name"}, {"api_name": "celery.shared_task", "line_number": 142, "usage_type": "call"}, {"api_name": "apps.modeling.tr55.utils.aoi_resolution", "line_number": 192, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 201, "usage_type": "call"}, {"api_name": "django.conf.settings.NODATA", "line_number": 202, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 202, "usage_type": "name"}, {"api_name": "mmw.settings.layer_classmaps.SOIL.items", "line_number": 205, "usage_type": "call"}, {"api_name": "mmw.settings.layer_classmaps.SOIL", "line_number": 205, "usage_type": "attribute"}, {"api_name": "mmw.settings.layer_classmaps", "line_number": 205, "usage_type": "name"}, {"api_name": "celery.shared_task", "line_number": 187, "usage_type": "call"}, {"api_name": "calendar.month_name", "line_number": 247, "usage_type": "name"}, {"api_name": "celery.shared_task", "line_number": 222, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 261, "usage_type": "name"}, {"api_name": "apps.modeling.tr55.utils.aoi_resolution", "line_number": 336, "usage_type": "call"}, {"api_name": "apps.modeling.geoprocessing.parse", "line_number": 338, "usage_type": "call"}, {"api_name": "mmw.settings.layer_classmaps.PROTECTED_LANDS.items", "line_number": 347, "usage_type": "call"}, {"api_name": "mmw.settings.layer_classmaps.PROTECTED_LANDS", "line_number": 347, "usage_type": "attribute"}, {"api_name": "mmw.settings.layer_classmaps", "line_number": 347, "usage_type": "name"}, {"api_name": "celery.shared_task", "line_number": 331, "usage_type": "name"}, {"api_name": "apps.geoprocessing_api.calcs.drexel_fast_zonal", "line_number": 367, "usage_type": "call"}, {"api_name": "mmw.settings.layer_classmaps.NLCD.items", "line_number": 376, "usage_type": "call"}, {"api_name": "mmw.settings.layer_classmaps.NLCD", "line_number": 376, "usage_type": "attribute"}, {"api_name": "mmw.settings.layer_classmaps", "line_number": 376, "usage_type": "name"}, {"api_name": "celery.shared_task", "line_number": 365, "usage_type": "name"}, {"api_name": "apps.modeling.tr55.utils.aoi_resolution", "line_number": 398, "usage_type": "call"}, {"api_name": "mmw.settings.layer_classmaps.NLCD.items", "line_number": 405, "usage_type": "call"}, {"api_name": "mmw.settings.layer_classmaps.NLCD", "line_number": 405, "usage_type": "attribute"}, {"api_name": "mmw.settings.layer_classmaps", "line_number": 405, "usage_type": "name"}, {"api_name": "apps.modeling.geoprocessing.parse", "line_number": 426, "usage_type": "call"}, {"api_name": "apps.modeling.mapshed.calcs.streams", "line_number": 428, "usage_type": "name"}, {"api_name": "apps.geoprocessing_api.calcs.stream_data", "line_number": 428, "usage_type": "call"}, {"api_name": "apps.modeling.mapshed.tasks.nlcd_streams", "line_number": 428, "usage_type": "call"}, {"api_name": "apps.modeling.mapshed.calcs.streams", "line_number": 430, "usage_type": "name"}, {"api_name": "celery.shared_task", "line_number": 410, "usage_type": "name"}, {"api_name": "apps.modeling.mapshed.tasks.convert_data", "line_number": 451, "usage_type": "call"}, {"api_name": "apps.modeling.mapshed.tasks.collect_data", "line_number": 455, "usage_type": "call"}, {"api_name": "apps.modeling.tasks.run_gwlfe", "line_number": 456, "usage_type": "call"}, {"api_name": "apps.geoprocessing_api.calcs.stream_data", "line_number": 462, "usage_type": "call"}, {"api_name": "apps.modeling.mapshed.tasks.nlcd_streams", "line_number": 462, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 435, "usage_type": "name"}, {"api_name": "apps.modeling.mapshed.tasks.NOCACHE", "line_number": 480, "usage_type": "name"}, {"api_name": "apps.geoprocessing_api.calcs.huc12s_with_aois", "line_number": 482, "usage_type": "call"}, {"api_name": "apps.modeling.mapshed.calcs.streams", "line_number": 485, "usage_type": "name"}, {"api_name": "apps.geoprocessing_api.calcs.streams_for_huc12s", "line_number": 485, "usage_type": "call"}, {"api_name": "apps.modeling.geoprocessing.multi", "line_number": 492, "usage_type": "call"}, {"api_name": "apps.modeling.mapshed.calcs.streams", "line_number": 492, "usage_type": "argument"}, {"api_name": "apps.modeling.geoprocessing.multi", "line_number": 500, "usage_type": "call"}, {"api_name": "apps.modeling.mapshed.calcs.streams", "line_number": 500, "usage_type": "argument"}, {"api_name": "celery.shared_task", "line_number": 469, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.GEOSGeometry", "line_number": 534, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 534, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 535, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.MultiPolygon", "line_number": 535, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 546, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 517, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.GEOSGeometry", "line_number": 565, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 565, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.GEOSGeometry", "line_number": 568, "usage_type": "call"}, {"api_name": "apps.modeling.mapshed.calcs.streams", "line_number": 568, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 572, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 574, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.MultiPolygon", "line_number": 574, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 585, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 550, "usage_type": "call"}]}
{"seq_id": "30237847114", "text": "import pickle\n\n'''\n - Need to check whether time is present and run if it is'''\nimport time\nimport pandas as pd\nimport json\nfrom openpyxl import load_workbook\nfrom copy import copy\n#Lines 4~197 for input\n\n#Read information to calculate from this file:\ntemplate = pd.read_excel(\"read.xlsx\")\ntemplate = pd.DataFrame(template)\n\n\n\ndate = str(template.iloc[8][1]).split(' ')[0]\n\n#Get numeric characters\ndef integerExtractor(var):\n    integers = []\n    inttemp = []\n    #only get integers\n    for x in var:\n        try:\n            x = int(x)\n        except:\n            pass\n        else:\n            integers.append(x)\n    for a in integers:\n        inttemp.append(str(a))\n    output = ''.join(inttemp)\n    integer = int(output)\n    return integer\n    \n#Add colon for xx:xx if it is needed\ndef colonorNot(data):\n    temp=[]\n    for x in data:\n        temp.append(str(x))\n    output = ''.join(temp)\n    \n    if len(output)==4:\n        output = f\"{output[:2]}:{output[2:]}\"\n    elif len(output)==3:\n        output = f\"{output[:1]}:{output[1:]}\"\n    else:\n        return\n    return output\n    \n#Detect whether the time is am or pm based off user input \ndef appendampm(x):\n    if x is None:\n        return\n    if 'am' in x.lower():\n        y = 'am'\n    elif 'pm' in x.lower():\n        y = 'pm'\n    else:\n        return None, x\n    return y, str(x)\n        \n#Add zeros for time computation\ndef zeros(data):\n    print(f'{data}, {len(data)}, {type(data)}')\n    if len(str(data))==1:\n        slice = 1\n        temp = list(str(data))\n        temp.insert(slice, '00')\n        data = int(''.join(temp))\n        return data\n    elif len(str(data))==2:\n        slice = 2\n        temp = list(str(data))\n        temp.insert(slice, '00')\n        data = int(''.join(temp))\n        return data\n    else:\n        return(int(data))\n\n\n#Calculate all values\ndef calculate(position):\n    endposition = position\n    c = 0\n    while c<=5:\n        if str(template.iloc[position+c][6])!='nan':\n            print(template.iloc[position+c][6])\n            endposition = position+c+1\n        else:\n            c=5\n        c=c+1\n\n    print(position)\n    print(endposition)\n    #detecting whether each time period is am or pm\n    dashindex = str(template.iloc[position][4]).index(\"-\")\n    ampm = ['', '']\n    ampm[0], Start = appendampm(str(template.iloc[position][4][:dashindex]))\n    print(ampm[0])\n    ampm[1], End = appendampm(str(template.iloc[position][4][dashindex:]))\n\n    #idnetify vehicle type based on Excel Form\n    vehicleTypes=[]\n    for i in range(position, endposition):\n        if template.iloc[i][6]:\n            vehicleTypes.append(f'{template.iloc[i][6]}')\n            print(template.iloc[i][6])\n\n    #Identify number of vehicles\n    numVehicles = []\n    for i in range(position,endposition):\n        if template.iloc[i][5]:\n            numVehicles.append(integerExtractor(str(template.iloc[i][5])))\n\n    numDrivers = template.iloc[i][1]\n    numSupervisors = template.iloc[i][3]\n\n\n    #Identify rate based on Excel Form\n    RateDict = {'Towing Package(s)': '561.84', \n    'Impact Suppression Vehicle(s)': '40', \n    'Motor Vehicle Operator(s)': '32',\n    'Supervisor(s)': '38.75',\n    'Tow Truck(s)': '83.22',\n    'Tow Truck (PEMA)': '83.22',\n    'Tow Truck Driver(s)': '37.59',\n    'Parking Enforcement Vehicle(s)': '40',\n    'Parking Enforcement Officer(s)': '35.09',\n    'Impact Suppression Vehicle(s)': '40',\n    'Dump Truck (SWMA-ISV)': '40',  \n    'Flusher (SWMA)': '40', \n    'Trash Truck (SWMA)': '40',\n    'Recycling Truck (SWMA)': '40', \n    'Dump Truck (SWMA)': '40', \n    'Sweeper(s)': '40',\n    'Liftgate (SWMA)': '40', \n    'Dumpster (SWMA)': '40', \n    'Flatbed Truck (FMA)': '40', \n    'Flatbed Truck (PEMA)': '40', \n    'Large Tow (FMA)': '40', \n    'Large Tow (PEMA)': '40', \n    'Gator (FMA)': '40', \n    'Solar Light Tower (FMA)': '40'}\n    RateArr = []\n    for vehicleType in vehicleTypes:\n        RateArr.append(RateDict[vehicleType])\n\n\n\n    #Convert values:\n    #xx00\n    intStart = integerExtractor(Start)\n    #xx00\n    intEnd = integerExtractor(End)\n    #xx00\n    intRates = []\n    for Rate in RateArr:\n        intRates.append(float(Rate))\n    #convert the start time into a computable number\n    intStart = zeros(str(intStart))\n    #convert the start time into xx:xx\n    timeStart = colonorNot(str(intStart))\n\n\n    #Calculate amt of time\n    intEnd = zeros(str(intEnd))\n    timeEnd = colonorNot(str(intEnd))\n\n\n    if intEnd == 1200:\n        intEnd = 0\n    \n    if intStart == 1200:\n        Hours = -1\n    else:\n        Hours = 0\n    if intEnd!=intStart:\n        while intStart!=intEnd and Hours<20:\n            Hours+=1\n            intStart+=100\n            if intStart>1100:\n                intStart = 0\n    else:\n        Hours = 12\n\n    #Dictionary template to fill \n    dictionaryArr = []\n    TotalAmounts = []\n    for i in range(len(intRates)): \n        totVehicles = round((numVehicles[i]*Hours)*intRates[i], 2)\n        totSupervisors = round(float(RateDict['Supervisor(s)'])*Hours*numSupervisors, 2)\n        totDrivers = round(numVehicles[i]*float(RateDict['Motor Vehicle Operator(s)'])*Hours, 2)\n        VehicleDict = {\n            \"Description\": f\"{numVehicles[i]} {vehicleTypes[i]}, each for {Hours} hours\",\n            \"Service Hours\": f\"{timeStart} {ampm[0]} - {timeEnd} {ampm[1]}\",\n            \"Total Hours\": f\"{Hours*numVehicles[i]} hours\",\n            \"Hourly Rate\": f\"{intRates[i]} dollars an hour\",\n            \"Total Amount\": f\"{totVehicles} dollars\"\n        }\n\n        dictionaryArr.append(VehicleDict)\n    DriverDict = {\n        \"Description\": f\"{numDrivers} drivers, each for {Hours} hours\",\n        \"Service Hours\": f\"{timeStart} {ampm[0]} - {timeEnd} {ampm[1]}\",\n        \"Total Hours\": f\"{Hours*numDrivers} hours\",\n        \"Hourly Rate\": f\"{float(RateDict['Motor Vehicle Operator(s)'])} dollars an hour\",\n        \"Total Amount\": f\"{totDrivers} dollars\"\n    }\n    SupervisorDict = {\n        \"Description\": f\"{numSupervisors} supervisor for {Hours} hours\",\n        \"Service Hours\": f\"{timeStart} {ampm[0]} - {timeEnd} {ampm[1]}\",\n        \"Total Hours\": f\"{Hours} hours\",\n        \"Hourly Rate\": f\"{float(RateDict['Supervisor(s)'])*numSupervisors} dollars an hour\",\n        \"Total Amount\": f\"{totSupervisors} dollars\"\n    }\n    dictionaryArr.append(DriverDict)\n    dictionaryArr.append(SupervisorDict)\n\n\n\n    #df = pd.DataFrame(data = dict, index=[0])\n\n    return dictionaryArr, round(totVehicles+totDrivers+totSupervisors, 2)\n\n\ndfarr = []\n#Sum all costs and get a sum for the receipt\ntotalvalues = []\n#Set up a final dataframe to append all dataframes to\nfinaldf = pd.DataFrame(data=None)\n\n#n is used for figuring out what rows to delete\nn=0\nif type(template.iloc[13][4]) is str:\n    dict0, value = calculate(13)\n    for i in dict0:\n        dfarr.append(i)\n        n=n+1\n    totalvalues.append(value)\n\n\nif type(template.iloc[17][4]) is str:\n    dict0, value = calculate(17)\n    for i in dict0:\n        dfarr.append(i)\n        n=n+1\n    totalvalues.append(value)\n\nif type(template.iloc[22][4]) is str:\n    dict0, value = calculate(22)\n    for i in dict0:\n        dfarr.append(i)\n        n=n+1\n    totalvalues.append(value)\n\n\n\n#Used for multiple calculations\n#main()\n\n#Summation of cost\nvalues = sum(totalvalues)\nprint(values)\n\ndict1 = {\n    \"Description\": '',\n    \"Service Hours\": '',\n    \"Total Hours\": '',\n    \"Hourly Rate\": '',\n    \"Total Amount\": f'{values} dollars'\n}\n#Fills the rest of the useless entries with dataframes of same size but no content\ndictfill = {\n    \"Description\": '',\n    \"Service Hours\": '',\n    \"Total Hours\": '',\n    \"Hourly Rate\": '',\n    \"Total Amount\": ''\n}\n\nsummation = pd.DataFrame(data = dict1, index=[0])\ndictfill = pd.DataFrame(data = dictfill, index=[0])\n\nwhile len(dfarr)<20:\n    dfarr.append(dictfill)\ndfarr.append(summation)\n\nprint(len(dfarr))\n\n#Copy is the template of the receipt that is used\npath = pd.read_excel('writeto.xlsx')   \npath = pd.DataFrame(path)\n\nfor i in range(19,40):\n    if str(dfarr[i-19][\"Total Amount\"])[0]!='0':\n        path.iloc[i][1] = str(dfarr[i-19][\"Description\"])\n        path.iloc[i][2] = str(dfarr[i-19][\"Service Hours\"])\n        path.iloc[i][3] = str(dfarr[i-19][\"Total Hours\"])\n        path.iloc[i][4] = str(dfarr[i-19][\"Hourly Rate\"])\n        path.iloc[i][5] = str(dfarr[i-19][\"Total Amount\"])\n    elif i==39:\n        path.iloc[i][5] = str(dfarr[i-19][\"Total Amount\"]).strip('0').strip('\\nName: Total Amount, dtype: object')\n    else:\n        path.iloc[i][1] = ''\n        path.iloc[i][2] = ''\n        path.iloc[i][3] = ''\n        path.iloc[i][4] = ''\n        path.iloc[i][5] = ''\npath.fillna(' ', inplace=True)\npath.replace('\\n', '', regex=True)\nprint(path)\n\n\nstyle_attrs = [\"alignment\", \"border\", \"fill\", \"font\", \"number_format\", \"protection\"]\n\n#Copy cell styling\ndef cells(worksheet):\n    \"\"\"Return a generator for the sequence of cells in the worksheet\"\"\"\n    for row in worksheet:\n        for cell in row:\n            yield cell\n\n\ndef copy_attrs(src, dst, attrs=style_attrs):\n    \"\"\"Copy attributes from src to dst. Attributes are shallow-copied to avoid\n    TypeError: unhashable type: 'StyleProxy'\"\"\"\n    for name in attrs:\n        setattr(dst, name, copy(getattr(src, name)))\n\n\ndef copy_column_attrs(worksheet_src, worksheet_dst, attrs=style_attrs + [\"width\"]):\n    \"\"\"Copy ColumnDimension properties from worksheet_src to worksheet_dst.\n    Only properties listed in attrs will be copied.\"\"\"\n    for column, dimensions in worksheet_src.column_dimensions.items():\n        copy_attrs(\n            src=dimensions,\n            dst=worksheet_dst.column_dimensions[column],\n            attrs=style_attrs + [\"width\"],\n        )\n\n\n\ndef copy_row_attrs(worksheet_src, worksheet_dst, attrs=style_attrs + [\"height\"]):\n    \"\"\"Copy RowDimension properties from worksheet_src to worksheet_dst.\n    Only properties listed in attrs will be copied.\"\"\"\n    for row, dimensions in worksheet_src.row_dimensions.items():\n        copy_attrs(\n            src=dimensions,\n            dst=worksheet_dst.row_dimensions[row],\n            attrs=style_attrs + [\"height\"],\n        )\nnum=0\n\n#write new data from df information\ndef write_new_data(worksheet_dst, data):\n    for i in range(19, 42):\n        worksheet_dst.cell(row=i, column=1, value=data.iloc[i-2][0])\n        worksheet_dst.cell(row=i, column=2, value=data.iloc[i-2][1])\n        worksheet_dst.cell(row=i, column=3, value=data.iloc[i-2][2])\n        worksheet_dst.cell(row=i, column=4, value=data.iloc[i-2][3])\n        worksheet_dst.cell(row=i, column=5, value=data.iloc[i-2][4])\n        worksheet_dst.cell(row=i, column=6, value=data.iloc[i-2][5])\n\n#we only copy cell styles here:\ndef copy_cells(worksheet_src, worksheet_dst, data, attrs=style_attrs):\n    global num\n    arr=[]\n    \"\"\"Copy cells from worksheet_src to worksheet_dst. If cells are styled\n    then also copy the attributes listed in attrs.\"\"\"\n    cellvalues = cells(worksheet_src)\n    for cell in cells(worksheet_src):\n        arr.append(cell)\n        cell_dst = worksheet_dst.cell(row=cell.row, column=cell.column)\n        if cell.has_style:\n            copy_attrs(cell, cell_dst, attrs=attrs)\n        cell_dst.value = cell.value\n        num=num+1\n    write_new_data(worksheet_dst, data)\n\n#Clean end product before populating with data\ndef delete_worksheet_cells(worksheet):\n    worksheet.delete_cols(1, worksheet.max_column + 1)\n    worksheet.delete_rows(1, worksheet.max_row + 1)\n\n#Template to copy style from\nwb_src = load_workbook(\"source.xlsx\")\nws_src = wb_src.active\n\n#Document to write to with new data\nwb_dst = load_workbook(\"writeto.xlsx\")\nws_dst = wb_dst.active\n\ndelete_worksheet_cells(ws_dst)\ncopy_column_attrs(ws_src, ws_dst)\ncopy_row_attrs(ws_src, ws_dst)\n#copy_cells and the functions used inside of it are the main problems\ncopy_cells(ws_src, ws_dst, path)\n\n\n#Auto fit columns\ndims = {}\nfor row in ws_dst.rows:\n    for cell in row:\n        if cell.value:\n            dims[cell.column_letter] = max((dims.get(cell.column_letter, 0), len(str(cell.value))))\nfor col, value in dims.items():\n    ws_dst.column_dimensions[col].width = 30\n\nlis = []\nf=[]\nfor col in range(48):\n    lis.append(ws_dst.row_dimensions[col].height)\nfor i in lis:\n    if i is not None:\n        f.append(i)\nws_dst.row_dimensions[2].height=max(f)\n\n#n was referenced when adding dataframes\nws_dst.delete_rows(21+n,41-(21+n))\n\n#Save as excel file\n\nclass MyClass():\n    def __init__(self, param):\n        self.param = param\n \ndef save_object(obj):\n    try:\n        with open(\"meta/data.pickle\", \"wb\") as f:\n            pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)\n    except Exception as ex:\n        print(\"Error during pickling object (Possibly unsupported):\", ex)\n        return None\n \ndef load_object(filename):\n    try:\n        with open(filename, \"rb\") as f:\n            return pickle.load(f)\n    except Exception as ex:\n        print(\"Error during unpickling object (Possibly unsupported):\", ex)\n        return None\n\n\n#Check whether .pickle contains data\nobj = int(load_object(\"meta/data.pickle\"))\nif obj == None:\n    save_object(1)\n    wb_dst.save(f'Outputs/output{str(obj).zfill(4)}.xlsx')\n    obj=obj+1\n    save_object(obj)\nelse:  \n    wb_dst.save(f'Outputs/output{str(obj).zfill(4)}.xlsx')\n    obj=obj+1\n    save_object(obj)", "repo_name": "DashDashhh/vehicle-calculation", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 13225, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pandas.read_excel", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 233, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 284, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 285, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 294, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 295, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 331, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 389, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 393, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 433, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 433, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 441, "usage_type": "call"}]}
{"seq_id": "18762457871", "text": "from flask import Blueprint, request, jsonify\nfrom app.models import db, Post, Comment, User\nfrom app.forms import PostForm, CommentForm\nfrom datetime import datetime\nfrom flask_login import login_required, current_user\nfrom app.aws_s3 import (\n    upload_file_to_s3, allowed_file, get_unique_filename)\n\n\npost_routes = Blueprint('posts', __name__)\n\ndef validation_errors_to_error_messages(validation_errors):\n    errorMessages = []\n    for field in validation_errors:\n        for error in validation_errors[field]:\n            errorMessages.append(f'{error}')\n    return errorMessages\n\n\n# Get all Posts\n@post_routes.route('/myfeed')\ndef posts():\n    posts = Post.query.all()\n    data = [post.to_dict() for post in posts]\n    return {'posts': data}\n\n# Create a Post\n\n@post_routes.route('/newpost', methods=['POST'])\ndef newpost():\n    form = PostForm()\n    form['csrf_token'].data = request.cookies['csrf_token']\n\n    if \"photo\" not in request.files:\n        return {\"errors\": \"image required\"}, 400\n\n    photo = request.files[\"photo\"]\n\n\n    if not allowed_file(photo.filename):\n        return {\"errors\": \"file type not permitted\"}, 400\n\n    photo.filename = get_unique_filename(photo.filename)\n\n    upload = upload_file_to_s3(photo)\n\n    if \"url\" not in upload:\n        # if the dictionary doesn't have a url key\n        # it means that there was an error when we tried to upload\n        # so we send back that error message\n        return upload, 400\n\n    url = upload[\"url\"]\n    # flask_login allows us to get the current user from the request\n    # new_image = Image(user=current_user, url=url)\n\n\n    if form.validate_on_submit():\n        new_post = Post(\n            user_id = form.data['user_id'],\n            photo = url,\n            caption = form.data['caption'],\n            location = form.data['location']\n        )\n        db.session.add(new_post)\n        db.session.commit()\n\n        return new_post.to_dict()\n\n    return {'errors': validation_errors_to_error_messages(form.errors)}, 400\n\n# Get a Single Post (when on a profile Page, this will be a modal)\n\n# Update a Post\n\n@post_routes.route('/<id>', methods=['PUT'])\ndef updatepost(id):\n    form = PostForm()\n\n    form['csrf_token'].data = request.cookies['csrf_token']\n    post = Post.query.get(id)\n\n\n    if form.validate_on_submit():\n        # post.photo = form.data['photo']\n        post.caption = form.data['caption']\n        post.location = form.data['location']\n\n        db.session.commit()\n        return post.to_dict()\n    return {'errors': validation_errors_to_error_messages(form.errors)}, 400\n\n# Delete a Post\n\n@post_routes.route('/<id>', methods=['DELETE'])\ndef deletepost(id):\n    post = Post.query.get(id)\n\n    db.session.delete(post)\n    db.session.commit()\n    return post.to_dict()\n\n# Leave and remove a like on a Post\n@post_routes.route('/<id>/like', methods=['POST'])\ndef post_like(id):\n    post = Post.query.get(id)\n\n    if current_user in post.post_likes:\n        post.post_likes.remove(current_user)\n        db.session.add(post)\n        db.session.commit()\n        return post.to_dict()\n\n    post.post_likes.append(current_user)\n\n    db.session.add(post)\n    db.session.commit()\n    return post.to_dict()\n", "repo_name": "joshsalcido/mescroll", "sub_path": "app/api/post_routes.py", "file_name": "post_routes.py", "file_ext": "py", "file_size_in_byte": 3192, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "flask.Blueprint", "line_number": 10, "usage_type": "call"}, {"api_name": "app.models.Post.query.all", "line_number": 23, "usage_type": "call"}, {"api_name": "app.models.Post.query", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app.models.Post", "line_number": 23, "usage_type": "name"}, {"api_name": "app.forms.PostForm", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "app.aws_s3.allowed_file", "line_number": 40, "usage_type": "call"}, {"api_name": "app.aws_s3.get_unique_filename", "line_number": 43, "usage_type": "call"}, {"api_name": "app.aws_s3.upload_file_to_s3", "line_number": 45, "usage_type": "call"}, {"api_name": "app.models.Post", "line_number": 59, "usage_type": "call"}, {"api_name": "app.models.db.session.add", "line_number": 65, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 65, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 65, "usage_type": "name"}, {"api_name": "app.models.db.session.commit", "line_number": 66, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 66, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 66, "usage_type": "name"}, {"api_name": "app.forms.PostForm", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "app.models.Post.query.get", "line_number": 81, "usage_type": "call"}, {"api_name": "app.models.Post.query", "line_number": 81, "usage_type": "attribute"}, {"api_name": "app.models.Post", "line_number": 81, "usage_type": "name"}, {"api_name": "app.models.db.session.commit", "line_number": 89, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 89, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 89, "usage_type": "name"}, {"api_name": "app.models.Post.query.get", "line_number": 97, "usage_type": "call"}, {"api_name": "app.models.Post.query", "line_number": 97, "usage_type": "attribute"}, {"api_name": "app.models.Post", "line_number": 97, "usage_type": "name"}, {"api_name": "app.models.db.session.delete", "line_number": 99, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 99, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 99, "usage_type": "name"}, {"api_name": "app.models.db.session.commit", "line_number": 100, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 100, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 100, "usage_type": "name"}, {"api_name": "app.models.Post.query.get", "line_number": 106, "usage_type": "call"}, {"api_name": "app.models.Post.query", "line_number": 106, "usage_type": "attribute"}, {"api_name": "app.models.Post", "line_number": 106, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 108, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 109, "usage_type": "argument"}, {"api_name": "app.models.db.session.add", "line_number": 110, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 110, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 110, "usage_type": "name"}, {"api_name": "app.models.db.session.commit", "line_number": 111, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 111, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 111, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 114, "usage_type": "argument"}, {"api_name": "app.models.db.session.add", "line_number": 116, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 116, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 116, "usage_type": "name"}, {"api_name": "app.models.db.session.commit", "line_number": 117, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 117, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 117, "usage_type": "name"}]}
{"seq_id": "12612840947", "text": "import asyncio\nimport json\n\nimport websockets\nfrom channels.layers import get_channel_layer\nfrom django.core.management import BaseCommand\n\n\nasync def message_handler(ws):\n    async for message in ws:\n        message = json.loads(message)\n        if message.get('table') == 'instrument' or message.get('subscribe') == 'instrument':\n            channel_layer = get_channel_layer()\n            results = message.get('data')[0]\n            price = results.get('markPrice') or results.get('fairPrice')\n            if price:\n                data = {\n                    'timestamp': results.get('timestamp'),\n                    'symbol': results.get('symbol'),\n                    'price': results.get('markPrice')\n                }\n                await channel_layer.group_send(\n                    \"bitmex_feed\",\n                    {\n                        \"type\": \"feed_message\",\n                        \"message\": json.dumps(data),\n                    },\n                )\n\n\nasync def consume(url):\n    async with websockets.connect(url) as ws:\n        await message_handler(ws)\n\n\nclass Command(BaseCommand):\n    def handle(self, *args, **options):\n        loop = asyncio.get_event_loop()\n        loop.run_until_complete(\n            consume('wss://testnet.bitmex.com/realtime?subscribe=instrument:XBTUSD')\n        )\n        loop.run_forever()\n", "repo_name": "NikolayPogoreliy/test_bitmex", "sub_path": "apps/subscription/management/commands/runws.py", "file_name": "runws.py", "file_ext": "py", "file_size_in_byte": 1347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "json.loads", "line_number": 11, "usage_type": "call"}, {"api_name": "channels.layers.get_channel_layer", "line_number": 13, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 26, "usage_type": "call"}, {"api_name": "websockets.connect", "line_number": 32, "usage_type": "call"}, {"api_name": "django.core.management.BaseCommand", "line_number": 36, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 38, "usage_type": "call"}]}
{"seq_id": "30621928119", "text": "from django.urls import path\nfrom rest_framework_simplejwt.views import (\n    TokenObtainPairView, TokenRefreshView)\nfrom gymticApp import views\nurlpatterns = [\n    path('login/', TokenObtainPairView.as_view()),\n    path('refresh/', TokenRefreshView.as_view()),\n    path('user/', views.UserCreateView.as_view()),\n    path('user/<int:pk>/', views.UserDetailView.as_view()),\n    path('userList/', views.UserListView.as_view()),\n    path('user/update/<int:pk>/', views.UserUpdateView.as_view()),\n    path('user/delete/<int:pk>/', views.UserDeleteView.as_view()),\n]\n", "repo_name": "johancuervo/GymTic", "sub_path": "Gym-be/gymticProyect/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 562, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenObtainPairView.as_view", "line_number": 6, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenObtainPairView", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenRefreshView.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenRefreshView", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "gymticApp.views.UserCreateView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "gymticApp.views.UserCreateView", "line_number": 8, "usage_type": "attribute"}, {"api_name": "gymticApp.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "gymticApp.views.UserDetailView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "gymticApp.views.UserDetailView", "line_number": 9, "usage_type": "attribute"}, {"api_name": "gymticApp.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "gymticApp.views.UserListView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "gymticApp.views.UserListView", "line_number": 10, "usage_type": "attribute"}, {"api_name": "gymticApp.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "gymticApp.views.UserUpdateView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "gymticApp.views.UserUpdateView", "line_number": 11, "usage_type": "attribute"}, {"api_name": "gymticApp.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "gymticApp.views.UserDeleteView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "gymticApp.views.UserDeleteView", "line_number": 12, "usage_type": "attribute"}, {"api_name": "gymticApp.views", "line_number": 12, "usage_type": "name"}]}
{"seq_id": "34644898587", "text": "import pyqtgraph as pg\nfrom pyqtgraph.Qt import QtCore, QtGui\nimport numpy as np\nfrom multiprocessing import Process\nimport time\nimport constants\n\nclass ContinousStream:\n    # Displays continous stream by buffering <samplesBuffered> samples\n    def __init__(self, q, secToDisp, SAMPLING_FREQ, graphUpdateFreq):\n        # Process in which the ploting runs\n        self.process = Process(target=self.run)\n        # Data queue\n        self.q = q\n        # Audio sampling frequency\n        self.SAMPLING_FREQ = SAMPLING_FREQ\n        # Buffer length\n        self.storedSamp = secToDisp * SAMPLING_FREQ\n        self.ybuffer = np.zeros(self.storedSamp, dtype=np.uint16)\n        # Update frequency of the plot\n        self.graphUpdateFreq = graphUpdateFreq\n        # Number of seconds of data displayed\n        self.xdata = np.arange(-secToDisp, 0, 1. / SAMPLING_FREQ)\n        self.ydata = np.full(self.xdata.size, constants.AUDIO_MEAN, dtype=np.uint16)\n        self.ptr = 0\n        # Number of samples used for computing the Spectrogram\n        self.sampToSpec = 1024\n        self.timer = pg.QtCore.QTimer()\n        self.app = QtGui.QApplication([])\n        self.app.aboutToQuit.connect(self.cleanUp)\n        self.win = pg.GraphicsWindow()\n    \n    def cleanUp(self):\n        # Stops QT timer\n        self.timer.stop()\n        # Closes app\n        self.win.close()\n        self.app.closeAllWindows()\n        print('Window closed')\n\n    def start(self):\n        self.process.start()\n\n    def run(self):        \n        self.win.setWindowTitle('Microphone data')\n        # Sound wave plot\n        plot = self.win.addPlot()\n        plot.setLabel('bottom', 'Time', 's')\n        plot.setLabel('left', 'Amplitude', '')\n        plot.showGrid(x=True, y=True)\n        self.curve = plot.plot(x=self.xdata, y=self.ydata, pen=(255,0,0))\n        \n        # Next row\n        self.win.nextRow()\n        \n        # Spectrogram plot\n        self.specImg = pg.ImageItem()\n        specPlot = self.win.addPlot()\n        specPlot.addItem(self.specImg)\n        self.imgArray = np.zeros((1000, self.sampToSpec/2+1))\n        \n        # Bipolar colormap\n        pos = np.array([0., 1., 0.5, 0.25, 0.75])\n        color = np.array([[0,255,255,255], [255,255,0,255], [0,0,0,255], (0, 0, 255, 255), (255, 0, 0, 255)], dtype=np.ubyte)\n        cmap = pg.ColorMap(pos, color)\n        lut = cmap.getLookupTable(0.0, 1.0, 256)\n\n        # Set colormap\n        self.specImg.setLookupTable(lut)\n        self.specImg.setLevels([-50,40])\n        \n        # Setup the correct scaling for y-axis\n        freq = np.arange((self.sampToSpec/2)+1)/(float(self.sampToSpec)/self.SAMPLING_FREQ)\n        yscale = 1.0/(self.imgArray.shape[1]/freq[-1])\n        self.specImg.scale((1./self.SAMPLING_FREQ)*self.sampToSpec, yscale)\n        \n        specPlot.setLabel('left', 'Frequency', units='Hz')\n        self.wind = np.hanning(self.sampToSpec)\n        self.timer.timeout.connect(self.update)\n        \n        # Timer init\n        self.timer.start(1./self.graphUpdateFreq * 1000)\n        self.app.exec_()\n\n    def update(self):\n        ptrOld = self.ptr\n        # Gets samples from queue\n        while not self.q.empty():\n            sampleVec = self.q.get()\n            if sampleVec.size == 19:\n                self.ybuffer[(self.ptr - ptrOld):(self.ptr - ptrOld + 19)] = sampleVec\n                self.ptr += 19\n        # Rolls vector\n        self.ydata = np.roll(self.ydata, -(self.ptr - ptrOld))\n        # Copies samples to the ploted vector\n        self.ydata[self.storedSamp - (self.ptr - ptrOld):] = self.ybuffer[:(self.ptr - ptrOld)]\n        # Plots data\n        self.curve.setData(x=self.xdata, y=self.ydata)\n    \n        # Chunk used in Spectrogram\n        chunk = self.ydata[(self.storedSamp-self.sampToSpec):]\n        spec = np.fft.rfft(chunk*self.wind) / self.sampToSpec\n        # Get magnitude \n        psd = abs(spec)\n        # Convert to dB scale\n        psd = 20 * np.log10(psd)            \n        # Roll down image array\n        self.imgArray = np.roll(self.imgArray, -1, 0)\n        self.imgArray[-1:] = psd\n        # Sets image\n        self.specImg.setImage(self.imgArray, autoLevels=False)\n", "repo_name": "diasdm/crazyf_ros", "sub_path": "mic_deck/scripts/continuousStream.py", "file_name": "continuousStream.py", "file_ext": "py", "file_size_in_byte": 4153, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "multiprocessing.Process", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 24, "usage_type": "call"}, {"api_name": "constants.AUDIO_MEAN", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pyqtgraph.QtCore.QTimer", "line_number": 28, "usage_type": "call"}, {"api_name": "pyqtgraph.QtCore", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pyqtgraph.Qt.QtGui.QApplication", "line_number": 29, "usage_type": "call"}, {"api_name": "pyqtgraph.Qt.QtGui", "line_number": 29, "usage_type": "name"}, {"api_name": "pyqtgraph.GraphicsWindow", "line_number": 31, "usage_type": "call"}, {"api_name": "pyqtgraph.ImageItem", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.ubyte", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pyqtgraph.ColorMap", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.hanning", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.fft.rfft", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.log10", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 108, "usage_type": "call"}]}
{"seq_id": "16066424349", "text": "from osgeo import gdal\nimport os, gc\n# import filetype as ftype\nfrom scipy import misc\n# from scipy.misc import imread\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.misc import imsave\n\nclass GRID:\n\n    # read image files\n    def read_data(self, filename):\n        dataset=gdal.Open(filename)       # open file\n\n        im_width = dataset.RasterXSize    # get width\n        im_height = dataset.RasterYSize   # get height\n\n        im_geotrans = dataset.GetGeoTransform()  # get geoTransform\n        im_proj = dataset.GetProjection() # get Projection\n        im_data = dataset.ReadAsArray(0,0,im_width,im_height) # read data as array\n\n        del dataset\n        return im_proj, im_geotrans, im_data\n\n    # write tiff file\n    def write_data(self, filename, im_proj, im_geotrans, im_data):\n        #gdal data types include:\n        #gdal.GDT_Byte,\n        #gdal .GDT_UInt16, gdal.GDT_Int16, gdal.GDT_UInt32, gdal.GDT_Int32,\n        #gdal.GDT_Float32, gdal.GDT_Float64\n\n        # check the datatype of raster data\n        if 'int8' in im_data.dtype.name:\n            datatype = gdal.GDT_Byte\n        elif 'int16' in im_data.dtype.name:\n            datatype = gdal.GDT_UInt16\n        else:\n            datatype = gdal.GDT_Float32\n\n        # get the dimension\n        if len(im_data.shape) == 3:\n            im_bands, im_height, im_width = im_data.shape\n        else:\n            im_bands, (im_height, im_width) = 1, im_data.shape\n\n        # create the output file\n        driver = gdal.GetDriverByName(\"GTiff\")  # specify the format\n        dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)\n\n        if (dataset != None):\n            dataset.SetGeoTransform(im_geotrans)  # write affine transformation parameter\n            dataset.SetProjection(im_proj)  # write Projection\n        else:\n            print(\"Fails to create output file !!!\")\n\n        if im_bands == 1:\n            dataset.GetRasterBand(1).WriteArray(im_data)  # write array data\n        else:\n            for i in range(im_bands):\n                dataset.GetRasterBand(i + 1).WriteArray(im_data[i])\n\n        del dataset\n\ndef scale_to_01(data):\n    data_plot = data.reshape([-1, 1])  # [np.where(data!=0)].reshape([-1,1])\n    MAX = max(data_plot)\n    MIN = min(data_plot)\n    data_plot01 = (data - MIN) / (MAX - MIN)\n    return data_plot01\n\n\ndef tif2rgb(dataPath, dataName, savePath, saveName):\n    run = GRID()\n\n    proj, geotrans, data = run.read_data(\n        dataPath + dataName + \".tif\")  # read data\n\n    if len(data.shape) == 2:\n        # jpg_data = scale_to_01(data)*255\n        jpg_data = data\n\n    if len(data.shape) == 3:\n        data = data[0:3, :, :]\n        jpg_data = data.transpose(1, 2, 0)\n\n    if not os.path.exists(savePath):\n        os.makedirs(savePath)\n\n    # print(\"jpg.shape: {}\".format(jpg_data.shape))\n    imsave(savePath + saveName + \".png\", jpg_data)\n\n\nif __name__ == \"__main__\":\n    dataPath = r\"F:\\Thomas_Wildfire_v2\\ref_maps_based_on_offical_docs\\\\\"\n    dataName = \"Thomas_offical_progression_map_from_pdf_100m\"\n    tif2rgb(dataPath, dataName, dataPath, saveName=dataName)\n\n# if __name__ == \"__main__\":\n#     # os.chdir(r'D:\\Desktop\\processing\\Thomas_fire_SWIR_PseudoColorImage_auxil')      # change path into your workspace\n#\n#     batchProcessingFlag = False\n#     if batchProcessingFlag: # batch processing\n#         dataPath = r\"F:\\Thomas_Wildfire\\png2tif\\\\\"\n#         savePath = r\"F:\\Thomas_Wildfire\\png2tif\\\\\"\n#\n#         if not os.path.exists(savePath):\n#             os.makedirs(savePath)\n#\n#         run = GRID()\n#         # fileNames = []\n#         for file in os.listdir(dataPath):\n#             gc.enable()\n#\n#             dataName = file[:-4]\n#             print(dataName)\n#             print(dataPath + dataName + \".tif\")\n#\n#             # read data\n#             proj, geotrans, data = run.read_data(dataPath + dataName + \".tif\")  # read data\n#\n#             # band operation\n#             # data = data[[5, 4, 5], :, :]  # operation on bands, SWIR-1/2\n#             #\n#             # NIR = data[0, :, :]\n#             # Red = data[1, :, :]\n#             # NDVI = (NIR - Red) / (NIR + Red)\n#             #\n#             # print(\"NDVI: \", NDVI)\n#\n#             SWIR2 = data[5, :, :]\n#\n#             # write data\n#             # export bands.\n#             saveBandPath = \"D:\\Desktop\\Thomas_Wildfire\\Thomas_fire_SWIR2_band_tif\\\\\"\n#             if not os.path.exists(saveBandPath):\n#                 os.makedirs(saveBandPath)\n#\n#             saveBandName = dataName + '_SWIR2'\n#             run.write_data(saveBandPath + saveBandName + \".tif\", proj, geotrans, SWIR2)  # write data\n#\n#             # jpgData = data.transpose(1, 2, 0)\n#             # misc.imsave(savePath + \"test_\" + saveName + \".jpg\", jpgData)\n#\n#     else: # non-batch processing\n#         run = GRID()\n#         dataPath = r\"F:\\Thomas_Wildfire_v2\\png2tif\\\\\"\n#         savePath = r\"F:\\Thomas_Wildfire_v2\\png2tif\\\\\"\n#\n#         if not os.path.exists(savePath):\n#             os.makedirs(savePath)\n#\n#         pngName = \"DSC_1104_1128_VV_VH_cnn_fireConfMap_lr_0.0001_acc_96.92_gray\"\n#         tifName = \"dem.elevation\"\n#\n#         data = misc.imread(dataPath + pngName + \".png\")[..., 0]/255.0\n#         print(np.unique(data))\n#\n#         # data[np.where(data == 32)] = 255\n#         # data[np.where(data == 253)] = 0\n#         # data[np.where(data == 68)] = 0\n#\n#\n#         print(\"read data: \" + dataPath + tifName + \".tif\")\n#         proj, geotrans, _ = run.read_data(dataPath + tifName + \".tif\")  # read data\n#\n#\n#         # data = data[[5, 4, 5], :, :]  # operation on bands, SWIR-1/2\n#         # data = data[[0, 1, 2], :, :]  # NIR\n#         #\n#         print(\"============ Info for the Written tiff =============\")\n#         print(\"Projection\", proj)\n#         print(\"GeoTransformation: \", geotrans)\n#         print(\"Shape of written data: \", data.shape)\n#         print(\"============ Info for the Written tiff =============\")\n#\n#         os.chdir(savePath)\n#         saveName = pngName\n#         run.write_data(savePath + pngName + \".tif\", proj, geotrans, data)  # write data\n#\n#         # plt.imsave(savePath + pngName + \"_nofire_confMap.png\", 1 - data, cmap = 'jet')\n#\n#         # export bands.\n#         # saveBandPath = \"D:\\Desktop\\Thomas_Wildfire\\Thomas_fire_SWIR2_band_tif\\\\\"\n#         # if not os.path.exists(saveBandPath):\n#         #     os.makedirs(saveBandPath)\n#         #\n#         # bands = ['NIR', 'R', 'G', 'B', 'SWIR1', 'SWIR2']\n#         # for i in range(0, len(bands)):\n#         #     band = data[i, :, :]\n#         #     saveBandName = dataName + '_' + bands[i] + \".tif\"\n#         #     run.write_data(saveBandPath + saveBandName, proj, geotrans, band)  # write data\n#\n#         # fileNames.append(file[:-4])\n#\n#     # sourcefile = r\"D:\\Desktop\\processing\\ThomasFire_SWIR_PseudoColorImages\\ThomasFire_S2_20171128_SWIR.tif\"\n#     # destinationfile = r\"D:\\Desktop\\processing\\ThomasFire_SWIR_PseudoColorImages\\ThomasFire_S2_20171128_SWIR_v3.jpg\"\n#     #\n#     # print(\"gdal_translate -of GTiff \" + sourcefile + \" \" +  destinationfile)\n#     # os.system(\"gdal_translate -of GTiff \" + sourcefile + \" \" +  destinationfile)\n", "repo_name": "KuntaHu/GEE_batch_download", "sub_path": "GDAL_read_and_write_tif.py", "file_name": "GDAL_read_and_write_tif.py", "file_ext": "py", "file_size_in_byte": 7154, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "46", "api": [{"api_name": "osgeo.gdal.Open", "line_number": 14, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 14, "usage_type": "name"}, {"api_name": "osgeo.gdal.GDT_Byte", "line_number": 35, "usage_type": "attribute"}, {"api_name": "osgeo.gdal", "line_number": 35, "usage_type": "name"}, {"api_name": "osgeo.gdal.GDT_UInt16", "line_number": 37, "usage_type": "attribute"}, {"api_name": "osgeo.gdal", "line_number": 37, "usage_type": "name"}, {"api_name": "osgeo.gdal.GDT_Float32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "osgeo.gdal", "line_number": 39, "usage_type": "name"}, {"api_name": "osgeo.gdal.GetDriverByName", "line_number": 48, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 88, "usage_type": "call"}, {"api_name": "scipy.misc.imsave", "line_number": 91, "usage_type": "call"}]}
{"seq_id": "30647238326", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n__date__= 'Dec 04, 2013 '\n__author__= 'samuel'\n\nimport logging\nlog = logging.getLogger(__name__)\n\nimport inspect\nimport traceback\n\nfrom petsquarebackend.services import BaseService\nfrom petsquarebackend.services import ServiceMethod\nfrom petsquarebackend.models.location import Location_TB\n\nclass LocationService(BaseService):\n    def __init__(self, request):\n        super(LocationService, self).__init__('LocationService', request)\n\n    @ServiceMethod\n    def list(self, user_id=None, offset=0, size=100):\n        status = self.status.copy()\n        if user_id:\n            success, models = Location_TB.list(filattr=('explorer_id', user_id),\n                                      offset=offset,\n                                      size=size)\n        else:\n            success, models = Location_TB.list(offset=offset,\n                                      size=size)\n\n        status = self.serv_rtn(status=status, success=success, model=models)\n        status['info']['count'] = len(models)\n        return status\n\n    @ServiceMethod\n    def create(self, name, description, longitude, latitude, address, user_id):\n        status = self.status.copy()\n        success, model = Location_TB.create(name=name, \n                                            description=description,\n                                            longitude=longitude,\n                                            latitude=latitude,\n                                            address=address, \n                                            explorer_id=user_id)\n        status = self.serv_rtn(status=status, success=success, model=model)\n        return status\n\n    @ServiceMethod\n    def show(self, id):\n        status = self.status.copy()\n        success, model = Location_TB.show(id)\n        status = self.serv_rtn(status=status, success=success, model=model)\n        return status\n\n    @ServiceMethod\n    def update(self, id, data):\n        status = self.status.copy()\n        success, model = Location_TB.update(id=id,\n                           name=data['name'],\n                           description=data['description'],\n                           longitude=data['longitude'],\n                           latitude=data['latitude'],\n                           address=data['address'],\n                           explorer_id=data['user_id'],)\n        status = self.serv_rtn(status=status, success=success, model=model)\n        return status\n\n    @ServiceMethod\n    def delete(self, id):\n        status = self.status.copy()\n        success, model = Location_TB.delete(id=id)\n        status = self.serv_rtn(status=status, success=success, model=model)\n        return status\n\n    def search_latlng(self, user_id, latitude, longitude, radius=0.00449661, size=100):\n        status = self.status.copy()\n        success, models = Location_TB.search_latlng_with_radius(latitude,\n                                                       longitude,\n                                                       radius,\n                                                       size)\n        status = self.serv_rtn(status=status, success=success, model=models)\n        models_len = len(models)\n        status['info']['count'] = models_len\n        if models_len > size:\n            status['info']['msg'] = 'radius too large.'\n        return status\n\n\ndef main():\n    pass\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "radjan/petsfaq", "sub_path": "petsadv/petsquare-backend/petsquarebackend/services/location.py", "file_name": "location.py", "file_ext": "py", "file_size_in_byte": 3409, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "46", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "petsquarebackend.services.BaseService", "line_number": 16, "usage_type": "name"}, {"api_name": "petsquarebackend.models.location.Location_TB.list", "line_number": 24, "usage_type": "call"}, {"api_name": "petsquarebackend.models.location.Location_TB", "line_number": 24, "usage_type": "name"}, {"api_name": "petsquarebackend.models.location.Location_TB.list", "line_number": 28, "usage_type": "call"}, {"api_name": "petsquarebackend.models.location.Location_TB", "line_number": 28, "usage_type": "name"}, {"api_name": "petsquarebackend.services.ServiceMethod", "line_number": 20, "usage_type": "name"}, {"api_name": "petsquarebackend.models.location.Location_TB.create", "line_number": 38, "usage_type": "call"}, {"api_name": "petsquarebackend.models.location.Location_TB", "line_number": 38, "usage_type": "name"}, {"api_name": "petsquarebackend.services.ServiceMethod", "line_number": 35, "usage_type": "name"}, {"api_name": "petsquarebackend.models.location.Location_TB.show", "line_number": 50, "usage_type": "call"}, {"api_name": "petsquarebackend.models.location.Location_TB", "line_number": 50, "usage_type": "name"}, {"api_name": "petsquarebackend.services.ServiceMethod", "line_number": 47, "usage_type": "name"}, {"api_name": "petsquarebackend.models.location.Location_TB.update", "line_number": 57, "usage_type": "call"}, {"api_name": "petsquarebackend.models.location.Location_TB", "line_number": 57, "usage_type": "name"}, {"api_name": "petsquarebackend.services.ServiceMethod", "line_number": 54, "usage_type": "name"}, {"api_name": "petsquarebackend.models.location.Location_TB.delete", "line_number": 70, "usage_type": "call"}, {"api_name": "petsquarebackend.models.location.Location_TB", "line_number": 70, "usage_type": "name"}, {"api_name": "petsquarebackend.services.ServiceMethod", "line_number": 67, "usage_type": "name"}, {"api_name": "petsquarebackend.models.location.Location_TB.search_latlng_with_radius", "line_number": 76, "usage_type": "call"}, {"api_name": "petsquarebackend.models.location.Location_TB", "line_number": 76, "usage_type": "name"}]}
{"seq_id": "35764976840", "text": "import time\nimport conf\n\nimport community as nxlouvain\n# import matplotlib.pyplot as plt\nimport networkx as nx\nimport pandas as pd\nfrom dateutil import rrule\nimport json\n\nfrom classes.neocontroller import Neo4jController\n\n\ndef analyze_repos(owners):\n\n    for owner in owners:\n        analyzer = Analyzer(owner)\n        analyzer.export_graphjson()\n    else:\n        pass\n\n\nclass Analyzer:\n    def __init__(self,\n                 owner: str,\n                 repo=None):\n        self._owner = owner\n        self._repo = repo\n\n        self._controller = Neo4jController()\n\n        self._startdt, self._enddt = self._controller.get_comment_timeframe(self._owner)\n\n        self._degree_centrality = None\n        self._betweenness_centrality = None\n        self._eigenvector_centrality = None\n        self._partition = None\n        self._modularity = None\n\n        self.run()\n\n    def run(self):\n        self._individual_measures()\n        self._export_measures()\n\n    def get_groups(self):\n        return self._partition\n\n    def get_betweenness_centrality(self):\n        return self._betweenness_centrality\n\n    def get_degree_centrality(self):\n        return self._degree_centrality\n\n    def get_modularity(self):\n        return self._modularity\n\n    def _louvain_networkx(self):\n\n        print(\"Running NX Louvain algorithm for {0} and timeframe length {1}\".format(self._owner,\n                                                                                     conf.a_length_timeframe))\n        res = {}\n        for dt in rrule.rrule(rrule.WEEKLY, dtstart=self._startdt, until=self._enddt):\n            time_start = time.time()\n\n            links = self._controller.get_communication_subgraph(self._owner, dt)\n\n            if not links.empty:\n                nxgraph = nx.from_pandas_dataframe(links, source=\"source\", target=\"target\", create_using=nx.MultiGraph())\n\n                partition = nxlouvain.best_partition(nxgraph)\n\n                # partition = self.convert_keys_to_string(partition)\n\n                res[dt.strftime(\"%Y-%m-%d\")] = partition\n\n            print(\"current: {0} - time: {1}\".format(dt.date(), time.time() - time_start))\n\n        return res\n\n    def _individual_measures(self):\n\n        print(\"Running NX analysis for {0} and timeframe length {1}\".format(self._owner,\n                                                                            conf.a_length_timeframe))\n        res_louvain = {}\n        res_degree_centrality = {}\n        res_betweenness_centrality = {}\n        res_eigenvector_centrality = {}\n        res_modularity = {}\n\n        time_start = time.time()\n        for dt in rrule.rrule(rrule.WEEKLY, dtstart=self._startdt, until=self._enddt):\n            lap_time = time.time()\n\n            links = self._controller.get_communication_subgraph(self._owner, dt)\n\n            if not links.empty:\n                multi_graph = nx.from_pandas_dataframe(links,\n                                                       source=\"source\",\n                                                       target=\"target\",\n                                                       create_using=nx.MultiGraph())\n\n                if conf.a_louvain:\n                    partition = nxlouvain.best_partition(multi_graph)\n                    res_louvain[dt.strftime(\"%Y-%m-%d\")] = partition\n\n                if conf.a_betweenness_centrality:\n                    bc = nx.betweenness_centrality(multi_graph, normalized=True)\n                    res_betweenness_centrality[dt.strftime(\"%Y-%m-%d\")] = bc\n\n                if conf.a_degree_centrality:\n                    dc = nx.degree_centrality(multi_graph)\n                    res_degree_centrality[dt.strftime(\"%Y-%m-%d\")] = dc\n\n                if conf.a_modularity:\n                    mod = nxlouvain.modularity(partition, multi_graph)\n                    res_modularity[dt.strftime(\"%Y-%m-%d\")] = mod\n\n                if conf.a_eigenvector_centrality:\n                    pass\n                    # simple_graph = self.convert_to_simple(multi_graph)\n\n                    # TODO: eigenvector centrality calculation fails occasionally\n                    # reason may be that nx.eigenvector_centrality() can't handle star graphs\n                    # https://stackoverflow.com/questions/43208737/using-networkx-to-calculate-eigenvector-centrality\n                    # ec = nx.eigenvector_centrality(simple_graph)\n                    # res_eigenvector_centrality[dt.strftime(\"%Y-%m-%d\")] = ec\n\n            if conf.output_verbose:\n                print(\"current: {0} - time: {0:.2f}s\".format(dt.date(), time.time() - lap_time))\n\n        print(\"{0:.2f}s\".format(time.time()-time_start))\n        print()\n\n        self._modularity = res_modularity\n        self._degree_centrality = res_degree_centrality\n        self._betweenness_centrality = res_betweenness_centrality\n        self._degree_centrality = res_degree_centrality\n        self._eigenvector_centrality = res_eigenvector_centrality\n        self._partition = res_louvain\n\n    def _export_measures(self):\n        if conf.a_betweenness_centrality:\n            pd.DataFrame.from_dict(self._betweenness_centrality)\\\n                .to_csv(conf.get_nx_path(self._owner, \"bc\",  self._repo))\n\n        if conf.a_degree_centrality:\n            pd.DataFrame.from_dict(self._degree_centrality)\\\n                .to_csv(conf.get_nx_path(self._owner, \"dc\", self._repo))\n\n        if conf.a_eigenvector_centrality:\n            pd.DataFrame.from_dict(self._eigenvector_centrality) \\\n                .to_csv(conf.get_nx_path(self._owner, \"ec\", self._repo))\n\n        if conf.a_modularity:\n            i = \"mod\"\n            df = pd.DataFrame.from_dict(self._modularity, orient=\"index\")\n            df.to_csv(conf.get_nx_path(self._owner, i, self._repo))\n            if conf.a_gen_charts:\n                pass\n                # df.plot(title=\"Modularity ({0}/{1})\".format(self._owner, self._repo),\n                #         legend=False)\n                #  plt.savefig(conf.get_plot_path(self._owner, self._repo, i))\n\n    def convert_keys_to_string(self, o):\n        \"\"\"\n        Recursively converts dictionary keys to strings. Type conversion is required for exporting JSON files.\n\n        :param o:      any object. If o is dictionary, method calls itself\n        :return:       --\n        \"\"\"\n\n        if not isinstance(o, dict):\n            return o\n        return dict((str(k), self.convert_keys_to_string(v))\n                    for k, v in o.items())\n\n    @staticmethod\n    def convert_to_simple(multi_graph):\n        simple_graph = nx.Graph()\n        for u, v, data in multi_graph.edges(data=True):\n            w = data['weight'] if 'weight' in data else 1.0\n\n            if simple_graph.has_edge(u, v):\n                simple_graph[u][v]['weight'] += w\n            else:\n                simple_graph.add_edge(u, v, weight=w)\n\n        return simple_graph\n\n    def export_graphjson(self):\n\n        \"\"\"\n        Exports link data for single repositories as JSON files suitable to serve as input for  the gitNetViewer tool.\n\n        Projects and export links to be exported can be configured in the conf module.\n\n        The resulting JSON string has the following fields:\n\n            \"info\":             a subset of repository related information fields containing\n                                    owner:          owner name\n                                    repo:           repository name\n                                    total_links:    total number of links\n                                    total_nodes:    total number of nodes\n                                    no_threads:     total number of threads\n                                    no_comments:    total number of comments\n\n            \"nodes\":            a list of nodes with the fields\n                                    name:       lowercase string representation of the users' login names\n                                    id:         node id as provided by the database\n\n            \"links\":            a list of links with the fields\n                                    source:         the source node's id as provided by the database\n                                    target:         the target node's id as provided by the database\n                                    rel_type:       the relation type\n                                    timestamp:      timestamp in the format 'yyyy-MM-dd'\n                                    link_id:        link id as provided by the database\n\n            \"groups\":           group attribution per node over time as provided by the Analyzer class\n            \"d_centrality\":     degree centrality per node over time as provided by the Analyzer class\n            \"b_centrality\":     betweenness centrality per node over time as provided by the Analyzer class\n            \"modularity\":       modularity over time as provided by the Analyzer class\n\n        :return:    --\n        \"\"\"\n\n        print(\"exporting data in graphJSON format for {0}\".format(self._owner))\n\n        groups = self.convert_keys_to_string(self._partition)\n        d_centrality = self.convert_keys_to_string(self._degree_centrality)\n        b_centrality = self.convert_keys_to_string(self._betweenness_centrality)\n        modularity = self.convert_keys_to_string(self._modularity)\n\n        nodes, links, info = self._controller.get_viz_data(self._owner)\n\n        data = {\"info\": info,\n                \"nodes\": nodes,\n                \"links\": links,\n                \"groups\": groups,\n                \"d_centrality\": d_centrality,\n                \"b_centrality\": b_centrality,\n                \"modularity\": modularity}\n\n        with open(conf.get_viz_data_path(self._owner, self._repo), \"w\") as fp:\n            json.dump(data, fp, indent=\"\\t\")\n\n\nif __name__ == '__main__':\n    owners = [\n        'OneDrive',\n        'waffleio',\n        'getnikola',\n        'Tribler',\n        'BobPalmer',\n        'novus',\n        'rathena',\n        'gatsbyjs']\n\n    # repos = [16727251]\n    analyze_repos(owners)\n", "repo_name": "maxthemillion/gitNet", "sub_path": "viz_export.py", "file_name": "viz_export.py", "file_ext": "py", "file_size_in_byte": 9988, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "classes.neocontroller.Neo4jController", "line_number": 30, "usage_type": "call"}, {"api_name": "conf.a_length_timeframe", "line_number": 61, "usage_type": "attribute"}, {"api_name": "dateutil.rrule.rrule", "line_number": 63, "usage_type": "call"}, {"api_name": "dateutil.rrule", "line_number": 63, "usage_type": "name"}, {"api_name": "dateutil.rrule.WEEKLY", "line_number": 63, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}, {"api_name": "networkx.from_pandas_dataframe", "line_number": 69, "usage_type": "call"}, {"api_name": "networkx.MultiGraph", "line_number": 69, "usage_type": "call"}, {"api_name": "community.best_partition", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 77, "usage_type": "call"}, {"api_name": "conf.a_length_timeframe", "line_number": 84, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 91, "usage_type": "call"}, {"api_name": "dateutil.rrule.rrule", "line_number": 92, "usage_type": "call"}, {"api_name": "dateutil.rrule", "line_number": 92, "usage_type": "name"}, {"api_name": "dateutil.rrule.WEEKLY", "line_number": 92, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}, {"api_name": "networkx.from_pandas_dataframe", "line_number": 98, "usage_type": "call"}, {"api_name": "networkx.MultiGraph", "line_number": 101, "usage_type": "call"}, {"api_name": "conf.a_louvain", "line_number": 103, "usage_type": "attribute"}, {"api_name": "community.best_partition", "line_number": 104, "usage_type": "call"}, {"api_name": "conf.a_betweenness_centrality", "line_number": 107, "usage_type": "attribute"}, {"api_name": "networkx.betweenness_centrality", "line_number": 108, "usage_type": "call"}, {"api_name": "conf.a_degree_centrality", "line_number": 111, "usage_type": "attribute"}, {"api_name": "networkx.degree_centrality", "line_number": 112, "usage_type": "call"}, {"api_name": "conf.a_modularity", "line_number": 115, "usage_type": "attribute"}, {"api_name": "community.modularity", "line_number": 116, "usage_type": "call"}, {"api_name": "conf.a_eigenvector_centrality", "line_number": 119, "usage_type": "attribute"}, {"api_name": "conf.output_verbose", "line_number": 129, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 130, "usage_type": "call"}, {"api_name": "time.time", "line_number": 132, "usage_type": "call"}, {"api_name": "conf.a_betweenness_centrality", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 144, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 144, "usage_type": "attribute"}, {"api_name": "conf.get_nx_path", "line_number": 145, "usage_type": "call"}, {"api_name": "conf.a_degree_centrality", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 148, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 148, "usage_type": "attribute"}, {"api_name": "conf.get_nx_path", "line_number": 149, "usage_type": "call"}, {"api_name": "conf.a_eigenvector_centrality", "line_number": 151, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 152, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 152, "usage_type": "attribute"}, {"api_name": "conf.get_nx_path", "line_number": 153, "usage_type": "call"}, {"api_name": "conf.a_modularity", "line_number": 155, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 157, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 157, "usage_type": "attribute"}, {"api_name": "conf.get_nx_path", "line_number": 158, "usage_type": "call"}, {"api_name": "conf.a_gen_charts", "line_number": 159, "usage_type": "attribute"}, {"api_name": "networkx.Graph", "line_number": 180, "usage_type": "call"}, {"api_name": "conf.get_viz_data_path", "line_number": 244, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 245, "usage_type": "call"}]}
{"seq_id": "16919164958", "text": "import dash\nimport dash_cytoscape as cyto\nimport dash_html_components as html\n\n# Dashインスタンスの生成\napp = dash.Dash(__name__)\n\n# Cytoscapeコンポーネントの生成\ncyto_compo = cyto.Cytoscape(\n    id=\"hello-dash-cyto\",\n    style={\"width\": \"400px\", \"height\": \"400px\"},\n    # ネットワークの構成要素の定義\n    elements=[\n        # ノードの定義\n        {\"data\": {\"id\": \"A\", \"name\": \"Alice\"}},\n        {\"data\": {\"id\": \"B\", \"name\": \"Bob\"}},\n        # エッジの定義\n        {\"data\": {\"source\": \"A\", \"target\": \"B\",\n                  \"name\": \"family\",\n                  \"source_name\": \"parent\", \"target_name\": \"child\"}},\n    ],\n    # ノード配置方法の定義\n    layout={\"name\": \"grid\"},\n    # スタイルの定義\n    stylesheet=[\n        # ❶ ノード全体のラベルを変更\n        {\"selector\": \"node\",\n         \"style\": {\"label\": \"data(name)\",  # ラベル文字列\n                   \"color\": \"white\",  # 文字色を白\n                   \"text-background-color\": \"green\",  # 背景色を緑\n                   \"text-background-opacity\": 1,  # 完全に不透明\n                   \"text-halign\": \"right\",  # ノードの右側\n                   \"text-valign\": \"center\",}}, # ノードの中央\n        # ❷ エッジ全体のラベルを変更\n        {\"selector\": \"edge\",\n         \"style\": {\"content\": \"data(name)\",  # ラベル文字列\n                   \"source-label\": \"data(source_name)\",  # 始点側のラベル文字列\n                   \"target-label\": \"data(target_name)\",  # 終点側のラベル文字列\n                   \"source-text-offset\": 25,  # 始点側のラベルの位置\n                   \"target-text-offset\": 25}},  # 終点側のラベルの位置\n    ],\n)\n\n# Cytoscapeコンポーネントをレイアウトに渡す\napp.layout = html.Div([cyto_compo])\n\nif __name__ == \"__main__\":\n    # サーバの起動\n    app.run_server(debug=True)\n", "repo_name": "plotly-dash-book/plotly-dash-book", "sub_path": "ch10_dash_cytoscape_basic/style/dash_cyto_style_label.py", "file_name": "dash_cyto_style_label.py", "file_ext": "py", "file_size_in_byte": 1925, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 35, "dataset": "github-code", "pt": "46", "api": [{"api_name": "dash.Dash", "line_number": 6, "usage_type": "call"}, {"api_name": "dash_cytoscape.Cytoscape", "line_number": 9, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 45, "usage_type": "call"}]}
{"seq_id": "33598822061", "text": "#!/usr/bin/env python\n# -*- coding: ascii -*-\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom future import standard_library\nstandard_library.install_aliases()\nfrom builtins import str\nfrom builtins import range\nfrom builtins import object\n\nimport random\n\nDEALER_HITS_BELOW = 17\n\nclass Hand( object ):\n    \"\"\"Either the dealer or player hand\"\"\"\n    def __init__(self, name='???', auto_fill_to_12=False):\n        self.name = name\n        self.new_hand(auto_fill_to_12=auto_fill_to_12)\n        \n    def new_hand(self, auto_fill_to_12=False): # can call directly for a new hand\n        self.cardL = []\n        self.usable_ace = False\n        self.get_card(2)\n        \n        if auto_fill_to_12:\n            while self.summ < 11:\n                self.get_card(1)\n\n    def get_card(self, N=1):\n        for i in range(N):\n            self.cardL.append( random.choice( (1,2,3,4,5,6,7,8,9,10,10,10,10) ) )\n        self.calc_summ()\n\n    def get_show_card(self):\n        return self.cardL[0] # use 1st card as show card.\n\n    def set_player_hand(self, summ=0, usable_ace=False ):\n        if usable_ace:\n            self.cardL = [1, summ-11]\n        else:\n            c1 = random.choice( tuple( range(2, summ-1, 1) ) )\n            c2 = summ - c1\n            self.cardL = [c1, c2]\n        \n        self.calc_summ()\n        \n    def set_dealer_hand(self, dealer_showing=0 ):\n        self.cardL = [dealer_showing]\n        self.get_card(1)\n\n    def calc_summ(self):\n        self.usable_ace = False\n        \n        self.summ = sum( self.cardL )\n        if 1 in self.cardL:\n            if (self.summ <= 11):\n                self.summ += 10 # use the ace as an 11\n                self.usable_ace = True\n                \n        self.is_bust = (self.summ > 21)\n        \n        self.is_natural = (len(self.cardL)==2) and (self.summ==21)\n        \n    def summ_print(self):\n        s = ''\n        if self.is_bust:\n            s = '  BUSTED'*4\n        elif self.is_natural:\n            s = '  NATURAL 21'\n        elif self.usable_ace:\n            s = '  USABLE ACE'\n            \n        print('___%s Hand Summary___'%self.name)\n        print('    Cards = ', ', '.join( ['%s'%i for i in self.cardL] ))\n        print('    Summ  =', self.summ, s)\n\nclass BlackJack( object ):\n    \n    \n    def __init__(self):\n        self.deal_hands()\n    \n    def deal_hands(self):\n        self.playerH = Hand('Player', auto_fill_to_12=True)\n        self.dealerH = Hand('Dealer')\n        self.game_over = False\n        self.dealer_is_done = False\n        self.reward = 0.0\n        self.evaluate()\n    \n    def re_deal(self):\n        self.playerH.new_hand( auto_fill_to_12=True )\n        self.dealerH.new_hand()\n        self.game_over = False\n        self.dealer_is_done = False\n        self.reward = 0.0\n        self.evaluate()\n    \n    def evaluate(self):\n        \"\"\"\n        Only evaluates the player move.  \n        Final reward happens when dealer plays.\n        \"\"\"\n        if self.playerH.is_bust:\n            self.reward = -1.0\n            self.game_over = True\n            \n        elif self.dealerH.is_bust:\n            self.reward = 1.0\n            self.game_over = True\n            \n        elif self.playerH.is_natural:\n            self.game_over = True\n            \n            if self.dealerH.is_natural:\n                self.reward = 0.0\n            else:\n                self.reward = 1.0 # player wins outright.\n            \n    def player_hits(self):\n        self.playerH.get_card()\n        self.evaluate()\n    \n    def dealer_plays(self):\n        \n        while (not self.game_over) and (self.dealerH.summ < DEALER_HITS_BELOW):\n            #print('--> Dealer Hits at:', self.dealerH.summ, end=' ')\n            self.dealerH.get_card()\n            self.evaluate()\n            #print('Card is:', self.dealerH.cardL[-1], 'game_over =',self.game_over)\n        \n        self.dealer_is_done = True\n        \n        # need to decide winner based on sum of cards\n        if not self.game_over: # i.e. nobody busted, so need to compare sums.\n            self.game_over = True\n            \n            if self.dealerH.summ > self.playerH.summ:\n                self.reward = -1.0\n            elif self.dealerH.summ < self.playerH.summ:\n                self.reward = 1.0\n            else:\n                self.reward = 0.0\n    \n    def get_state_hash(self):\n        \"\"\"State Hash is (player sum, usable ace flage, dealer show card)\"\"\"\n        return (self.playerH.summ, self.playerH.usable_ace, self.dealerH.get_show_card() )\n    \n    def set_state_hash(self, state_hash ):\n        (player_sum, usable_ace, dealer_showing) = state_hash\n        \n        self.playerH.set_player_hand( summ=player_sum, usable_ace=usable_ace )\n        self.dealerH.set_dealer_hand( dealer_showing=dealer_showing )\n        \n        self.game_over = False\n        self.dealer_is_done = False\n        self.reward = 0.0\n        self.evaluate()\n    \n    def summ_print(self):\n        print('___BlackJack___  state_hash =', self.get_state_hash() )\n        if self.game_over:\n            print('***GAME OVER*** Reward=',self.reward, end=' ')\n        \n        if self.reward > 0.1:\n            print('PLAYER WINS')\n        elif self.reward < -0.1:\n            print('PLAYER LOSES')\n        else:\n            print('DRAW GAME')\n        \n        self.playerH.summ_print()\n        self.dealerH.summ_print()\n\ndef play_policy_game( BJ, policyD ):\n    \n    if not BJ.game_over:\n        action = policyD[ BJ.get_state_hash() ]\n        \n        while action == 'Hit':\n            #print('--> Player Hits at:', BJ.playerH.summ, end=' ')\n            BJ.player_hits()\n            #print('Card is:', BJ.playerH.cardL[-1], \n            #      'Hand is:',BJ.playerH.cardL,\n            #      '  game_over=',BJ.game_over)\n            \n            if BJ.game_over:\n                action = 'Stay'\n            else:\n                if BJ.get_state_hash() not in policyD:\n                    print('ERROR... BJ.get_state_hash() =',BJ.get_state_hash())\n                action = policyD[ BJ.get_state_hash() ]\n            \n    if not BJ.game_over:\n        BJ.dealer_plays()\n\n\ndef mock_game( BJ ):\n    if not BJ.game_over:\n        if BJ.playerH.summ < 17:\n            print('--> Player Hits at:', BJ.playerH.summ, end=' ')\n            BJ.player_hits()\n            print('Card is:', BJ.playerH.cardL[-1], '  game_over=',BJ.game_over)\n    \n    if not BJ.game_over:\n        BJ.dealer_plays()\n\nif __name__ == \"__main__\":\n    \n    BJ = BlackJack()\n    mock_game( BJ )\n    BJ.summ_print()\n    print('-'*55)\n    \n    BJ.re_deal()\n    mock_game( BJ )\n    BJ.summ_print()\n    print('-'*55)\n    \n    BJ.set_state_hash( (11, False, 5) )\n    mock_game( BJ )\n    BJ.summ_print()\n    ", "repo_name": "sonofeft/IntroRL", "sub_path": "introrl/black_box_sims/blackjack_supt/blackjack.py", "file_name": "blackjack.py", "file_ext": "py", "file_size_in_byte": 6723, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "future.standard_library.install_aliases", "line_number": 6, "usage_type": "call"}, {"api_name": "future.standard_library", "line_number": 6, "usage_type": "name"}, {"api_name": "builtins.object", "line_number": 15, "usage_type": "name"}, {"api_name": "builtins.range", "line_number": 31, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 32, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 42, "usage_type": "call"}, {"api_name": "builtins.range", "line_number": 42, "usage_type": "call"}, {"api_name": "builtins.object", "line_number": 78, "usage_type": "name"}]}
{"seq_id": "22786571438", "text": "from data.data import load_data_config_dict\n\ndef process_regression_models(load_data_config_dict):\n    models = load_data_config_dict['models']\n    \n    models_dict = {}\n    for model_name, model in models.items():\n        model_dict = {}\n        variables = []\n        correction_variable = None\n        description = ''\n        training = {}\n        \n        if 'variables' in model:\n            variables.extend(model['variables'].keys())\n        if 'differential variables' in model:\n            variables.extend(model['differential variables'].keys())\n        if 'correction variable' in model:\n            correction_variable = list(model['correction variable'].keys())[0]\n        if 'description' in model:\n            description = model['description']\n        if 'training' in model:\n            training = model['training']\n            \n        model_dict = dict(variables=variables, correction_variable=correction_variable,\n        description=description, training=training)\n        models_dict[model_name] = model_dict\n    return models_dict\n\nregression_models = process_regression_models(load_data_config_dict)", "repo_name": "JBailey96/short-term-load-forecasting-visualisation-and-interaction-tool", "sub_path": "data/util/data_regression_models.py", "file_name": "data_regression_models.py", "file_ext": "py", "file_size_in_byte": 1124, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "data.data.load_data_config_dict", "line_number": 4, "usage_type": "name"}, {"api_name": "data.data.load_data_config_dict", "line_number": 30, "usage_type": "argument"}]}
{"seq_id": "72536448778", "text": "import datetime\nimport os\nimport sys\nfrom typing import Callable\n\nimport dill\nimport shutil\nimport sys\nimport time\nimport traceback\nimport multiprocessing\n\nfrom multiprocessing import Process, Queue\n\nfrom sessions.dirtools import Dir\nfrom git import Repo\nfrom pathlib import Path\n\n\nclass Logger:\n    \"\"\"Copies terminal output to a file\"\"\"\n    stderr = False\n\n    def __init__(self, filepath: str,  stderr=False):\n\n        self.stderr = stderr\n        if self.stderr:\n            self.terminal = sys.stderr\n        else:\n            self.terminal = sys.stdout\n        target = Path(filepath)\n        self.log = open(target, \"a\")\n\n    def write(self, message):\n        if self.stderr:\n            self.terminal.write('\\033[91m' + message + '\\033[0m')\n        else:\n            self.terminal.write(message)\n        self.log.write(message)\n        self.log.flush()\n\n    def __del__(self):\n        try:\n            self.stop()\n            self.log.close()\n        except AttributeError:\n            pass\n\n    def stop(self):\n        if self.stderr:\n            sys.stderr = self.terminal\n        else:\n            sys.stdout = self.terminal\n\n    def start(self):\n        if self.stderr:\n            sys.stderr = self\n        else:\n            sys.stdout = self\n\n    def flush(self):\n        self.terminal.flush()\n\n\ndef get_input(valid_inputs=(\"y\", \"n\")):\n    \"\"\"\n    Queries the user for a terminal input which must be one of the valid inputs specified\n    :param valid_inputs: A iterable of string the represents valid inputs\n    :return:\n    \"\"\"\n    res = input().lower()\n    while res not in valid_inputs:\n        print(f'Valid responses: ({\"/\".join(valid_inputs)})')\n        res = input().lower()\n    return res\n\n\ndef load_session(path, use_backup=True):\n    \"\"\"\n    Loads a session\n    :param path: The session folder (labelled .ses) containng the session.dill and session.back file\n    :param use_backup: If True, will attempt to load backup if main fails\n    :return:\n    \"\"\"\n    target = Path(path) / \"session.dill\"\n\n    try:\n        with open(target, \"rb\") as fp:\n            return dill.load(fp)[0]\n    except Exception as e:\n        if use_backup:\n            print(f\"failed to load {target.name}. Reason:\")\n            target = target.with_suffix(\".back\")\n            time.sleep(0.1)  # Get nicer print statements\n            traceback.print_exc()\n            time.sleep(0.1)  # Get nicer print statements\n            print(\"Trying to load backup\")\n            with open(target, \"rb\") as fp:\n                return dill.load(fp)[0]\n        else:\n            raise e\n\n\nclass Session:\n    \"\"\"A session represents some work that needs to be done and saved, and possibly paused\"\"\"\n    is_finished = False  # True when the session has finished all the work\n    ignore_uncommited_changes_to_main = True\n    runtime = datetime.timedelta()\n    _repo_dir = Path(os.path.dirname(os.path.abspath(__file__)))\n\n    def __init__(self, worker, name, save_folder=None, repo_dir=None, ignore_file='.ignore', ignore_warnings=True):\n        \"\"\"\n        :param worker: A object that implements the work() methods which throws a StopIteration when finished.\n        :param name: The name of the session. Unless otherwise specified it will also be the name of the data folder\n        :param save_folder: The folder where the data is stored. Default is a folder in the same directory as the\n                            main script with same name as the session\n        :param repo_dir: The repository in for which the commit state at session runtime is stored if the session is\n                            to be restarted at a later time\n        :param ignore_file: A file similar to a .gitignore that allows you to specify certain files which can be\n                            modified without affecting the result of the worker\n        \"\"\"\n        self.ignore_warnings = ignore_warnings\n        self.terminate = False\n        if repo_dir is not None:\n            self._repo_dir = str(repo_dir)\n        while \".git\" not in os.listdir(self.repo_dir):\n            if self.repo_dir == self.repo_dir.parent:\n                raise Exception(f\"Could not find a .git folder while searching the directory tree\")\n            self._repo_dir = str(self.repo_dir.parent)\n        self.repo = Repo(self.repo_dir)\n\n        self.worker = worker\n        self.name = name\n        self.ignore_file = ignore_file\n        if save_folder is None:\n            self._save_folder = f\"Experiments/\"\n        else:\n            self._save_folder = str(Path(\"Experiments\") / save_folder)\n        os.makedirs(self.save_folder, exist_ok=True)\n        self._loggers = [Logger(self.save_folder / \"log.txt\"), Logger(self.save_folder / \"log.txt\", stderr=True)]\n\n    @property\n    def loggers(self):\n        if not hasattr(self, \"_loggers\"):\n            self._loggers = [Logger(self.save_folder / \"log.txt\"), Logger(self.save_folder / \"log.txt\", stderr=True)]\n        return self._loggers\n\n    @property\n    def repo_dir(self):\n        return Path(self._repo_dir)\n\n    @property\n    def save_folder(self):\n        return Path(os.path.dirname(sys.argv[0])) / self._save_folder / (self.name + \".ses\")\n\n    def _session_data(self):\n        return self.worker, self.repo.head.commit.hexsha, self.is_finished, self.runtime\n\n    def load_results(self):\n        \"\"\"This method is for loading session results after the session has finished\"\"\"\n        (worker, repo, is_finished) = self.load_data(\"session\")\n        commit = repo.index.commit\n        if self.repo.index.commit != commit:\n            print(\"Warning: Loaded data belongs to a different commit\")\n\n        if not is_finished:\n            print(\"Warning: Loaded data is has not yet finished iterating\")\n        return worker\n\n    def check_git_status(self):\n        \"\"\"Checks if the there are uncommitted changes to the git head that should be committed before session start\"\"\"\n        try:\n            d = Dir(self.repo.working_dir, exclude_file=self.ignore_file)\n\n            changed_files = [i.a_path for i in self.repo.index.diff(self.repo.head.commit) if\n                             not d.is_excluded(Path(self.repo.working_dir) / i.a_path)]\n            untracked_files = [f for f in self.repo.untracked_files if\n                               not d.is_excluded(Path(self.repo.working_dir) / f)]\n            dirty_files = changed_files + untracked_files\n            if self.ignore_uncommited_changes_to_main:\n                target = sys.argv[0].replace(Path(self.repo_dir).as_posix() + \"/\", \"\")\n                if target in dirty_files:\n                    dirty_files.remove(target)\n            if len(dirty_files) > 0:\n                print(\"The following files were untracked or had uncommitted changes:\")\n                for f in dirty_files:\n                    print(\"- \" + f)\n                return False\n        except Exception as e:\n            print(\"Encountered exception while checking git status\")\n            traceback.print_exc()\n            return False\n        return True\n\n    def save_data(self, filename, data, mode=\"wb\", use_backup=True):\n        \"\"\"Saves data in the data folder\"\"\"\n        with open(Path(self.save_folder) / f\"{filename}.dill\", mode) as fp:\n            dill.dump(data, fp)\n        if use_backup:\n            with open(Path(self.save_folder) / f\"{filename}.back\", mode) as fp:\n                dill.dump(data, fp)\n\n    def load_data(self, filename, mode=\"rb\", use_backup=True):\n        \"\"\"loads data from the data folder\"\"\"\n        assert \"b\" in mode\n        try:\n            with open(Path(self.save_folder) / f\"{filename}.dill\", mode) as fp:\n                return dill.load(fp)\n        except Exception as e:\n            if use_backup:\n                print(f\"failed to load {filename}.pickle. Reason:\")\n                time.sleep(0.1)  # Get nicer print statements\n                traceback.print_exc()\n                time.sleep(0.1)  # Get nicer print statements\n                print(\"Trying to load backup\")\n                with open(Path(self.save_folder) / f\"{filename}.back\", mode) as fp:\n                    return dill.load(fp)\n            else:\n                raise e\n\n    def start(self, on_load: Callable[['Session'], None] = None):\n        \"\"\"Starts the session. It will guide the user throug a series of questions about choices for the session\n        regarding git status and restarting/overwriting previous sessions\"\"\"\n        if not os.path.exists(self.save_folder):\n            os.makedirs(self.save_folder)\n        for logger in self.loggers:\n            logger.start()\n        print(f\"--- {self.name} ---\")\n        status = self.check_git_status()\n        if not status:\n            if self.ignore_warnings:\n                print(\"Git status is not clean (Ignored)\")\n            else:\n                print(\"Git status is not clean. Would you still like to continue with the session? (y/n)\")\n                response = get_input(valid_inputs=(\"y\", \"n\"))\n                if response == \"n\":\n                    return\n\n        if os.path.exists(self.save_folder / \"session.dill\"):\n            print(f\"The save folder already exists. (Path: {self.save_folder})\")\n            if self.ignore_warnings:\n                print(f\"Loading session {self.name}\")\n                response = \"l\"\n            else:\n                choices = \"Choose one:\\n- Restart session and overwrite data folder (r)\\n- Load folder (l)\\n- Exit (q)\"\n                print(choices)\n                response = get_input(valid_inputs=(\"r\", \"l\", \"q\"))\n            if response == \"l\":\n                data = self.load_data(\"session\")\n                if len(data) == 3:\n                    (worker, repo, is_finished) = data\n                elif len(data) == 4:\n                    (worker, repo, is_finished, runtime) = data\n                    self.runtime = runtime\n                else:\n                    raise AssertionError()\n\n                if isinstance(repo, Repo):\n\n                    try:\n                        hexsha = repo.head.commit.hexsha\n                    except ValueError:\n                        print(\"(Probably) got error when comparing loaded head with current. This most likely is an \"\n                              \"issue of data stored with an old version of session when loading data on a different PC\")\n                        traceback.print_exc()\n                        hexsha = None\n                else:\n                    assert isinstance(repo, str)\n                    hexsha = repo\n                if self.repo.head.commit.hexsha != hexsha:\n                    print(\"WARNING: The loaded session belonged to a different commit and cannot be loaded\")\n                    print(f\"Consider rerunning script after checking out commit({hexsha})\")\n                    if not self.ignore_warnings:\n                        print(\"Continue? (Y/N)\")\n                        response = get_input(valid_inputs=(\"y\", \"n\",))\n                        if response == \"n\":\n                            return\n                self.worker = worker\n                if on_load is not None:\n                    print(f\"Calling on_load method: {on_load.__name__}\")\n                    on_load(self)\n                self._run()\n                return\n            elif response == \"r\":\n                print(\"Confirm (y/n)\")\n                if get_input((\"y\", \"n\")) == \"y\":\n                    shutil.rmtree(self.save_folder)\n                    os.makedirs(self.save_folder)\n                else:\n                    return\n            else:\n                return\n        self.save_data(\"session\", self._session_data())\n        shutil.copyfile(Path(sys.argv[0]), Path(self.save_folder) / \"script_copy.py\")\n        print(f\"Starting session: {self.name}\")\n        self._run()\n\n    def _run(self):\n        \"\"\"Don't call explicitly. Instead use start()\n        Lets the worker (continue) work until interrupted or finished\n        \"\"\"\n        while True:\n            try:\n                starttime = datetime.datetime.now()\n                self.worker.iterate()\n                self.runtime += (datetime.datetime.now() - starttime)\n                self.save_data(\"session\", self._session_data())\n                if self.terminate:\n                    print(\"Session terminated\")\n                    return\n            except StopIteration:\n                break\n        self.is_finished = True\n        self.save_data(\"session\", self._session_data())\n        print(f\"Session done ({self.name}) in total time: {self.runtime}\")\n        for logger in self.loggers:\n            logger.stop()\n\n    # serialization\n    def __getstate__(self):\n        state = self.__dict__.copy()\n        if \"_loggers\" in state:\n            del state['_loggers']\n        return state\n\n    # # serialization\n    # def __setstate__(self, state):\n    #     for s in [\"logger\", \"_logger\", \"loggers\", \"_logger\"]:\n    #         if s in state:\n    #             del state[s]\n    #     self.__dict__ = state\n\n\nclass MultiSession(Session):\n    \"\"\"Works like a session except it accepts a list of workers and executes them sequentially\"\"\"\n\n    def __init__(self, workers, name, save_folder=None, repo_dir=None, ignore_file='.ignore',\n                 ignore_warnings=True, parallel_execution=False):\n        self.workers = workers\n        self.current_worker = 0\n        super().__init__(self, name, save_folder=save_folder, repo_dir=repo_dir, ignore_file=ignore_file,\n                         ignore_warnings=ignore_warnings)\n        self.parallel_execution = parallel_execution  # TODO: This implementation is quick and dirty hand has potential problems for speical uses\n        self.completed = [False] * len(self.worker.workers)\n        self.errors = [False] * len(self.worker.workers)\n\n    def _work(self):\n        try:\n            self.worker.workers[self.current_worker].iterate()\n        except StopIteration:\n            print(f\"Finished worker ({self.current_worker})\")\n            self.completed[self.current_worker] = True\n        except Exception as e:\n            print(f\"Error in worker ({self.current_worker})\")\n            traceback.print_exc()\n            self.completed[self.current_worker] = True\n            self.errors[self.current_worker] = True\n        return self.completed[self.current_worker]\n\n    def iterate(self):\n        if not all(self.completed):\n            if self.parallel_execution:\n                for _ in range(len(self.worker.workers)):\n                    if not self.completed[self.current_worker]:\n                        break\n                    self.current_worker = (self.current_worker + 1) % len(self.worker.workers)\n                self._work()\n                self.current_worker = (self.current_worker + 1) % len(self.worker.workers)\n            else:\n                done = self._work()\n                if done:\n                    self.current_worker = (self.current_worker + 1) % len(self.worker.workers)\n\n        else:\n            if any(self.errors):\n                error_idx = [i for i, err in enumerate(self.errors) if err]\n                print(f\"WARNING: Unhandled exceptions occurred in thread {error_idx}\")\n            raise StopIteration()\n\n\nclass MultiThreadedSession(Session):\n    \"\"\"Works like a MultiSession except all workers work concurrently\"\"\"\n\n    def __init__(self, workers, name, save_folder=None, repo_dir=None, ignore_file='.ignore', ignore_warnings=True,\n                 thread_count: int = None):\n        self.workers = workers\n\n        self.status_done = [False] * len(workers)\n        self.status_error = [False] * len(workers)\n        self.status_working = [False] * len(workers)\n        super().__init__(None, name, save_folder=save_folder, repo_dir=repo_dir, ignore_file=ignore_file,\n                         ignore_warnings=ignore_warnings)\n        self.worker = self\n        self.i = 0\n        self._thread_count = thread_count\n        # raise NotImplementedError(\"Does not currently work\")\n\n    @property\n    def thread_count(self):\n        return min(len(self.workers),\n                   self._thread_count if self._thread_count is not None else multiprocessing.cpu_count())\n\n    def _get_next_worker(self):\n        i = int(self.i)\n        self.i = (self.i + 1) % len(self.workers)\n        for _ in range(len(self.workers)):\n            if not self.status_done[i] and not self.status_working[i]:\n                return i, self.workers[i]\n            i = (i + 1) % len(self.workers)\n\n    def _run(self):\n        \"\"\"Don't call explicitly. Instead use start()\n                Lets the worker (continue) work until interrupted or finished\n                \"\"\"\n        queue = Queue()\n\n        for i in range(self.thread_count):\n            res = self._get_next_worker()\n            if res:\n                i, worker = res\n                self.status_working[i] = True\n                p = Process(target=self._process_worker, args=(i, worker, queue))\n                p.start()\n\n        while any(self.status_working):\n            i, worker, done, error, runtime = queue.get()\n\n            self.workers[i] = worker\n            self.status_error[i] = error\n            self.status_done[i] = done\n            self.status_working[i] = False\n            self.runtime += runtime\n\n            res = self._get_next_worker()\n            if res:\n                i, worker = res\n                self.status_working[i] = True\n                p = Process(target=self._process_worker, args=(i, worker, queue))\n                p.start()\n        self.is_finished = True\n        self.save_data(\"session\", self._session_data())\n\n        print(f\"Session done ({self.name}) in total time: {self.runtime}\")\n        for logger in self.loggers:\n            logger.stop()\n\n    def _process_worker(self, worker_id: int, worker, queue: Queue):\n        done, error = False, False\n        starttime = datetime.datetime.now()\n        try:\n            worker.iterate()\n        except StopIteration:\n            print(f\"Finished worker ({worker_id})\")\n            done = True\n        except Exception as e:\n            done, error = True, True\n            print(f\"Error in worker ({worker_id})\")\n            traceback.print_exc()\n        runtime = (datetime.datetime.now() - starttime)\n        queue.put((worker_id, worker, done, error, runtime))\n", "repo_name": "Chris-Carvelli/ContinualLearning-ITU19", "sub_path": "sessions/session.py", "file_name": "session.py", "file_ext": "py", "file_size_in_byte": 18222, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sys.stderr", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 85, "usage_type": "call"}, {"api_name": "dill.load", "line_number": 89, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 95, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}, {"api_name": "dill.load", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 108, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 109, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 126, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 130, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 138, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 139, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 150, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 154, "usage_type": "attribute"}, {"api_name": "sessions.dirtools.Dir", "line_number": 173, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 176, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 178, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 181, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 181, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 191, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 197, "usage_type": "call"}, {"api_name": "dill.dump", "line_number": 198, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 200, "usage_type": "call"}, {"api_name": "dill.dump", "line_number": 201, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 207, "usage_type": "call"}, {"api_name": "dill.load", "line_number": 208, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 212, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 213, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 214, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 216, "usage_type": "call"}, {"api_name": "dill.load", "line_number": 217, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 221, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path", "line_number": 224, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "git.Repo", "line_number": 258, "usage_type": "argument"}, {"api_name": "traceback.print_exc", "line_number": 265, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 287, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 288, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 294, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 294, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 294, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 304, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 304, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 306, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 306, "usage_type": "attribute"}, {"api_name": "traceback.print_exc", "line_number": 355, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 401, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 415, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 422, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 438, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 447, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 449, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 449, "usage_type": "attribute"}, {"api_name": "traceback.print_exc", "line_number": 458, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 459, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 459, "usage_type": "attribute"}]}
{"seq_id": "42646228187", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom torchvision import transforms\nfrom torchvision.datasets import MNIST\nimport torchvision.models as models\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom advertorch.attacks import GradientSignAttack as FGSM\n\nclass MnistModel(nn.Module):\n    def __init__(self):\n        super(MnistModel, self).__init__()\n        # mnist의 경우 28*28의 흑백이미지(input channel=1)이다.\n        self.conv1 = nn.Conv2d(1, 32, kernel_size = 5, padding=2)\n        # feature map의 크기는 14*14가 된다\n        # 첫번재 convolution layer에서 나온 output channel이 32이므로 2번째 input도 32\n        self.conv2 = nn.Conv2d(32, 64, kernel_size = 5, padding=2)\n        # feature map의 크기는 7*7이 된다\n        # fc -> fully connected, fc는 모든 weight를 고려해서 만들기 때문에 cnn에서는 locally connected를 이용하여 만든다.\n        # nn.Linear에서는 conv를 거친 feature map을 1차원으로 전부 바꿔서 input을 한다. 이게 64*7*7\n        self.fc1 = nn.Linear(64*7*7, 1024)\n        self.fc2 = nn.Linear(1024, 10)\n\n    def forward(self, x):\n        x = F.max_pool2d(F.relu(self.conv1(x)), 2)\n        x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n        x = x.view(-1, 64*7*7) # linear에 들어갈 수 있도록 reshape\n        x = F.relu(self.fc1(x)) # fully connected에 relu 적용\n        x = F.dropout(x, training=self.training) # 가중치 감소만으로는 overfit을 해결하기가 어려움, 그래서 뉴런의 연결을 임의로 삭제\n        x = self.fc2(x)\n        return F.log_softmax(x)\n    \ndef advtrain(model, device, train_loader, optimizer, epoch, log_interval):\n    model.train()\n    avg_loss = 0\n    # in training loop:\n    \n    adversary = FGSM(model, loss_fn=nn.NLLLoss(reduction='sum'), \n                    eps=0.3, clip_min=0., clip_max=1., targeted=False)\n\n    for batch_idx, (data, target) in enumerate(train_loader):\n        # gpu나 cpu로 데이터를 옮김\n        data, target = data.to(device), target.to(device)\n        data = adversary.perturb(data, target)\n        # gradient descent전에 gradient buffer를 0으로 초기화 하는 역할을 한다\n        optimizer.zero_grad()\n        output = model(data)\n        # negative log-likelihood: nll loss, 딥러닝 모델의 손실함수이다\n        loss = F.nll_loss(output, target)\n        loss.backward()\n        optimizer.step() # Does the update based on the current gradient stored in grad\n        # 여기서 기존에 저장되어있던 gradient를 기반으로 update 하기 때문에 위의 초기화가 필요함\n        avg_loss+=F.nll_loss(output, target, reduction='sum').item()\n        \n        if batch_idx % log_interval == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n    avg_loss/=len(train_loader.dataset)\n    return avg_loss\n\ntrain_batch_size=64\ntest_batch_size=1\nepochs=30\nlr = 0.01\nmomentum = 0.5\nseed = 1\nlog_interval = 1000\n\nis_cuda = torch.cuda.is_available()\ndevice = torch.device('cuda' if is_cuda else 'cpu')\n\npretrained_model = './data/mnist_model.pth'\nmodel = MnistModel().to(device)\nmodel.load_state_dict(torch.load(pretrained_model, map_location='cpu'))\n\n# momentum: 기울기에서 속도의 개념을 추가, 기울기 업데이트시 폭을 조절한다\n# lr: learning rate\noptimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)\n\n# mean=0.5 std=1.0으로 Normalize한다\nmnist_transform = transforms.Compose([\n    transforms.ToTensor(), \n    # transforms.Normalize((0.5,), (1.0,))\n])\n\ndownload_path = './data'\ntrain_dataset = MNIST(download_path, transform=mnist_transform, train=True, download=True)\ntest_dataset = MNIST(download_path, transform=mnist_transform, train=False, download=True)\n\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n                                           batch_size=train_batch_size,\n                                           shuffle=True)\n\ntest_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n                                          batch_size=test_batch_size,\n                                          shuffle=True)\n\ntrain_losses = []\ntest_losses = []\naccuracy_list = []\n\nfor epoch in range(1, epochs + 1):\n    trn_loss = advtrain(model, device, train_loader, optimizer, epoch, log_interval)\n    #test_loss,accuracy = test(model, device, test_loader)\n    train_losses.append(trn_loss)\n    #test_losses.append(test_loss)\n    #accuracy_list.append(accuracy)\n\nPATH = './data/mnist_fgsm_model.pth'\ntorch.save(model.state_dict(), PATH)", "repo_name": "sanglee325/fgsm", "sub_path": "fgsm_defense_model.py", "file_name": "fgsm_defense_model.py", "file_ext": "py", "file_size_in_byte": 4768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "torch.nn.Module", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 36, "usage_type": "name"}, {"api_name": "advertorch.attacks.GradientSignAttack", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.NLLLoss", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 84, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 87, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 87, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 88, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 88, "usage_type": "name"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 93, "usage_type": "call"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 96, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 116, "usage_type": "call"}]}
{"seq_id": "7040823287", "text": "#importing the openCV and numpy packages\nimport cv2\nimport numpy as np\n\n#creating a balck image of 300x300\ncanvas = np.zeros((300, 300, 3), dtype = \"uint8\")\n\n#creating a green line from 0,0 to 300,300 and another line with thickness of 3 pixel from 300,0 to 0,300\n#syntax: cv2.line(image,starting_point,ending_point,color)\ngreen = (0, 255, 0)\ncv2.line(canvas, (0, 0), (300, 300), green)\ncv2.imshow(\"Canvas\", canvas)\ncv2.waitKey(0)\n\n#syntax: cv2.line(image,starting_point,ending_point,color,thickness)\nred = (0, 0, 255)\ncv2.line(canvas, (300, 0), (0, 300), red, 3)\ncv2.imshow(\"Canvas\", canvas)\ncv2.waitKey(0)\n\n#green rectangle\ncv2.rectangle(canvas, (10, 10), (60, 60), green) \ncv2.imshow(\"Canvas\", canvas)\ncv2.waitKey(0)\n\n#thick red rectangle\ncv2.rectangle(canvas, (50, 200), (200, 225), red, 5)\ncv2.imshow(\"Canvas\", canvas)\ncv2.waitKey(0)\n\n#thcikness as -1 will cause color fill\nblue = (255, 0, 0)\ncv2.rectangle(canvas, (200, 50), (225, 125), blue, -1)\ncv2.imshow(\"Canvas\", canvas)\ncv2.waitKey(0)\n\n#creating circles\nwhite = (255, 255, 255)\ncenterX = 125\ncenterY = 125\nr=50\ncv2.circle(canvas, (centerX, centerY), r, white,2)\ncv2.imshow(\"Canvas\", canvas)\ncv2.waitKey(0)\ncv2.destroyAllWindows()", "repo_name": "imhighoncoffee/sosc_cv101", "sub_path": "drawing.py", "file_name": "drawing.py", "file_ext": "py", "file_size_in_byte": 1191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "numpy.zeros", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 45, "usage_type": "call"}]}
{"seq_id": "17571914982", "text": "import os\r\nimport numpy as np\r\nimport cv2\r\nimport torch\r\nimport torch.nn as nn\r\nimport torchvision as tv\r\n\r\nimport network\r\nimport dataset\r\n\r\n# ----------------------------------------\r\n#                 Network\r\n# ----------------------------------------\r\ndef create_generator(opt):\r\n    # Initialize the networks\r\n    generator = network.GatedGenerator(opt)\r\n    print('Generator is created!')\r\n    if opt.load_name:\r\n        generator = load_dict(generator, opt.load_name)\r\n    else:\r\n        # Init the networks\r\n        network.weights_init(generator, init_type = opt.init_type, init_gain = opt.init_gain)\r\n        print('Initialize generator with %s type' % opt.init_type)\r\n    return generator\r\n\r\ndef create_discriminator(opt):\r\n    # Initialize the networks\r\n    discriminator = network.PatchDiscriminator(opt)\r\n    print('Discriminator is created!')\r\n    # Init the networks\r\n    network.weights_init(discriminator, init_type = opt.init_type, init_gain = opt.init_gain)\r\n    print('Initialize discriminator with %s type' % opt.init_type)\r\n    return discriminator\r\n\r\ndef create_perceptualnet():\r\n    # Get the first 15 layers of vgg16, which is conv3_3\r\n    perceptualnet = network.PerceptualNet()\r\n    # Pre-trained VGG-16\r\n    vgg16 = torch.load('./vgg16_pretrained.pth')\r\n    load_dict(perceptualnet, vgg16)\r\n    # It does not gradient\r\n    for param in perceptualnet.parameters():\r\n        param.requires_grad = False\r\n    print('Perceptual network is created!')\r\n    return perceptualnet\r\n\r\ndef load_dict(process_net, pretrained_net):\r\n    # Get the dict from pre-trained network\r\n    pretrained_dict = pretrained_net\r\n    # Get the dict from processing network\r\n    process_dict = process_net.state_dict()\r\n    # Delete the extra keys of pretrained_dict that do not belong to process_dict\r\n    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in process_dict}\r\n    # Update process_dict using pretrained_dict\r\n    process_dict.update(pretrained_dict)\r\n    # Load the updated dict to processing network\r\n    process_net.load_state_dict(process_dict)\r\n    return process_net\r\n    \r\n# ----------------------------------------\r\n#             PATH processing\r\n# ----------------------------------------\r\ndef text_readlines(filename):\r\n    # Try to read a txt file and return a list.Return [] if there was a mistake.\r\n    try:\r\n        file = open(filename, 'r')\r\n    except IOError:\r\n        error = []\r\n        return error\r\n    content = file.readlines()\r\n    # This for loop deletes the EOF (like \\n)\r\n    for i in range(len(content)):\r\n        content[i] = content[i][:len(content[i])-1]\r\n    file.close()\r\n    return content\r\n\r\ndef savetxt(name, loss_log):\r\n    np_loss_log = np.array(loss_log)\r\n    np.savetxt(name, np_loss_log)\r\n\r\ndef get_files(path):\r\n    # read a folder, return the complete path\r\n    ret = []\r\n    for root, dirs, files in os.walk(path):\r\n        for filespath in files:\r\n            ret.append(os.path.join(root, filespath))\r\n    return ret\r\n\r\ndef get_names(path):\r\n    # read a folder, return the image name\r\n    ret = []\r\n    for root, dirs, files in os.walk(path):\r\n        for filespath in files:\r\n            ret.append(filespath)\r\n    return ret\r\n\r\ndef text_save(content, filename, mode = 'a'):\r\n    # save a list to a txt\r\n    # Try to save a list variable in txt file.\r\n    file = open(filename, mode)\r\n    for i in range(len(content)):\r\n        file.write(str(content[i]) + '\\n')\r\n    file.close()\r\n\r\ndef check_path(path):\r\n    if not os.path.exists(path):\r\n        os.makedirs(path)\r\n    \r\n# ----------------------------------------\r\n#    Validation and Sample at training\r\n# ----------------------------------------\r\ndef save_sample_png(sample_folder, sample_name, img_list, name_list, pixel_max_cnt = 255):\r\n    # Save image one-by-one\r\n    for i in range(len(img_list)):\r\n        img = img_list[i]\r\n        # Recover normalization: * 255 because last layer is sigmoid activated\r\n        img = img * 255\r\n        # Process img_copy and do not destroy the data of img\r\n        img_copy = img.clone().data.permute(0, 2, 3, 1)[0, :, :, :].cpu().numpy()\r\n        img_copy = np.clip(img_copy, 0, pixel_max_cnt)\r\n        img_copy = img_copy.astype(np.uint8)\r\n        # Save to certain path\r\n        save_img_name = sample_name + '_' + name_list[i] + '.png'\r\n        save_img_path = os.path.join(sample_folder, save_img_name)\r\n        cv2.imwrite(save_img_path, img_copy)\r\n\r\ndef psnr(pred, target, pixel_max_cnt = 255):\r\n    mse = torch.mul(target - pred, target - pred)\r\n    rmse_avg = (torch.mean(mse).item()) ** 0.5\r\n    p = 20 * np.log10(pixel_max_cnt / rmse_avg)\r\n    return p\r\n\r\ndef grey_psnr(pred, target, pixel_max_cnt = 255):\r\n    pred = torch.sum(pred, dim = 0)\r\n    target = torch.sum(target, dim = 0)\r\n    mse = torch.mul(target - pred, target - pred)\r\n    rmse_avg = (torch.mean(mse).item()) ** 0.5\r\n    p = 20 * np.log10(pixel_max_cnt * 3 / rmse_avg)\r\n    return p\r\n\r\ndef ssim(pred, target):\r\n    pred = pred.clone().data.permute(0, 2, 3, 1).cpu().numpy()\r\n    target = target.clone().data.permute(0, 2, 3, 1).cpu().numpy()\r\n    target = target[0]\r\n    pred = pred[0]\r\n    ssim = skimage.measure.compare_ssim(target, pred, multichannel = True)\r\n    return ssim\r\n", "repo_name": "zhaoyuzhi/deepfillv2", "sub_path": "deepfillv2/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 5235, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 218, "dataset": "github-code", "pt": "46", "api": [{"api_name": "network.GatedGenerator", "line_number": 16, "usage_type": "call"}, {"api_name": "network.weights_init", "line_number": 22, "usage_type": "call"}, {"api_name": "network.PatchDiscriminator", "line_number": 28, "usage_type": "call"}, {"api_name": "network.weights_init", "line_number": 31, "usage_type": "call"}, {"api_name": "network.PerceptualNet", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 79, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 138, "usage_type": "call"}]}
{"seq_id": "17296945874", "text": "from pylab import *\nimport scipy\nfrom scipy.signal import lfilter\ndef dirac(n):\n    d=zeros(n)\n    d[0]=1\n    return d\na1=-1.76\na2=0.8\nN=100\nx=dirac(N)\ny=lfilter([1,1/2],[1, a1 , a2],x)\nstem(y)\n", "repo_name": "Bilal-Belli/personalAcademicDocuments", "sub_path": "TSG (Signal Processing)/TP2.py", "file_name": "TP2.py", "file_ext": "py", "file_size_in_byte": 194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "scipy.signal.lfilter", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "8784236498", "text": "import llvmlite.ir as ll\nimport llvmlite.binding as llvm\n\nimport logging\nimport time\n\nfrom . import tp\nfrom . import ir\nfrom . import exc\n\n\nclass CGEnv(object):\n    module = None\n    builder = None\n\n\nclass Program(object):\n    def __init__(self, module):\n        llvm.initialize()\n        llvm.initialize_native_target()\n        llvm.initialize_native_asmprinter()\n\n        self.module = module\n        self.module.translate()\n\n        self.cge = CGEnv()\n        self.cge.module = module\n\n        self.llvm = self.makeStub()\n\n        for _, func in self.module.namestore.all(ir.Function):\n            self.blockAndCode(func)\n\n        self.target_machine = llvm.Target.from_default_triple().create_target_machine()\n\n        logging.debug(\"Verifying... \")\n        self._llmod = None\n\n    def llmod(self):\n        if not self._llmod:\n            self._llmod = llvm.parse_assembly(str(self.module.llvm))\n        return self._llmod\n\n    def blockAndCode(self, impl):\n        func = impl.llvm\n        # create blocks\n        bb = func.append_basic_block(\"entry\")\n\n        for bc in impl.bytecodes:\n            if bc.discard:\n                impl.remove(bc)\n                impl.log.debug(\"BLOCK skipped {0}\".format(bc))\n                continue\n\n            newblock = ''\n            if bc in impl.incoming_jumps:\n                assert not bc.block\n                bc.block = func.append_basic_block(str(bc.loc))\n                bb = bc.block\n                newblock = ' NEW BLOCK (' + str(bc.loc) + ')'\n            else:\n                bc.block = bb\n            impl.log.debug(\"BLOCK'D {0}{1}\".format(bc, newblock))\n\n        for ext_module in self.module.getExternalModules():\n            ext_module.translate(self.module.llvm)\n\n        impl.log.debug(\"Printing all bytecodes:\")\n        impl.bytecodes.printAll(impl.log)\n\n        impl.log.debug(\"Emitting code:\")\n        bb = None\n        cge = self.cge\n        for bc in impl.bytecodes:\n            try:\n                if bb != bc.block:\n                    # new basic block, use a new builder\n                    cge.builder = ll.IRBuilder(bc.block)\n\n                if bc.reachable:\n                    bc.translate(cge)\n                    impl.log.debug(\"TRANS'D {0}\".format(bc.locStr()))\n                else:\n                    # eliminate unreachable code, which may occur in the middle of a function\n                    impl.log.debug(\"UNREACH {}\".format(bc.locStr()))\n            except exc.StellaException as e:\n                e.addDebug(bc.debuginfo)\n                raise\n\n    def makeStub(self):\n        impl = self.module.entry\n        func_tp = ll.FunctionType(impl.result.type.llvmType(self.module), [])\n        func = ll.Function(self.module.llvm, func_tp, name=str(impl.function)+'__stub__')\n        bb = func.append_basic_block(\"entry\")\n        builder = ll.IRBuilder(bb)\n        self.cge.builder = builder\n\n        for name, var in self.module.namestore.all(ir.GlobalVariable):\n            var.translate(self.cge)\n\n        llvm_args = [arg.translate(self.cge) for arg in self.module.entry_args]\n\n        call = builder.call(impl.llvm, llvm_args)\n\n        if impl.result.type is tp.Void:\n            builder.ret_void()\n        else:\n            builder.ret(call)\n        return func\n\n    def elapsed(self):\n        if self.start is None or self.end is None:\n            return None\n        return self.end - self.start\n\n    def optimize(self, opt):\n        if opt is not None:\n            logging.warn(\"Running optimizations level {0}... \".format(opt))\n\n            # TODO was build_pass_managers(tm, opt=opt, loop_vectorize=True, fpm=False)\n            pmb = llvm.create_pass_manager_builder()\n            pmb.opt_level = opt\n            pm = llvm.create_module_pass_manager()\n            pmb.populate(pm)\n            pm.run(self.llmod())\n\n    def destruct(self):\n        self.module.destruct()\n        del self.module\n\n    def __del__(self):\n        logging.debug(\"DEL  {}: {}\".format(repr(self), hasattr(self, 'module')))\n\n    def run(self, stats):\n        logging.debug(\"Preparing execution...\")\n\n        import ctypes\n        import llvmlite\n        import os\n\n        _lib_dir = os.path.dirname(llvm.ffi.__file__)\n        clib = ctypes.CDLL(os.path.join(_lib_dir, llvmlite.utils.get_library_name()))\n        # Direct access as below mangles the name\n        # f = clib.__powidf2\n        f = getattr(clib, '__powidf2')\n        llvm.add_symbol('__powidf2', ctypes.cast(f, ctypes.c_void_p).value)\n\n        with llvm.create_mcjit_compiler(self.llmod(), self.target_machine) as ee:\n            ee.finalize_object()\n\n            entry = self.module.entry\n            ret_type = entry.result.type\n\n            logging.info(\"running {0}{1}\".format(entry,\n                                                 list(zip(entry.type_.arg_types,\n                                                          self.module.entry_args))))\n\n            entry_ptr = ee.get_pointer_to_global(self.llmod().get_function(self.llvm.name))\n            ret_ctype = entry.result.type.Ctype()\n            if ret_type.on_heap:\n                ret_ctype = ctypes.POINTER(ret_ctype)\n            cfunc = ctypes.CFUNCTYPE(ret_ctype)(entry_ptr)\n\n            time_start = time.time()\n            retval = cfunc()\n            stats['elapsed'] = time.time() - time_start\n\n        for arg in self.module.entry_args:\n            arg.ctype2Python(self.cge)  # may be a no-op if not necessary\n\n        retval = ret_type.unpack(retval)\n\n        logging.debug(\"Returning...\")\n        self.destruct()\n\n        return retval\n\n    def getAssembly(self):\n        return self.target_machine.emit_assembly(self.llmod())\n\n    def getLlvmIR(self):\n        ret = self.module.getLlvmIR()\n\n        logging.debug(\"Returning...\")\n        self.destruct()\n\n        return ret\n", "repo_name": "squisher/stella", "sub_path": "stella/codegen.py", "file_name": "codegen.py", "file_ext": "py", "file_size_in_byte": 5787, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "46", "api": [{"api_name": "llvmlite.binding.initialize", "line_number": 19, "usage_type": "call"}, {"api_name": "llvmlite.binding", "line_number": 19, "usage_type": "name"}, {"api_name": "llvmlite.binding.initialize_native_target", "line_number": 20, "usage_type": "call"}, {"api_name": "llvmlite.binding", "line_number": 20, "usage_type": "name"}, {"api_name": "llvmlite.binding.initialize_native_asmprinter", "line_number": 21, "usage_type": "call"}, {"api_name": "llvmlite.binding", "line_number": 21, "usage_type": "name"}, {"api_name": "llvmlite.binding.Target.from_default_triple", "line_number": 34, "usage_type": "call"}, {"api_name": "llvmlite.binding.Target", "line_number": 34, "usage_type": "attribute"}, {"api_name": "llvmlite.binding", "line_number": 34, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 36, "usage_type": "call"}, {"api_name": "llvmlite.binding.parse_assembly", "line_number": 41, "usage_type": "call"}, {"api_name": "llvmlite.binding", "line_number": 41, "usage_type": "name"}, {"api_name": "llvmlite.ir.IRBuilder", "line_number": 78, "usage_type": "call"}, {"api_name": "llvmlite.ir", "line_number": 78, "usage_type": "name"}, {"api_name": "llvmlite.ir.FunctionType", "line_number": 92, "usage_type": "call"}, {"api_name": "llvmlite.ir", "line_number": 92, "usage_type": "name"}, {"api_name": "llvmlite.ir.Function", "line_number": 93, "usage_type": "call"}, {"api_name": "llvmlite.ir", "line_number": 93, "usage_type": "name"}, {"api_name": "llvmlite.ir.IRBuilder", "line_number": 95, "usage_type": "call"}, {"api_name": "llvmlite.ir", "line_number": 95, "usage_type": "name"}, {"api_name": "logging.warn", "line_number": 118, "usage_type": "call"}, {"api_name": "llvmlite.binding.create_pass_manager_builder", "line_number": 121, "usage_type": "call"}, {"api_name": "llvmlite.binding", "line_number": 121, "usage_type": "name"}, {"api_name": "llvmlite.binding.create_module_pass_manager", "line_number": 123, "usage_type": "call"}, {"api_name": "llvmlite.binding", "line_number": 123, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 132, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "llvmlite.binding.ffi", "line_number": 141, "usage_type": "attribute"}, {"api_name": "llvmlite.binding", "line_number": 141, "usage_type": "name"}, {"api_name": "ctypes.CDLL", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "llvmlite.utils.get_library_name", "line_number": 142, "usage_type": "call"}, {"api_name": "llvmlite.utils", "line_number": 142, "usage_type": "attribute"}, {"api_name": "llvmlite.binding.add_symbol", "line_number": 146, "usage_type": "call"}, {"api_name": "llvmlite.binding", "line_number": 146, "usage_type": "name"}, {"api_name": "ctypes.cast", "line_number": 146, "usage_type": "call"}, {"api_name": "ctypes.c_void_p", "line_number": 146, "usage_type": "attribute"}, {"api_name": "llvmlite.binding.create_mcjit_compiler", "line_number": 148, "usage_type": "call"}, {"api_name": "llvmlite.binding", "line_number": 148, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 154, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 161, "usage_type": "call"}, {"api_name": "ctypes.CFUNCTYPE", "line_number": 162, "usage_type": "call"}, {"api_name": "time.time", "line_number": 164, "usage_type": "call"}, {"api_name": "time.time", "line_number": 166, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 173, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 184, "usage_type": "call"}]}
{"seq_id": "2375828054", "text": "from parse_page import parse_page\nfrom selenium import webdriver\n\nbrowser = webdriver.Firefox()\ncnt = 1\n\nwhile True:\n    next_page = parse_page(\n        f'https://www.kijiji.ca/b-apartments-condos/city-of-toronto/page-{cnt}/c37l1700273',\n        browser)\n    if not next_page:\n        break\n    print(f'End of page {cnt}')\n    cnt += 1\n\nbrowser.close()\n", "repo_name": "fatam0rgana/parsing_test", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 4, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 4, "usage_type": "name"}, {"api_name": "parse_page.parse_page", "line_number": 8, "usage_type": "call"}]}
{"seq_id": "72470109882", "text": "from base_test import BaseTestApi\nimport requests\nimport xmltodict\n\n\nclass TestUpdateIssue(BaseTestApi):\n\n    def setUp(self):\n        super(TestUpdateIssue, self).setUp()\n        self.url = self.base_url + '/issue/'\n\n        self.updateData = {\n            'summary': 'updated_test_summary',\n            'description': 'updated_test_description'\n        }\n\n    def test_update_issue(self):\n        issue_id = self.create_issue()\n        url = self.url + issue_id\n\n        r = requests.post(url, self.updateData, cookies=self.cookies)\n\n        self.assertEquals(r.status_code, 200)\n\n        response = requests.get(url, cookies=self.cookies)\n        response_dict = xmltodict.parse(response.text)\n\n        self.assertEquals(response_dict['issue']['field'][2]['value'], self.updateData['summary'])\n        self.assertEquals(response_dict['issue']['field'][3]['value'], self.updateData['description'])\n\n        #remove created issue after all checks\n        self.delete_issue(issue_id)\n\n    def test_update_nonexisting_issue(self):\n        url = self.base_url + 'ZZZ'\n\n        r = requests.post(url, self.updateData, cookies=self.cookies)\n\n        self.assertEquals(r.status_code, 404)", "repo_name": "igurkina/codespaceHW", "sub_path": "update_issue.py", "file_name": "update_issue.py", "file_ext": "py", "file_size_in_byte": 1183, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "base_test.BaseTestApi", "line_number": 6, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "xmltodict.parse", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 37, "usage_type": "call"}]}
{"seq_id": "24009899832", "text": "# Author: HZHtat\r\n# Date: Jan-2020\r\n# Version: 0.8\r\n\r\nimport smtplib\r\nfrom email.message import EmailMessage\r\n\r\n\r\ndef alerter(emailpackage, mode, to, body):\r\n    '''\r\n    emailpackage is a list:\r\n    [sender, [receiver(s),], error_subject, success_subject, mailsvr]\r\n    Pass through a list of following structure. e.g.\r\n    ([\r\n    'from',\r\n    {'prim': x, 'sec': y, 'ter': z},\r\n    {'err': ..., 'success': ..., 'info': ...},\r\n    mailsvr\r\n    ],\r\n    'mode',\r\n    'to',\r\n    'body')\r\n    Arg exception is either True or False\r\n    '''\r\n    msg = EmailMessage()\r\n    msg['From'] = emailpackage[0]\r\n\r\n    if to == 'prim':\r\n        msg['To'] = emailpackage[1]['prim']\r\n    elif to == 'sec':\r\n        msg['To'] = emailpackage[1]['sec']\r\n    elif to == 'ter':  # tertiary\r\n        msg['To'] = emailpackage[1]['ter']\r\n\r\n    msg.set_content(body)\r\n\r\n    # subject lines\r\n    if mode == 'err':\r\n        msg['Subject'] = emailpackage[2]['err']\r\n    elif mode == 'success':\r\n        msg['Subject'] = emailpackage[2]['success']\r\n    elif mode == 'info':\r\n        msg['Subject'] = emailpackage[2]['info']\r\n\r\n    s = smtplib.SMTP(emailpackage[3])  # mailserver\r\n    s.send_message(msg)\r\n    s.quit()\r\n", "repo_name": "hzht/jitterbit-clone", "sub_path": "emailalert.py", "file_name": "emailalert.py", "file_ext": "py", "file_size_in_byte": 1190, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "email.message.EmailMessage", "line_number": 25, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 45, "usage_type": "call"}]}
{"seq_id": "11513816877", "text": "#!/usr/bin/env python\n\n\nimport netCDF4 as nc4\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nimport datetime\nimport numpy as np\n\nimport glob\nimport sys, cftime\n\n\n\n#ilamb_root=\"/qfs/people/xumi699/scratch/proc_ilamb4xj/ilamb_wks/_build/EcosystemandCarbonCycle/GlobalNetEcosystemCarbonBalance\"\nilamb_root=\"/qfs/people/xumi699/scratch/proc_ilamb4xj/ilamb_wks/_build_useneedirectly/\"\n\n\nncfiles = glob.glob(ilamb_root + \"/EcosystemandCarbonCycle/GlobalNetEcosystemCarbonBalance/GCP/*.nc\")\n\nprint (ncfiles)\n\n\n\nwith nc4.Dataset(\"GCP_Benchmark.nc\", \"r\") as newncf:\n\n    xxnew = newncf.groups['MeanState'].variables['time_'][:] / 365. + 1851\n    yynew = newncf.groups['MeanState'].variables['accumulate_of_nbp_over_global'][:] * (-1.)\n    ybnew = newncf.groups['MeanState'].variables['accumulate_of_nbp_over_global_bnds'][:,0] * (-1.)\n    ytnew = newncf.groups['MeanState'].variables['accumulate_of_nbp_over_global_bnds'][:,1] * (-1.)\n\n\nfig, ax = plt.subplots()\n\nfor ncfile in ncfiles:\n    print (ncfile)\n    with nc4.Dataset(ncfile, 'r') as ncf:\n\n\n         xx = ncf.groups['MeanState'].variables['time_'][:] / 365. + 1851\n         yy = ncf.groups['MeanState'].variables['accumulate_of_nbp_over_global'][:] * (-1.)\n\n\n         if 'Benchmark' in ncfile:\n             ax.fill_between(xxnew, ybnew, ytnew, lw=0, color = 'k', alpha = 0.25)\n             #ax.plot(xxnew, yynew, '--', color='black', label='Benchmark New')\n             ax.plot(xx, yy, '-', color='black', label='Benchmark')\n\n             \n         elif 'CNP' in ncfile:\n             ax.plot(xx, yy, '-', color='blue', label='ELMv1-CNP')\n         elif 'CNonly' in ncfile:\n             ax.plot(xx, yy, '-', color='red', label='ELMv1-CN')\n\n\n         ax.set_xlabel(\"Year\", fontsize=\"large\", fontweight=\"bold\")\n         ax.set_xlim(1960, 2010)\n         ax.set_ylabel(\"Accumulative land carbon sink (PgC)\", fontsize=\"large\", fontweight=\"bold\")\n         ax.set_ylim(-30, 80)\n\n         ax.tick_params(labelsize='large', width=1)\n         ax.legend(bbox_to_anchor=(0.05, 1), loc='upper left', borderaxespad=0.)\n\nax.hlines(0, 1850, 2010, colors='grey', linestyles='--')\n\nplt.savefig(\"ngepbal_gcp_withuncertainty.png\", dpi=300)\n", "repo_name": "minxu74/plot_gnecbal", "sub_path": "plot_ngepbal_gcp_withuncertainty.py", "file_name": "plot_ngepbal_gcp_withuncertainty.py", "file_ext": "py", "file_size_in_byte": 2179, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "glob.glob", "line_number": 21, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "netCDF4.Dataset", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}]}
{"seq_id": "24441284650", "text": "\"\"\"Platform for Wattio integration testing.\"\"\"\nimport logging\n\nfrom homeassistant.const import (\n    ATTR_BATTERY_LEVEL\n)\nfrom homeassistant.helpers.entity import Entity\n\nfrom . import WattioDevice\n\nfrom .const import DOMAIN, ICON, MEASUREMENT, SENSORS\n\n_LOGGER = logging.getLogger(__name__)\n\n\nasync def async_setup_platform(hass, config, async_add_entities, discovery_info=None):\n    \"\"\"Wattio Sensor setup platform.\"\"\"\n    _LOGGER.debug(\"Wattio Sensor component running ...\")\n    if discovery_info is None:\n        _LOGGER.error(\"No Sensor(s) discovered\")\n        return\n    devices = []\n    for device in hass.data[DOMAIN][\"devices\"]:\n        if device[\"type\"] in SENSORS:\n            if device[\"type\"] == \"bat\":\n                channel = device[\"channel\"]\n            else:\n                channel = None\n            devices.append(\n                WattioSensor(\n                    device[\"name\"],\n                    device[\"type\"],\n                    MEASUREMENT[device[\"type\"]],\n                    ICON[device[\"type\"]],\n                    device[\"ieee\"],\n                    channel,\n                )\n            )\n            _LOGGER.debug(\"Adding device: %s\", device[\"name\"])\n\n    async_add_entities(devices)\n\n\nclass WattioSensor(WattioDevice, Entity):\n    \"\"\"Representation of Sensor.\"\"\"\n\n    # pylint: disable=too-many-instance-attributes\n\n    def __init__(self, name, devtype, measurement, icon, ieee, channel=None):\n        \"\"\"Initialize the sensor.\"\"\"\n        self._pre = \"s_\"\n        self._name = name\n        self._state = None\n        self._measurement = measurement\n        self._icon = icon\n        self._ieee = ieee\n        self._devtype = devtype\n        self._channel = None\n        self._battery = None\n        self._data = None\n        self._available = 0\n        if channel is not None:\n            self._channel = channel - 1\n\n    @property\n    def name(self):\n        \"\"\"Return the name of the sensor.\"\"\"\n        return self._name\n\n    @property\n    def available(self):\n        \"\"\"Return availability.\"\"\"\n        _LOGGER.debug(\"Device %s - availability: %s\", self._name, self._available)\n        return True if self._available == 1 else False\n\n    @property\n    def should_poll(self):\n        \"\"\"No polling needed.\"\"\"\n        return False\n\n    @property\n    def unit_of_measurement(self):\n        \"\"\"Return the unit of measurement.\"\"\"\n        return self._measurement\n\n    @property\n    def icon(self):\n        \"\"\"Return the image of the sensor.\"\"\"\n        return self._icon\n\n    @property\n    def state(self):\n        \"\"\"Return sensor state.\"\"\"\n        return self._state\n\n    @property\n    def device_state_attributes(self):\n        \"\"\"Return the state attributes of this device.\"\"\"\n        attr = {}\n        if self._battery is not None:\n            attr[ATTR_BATTERY_LEVEL] = self.get_battery_level()\n        return attr\n\n    def get_battery_level(self):\n        \"\"\"Return device battery level.\"\"\"\n        if self._battery is not None:\n            battery_level = round((self._battery * 100) / 4)\n            return battery_level\n        return False\n\n    async def async_update(self):\n        \"\"\"Update sensor data.\"\"\"\n        self._data = self.hass.data[DOMAIN][\"data\"]\n        _LOGGER.debug(\"ACTUALIZANDO SENSOR %s - %s\", self._name, self._ieee)\n        if self._data is not None:\n            self._available = 0\n            for device in self._data:\n                if device[\"ieee\"] == self._ieee:\n                    self._available = 1\n                    if self._channel is not None and self._devtype == \"bat\":\n                        sensorvalue = device[\"status\"][\"consumption\"][self._channel]\n                    elif self._devtype == \"therm\":\n                        sensorvalue = device[\"status\"][\"current\"]\n                    elif self._devtype == \"motion\":\n                        sensorvalue = device[\"status\"][\"temperature\"]\n                        self._battery = device[\"status\"][\"battery\"]\n                    elif self._devtype == \"pod\":\n                        sensorvalue = device[\"status\"][\"consumption\"]\n                    else:\n                        sensorvalue = None\n                    self._state = sensorvalue\n                    break\n            _LOGGER.debug(\"Valor: %s\", self._state)\n            return self._state\n        return False\n", "repo_name": "dmoranf/home-assistant-custom-components", "sub_path": "wattio/sensor.py", "file_name": "sensor.py", "file_ext": "py", "file_size_in_byte": 4313, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "46", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "const.DOMAIN", "line_number": 23, "usage_type": "name"}, {"api_name": "const.SENSORS", "line_number": 24, "usage_type": "name"}, {"api_name": "const.MEASUREMENT", "line_number": 33, "usage_type": "name"}, {"api_name": "const.ICON", "line_number": 34, "usage_type": "name"}, {"api_name": "homeassistant.helpers.entity.Entity", "line_number": 44, "usage_type": "name"}, {"api_name": "homeassistant.const.ATTR_BATTERY_LEVEL", "line_number": 101, "usage_type": "name"}, {"api_name": "const.DOMAIN", "line_number": 113, "usage_type": "name"}]}
{"seq_id": "4537587295", "text": "from django.shortcuts import render, get_object_or_404\nfrom django.http import HttpResponse\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated, IsAdminUser\nfrom .serializers import MovieSerializer, MovieListSerializer, GenreSerializer, ReviewSerializer, ReviewListSerializer\nfrom .models import Movie, Genre, Review\n\n\n# Create your views here.\n@api_view(['GET'])\ndef genre_list(request):\n    genres = Genre.objects.all()\n    serializer = GenreSerializer(genres, many=True)\n    return Response(serializer.data)\n\n@api_view(['GET'])\ndef movie_list(request):\n    movies = Movie.objects.all()\n    serializer = MovieListSerializer(movies, many=True)\n    return Response(serializer.data)\n\n@api_view(['POST'])\n@permission_classes([IsAdminUser])\ndef movie_create(request):\n    serializer = MovieSerializer(data=request.data)\n    if serializer.is_valid(raise_exception=True):\n        serializer.save()\n        return Response(serializer.data)\n\n@api_view(['GET'])\ndef movie_detail(request, movie_pk):\n    movie = get_object_or_404(Movie, pk=movie_pk)\n    if request.method == 'GET':\n        serializer = MovieSerializer(movie)\n        return Response(serializer.data)\n\n@api_view(['PUT', 'DELETE'])\n@permission_classes([IsAdminUser])\ndef movie_update(request, movie_pk):\n    movie = get_object_or_404(Movie, pk=movie_pk)\n    if request.method == 'PUT':\n        serializer = MovieSerializer(data=request.data, instance=movie)\n        if serializer.is_valid(raise_exception=True):\n            serializer.save()\n            return Response(serializer.data)\n    else:\n        movie.delete()\n        return Response({'message': 'Movie has been deleted!'})\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef user_reviews(request, user_name):\n    if request.user.username == user_name:\n        reviews = Review.objects.filter(user=request.user.id)\n        serializer = ReviewListSerializer(reviews, many=True)\n        return Response(serializer.data)\n    else:\n        return Response({'message': 'You do not have permission!'})\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef recommend_movie(request, user_name):\n    if request.user.username == user_name:\n        # request user's reviews\n        reviews = Review.objects.filter(user=request.user.id)\n        # number of reviews less than 3\n        if len(reviews) < 3:\n            # random\n            movies = Movie.objects.order_by(\"?\")[:12]\n            serializer = MovieListSerializer(movies, many=True)\n            return Response(serializer.data)\n        else:\n            movie_dict = {}\n            for review in reviews:\n                movies = Movie.objects.filter(pk=review.movie_id)\n                serializer = MovieListSerializer(movies, many=True)\n                # genre count\n                for movie in serializer.data:\n                    for genre in movie['genres']:\n                        if genre not in movie_dict:\n                            movie_dict[genre] = 1\n                        else:\n                            movie_dict[genre] += 1\n            # sort genre\n            movies_arr = sorted(movie_dict.items(), key= lambda x: -x[1])\n            # the most seleted genre\n            genre = Genre.objects.filter(pk=movies_arr[0][0])\n            genre_serializer = GenreSerializer(genre, many=True)\n            # Search for a movie that corresponds to the genre\n            recommend = genre_serializer.data[0]['movie_genres']\n            recommend = recommend[:12]\n            movies = Movie.objects.filter(id__in=recommend)\n            serializer = MovieListSerializer(movies, many=True)\n            return Response(serializer.data)\n    else:\n        return Response({'message': 'You do not have permission!'})\n\n@api_view(['GET'])\ndef review_list(request, movie_pk):\n    reviews = Review.objects.filter(movie=movie_pk)\n    serializer = ReviewListSerializer(reviews, many=True)\n    return Response(serializer.data)\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef review_create(request, movie_pk):\n    movie = get_object_or_404(Movie, pk=movie_pk)\n    serializer = ReviewSerializer(data=request.data)\n    if serializer.is_valid(raise_exception=True):\n        serializer.save(user=request.user, movie=movie)\n        return Response(serializer.data)\n\n@api_view(['PUT', 'DELETE'])\n@permission_classes([IsAuthenticated])\ndef review_update(request, movie_pk, review_pk):\n    review = get_object_or_404(Review, pk=review_pk)\n    if request.user.id == review.user.id:\n        if request.method == 'PUT':\n            serializer = ReviewSerializer(data=request.data, instance=review)\n            if serializer.is_valid(raise_exception=True):\n                serializer.save()\n                return Response(serializer.data)\n        else:\n            review.delete()\n            return Response({'message': 'Review has been deleted!'})\n    else:\n        return Response({'message': 'Author only'})", "repo_name": "surplus112/Final_pjt", "sub_path": "django-back/movies/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5017, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "models.Genre.objects.all", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Genre.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Genre", "line_number": 13, "usage_type": "name"}, {"api_name": "serializers.GenreSerializer", "line_number": 14, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Movie.objects.all", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 19, "usage_type": "name"}, {"api_name": "serializers.MovieListSerializer", "line_number": 20, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 21, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 17, "usage_type": "call"}, {"api_name": "serializers.MovieSerializer", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 29, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 23, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 33, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 35, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 36, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 41, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 43, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 49, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 39, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 39, "usage_type": "name"}, {"api_name": "models.Review.objects.filter", "line_number": 55, "usage_type": "call"}, {"api_name": "models.Review.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.Review", "line_number": 55, "usage_type": "name"}, {"api_name": "serializers.ReviewListSerializer", "line_number": 56, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 57, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 59, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 51, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 52, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 52, "usage_type": "name"}, {"api_name": "models.Review.objects.filter", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Review.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "models.Review", "line_number": 66, "usage_type": "name"}, {"api_name": "models.Movie.objects.order_by", "line_number": 70, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 70, "usage_type": "name"}, {"api_name": "serializers.MovieListSerializer", "line_number": 71, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 72, "usage_type": "call"}, {"api_name": "models.Movie.objects.filter", "line_number": 76, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 76, "usage_type": "name"}, {"api_name": "serializers.MovieListSerializer", "line_number": 77, "usage_type": "call"}, {"api_name": "models.Genre.objects.filter", "line_number": 88, "usage_type": "call"}, {"api_name": "models.Genre.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "models.Genre", "line_number": 88, "usage_type": "name"}, {"api_name": "serializers.GenreSerializer", "line_number": 89, "usage_type": "call"}, {"api_name": "models.Movie.objects.filter", "line_number": 93, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 93, "usage_type": "name"}, {"api_name": "serializers.MovieListSerializer", "line_number": 94, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 97, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 61, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 62, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 62, "usage_type": "name"}, {"api_name": "models.Review.objects.filter", "line_number": 101, "usage_type": "call"}, {"api_name": "models.Review.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.Review", "line_number": 101, "usage_type": "name"}, {"api_name": "serializers.ReviewListSerializer", "line_number": 102, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 103, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 99, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 108, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 108, "usage_type": "argument"}, {"api_name": "serializers.ReviewSerializer", "line_number": 109, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 112, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 105, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 106, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 106, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 117, "usage_type": "call"}, {"api_name": "models.Review", "line_number": 117, "usage_type": "argument"}, {"api_name": "serializers.ReviewSerializer", "line_number": 120, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 123, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 126, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 128, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 114, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 115, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 115, "usage_type": "name"}]}
{"seq_id": "27710581469", "text": "import cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pyzbar.pyzbar as pyzbar\n\nwidth, height = 0, 0\n\n\ndef decode(img):\n    # if height < width, then rotate the image\n    img = img if img.shape[0] > img.shape[1] else np.rot90(img)\n\n    global height, width\n    width, height = img.shape[1], img.shape[0]\n\n    roi_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n    roi_map = cv2.convertScaleAbs(roi_gray, alpha=1, beta=0)  # 將灰階轉換成黑白圖，增強對比度\n\n    _, roi_thresh = cv2.threshold(\n        roi_map, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)  # 大津法閾值化\n\n    # 一些可能用的功能\n    # roi_thresh = cv2.bitwise_not(roi_thresh)  # 反轉圖像\n\n    roi_thresh = cv2.inpaint(roi_thresh, np.zeros(\n        roi_thresh.shape, dtype=np.uint8), 3, cv2.INPAINT_TELEA)  # 填充黑色區域\n\n    roi_thresh = fill_color(roi_thresh, 255)\n    # Border\n    borders = 250\n    canvas = np.zeros((height+2*borders, width+2*borders), dtype=np.uint8)\n    canvas[:] = 255\n\n    canvas[borders:-borders, borders:-borders] = roi_thresh\n\n    # cv2.imshow('canvas', canvas)\n    # cv2.waitKey(0)\n\n    return process1(canvas)\n\n\ndef process1(img):\n    img_cp = img.copy()\n    roi_blur = cv2.GaussianBlur(img_cp, (5, 5), 0)\n    roi_edge = cv2.Canny(roi_blur, 50, 100, apertureSize=3)\n\n    contours, _ = cv2.findContours(\n        roi_edge, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)  # 找輪廓\n\n    # 依照凸包面積大小來排序\n    contours = sorted(contours, key=lambda c: cv2.contourArea(\n        cv2.convexHull(c)), reverse=True)\n\n    contours = [contours[0]]  # 只留下最大的輪廓\n\n    # # show image\n    # cp = roi_edge.copy()\n    # convex = [cv2.convexHull(contours) for contours in contours]  # 找輪廓的凸包\n    # cv2.polylines(cp, convex, True, 255, 3)\n    # cv2.namedWindow(\"convex\", cv2.WINDOW_NORMAL)\n    # cv2.imshow(\"convex\", cp)\n\n    # 畫出凸包\n    convex = draw_approx_hull_polygon(img_cp, contours)  # 繪製凸包\n    # 取得角點\n    pts = getcorner(convex)\n\n    # pt0--------pt1\n    # |           |\n    # pt3--------pt2\n    h, w = pts[3][1]-pts[0][1], pts[2][0]-pts[3][0]\n\n    img = Perspective(img, pts, w, h)\n\n    return process2(img)\n\n\ndef process2(img):\n    # 上一步已經取得了QR code的4個角點，並根據角點的位置\n    # 把圖片不需要的部分裁切掉了\n\n    # 去除黑框\n\n    dst = fill_color(img, 255)\n\n    cv2.inpaint(dst, np.ones(dst.shape, dtype=np.uint8),\n                3, cv2.INPAINT_TELEA, dst)\n    # cv2.imshow('dst', dst)\n    dst = np.where(dst < 120, 0, 255).astype(np.uint8)\n    # 我們現在要找的東西是這樣的:\n\n    # w w w w w w w w #   <- start #1\n    # ............... #\n    #                 #\n    #     上半部\n    #                 #\n    # ............... #\n    # w w w w w w w w #   <- end #1\n    # w w w w w w w w #   <- start #2\n    # *************** #\n    #\n    #     下半部\n    #\n    # *************** #\n    # w w w w w w w w #   <- end #2\n\n    # wt_row = np.where((dst == np.full(w, 255, dtype=np.uint8)).all(axis=1))[0]\n    # if len(wt_row) < 3:\n    #     return None\n\n    # 先找到上半部的起始和結束點\n    h, w = np.int0(dst.shape)\n\n    res = np.where((dst == np.full(w, 255, dtype=np.uint8)).all(axis=1))[0]\n    y1 = res[np.where((res < h/10))][-1]\n    y2 = res[np.where((res > h/3) & (res < h*2/3))][0]  # <- start #2\n    y3 = res[np.where((res > h/3) & (res < h*2/3))][-1]  # <- end #1, start #2\n    y4 = res[np.where((res > h*9/10))][0]   # <- end #2\n\n    half_1 = dst[y1:y2, :]\n    half_2 = dst[y3:y4, :]\n\n    half_2 = np.flip(half_2, axis=0)  # 翻轉\n\n    # # resize\n    # ratio = half_1.shape[0] / half_2.shape[0]\n    # h2, w2 = half_1.shape[0], int(half_2.shape[1]*ratio)\n    # half_2 = cv2.resize(half_2, (w2, h2), interpolation=cv2.INTER_CUBIC)\n\n    half_2 = cv2.resize(half_2, (half_2.shape[1], half_1.shape[0]))\n\n    half_1 = fill_color(half_1, 255)\n    half_2 = fill_color(half_2, 255)\n\n    return process3(half_1, half_2)\n\n\ndef process3(half_1, half_2):\n\n    half_1 = np.where(half_1 < 120, 0, 255).astype(\"uint8\")\n    half_2 = np.where(half_2 < 120, 0, 255).astype(\"uint8\")\n\n    w1, w2 = half_1.shape[1], half_2.shape[1]\n    x1, x2 = w1-1, 0\n\n    # 這個部分已經驗證過了，如果用一次多行的方式計算白色比例，最終會有微妙的誤差，\n    # 這個微妙的誤差會導致最後decode失敗(真的是龜公)\n    for x in range(w1-1, 0, -1):\n\n        if (half_1[:, x] == 255).sum() / (half_1.shape[0]) < 0.7:\n            x1 = x\n            break\n    for x in range(0, w2):\n        if (half_2[:, x] == 255).sum() / (half_2.shape[0]) < 0.7:\n            x2 = x\n            break\n\n    flag = 1\n    for x in range(x2, w2//2):\n        col = half_2[:, x]\n        cnt = 0\n        for c in range(1, len(col)):\n            if col[c-1] < 120 and col[c] > 120:\n                cnt += 1\n        if cnt < 9 and flag == 0:\n            x2 = x\n            break\n        if flag == 1 and cnt > 9:\n            flag -= 1\n        # print(cnt)\n\n    # cv2.namedWindow(\"half_1\", cv2.WINDOW_NORMAL)\n    # cv2.imshow('half_1', half_1[:, :x1])\n    # cv2.namedWindow(\"half_2\", cv2.WINDOW_NORMAL)\n    # cv2.imshow('half_2', half_2[:, x2:])\n\n    qr_code = np.hstack((half_1[:, :x1], half_2[:, x2:]))\n\n    return show(qr_code)\n\n\ndef show(qr_code):\n    borders = round(qr_code.shape[0]/21*4)\n    canvas = np.zeros(\n        (qr_code.shape[0]+2*borders, qr_code.shape[1]+2*borders), dtype=np.uint8)\n    canvas[:, :] = 255\n    canvas[borders:qr_code.shape[0]+borders,\n           borders:qr_code.shape[1]+borders] = qr_code\n    # cv2.imshow('qr_code', canvas)\n    # cv2.imwrite('qr_code-2.png', canvas)\n    # cv2.waitKey(0)\n\n    text = pyzbar.decode(canvas)\n    if text == []:\n        blur = cv2.GaussianBlur(canvas, (3, 3), 0)\n        # closing = cv2.morphologyEx(canvas, cv2.MORPH_CLOSE, np.zeros((3, 3), np.uint8))\n        text = pyzbar.decode(blur)\n        cv2.imshow('qr_code', blur)\n\n    if text == []:\n        return None\n    else:\n        return text[0].data.decode('utf-8')\n\n\ndef getcorner(canny):\n    global width, height\n    # shi-tomasi corner detection\n    corners = cv2.goodFeaturesToTrack(\n        canny, 4, 0.01, width//8, blockSize=10, useHarrisDetector=False, k=0.04)\n\n    # # 另一種方法：亞像素角偵測\n    # criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 40, 0.001)\n    # cv2.cornerSubPix(canny, np.float32(corners), (5, 5), (-1, -1), criteria)\n\n    corners = np.int0(corners)\n    pts = []\n    for c in corners:\n        x, y = c.ravel()\n        pts.append([x, y])\n\n    if len(pts) != 4:\n        return None\n\n    def sort(pt):\n        pt = sorted(pt, key=lambda x: x[1])\n        if pt[0][0] > pt[1][0]:\n            pt[0], pt[1] = pt[1], pt[0]\n        if pt[3][0] > pt[2][0]:\n            pt[2], pt[3] = pt[3], pt[2]\n        return pt\n\n    pts = sort(pts)\n    # # 畫出角點\n    # for x, y in pts:\n    #     cv2.circle(canny, (x, y), 3, (0, 0, 255), -1)\n    # cv2.imshow('corners', canny)\n\n    return pts\n\n\ndef draw_approx_hull_polygon(img, contours):  # 畫出多邊形\n    # 取出多邊形凸包\n    hulls = [cv2.convexHull(cnt) for cnt in contours]  # 找輪廓的凸包\n\n    # show image\n    img_cp = np.zeros(img.shape, dtype=np.uint8)\n    cv2.polylines(img_cp, hulls, isClosed=True, color=255, thickness=1)\n    # cv2.imshow('convex hull', img_cp)\n\n    return img_cp\n\n\ndef fill_color(img, color=255):\n    h, w = img.shape[:2]\n    img = np.asarray(img)\n    _, img = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY)\n    seedPoint = [(0, 0), (0, h-1), (w-1, h-1), (w-1, 0)]\n    for sp in seedPoint:\n        cv2.floodFill(img, None, sp, (color, color, color))\n    # floodFill(image, mask, seedPoint, newVal, loDiff=None, upDiff=None, flags=None)\n    return img\n\n\ndef Perspective(image, pts1, w, h):\n    w, h = int(w), int(h)\n    if w > h:\n        w, h = h, w\n    # src: 源點\n    pts1 = np.float32(pts1)\n    # dst: 目標點（必須以與src點相同的順序列出！）\n    pts2 = np.float32([[0, 0], [w, 0], [w, h], [0, h]])\n\n    # 轉換矩陣\n    M = cv2.getPerspectiveTransform(pts1, pts2)\n    # 套用矩陣\n    dst = cv2.warpPerspective(image, M, (w, h))\n    return dst\n", "repo_name": "ketty0712/yolov4", "sub_path": "opencv/recover.py", "file_name": "recover.py", "file_ext": "py", "file_size_in_byte": 8192, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "numpy.rot90", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.inpaint", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.INPAINT_TELEA", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.convexHull", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.inpaint", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 87, "usage_type": "attribute"}, {"api_name": "cv2.INPAINT_TELEA", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.int0", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 124, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 186, "usage_type": "attribute"}, {"api_name": "pyzbar.pyzbar.decode", "line_number": 194, "usage_type": "call"}, {"api_name": "pyzbar.pyzbar", "line_number": 194, "usage_type": "name"}, {"api_name": "cv2.GaussianBlur", "line_number": 196, "usage_type": "call"}, {"api_name": "pyzbar.pyzbar.decode", "line_number": 198, "usage_type": "call"}, {"api_name": "pyzbar.pyzbar", "line_number": 198, "usage_type": "name"}, {"api_name": "cv2.imshow", "line_number": 199, "usage_type": "call"}, {"api_name": "cv2.goodFeaturesToTrack", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.int0", "line_number": 217, "usage_type": "call"}, {"api_name": "cv2.convexHull", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 248, "usage_type": "attribute"}, {"api_name": "cv2.polylines", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 257, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 258, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 258, "usage_type": "attribute"}, {"api_name": "cv2.floodFill", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 273, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 276, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 278, "usage_type": "call"}]}
{"seq_id": "70203334844", "text": "\"\"\"\nContains class Density for the calculation of densities of gray images along \nsegments.\n\n# Author: Vladan Lucic (MPI for Biochemistry)\n# $Id$\n\"\"\"\nfrom __future__ import unicode_literals\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom builtins import zip\nfrom builtins import range\n#from past.utils import old_div\nfrom past.builtins import basestring\n\n__version__ = \"$Revision$\"\n\nimport sys\nimport logging\nimport inspect\nfrom copy import copy, deepcopy\n\nimport numpy\nimport scipy\nimport scipy.ndimage as ndimage\n\nfrom .features import Features\nfrom pyto.core.image import Image\nfrom .segment import Segment\nfrom .statistics import Statistics\nfrom .morphology import Morphology\n\n\nclass Density(Features):\n    \"\"\"\n    Calculates basic statistics (mean, std, min, max) and volume for\n    segments of a (typically greyscale) image.\n\n    Attributes holding calculated data:\n      - mean\n      - std\n      - min\n      - max\n      - volume\n\n    Each of these data attributes is an ndarray indexed by a segment id (from 1 \n    up). Elements at position 0 contain values for all segments taken together.\n\n    Methods:\n      - calculate(): calculated all data arrays\n      - aggregate(): combines calculated data for more than one segment\n\n    Usage:\n      density = Density()\n      density.calculate(image=grey_image, segments_segmented_image)\n    \"\"\"\n    \n    #############################################################\n    #\n    # Initialization\n    #\n    #############################################################\n\n    def __init__(self):\n        \"\"\"\n        Initializes segments and ids.\n        \n        Note (r527): don't want to accept segments as argument because super\n        sets the argument segments the the actual array (and loses other info \n        like inset). \n        \"\"\"\n\n        # call super\n        super(Density, self).__init__()\n\n        # local attributes\n        self.dataNames = ['mean', 'std', 'min', 'max', 'volume']\n        \n\n\n    #############################################################\n    #\n    # Calculations\n    #\n    ############################################################\n\n    def calculate(self, image, segments, ids=None):\n        \"\"\"\n        Calculates gray value mean, std, min, max and volume for each segment\n        from arg segments specified in arg ids. The calculated values are saved\n        as attributes named mean, std, min, max and volume, respectivly.\n\n        If arg segments is not specified self.segments is used. If arg ids is\n        not specified self.ids is used if found, or if not segments.ids is used.\n\n        Uses minimal possible size of segments and image arrays to speed up\n        the calculations, but these arguments are not changed. This method \n        respects positioning of image and segments (attribute inset) but \n        image.data has to contain the whole inset covered by segments.\n\n        Arguments:\n          - image: (core.Image) grayscale image\n          - segments: (Segment) segmented image\n          - ids: segment ids\n\n        Sets attributes: \n          - mean, std (N-1 degrees of fereedom), min, max and volume to \n          calculated values\n          - ids: to arg ids or segments.ids\n        \"\"\"\n\n        self.setIds(ids=ids, segments=segments)\n        \n\n        # work on array insets \n        if self.maxId > 0:\n            segments_data, segments_inset = segments.getDataInset()\n            segments.makeInset(ids=self.ids)\n            image_data, image_inset = image.getDataInset()\n            image.useInset(inset=segments.inset, mode='abs')\n\n        # calculate density statistics for each segment\n        stats = Statistics(data=image.data, labels=segments.data, ids=self.ids)\n        stats.calculate()\n\n        # calculate volume\n        mor = Morphology(segments=segments.data, ids=self.ids)\n        mor.getVolume()\n            \n        # set attributes\n        self.mean = stats.mean\n        self.std = stats.std\n        self.min = stats.min\n        self.max = stats.max\n        self.volume = mor.volume\n\n        # revert to the original data\n        if self.maxId > 0:\n            segments.setDataInset(data=segments_data, inset=segments_inset)\n            image.setDataInset(data=image_data, inset=image_inset)\n\n    def aggregate(self, ids):\n        \"\"\"\n        Returns a new instance with data corresponding to segments combined\n        according to arg ids.\n\n        Elements of ids defines segments in the new instance, element i of\n        this list defines segment with id i+1 of the new instance. Each element\n        of ids should be a list containing segment ids of the current instance\n        that are to be taken together.\n\n        For example:\n\n          aggregate(ids=[[1,2], [3,6,7]])\n\n        returns instance with ids 1 and 2, where the new segment 1 contains data\n        for actual segments 1 and 3 together, and the new segment 2 for actual\n        3, 6 and 7.\n\n        Note that segment ids can be repeated in the argument ids and that not\n        all ids need to be present in the argument ids. In those cases \n        0-elements of data vectors might be meaningless.\n\n        Argument:\n          - ids: (list of lists) segment ids\n\n        Returns: new instance of this class.\n        \"\"\"\n\n        # make new instance\n        new = self.__class__()\n        new_len = len(ids) + 1\n        new.setIds(ids=list(range(1, new_len)))\n\n        # initrialize new data arrays\n        new.mean = numpy.zeros(new_len) - 1\n        new.std = numpy.zeros(new_len) - 1\n        new.min = numpy.zeros(new_len) - 1\n        new.max = numpy.zeros(new_len) - 1\n        new.volume = numpy.zeros(new_len, dtype=int) - 1\n        sum_sq = numpy.zeros(new_len) - 1\n\n        # agregated segments\n        for old_ids, new_id in zip(ids, new.ids):\n            new.volume[new_id] = self.volume[old_ids].sum()\n            new.mean[new_id] = (\n                numpy.inner(self.mean[old_ids], self.volume[old_ids])\n                / new.volume[new_id].astype(float))\n            new.min[new_id] = self.min[old_ids].min()\n            new.max[new_id] = self.max[old_ids].max()\n\n            # std\n            ddof = 0.\n            sum_sq[new_id] = \\\n                ((self.std[old_ids] ** 2 * (self.volume[old_ids] - ddof))\n                 + self.mean[old_ids] ** 2 * self.volume[old_ids]).sum()\n            new.std[new_id] = numpy.sqrt(\n                (sum_sq[new_id] - new.mean[new_id] ** 2 * new.volume[new_id])\n                / (new.volume[new_id] - ddof))\n\n        # set total (index 0) values\n        new.setTotal()\n\n        return new\n\n    def restrict(self, ids):\n        \"\"\"\n        Effectivly removes data that correspond to ids that are not specified \n        in the arg ids. Arg ids should not contain ids that are not in self.ids.\n\n        Sets self.ids to ids and recalculates totals (index 0). Currently\n        it doesn't actually remove non-id values from data.\n\n        Argument:\n          - ids: new ids\n        \"\"\"\n\n        # set ids\n        super(Density, self).restrict(ids=ids)\n\n        # remove non-id values?\n\n        # set total\n        self.setTotal()\n\n    def calculateTotal(self):\n        \"\"\"\n        Calculates total values based on the individual segments data \n        and saves them at index 0.\n\n        Acts on attributes min, max, volume, mean and std, if in self.dataNames.\n\n        Doesn't do anything if there are no ids (self.ids).\n        \"\"\"\n\n        # don't do anything if no ids\n        if len(self.ids) == 0: return \n\n        if 'min' in self.dataNames:\n            self.min[0] = self.min[self.ids].min()\n        if 'max' in self.dataNames:\n            self.max[0] = self.max[self.ids].max()\n        if 'volume' in self.dataNames:\n            self.volume[0] = self.volume[self.ids].sum()\n\n        # mean\n        if ('mean' in self.dataNames) and ('volume' in self.dataNames):\n            self.mean[0] = (\n                numpy.dot(self.mean[self.ids], self.volume[self.ids])\n                / float(self.volume[0]))\n\n        # std\n        if (('std' in self.dataNames) and ('mean' in self.dataNames) \n            and ('volume' in self.dataNames)):\n            variances = numpy.dot(\n                (self.std[self.ids]**2 + self.mean[self.ids]**2),\n                self.volume[self.ids])\n            means = numpy.dot(\n                self.mean[self.ids], self.volume[self.ids]) \n            self.std[0] = numpy.sqrt(\n                variances / float(self.volume[0])\n                - (means / float(self.volume[0]))**2)\n\n    def merge(\n            self, new, names=None, mode='replace', mode0='consistent',\n            volume=None):\n        \"\"\"\n        Merges data of this object new with the data of this instance.\n        The values of all attributes listed in self.dataNames are added.\n\n        The data attributes whose values are meged are those listed in names or\n        in self.dataNames if names is None.\n\n        If mode is 'add' the data is simply added. If mode is \n        'replace' the new values replace the old ones for id that are in \n        new.ids. \n\n        Recommended value for arg mode0 is 'consistent', because in this case\n        index 0 of arrays mean, std min and max will contain appropriate values\n        for all segments taken together.\n\n        Arguments:\n          - new: instance of Morphology\n          - names: (list of strings, or a single string) names of data \n          attributes\n          - mode: merge mode for data (indices 1+) 'add' or 'replace'\n          - mode0: merge mode for index 0, 'consistent', 'add', 'replace' \n          or a numerical value\n        \"\"\"\n\n        # \n        mode0_consistent = False\n        if isinstance(mode0, basestring) and (mode0 == 'consistent'):\n            mode0_consistent = True\n            mode0 = -1\n\n        # merge data listed in self.dataNames\n        super(Density, self).merge(\n            new=new, names=names, mode=mode, mode0=mode0)\n\n        # set volume\n        if (volume is None) and ('volume' in self.dataNames):\n            volume = self.volume\n\n        # calculate index 0 values if needed\n        if mode0_consistent:\n            #if 'min' in self.dataNames:\n            #    self.min[0] = self.min[self.ids].min()\n            #if 'max' in self.dataNames:\n            #    self.max[0] = self.max[self.ids].max()\n            #if 'volume' in self.dataNames:\n            #    self.volume[0] = self.volume[self.ids].sum()\n            #if ('mean' in self.dataNames) and (volume is not None):\n            #    total_dens = numpy.dot(self.mean[self.ids], volume[self.ids]) \n            #    self.mean[0] = total_dens / float(self.volume[0])\n            #if ('std' in self.dataNames) and (volume is not None):\n            #    variances = numpy.dot(\n            #        (self.std[self.ids]**2 + self.mean[self.ids]**2),\n            #        self.volume[self.ids])\n            #    means = numpy.dot(\n            #        self.mean[self.ids], self.volume[self.ids]) \n            #    self.std[0] = numpy.sqrt(\n            #        variances / float(self.volume[0]) \n            #        - (means / float(self.volume[0]))**2)\n            self.calculateTotal()\n\n    def setTotal(self):\n        \"\"\"\n        Sets total values (index 0) for all data.\n\n        In particular, total volume is the sum of all volumes, while total mean,\n        std, min and max are values obtained for all segments taken together\n        (and not mean of the segment means and so on. Takes into account only\n        segments listed in self.ids.\n\n        Sets index 0 of data arrays. \n        \"\"\"\n\n        ids = self.ids\n\n        # don't do anything if no ids\n        if (ids is None) or (len(ids) == 0):\n            return\n\n        # volume\n        self.volume[0] = self.volume[ids].sum()\n\n        # mean, min, max\n        self.mean[0] = (\n            numpy.inner(self.mean[ids], self.volume[ids])\n            / float(self.volume[0]))\n        self.max[0] = self.max[ids].max()\n        self.min[0] = self.min[ids].min()\n\n        # std\n        ddof = 0\n        sum_sq = (self.volume[ids] - ddof) * self.std[ids]**2 \\\n            + self.volume[ids] * (self.mean[ids] - self.mean[0])**2 \n        self.std[0] = numpy.sqrt(sum_sq.sum() / (self.volume[0] - ddof))\n\n", "repo_name": "anmartinezs/pyseg_system", "sub_path": "code/pyto/segmentation/density.py", "file_name": "density.py", "file_ext": "py", "file_size_in_byte": 12205, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "41", "api": [{"api_name": "features.Features", "line_number": 34, "usage_type": "name"}, {"api_name": "statistics.Statistics", "line_number": 123, "usage_type": "call"}, {"api_name": "morphology.Morphology", "line_number": 127, "usage_type": "call"}, {"api_name": "builtins.range", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 181, "usage_type": "call"}, {"api_name": "builtins.zip", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.inner", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 260, "usage_type": "call"}, {"api_name": "past.builtins.basestring", "line_number": 293, "usage_type": "argument"}, {"api_name": "numpy.inner", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 359, "usage_type": "call"}]}
{"seq_id": "40210997897", "text": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport matplotlib.pyplot as plt\nimport torch.nn.functional as F\nimport time\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\n\n## interactive off\nplt.ioff()\n## setup torch enviro\ntorch.manual_seed(42)\ntorch.cuda.manual_seed_all(42)\ntorch.autograd.set_detect_anomaly(True)\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\ndef single_to_multi_label(y):\n  \"\"\"\n    Input: labels in {0,...,9}\n    Output: one-hot encoding of y\n  \"\"\"\n  y_onehot = torch.FloatTensor(y.shape[0], 10)\n  y = y.unsqueeze(1)\n  y_onehot.zero_()\n  y_onehot.scatter_(1, y, 1)\n  return y_onehot\n\n## PLOT HELPER\ndef plot_history(key, train, history):\n  \"\"\" \n    Plot loss and accuracy history during model run\n    Input:\n          key : str => name of the model\n          train : bool => training 1 or test 0\n          history : dict{str : list of floats}\n  \"\"\"\n  if train:\n    when = \"train\"\n  else:\n    when = \"test\"\n\n  fig, ax = plt.subplots( 1, 2, figsize = (12,4) )\n  ax[0].plot(history['loss'], label = \"loss\")\n  ax[0].set_title( \"Loss\" )\n  ax[0].set_xlabel( \"Epochs\" )\n  ax[0].set_ylabel( \"Loss\" )\n  ax[0].grid( True )\n  ax[0].legend()\n\n  ax[1].plot(history['accuracy'], label = \"accuracy\")\n  ax[1].set_title( \"Accuracy\" )\n  ax[1].set_xlabel( \"Epochs\" )\n  ax[1].set_ylabel( \"Accuracy\" )\n  ax[1].grid( True )\n  ax[1].legend()\n\n  plt.savefig(f\"./images/{key}_{when}.png\")\n  plt.close()\n\n## TRAIN\ndef train(logger, model, dataHandler, num_epochs, lr=0.001, momentum=0.9, l1_ratio=0.5, l1l2_penalty=0.0000001, l1_penalty=0.01, l2_penalty=0.01, optim_algo='SGD', loss='MSE', regularizer='None'):\n  print(\"Using: \", device)\n  if regularizer == 'None':\n    l2_penalty = 0\n    l1_penalty = 0\n  elif regularizer == 'L1':\n    l2_penalty = 0\n  elif regularizer == 'L2':\n    l1_penalty = 0\n  elif regularizer == 'Elastic':\n    l1_penalty = l1_ratio*l1l2_penalty\n    l2_penalty = l1l2_penalty*(1-l1_ratio)\n  else:\n    print(\"regularizer has to be None, L1, L2 or Elastic\")\n    exit()\n\n  if optim_algo == 'Adam':\n    optimizer = optim.Adam(model.parameters(),lr=lr, weight_decay=l2_penalty, amsgrad=True)\n  elif optim_algo == 'SGD':\n    optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum, weight_decay=l2_penalty, nesterov=True)\n  else:\n    print(\"Optimizer is Adam or SGD\")\n    exit\n  if loss == 'CSE':\n    criterion = nn.CrossEntropyLoss()\n  elif loss == 'MSE':\n    criterion = nn.MSELoss()  \n  else:\n    print(\"Loss is CrossEntropy or MSE\")\n    exit\n  \n  scheduler = ReduceLROnPlateau(optimizer, 'min'\n                                  ,patience=3,factor=0.9817\n                                 ,verbose=True,threshold=1e-2)\n  \n  print(f\"Training summary:\\n lr={lr}\\n epochs={num_epochs}\\n optim={optim_algo}\\n loss={loss}\\n regularizer={regularizer}\")\n\n  trainHistory = {}\n  trainHistory['loss'] = []\n  trainHistory['accuracy'] = []\n\n  model.to(device)\n  model.train()\n  for epoch in range(num_epochs):\n    \n    start = time.time()\n    epoch_loss = 0\n    num_correct = 0.0\n    num_samples = 0.0\n    \n    for i, (data, labels) in enumerate(dataHandler.train_dl):\n      data = data.to(device=device)\n      if loss == 'MSE':\n        labels_one_hot = single_to_multi_label(labels)\n        labels_one_hot = labels_one_hot.to(device=device)\n      labels = labels.to(device)\n      \n      optimizer.zero_grad()\n            \n      predictions = model(data)\n      if loss == 'MSE':\n        #predictions, labels_one_hot = predictions.type('torch.FloatTensor'), labels_one_hot.type('torch.FloatTensor')\n        loss_value = criterion(predictions, labels_one_hot)\n      else:\n        #predictions, labels = predictions.type('torch.FloatTensor'), labels.type('torch.FloatTensor')\n        loss_value = criterion(predictions, labels)\n      \n      if regularizer == 'L1':\n        parameters = []\n        for parameter in model.parameters():\n          parameters.append(parameter.view(-1))\n        l1_norm = torch.abs(torch.cat(parameters)).sum()\n        loss_value = loss_value + l1_penalty*l1_norm\n      \n      loss_value.backward()\n      optimizer.step()\n\n\n      epoch_loss += loss_value.item()\n      _, predicted_labels = predictions.max(1)\n      correct = (predicted_labels == labels).sum().item()\n      samples = predicted_labels.size(0)\n      num_correct += correct\n      num_samples += samples\n\n      if model.verbose and (i+1)%100 == 0:\n        print(f\"[?] Step {i+1}/{len(dataHandler.train_dl)} Epoch {epoch+1}/{num_epochs} Loss {loss_value.item()} Accuracy {correct/samples:.4f}\")\n\n    end = time.time()\n    print(f\"[?] {logger.name} Epoch {epoch+1}/{num_epochs} Loss {epoch_loss/(i+1):.4f} Accuracy {num_correct/num_samples:.4f} Time: {end-start:.4f}s\")\n    logger.log_step(epoch, i, epoch_loss/(i+1), num_correct/num_samples)  \n    trainHistory['loss'].append(epoch_loss/(i+1))\n    trainHistory['accuracy'].append(num_correct/num_samples)\n    \n    scheduler.step(epoch_loss/(i+1))\n    \n  plot_history(logger.name, True, trainHistory)\n\n## EVAL \ndef eval(logger, model, dataHandler, loss='MSE'):\n  num_correct = 0\n  num_samples = 0\n\n  if loss == 'CSE':\n    criterion = nn.CrossEntropyLoss()\n  elif loss == 'MSE':\n    criterion = nn.MSELoss()  \n  else:\n    print(\"Loss is CrossEntropy or MSE\")\n    exit\n  \n  model.eval()\n  \n  testHistory = {}\n  testHistory['loss'] = []\n  testHistory['accuracy'] = []\n  test_loss = 0\n  test_accuracy = 0\n  \n  with torch.no_grad():\n    for batch, (data,labels) in enumerate(dataHandler.test_dl):\n        data = data.to(device=device)\n        if loss == 'MSE':\n          labels_one_hot = single_to_multi_label(labels)\n          labels_one_hot = labels_one_hot.to(device=device)\n        else:\n          labels = labels.to(device=device)\n        \n        predictions = model(data)\n        if loss == 'MSE':\n          predictions, labels_one_hot = predictions.type('torch.FloatTensor'), labels_one_hot.type('torch.FloatTensor')\n          loss_value = criterion(predictions, labels_one_hot).item()\n        else:\n          predictions = model(data)\n          #predictions, labels = predictions.type('torch.FloatTensor'), labels.type('torch.FloatTensor')\n          loss_value = criterion(predictions, labels).item()\n        \n        test_loss += loss_value\n        _, predicted_labels = predictions.max(1)\n        num_correct += (predicted_labels == labels).sum().item()\n        num_samples += predicted_labels.size(0)\n        \n        testHistory['loss'].append(loss_value)\n        testHistory['accuracy'].append(float(num_correct) / float(num_samples))\n        \n        logger.log_batch(batch+1, loss_value, float(num_correct) / float(num_samples))\n    \n    test_accuracy = float(num_correct) / float(num_samples)\n    test_loss = test_loss/len(dataHandler.test_dl) ## avg loss\n    logger.finalize(test_loss, test_accuracy)\n\n    plot_history(logger.name, False, testHistory)\n\n  return test_loss, test_accuracy\n", "repo_name": "ldsec/slytHErin", "sub_path": "training/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6897, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "41", "api": [{"api_name": "matplotlib.pyplot.ioff", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.manual_seed", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.autograd.set_detect_anomaly", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.ReduceLROnPlateau", "line_number": 92, "usage_type": "call"}, {"api_name": "time.time", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 132, "usage_type": "call"}, {"api_name": "time.time", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 165, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 167, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 180, "usage_type": "call"}]}
{"seq_id": "39453149577", "text": "from typing import Any\n\nimport fastapi\n\nfrom schemas.documentation import DocumentationOut\n\nrouter = fastapi.APIRouter()\n\n\n@router.get(\"/documentation\", response_model=DocumentationOut)\ndef get_doc() -> dict[str, Any]:\n    docs: dict[str, Any] = {\n        \"model_name\": \"image_classification_vissl\",\n        \"short_desc\": (\n            \"ViSSL is a computer vision library for state-of-the-art deep learning\"\n            \" models. It provides pre-trained models, tools for training new models,\"\n            \" and a framework for building custom models.\"\n        ),\n        \"ideal\": (\n            \"Ideal for computer vision tasks that require high accuracy and advanced\"\n            \" features, such as object detection, semantic segmentation, and instance\"\n            \" segmentation.\"\n        ),\n        \"not_ideal\": (\n            \"May not be ideal for low-resource or real-time applications due to the\"\n            \" computational intensity of the models.\"\n        ),\n        \"input_args\": {\"example1\": \"Image file in a format supported by the library.\"},\n        \"output_args\": {\"example1\": \"BMP\"},\n    }\n    return docs\n", "repo_name": "hb0313/Jenkins-cicd", "sub_path": "api/v1/routers/documentation.py", "file_name": "documentation.py", "file_ext": "py", "file_size_in_byte": 1123, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "fastapi.APIRouter", "line_number": 7, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 12, "usage_type": "name"}, {"api_name": "schemas.documentation.DocumentationOut", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 11, "usage_type": "name"}]}
{"seq_id": "29729364483", "text": "import subprocess\nfrom contextlib import asynccontextmanager\n\nfrom sqlalchemy import create_engine, MetaData, Table\nfrom sqlalchemy.exc import NoSuchTableError\n\nfrom contextqa import settings\n\n\ndef _migrations_already_applied() -> bool:\n    engine = create_engine(settings.sqlalchemy_url)\n    metadata = MetaData()\n\n    # Pass the 'bind' argument when creating the Table\n    try:\n        Table(\"alembic_version\", metadata, autoload_with=engine)\n    except NoSuchTableError:\n        return False\n    return True\n\n\n@asynccontextmanager\nasync def check_migrations(*_):\n    \"\"\"Apply migrations if necessary\"\"\"\n    if not _migrations_already_applied():\n        code = subprocess.check_call([\"alembic\", \"upgrade\", \"head\"])\n        print(\"Migration finished with exit code:\", code)\n    yield\n    print(\"Terminating...\")\n", "repo_name": "zaldivards/ContextQA", "sub_path": "api/contextqa/utils/migrations.py", "file_name": "migrations.py", "file_ext": "py", "file_size_in_byte": 813, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 11, "usage_type": "call"}, {"api_name": "contextqa.settings.sqlalchemy_url", "line_number": 11, "usage_type": "attribute"}, {"api_name": "contextqa.settings", "line_number": 11, "usage_type": "name"}, {"api_name": "sqlalchemy.MetaData", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.exc.NoSuchTableError", "line_number": 17, "usage_type": "name"}, {"api_name": "subprocess.check_call", "line_number": 26, "usage_type": "call"}, {"api_name": "contextlib.asynccontextmanager", "line_number": 22, "usage_type": "name"}]}
{"seq_id": "11337683749", "text": "# function_import --------------------\n\nfrom transformers import pipeline\n\n# function_code --------------------\n\ndef image_question_answering(image_path: str, question: str) -> str:\n    \"\"\"\n    This function uses the 'uclanlp/visualbert-vqa' model from Hugging Face Transformers to answer questions based on the contents of an image.\n\n    Args:\n        image_path (str): The path to the image file.\n        question (str): The question related to the image.\n\n    Returns:\n        str: The answer to the question based on the image contents.\n    \"\"\"\n    model = pipeline('question-answering', model='uclanlp/visualbert-vqa')\n    result = model(image_path, question)\n    return result\n\n# test_function_code --------------------\n\ndef test_image_question_answering():\n    \"\"\"\n    This function tests the 'image_question_answering' function by providing a sample image and question.\n    \"\"\"\n    image_path = 'path/to/sample_image.jpg'\n    question = 'What is the main color of the object in the image?'\n    result = image_question_answering(image_path, question)\n    assert isinstance(result, str), 'The result should be a string.'\n\n# call_test_function_code --------------------\n\ntest_image_question_answering()", "repo_name": "vixuowis/Research-2309", "sub_path": "Exp-2/output/hf-eval-data-v2/f00394_image_question_answering.py", "file_name": "f00394_image_question_answering.py", "file_ext": "py", "file_size_in_byte": 1207, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "transformers.pipeline", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "41233262643", "text": "from lasif.utils import get_event_filename\nimport glob\nimport inspect\nimport numpy as np\nimport obspy\nfrom obspy.core.event import Catalog\nimport os\nimport random\nimport pyasdf\nimport datetime\nimport shutil\nfrom scipy.spatial import cKDTree\n\nfrom lasif.exceptions import LASIFNotFoundError, LASIFError\n\nEARTH_RADIUS = 6371.00\n\n\nclass SphericalNearestNeighbour(object):\n    \"\"\"\n    Spherical nearest neighbour queries using scipy's fast\n    kd-tree implementation.\n    \"\"\"\n\n    def __init__(self, data):\n        cart_data = self.spherical2cartesian(data)\n        self.data = data\n        self.kd_tree = cKDTree(data=cart_data, leafsize=10)\n\n    def query(self, points, k=10):\n        points = self.spherical2cartesian(points)\n        d, i = self.kd_tree.query(points, k=k)\n        return d, i\n\n    @staticmethod\n    def spherical2cartesian(data):\n        \"\"\"\n        Converts an array of shape (x, 2) containing latitude/longitude\n        pairs into an array of shape (x, 3) containing x/y/z assuming a\n        radius of one for points on the surface of a sphere.\n        \"\"\"\n        lat = data[:, 0]\n        lng = data[:, 1]\n        # Convert data from lat/lng to x/y/z, assume radius of 1\n        colat = 90 - lat\n        cart_data = np.empty((lat.shape[0], 3))\n\n        cart_data[:, 0] = np.sin(np.deg2rad(colat)) * np.cos(np.deg2rad(lng))\n        cart_data[:, 1] = np.sin(np.deg2rad(colat)) * np.sin(np.deg2rad(lng))\n        cart_data[:, 2] = np.cos(np.deg2rad(colat))\n\n        return cart_data\n\n\ndef _read_GCMT_catalog(min_year=None, max_year=None):\n    \"\"\"\n    Helper function reading the GCMT data shipping with LASIF.\n\n    :param min_year: The minimum year to read.\n    :type min_year: int, optional\n    :param max_year: The maximum year to read.\n    :type max_year: int, optional\n    \"\"\"\n    # easier tests\n    if min_year is None:\n        min_year = 0\n    else:\n        min_year = int(min_year)\n    if max_year is None:\n        max_year = 3000\n    else:\n        max_year = int(max_year)\n\n    data_dir = os.path.join(\n        os.path.dirname(\n            os.path.dirname(\n                os.path.abspath(inspect.getfile(inspect.currentframe()))\n            )\n        ),\n        \"data\",\n        \"GCMT_Catalog\",\n    )\n    available_years = [_i for _i in os.listdir(data_dir) if _i.isdigit()]\n    available_years.sort()\n    print(\n        \"LASIF currently contains GCMT data from %s to %s/%i.\"\n        % (\n            available_years[0],\n            available_years[-1],\n            len(\n                glob.glob(\n                    os.path.join(data_dir, available_years[-1], \"*.ndk*\")\n                )\n            ),\n        )\n    )\n\n    available_years = [\n        _i\n        for _i in os.listdir(data_dir)\n        if _i.isdigit() and (min_year <= int(_i) <= max_year)\n    ]\n    available_years.sort()\n\n    print(\"Parsing the GCMT catalog. This might take a while...\")\n    cat = Catalog()\n    for year in available_years:\n        print(\"\\tReading year %s ...\" % year)\n        if float(year) < 2005:\n            filename = glob.glob(os.path.join(data_dir, str(year), \"*.xml*\"))[0]\n            cat += obspy.read_events(filename, format=\"QuakeML\")\n        else:\n            for filename in glob.glob(os.path.join(data_dir, str(year), \"*.ndk*\")):\n                cat += obspy.read_events(filename, format=\"ndk\")\n\n    return cat\n\n\ndef update_GCMT_catalog():\n    \"\"\"\n    Helper function updating the GCMT data shipped with LASIF.\n    \"\"\"\n    data_dir = os.path.join(\n        os.path.dirname(\n            os.path.dirname(\n                os.path.abspath(inspect.getfile(inspect.currentframe()))\n            )\n        ),\n        \"data\",\n        \"GCMT_Catalog\",\n    )\n\n    start_year = 2005\n    end_year = datetime.datetime.now().year\n    years = np.arange(\n        start_year, end_year + 1\n    )  # begin and end year, does not include end\n    months = [\n        \"jan\",\n        \"feb\",\n        \"mar\",\n        \"apr\",\n        \"may\",\n        \"jun\",\n        \"jul\",\n        \"aug\",\n        \"sep\",\n        \"oct\",\n        \"nov\",\n        \"dec\",\n    ]\n\n    web_address = \"https://www.ldeo.columbia.edu/\"\n    web_address += \"~gcmt/projects/CMT/catalog/NEW_MONTHLY\"\n    os.chdir(data_dir)\n    for year in years:\n        year_str = str(year)\n        os.chdir(data_dir)\n        web_address_year = web_address + f\"/{year_str}/\"\n\n        if not os.path.exists(year_str):\n            # check if january exists\n            january = web_address_year + \"jan\" + year_str[-2:] + \".ndk\"\n            if os.system(f\"wget -q --spider {january}\") == 0:\n                os.makedirs(year_str)\n            else:\n                print(f\"Could not find anything yet for year: {year_str}\")\n                # nothing from this year, skip it\n                continue\n\n        os.chdir(year_str)\n        for month in months:\n            month_name = month + year_str[-2:] + \".ndk\"\n            filename = web_address_year + month_name\n            if (\n                not os.path.exists(month_name + \".tar.bz2\")\n                and os.system(f\"wget -q --spider {filename}\") == 0\n            ):\n                if (\n                    os.system(f\"wget -q {filename}\") == 0\n                    and os.system(\n                        f\"tar -cjf {month_name}.tar.bz2 {month_name}\"\n                    )\n                    == 0\n                ):\n                    os.remove(month_name)\n                    print(f\"Successfully retrieved: {month_name}\")\n\n\ndef add_new_events(\n    comm,\n    count,\n    min_magnitude,\n    max_magnitude,\n    min_year=None,\n    max_year=None,\n    threshold_distance_in_km=50.0,\n    return_events=False,\n):\n    min_magnitude = float(min_magnitude)\n    max_magnitude = float(max_magnitude)\n\n    # Get the catalog.\n    cat = _read_GCMT_catalog(min_year=min_year, max_year=max_year)\n    # Filter with the magnitudes\n    cat = cat.filter(\n        \"magnitude >= %.2f\" % min_magnitude,\n        \"magnitude <= %.2f\" % max_magnitude,\n    )\n\n    # Filtering catalog to only contain events in the domain.\n    print(\"Filtering to only include events inside domain...\")\n    # Coordinates and the Catalog will have the same order!\n    temp_cat = Catalog()\n    coordinates = []\n    for event in cat:\n        org = event.preferred_origin() or event.origins[0]\n        if not comm.query.point_in_domain(\n            org.latitude, org.longitude, org.depth\n        ):\n            continue\n        temp_cat.events.append(event)\n        coordinates.append((org.latitude, org.longitude))\n    cat = temp_cat\n\n    chosen_events = []\n    if len(cat) == 0:\n        print(\n            \"No valid events were found. Consider your query parameters \"\n            \"and domain size and try again. Events might be inside\"\n            \" your buffer elements as well.\"\n        )\n        return\n\n    print(\"%i valid events remain. Starting selection process...\" % len(cat))\n\n    existing_events = comm.events.get_all_events().values()\n    # Get the coordinates of all existing events.\n    existing_coordinates = [\n        (_i[\"latitude\"], _i[\"longitude\"]) for _i in existing_events\n    ]\n    existing_origin_times = [_i[\"origin_time\"] for _i in existing_events]\n\n    # Special case handling in case there are no preexisting events.\n    if not existing_coordinates:\n        idx = random.randint(0, len(cat) - 1)\n\n        chosen_events.append(cat[idx])\n        del cat.events[idx]\n        existing_coordinates.append(coordinates[idx])\n        del coordinates[idx]\n\n        _t = cat[idx].preferred_origin() or cat[idx].origins[0]\n        existing_origin_times.append(_t.time)\n\n        count -= 1\n\n    while count > 0:\n        if not coordinates:\n            print(\"\\tNo events left to select from. Stopping here.\")\n            break\n        # Build kdtree and query for the point furthest away from any other\n        # point.\n        kdtree = SphericalNearestNeighbour(np.array(existing_coordinates))\n        distances = kdtree.query(np.array(coordinates), k=1)[0]\n        idx = np.argmax(distances)\n\n        event = cat[idx]\n        coords = coordinates[idx]\n        del cat.events[idx]\n        del coordinates[idx]\n\n        # Actual distance.\n        distance = EARTH_RADIUS * distances[idx]\n\n        if distance < threshold_distance_in_km:\n            print(\n                \"\\tNo events left with distance to the next closest event \"\n                \"of more than %.1f km. Stopping here.\"\n                % threshold_distance_in_km\n            )\n            break\n\n        # Make sure it did not happen within one day of an existing event.\n        # This should also filter out duplicates.\n        _t = event.preferred_origin() or event.origins[0]\n        origin_time = _t.time\n\n        if (\n            min([abs(origin_time - _i) for _i in existing_origin_times])\n            < 86400\n        ):\n            print(\n                \"\\tSelected event temporally to close to existing event. \"\n                \"Will not be chosen. Skipping to next event.\"\n            )\n            continue\n\n        print(\n            \"\\tSelected event with the next closest event being %.1f km \"\n            \"away.\" % distance\n        )\n\n        chosen_events.append(event)\n        existing_coordinates.append(coords)\n        count -= 1\n    print(\"Selected %i events.\" % len(chosen_events))\n    folder = os.path.join(comm.project.paths[\"root\"], \"tmp\")\n    os.mkdir(folder)\n    data_dir = comm.project.paths[\"eq_data\"]\n    event_paths = []\n    for event in chosen_events:\n        filename = os.path.join(folder, get_event_filename(event, \"GCMT\"))\n        Catalog(events=[event]).write(\n            filename, format=\"quakeml\", validate=True\n        )\n        asdf_filename = os.path.join(\n            data_dir,\n            get_event_filename(event, \"GCMT\").rsplit(\".\", 1)[0] + \".h5\",\n        )\n        ds = pyasdf.ASDFDataSet(asdf_filename, compression=\"gzip-3\")\n        ds.add_quakeml(filename)\n        event_paths.append(asdf_filename)\n        print(\"Written %s\" % (os.path.relpath(asdf_filename)))\n    shutil.rmtree(folder)\n    if return_events:\n        return event_paths\n\n\ndef get_subset_of_events(comm, count, events, existing_events=None):\n    \"\"\"\n    This function gets an optimally distributed set of events,\n    NO QA.\n    :param comm: LASIF communicator\n    :param count: number of events to choose.\n    :param events: list of event_names, from which to choose from. These\n    events must be known to LASIF\n    :param existing_events: list of events, that have been chosen already\n    and should thus be excluded from the selected options, but are also\n    taken into account when ensuring a good spatial distribution. The\n    function assumes that there are no common occurences between\n    events and existing events\n    :return: a list of chosen events.\n    \"\"\"\n    available_events = comm.events.list()\n\n    if len(events) < count:\n        raise LASIFError(\"Insufficient amount of events specified.\")\n    if not type(count) == int:\n        raise ValueError(\"count should be an integer value.\")\n    if count < 1:\n        raise ValueError(\"count should be at least 1.\")\n    for event in events:\n        if event not in available_events:\n            raise LASIFNotFoundError(f\"event : {event} not known to LASIF.\")\n\n    if existing_events is None:\n        existing_events = []\n    else:\n        for event in events:\n            if event in existing_events:\n                raise LASIFError(\n                    f\"event: {event} was existing already,\"\n                    f\"but still supplied to choose from.\"\n                )\n\n    cat = obspy.Catalog()\n    for event in events:\n        event_file_name = comm.waveforms.get_asdf_filename(\n            event, data_type=\"raw\"\n        )\n        with pyasdf.ASDFDataSet(event_file_name, mode=\"r\") as ds:\n            ev = ds.events[0]\n            # append event_name to comments, such that it can later be\n            # retrieved\n            ev.comments.append(event)\n            cat += ev\n\n    # Coordinates and the Catalog will have the same order!\n    coordinates = []\n    for event in cat:\n        org = event.preferred_origin() or event.origins[0]\n        coordinates.append((org.latitude, org.longitude))\n\n    chosen_events = []\n    existing_coordinates = []\n    for event in existing_events:\n        ev = comm.events.get(event)\n        existing_coordinates.append((ev[\"latitude\"], ev[\"longitude\"]))\n\n    # randomly start with one of the specified events\n    if not existing_coordinates:\n        idx = random.randint(0, len(cat) - 1)\n        chosen_events.append(cat[idx])\n        del cat.events[idx]\n        existing_coordinates.append(coordinates[idx])\n        del coordinates[idx]\n        count -= 1\n\n    while count:\n        if not coordinates:\n            print(\"\\tNo events left to select from. Stopping here.\")\n            break\n        # Build kdtree and query for the point furthest away from any other\n        # point.\n        kdtree = SphericalNearestNeighbour(np.array(existing_coordinates))\n        distances = kdtree.query(np.array(coordinates), k=1)[0]\n        idx = np.argmax(distances)\n\n        event = cat[idx]\n        coods = coordinates[idx]\n        del cat.events[idx]\n        del coordinates[idx]\n\n        chosen_events.append(event)\n        existing_coordinates.append(coods)\n        count -= 1\n\n    list_of_chosen_events = []\n    for ev in chosen_events:\n        list_of_chosen_events.append(ev.comments.pop())\n    if len(list_of_chosen_events) < count:\n        raise ValueError(\"Could not select a sufficient amount of events\")\n\n    return list_of_chosen_events\n\n\ndef get_random_mitchell_subset(comm, count, events, p_dict=None,\n                               existing_events=None,) -> list:\n    \"\"\"\n    This function gets an optimally distributed set of events,\n    NO QA.\n    :param comm: LASIF communicator\n    :param count: number of events to choose.\n    :param events: list of event_names, from which to choose from. These\n    events must be known to LASIF\n    :param existing_events: list of events, that have been chosen already\n    and should thus be excluded from the selected options, but are also\n    taken into account when ensuring a good spatial distribution. The\n    function assumes that there are no common occurences between\n    events and existing events\n    :param p_dict: initial distribution of probabilities that could be based\n    on event quality for example.. keys are events, and values are p vvalues.\n    :type p_dict: dict\n    :return: a list of chosen events.\n    \"\"\"\n    available_events = comm.events.list()\n\n    # work on a copy\n    events = events.copy()\n    if len(events) < count:\n        raise LASIFError(\"Insufficient amount of events specified.\")\n    if not type(count) == int:\n        raise ValueError(\"count should be an integer value.\")\n    if count < 1:\n        raise ValueError(\"count should be at least 1.\")\n    for event in events:\n        if event not in available_events:\n            raise LASIFNotFoundError(f\"event : {event} not known to LASIF.\")\n\n    if existing_events is None:\n        existing_events = []\n    else:\n        for event in events:\n            if event in existing_events:\n                raise LASIFError(\n                    f\"event: {event} was existing already,\"\n                    f\"but still supplied to choose from.\"\n                )\n\n    # Fill catalog\n    cat = obspy.Catalog()\n    for event in events:\n        event_file_name = comm.waveforms.get_asdf_filename(\n            event, data_type=\"raw\"\n        )\n        with pyasdf.ASDFDataSet(event_file_name, mode=\"r\") as ds:\n            ev = ds.events[0]\n            # append event_name to comments, such that it can later be\n            # retrieved\n            ev.comments.append(event)\n            cat += ev\n\n    # Coordinates and the Catalog will have the same order!\n    coordinates = []\n    for event in cat:\n        org = event.preferred_origin() or event.origins[0]\n        coordinates.append((org.latitude, org.longitude))\n\n    chosen_events = []\n    existing_coordinates = []\n    for event in existing_events:\n        ev = comm.events.get(event)\n        existing_coordinates.append((ev[\"latitude\"], ev[\"longitude\"]))\n\n    # randomly start with one of the specified events\n    if not existing_coordinates:\n        idx = random.randint(0, len(cat) - 1)\n        chosen_events.append(cat[idx])\n        del cat.events[idx]\n        del events[idx]\n        existing_coordinates.append(coordinates[idx])\n        del coordinates[idx]\n        count -= 1\n    while count:\n        if not coordinates:\n            print(\"\\tNo events left to select from. Stopping here.\")\n            break\n        # Build kdtree and query for the point furthest away from any other\n        # point.\n        kdtree = SphericalNearestNeighbour(np.array(existing_coordinates))\n        distances = kdtree.query(np.array(coordinates), k=1)[0]\n        # p_dict must contain values for all events, if given\n        if p_dict is None:\n            p_values = np.ones(len(events))\n        else:\n            p_values = []\n            for ev in events:\n                p_values.append(p_dict[ev])\n                if p_dict[ev] is None:\n                    print(ev, \"is none\")\n                    raise Exception(\"\")\n\n        p_values_comb = p_values * distances\n        p_values_comb /= np.sum(p_values_comb)\n\n        chosen_ev = list(np.random.choice(events, 1 ,\n                                          replace=False, p=p_values_comb))[0]\n        idx = events.index(chosen_ev)\n\n        event = cat[idx]\n        coords = coordinates[idx]\n\n        # clear chosen event\n        del events[idx]\n        del cat.events[idx]\n        del coordinates[idx]\n\n        chosen_events.append(event)\n        existing_coordinates.append(coords)\n        count -= 1\n\n    list_of_chosen_events = []\n    for ev in chosen_events:\n        list_of_chosen_events.append(ev.comments.pop())\n    if len(list_of_chosen_events) < count:\n        raise ValueError(\"Could not select a sufficient amount of events\")\n\n    return list_of_chosen_events\n", "repo_name": "cjschiller/SWP_Carls_kindergarden_sandbox", "sub_path": "lasif/tools/query_gcmt_catalog.py", "file_name": "query_gcmt_catalog.py", "file_ext": "py", "file_size_in_byte": 17940, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "46", "api": [{"api_name": "scipy.spatial.cKDTree", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "inspect.getfile", "line_number": 77, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 77, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 83, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 100, "usage_type": "call"}, {"api_name": "obspy.core.event.Catalog", "line_number": 106, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "obspy.read_events", "line_number": 111, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "obspy.read_events", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "inspect.getfile", "line_number": 126, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 134, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 135, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 155, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 164, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 165, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 177, "usage_type": "call"}, {"api_name": "os.system", "line_number": 180, "usage_type": "call"}, {"api_name": "os.system", "line_number": 181, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 186, "usage_type": "call"}, {"api_name": "obspy.core.event.Catalog", "line_number": 214, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 308, "usage_type": "call"}, {"api_name": "os.path", "line_number": 308, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 309, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "lasif.utils.get_event_filename", "line_number": 313, "usage_type": "call"}, {"api_name": "obspy.core.event.Catalog", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 317, "usage_type": "call"}, {"api_name": "os.path", "line_number": 317, "usage_type": "attribute"}, {"api_name": "lasif.utils.get_event_filename", "line_number": 319, "usage_type": "call"}, {"api_name": "pyasdf.ASDFDataSet", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 324, "usage_type": "call"}, {"api_name": "os.path", "line_number": 324, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 325, "usage_type": "call"}, {"api_name": "lasif.exceptions.LASIFError", "line_number": 348, "usage_type": "call"}, {"api_name": "lasif.exceptions.LASIFNotFoundError", "line_number": 355, "usage_type": "call"}, {"api_name": "lasif.exceptions.LASIFError", "line_number": 362, "usage_type": "call"}, {"api_name": "obspy.Catalog", "line_number": 367, "usage_type": "call"}, {"api_name": "pyasdf.ASDFDataSet", "line_number": 372, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 408, "usage_type": "call"}, {"api_name": "lasif.exceptions.LASIFError", "line_number": 452, "usage_type": "call"}, {"api_name": "lasif.exceptions.LASIFNotFoundError", "line_number": 459, "usage_type": "call"}, {"api_name": "lasif.exceptions.LASIFError", "line_number": 466, "usage_type": "call"}, {"api_name": "obspy.Catalog", "line_number": 472, "usage_type": "call"}, {"api_name": "pyasdf.ASDFDataSet", "line_number": 477, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 511, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 512, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 515, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 525, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 527, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 527, "usage_type": "attribute"}]}
{"seq_id": "39656422030", "text": "import torch\nfrom torch.nn import functional as F\nfrom tqdm import tqdm\nn_iters = 0\n\ndef train_fol_ego(epoch, args, fol_model, ego_pred_model, optimizer, train_gen, verbose=True):\n    fol_model.train()\n    ego_pred_model.train()\n\n    avg_fol_loss = 0\n    avg_ego_pred_loss = 0\n\n    loader = tqdm(train_gen, total=len(train_gen))\n    with torch.set_grad_enabled(True):\n        for batch_idx, data in enumerate(loader):\n            input_bbox, input_flow, input_ego_motion, target_bbox, target_ego_motion = data\n\n            # forward\n            ego_predictions = ego_pred_model(input_ego_motion)\n            fol_predictions = fol_model(input_bbox, input_flow, ego_predictions)\n\n            # compute loss\n            fol_loss = rmse_loss_fol(fol_predictions, target_bbox)\n            ego_pred_loss = rmse_loss_fol(ego_predictions, target_ego_motion)\n            loss_to_optimize = args.lambda_fol * fol_loss +  args.lambda_ego * ego_pred_loss\n\n            avg_fol_loss += fol_loss.item()\n            avg_ego_pred_loss += ego_pred_loss.item()\n\n            # optimize\n            optimizer.zero_grad()\n            loss_to_optimize.backward()\n            optimizer.step()\n\n            if verbose and batch_idx % 100 == 0:\n                print('Train Epoch: {} [{}/{} ({:.0f}%)]\\t FOL loss: {:.4f}\\t Ego pred loss: {:.4f}'.format(\n                    epoch, batch_idx * len(data), len(train_gen.dataset),\n                    100. * batch_idx / len(train_gen), fol_loss.item()/input_bbox.shape[0], ego_pred_loss.item()/input_ego_motion.shape[0]))\n        avg_fol_loss /= len(train_gen.dataset)\n        avg_ego_pred_loss /= len(train_gen.dataset)\n        avg_train_loss = avg_fol_loss + avg_ego_pred_loss\n        print('Train Epoch: {} [{}/{} ({:.0f}%)]\\t FOL loss: {:.4f}\\t Ego pred loss: {:.4f} Total: {:.4f}'.format(\n                    epoch, batch_idx * len(data), len(train_gen.dataset),\n                    100. * batch_idx / len(train_gen), avg_fol_loss, avg_ego_pred_loss, avg_train_loss))\n\n\n    return avg_train_loss, avg_fol_loss, avg_ego_pred_loss\n\ndef val_fol_ego(epoch, args, fol_model, ego_pred_model, val_gen, verbose=True):\n    fol_model.eval()\n    ego_pred_model.eval()\n\n    fol_loss = 0\n    ego_pred_loss = 0\n    loader = tqdm(val_gen, total=len(val_gen))\n    with torch.set_grad_enabled(False):\n        for batch_idx, data in enumerate(loader):\n            input_bbox, input_flow, input_ego_motion, target_bbox, target_ego_motion = data\n\n            # forward\n            ego_predictions = ego_pred_model(input_ego_motion)\n            fol_predictions = fol_model(input_bbox, input_flow, ego_predictions)\n\n            # compute loss\n            fol_loss += rmse_loss_fol(fol_predictions, target_bbox).item()\n            ego_pred_loss += rmse_loss_fol(ego_predictions, target_ego_motion).item()\n\n    fol_loss /= len(val_gen.dataset)\n    ego_pred_loss /= len(val_gen.dataset)\n    avg_val_loss = fol_loss + ego_pred_loss\n    if verbose:\n        print('\\nVal set: Average FOL loss: {:.4f}, Average Ego pred loss: {:.4f}, Total: {:.4f}\\n'.format(fol_loss, ego_pred_loss, avg_val_loss))\n\n    return avg_val_loss, fol_loss, ego_pred_loss\n\n\ndef rmse_loss_fol(x_pred, x_true):\n    L2_diff = torch.sqrt(torch.sum((x_pred - x_true)**2, dim=3))\n    L2_all_pred = torch.sum(L2_diff, dim=2)\n\n    L2_mean_pred = torch.mean(L2_all_pred, dim=1)\n    L2_mean_pred = torch.mean(L2_mean_pred, dim=0)\n\n    return L2_mean_pred\n", "repo_name": "Kim-Ha-Jeong/Capstone_model", "sub_path": "주행중/lib/utils/train_val_utils.py", "file_name": "train_val_utils.py", "file_ext": "py", "file_size_in_byte": 3418, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "tqdm.tqdm", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.set_grad_enabled", "line_number": 14, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.set_grad_enabled", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 82, "usage_type": "call"}]}
{"seq_id": "20481505059", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 22 19:35:17 2019\n\n@author: sarde\n\"\"\"\nfrom collections import deque\n#from skimage.color import rgb2gray\nimport numpy as np\nimport matplotlib.image as mpimg\nimport time\n\ndef bfs(graph, start, end):\n    \"\"\"\n    BFS is used as weights for all the edges are 1, in any case of neighborhood\n    or connectivity. \n    As we only need 1 shortest path between the start and end point, BFS algorithm\n    can be faster and space saving than say Dijkstra's algorithm which calculates \n    shortest distance to every other node.\n    \"\"\"\n    if start == end:\n        return [start]  #return start if start and end node are the same\n    visited = {start}   #add start to visited set\n    queue = deque([(start, [])])    #queue implementation using dequeue from collections\n                                    #start element and empty path is added to the queue\n    while queue:    #loop until queue is empty\n        current, path = queue.popleft() #pop current element and path from the queue\n        visited.add(current)    #add current element to visited\n        for neighbor in graph[current]: #loop through all neighbors of current element\n            if neighbor == end:\n                return path + [current, neighbor]   #if neighbor is equal to end, return the path as path+[current,neighbor]\n            if neighbor in visited:\n                continue    #If neighbor is visited, continue to next iteration                                 \n            queue.append((neighbor, path + [current]))  #append neighbor, new path to the queue. \n            visited.add(neighbor)    #add neighbor to visited\n    return None  # no path found.\n\nif __name__ == '__main__':\n    img = np.array([[3,1,2,1],[2,2,0,2],[1,2,1,1],[1,0,1,2]])   #example 1. for part a and b. \n#    img = np.array([[2,3,1,0,1],[0,2,5,1,2],[0,4,1,3,1],[0,1,2,3,1]])\n#    img = mpimg.imread('lena.jpg')  #imread image for part c. \n    #img = rgb2gray(img)\n    img1 = img[:,:]\n    v = [0,1] #V_set for example 1. part a and b\n#    v = {img1[i][j] for i in range(img1.shape[0]) for j in range(img1.shape[1]) if i%2==0 and j%2==0} #create random predefined v set \n    ht,wd = img1.shape    #get height and width of the image\n#    img_graph = np.zeros((ht,wd),dtype='uint8') #debug\n    neighbor_type = input('Choose path \\n1. 4 path\\n2. 8 path\\n3. m path\\n')   #take users input for connectivity \n    promptp = 'Set start point as (x,y) within the image dimensions of {} x {} - 0 based indexing\\t'.format(ht,wd)\n    promptq = 'Set end point as (x,y) within the image dimensions of {} x {} - 0 based indexing\\t'.format(ht,wd)\n    px = qx = py = qy = -1  #initialize start and end points \n    while px<0 or qx<0 or py<0 or qy<0 or px>ht or qx>ht or qy>wd or py>wd: #handle start, end pixels out of bounds\n        px, py = tuple(int(x.strip()) for x in  input(promptp).split(','))  #get user input for px, py\n        qx, qy = tuple(int(x.strip()) for x in  input(promptq).split(','))  #get user input for px, py\n    start_time = time.time()    #start to record time\n    graph = {}  #initialize adjacency list as a dictionary \n    #creation of adjacency list\n    img_ex = np.full((ht+2,wd+2), -1) #extended image to handle edge pixel cases\n    img_ex[1:-1,1:-1] = img1   #copy original image in the center of the extended image.\n    for i in range(ht): \n        for j in range(wd): #traverse through original image\n            if img1[i][j] in v: #if the pixels are in set v, add keys to dictionary \n                graph.update({(i,j):set([])})   \n                try:\n                    if neighbor_type == '4':    #if neighbor type 4\n                        adjacents = {(-1,0),(0,1),(1,0),(0,-1)} #4 neighbors defined by the following list\n                        for n1, n2 in adjacents:\n                            if((i+n1!=i or j+n2!=j) and (0<=i+n1<=ht) \\\n                               and (0<=i+n2<=wd) and (i+n1!=i+n2) and (i+n1!=-(i+n2))\\\n                               and img_ex[i+1+n1][j+1+n2] in v):    #all conditions to check validity of 4 neighbors\n                                graph[(i,j)].add((i+n1,j+n2))            \n                    if neighbor_type == '8':    #if 8 neighbors specified by the user \n                        for n1 in range(-1,2): \n                            for n2 in range(-1, 2):\n                                if((i+n1!=i or j+n2!=j) and (0<=i+n1<=ht) \\\n                                   and (0<=i+n2<=wd) and img_ex[i+1+n1][j+1+n2] in v):\n                                    graph[(i,j)].add((i+n1,j+n2))   #all conditions to check validity \n                    if neighbor_type == 'm':    #if neighbor type - m neighbors \n                        n4 = [(-1,0),(0,1),(1,0),(0,-1)]\n                        diag = [(-1,-1),(-1,1),(1,-1),(1,1)]\n                        for d in range(len(diag)):  #check diagonals first\n                            d1,d2 = diag[d]\n                            if((0<=i+d1<=ht) and (0<=i+d2<=wd)\\\n                               and img_ex[i+1+d1][j+1+d2] in v):\n                                if(d==0 and (img_ex[i+1+d1+n4[1][0]][j+1+d2+n4[1][1]] not in v and d==0 \\\n                                             and (img_ex[i+1+d1+n4[2][0]][j+1+d2+n4[2][1]] not in v))):   #check their 4 neighbors\n                                    graph[(i,j)].add((i+d1,j+d2))\n                                    continue\n                                if(d==1 and (img_ex[i+1+d1+n4[2][0]][j+1+d2+n4[2][1]] not in v) and\\\n                                   d==1 and (img_ex[i+1+d1+n4[3][0]][j+1+d2+n4[3][1]] not in v)):\n                                    graph[(i,j)].add((i+d1,j+d2))\n                                    continue\n                                if(d==2 and (img_ex[i+1+d1+n4[0][0]][j+1+d2+n4[0][1]] not in v) and\\\n                                   d==2 and (img_ex[i+1+d1+n4[1][0]][j+1+d2+n4[1][1]] not in v)):\n                                    graph[(i,j)].add((i+d1,j+d2))\n                                    continue\n                                if(d==3 and (img_ex[i+1+d1+n4[3][0]][j+1+d2+n4[3][1]] not in v) and\\\n                                   d==3 and (img_ex[i+1+d1+n4[0][0]][j+1+d2+n4[0][1]] not in v)):\n                                    graph[(i,j)].add((i+d1,j+d2))\n                                    continue\n                            else:\n                                adjacents = {(-1,0),(0,1),(1,0),(0,-1)} #4 neighbors defined by the following list\n                                for n1, n2 in adjacents:\n                                    if((i+n1!=i or j+n2!=j) and (0<=i+n1<=ht) \\\n                                       and (0<=i+n2<=wd) and (i+n1!=i+n2) and (i+n1!=-(i+n2))\\\n                                       and img_ex[i+1+n1][j+1+n2] in v):    #all conditions to check validity of 4 neighbors\n                                        graph[(i,j)].add((i+n1,j+n2))\n                                \n                except IndexError:\n                    continue              \n\n    path = bfs(graph, (px,py), (qx, qy) ) #use bfs algorithm for searching from p to q\n    if path:\n        print(path,'\\nLength of path is ', len(path)-1)  #return path and length of the path\n    else:\n        print('no path found')        \n    print(\"--- %s seconds ---\" % (time.time() - start_time))", "repo_name": "sardesaim/Image_Processing_Projects", "sub_path": "1_SmoothnessPrior_ShortestPath/project01_part2_shortestpath.py", "file_name": "project01_part2_shortestpath.py", "file_ext": "py", "file_size_in_byte": 7305, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "collections.deque", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 58, "usage_type": "call"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}]}
{"seq_id": "15688048768", "text": "from PIL import Image\nimport os, os.path\nimport numpy as np\nimport keras\n\n\ndef load_images(dir_path, files_ext):\n    images = []\n    valid_extensions = files_ext\n    for f in os.listdir(dir_path):\n        ext = os.path.splitext(f)[1]\n        if ext.lower() not in valid_extensions:\n            continue\n        img = Image.open(os.path.join(dir_path, f))\n        images.append(np.array(img))\n        img.close()\n\n    return images\n\ndef load_train_data(x_dir_path, y_dir_path, files_ext):\n    x_images = load_images(x_dir_path, files_ext)\n    y_images = load_images(y_dir_path, files_ext)\n\n    y_and_quad_images = []\n\n    # for img in x_images:\n", "repo_name": "niros7/butman2", "sub_path": "matala3/data_loader.py", "file_name": "data_loader.py", "file_ext": "py", "file_size_in_byte": 644, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.listdir", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 14, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}]}
{"seq_id": "43628938801", "text": "from rest_framework import serializers\nfrom django.contrib.auth.models import User\nfrom django.core.mail import EmailMessage\nfrom django.template.loader import render_to_string\nfrom django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode\nfrom django.utils.encoding import force_bytes, force_text\nfrom django.contrib.auth.tokens import default_token_generator\nfrom .models import Farmer, Buyer, Farm, Batch, Crop\nfrom kilola import email_credentials\n\n\nclass UserSerializer(serializers.ModelSerializer):\n    type = serializers.SerializerMethodField('get_type')\n\n    def get_type(self, data):\n        has_farmer_profile = len(data.farmer_set.all()) > 0\n        has_buyer_profile = len(data.buyer_set.all()) > 0\n        if has_farmer_profile and not has_buyer_profile:\n            return 'farmer'\n        if has_buyer_profile and not has_farmer_profile:\n            return 'buyer'\n        if has_farmer_profile and has_buyer_profile:\n            return 'buymer'\n        else:\n            return ''\n\n    class Meta:\n        model = User\n        fields = [\n            \"id\",\n            \"last_login\",\n            \"is_superuser\",\n            \"username\",\n            \"first_name\",\n            \"last_name\",\n            \"email\",\n            \"is_staff\",\n            \"is_active\",\n            \"date_joined\",\n            \"groups\",\n            \"user_permissions\",\n            \"type\"\n        ]\n\n\nclass SignUpSerializer(serializers.Serializer):\n    username = serializers.CharField(max_length=200)\n    first_name = serializers.CharField(max_length=200)\n    last_name = serializers.CharField(max_length=200)\n    password = serializers.CharField(max_length=500)\n    email = serializers.EmailField()\n    type = serializers.CharField(\n        max_length=200,\n        help_text=\"User Type. Can be either of the string\\\n        values 'farmer' or 'buyer'\"\n    )\n    status = serializers.CharField(read_only=True)\n\n    def validate(self, data):\n        # username = data['username']\n        # first_name = data['first_name']\n        # last_name = data['last_name']\n        # email = data['email']\n        type = data['type']\n        if type not in ['farmer', 'buyer']:\n            raise serializers.ValidationError(\n                \"Attribute 'type' must be either 'farmer' or 'buyer'\"\n            )\n        return super().validate(data)\n\n    def create(self, validated_data):\n        username = validated_data['username']\n        first_name = validated_data['first_name']\n        last_name = validated_data['last_name']\n        password = validated_data['password']\n        email = validated_data['email']\n        type = validated_data['type']\n        user = User(\n            username=username,\n            first_name=first_name,\n            last_name=last_name,\n            email=email,\n            password=password,\n            is_active=False\n        )\n        user.set_password(password)\n        user.save()\n        mail_subject = 'Activate your Kilola account.'\n        message = render_to_string('account_activation_email.html', {\n            'user': user,\n            'domain': 'kilola-beta.portacode.com',\n            'token': default_token_generator.make_token(user),\n            'uid': urlsafe_base64_encode(force_bytes(user.id))\n        })\n        email = EmailMessage(\n            mail_subject,\n            message,\n            email_credentials.user,\n            to=[email]\n        )\n        email.send(fail_silently=False)\n        validated_data['status'] = 'SUCCESS'\n        if type == 'farmer':\n            Farmer.objects.create(user=user)\n        elif type == 'buyer':\n            Buyer.objects.create(user=user)\n        return validated_data\n\n\nclass ConfirmEmailSerializer(serializers.Serializer):\n    uidb64 = serializers.CharField(max_length=200)\n    token = serializers.CharField(max_length=200)\n    status = serializers.CharField(read_only=True)\n\n    def create(self, data):\n        uidb64 = data['uidb64']\n        token = data['token']\n        data['status'] = 'FAILED'\n        try:\n            uid = force_text(urlsafe_base64_decode(uidb64))\n            user = User.objects.get(pk=uid)\n        except(TypeError, ValueError, OverflowError, User.DoesNotExist):\n            user = None\n            serializers.ValidationError('Invalid activation link')\n        if user is not None and default_token_generator\\\n                .check_token(user, token):\n            user.is_active = True\n            user.save()\n            data['status'] = 'SUCCESS'\n        else:\n            serializers.ValidationError('Invalid activation link')\n        return data\n\n\nclass FarmerSerializer(serializers.ModelSerializer):\n    user = serializers.SlugRelatedField(\n        read_only=True,\n        slug_field='username'\n    )\n\n    class Meta:\n        model = Farmer\n        fields = ['user']\n\n\nclass FarmSerializer(serializers.ModelSerializer):\n    farmer = FarmerSerializer(read_only=True)\n\n    class Meta:\n        model = Farm\n        fields = ['id', 'name', 'farmer', 'location', 'size']\n\n\nclass BatchSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Batch\n        fields = ['id', 'crop', 'farm', 'area', 'weight', 'planting_date',\n                  'harvesting_date', 'description']\n\n\nclass CropSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Crop\n        fields = '__all__'\n\n\nclass BatchDetailsSerializer(serializers.ModelSerializer):\n    crop = CropSerializer(read_only=True)\n    farm = FarmSerializer(read_only=True)\n\n    class Meta:\n        model = Batch\n        fields = ['id', 'crop', 'farm', 'area', 'weight', 'planting_date',\n                  'harvesting_date', 'description']\n", "repo_name": "meena-erian/kilola-beta", "sub_path": "kilola/api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 5633, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 13, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 47, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 48, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 49, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 50, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 50, "usage_type": "name"}, {"api_name": "rest_framework.serializers.EmailField", "line_number": 51, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 51, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 52, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 52, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 57, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 57, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 66, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 78, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 89, "usage_type": "call"}, {"api_name": "django.contrib.auth.tokens.default_token_generator.make_token", "line_number": 92, "usage_type": "call"}, {"api_name": "django.contrib.auth.tokens.default_token_generator", "line_number": 92, "usage_type": "name"}, {"api_name": "django.utils.http.urlsafe_base64_encode", "line_number": 93, "usage_type": "call"}, {"api_name": "django.utils.encoding.force_bytes", "line_number": 93, "usage_type": "call"}, {"api_name": "django.core.mail.EmailMessage", "line_number": 95, "usage_type": "call"}, {"api_name": "kilola.email_credentials.user", "line_number": 98, "usage_type": "attribute"}, {"api_name": "kilola.email_credentials", "line_number": 98, "usage_type": "name"}, {"api_name": "models.Farmer.objects.create", "line_number": 104, "usage_type": "call"}, {"api_name": "models.Farmer.objects", "line_number": 104, "usage_type": "attribute"}, {"api_name": "models.Farmer", "line_number": 104, "usage_type": "name"}, {"api_name": "models.Buyer.objects.create", "line_number": 106, "usage_type": "call"}, {"api_name": "models.Buyer.objects", "line_number": 106, "usage_type": "attribute"}, {"api_name": "models.Buyer", "line_number": 106, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 110, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 110, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 111, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 111, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 112, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 112, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 113, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 113, "usage_type": "name"}, {"api_name": "django.utils.encoding.force_text", "line_number": 120, "usage_type": "call"}, {"api_name": "django.utils.http.urlsafe_base64_decode", "line_number": 120, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 121, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 121, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.DoesNotExist", "line_number": 122, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 122, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 124, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 124, "usage_type": "name"}, {"api_name": "django.contrib.auth.tokens.default_token_generator.check_token", "line_number": 125, "usage_type": "call"}, {"api_name": "django.contrib.auth.tokens.default_token_generator", "line_number": 125, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 131, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 131, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 135, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 135, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SlugRelatedField", "line_number": 136, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 136, "usage_type": "name"}, {"api_name": "models.Farmer", "line_number": 142, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 146, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 146, "usage_type": "name"}, {"api_name": "models.Farm", "line_number": 150, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 154, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 154, "usage_type": "name"}, {"api_name": "models.Batch", "line_number": 156, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 161, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 161, "usage_type": "name"}, {"api_name": "models.Crop", "line_number": 163, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 167, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 167, "usage_type": "name"}, {"api_name": "models.Batch", "line_number": 172, "usage_type": "name"}]}
{"seq_id": "42453212544", "text": "from rich.console import Console\n\nfrom dbt_coves.tasks.base import NonDbtBaseTask\n\nfrom .dbt import SetupDbtTask\nfrom .git import SetupGitTask\nfrom .pre_commit import SetupPrecommitTask\nfrom .ssh import SetupSSHTask\n\nconsole = Console()\n\n\nclass DbtCovesSetupException(Exception):\n    pass\n\n\nclass SetupTask(NonDbtBaseTask):\n    \"\"\"\n    Task that code-gen dbt resources\n    \"\"\"\n\n    tasks = [\n        SetupGitTask,\n        SetupDbtTask,\n        SetupSSHTask,\n        SetupPrecommitTask,\n    ]\n\n    key_column_with = 20\n    value_column_with = 50\n    arg_parser = None\n\n    @classmethod\n    def register_parser(cls, sub_parsers, base_subparser):\n        ext_subparser = sub_parsers.add_parser(\n            \"setup\",\n            parents=[base_subparser],\n            help=\"Set up project components (git, dbt, vscode, sqlfluff, pre-commit, etc)\",\n        )\n        ext_subparser.set_defaults(cls=cls, which=\"setup\")\n        sub_parsers = ext_subparser.add_subparsers(title=\"dbt-coves setup commands\", dest=\"task\")\n        # Register a separate sub parser for each sub task.\n        [x.register_parser(sub_parsers, base_subparser) for x in cls.tasks]\n        cls.arg_parser = ext_subparser\n        return ext_subparser\n", "repo_name": "datacoves/dbt-coves", "sub_path": "dbt_coves/tasks/setup/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1214, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 180, "dataset": "github-code", "pt": "46", "api": [{"api_name": "rich.console.Console", "line_number": 10, "usage_type": "call"}, {"api_name": "dbt_coves.tasks.base.NonDbtBaseTask", "line_number": 17, "usage_type": "name"}, {"api_name": "git.SetupGitTask", "line_number": 23, "usage_type": "name"}, {"api_name": "dbt.SetupDbtTask", "line_number": 24, "usage_type": "name"}, {"api_name": "ssh.SetupSSHTask", "line_number": 25, "usage_type": "name"}, {"api_name": "pre_commit.SetupPrecommitTask", "line_number": 26, "usage_type": "name"}]}
{"seq_id": "11020027208", "text": "import configparser\nimport requests, time\nfrom multiprocessing import Process, SimpleQueue\nfrom tools import log as lo\nfrom custom import functions\n\n\nlog = lo(\"Main\", \"main.log\")\nconfig = configparser.ConfigParser()\nconfig.read('setting.conf')\n\nargs = config['site']\nURL  = args['url']\nPATH = args['result_path']\nTIMEOUT = int( args['timeout'] )\n\ndef comma(float):\n    res = \"{}\".format( float )\n    res = res.replace(\".\",\",\")\n    return res\n\ndef get_data():\n    response = requests.get( URL )\n    if response.status_code == 200:\n        return response.json()\n    raise ValueError\n\ndef wrapper_run(*args, **kwargs):\n\n    queue    = args[0]\n    function = args[1]\n    data = args[2]\n    index  = args[3]\n\n    start_time = time.time()\n    \n    res = function(data)\n\n    end_time = time.time()\n    end_time = end_time - start_time\n\n    log.info( \"Run time: {:.10f} sec for function {}\".format( end_time, index ) )\n    queue.put( ( res, index ) )\n\n\n\n\ndef save( data ):\n    data = sorted( data, key=lambda x : x[1])\n    res = [comma(x[0]) for x in data]\n    string = \";\".join( res )\n    with open(PATH, \"w\", encoding=\"utf8\") as f:\n        f.write( string )\n    log.info( \"Save {} in {}\".format( string, PATH ) )\n\n\ndef _main():\n    q = SimpleQueue()\n    data = get_data()\n    processes = []\n\n    for e, func in enumerate( functions , start=1):\n        processes.append( Process(target=wrapper_run, args =  (q, func, data, e ) ) )\n\n    [x.start() for x in processes]\n    [x.join() for x in processes]\n    results = []\n    while True:\n        items = q.get()\n        results.append( items )\n        if q.empty():\n            break\n    save( results )\n\n\ndef main():\n    _main()\n\ndef work():\n    while True:\n        try:\n            main()\n        except Exception as Er:\n            log.error(\"Error {}\".format(str(Er)) ,exc_info=True)\n        finally:\n            log.info(\"Sleep {}\".format(TIMEOUT))\n            time.sleep( TIMEOUT )\n\n\nif __name__ == '__main__':\n    # main()\n    work()", "repo_name": "repen/92930809", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1980, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "tools.log", "line_number": 8, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 39, "usage_type": "call"}, {"api_name": "multiprocessing.SimpleQueue", "line_number": 58, "usage_type": "call"}, {"api_name": "custom.functions", "line_number": 62, "usage_type": "argument"}, {"api_name": "multiprocessing.Process", "line_number": 63, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 87, "usage_type": "call"}]}
{"seq_id": "38324293672", "text": "import gym\n\nfrom agent import Agent\nfrom environment import Environment\nfrom mdp.MDP import MDP\nfrom strategies.deepqlearning import DeepQLearning\nfrom strategies.doubledeepqlearning import DoubleDeepQLearning\nfrom strategies.montecarlostrategy import MonteCarloStrategy\nfrom strategies.nstepqlearnstrategy import NStepQLearnStrategy\nfrom strategies.qlearnstrategy import QLearnStrategy\nfrom strategies.valueiteration import ValueIteration\n\n\ndef run_frozenlake():\n    # region Frozenlake\n    env = gym.make(\"FrozenLake-v0\")\n    # env = gym.make(\"CartPole-v0\")\n    environment = Environment(env)\n\n    qlearnstrat = QLearnStrategy(learning_rate=0.85,\n                                 expl_decay_rate=0.01,\n                                 discount_factor=0.95,\n                                 expl_prob_max=1,\n                                 expl_prob_min=0.0001,\n                                 environment=environment)\n\n    nstepqlearnstrat = NStepQLearnStrategy(learning_rate=0.8,\n                                           expl_decay_rate=0.01,\n                                           discount_factor=0.95,\n                                           expl_prob_max=1,\n                                           expl_prob_min=0.0001,\n                                           environment=environment,\n                                           steps_amount=10)\n\n    montecarlostrat = MonteCarloStrategy(learning_rate=0.8,\n                                         expl_decay_rate=0.01,\n                                         discount_factor=0.95,\n                                         expl_prob_max=1,\n                                         expl_prob_min=0.0001,\n                                         environment=environment)\n\n    mdp = MDP(environment.observation_space_size, environment.action_space_size)\n    valueiteration = ValueIteration(mdp=mdp,\n                                    precision=0.01,\n                                    discount_factor=0.95,\n                                    expl_prob_max=1,\n                                    expl_prob_min=0.0000001,\n                                    expl_decay_rate=0.002,\n                                    environment=environment,\n                                    learning_rate=0.8)\n\n    agent = Agent(environment, qlearnstrat)\n    agent.learn(2001)\n    # endregion\n\n\ndef run_cartpole():\n    # region cartpole (DQN)\n    env_cp = gym.make(\"CartPole-v0\")\n    environment = Environment(env_cp)\n\n    deepqlearning = DeepQLearning(batch_size=32, update_interval=8, learning_rate=0.001, expl_decay_rate=0.995,\n                                  discount_factor=0.95, expl_prob_max=1,\n                                  expl_prob_min=0.0000001, environment=environment, max_experience_size=1000)\n    doubledeepqlearning = DoubleDeepQLearning(batch_size=32, update_interval=8, learning_rate=0.001,\n                                              expl_decay_rate=0.995,\n                                              discount_factor=0.95, expl_prob_max=1,\n                                              expl_prob_min=0.0000001, environment=environment,\n                                              max_experience_size=1000)\n    agent = Agent(environment, doubledeepqlearning)\n    agent.learn(1001)\n    # endregion\n\n\ndef main():\n    run_frozenlake()\n    # run_cartpole()\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "wpessers/RLFramework", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3381, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "gym.make", "line_number": 16, "usage_type": "call"}, {"api_name": "environment.Environment", "line_number": 18, "usage_type": "call"}, {"api_name": "strategies.qlearnstrategy.QLearnStrategy", "line_number": 20, "usage_type": "call"}, {"api_name": "strategies.nstepqlearnstrategy.NStepQLearnStrategy", "line_number": 27, "usage_type": "call"}, {"api_name": "strategies.montecarlostrategy.MonteCarloStrategy", "line_number": 35, "usage_type": "call"}, {"api_name": "mdp.MDP", "line_number": 42, "usage_type": "name"}, {"api_name": "mdp.MDP.MDP", "line_number": 42, "usage_type": "call"}, {"api_name": "environment.observation_space_size", "line_number": 42, "usage_type": "attribute"}, {"api_name": "environment.action_space_size", "line_number": 42, "usage_type": "attribute"}, {"api_name": "strategies.valueiteration.ValueIteration", "line_number": 43, "usage_type": "call"}, {"api_name": "mdp.MDP", "line_number": 43, "usage_type": "name"}, {"api_name": "agent.Agent", "line_number": 52, "usage_type": "call"}, {"api_name": "agent.learn", "line_number": 53, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 59, "usage_type": "call"}, {"api_name": "environment.Environment", "line_number": 60, "usage_type": "call"}, {"api_name": "strategies.deepqlearning.DeepQLearning", "line_number": 62, "usage_type": "call"}, {"api_name": "strategies.doubledeepqlearning.DoubleDeepQLearning", "line_number": 65, "usage_type": "call"}, {"api_name": "agent.Agent", "line_number": 70, "usage_type": "call"}, {"api_name": "agent.learn", "line_number": 71, "usage_type": "call"}]}
{"seq_id": "38265479693", "text": "import os\nimport pika\nimport sys\nimport logging as logger\nfrom util.http_status_server import HttpHealthServer\nfrom util.task_args import get_input_channel, get_output_channel, get_name, get_reverse_string\n\nlogger.basicConfig(level=logger.INFO)\nHttpHealthServer.run_thread()\n\nhost = os.environ['SPRING_RABBITMQ_HOST']\nport = int(os.environ['SPRING_RABBITMQ_PORT'])\n\nlogger.info(\"intput: {}\".format(get_input_channel()))\nlogger.info(\"output: {}\".format(get_output_channel()))\nlogger.info(\"name: {}\".format(get_name()))\n\ninput = \"{0}.{1}\".format(get_input_channel(), get_name())\noutput = \"{0}.{1}\".format(get_output_channel(), get_name())\n\n# Consumer\nconsumer = pika.BlockingConnection(pika.ConnectionParameters(host=host, port=port))\nchannel_consumer = consumer.channel()\nchannel_consumer.queue_declare(queue=input, durable=True)\n# Consumer\n\n# Producer\nproducer = pika.BlockingConnection(pika.ConnectionParameters(host=host, port=port))\nchannel_producer = producer.channel()\nchannel_producer.queue_declare(queue=output, durable=True)\n# Producer\n\n\ndef on_message(ch, method, properties, body):\n    # print(method_frame.delivery_tag)\n    logger.info(\" [x] Received %r\" % body)\n    channel_producer.basic_publish(exchange='',\n                                   routing_key=output,\n                                   body=get_reverse_string(body))\n\n\nchannel_consumer.basic_consume(queue=input,\n                               auto_ack=True,\n                               on_message_callback=on_message)\ntry:\n    channel_consumer.start_consuming()\nexcept KeyboardInterrupt:\n    raise\n# channel.stop_consuming()\n# connection.close()\n", "repo_name": "petrospgithub/python-processor", "sub_path": "python_processor.py", "file_name": "python_processor.py", "file_ext": "py", "file_size_in_byte": 1626, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 8, "usage_type": "attribute"}, {"api_name": "util.http_status_server.HttpHealthServer.run_thread", "line_number": 9, "usage_type": "call"}, {"api_name": "util.http_status_server.HttpHealthServer", "line_number": 9, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 14, "usage_type": "call"}, {"api_name": "util.task_args.get_input_channel", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 15, "usage_type": "call"}, {"api_name": "util.task_args.get_output_channel", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 16, "usage_type": "call"}, {"api_name": "util.task_args.get_name", "line_number": 16, "usage_type": "call"}, {"api_name": "util.task_args.get_input_channel", "line_number": 18, "usage_type": "call"}, {"api_name": "util.task_args.get_name", "line_number": 18, "usage_type": "call"}, {"api_name": "util.task_args.get_output_channel", "line_number": 19, "usage_type": "call"}, {"api_name": "util.task_args.get_name", "line_number": 19, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 22, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 22, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 28, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 36, "usage_type": "call"}, {"api_name": "util.task_args.get_reverse_string", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "75325152778", "text": "from django.utils.translation import ugettext_lazy as _\n\nfrom core.errors import Error\n\n\nclass PluginError(Error):\n    status_code = 400\n    code = 3310001\n    name = _(\"插件模块错误\")\n    message_tpl = _(\"插件模块错误：{msg}\")\n\n\nclass PluginParseError(PluginError):\n    code = 3310002\n    name = _(\"插件包解析错误\")\n    message_tpl = _(\"插件包解析错误: {msg}\")\n\n\nclass PluginIDExist(PluginError):\n    status_code = 200\n    code = 3310003\n    name = _(\"插件ID已存在\")\n    message_tpl = _(\"插件ID错误: 已存在的插件ID({msg})\")\n\n\nclass PluginIDNotExist(PluginParseError):\n    code = 3310004\n    name = _(\"插件ID不存在\")\n    message_tpl = _(\"插件ID错误: 不存在的plugin_id\")\n\n\nclass MakePackageError(PluginParseError):\n    code = 3310005\n    name = _(\"打包失败\")\n    message_tpl = _(\"打包失败: {msg}\")\n\n\nclass RegisterPackageError(PluginParseError):\n    code = 3310006\n    name = _(\"注册插件失败\")\n    message_tpl = _(\"注册插件失败: {msg}\")\n\n\nclass PluginVersionNotExist(PluginParseError):\n    code = 3310007\n    name = _(\"插件版本不存在\")\n    message_tpl = _(\"插件版本不存在: 不存在的插件版本\")\n\n\nclass SignatureTypeError(PluginError):\n    code = 3310008\n    name = _(\"插件签名格式无效\")\n    message_tpl = _(\"插件签名解析失败，无效的签名格式\")\n\n\nclass SignatureProtocolNotExist(PluginError):\n    code = 3310009\n    name = _(\"插件签名协议不存在\")\n    message_tpl = _(\"{plugin}插件签名协议: {protocol} 不存在\")\n\n\nclass SignatureNotSupported(PluginError):\n    code = 3310010\n    name = _(\"不支持签名的插件类型\")\n    message_tpl = _(\"不支持签名的插件类型: {plugin_type}\")\n\n\nclass EditPermissionDenied(PluginError):\n    code = 3310011\n    name = _(\"无插件编辑权限\")\n    message_tpl = _(\"编辑失败！无插件编辑权限: {plugin_id}\")\n\n\nclass DeletePermissionDenied(PluginError):\n    code = 3310012\n    name = _(\"无插件删除权限\")\n    message_tpl = _(\"删除失败！无插件删除权限: {plugin_id}\")\n\n\nclass UnsupportedPluginTypeError(PluginError):\n    code = 3310013\n    name = _(\"不支持的插件类型\")\n    message_tpl = _(\"不支持的插件类型: {plugin_type}\")\n\n\nclass RelatedItemsExist(PluginError):\n    code = 3310014\n    name = _(\"插件存在关联项\")\n    message_tpl = _(\"插件存在关联项: {msg}\")\n\n\nclass ExportPluginError(PluginError):\n    code = 3310015\n    name = _(\"插件导出错误\")\n    message_tpl = _(\"插件导出错误: {msg}\")\n\n\nclass RemoteCollectError(PluginError):\n    code = 3310016\n    name = _(\"是否支持远程采集冲突\")\n    message_tpl = _(\"是否支持远程采集冲突: {msg}\")\n\n\nclass ExportPluginTimeout(PluginError):\n    code = 3310017\n    name = _(\"节点管理导出超时\")\n    message_tpl = _(\"节点管理导出超时，请联系管理员检查后台环境\")\n\n\nclass NodeManDeleteError(PluginError):\n    code = 3310018\n    name = _(\"节点管理删除插件失败\")\n    message_tpl = _(\"节点管理删除插件失败，请联系管理员检查后台环境\")\n\n\nclass BizChangedError(PluginError):\n    code = 3310018\n    name = _(\"插件业务切换错误\")\n    message_tpl = _(\"不支持单业务之间的互相切换\")\n\n\nclass SNMPMetricNumberError(PluginError):\n    code = 3310020\n    name = _(\"SNMP指标设置错误\")\n    message_tpl = _(\"SNMP设置指标数量超过{snmp_max_metric_num}，请删减非必要指标\")\n\n\nclass MetricNumberError(PluginError):\n    code = 3310021\n    name = _(\"指标设置错误\")\n    message_tpl = _(\"设置指标数量超过{max_metric_num}，请删减非必要指标\")\n\n\nclass PluginIDFormatError(PluginError):\n    status_code = 200\n    code = 3310022\n    name = _(\"插件ID格式不规范\")\n    message_tpl = _(\"插件ID格式错误: {msg}\")\n\n\nclass ParsingDataError(PluginError):\n    code = 3310023\n    name = _(\"解析数据错误\")\n    message_tpl = _(\"解析节点管理返回数据异常，错误原因可能为：1. 插件采集的指标数量过多，2. 插件采集的数据量过大\")\n", "repo_name": "TencentBlueKing/bk-monitor", "sub_path": "bkmonitor/core/errors/plugin.py", "file_name": "plugin.py", "file_ext": "py", "file_size_in_byte": 4066, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "46", "api": [{"api_name": "core.errors.Error", "line_number": 6, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 9, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 10, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 15, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 16, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 23, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 28, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 29, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 34, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 35, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 40, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 41, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 46, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 47, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 52, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 53, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 58, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 59, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 64, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 65, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 70, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 71, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 76, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 77, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 82, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 83, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 88, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 89, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 94, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 95, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 100, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 101, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 106, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 107, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 112, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 113, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 118, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 119, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 124, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 125, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 130, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 131, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 137, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 138, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 143, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 144, "usage_type": "call"}]}
{"seq_id": "9387721669", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n@Date     : 2020/11/16 17:49:07\n@Author   : milier00\n@FileName : TipApproach.py\n\"\"\"\nimport sys\nsys.path.append(\"../ui/\")\nsys.path.append(\"../MainMenu/\")\nsys.path.append(\"../Setting/\")\nsys.path.append(\"../Model/\")\nsys.path.append(\"../TipApproach/\")\nsys.path.append(\"../Scan/\")\nsys.path.append(\"../Etest/\")\nfrom PyQt5.QtWidgets import QApplication , QWidget , QDesktopWidget, QMessageBox\nfrom PyQt5.QtCore import pyqtSignal\nfrom TipApproach_ui import Ui_TipApproach\nimport conversion as cnv\n\nclass myTipApproach(QWidget, Ui_TipApproach):\n    close_signal = pyqtSignal()\n    stop_signal = pyqtSignal()\n    mode_signal = pyqtSignal(int, bool)\n    giant_signal = pyqtSignal(int, int, int, int, int, int)\n    approach_signal = pyqtSignal(int, int, int, int, int, int, int)\n\n    def __init__(self):\n        super().__init__()\n        self.setupUi(self)\n        self.init_UI()\n        self.idling = True\n\n    # Init UI for tip approach\n    def init_UI(self):\n        # Set window geometry\n        screen = QDesktopWidget().screenGeometry()\n        size = self.frameGeometry()\n        self.move(int((screen.width()-size.width())/2), int((screen.height()-size.height())/2))\n        self.setFixedSize(self.width(), self.height())\n        \n        #\n        # Connect signals\n        #\n        # Waveform\n        self.spinBox_Delay.editingFinished.connect(lambda: self.delay_slot(True))\n        self.scrollBar_Delay.valueChanged.connect(lambda: self.delay_slot(False))\n        self.spinBox_Xstep.editingFinished.connect(lambda: self.x_slot(True))\n        self.scrollBar_Xstep.valueChanged.connect(lambda: self.x_slot(False))\n        self.spinBox_Zstep.editingFinished.connect(lambda: self.z_slot(True))\n        self.scrollBar_Zstep.valueChanged.connect(lambda: self.z_slot(False))\n        self.spinBox_Accel.editingFinished.connect(lambda: self.accel_slot(True))\n        self.scrollBar_Accel.valueChanged.connect(lambda: self.accel_slot(False))\n        self.spinBox_MInCurr.editingFinished.connect(lambda: self.min_curr_slot(True))\n        self.scrollBar_MInCurr.valueChanged.connect(lambda: self.min_curr_slot(False))\n        # Approach parameters\n        self.spinBox_Giant.valueChanged.connect(self.scrollBar_Giant.setValue)\n        self.scrollBar_Giant.valueChanged.connect(self.spinBox_Giant.setValue)\n        self.spinBox_Baby.valueChanged.connect(self.scrollBar_Baby.setValue)\n        self.scrollBar_Baby.valueChanged.connect(self.spinBox_Baby.setValue)\n        self.spinBox_StepNum.valueChanged.connect(self.scrollBar_StepNum.setValue)\n        self.scrollBar_StepNum.valueChanged.connect(self.spinBox_StepNum.setValue)\n        # Translation\n        self.groupBox_Trans_TipAppr.toggled.connect(self.mode_change)\n        self.pushButton_Xfwd_Trans.clicked.connect(lambda: self.giant_step(0x10, True))\n        self.pushButton_Xbkwd_Trans.clicked.connect(lambda: self.giant_step(0x10, False))\n        self.pushButton_Yfwd_Trans.clicked.connect(lambda: self.giant_step(0x1f, True))\n        self.pushButton_Ybkwd_Trans.clicked.connect(lambda: self.giant_step(0x1f, False))\n        # Rotation\n        self.pushButton_TipAppr_Steps.clicked.connect(self.tip_approach)\n        self.pushButton_Down_Steps.clicked.connect(lambda: self.giant_step(0x10, False))\n        self.pushButton_Up_Steps.clicked.connect(lambda: self.giant_step(0x10, True))\n        # Stop\n        self.pushButton_Stop.clicked.connect(self.stop)\n\n    # Re-init tip approach view\n    def init_tipAppr(self, succeed, lastdigital):\n        # Enable serial related buttons\n        self.groupBox_Trans_TipAppr.setEnabled(succeed)\n        self.pushButton_Up_Steps.setEnabled(succeed)\n        self.pushButton_Down_Steps.setEnabled(succeed)\n        self.pushButton_TipAppr_Steps.setEnabled(succeed)\n        \n        # Set push buttons enable based on dsp translation status\n        self.groupBox_Trans_TipAppr.setChecked(lastdigital[5])\n        self.set_translation(lastdigital[5])\n        \n    # Set translation based on variable\n    def set_translation(self, translation):\n        self.pushButton_Up_Steps.setEnabled(not translation)\n        self.pushButton_Down_Steps.setEnabled(not translation)\n        self.pushButton_TipAppr_Steps.setEnabled(not translation)\n    \n    # Delay conversion slot\n    def delay_slot(self, box_to_bar):\n        if box_to_bar:\n            self.scrollBar_Delay.setValue(int(self.spinBox_Delay.value() * 1000))\n        else:\n            self.spinBox_Delay.setValue(int(self.scrollBar_Delay.value() / 1000))\n            \n    # X step conversion slot\n    def x_slot(self, box_to_bar):\n        if box_to_bar:\n            self.scrollBar_Xstep.setValue(cnv.vb(self.spinBox_Xstep.value(), 'd', 10) - 0x8000)\n        else:\n            self.spinBox_Xstep.setValue(cnv.bv(self.scrollBar_Xstep.value() + 0x8000, 'd', 10))\n            \n    # Z step conversion slot\n    def z_slot(self, box_to_bar):\n        if box_to_bar:\n            self.scrollBar_Zstep.setValue(cnv.vb(self.spinBox_Zstep.value(), 'd', 10) - 0x8000)\n        else:\n            self.spinBox_Zstep.setValue(cnv.bv(self.scrollBar_Zstep.value() + 0x8000, 'd', 10))\n            \n    # Acceleration conversion slot\n    def accel_slot(self, box_to_bar):\n        if box_to_bar:\n            self.scrollBar_Accel.setValue(int(self.spinBox_Accel.value() * 8.163))\n        else:\n            self.spinBox_Accel.setValue(self.scrollBar_Accel.value() / 8.163)\n            \n    # Minimum tunneling current conversion slot\n    def min_curr_slot(self, box_to_bar):\n        if box_to_bar:\n            self.scrollBar_MInCurr.setValue(cnv.vb(self.spinBox_MInCurr.value(), 'a') - 0x8000)\n        else:\n            self.spinBox_MInCurr.setValue(cnv.bv(self.scrollBar_MInCurr.value() + 0x8000, 'a'))\n    \n    # Giant step slot\n    def giant_step(self, channel, direction):\n        # Obtain all required information\n        x = self.scrollBar_Xstep.value()\n        z = self.scrollBar_Zstep.value() + 0x8000\n        delay = self.scrollBar_Delay.value()\n        g = self.scrollBar_Accel.value()\n        stepnum = self.scrollBar_StepNum.value()\n        \n        x = (0x8000 + x) if direction else (0x8000 - x )            # Calculate the real x based on direction\n        chtxt = 'X' if channel == 0x10 else 'Y'                     # Figure out channel text\n        dirtxt = '+' if direction else '-'                          # Figure out direction text\n        labeltxt = 'Stepping Up' if direction else 'Stepping Down'  # Figure out steping direction text\n\n        # Set status label\n        if self.groupBox_Trans_TipAppr.isChecked():\n            self.label_Status_Status.setText('Translating to' + ' ' + dirtxt + chtxt +' direction')\n        else:\n            self.label_Status_Status.setText(labeltxt)\n        \n        # Emit signal for giant steps\n        self.giant_signal.emit(channel, x, z, delay, g, stepnum)\n        \n    # Tip approach slot\n    def tip_approach(self):\n        # Obtain all required information\n        x = 0x8000 - self.scrollBar_Xstep.value() \n        z = self.scrollBar_Zstep.value() + 0x8000\n        delay = self.scrollBar_Delay.value()\n        g = self.scrollBar_Accel.value()\n        giant = self.scrollBar_Giant.value()\n        baby = self.scrollBar_Baby.value()\n        minCurr = self.scrollBar_MInCurr.value()\n\n        self.label_Status_Status.setText('Tip approaching')     # Set status label\n        \n        # Emit signal for tip approach\n        self.approach_signal.emit(x, z, delay, g, giant, baby, minCurr)\n    \n    # Change translation mode\n    def mode_change(self, translation):\n        # Set push buttons enable based on dsp translation status\n        self.set_translation(translation)\n        # Emit signal to change dsp translation mode\n        self.mode_signal.emit(5, translation)\n    \n    # Stop button\n    def stop(self):\n        self.pushButton_Stop.setEnabled(False)      # Disable Stop button to prevent emitting signal twice\n        self.stop_signal.emit()                     # Emit stop signal\n    \n    # Emit close signal\n    def closeEvent(self, event):\n        if self.idling:\n            self.close_signal.emit()    # Emit close signal\n            event.accept()              # Accept close signal\n        else:\n            QMessageBox.warning(None, \"Tip approach\", \"Operation is on going\", QMessageBox.Ok)\n            event.ignore()\n    \n    # Enable serial\n    def enable_serial(self, enable):\n        self.groupBox_WaveForm_TipAppr.setEnabled(enable)\n        self.groupBox_ApprPar_TipAppr.setEnabled(enable)\n        self.groupBox_Steps_TipAppr.setEnabled(enable)\n        self.groupBox_Trans_TipAppr.setEnabled(enable)\n        self.groupBox_MInCurr_TipAppr.setEnabled(enable)\n        self.pushButton_Stop.setEnabled(False)\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    window = myTipApproach()\n    window.enable_serial(True)\n    window.show()\n    sys.exit(app.exec_())", "repo_name": "freestylexyz/STM-pyqt5-software", "sub_path": "pyqt5 STM software/TipApproach/TipApproach.py", "file_name": "TipApproach.py", "file_ext": "py", "file_size_in_byte": 8885, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 20, "usage_type": "name"}, {"api_name": "TipApproach_ui.Ui_TipApproach", "line_number": 20, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 21, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDesktopWidget", "line_number": 36, "usage_type": "call"}, {"api_name": "conversion.vb", "line_number": 103, "usage_type": "call"}, {"api_name": "conversion.bv", "line_number": 105, "usage_type": "call"}, {"api_name": "conversion.vb", "line_number": 110, "usage_type": "call"}, {"api_name": "conversion.bv", "line_number": 112, "usage_type": "call"}, {"api_name": "conversion.vb", "line_number": 124, "usage_type": "call"}, {"api_name": "conversion.bv", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 185, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 185, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 185, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 198, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 198, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 202, "usage_type": "call"}]}
{"seq_id": "11336190799", "text": "# function_import --------------------\n\nfrom transformers import AutoFeatureExtractor, RegNetForImageClassification\nimport torch\nfrom PIL import Image\nimport requests\nfrom io import BytesIO\n\n# function_code --------------------\n\ndef classify_image(image_url):\n    \"\"\"\n    Classify an image using the pretrained RegNetForImageClassification model.\n\n    Args:\n        image_url (str): The URL of the image to classify.\n\n    Returns:\n        str: The predicted label of the image.\n    \"\"\"\n    # Load the pretrained model and feature extractor\n    model = RegNetForImageClassification.from_pretrained('zuppif/regnet-y-040')\n    feature_extractor = AutoFeatureExtractor.from_pretrained('zuppif/regnet-y-040')\n\n    # Load the image from the URL\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    # Extract the features from the image\n    inputs = feature_extractor(images=img, return_tensors='pt')\n\n    # Classify the image\n    with torch.no_grad():\n        logits = model(**inputs).logits\n\n    # Get the predicted label\n    predicted_label = logits.argmax(-1).item()\n\n    return model.config.id2label[predicted_label]\n\n# test_function_code --------------------\n\ndef test_classify_image():\n    \"\"\"\n    Test the classify_image function.\n    \"\"\"\n    # Define a test image URL\n    test_image_url = 'https://example.com/test_image.jpg'\n\n    # Call the classify_image function\n    predicted_label = classify_image(test_image_url)\n\n    # Assert that the function returns a string (the predicted label)\n    assert isinstance(predicted_label, str)\n\n# call_test_function_code --------------------\n\ntest_classify_image()", "repo_name": "vixuowis/Research-2309", "sub_path": "Exp-2/output/hf-eval-data-v2/f00188_classify_image.py", "file_name": "f00188_classify_image.py", "file_ext": "py", "file_size_in_byte": 1645, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "transformers.RegNetForImageClassification.from_pretrained", "line_number": 22, "usage_type": "call"}, {"api_name": "transformers.RegNetForImageClassification", "line_number": 22, "usage_type": "name"}, {"api_name": "transformers.AutoFeatureExtractor.from_pretrained", "line_number": 23, "usage_type": "call"}, {"api_name": "transformers.AutoFeatureExtractor", "line_number": 23, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 27, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 27, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 33, "usage_type": "call"}]}
{"seq_id": "30778633951", "text": "import subprocess\nimport numpy as np\nimport shlex\nimport time\nimport os\nimport click\n\n\n@click.command()\n@click.option('--optimized_function', type=str, default='YModel')\n@click.option('--init_psi', type=str, default=\"0., 0.\")\ndef main(optimized_function, init_psi):\n    ns = [3, 5, 7, 9]\n    hs = np.logspace(-3, 0, 19)\n    command = \"python baseline.py --project_name grid_search_num_diff_{0} \\\n    --work_space USERNAME --tags n_{2},h_{3},grid_search  \\\n    --n {2} --h {3} --optimizer_config_file  optimizer_config_num_diff \\\n    --optimizer TorchOptimizer --optimized_function {0}  --init_psi {1}\"\n    processes = []\n    for n in ns:\n        for h in hs:\n            command_pre = command.format(\n                    optimized_function,  # 0\n                    init_psi,  # 1\n                    n,  # 2\n                    h,  # 3\n                )\n            print(command_pre)\n            command_pre = shlex.split(command_pre)\n            print(command_pre)\n            process = subprocess.Popen(command_pre,\n                                       shell=False,\n                                       close_fds=True,\n                                       stdout=subprocess.DEVNULL,\n                                       stderr=subprocess.DEVNULL,\n                                       preexec_fn=os.setsid)\n            processes.append(process)\n            time.sleep(5.)\n\n    for process in processes:\n        print(process.pid)\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "shir994/L-GSO", "sub_path": "local_train/baseline_scripts/grid_search_num_diff.py", "file_name": "grid_search_num_diff.py", "file_ext": "py", "file_size_in_byte": 1483, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.logspace", "line_number": 14, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 29, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 31, "usage_type": "call"}, {"api_name": "subprocess.DEVNULL", "line_number": 34, "usage_type": "attribute"}, {"api_name": "subprocess.DEVNULL", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.setsid", "line_number": 36, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "click.command", "line_number": 9, "usage_type": "call"}, {"api_name": "click.option", "line_number": 10, "usage_type": "call"}, {"api_name": "click.option", "line_number": 11, "usage_type": "call"}]}
{"seq_id": "18407302334", "text": "import time\n\nfrom starlette.middleware.base import BaseHTTPMiddleware, RequestResponseEndpoint\nfrom fastapi import Request, Response\nfrom fastapi.encoders import jsonable_encoder\nfrom fastapi.responses import JSONResponse\nfrom datetime import datetime\nfrom jose import jwt\nfrom app.errors.exceptions import APIException\n\nfrom app.utils.logging import logger\nfrom app.database.connection import db\nfrom app.utils.logger import api_logger\nfrom app.middlewares.exception_handler import exception_handler\nfrom app.common.const import get_settings\nfrom app.utils.utils import cal_time_elapsed_seconds\nfrom app.schemas import error_models as ErrorResponse\n\n\nsettings = get_settings()\n\n\nclass LoggingMiddleware(BaseHTTPMiddleware):\n    async def dispatch(\n        self, request: Request, call_next: RequestResponseEndpoint\n    ) -> Response:\n        request_datetime = datetime.now()\n        try:\n            request.state.req_time = request_datetime\n            logger.info(f\"Request time: {request.state.req_time}\")\n            request.state.start = time.time()\n            request.state.inspect = None\n            request.state.user = None\n            if settings.USE_TEXTSCOPE_DATABASE:\n                request.state.db = next(db.session())\n            request.state.email = \"none@none.none\"\n            headers = request.headers\n            ip = (\n                headers[\"x-forwarded-for\"]\n                if \"x-forwarded-for\" in request.headers.keys()\n                else request.client.host\n            )\n            ip = ip if ip is not None else \"127.0.0.1\"\n            request.state.ip = ip.split(\",\")[0] if \",\" in ip else ip\n            if \"authorization\" in headers.keys():\n                token = headers.get(\"Authorization\")\n                payload = jwt.decode(\n                    token.replace(\"Bearer \", \"\"),\n                    settings.SECRET_KEY,\n                    algorithms=[settings.ALGORITHM],\n                )\n                request.state.email = payload.get(\"sub\")\n            response = await call_next(request)\n            api_logger(request=request, response=response)\n        except jwt.ExpiredSignatureError as e:\n            error = await exception_handler(e)\n            error_dict = parse_error_dict(error)\n            status_code, error_model = ErrorResponse.ErrorCode.get(2402)\n            response = JSONResponse(status_code=status_code, content=jsonable_encoder({\"error\": error_model}))\n            api_logger(request=request, error=error)\n        except jwt.JWTError as e:\n            error = await exception_handler(e)\n            error_dict = parse_error_dict(error)\n            status_code, error_model = ErrorResponse.ErrorCode.get(2403)\n            response = JSONResponse(status_code=status_code, content=jsonable_encoder({\"error\": error_model}))\n            api_logger(request=request, error=error)\n        except Exception as e:\n            error = await exception_handler(e)\n            error_dict = parse_error_dict(error)\n            response = JSONResponse(status_code=error.status_code, content=error_dict)\n            api_logger(request=request, error=error)\n        finally:\n            if settings.USE_TEXTSCOPE_DATABASE:\n                request.state.db.close()\n            response_datetime = datetime.now()\n            elapsed = cal_time_elapsed_seconds(request_datetime, response_datetime)\n            logger.info(f\"Response time: {response_datetime}\")\n            logger.info(f\"Elapsed time: {elapsed}\")\n        return response\n\n\ndef parse_error_dict(exection: APIException):\n    return dict(\n        status=exection.status_code,\n        msg=exection.msg,\n        detail=exection.detail,\n        code=exection.code,\n        error=dict(\n            error_code=exection.code,\n            error_message=str(exection.detail) + \", \" + str(exection.msg)\n        )\n    )", "repo_name": "naknakLEE/lomin-textscope-core", "sub_path": "app/middlewares/logging.py", "file_name": "logging.py", "file_ext": "py", "file_size_in_byte": 3823, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "app.common.const.get_settings", "line_number": 20, "usage_type": "call"}, {"api_name": "starlette.middleware.base.BaseHTTPMiddleware", "line_number": 23, "usage_type": "name"}, {"api_name": "fastapi.Request", "line_number": 25, "usage_type": "name"}, {"api_name": "starlette.middleware.base.RequestResponseEndpoint", "line_number": 25, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "app.utils.logging.logger.info", "line_number": 30, "usage_type": "call"}, {"api_name": "app.utils.logging.logger", "line_number": 30, "usage_type": "name"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "app.database.connection.db.session", "line_number": 35, "usage_type": "call"}, {"api_name": "app.database.connection.db", "line_number": 35, "usage_type": "name"}, {"api_name": "jose.jwt.decode", "line_number": 47, "usage_type": "call"}, {"api_name": "jose.jwt", "line_number": 47, "usage_type": "name"}, {"api_name": "app.utils.logger.api_logger", "line_number": 54, "usage_type": "call"}, {"api_name": "jose.jwt.ExpiredSignatureError", "line_number": 55, "usage_type": "attribute"}, {"api_name": "jose.jwt", "line_number": 55, "usage_type": "name"}, {"api_name": "app.middlewares.exception_handler.exception_handler", "line_number": 56, "usage_type": "call"}, {"api_name": "app.schemas.error_models.ErrorCode.get", "line_number": 58, "usage_type": "call"}, {"api_name": "app.schemas.error_models.ErrorCode", "line_number": 58, "usage_type": "attribute"}, {"api_name": "app.schemas.error_models", "line_number": 58, "usage_type": "name"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 59, "usage_type": "call"}, {"api_name": "fastapi.encoders.jsonable_encoder", "line_number": 59, "usage_type": "call"}, {"api_name": "app.utils.logger.api_logger", "line_number": 60, "usage_type": "call"}, {"api_name": "jose.jwt.JWTError", "line_number": 61, "usage_type": "attribute"}, {"api_name": "jose.jwt", "line_number": 61, "usage_type": "name"}, {"api_name": "app.middlewares.exception_handler.exception_handler", "line_number": 62, "usage_type": "call"}, {"api_name": "app.schemas.error_models.ErrorCode.get", "line_number": 64, "usage_type": "call"}, {"api_name": "app.schemas.error_models.ErrorCode", "line_number": 64, "usage_type": "attribute"}, {"api_name": "app.schemas.error_models", "line_number": 64, "usage_type": "name"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 65, "usage_type": "call"}, {"api_name": "fastapi.encoders.jsonable_encoder", "line_number": 65, "usage_type": "call"}, {"api_name": "app.utils.logger.api_logger", "line_number": 66, "usage_type": "call"}, {"api_name": "app.middlewares.exception_handler.exception_handler", "line_number": 68, "usage_type": "call"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "app.utils.logger.api_logger", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "name"}, {"api_name": "app.utils.utils.cal_time_elapsed_seconds", "line_number": 76, "usage_type": "call"}, {"api_name": "app.utils.logging.logger.info", "line_number": 77, "usage_type": "call"}, {"api_name": "app.utils.logging.logger", "line_number": 77, "usage_type": "name"}, {"api_name": "app.utils.logging.logger.info", "line_number": 78, "usage_type": "call"}, {"api_name": "app.utils.logging.logger", "line_number": 78, "usage_type": "name"}, {"api_name": "fastapi.Response", "line_number": 26, "usage_type": "name"}, {"api_name": "app.errors.exceptions.APIException", "line_number": 82, "usage_type": "name"}]}
{"seq_id": "8953359209", "text": "#!/usr/bin/env python3\n\nimport rospy\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom ideasclinic.msg import class_info\nimport cv2\nimport numpy as np\nimport process\nimport tensorflow as tf\nfrom tensorflow.keras.models import model_from_json\n\n\nclass client:\n    def __init__(self):\n        json_file = open(\"/home/kaeun/catkin_ws/src/ideasclinic/src/model.json\", \"r\")\n        loaded_model_json = json_file.read()\n        json_file.close()\n        self.loaded_model = model_from_json(loaded_model_json)\n\n        self.loaded_model.load_weights(\"/home/kaeun/catkin_ws/src/ideasclinic/src/model.h5\")\n        print(\"Loaded model from disk\")\n\n        self.loaded_model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) \n        self.camera_rgb = rospy.Subscriber(\"class_detection/image_raw\", Image, self.callback, queue_size=1)\n        self.pub = rospy.Publisher(\"class_info\", class_info, queue_size = 10)\n        self.last_time = rospy.Time().now().to_sec()\n        self.latest_img = None\n        self.iter = 1\n        \n    def callback(self, img): \n        print(\"got image: %d\" % self.iter)\n        self.iter = self.iter + 1\n        if self.iter % 30 != 0:\n            return\n\n        height = img.height\n        width = img.width\n        blank_image = np.zeros((height,width,3), np.uint8)\n\n        row_size = width * 3\n\n        for i in range(height):\n            for j in range(width):\n                for k in range(3):\n                    idx = i*row_size + j*3 + k\n                    blank_image[i, j, k] = img.data[idx]\n        print(\"copied\")\n        #try:\n        #     cv_image = CvBridge().imgmsg_to_cv2(img, \"bgr8\")\n        #cv2.imshow(\"Classification Camera\", blank_image)\n        #cv2.waitKey(5)\n\n        #except CvBridgeError as e:\n        #    print(e)\n\n        self.latest_img = blank_image\n\n    def process(self):\n        if self.latest_img is None:\n            return\n        cv_image = self.latest_img\n        \n        IMG_SIZE = 50\n#        img2, contours, hierarchy = cv2.findContours(cv_image, cv2.CV_RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n #       cnt = contours[0]\n  #      M = cv2.moments(cnt)\n   #     print(M)\n        crop_img = cv_image[250:450, 250:450]\n        cv2.imshow(\"cropped\", crop_img)\n        cv2.waitKey(5)\n        img_array = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)\n        \n                \n       # img2, contours, hierarchy = cv2.findContours(img_array, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n       # cnt = contours[0]\n       # M = cv2.moments(cnt)\n       # print(M)\n        #cnt = []\n        #for i in range(len(contours)):\n            #if cv2.contourArea(contours[i]) < 1000000:\n                #cnt.append(contours[i]) \n\n        #print(\"area is\", cnt)\n\n        #pts_src = np.array([[250, 131], [340, 131], [340, 321], [250, 321]])\n        #pts_dst = np.array([[0, 0], [299, 0], [299, 399], [0, 399]])\n        \n        #h, status = cv2.findHomography(pts_src, pts_dst)\n        #im_out = cv2.warpPerspective(img_array, h, (img_array.shape[1], img_array.shape[0]))\n        #cv2.imshow(\"Wraped Source Image\", im_out)\n        #cv2.waitKey(5)\n        new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n        new_array = np.reshape(new_array, (-1, IMG_SIZE, IMG_SIZE, 1))\n        #pts_src = np.array([[141, 131], [480, 159], [493, 630], [64, 601]])\n        #pts_dst = np.array([[318, 256], [534, 372], [316, 670], [73, 473]])\n\n        #h, status = cv2.findHomography(pts_src, pts_dst)\n        #im_out = cv2.warpPerspective(cv_image, h, (cv_image.shape[1], cv_image.shape[0]))\n       # cv2.imshow(\"Wraped Source Image\", im_out)\n        #new_array.reshape(1, IMG_SIZE, IMG_SIZE, 1)\n        \n       # model = tf.keras.models.load_model(\"CNN.model\")\n#        image = new_img\n        '''json_file = open(\"/home/kaeun/catkin_ws/src/ideasclinic/src/model.json\", \"r\")\n        loaded_model_json = json_file.read()\n        json_file.close()\n        loaded_model = model_from_json(loaded_model_json)\n\n        loaded_model.load_weights(\"/home/kaeun/catkin_ws/src/ideasclinic/src/model.h5\")\n        print(\"Loaded model from disk\")\n\n        loaded_model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])'''\n        CATEGORIES=[\"cat\", \"dog\", \"spider\", \"squirrel\"]\n        prediction = self.loaded_model.predict_classes(new_array)\n        #prediction = list(prediction[0])\n        print(CATEGORIES[max(prediction)])\n        print(\"prediction is\", prediction)\n        #model = keras.models.load_model(\"model.h5\")\n        #prediction = model.predict(cv_image)\n        #prediction = list(prediction[0])\n        #print(CATEGORIES[prediction.index(max(prediction))])\n\n        msg = class_info()\n        if prediction is not None:\n            msg.symbol = prediction\n\n            self.pub.publish(msg)\n\nif __name__ == '__main__':\n    rospy.init_node('img_init')\n    c = client()\n#    rospy.spin()\n    while not rospy.is_shutdown():\n        rospy.sleep(1)\n#        time in seconds\n        c.process()\n\n", "repo_name": "k223kim/IdeasClinic", "sub_path": "catkin_ws/src/ideasclinic/src/classification.py", "file_name": "classification.py", "file_ext": "py", "file_size_in_byte": 5036, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "tensorflow.keras.models.model_from_json", "line_number": 19, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 25, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 25, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 26, "usage_type": "call"}, {"api_name": "ideasclinic.msg.class_info", "line_number": 26, "usage_type": "argument"}, {"api_name": "rospy.Time", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 72, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 94, "usage_type": "call"}, {"api_name": "ideasclinic.msg.class_info", "line_number": 124, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 131, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 134, "usage_type": "call"}, {"api_name": "rospy.sleep", "line_number": 135, "usage_type": "call"}]}
{"seq_id": "24449537292", "text": "\"\"\"Module containing TSV io support.\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import TYPE_CHECKING\n\nfrom glotaran.io import ProjectIoInterface\nfrom glotaran.io import load_parameters\nfrom glotaran.io import register_project_io\nfrom glotaran.io import save_parameters\n\nif TYPE_CHECKING:\n    from glotaran.parameter import Parameters\n\n\n@register_project_io([\"tsv\"])\nclass TsvProjectIo(ProjectIoInterface):\n    \"\"\"Plugin for TSV data io.\"\"\"\n\n    def load_parameters(self, file_name: str) -> Parameters:\n        \"\"\"Load parameters from TSV file.\n\n        Parameters\n        ----------\n        file_name : str\n            Name of file to be loaded.\n\n        Returns\n        -------\n            :class:`Parameters`\n        \"\"\"\n        return load_parameters(file_name, format_name=\"csv\", sep=\"\\t\")\n\n    def save_parameters(\n        self,\n        parameters: Parameters,\n        file_name: str,\n        *,\n        replace_infinfinity: bool = True,\n    ) -> None:\n        \"\"\"Save a :class:`Parameters` to a TSV file.\n\n        Parameters\n        ----------\n        parameters : Parameters\n            Parameters to be saved to file.\n        file_name : str\n            File to write the parameters to.\n        replace_infinfinity : bool\n            Weather to replace infinity values with empty strings.\n        \"\"\"\n        save_parameters(\n            parameters,\n            file_name,\n            format_name=\"csv\",\n            sep=\"\\t\",\n            replace_infinfinity=replace_infinfinity,\n        )\n", "repo_name": "glotaran/pyglotaran", "sub_path": "glotaran/builtin/io/pandas/tsv.py", "file_name": "tsv.py", "file_ext": "py", "file_size_in_byte": 1506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 44, "dataset": "github-code", "pt": "41", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 12, "usage_type": "name"}, {"api_name": "glotaran.io.ProjectIoInterface", "line_number": 17, "usage_type": "name"}, {"api_name": "glotaran.io.load_parameters", "line_number": 32, "usage_type": "call"}, {"api_name": "glotaran.parameter.Parameters", "line_number": 20, "usage_type": "name"}, {"api_name": "glotaran.parameter.Parameters", "line_number": 36, "usage_type": "name"}, {"api_name": "glotaran.io.save_parameters", "line_number": 52, "usage_type": "call"}, {"api_name": "glotaran.io.register_project_io", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "25262993294", "text": "from mesa.visualization.ModularVisualization import ModularServer\n\nfrom aufgabe_02 import *\nimport mesa\n\ndef agent_portrayal(agent):\n    portrayal = {\"Shape\": \"circle\",\n                 \"Filled\": \"true\",\n                 \"Layer\": 0,\n                 \"Color\": \"red\",\n                 \"r\": 0.5}\n\n    if isinstance(agent, ParticleAgent) and agent.type == \"Nuss\":\n        portrayal[\"Color\"] = \"brown\"\n        portrayal[\"Layer\"] = 1\n    elif isinstance(agent, ParticleAgent) and agent.type == \"Blatt\":\n        portrayal[\"Color\"] = \"green\"\n        portrayal[\"Layer\"] = 1\n        #portrayal[\"r\"] = 0.2\n    elif isinstance(agent, ParticleAgent) and agent.type == \"Stein\":\n        portrayal[\"Color\"] = \"grey\"\n        portrayal[\"Layer\"] = 1\n        #portrayal[\"r\"] = 0.2\n    else:\n        portrayal[\"Color\"] = \"black\"\n        portrayal[\"Layer\"] = 2\n        portrayal[\"r\"] = 0.2\n\n    return portrayal\n\nif __name__ == \"__main__\":\n\n\n\n    grid = mesa.visualization.CanvasGrid(agent_portrayal, 15, 15, 500, 500)\n    server = mesa.visualization.ModularServer(\n        AntModel, [grid], \"Ant Model\",\n        {\"N\": 10, \"density\": 0.1, \"s\": 1, \"j\": 3, \"width\": 15, \"height\": 15, \"middleInit\": False, \"cluster_cond\": 3}\n    )\n    server = ModularServer(AntModel,\n                           [grid],\n                           \"And Model\",\n                           {\"N\": 10, \"density\": 0.1, \"s\": 1, \"j\": 3, \"width\": 15, \"height\": 15, \"middleInit\": False,\n                            \"cluster_cond\": 3})\n    server.port = 8521  # The default\n    server.launch()\n\n", "repo_name": "robertik10/OrganicComputing", "sub_path": "blatt01_01/aufgabe_02_vis.py", "file_name": "aufgabe_02_vis.py", "file_ext": "py", "file_size_in_byte": 1542, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "mesa.visualization.CanvasGrid", "line_number": 35, "usage_type": "call"}, {"api_name": "mesa.visualization", "line_number": 35, "usage_type": "attribute"}, {"api_name": "mesa.visualization.ModularServer", "line_number": 36, "usage_type": "call"}, {"api_name": "mesa.visualization", "line_number": 36, "usage_type": "attribute"}, {"api_name": "mesa.visualization.ModularVisualization.ModularServer", "line_number": 40, "usage_type": "call"}]}
{"seq_id": "28303156152", "text": "from django.utils.decorators import method_decorator\nfrom django.views.decorators.cache import cache_page\n\nfrom django.shortcuts import get_object_or_404\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import permissions, status, generics\n\nfrom quizzz.communities.permissions import IsCommunityMember, IsCommunityAdmin\nfrom quizzz.common.permissions import IsSafeMethod, IsAuthenticated\n\nfrom .models import Tournament, Round\nfrom .serializers import (\n    TournamentSerializer, \n    ListedRoundSerializer,\n    ListedQuizSerializer, \n    EditableRoundSerializer,\n)\nfrom quizzz.quizzes.models import Quiz\n\n\nclass TournamentListOrCreate(APIView):\n    \"\"\"\n    Create a new tournament or list group's tournaments.\n    \"\"\"\n    permission_classes = [\n        IsAuthenticated,\n        (IsSafeMethod & IsCommunityMember) | IsCommunityAdmin,\n    ]\n\n    def get(self, request, community_id):\n        tournaments = Tournament.objects.filter(community_id=community_id).all()\n        serializer = TournamentSerializer(tournaments, many=True)\n        return Response(serializer.data)\n\n    def post(self, request, community_id):\n        serializer = TournamentSerializer(data=request.data)\n        if serializer.is_valid(raise_exception=True):\n            serializer.save(community_id=community_id)\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n\n\nclass TournamentDetail(generics.RetrieveUpdateDestroyAPIView):\n    \"\"\"\n    Retrieve/update/delete existing tournament.\n    \"\"\"\n    permission_classes = [\n        IsAuthenticated,\n        (IsSafeMethod & IsCommunityMember) | IsCommunityAdmin,\n    ]\n    queryset = Tournament.objects.all()\n    lookup_url_kwarg = \"tournament_id\"\n    serializer_class = TournamentSerializer\n\n\n\nclass RoundListOrCreate(APIView):\n    \"\"\"\n    Create a new round or list tournament rounds.\n    \"\"\"\n    permission_classes = [\n        IsAuthenticated,\n        (IsSafeMethod & IsCommunityMember) | IsCommunityAdmin,\n    ]\n\n    def get(self, request, community_id, tournament_id):\n        \n        rounds = Round.objects\\\n            .filter(tournament_id=tournament_id)\\\n            .select_related('quiz').select_related('quiz__user')\\\n            .prefetch_related(Round.get_user_plays_prefetch_object(request.user.id))\\\n            .all()\n        serializer = ListedRoundSerializer(rounds, many=True, context={'request': request})\n\n        return Response(serializer.data)\n\n    def post(self, request, community_id, tournament_id):\n        serializer = EditableRoundSerializer(data=request.data)\n        if serializer.is_valid(raise_exception=True):\n            round_obj = serializer.save(tournament_id=tournament_id)\n\n            round_obj.load_user_plays(request.user.id)\n            detailed_serializer = ListedRoundSerializer(round_obj, context={'request': request})\n            \n            return Response(detailed_serializer.data, status=status.HTTP_201_CREATED)\n\n\n\nclass RoundDetail(generics.RetrieveDestroyAPIView):\n    \"\"\"\n    Retrieve/update/delete existing round.\n    \"\"\"\n    permission_classes = [\n        IsAuthenticated,\n        (IsSafeMethod & IsCommunityMember) | IsCommunityAdmin,\n    ]\n    queryset = Round.objects.all()\n    lookup_url_kwarg = \"round_id\"\n\n    def get_serializer_class(self):\n        if self.request.method == 'DELETE':\n            return EditableRoundSerializer\n\n    def get(self, request, community_id, round_id):\n        obj = get_object_or_404(Round.objects.filter(pk=round_id))\n        self.check_object_permissions(self.request, obj)\n\n        obj.load_user_plays(request.user.id)\n        serializer = ListedRoundSerializer(obj, context={'request': request})\n\n        return Response({\n            \"round\": serializer.data,\n            \"standings\": obj.get_standings(),\n        })\n\n    def put(self, request, community_id, round_id):\n        obj = get_object_or_404(Round.objects.filter(pk=round_id))\n        self.check_object_permissions(self.request, obj)\n\n        serializer = EditableRoundSerializer(obj, data=request.data)\n        if serializer.is_valid(raise_exception=True):\n            round_obj = serializer.save(tournament_id=obj.tournament_id)\n\n            round_obj.load_user_plays(request.user.id)\n            detailed_serializer = ListedRoundSerializer(round_obj, context={'request': request})\n            \n            return Response(detailed_serializer.data)\n\n\n\nclass QuizPool(APIView):\n    \"\"\"\n    List group's available quizzes.\n    \"\"\"\n    permission_classes = [ IsAuthenticated, IsCommunityAdmin ]\n\n    def get(self, request, community_id):\n        quizzes = Quiz.objects\\\n            .filter(community_id=community_id)\\\n            .filter(is_finalized=True)\\\n            .filter(round__id=None)\\\n            .order_by('-time_created')\\\n            .all()\n        serializer = ListedQuizSerializer(quizzes, many=True)\n        return Response(serializer.data)\n\n\nclass TournamentStandings(APIView):\n    permission_classes = [ IsAuthenticated,  IsCommunityMember ]\n    \n    @method_decorator(cache_page(30))\n    def get(self, request, community_id, tournament_id):\n        tournament = get_object_or_404(Tournament.objects.filter(pk=tournament_id))\n        standings = tournament.get_standings()\n        return Response(standings)", "repo_name": "pyvlad/quizzz-spa", "sub_path": "drf/quizzz/tournaments/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5287, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 22, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsAuthenticated", "line_number": 27, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsSafeMethod", "line_number": 28, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityMember", "line_number": 28, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityAdmin", "line_number": 28, "usage_type": "name"}, {"api_name": "models.Tournament.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Tournament.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Tournament", "line_number": 32, "usage_type": "name"}, {"api_name": "serializers.TournamentSerializer", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 34, "usage_type": "call"}, {"api_name": "serializers.TournamentSerializer", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 40, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.generics.RetrieveUpdateDestroyAPIView", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 44, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsAuthenticated", "line_number": 49, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsSafeMethod", "line_number": 50, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityMember", "line_number": 50, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityAdmin", "line_number": 50, "usage_type": "name"}, {"api_name": "models.Tournament.objects.all", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Tournament.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.Tournament", "line_number": 52, "usage_type": "name"}, {"api_name": "serializers.TournamentSerializer", "line_number": 54, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 58, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsAuthenticated", "line_number": 63, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsSafeMethod", "line_number": 64, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityMember", "line_number": 64, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityAdmin", "line_number": 64, "usage_type": "name"}, {"api_name": "models.Round.objects.filter", "line_number": 69, "usage_type": "call"}, {"api_name": "models.Round.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.Round", "line_number": 69, "usage_type": "name"}, {"api_name": "models.Round.get_user_plays_prefetch_object", "line_number": 72, "usage_type": "call"}, {"api_name": "models.Round", "line_number": 72, "usage_type": "name"}, {"api_name": "serializers.ListedRoundSerializer", "line_number": 74, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 76, "usage_type": "call"}, {"api_name": "serializers.EditableRoundSerializer", "line_number": 79, "usage_type": "call"}, {"api_name": "serializers.ListedRoundSerializer", "line_number": 84, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 86, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 86, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 86, "usage_type": "name"}, {"api_name": "rest_framework.generics.RetrieveDestroyAPIView", "line_number": 90, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 90, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsAuthenticated", "line_number": 95, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsSafeMethod", "line_number": 96, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityMember", "line_number": 96, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityAdmin", "line_number": 96, "usage_type": "name"}, {"api_name": "models.Round.objects.all", "line_number": 98, "usage_type": "call"}, {"api_name": "models.Round.objects", "line_number": 98, "usage_type": "attribute"}, {"api_name": "models.Round", "line_number": 98, "usage_type": "name"}, {"api_name": "serializers.EditableRoundSerializer", "line_number": 103, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 106, "usage_type": "call"}, {"api_name": "models.Round.objects.filter", "line_number": 106, "usage_type": "call"}, {"api_name": "models.Round.objects", "line_number": 106, "usage_type": "attribute"}, {"api_name": "models.Round", "line_number": 106, "usage_type": "name"}, {"api_name": "serializers.ListedRoundSerializer", "line_number": 110, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 112, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 118, "usage_type": "call"}, {"api_name": "models.Round.objects.filter", "line_number": 118, "usage_type": "call"}, {"api_name": "models.Round.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.Round", "line_number": 118, "usage_type": "name"}, {"api_name": "serializers.EditableRoundSerializer", "line_number": 121, "usage_type": "call"}, {"api_name": "serializers.ListedRoundSerializer", "line_number": 126, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 128, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 132, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsAuthenticated", "line_number": 136, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityAdmin", "line_number": 136, "usage_type": "name"}, {"api_name": "quizzz.quizzes.models.Quiz.objects.filter", "line_number": 139, "usage_type": "call"}, {"api_name": "quizzz.quizzes.models.Quiz.objects", "line_number": 139, "usage_type": "attribute"}, {"api_name": "quizzz.quizzes.models.Quiz", "line_number": 139, "usage_type": "name"}, {"api_name": "serializers.ListedQuizSerializer", "line_number": 145, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 146, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 149, "usage_type": "name"}, {"api_name": "quizzz.common.permissions.IsAuthenticated", "line_number": 150, "usage_type": "name"}, {"api_name": "quizzz.communities.permissions.IsCommunityMember", "line_number": 150, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 154, "usage_type": "call"}, {"api_name": "models.Tournament.objects.filter", "line_number": 154, "usage_type": "call"}, {"api_name": "models.Tournament.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "models.Tournament", "line_number": 154, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 156, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 152, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.cache_page", "line_number": 152, "usage_type": "call"}]}
{"seq_id": "18895109804", "text": "# 数组中有一个数字出现的次数超过数组长度的一半，请找出这个数字。\n#\n# 你可以假设数组是非空的，并且给定的数组总是存在多数元素。\n#\n# 示例 1:\n#\n# 输入: [1, 2, 3, 2, 2, 2, 5, 4, 2]\n# 输出: 2\n\n\nimport collections\n\n\nclass Solution(object):\n    def majorityElement(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        dic = dict(collections.Counter(nums))\n        # print(dic)\n        for i, v in dic.items():\n            if v == max(dic.values()):\n                return i\n\nif __name__ == '__main__':\n    ob = Solution()\n    print(ob.majorityElement([1, 2, 3, 2, 2, 2, 5, 4, 2]))\n", "repo_name": "ddz-mark/LeetCode", "sub_path": "jianzhioffer/39. 数组中出现次数超过一半的数字.py", "file_name": "39. 数组中出现次数超过一半的数字.py", "file_ext": "py", "file_size_in_byte": 681, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.Counter", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "13323237305", "text": "# Python 3.x compat\nfrom __future__ import (absolute_import, division,\n                        print_function, unicode_literals)\nfrom builtins import (  # NOQA\n         bytes, dict, int, list, object, range, str,\n         ascii, chr, hex, input, next, oct, open,\n         pow, round, super,\n         filter, map, zip)\n\nimport json\n\n\nclass Topology:\n    clusters = []\n\n    def __init__(self, clusters):\n        self.clusters = clusters\n\n    @staticmethod\n    def load_file(handle, parameters):\n        t = json.load(handle)\n\n        for c in t['clusters']:\n            assert c['name'] != 'hq'\n\n        return Topology([\n            Cluster(c['name'], c, parameters)\n            for c in t['clusters']\n        ])\n\n\nclass Cluster:\n    def __init__(self, name, attrs, params):\n        self.name = name\n        self.attrs = {\n            \"expose\": False,\n            \"internet\": True,\n            \"image\": \"ami-d05e75b8\",  # Ubuntu Server 14.04 LTS\n            \"itype\": \"t2.nano\",\n            \"count\": 1,\n        }\n\n        for k, v in attrs.items():\n            if k in self.attrs:\n                if isinstance(v, str) and v.startswith('$'):\n                    self.attrs[k] = params[attrs[k].lstrip('$')]\n                else:\n                    self.attrs[k] = attrs[k]\n            elif k != \"name\":\n                raise KeyError(\"Unknown cluster attribute '{}'\".format(k))\n\n        assert not self.attrs['expose'] or self.attrs['internet']\n\n    def __getattr__(self, name):\n        if name in self.attrs:\n            return self.attrs[name]\n        raise AttributeError(name)\n", "repo_name": "jonhoo/salvo", "sub_path": "salvo/topology.py", "file_name": "topology.py", "file_ext": "py", "file_size_in_byte": 1580, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 45, "usage_type": "argument"}]}
{"seq_id": "9406815198", "text": "from elasticsearch import Elasticsearch\n\nfrom django.conf import settings\n\ndef elasticsearch(corpus_name):\n    '''\n    Create ElasticSearch instance with default configuration.\n    '''\n    server_name = settings.CORPUS_SERVER_NAMES.get(corpus_name, 'default')\n    server_config = settings.SERVERS[server_name]\n\n    node = {'host': server_config['host'],\n            'port': int(server_config['port']),\n            'scheme': 'http'\n    }\n    kwargs = {\n        'max_retries': 15,\n        'retry_on_timeout': True,\n        'timeout': 60\n    }\n    if server_config.get('certs_location') and server_config.get('api_key'):\n        # settings to connect via SSL are present\n        node['scheme'] = 'https'\n        kwargs['ca_certs'] = server_config.get('certs_location')\n        kwargs['api_key'] = (server_config.get('api_id'), server_config.get('api_key'))\n    client = Elasticsearch([node], **kwargs)\n    return client\n", "repo_name": "UUDigitalHumanitieslab/I-analyzer", "sub_path": "backend/ianalyzer/elasticsearch.py", "file_name": "elasticsearch.py", "file_ext": "py", "file_size_in_byte": 917, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.conf.settings.CORPUS_SERVER_NAMES.get", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.settings.CORPUS_SERVER_NAMES", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.settings.SERVERS", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 10, "usage_type": "name"}, {"api_name": "elasticsearch.Elasticsearch", "line_number": 26, "usage_type": "call"}]}
{"seq_id": "4358901076", "text": "import pymorphy2\n\n\nmorph = pymorphy2.MorphAnalyzer()\nword = input()\nword = morph.parse(word)[0]\ntenses = (('past', 'прошедшее'),\n          ('pres', 'настоящее'))\ngenders = ('masc', 'femn', 'neut')\npersons = tuple(sorted(word.tag.PERSONS))\nnumbers = ('sing', 'plur')\n\nif {'VERB', 'INFN'} & {word.tag.POS}:\n    for lat_ten, ru_ten in tenses:\n        print(f'{ru_ten.capitalize()} время:')\n        if lat_ten == 'past':\n            for gender in genders:\n                print(f'{word.inflect({lat_ten, gender}).word}')\n            print(f'{word.inflect({lat_ten, numbers[-1]}).word}')\n        else:\n            for person in persons:\n                for number in numbers:\n                    print(f'{word.inflect({lat_ten, person, number}).word}')\nelse:\n    print('Не глагол')", "repo_name": "hom1c1d3/yandex_lyceum", "sub_path": "Основы программирования на языке Python/30. Библиотеки Python. Морфология/conjugate.py", "file_name": "conjugate.py", "file_ext": "py", "file_size_in_byte": 807, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pymorphy2.MorphAnalyzer", "line_number": 4, "usage_type": "call"}]}
{"seq_id": "72268086525", "text": "\"\"\"\nUtils for Training\n\"\"\"\n\nimport numpy as np\nimport theano\n\nFLOAT_X = theano.config.floatX\n\ndef shuffle_data(samples, labels):\n    \"\"\" Shuffle data (from begin_project_1b.py) \"\"\"\n    idx = np.arange(samples.shape[0])\n    np.random.shuffle(idx)\n    samples, labels = samples[idx], labels[idx]\n    return samples, labels\n\ndef get_weigh(prev_layer, cur_layer):\n    \"\"\" get weigh \"\"\"\n    return theano.shared(np.random.randn(prev_layer, cur_layer)*0.01, FLOAT_X)\n\ndef get_bias(cur_layer):\n    \"\"\" get weigh \"\"\"\n    return theano.shared(np.random.randn(cur_layer)*0.01, FLOAT_X)\n\ndef get_fold_data(data, fold_num):\n    \"\"\" split the data into different fold \"\"\"\n    train = []\n    validation = []\n    for i in range(len(data)):\n        if i == fold_num:\n            validation = data[i]\n        else:\n            if len(train) == 0:\n                train = data[i]\n            else:\n                train = np.concatenate((train, data[i]))\n\n    return train, validation", "repo_name": "cacad101/assignment-neural-network-1", "sub_path": "problem_2_regression/housing_training/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 966, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "theano.config", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "14763199687", "text": "import random\nimport chess\nimport sys\n\n\nclass Board:\n    def __init__(self, trace=None):\n\n        self.string_of_board = \".\"\n        self.string_of_p1_side = None\n        self.string_of_p2_side = None\n        self.player1_color = None\n        self.player2_color = None\n        self.board_history = []\n        self.board = board = chess.Board()\n        self._trace = []\n        self.game_over = False\n        if not (trace is None):\n            for c in trace:\n                if not self.isValidMove(int(c)):\n                    print(\"Warning: invalid move skipped.\")\n                    continue\n                self.makeMove(int(c))\n\n    @property\n    def p1_color_set(self):\n        colored_player1_pieces = list[1, 2, 3, 4, 5, 6]\n        for i in colored_player1_pieces:\n            self.player1_color = chess.BaseBoard.pieces(self.board, i, 0)\n            return hex(self.player1_color)\n\n    @property\n    def p2_color_set(self):\n        colored_player2_pieces = list[1, 2, 3, 4, 5, 6]\n        for i in colored_player2_pieces:\n            self.player2_color = chess.BaseBoard.pieces(self.board, i, 1)\n            return hex(self.player2_color)\n\n    @property\n    def p1_pieces(self):\n        self.string_of_board = str(self.board)\n        for i in enumerate(self.string_of_board[0:15]):\n            if \".\" in str(self.string_of_board) + (str(self.board)):\n                # self.string_of_board(\".\")\n                self.string_of_board = self.string_of_p1_side\n        return sys.getsizeof(self.string_of_p1_side) / 4\n\n    @property\n    def p2_pieces(self):\n        self.string_of_board = str(self.board)\n        for i in enumerate(self.string_of_board[15:25]):\n            if \".\" in str(self.string_of_board) + (str(self.board)):\n                # self.string_of_board.remove(\".\")\n\n                self.string_of_board = self.string_of_p2_side\n        return sys.getsizeof(self.string_of_p2_side) / 4\n\n    @property\n    def turn(self):\n        p1color = self.player1_color\n        # p2color = self.player2_color\n        # if p1color == hex(FFFFFF):\n        #     return 0\n        # else:\n        #     return 1\n        return p1color\n\n    @property\n    def winner(self):\n        if not self.game_over:\n            return None\n        if self.p1_pieces > self.p2_pieces:\n            return self.board.outcome()\n        if self.p2_pieces > self.p1_pieces:\n            return not (self.board.outcome())\n        if self.p2_pieces == self.p1_pieces:\n            return self.board.is_stalemate() or self.board.is_insufficient_material()\n        return 0\n\n    def isValidMove(self, pieces):\n        # to do add cases for pinning, attacking, etc\n        if self.p2_pieces == self.p1_pieces:\n            return self.board.is_stalemate()\n        if self.game_over:\n            return self.board.is_check()\n\n        else:\n            return self.board.is_insufficient_material()\n\n    def generateVaildMove(self):\n        return list(self.board.legal_moves)\n\n    def makemove(self, move):\n        self.trace = list(self.board.legal_moves)\n        # create new board in stack\n        self.board_history = self.board\n        player = self.turn\n\n        while self.p1_pieces > 0:\n            # move a randompiece forward\n            if self.p1_pieces == 16 and player == 0:\n                move.from_uci(str(random.choice(self.generatedVaildMoves)))\n                if self.isVaildMove(self, self.pieces) == self.board.is_stalemate():\n                    print(\"The game is a Stalemate\")\n                if self.isVaildMove(self, self.pieces) == self.board.is_check():\n                    print(\"checkmate oof\")\n                if (\n                    self.isVaildMove(self, self.pieces)\n                    == self.board.is_insufficient_material()\n                ):\n                    print(\"we can't countie the game\")\n            # else:\n            #     Move.from_uci(str(random.choice(self.generatedVaildMoves))\n", "repo_name": "noahpujol/B351-Final-Project", "sub_path": "board.py", "file_name": "board.py", "file_ext": "py", "file_size_in_byte": 3914, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "chess.Board", "line_number": 15, "usage_type": "call"}, {"api_name": "chess.BaseBoard.pieces", "line_number": 29, "usage_type": "call"}, {"api_name": "chess.BaseBoard", "line_number": 29, "usage_type": "attribute"}, {"api_name": "chess.BaseBoard.pieces", "line_number": 36, "usage_type": "call"}, {"api_name": "chess.BaseBoard", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sys.getsizeof", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.getsizeof", "line_number": 56, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 102, "usage_type": "call"}]}
{"seq_id": "73661347002", "text": "\nimport os\nimport argparse\nimport pandas as pd\nfrom ultrasound_dataset import USDataModule\n\nimport torch\nimport pytorch_lightning as pl\n\nfrom pytorch_lightning import Trainer\nfrom pytorch_lightning.callbacks.early_stopping import EarlyStopping\nfrom pytorch_lightning.callbacks import ModelCheckpoint\nfrom pytorch_lightning.strategies.ddp import DDPStrategy\nfrom pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger\n\nfrom pl_bolts.models.self_supervised import SimCLR\nfrom pl_bolts.models.self_supervised.simclr.transforms import (\n    SimCLREvalDataTransform, SimCLRTrainDataTransform)\n\n\ndef main(args):\n\n    checkpoint_callback = ModelCheckpoint(\n        dirpath=args.out,\n        filename='{epoch}-{val_loss:.2f}',\n        save_top_k=2,\n        monitor='val_loss'\n    )\n    \n    ga = 'ga_boe'\n    df_train = pd.read_csv(os.path.join(args.mount_point, 'CSV_files', 'extract_frames.csv'))\n    df_train = df_train.query('197 <= ' + ga + ' and ' + ga + ' <= 300')\n\n    df_val = pd.read_csv(os.path.join(args.mount_point, 'CSV_files', 'extract_frames_valid.csv'))\n    df_val = df_val.query('197 <= ' + ga + ' and ' + ga + ' <= 300')\n    \n    df_test = pd.read_csv(os.path.join(args.mount_point, 'CSV_files', 'extract_frames_test.csv'))\n    df_test = df_test.query('197 <= ' + ga + ' and ' + ga + ' <= 300')\n\n    usdata = USDataModule(df_train, df_val, df_test, mount_point=args.mount_point, batch_size=args.batch_size, num_workers=args.num_workers)\n\n    early_stop_callback = EarlyStopping(monitor=\"val_loss\", min_delta=0.00, patience=30, verbose=True, mode=\"min\")\n    \n    usdata.train_transforms = SimCLRTrainDataTransform(224)\n    usdata.val_transforms = SimCLREvalDataTransform(224)\n\n    logger = TensorBoardLogger(save_dir=args.tb_dir, name=args.tb_name)\n\n    trainer = Trainer(\n        logger=logger,\n        max_epochs=args.epochs,\n        callbacks=[early_stop_callback, checkpoint_callback],\n        devices=torch.cuda.device_count(), \n        accelerator=\"gpu\", \n        strategy=DDPStrategy(find_unused_parameters=False)\n    )\n\n    # model\n    model = SimCLR(learning_rate=args.lr, hidden_mlp=args.hidden_mlp, temperature=args.temperature)\n\n    # fit\n    trainer = pl.Trainer()\n    trainer.fit(model, datamodule=dm)\n\nif __name__ == '__main__':\n\n\n    parser = argparse.ArgumentParser(description='Autoencoder Training')\n    parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, help='Learning rate')\n    parser.add_argument('--epochs', help='Max number of epochs', type=int, default=200)\n    parser.add_argument('--model', help='Model to continue training', type=str, default= None)\n    parser.add_argument('--out', help='Output', type=str, default=\"./\")\n    parser.add_argument('--mount_point', help='Dataset mount directory', type=str, default=\"./\")    \n    parser.add_argument('--num_workers', help='Number of workers for loading', type=int, default=4)\n    parser.add_argument('--batch_size', help='Batch size', type=int, default=256)\n    \n    parser.add_argument('--hidden_mlp', help='Dimension of the hidden MLP', type=int, default=2048)\n    parser.add_argument('--temperature', help='Temperature, used for the model effnet_simclr', type=float, default=0.1)\n    parser.add_argument('--feat_dim', help='Feature dimension', type=int, default=128)\n\n    parser.add_argument('--tb_dir', help='Tensorboard output dir', type=str, default=None)\n    parser.add_argument('--tb_name', help='Tensorboard experiment name', type=str, default=\"SimCLR\")\n\n    args = parser.parse_args()\n\n    main(args)", "repo_name": "juanprietob/us-famli-pl", "sub_path": "src/simclr.py", "file_name": "simclr.py", "file_ext": "py", "file_size_in_byte": 3529, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pytorch_lightning.callbacks.ModelCheckpoint", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "ultrasound_dataset.USDataModule", "line_number": 40, "usage_type": "call"}, {"api_name": "pytorch_lightning.callbacks.early_stopping.EarlyStopping", "line_number": 42, "usage_type": "call"}, {"api_name": "pl_bolts.models.self_supervised.simclr.transforms.SimCLRTrainDataTransform", "line_number": 44, "usage_type": "call"}, {"api_name": "pl_bolts.models.self_supervised.simclr.transforms.SimCLREvalDataTransform", "line_number": 45, "usage_type": "call"}, {"api_name": "pytorch_lightning.loggers.TensorBoardLogger", "line_number": 47, "usage_type": "call"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.cuda.device_count", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pytorch_lightning.strategies.ddp.DDPStrategy", "line_number": 55, "usage_type": "call"}, {"api_name": "pl_bolts.models.self_supervised.SimCLR", "line_number": 59, "usage_type": "call"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 62, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 68, "usage_type": "call"}]}
{"seq_id": "42155945817", "text": "from django import forms\nfrom django.conf import settings\nfrom django.forms import TextInput, RadioSelect, modelformset_factory\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom accounts.models import CircusUser\nfrom clients.models import Client\nfrom finance.models import PAYMENT_CHOICES\nfrom projects.models import Project, ProjectJobOptions\nfrom services.managers import DEFAULT_INDUSTRY_CODE\nfrom shared.widgets import DateTimeWidget\nfrom django.forms.widgets import HiddenInput\n\nfrom services.models import ServiceType\nfrom django.forms.widgets import CheckboxSelectMultiple\nfrom tinymce import TinyMCE\nfrom shared.group_permissions import PROTECTED_HEALTH_INFORMATION_GROUP\n\n\nRESTRICTED_CHOICES = (\n        (True, _(u'Restricted Access')),\n        (False, _(u'Unrestricted Access')),\n    )\n\nSECURE_JOB_CHOICES= (\n        (True, _(u'Limit Team')),\n        (False, _(u'Full Team')),\n    )\n\n\nclass ProjectNewAutoJobForm(forms.ModelForm):\n    project_reference_name = forms.CharField(required=False, max_length=100)\n\n    def __init__(self, *args, **kwargs):\n        super(ProjectNewAutoJobForm, self).__init__(*args, **kwargs)\n        self.fields['name'].label = _('Job Name')\n        self.fields['name'].help_text = ''\n        self.fields['name'].required = True\n\n        self.fields['job_number'].required = False\n\n        self.fields['client_poc'].queryset = CircusUser.objects.none()\n\n        self.fields['project_reference_name'].label = _('Client Job Reference')\n        self.fields['internal_via_project'].label = _('No Client View')\n\n        clients = Client.objects.filter(is_deleted=False).order_by('name')\n        self.fields['client'].queryset = clients\n        if len(clients) == 1:\n            self.fields['client'].initial = clients[0].pk\n\n        from shared.forms import form_get_client_id\n        client_id = form_get_client_id(self)\n\n        if client_id:\n            # client is known. Now I can display the matching children.\n            from shared.forms import form_get_client_poc\n            client_poc =  form_get_client_poc(client_id)\n            self.fields['client_poc'].queryset = client_poc\n            if len(client_poc) == 1:\n                self.fields['client_poc'].initial = client_poc[0].pk\n\n    class Meta:\n        model = Project\n        exclude = ['kit', 'status', 'estimate_type']\n        fields = [\n            'job_number',\n            'name',\n            'client',\n            'client_poc',\n            'project_reference_name',\n            'internal_via_project',\n        ]\n        widgets = {\n            'job_number': TextInput(attrs={'placeholder': '(Leave this to auto-generate)'}),\n        }\n\n\nclass ProjectAutoJobContinueForm(forms.ModelForm):\n    def __init__(self, *args, **kwargs):\n        super(ProjectAutoJobContinueForm, self).__init__(*args, **kwargs)\n        self.fields['source_locale'].queryset = self.fields['source_locale'].queryset.filter(available=True)\n        self.fields['source_locale'].required = True\n        self.fields['target_locales'].help_text = ''\n        self.fields['target_locales'].required = True\n        self.fields['target_locales'].queryset = self.fields['target_locales'].queryset.filter(available=True)\n        self.fields['services'].label = ''\n        self.fields['services'].help_text = ''\n        self.fields['services'].required = True\n\n        self.fields['is_restricted_job'].label = _('Access')\n        self.fields['is_restricted_job'].help_text = ''\n        self.fields['is_restricted_job'].initial = False\n\n        self.fields['is_secure_job'].label = _('Secure Job')\n        self.fields['is_secure_job'].help_text = ''\n        self.fields['is_secure_job'].initial = False\n\n        self.fields['services'].queryset = ServiceType.objects.filter(\n            clientservice__client=self.instance.client,\n            clientservice__available=True,\n            available=True\n        ).order_by('-description')\n\n        self.fields['industry'].queryset = self.fields['industry'].queryset.exclude(code=DEFAULT_INDUSTRY_CODE)\n\n    class Meta:\n        model = Project\n        fields = [\n            'industry',\n            'source_locale',\n            'target_locales',\n            'services',\n            'is_restricted_job',\n            'is_secure_job'\n        ]\n        widgets = {\n            'services': CheckboxSelectMultiple(),\n            'is_restricted_job': RadioSelect(choices=RESTRICTED_CHOICES),\n            'is_secure_job': RadioSelect(choices=SECURE_JOB_CHOICES),\n        }\n\n    def clean(self):\n        cleaned_data = super(ProjectAutoJobContinueForm, self).clean()\n        if not self.instance.kit.file_count():\n            raise forms.ValidationError(\"Please upload at least one file for translation.\")\n        return cleaned_data\n\n\nclass ProjectForm(forms.ModelForm):\n\n    payment_method = forms.ChoiceField(choices=PAYMENT_CHOICES, initial='ca', widget=forms.RadioSelect())\n    ca_invoice_number = forms.CharField(required=False, max_length=100)\n    rush_estimate = forms.BooleanField(required=False)\n    project_reference_name = forms.CharField(required=False, max_length=100)\n    reschedule_all_due_dates = forms.BooleanField(required=False, widget=forms.HiddenInput())\n\n    instructions = forms.CharField(widget=TinyMCE(attrs={'cols': settings.TINYMCE_COLS, 'rows': settings.TINYMCE_ROWS}), required=False)\n    instructions_via = forms.CharField(widget=TinyMCE(attrs={'cols': settings.TINYMCE_COLS, 'rows': settings.TINYMCE_ROWS}), required=False)\n    instructions_vendor = forms.CharField(widget=TinyMCE(attrs={'cols': settings.TINYMCE_COLS, 'rows': settings.TINYMCE_ROWS}), required=False)\n\n    def __init__(self, *args, **kwargs):\n        super(ProjectForm, self).__init__(*args, **kwargs)\n        self.fields['name'].label = _('Job Name')\n        self.fields['name'].help_text = ''\n        self.fields['name'].required = True\n\n        self.fields['source_locale'].help_text = ''\n        self.fields['source_locale'].required = True\n        self.fields['source_locale'].queryset = self.fields['source_locale'].queryset.filter(available=True)\n\n        self.fields['target_locales'].help_text = ''\n        self.fields['target_locales'].required = True\n        self.fields['target_locales'].queryset = self.fields['target_locales'].queryset.filter(available=True)\n        if self.instance.id:\n            target_count = u\" (%s)\" % (self.instance.target_locales_count(),)\n            self.fields['target_locales'].label += target_count\n\n        if self.instance.is_phi_secure_job:\n            via_users = CircusUser.objects.filter(user_type=settings.VIA_USER_TYPE, groups__name=PROTECTED_HEALTH_INFORMATION_GROUP).order_by('first_name')\n        else:\n            via_users = CircusUser.objects.filter(user_type=settings.VIA_USER_TYPE).order_by('first_name')\n\n        self.fields['project_manager'].queryset = via_users\n        self.fields['account_executive'].queryset = via_users\n        self.fields['estimator'].queryset = via_users\n        self.fields['industry'].queryset = self.fields['industry'].queryset.exclude(code=DEFAULT_INDUSTRY_CODE)\n\n        self.fields['is_restricted_job'].label = _('Access')\n        self.fields['is_restricted_job'].help_text = ''\n        self.fields['is_restricted_job'].initial = False\n\n        self.fields['is_secure_job'].label = _('Secure Job')\n        self.fields['is_secure_job'].help_text = ''\n        self.fields['is_secure_job'].initial = False\n\n        self.fields['is_phi_secure_job'].label = _('PHI Job')\n        self.fields['is_phi_secure_job'].help_text = ''\n        self.fields['is_phi_secure_job'].initial = False\n\n        self.fields['delay_job_po'].label = _('PO Basis')\n        self.fields['delay_job_po'].help_text = ''\n        self.fields['delay_job_po'].initial = False\n\n        self.fields['restricted_locations'].help_text = ''\n        self.fields['restricted_locations'].label = _('Restricted Region')\n        self.fields['restricted_locations'].required = False\n        if self.instance.id:\n            restricted_locations_count = u\" (%s)\" % (self.instance.restricted_locations.count(),)\n            self.fields['restricted_locations'].label += restricted_locations_count\n\n        self.fields['completed'].label = _('Downloaded Date')\n\n        self.fields['job_number'].required = False\n\n        self.fields['project_reference_name'].label = _('Client Job Reference')\n        self.fields['internal_via_project'].label = _('No Client Access')\n        self.fields['no_express_option'].label = _('No Client Express')\n        self.fields['ignore_holiday_flag'].label = _('Holidays Ignored')\n\n        if not self.instance.can_edit_locales():\n            # select widgets don't necessarily have a readonly attribute, but\n            # we can use this to get select2.js to use read-only mode.\n            self.fields['source_locale'].widget.attrs['readonly'] = True\n            self.fields['target_locales'].widget.attrs['readonly'] = True\n            # if select2 isn't working, we can discourage editing by limiting\n            # the querysets to the current values. Not entirely bulletproof\n            # against removing current targets, but will discourage other changes.\n            self.fields['source_locale'].queryset = self.fields['source_locale'].queryset.filter(id=self.instance.source_locale_id)\n            self.fields['target_locales'].queryset = self.instance.target_locales\n\n        self.fields['ca_invoice_number'].label = 'Purchase Order'\n        if self.instance.payment_details:\n            self.fields['payment_method'].initial = self.instance.payment_details.payment_method\n            self.fields['ca_invoice_number'].initial = self.instance.payment_details.ca_invoice_number\n\n        # chained select filter based upon the chosen Client Company\n\n        self.fields['client_poc'].queryset = CircusUser.objects.none()\n\n        clients = Client.objects.filter(is_deleted=False).order_by('name')\n        self.fields['client'].queryset = clients\n        if len(clients) == 1:\n            self.fields['client'].initial = clients[0].pk\n\n        # client_id = self.fields['client'].initial or \\\n        #             self.initial.get('client')\n        from shared.forms import form_get_client_id\n        client_id = form_get_client_id(self)\n\n        if client_id:\n            # client is known. Now I can display the matching children.\n            from shared.forms import form_get_client_poc\n            client_poc =  form_get_client_poc(client_id)\n            self.fields['client_poc'].queryset = client_poc\n            if len(client_poc) == 1:\n                self.fields['client_poc'].initial = client_poc[0].pk\n\n        if self.instance.canceled or self.instance.is_canceled_status():\n            self.fields['canceled'].label = _('Canceled')\n            self.fields['canceled'].initial = self.instance.canceled\n        else:\n            self.fields['canceled'].label = ''\n            self.fields[\"canceled\"].widget = HiddenInput()\n\n    class Meta:\n        model = Project\n        exclude = ['kit', 'status', 'estimate_type']\n        fields = [\n            'job_number',\n            'name',\n            'project_speed',\n            'client',\n            'client_poc',\n            'industry',\n            'project_manager',\n            'account_executive',\n            'estimator',\n            'source_locale',\n            'target_locales',\n            'quote_due',\n            'quoted',\n            'started_timestamp',\n            'due',\n            'delivered',\n            'completed',\n            'payment_method',\n            'ca_invoice_number',\n            'rush_estimate',\n            'instructions',\n            'instructions_via',\n            'instructions_vendor',\n            'is_restricted_job',\n            'delay_job_po',\n            'restricted_locations',\n            'project_reference_name',\n            'internal_via_project',\n            'canceled',\n            'no_express_option',\n            'ignore_holiday_flag',\n            'price_per_document',\n            'is_secure_job',\n            'is_phi_secure_job'\n        ]\n        widgets = {\n            'quote_due': DateTimeWidget(),\n            'quoted': DateTimeWidget(),\n            'started_timestamp': DateTimeWidget(),\n            'due': DateTimeWidget(),\n            'delivered': DateTimeWidget(),\n            'completed': DateTimeWidget(),\n            'job_number': TextInput(attrs={'placeholder': '(Leave this to auto-generate)'}),\n            'is_restricted_job': RadioSelect(choices=RESTRICTED_CHOICES),\n            'is_secure_job': RadioSelect(choices=SECURE_JOB_CHOICES),\n            'delay_job_po': RadioSelect(),\n            'canceled': DateTimeWidget(),\n        }\n\n\nclass ViaInstructionsForm(forms.Form):\n    via_instructions = forms.CharField()\n\n\nclass ProjectJobOptionsForm(forms.ModelForm):\n    editable_source = forms.BooleanField(required=False)\n    recreation_source = forms.BooleanField(required=False)\n    translation_unformatted = forms.BooleanField(required=False)\n    translation_billingual = forms.BooleanField(required=False)\n\n    class Meta:\n        model = ProjectJobOptions\n        fields = '__all__'\n\n    def __init__(self, *args, **kwargs):\n        super(ProjectJobOptionsForm, self).__init__(*args, **kwargs)\n        self.fields['editable_source'].label = 'Editable Source on Approval (MS Office, InDesign, etc.)'\n        self.fields['recreation_source'].label = 'Full Recreation of Source from PDFs (MS Office) '\n        self.fields['translation_unformatted'].label = 'Translation Only as an Unformatted MS Word document'\n        self.fields['translation_billingual'].label = 'Translation Only as a Bilingual MS Word document (two columns)'\n\nProjectJobOptionsSet = modelformset_factory(ProjectJobOptions, fields='__all__', exclude=None, can_delete=True)\n\n\nclass ViaWorkflowForm(forms.ModelForm):\n    cost = forms.DecimalField(required=False)\n    price = forms.DecimalField(required=False)\n    express_cost = forms.DecimalField(required=False)\n    express_price = forms.DecimalField(required=False)\n\n    def __init__(self, *args, **kwargs):\n        super(ViaWorkflowForm, self).__init__(*args, **kwargs)\n        self.fields['name'].label = _('Job Name')\n        self.fields['name'].help_text = ''\n        self.fields['name'].required = True\n\n        self.fields['source_locale'].help_text = ''\n        self.fields['source_locale'].required = True\n        self.fields['source_locale'].queryset = self.fields['source_locale'].queryset.filter(available=True)\n\n        self.fields['target_locales'].help_text = ''\n        self.fields['target_locales'].required = True\n        self.fields['target_locales'].queryset = self.fields['target_locales'].queryset.filter(available=True)\n        if self.instance.id:\n            target_count = u\" (%s)\" % (self.instance.target_locales.count(),)\n            self.fields['target_locales'].label += target_count\n\n        self.fields['services'].label = ''\n        self.fields['services'].help_text = ''\n        self.fields['services'].required = True\n        self.fields['services'].queryset = ServiceType.objects.filter(available=True, workflow=True).order_by('category', 'description')\n\n        self.fields['job_number'].required = True\n        self.fields['industry'].queryset = self.fields['industry'].queryset.exclude(code=DEFAULT_INDUSTRY_CODE)\n\n        self.fields['client_poc'].queryset = CircusUser.objects.none()\n\n        clients = Client.objects.filter(is_deleted=False).order_by('name')\n        self.fields['client'].queryset = clients\n        if len(clients) == 1:\n            self.fields['client'].initial = clients[0].pk\n\n        from shared.forms import form_get_client_id\n        client_id = form_get_client_id(self)\n\n        if client_id:\n            # client is known. Now I can display the matching children.\n            from shared.forms import form_get_client_poc\n            client_poc = form_get_client_poc(client_id)\n            self.fields['client_poc'].queryset = client_poc\n            if len(client_poc) == 1:\n                self.fields['client_poc'].initial = client_poc[0].pk\n\n    class Meta:\n        model = Project\n        fields = [\n            'job_number',\n            'client',\n            'client_poc',\n            'name',\n            'source_locale',\n            'target_locales',\n            'industry',\n            'services',\n            'cost',\n            'price',\n            'express_cost',\n            'express_price',\n        ]\n        widgets = {\n            'services': CheckboxSelectMultiple(),\n        }\n\n", "repo_name": "Kiranoneandonly/MyProjects", "sub_path": "ViaDelivers/VTP/src/circus/projects/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 16502, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "django.utils.translation.ugettext_lazy", "line_number": 21, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 26, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 27, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 31, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 32, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 36, "usage_type": "call"}, {"api_name": "accounts.models.CircusUser.objects.none", "line_number": 42, "usage_type": "call"}, {"api_name": "accounts.models.CircusUser.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "accounts.models.CircusUser", "line_number": 42, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 44, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 45, "usage_type": "call"}, {"api_name": "clients.models", "line_number": 47, "usage_type": "name"}, {"api_name": "clients.models.Client.objects.filter", "line_number": 47, "usage_type": "call"}, {"api_name": "clients.models.Client.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "clients.models.Client", "line_number": 47, "usage_type": "name"}, {"api_name": "clients.models", "line_number": 48, "usage_type": "name"}, {"api_name": "clients.models", "line_number": 49, "usage_type": "argument"}, {"api_name": "clients.models", "line_number": 50, "usage_type": "name"}, {"api_name": "shared.forms.form_get_client_id", "line_number": 53, "usage_type": "call"}, {"api_name": "shared.forms.form_get_client_poc", "line_number": 58, "usage_type": "call"}, {"api_name": "projects.models.Project", "line_number": 64, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 75, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 79, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 79, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 91, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 95, "usage_type": "call"}, {"api_name": "services.models.ServiceType.objects.filter", "line_number": 99, "usage_type": "call"}, {"api_name": "services.models.ServiceType.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "services.models.ServiceType", "line_number": 99, "usage_type": "name"}, {"api_name": "services.managers.DEFAULT_INDUSTRY_CODE", "line_number": 105, "usage_type": "name"}, {"api_name": "projects.models.Project", "line_number": 108, "usage_type": "name"}, {"api_name": "django.forms.widgets.CheckboxSelectMultiple", "line_number": 118, "usage_type": "call"}, {"api_name": "django.forms.RadioSelect", "line_number": 119, "usage_type": "call"}, {"api_name": "django.forms.RadioSelect", "line_number": 120, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 126, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 126, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 130, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 130, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 132, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 132, "usage_type": "name"}, {"api_name": "finance.models.PAYMENT_CHOICES", "line_number": 132, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 132, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 133, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 133, "usage_type": "name"}, {"api_name": "django.forms.BooleanField", "line_number": 134, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 134, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 135, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 135, "usage_type": "name"}, {"api_name": "django.forms.BooleanField", "line_number": 136, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 136, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 136, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 138, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 138, "usage_type": "name"}, {"api_name": "tinymce.TinyMCE", "line_number": 138, "usage_type": "call"}, {"api_name": "django.conf.settings.TINYMCE_COLS", "line_number": 138, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 138, "usage_type": "name"}, {"api_name": "django.conf.settings.TINYMCE_ROWS", "line_number": 138, "usage_type": "attribute"}, {"api_name": "django.forms.CharField", "line_number": 139, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 139, "usage_type": "name"}, {"api_name": "tinymce.TinyMCE", "line_number": 139, "usage_type": "call"}, {"api_name": "django.conf.settings.TINYMCE_COLS", "line_number": 139, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 139, "usage_type": "name"}, {"api_name": "django.conf.settings.TINYMCE_ROWS", "line_number": 139, "usage_type": "attribute"}, {"api_name": "django.forms.CharField", "line_number": 140, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 140, "usage_type": "name"}, {"api_name": "tinymce.TinyMCE", "line_number": 140, "usage_type": "call"}, {"api_name": "django.conf.settings.TINYMCE_COLS", "line_number": 140, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 140, "usage_type": "name"}, {"api_name": "django.conf.settings.TINYMCE_ROWS", "line_number": 140, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 144, "usage_type": "call"}, {"api_name": "accounts.models.CircusUser.objects.filter", "line_number": 160, "usage_type": "call"}, {"api_name": "accounts.models.CircusUser.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "accounts.models.CircusUser", "line_number": 160, "usage_type": "name"}, {"api_name": "django.conf.settings.VIA_USER_TYPE", "line_number": 160, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 160, "usage_type": "name"}, {"api_name": "shared.group_permissions.PROTECTED_HEALTH_INFORMATION_GROUP", "line_number": 160, "usage_type": "name"}, {"api_name": "accounts.models.CircusUser.objects.filter", "line_number": 162, "usage_type": "call"}, {"api_name": "accounts.models.CircusUser.objects", "line_number": 162, "usage_type": "attribute"}, {"api_name": "accounts.models.CircusUser", "line_number": 162, "usage_type": "name"}, {"api_name": "django.conf.settings.VIA_USER_TYPE", "line_number": 162, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 162, "usage_type": "name"}, {"api_name": "services.managers.DEFAULT_INDUSTRY_CODE", "line_number": 167, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 169, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 173, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 177, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 181, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 186, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 192, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 196, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 197, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 198, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 199, "usage_type": "call"}, {"api_name": "accounts.models.CircusUser.objects.none", "line_number": 219, "usage_type": "call"}, {"api_name": "accounts.models.CircusUser.objects", "line_number": 219, "usage_type": "attribute"}, {"api_name": "accounts.models.CircusUser", "line_number": 219, "usage_type": "name"}, {"api_name": "clients.models", "line_number": 221, "usage_type": "name"}, {"api_name": "clients.models.Client.objects.filter", "line_number": 221, "usage_type": "call"}, {"api_name": "clients.models.Client.objects", "line_number": 221, "usage_type": "attribute"}, {"api_name": "clients.models.Client", "line_number": 221, "usage_type": "name"}, {"api_name": "clients.models", "line_number": 222, "usage_type": "name"}, {"api_name": "clients.models", "line_number": 223, "usage_type": "argument"}, {"api_name": "clients.models", "line_number": 224, "usage_type": "name"}, {"api_name": "shared.forms.form_get_client_id", "line_number": 229, "usage_type": "call"}, {"api_name": "shared.forms.form_get_client_poc", "line_number": 234, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 240, "usage_type": "call"}, {"api_name": "django.forms.widgets.HiddenInput", "line_number": 244, "usage_type": "call"}, {"api_name": "projects.models.Project", "line_number": 247, "usage_type": "name"}, {"api_name": "shared.widgets.DateTimeWidget", "line_number": 286, "usage_type": "call"}, {"api_name": "shared.widgets.DateTimeWidget", "line_number": 287, "usage_type": "call"}, {"api_name": "shared.widgets.DateTimeWidget", "line_number": 288, "usage_type": "call"}, {"api_name": "shared.widgets.DateTimeWidget", "line_number": 289, "usage_type": "call"}, {"api_name": "shared.widgets.DateTimeWidget", "line_number": 290, "usage_type": "call"}, {"api_name": "shared.widgets.DateTimeWidget", "line_number": 291, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 292, "usage_type": "call"}, {"api_name": "django.forms.RadioSelect", "line_number": 293, "usage_type": "call"}, {"api_name": "django.forms.RadioSelect", "line_number": 294, "usage_type": "call"}, {"api_name": "django.forms.RadioSelect", "line_number": 295, "usage_type": "call"}, {"api_name": "shared.widgets.DateTimeWidget", "line_number": 296, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 300, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 300, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 301, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 301, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 304, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 304, "usage_type": "name"}, {"api_name": "django.forms.BooleanField", "line_number": 305, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 305, "usage_type": "name"}, {"api_name": "django.forms.BooleanField", "line_number": 306, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 306, "usage_type": "name"}, {"api_name": "django.forms.BooleanField", "line_number": 307, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 307, "usage_type": "name"}, {"api_name": "django.forms.BooleanField", "line_number": 308, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 308, "usage_type": "name"}, {"api_name": "projects.models.ProjectJobOptions", "line_number": 311, "usage_type": "name"}, {"api_name": "django.forms.modelformset_factory", "line_number": 321, "usage_type": "call"}, {"api_name": "projects.models.ProjectJobOptions", "line_number": 321, "usage_type": "argument"}, {"api_name": "django.forms.ModelForm", "line_number": 324, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 324, "usage_type": "name"}, {"api_name": "django.forms.DecimalField", "line_number": 325, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 325, "usage_type": "name"}, {"api_name": "django.forms.DecimalField", "line_number": 326, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 326, "usage_type": "name"}, {"api_name": "django.forms.DecimalField", "line_number": 327, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 327, "usage_type": "name"}, {"api_name": "django.forms.DecimalField", "line_number": 328, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 328, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 332, "usage_type": "call"}, {"api_name": "services.models.ServiceType.objects.filter", "line_number": 350, "usage_type": "call"}, {"api_name": "services.models.ServiceType.objects", "line_number": 350, "usage_type": "attribute"}, {"api_name": "services.models.ServiceType", "line_number": 350, "usage_type": "name"}, {"api_name": "services.managers.DEFAULT_INDUSTRY_CODE", "line_number": 353, "usage_type": "name"}, {"api_name": "accounts.models.CircusUser.objects.none", "line_number": 355, "usage_type": "call"}, {"api_name": "accounts.models.CircusUser.objects", "line_number": 355, "usage_type": "attribute"}, {"api_name": "accounts.models.CircusUser", "line_number": 355, "usage_type": "name"}, {"api_name": "clients.models", "line_number": 357, "usage_type": "name"}, {"api_name": "clients.models.Client.objects.filter", "line_number": 357, "usage_type": "call"}, {"api_name": "clients.models.Client.objects", "line_number": 357, "usage_type": "attribute"}, {"api_name": "clients.models.Client", "line_number": 357, "usage_type": "name"}, {"api_name": "clients.models", "line_number": 358, "usage_type": "name"}, {"api_name": "clients.models", "line_number": 359, "usage_type": "argument"}, {"api_name": "clients.models", "line_number": 360, "usage_type": "name"}, {"api_name": "shared.forms.form_get_client_id", "line_number": 363, "usage_type": "call"}, {"api_name": "shared.forms.form_get_client_poc", "line_number": 368, "usage_type": "call"}, {"api_name": "projects.models.Project", "line_number": 374, "usage_type": "name"}, {"api_name": "django.forms.widgets.CheckboxSelectMultiple", "line_number": 390, "usage_type": "call"}]}
{"seq_id": "44010673911", "text": "from uuid import UUID\n\nimport pytest\nfrom flask import Flask\nfrom geolite2 import geolite2\n\nfrom sinfonia.deployment_repository import DeploymentRepository\nfrom sinfonia.matchers import match_by_location, match_by_network, match_random\n\nGOOD_UUID = \"00000000-0000-0000-0000-000000000000\"\nGOOD_CONTENT = \"\"\"\\\nchart: example\nversion: 0.1.0\nrestricted: false\n\"\"\"\nBAD_UUID = \"00000000-0000-0000-0000-000000000001\"\nBAD_CONTENT = \"\"\"\\\nchart: example\n\"\"\"\nRESTRICTED_UUID = \"00000000-0000-0000-0000-000000000002\"\nRESTRICTED_CONTENT = \"\"\"\\\nchart: private\nversion: 0.1.0\n\"\"\"\n\n\n@pytest.fixture(scope=\"session\")\ndef repository(tmp_path_factory):\n    repo = tmp_path_factory.mktemp(\"repository\")\n    (repo / GOOD_UUID).with_suffix(\".yaml\").write_text(GOOD_CONTENT)\n    (repo / BAD_UUID).with_suffix(\".yaml\").write_text(BAD_CONTENT)\n    (repo / RESTRICTED_UUID).with_suffix(\".yaml\").write_text(RESTRICTED_CONTENT)\n    return DeploymentRepository(repo)\n\n\n@pytest.fixture(scope=\"session\")\ndef good_uuid():\n    return UUID(GOOD_UUID)\n\n\n@pytest.fixture(scope=\"session\")\ndef bad_uuid():\n    return UUID(BAD_UUID)\n\n\n@pytest.fixture(scope=\"session\")\ndef restricted_uuid():\n    return UUID(RESTRICTED_UUID)\n\n\n@pytest.fixture(scope=\"session\")\ndef flask_app():\n    app = Flask(\"test\")\n    app.config[\"geolite2_reader\"] = geolite2.reader()\n    app.config[\"match_functions\"] = [match_by_network, match_by_location, match_random]\n    return app\n\n\n@pytest.fixture(scope=\"session\")\ndef example_wgkey():\n    return \"YpdTsMtb/QCdYKzHlzKkLcLzEbdTK0vP4ILmdcIvnhc=\"\n", "repo_name": "cmusatyalab/sinfonia", "sub_path": "tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 1532, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sinfonia.deployment_repository.DeploymentRepository", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 27, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 36, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 43, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 41, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 48, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 53, "usage_type": "call"}, {"api_name": "geolite2.geolite2.reader", "line_number": 54, "usage_type": "call"}, {"api_name": "geolite2.geolite2", "line_number": 54, "usage_type": "name"}, {"api_name": "sinfonia.matchers.match_by_network", "line_number": 55, "usage_type": "name"}, {"api_name": "sinfonia.matchers.match_by_location", "line_number": 55, "usage_type": "name"}, {"api_name": "sinfonia.matchers.match_random", "line_number": 55, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 51, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 59, "usage_type": "call"}]}
{"seq_id": "27450415393", "text": "import random\nimport sys\nimport os\n\nimport pygame as pg\n\nmain_dir = os.path.split(os.path.abspath(__file__))[0]\n\nclass Screen:\n    def __init__(self, title, wh, img_path):\n        # 練習１\n        pg.display.set_caption(title) \n        self.sfc = pg.display.set_mode(wh)\n        self.rct = self.sfc.get_rect()\n        self.bgi_sfc = pg.image.load(img_path) \n        self.bgi_rct = self.bgi_sfc.get_rect()\n\n    def blit(self):\n        self.sfc.blit(self.bgi_sfc, self.bgi_rct) \n\n    \nclass Bird:\n    key_delta = {\n        pg.K_UP:    [0, -1],\n        pg.K_DOWN:  [0, +1],\n        pg.K_LEFT:  [-1, 0],\n        pg.K_RIGHT: [+1, 0],\n    }\n\n    def __init__(self, img_path, ratio, xy):\n        self.sfc = pg.image.load(img_path) #\"fig/6.png\"\n        self.sfc = pg.transform.rotozoom(self.sfc, 0, ratio) #2.0\n        self.rct = self.sfc.get_rect()\n        self.rct.center = xy #900, 400\n\n\n    def blit(self, scr:Screen):\n        scr.sfc.blit(self.sfc, self.rct)\n\n    def update(self, scr:Screen):\n        key_dct = pg.key.get_pressed()\n        for key, delta in Bird.key_delta.items():\n            if key_dct[key]:\n                self.rct.centerx += delta[0]\n                self.rct.centery += delta[1]\n            if check_bound(self.rct, scr.rct) != (+1, +1):\n                self.rct.centerx -= delta[0]\n                self.rct.centery -= delta[1]\n        self.blit(scr)\n\n\nclass Brids:\n    def __init__(self, img_path, ratio, xy):\n        self.sfc = pg.image.load(img_path)\n        self.sfc = pg.transform.rotozoom(self.sfc, 0, ratio) #2.0\n        self.rct = self.sfc.get_rect()\n        self.rct.center = xy\n\n    def blit(self, scr:Screen):\n        scr.sfc.blit(self.sfc, self.rct)\n\n\n\nclass Bomb:\n    def __init__(self, color, rad, vxy, scr:Screen):\n\n        self.sfc = pg.Surface((2*rad, 2*rad)) # 正方形の空のSurface\n        self.sfc.set_colorkey((0, 0, 0))\n        self.color = color\n        pg.draw.circle(self.sfc, color, (rad, rad), rad)\n        self.rct = self.sfc.get_rect()\n        self.rct.centerx = random.randint(0, scr.rct.width)\n        self.rct.centery = random.randint(0, scr.rct.height)\n        self.vx, self.vy = vxy\n        \n    def blit(self, scr:Screen):\n        scr.sfc.blit(self.sfc, self.rct) \n        \n    def update(self, scr:Screen):\n        self.rct.move_ip(self.vx, self.vy)\n        yoko, tate = check_bound(self.rct, scr.rct)\n        self.vx *= yoko\n        self.vy *= tate\n        self.blit(scr) \n\n\nclass Shield: #保護場の追加\n    def __init__(self, color, x, y, hw, scr:Screen):\n        self.sfc = pg.Surface((hw+50, hw+50))\n        pg.draw.rect(self.sfc, color, (x, y, hw, hw))\n        self.rct = self.sfc.get_rect()\n        self.rct.centerx = random.randint(0, scr.rct.width)\n        self.rct.centery = random.randint(0, scr.rct.height)\n\n    def blit(self, scr:Screen):\n        scr.sfc.blit(self.sfc, self.rct) \n\n\n    \ndef load_sound(file):#音声の追加\n    \"\"\"because pygame can be be compiled without mixer.\"\"\"\n    if not pg.mixer:\n        return None\n    file = os.path.join(main_dir, \"data\", file)\n    try:\n        sound = pg.mixer.Sound(file)\n        return sound\n    except pg.error:\n        print(\"Warning, unable to load, %s\" % file)\n    return None\n\n\ndef check_bound(obj_rct, scr_rct):\n    \"\"\"\n    第1引数：こうかとんrectまたは爆弾rect\n    第2引数：スクリーンrect\n    範囲内：+1／範囲外：-1\n    \"\"\"\n    yoko, tate = +1, +1\n    if obj_rct.left < scr_rct.left or scr_rct.right < obj_rct.right:\n        yoko = -1\n    if obj_rct.top < scr_rct.top or scr_rct.bottom < obj_rct.bottom:\n        tate = -1\n    return yoko, tate\n\n\ndef main():\n    clock =pg.time.Clock()\n\n    # 練習１\n    scr = Screen(\"負けるな！こうかとん\", (1600, 900), \"fig/pg_bg.jpg\" )\n\n    if pg.mixer:\n        music = os.path.join(main_dir, \"data\", \"house_lo.wav\")\n        pg.mixer.music.load(music)\n        pg.mixer.music.play(-1)\n\n    hogo = Shield(\"green\", 300, 300, 100, scr)\n    hogo.blit(scr)\n\n    # 練習３\n    kkt = Bird(\"fig/6.png\", 2.0, (900, 400))\n    kkt_friend = Bird(\"fig/8.png\", 1.0, (1400, 100))\n    kkt_friend.blit(scr)\n    # scrn_sfcにtori_rctに従って，tori_sfcを貼り付ける\n    kkt.update(scr)\n\n\n\n    # 練習５\n    bkd_lst = []\n    colors = [\"red\", \"green\", \"blue\", \"yellow\", \"magenta\"]\n    for i in range(0, 10):\n        color = colors[i%5]\n        vx = random.choice([-1, +1])\n        vy = random.choice([-1, +1])\n        bkd = Bomb(color, 10, (vx, vy), scr)\n        bkd_lst.append(bkd)\n\n\n    # 練習２\n    while True:\n        scr.blit()   # scrn_sfc.blit(pgbg_sfc, pgbg_rct) \n        for event in pg.event.get():\n            if event.type == pg.QUIT:\n                return\n        hogo.blit(scr)\n        kkt.update(scr)\n        kkt.blit(scr)\n        for i in range(10):\n            bkd_lst[i].update(scr)    \n            if kkt.rct.colliderect(bkd_lst[i].rct):\n                if kkt.rct in hogo.rct: #こうかとんが保護場にいると\n                    pass\n                else:\n                    return\n\n        pg.display.update()\n        clock.tick(1000)\n\n\nif __name__ == \"__main__\":\n    pg.init()\n    main()\n    pg.quit()\n    sys.exit()\n", "repo_name": "Liunan0721/ProjExD", "sub_path": "ex05/fight_kokaton.py", "file_name": "fight_kokaton.py", "file_ext": "py", "file_size_in_byte": 5161, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.split", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 70, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 72, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 90, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 92, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 106, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pygame.error", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 128, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 135, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 135, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 136, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 136, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 155, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 156, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 164, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 178, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 178, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 183, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 185, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 186, "usage_type": "call"}]}
{"seq_id": "73252890683", "text": "from sympy import expand, poly, simplify\nfrom cartesian import *\n\ndef foot(P1, P2, P3):\n    x, y = symbols('x, y')\n    P1P2 = line(P1, P2)\n    P3H = Eq((P1[0] - P2[0])*(x - P3[0]) + (P1[1] - P2[1])*(y - P3[1]), 0)\n    return intersect(P1P2, P3H)\n\ndef circle(P1, P2, P3):\n    # return F(x, y) such that F(x, y) = 0 is the circle's equation\n    D, E, F, x, y = symbols('D, E, F, x, y')\n    circle_eq = Eq(x**2 + y**2 + D*x + E*y + F, 0)\n    circle_eqs = []\n    circle_eqs.append(circle_eq.subs(x, P1[0]).subs(y, P1[1]))\n    circle_eqs.append(circle_eq.subs(x, P2[0]).subs(y, P2[1]))\n    circle_eqs.append(circle_eq.subs(x, P3[0]).subs(y, P3[1]))\n    s = solve(circle_eqs, (D, E, F))\n    return x**2 + y**2 + s[D]*x + s[E]*y + s[F]\n\ndef center(c):\n    x, y = symbols('x, y')\n    return cancel(-poly(c, x).nth(1)/2), cancel(-poly(c, y).nth(1)/2)\n\ndef cos2eq(a2, b2, c2, d2, e2, f2):\n    left_num = fraction(cancel(a2 + b2 - c2))\n    left_den1 = fraction(cancel(a2))\n    left_den2 = fraction(cancel(b2))\n    right_num = fraction(cancel(d2 + e2 - f2))\n    right_den1 = fraction(cancel(d2))\n    right_den2 = fraction(cancel(e2))\n    left = left_num[0]**2*left_den1[1]*left_den2[1]*right_num[1]**2*right_den1[0]*right_den2[0]\n    right = right_num[0]**2*right_den1[1]*right_den2[1]*left_num[1]**2*left_den1[0]*left_den2[0]\n    return expand(left - right) == 0\n\ndef main():\n    a, b, c, d, e, x, y = symbols('a, b, c, d, e, x, y')\n    A, B, C, P = (0, a), (b, 0), (c, 0), (d, e)\n    D, E, F = foot(B, C, P), foot(C, A, P), foot(A, B, P)\n    print('D:', D)\n    print('E:', E)\n    print('F:', F)\n    pedal_circle = circle(D, E, F)\n    print('Pedal Circle:', pedal_circle, '= 0')\n    M = center(pedal_circle)\n    print('M:', M)\n    Q = (2*M[0] - P[0], 2*M[1] - P[1])\n    print('Q:', Q)\n    G, H, J = foot(B, C, Q), foot(C, A, Q), foot(A, B, Q)\n    print('G:', G)\n    print('H:', H)\n    print('J:', J)\n    print('Is G on Pedal Circle?', simplify(pedal_circle.subs(x, G[0]).subs(y, G[1])) == 0)\n    print('Is H on Pedal Circle?', simplify(pedal_circle.subs(x, H[0]).subs(y, H[1])) == 0)\n    print('Is J on Pedal Circle?', simplify(pedal_circle.subs(x, J[0]).subs(y, J[1])) == 0)\n    AB2, AC2, BC2 = dist2(A, B), dist2(A, C), dist2(B, C)\n    PA2, PB2, PC2 = dist2(P, A), dist2(P, B), dist2(P, C)\n    QA2, QB2, QC2 = dist2(Q, A), dist2(Q, B), dist2(Q, C)\n    print('cos²∠PAB = cos²∠QAC?', cos2eq(PA2, AB2, PB2, QA2, AC2, QC2))\n    print('cos²∠PAC = cos²∠QAB?', cos2eq(PA2, AC2, PC2, QA2, AB2, QB2))\n    print('cos²∠PBA = cos²∠QBC?', cos2eq(PB2, AB2, PA2, QB2, BC2, QC2))\n    print('cos²∠PBC = cos²∠QBA?', cos2eq(PB2, BC2, PC2, QB2, AB2, QA2))\n    print('cos²∠PCA = cos²∠QCB?', cos2eq(PC2, AC2, PA2, QC2, BC2, QB2))\n    print('cos²∠PCB = cos²∠QCA?', cos2eq(PC2, BC2, PB2, QC2, AC2, QA2))\n\nif __name__ == '__main__':\n    main()", "repo_name": "auntyellow/math", "sub_path": "pythagoras/isogonal.py", "file_name": "isogonal.py", "file_ext": "py", "file_size_in_byte": 2845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sympy.poly", "line_number": 23, "usage_type": "call"}, {"api_name": "sympy.expand", "line_number": 34, "usage_type": "call"}, {"api_name": "sympy.simplify", "line_number": 53, "usage_type": "call"}, {"api_name": "sympy.simplify", "line_number": 54, "usage_type": "call"}, {"api_name": "sympy.simplify", "line_number": 55, "usage_type": "call"}]}
{"seq_id": "13473522435", "text": "#! python3\r\n# combinePdfs.py - Combines all the PDFs in the current working directory into\r\n#                   a single pdf.\r\n\r\nimport os, PyPDF2\r\n\r\n# Get all the PDF filename\r\npdfFiles = []\r\nfor filename in os.listdir('.'):\r\n    if filename.endswith('.pdf'):\r\n        pdfFiles.append(filename)\r\npdfFiles.sort(key=str.lower) # case insensitive sorting\r\n\r\npdfWriter = PyPDF2.PdfFileWriter()\r\ncounter = 0\r\n# Open each PDF\r\nfor pdf in pdfFiles:\r\n    pdfFileObj = open(pdf, 'rb')\r\n    pdfReader = PyPDF2.PdfFileReader(pdfFileObj)\r\n    for page in range(1, pdfReader.numPages):\r\n        pageObj = pdfReader.getPage(page)\r\n        pdfWriter.addPage(pageObj)\r\n\r\npdfOutput = open('Chapter 13_all.pdf', 'wb')\r\npdfWriter.write(pdfOutput)\r\npdfOutput.close()\r\n\r\n\r\n# Similar Programs:\r\n## Cut out specific pages from PDFs.\r\n## Reorder pages in a PDF.\r\n## Create a PDF from only those pages that have some specific text,\r\n    ##identified by 'extractText()'\r\n", "repo_name": "artsR/Automate-the-Boring-Stuff-with-Python", "sub_path": "Chapter 13/Chapter 13 - Project - Combining Select Pages from many PDFs.py", "file_name": "Chapter 13 - Project - Combining Select Pages from many PDFs.py", "file_ext": "py", "file_size_in_byte": 946, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "46", "api": [{"api_name": "os.listdir", "line_number": 9, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileWriter", "line_number": 14, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "18647083918", "text": "import pytest\nfrom pydantic import ValidationError\nfrom geocoder_studitemps.geocoder import Address\nfrom i18naddress import InvalidAddress\n\n\ndef test_works_on_valid_addresses():\n    addresses = [\n        {\n            \"data\" : {\n                \"street\" : \"im mediapark 4a\",\n                \"postal_code\" : \"50670\",\n                \"city\" : \"köln\"\n            },\n            \"str\" : \"<Address (im mediapark 4a, 50670 köln) (DE)>\",\n            \"req_str\" : \"im mediapark 4a, 50670 köln\",\n        }\n    ]\n\n    for addr_info in addresses:\n        addr = Address(**addr_info[\"data\"])\n\n        assert addr.country_code == \"DE\"\n        assert addr.to_request_string() == addr_info[\"req_str\"]\n        assert str(addr) == addr_info[\"str\"]\n\n\ndef test_raises_validation_error_on_invalid_addresses():\n    addresses = [\n        {\n            \"data\" : {\n                \"street\" : \"im mediapark 4a\",\n                \"postal_code\" : \"800 45\",\n                \"city\" : \"Madrid\"\n            },\n            \"err\" : r\"Invalid address*\"\n        }\n    ]\n\n    for addr_info in addresses:\n        with pytest.raises(InvalidAddress, match=addr_info[\"err\"]):\n            addr = Address(**addr_info[\"data\"])\n            addr.validate()", "repo_name": "STUDITEMPS/py-geocoder-studitemps", "sub_path": "tests/address_validation_test.py", "file_name": "address_validation_test.py", "file_ext": "py", "file_size_in_byte": 1212, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "geocoder_studitemps.geocoder.Address", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 41, "usage_type": "call"}, {"api_name": "i18naddress.InvalidAddress", "line_number": 41, "usage_type": "argument"}, {"api_name": "geocoder_studitemps.geocoder.Address", "line_number": 42, "usage_type": "call"}]}
{"seq_id": "8035154823", "text": "#!/usr/bin/env python\n\"\"\"\n\nNeural SPARQL Machines - Script to filter data by a given criterion.\n\n'SPARQL as a Foreign Language' by Tommaso Soru and Edgard Marx et al., SEMANTiCS 2017\nhttps://arxiv.org/abs/1708.07624\n\nVersion 2.0.0\n\n\"\"\"\nimport argparse\nimport collections\nimport json\nimport os\nimport sys\n\nfrom generator_utils import encode, save_cache, extract_encoded_entities\nimport importlib\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    requiredNamed = parser.add_argument_group('required named arguments')\n    requiredNamed.add_argument('--dataset', dest='dataset', metavar='data_300.en', help='dataset', required=True)\n    requiredNamed.add_argument('--used_resources', dest='used_resources', metavar='used_resources.json', help='json file', required=True)\n    requiredNamed.add_argument('--minimum', dest='minimum', metavar='15', help='minimum number of occurence', required=True)\n    requiredNamed.add_argument('--comp', dest='comp', metavar='all|any', help='require minimum for all/any resources in the query', required=True)\n    args = parser.parse_args()\n\n    dataset_file = args.dataset\n    used_resources_file = args.used_resources\n    MINIMUM = int(args.minimum)\n    COMP = any if args.comp == 'any' else all\n\n    importlib.reload(sys)\n    sys.setdefaultencoding(\"utf-8\")\n\n\n    dataset_root, _ = os.path.splitext(dataset_file)\n    used_resources_root, _ = os.path.splitext(used_resources_file)\n    filtered_sparql_file = '{}_filtered_{:d}_{}.sparql'.format(dataset_root, MINIMUM, COMP.__name__)\n    filtered_en_file = '{}_filtered_{:d}_{}.en'.format(dataset_root, MINIMUM, COMP.__name__)\n\n    used_resources = collections.Counter(json.loads(open(used_resources_file).read()))\n    filtered_resources = [elem_cnt for elem_cnt in list(used_resources.items()) if elem_cnt[1] >= MINIMUM]\n    save_cache('{}_filter_{:d}.json'.format(used_resources_root, MINIMUM), collections.Counter(dict(filtered_resources)))\n    valid_encoded_resources = [encode(elem_cnt1[0]) for elem_cnt1 in filtered_resources]\n    check = lambda encoded_entity : encoded_entity in valid_encoded_resources\n\n    valid_lines = []\n    filtered_queries = []\n    with open(dataset_root+'.sparql', 'r') as sparql_file:\n        for linenumber, line in enumerate(sparql_file):\n            entities = extract_encoded_entities(line)\n            valid = COMP(list(map(check, entities)))\n            if valid:\n                filtered_queries.append(line)\n                valid_lines.append(linenumber)\n\n    filtered_questions = []\n    with open(dataset_root+'.en', 'r') as en_file:\n        for linenumber, line in enumerate(en_file):\n            if linenumber in valid_lines:\n                filtered_questions.append(line)\n\n    with open(filtered_en_file, 'w') as filtered:\n        filtered.writelines(filtered_questions)\n\n    with open(filtered_sparql_file, 'w') as filtered:\n        filtered.writelines(filtered_queries)\n", "repo_name": "LiberAI/NSpM", "sub_path": "nspm/filter_dataset.py", "file_name": "filter_dataset.py", "file_ext": "py", "file_size_in_byte": 2919, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 212, "dataset": "github-code", "pt": "46", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "importlib.reload", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.setdefaultencoding", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 45, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "generator_utils.save_cache", "line_number": 47, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 47, "usage_type": "call"}, {"api_name": "generator_utils.encode", "line_number": 48, "usage_type": "call"}, {"api_name": "generator_utils.extract_encoded_entities", "line_number": 55, "usage_type": "call"}]}
{"seq_id": "73714353740", "text": "''' Implements tests for the Kako configuration parser. '''\n\nimport os\nimport yaml\nimport unittest\nimport coverage\n\nimport kako\n\n\nclass KakoConfigTestCase(unittest.TestCase):\n    ''' Defines tests for the Kako configuration parser. '''\n\n    def setUp(self):\n        ''' Ensure the application is setup for testing. '''\n        self.fixtures_path = os.path.join(\n            os.path.dirname(os.path.abspath(__file__)),\n            'fixtures/'\n        )\n\n    def tearDown(self):\n        ''' Ensure everything is torn down between tests. '''\n        pass\n\n    def test_config_validation(self):\n        ''' Ensures that configurations can be validated. '''\n        # Ensure a valid config loads without error.\n        valid = yaml.safe_load(\n            open(os.path.join(self.fixtures_path, 'valid_config.yaml'))\n        )\n        self.assertIsNone(kako.config.validate(valid))\n\n        # Ensure an invalid config throws an AttributeError\n        invalid = yaml.safe_load(\n            open(os.path.join(self.fixtures_path, 'invalid_config.yaml'))\n        )\n        self.assertRaises(AttributeError, kako.config.validate, invalid)\n", "repo_name": "darkarnium/kako", "sub_path": "tests/test_config.py", "file_name": "test_config.py", "file_ext": "py", "file_size_in_byte": 1127, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "46", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "kako.config.validate", "line_number": 31, "usage_type": "call"}, {"api_name": "kako.config", "line_number": 31, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "kako.config", "line_number": 37, "usage_type": "attribute"}]}
{"seq_id": "40763252327", "text": "from fastapi import FastAPI, HTTPException, Body\nfrom fastapi.encoders import jsonable_encoder\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom datetime import datetime\n\n#App object\napp = FastAPI()\n\nfrom models import Recipe, UpdateRecipeModel\n\nfrom database import(\n    fetch_one_recipe,\n    fetch_all_recipes,\n    create_recipe,\n    update_recipe,\n    remove_recipe\n)\n\norigins = [\"*\"]\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=origins,\n    allow_credentials = True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n# @app.get(\"/\")\n# def read_root():\n#     return {\"Ping\":\"Pong\"}\n\n@app.get(\"/\")\nasync def read_root():\n    response = {\"hello\": \"world\"}\n    return response\n\n@app.get(\"/api/recipe\", tags=[\"Recipes\"])\nasync def get_recipes():\n    response = await fetch_all_recipes()\n    return response\n\n@app.get(\"/api/recipe/{id}\", response_description=\"Get a single recipe\", response_model=Recipe, tags=[\"Recipes\"])\nasync def get_recipe_by_id(id: str):\n    response = await fetch_one_recipe(id)\n    if response:\n        return response\n    raise HTTPException(404, f\"ID {id} not found\")\n\n@app.post(\"/api/recipe\", response_description=\"Add a new recipe\", response_model=Recipe, tags=[\"Recipes\"])\nasync def post_recipe(recipe: Recipe = Body(...)):\n    recipe = jsonable_encoder(recipe)\n    response = await create_recipe(recipe)\n    if response:\n        return response\n    raise HTTPException(400, \"Something went wrong / Bad Request\")\n\n@app.put(\"/api/recipe/{id}\", response_description=\"Update a recipe\", response_model=Recipe, tags=[\"Recipes\"])\nasync def put_recipe(id: str, recipe: UpdateRecipeModel = Body(...)):\n    recipe = {k: v for k, v in recipe.dict().items() if v is not None}\n    if len(recipe) >= 1:\n        response = await update_recipe(id, recipe)\n    if response:\n        return response\n    raise HTTPException(404, f\"There is no recipe with this id {id}\")\n\n@app.delete(\"/api/recipe/{id}\", response_description=\"Delete a recipe\", tags=[\"Recipes\"])\nasync def delete_recipe(id: str):\n    response = await remove_recipe(id)\n    if response:\n        return \"Successfully deleted recipe item\"\n    raise HTTPException(404, f\"There is no recipe item with this id:{id}\")", "repo_name": "CCKJon/cookbook", "sub_path": "backend/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2208, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "fastapi.FastAPI", "line_number": 7, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 22, "usage_type": "argument"}, {"api_name": "database.fetch_all_recipes", "line_number": 40, "usage_type": "call"}, {"api_name": "database.fetch_one_recipe", "line_number": 45, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Recipe", "line_number": 43, "usage_type": "name"}, {"api_name": "models.Recipe", "line_number": 51, "usage_type": "name"}, {"api_name": "fastapi.Body", "line_number": 51, "usage_type": "call"}, {"api_name": "fastapi.encoders.jsonable_encoder", "line_number": 52, "usage_type": "call"}, {"api_name": "database.create_recipe", "line_number": 53, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Recipe", "line_number": 50, "usage_type": "name"}, {"api_name": "models.UpdateRecipeModel", "line_number": 59, "usage_type": "name"}, {"api_name": "fastapi.Body", "line_number": 59, "usage_type": "call"}, {"api_name": "database.update_recipe", "line_number": 62, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Recipe", "line_number": 58, "usage_type": "name"}, {"api_name": "database.remove_recipe", "line_number": 69, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 72, "usage_type": "call"}]}
{"seq_id": "44062954282", "text": "import unittest\nfrom logging import INFO\n\nfrom common import exceptions\n\nimport logger\nfrom series import FileParser\n\n\nclass KnownValues(unittest.TestCase):\n    File_SxxExx = {}\n    File_SxxExx['FileName'] = \"\"\n    File_SxxExx['SeriesName'] = 'Covert Affairs'\n    File_SxxExx['SeasonNum'] = 1\n    File_SxxExx['EpisodeNums'] = [1]\n    File_SxxExx['type'] = 'episode'\n    File_SxxExx['Ext'] = 'ext'\n\nclass FileParserExceptions(unittest.TestCase):\n\n    def setUp(self):\n\n        TRACE = 5\n        VERBOSE = 15\n\n        logger.initialize(unit_test=True, level=INFO)\n#        logger.start(level=ERROR)\n\n        self.library = FileParser()\n\n    def test_FileParser_exception_case_001(self):\n        # should raise an exception for missing or invalid patient_id\n        KnownValues.File_SxxExx['FileName'] = '/mnt/Download/Bittorrent/the.big.bang.theory.season.1.avi'\n        self.assertRaises(exceptions.InvalidFilename, self.library.getFileDetails, KnownValues.File_SxxExx['FileName'])\n\n    def theSuite(self):\n        suite = unittest.TestLoader().loadTestsFromTestCase(self)\n        return suite\n\nif __name__ == '__main__':\n    suite = FileParserExceptions.theSuite()\n    unittest.TextTestRunner(verbosity=2).run(suite)\n\n", "repo_name": "stampedeboss/DadVision", "sub_path": "tests/library/series/fileparser/exceptions/test_exceptions_grp_1.py", "file_name": "test_exceptions_grp_1.py", "file_ext": "py", "file_size_in_byte": 1218, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logger.initialize", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "name"}, {"api_name": "series.FileParser", "line_number": 29, "usage_type": "call"}, {"api_name": "common.exceptions.InvalidFilename", "line_number": 34, "usage_type": "attribute"}, {"api_name": "common.exceptions", "line_number": 34, "usage_type": "name"}, {"api_name": "unittest.TestLoader", "line_number": 37, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 42, "usage_type": "call"}]}
{"seq_id": "69828697740", "text": "import logging\nimport os\nimport copy\n\nlog = logging.getLogger(__name__)\n\n# Array of all valid language codes.\nVALID_LANGUAGE_CODES = (\n    \"eng bas n6x hex\"\n)\n\n# Codes for which we have webtext but no fonts:\nUNUSABLE_LANGUAGE_CODES = \"\"\n\n# ASCIIをカバーしたフォント\nBASE_FONTS = [\n    \"PixelMplus10\",\n    \"PixelMplus12\",\n    \"VL Gothic\",\n    \"TakaoGothic\",\n    \"TakaoMincho\",\n    \"Noto Sans Mono CJK JP Regular\",\n    \"Noto Serif CJK JP SemiBold\",\n    \"P6mk2mode1page1font\",\n    \"P6mk2mode5page1font\",\n    \"P6 Printer Routine\",\n    \"PiO Printer A\",\n    \"N-Font_Original\",\n    \"MSX-FONT-Wide\",\n]\n\n# ASCII特化フォント(ひらがなが出せない)\nBAS_FONTS = copy.deepcopy(BASE_FONTS)\nBAS_FONTS.extend([\n    \"DotMatrix weight=101\",\n    \"Commodore PET\",\n    \"lcdfont\",\n    \"Verily Serif Mono\",\n    \"Arcade\",\n    \"Go Mono\",\n])\n\nHEX_FONTS = BAS_FONTS\n\n# 国産BASIC特化フォント\n# 美咲フォントは半角文字が小さすぎるのでASCIIのみの言語には含めない\n# (ひらがな、グラフィック文字の学習に使いたい)\nN6X_FONTS = copy.deepcopy(BASE_FONTS)\nN6X_FONTS.extend([\n    \"MisakiGothic\",\n    \"MisakiMincho\",\n])\n\nVERTICAL_FONTS = []\n\nFLAGS_webtext_prefix = os.environ.get(\"FLAGS_webtext_prefix\", \"\")\n\n\n# Set language-specific values for several global variables, including\n#   ${TEXT_CORPUS}\n#      holds the text corpus file for the language, used in phase F\n#   ${FONTS[@]}\n#      holds a sequence of applicable fonts for the language, used in\n#      phase F & I. only set if not already set, i.e. from command line\n#   ${TRAINING_DATA_ARGUMENTS}\n#      non-default arguments to the training_data program used in phase T\n#   ${FILTER_ARGUMENTS}[ -]\n#      character-code-specific filtering to distinguish between scripts\n#      (eg. CJK) used by filter_borbidden_characters in phase F\n#   ${WORDLIST2DAWG_ARGUMENTS}\n#      specify fixed length dawg generation for non-space-delimited lang\n# TODO(dsl): We can refactor these into functions that assign FONTS,\n# TEXT_CORPUS, etc. separately.\ndef set_lang_specific_parameters(ctx, lang):\n    # The default text location is now given directly from the language code.\n    TEXT_CORPUS = f\"{FLAGS_webtext_prefix}/{lang}.corpus.txt\"\n    FILTER_ARGUMENTS = []\n    WORDLIST2DAWG_ARGUMENTS = \"\"\n    # These dawg factors represent the fraction of the corpus not covered by the\n    # dawg, and seem like reasonable defaults, but the optimal value is likely\n    # to be highly corpus-dependent, as well as somewhat language-dependent.\n    # Number dawg factor is the fraction of all numeric strings that are not\n    # covered, which is why it is higher relative to the others.\n    PUNC_DAWG_FACTOR = None\n    NUMBER_DAWG_FACTOR = 0.125\n    WORD_DAWG_FACTOR = 0.05\n    BIGRAM_DAWG_FACTOR = 0.015\n    TRAINING_DATA_ARGUMENTS = []\n    FRAGMENTS_DISABLED = \"y\"\n    RUN_SHAPE_CLUSTERING = False\n    AMBIGS_FILTER_DENOMINATOR = \"100000\"\n    MEAN_COUNT = 40  # Default for latin script.\n    # Language to mix with the language for maximum accuracy. Defaults to eng.\n    # If no language is good, set to the base language.\n    MIX_LANG = \"eng\"\n    FONTS = ctx.fonts\n    TEXT2IMAGE_EXTRA_ARGS = [\"--invert=false\"]\n    LEADINGS = \"-6 0\".split()\n    EXPOSURES = \"0 2 4 -2 -4 -6\".split()\n    CHAR_SPACINGS = \"0 0.7 1.4\".split()\n\n    GENERATE_WORD_BIGRAMS = None\n    WORD_DAWG_SIZE = None\n\n    LANG_IS_RTL = False\n    NORM_MODE = 1\n\n    # languages.\n    if lang == \"eng\":\n        MEAN_COUNT = \"15\"\n        WORD_DAWG_FACTOR = 0.015\n        GENERATE_WORD_BIGRAMS = 0\n        TRAINING_DATA_ARGUMENTS += [\"--infrequent_ratio=10000\"]\n        TRAINING_DATA_ARGUMENTS += [\"--no_space_in_output --desired_bigrams=\"]\n        FILTER_ARGUMENTS = [\"--charset_filter=eng --segmenter_lang=eng\"]\n        if not FONTS:\n            FONTS = HEX_FONTS\n    if lang == \"hex\":\n        MEAN_COUNT = \"15\"\n        WORD_DAWG_FACTOR = 0.015\n        GENERATE_WORD_BIGRAMS = 0\n        TRAINING_DATA_ARGUMENTS += [\"--infrequent_ratio=10000\"]\n        TRAINING_DATA_ARGUMENTS += [\"--no_space_in_output --desired_bigrams=\"]\n        FILTER_ARGUMENTS = [\"--charset_filter=hex --segmenter_lang=hex\"]\n        if not FONTS:\n            FONTS = HEX_FONTS\n    elif lang == \"jpn\":\n        MEAN_COUNT = \"15\"\n        WORD_DAWG_FACTOR = 0.015\n        GENERATE_WORD_BIGRAMS = 0\n        TRAINING_DATA_ARGUMENTS += [\"--infrequent_ratio=10000\"]\n        TRAINING_DATA_ARGUMENTS += [\"--no_space_in_output --desired_bigrams=\"]\n        FILTER_ARGUMENTS = [\"--charset_filter=jpn --segmenter_lang=jpn\"]\n        if not FONTS:\n            FONTS = N6X_FONTS\n    elif lang == \"n6x\":\n        MEAN_COUNT = \"15\"\n        WORD_DAWG_FACTOR = 0.015\n        GENERATE_WORD_BIGRAMS = 0\n        TRAINING_DATA_ARGUMENTS += [\"--infrequent_ratio=10000\"]\n        TRAINING_DATA_ARGUMENTS += [\"--no_space_in_output --desired_bigrams=\"]\n        FILTER_ARGUMENTS = [\"--charset_filter=n6x --segmenter_lang=n6x\"]\n        if not FONTS:\n            FONTS = N6X_FONTS\n    elif lang == \"bas\":\n        MEAN_COUNT = \"15\"\n        WORD_DAWG_FACTOR = 0.015\n        GENERATE_WORD_BIGRAMS = 0\n        TRAINING_DATA_ARGUMENTS += [\"--infrequent_ratio=10000\"]\n        TRAINING_DATA_ARGUMENTS += [\"--no_space_in_output --desired_bigrams=\"]\n        FILTER_ARGUMENTS = [\"--charset_filter=bas --segmenter_lang=bas\"]\n        if not FONTS:\n            FONTS = BAS_FONTS\n\n    FLAGS_mean_count = int(os.environ.get(\"FLAGS_mean_count\", -1))\n    if FLAGS_mean_count > 0:\n        TRAINING_DATA_ARGUMENTS += [f\"--mean_count={FLAGS_mean_count}\"]\n    elif not MEAN_COUNT:\n        TRAINING_DATA_ARGUMENTS += [f\"--mean_count={MEAN_COUNT}\"]\n\n    # Default to Latin fonts if none have been set\n    if not FONTS:\n        FONTS = LATIN_FONTS\n\n    # Default to 0 exposure if it hasn't been set\n    if not EXPOSURES:\n        EXPOSURES = [0]\n\n    vars_to_transfer = {\n        'ambigs_filter_denominator': AMBIGS_FILTER_DENOMINATOR,\n        'bigram_dawg_factor': BIGRAM_DAWG_FACTOR,\n        'exposures': EXPOSURES,\n        'char_spacings': CHAR_SPACINGS,\n        'filter_arguments': FILTER_ARGUMENTS,\n        'fonts': FONTS,\n        'fragments_disabled': FRAGMENTS_DISABLED,\n        'generate_word_bigrams': GENERATE_WORD_BIGRAMS,\n        'lang_is_rtl': LANG_IS_RTL,\n        'leadings': LEADINGS,\n        'mean_count': MEAN_COUNT,\n        'mix_lang': MIX_LANG,\n        'norm_mode': NORM_MODE,\n        'number_dawg_factor': NUMBER_DAWG_FACTOR,\n        'punc_dawg_factor': PUNC_DAWG_FACTOR,\n        'run_shape_clustering': RUN_SHAPE_CLUSTERING,\n        'text2image_extra_args': TEXT2IMAGE_EXTRA_ARGS,\n        'text_corpus': TEXT_CORPUS,\n        'training_data_arguments': TRAINING_DATA_ARGUMENTS,\n        'word_dawg_factor': WORD_DAWG_FACTOR,\n        'word_dawg_size': WORD_DAWG_SIZE,\n        'wordlist2dawg_arguments': WORDLIST2DAWG_ARGUMENTS,\n    }\n\n    for attr, value in vars_to_transfer.items():\n        if hasattr(ctx, attr):\n            if getattr(ctx, attr) != value:\n                log.debug(f\"{attr} = {value} (was {getattr(ctx, attr)})\")\n                setattr(ctx, attr, value)\n            else:\n                log.debug(f\"{attr} = {value} (set on cmdline)\")\n        else:\n            log.debug(f\"{attr} = {value}\")\n            setattr(ctx, attr, value)\n\n    return ctx\n\n# =============================================================================\n# END of Language specific info\n# =============================================================================\n", "repo_name": "eighttails/ProgramListOCR", "sub_path": "training/language_specific.py", "file_name": "language_specific.py", "file_ext": "py", "file_size_in_byte": 7432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "46", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 33, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 48, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 56, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 155, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 155, "usage_type": "attribute"}]}
{"seq_id": "21019257582", "text": "from django.contrib import messages\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.shortcuts import render, redirect, get_object_or_404\r\n\r\nfrom .forms import PostForm, CommentForm\r\nfrom .models import Post, Comment\r\n\r\n\r\n@login_required(login_url='login')\r\ndef post_list(request):\r\n    posts = Post.objects.all().order_by('-created_at')\r\n    return render(request, 'board/post_list.html',\r\n                  {'posts': posts})\r\n\r\n\r\n@login_required(login_url='login')\r\ndef post_detail(request, pk):\r\n    post = get_object_or_404(Post, pk=pk)\r\n    comments = Comment.objects.filter(post_id=pk).order_by('-comment_created')\r\n\r\n    if request.method == 'POST':\r\n        form = CommentForm(request.POST)\r\n        if form.is_valid():\r\n            comment_form = form.save(commit=False)\r\n            comment_form.post = post\r\n            comment_form.comment_writer = request.user\r\n            comment_form.save()\r\n            return redirect('board:post_detail', pk=post.pk)\r\n\r\n    else:\r\n        form = CommentForm()\r\n        context = {\r\n            'post': post,\r\n            'comments': comments,\r\n            'comment_form': form\r\n        }\r\n        return render(request, 'board/post_detail.html', context)\r\n\r\n\r\n@login_required(login_url='login')\r\ndef post_upload(request):\r\n    if request.method == 'POST':\r\n        form = PostForm(request.POST)\r\n        if form.is_valid():\r\n            post = form.save(commit=False)\r\n            post.author = request.user\r\n            post.save()\r\n            return redirect('board:post_list')\r\n    else:\r\n        form = PostForm()\r\n    return render(request, 'board/post_upload.html', {\r\n        'form': form,\r\n    })\r\n\r\n\r\n@login_required(login_url='login')\r\ndef post_edit(request, pk):\r\n    item = get_object_or_404(Post, pk=pk)\r\n    # 그 사람 id인거 인증하는거 있어야함\r\n    if request.method == 'POST':\r\n        form = PostForm(instance=item)\r\n        if form.is_valid():\r\n            item = form.save()\r\n            messages.success(request, '포스트를 수정했습니다.')\r\n            return redirect(item)\r\n    else:\r\n        form = PostForm(instance=item)\r\n    return render(request, 'board/post_edit.html', {\r\n        'form': form,\r\n    })\r\n\r\n\r\n@login_required(login_url='login')\r\ndef post_delete(request, pk):\r\n    post = Post.objects.get(pk=pk)\r\n    if request.method == 'POST':\r\n        # 그 사람 id인거 인증하는거 있어야함\r\n        post.delete()\r\n        messages.success(request, '포스팅을 삭제했습니다.')\r\n        return redirect('board:post_list')\r\n\r\n", "repo_name": "rrabit42/Cyber_Graduation_Project", "sub_path": "EwhaEverytimeEverywhere/board/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "46", "api": [{"api_name": "models.Post.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 9, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 18, "usage_type": "argument"}, {"api_name": "models.Comment.objects.filter", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Comment.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 19, "usage_type": "name"}, {"api_name": "forms.CommentForm", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 28, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 16, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 50, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 58, "usage_type": "argument"}, {"api_name": "forms.PostForm", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 64, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 64, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 67, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Post.objects.get", "line_number": 75, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 75, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 79, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 79, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 73, "usage_type": "call"}]}
{"seq_id": "26451621268", "text": "import constants as keys;\nfrom telegram.ext import *\nimport random\nimport json\n\nimport torch\n\nfrom model import NeuralNet\nfrom nltk_utils import bag_of_words, tokenize\nfrom PyDictionary import PyDictionary\ndictionary = PyDictionary()\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nwith open('intents.json', 'r') as json_data:\n    intents = json.load(json_data)\n\nFILE = \"data.pth\"\ndata = torch.load(FILE)\n\ninput_size = data[\"input_size\"]\nhidden_size = data[\"hidden_size\"]\noutput_size = data[\"output_size\"]\nall_words = data['all_words']\ntags = data['tags']\nmodel_state = data[\"model_state\"]\n\nmodel = NeuralNet(input_size, hidden_size, output_size).to(device)\nmodel.load_state_dict(model_state)\nmodel.eval()\n\nbot_name = \"levi\"\nprint(\"Let's chat! (type 'quit' to exit)\")\n\ndef start_command(update, context):\n    update.message.reply_text(\"hi there! type anything you like\")\n\n\ndef help_command(update, context):\n    update.message.reply_text(\"try typing something else maybe?\")\n\ndef error(update, context):\n    return print(f\"Update {update} caused error {context.error}\")\n\ndef define_command(update, context):\n    updater = Updater(keys.API_KEY, use_context=True)\n    dp = updater.dispatcher\n    update.message.reply_text(\"what word would it be?\")\n    WORD = update.message.text\n    DEFINITION = dictionary.meaning(WORD)\n    update.message.reply_text(DEFINITION)\n\n\ndef handleInput(sentence):\n    if sentence == \"quit\":\n        print(f\"{bot_name}: see you!\")\n\n    sentence = tokenize(sentence)\n    X = bag_of_words(sentence, all_words)\n    X = X.reshape(1, X.shape[0])\n    X = torch.from_numpy(X).to(device)\n\n    output = model(X)\n    _, predicted = torch.max(output, dim=1)\n\n    tag = tags[predicted.item()]\n\n    probs = torch.softmax(output, dim=1)\n    prob = probs[0][predicted.item()]\n    if prob.item() > 0.75:\n        for intent in intents['intents']:\n            if tag == intent[\"tag\"]:\n                return str(random.choice(intent['responses']))\n    else:\n        return \"i'm sorry could you type something else\"\n\ndef handle_message(update, context):\n    sentence = str(update.message.text).lower();\n    response = handleInput(sentence)\n    update.message.reply_text(response)\n\ndef main():\n    updater = Updater(keys.API_KEY, use_context=True)\n    dp = updater.dispatcher\n    \n    dp.add_handler(CommandHandler(\"start\", start_command))\n    dp.add_handler(CommandHandler(\"help\", help_command))\n    dp.add_handler(MessageHandler(Filters.text, handle_message))\n    \n    # dp.add_error_handler(error)\n\n    updater.start_polling() # code that starts the bot\n    updater.idle() # to ensure that bot continues to stay active\n\nmain()\n", "repo_name": "kvihashini/LeviBot", "sub_path": "chatbot.py", "file_name": "chatbot.py", "file_ext": "py", "file_size_in_byte": 2657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "PyDictionary.PyDictionary", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 12, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 18, "usage_type": "call"}, {"api_name": "model.NeuralNet", "line_number": 27, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 28, "usage_type": "call"}, {"api_name": "model.eval", "line_number": 29, "usage_type": "call"}, {"api_name": "constants.API_KEY", "line_number": 45, "usage_type": "attribute"}, {"api_name": "nltk_utils.tokenize", "line_number": 57, "usage_type": "call"}, {"api_name": "nltk_utils.bag_of_words", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 67, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 72, "usage_type": "call"}, {"api_name": "constants.API_KEY", "line_number": 82, "usage_type": "attribute"}]}
{"seq_id": "43605509080", "text": "import threading\r\n\r\nfrom transitions.extensions import GraphMachine as Machine\r\n\r\n\r\nclass Model:\r\n    def __init__(self, name, id):\r\n        self.name = name\r\n        self.id = id\r\n\r\n    def on_enter_load(self):\r\n        print(f\"{self.name}_{self.id} {self.state} state ...\")\r\n        import time\r\n        if self.id % 2 == 0:\r\n            print(f\"{self.name}_{self.id} {self.state} sleep ...\")\r\n            time.sleep(1)\r\n        self.trigger(\"next\")\r\n\r\n    def on_enter_write_plc(self):\r\n        print(f\"{self.name}_{self.id} {self.state} state ...\")\r\n        self.trigger(\"next\")\r\n\r\n    def on_enter_wait_plc(self):\r\n        print(f\"{self.name}_{self.id} {self.state} state ...\")\r\n        self.trigger(\"next\")\r\n\r\n    def on_enter_end(self):\r\n        print(f\"{self.name}_{self.id} {self.state} state ...\")\r\n        self.trigger(\"next\")  # 接回去會無窮迴圈\r\n\r\n\r\nclass ModelThreading(threading.Thread):\r\n\r\n    def __init__(self, station_id):\r\n        threading.Thread.__init__(self)\r\n        self.station_id = station_id\r\n        self.model = Model(self.name, station_id)\r\n        self.machine = Machine(model=self.model,\r\n                            #    queued=False,\r\n                               states=[\r\n                                   'start',\r\n                                   'load',\r\n                                   'write_plc',\r\n                                   'wait_plc',\r\n                                   'end',\r\n                               ],\r\n                               transitions=[\r\n                                   {'trigger': 'next', 'source': 'start', 'dest': 'load'},\r\n                                   {'trigger': 'next', 'source': 'load', 'dest': 'write_plc'},\r\n                                   {'trigger': 'next', 'source': 'write_plc', 'dest': 'wait_plc'},\r\n                                   {'trigger': 'next', 'source': 'wait_plc', 'dest': 'end'},\r\n                                   {'trigger': 'next', 'source': 'end', 'dest': 'start'}\r\n                               ],\r\n                               initial='start',\r\n                               show_conditions=True)\r\n        self.model.next()\r\n\r\n    # def run(self):\r\n    #     print(f\"{self.name}_{self.station_id} run ...\")\r\n    #     self.model.trigger(\"next\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    machines = []\r\n    for i in range(1):\r\n        machines.append(ModelThreading(i))\r\n        # machines[i].next()\r\n\r\n'''\r\nThread-1_0 run ...\r\nThread-1_0 load state ...\r\nThread-1_0 load sleep ...\r\nThread-2_1 run ...\r\nThread-2_1 load state ...\r\nThread-2_1 write_plc state ...\r\nThread-2_1 wait_plc state ...\r\nThread-2_1 end state ...\r\nThread-3_2 run ...\r\nThread-3_2 load state ...\r\nThread-3_2 load sleep ...\r\nThread-4_3 run ...\r\nThread-4_3 load state ...\r\nThread-4_3 write_plc state ...\r\nThread-4_3 wait_plc state ...\r\nThread-4_3 end state ...\r\nThread-5_4 run ...\r\nThread-5_4 load state ...\r\nThread-5_4 load sleep ...\r\nThread-1_0 write_plc state ...\r\nThread-1_0 wait_plc state ...\r\nThread-1_0 end state ...\r\nThread-3_2 write_plc state ...\r\nThread-3_2 wait_plc state ...\r\nThread-3_2 end state ...\r\nThread-5_4 write_plc state ...\r\nThread-5_4 wait_plc state ...\r\nThread-5_4 end state ...\r\n'''\r\n", "repo_name": "RyanJhang/note", "sub_path": "StateMachine/creat_state_machine_by_thread.py", "file_name": "creat_state_machine_by_thread.py", "file_ext": "py", "file_size_in_byte": 3210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 32, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 35, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 35, "usage_type": "attribute"}, {"api_name": "transitions.extensions.GraphMachine", "line_number": 38, "usage_type": "call"}]}
{"seq_id": "32301892300", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul  6 16:25:52 2017\n\n@author: JFan\n\"\"\"\n\n#!python\n\nfrom numpy import sin, pi, absolute, arange, linspace, sqrt\nfrom scipy.signal import lfilter\nfrom pylab import figure, xlabel, ylabel, ylim, title, grid, show, subplots_adjust\nimport matplotlib.pyplot as plt\nfrom matplotlib import gridspec\nfrom scipy import fft, signal\nfrom IIRFilter import IIRFilter\n\n#------------------------------------------------\n# Digtal filter parameters\n#------------------------------------------------\nGAIN = 0\nlowcut = 0\nhighcut = 0\n\n#------------------------------------------------\n# Signal parameters\n#------------------------------------------------\nsample_rate = 0  # sampling rate\n\ndef plotSpectrum(ax, y, Fs, color):\n    \"\"\"\n    Plots a Single-Sided Amplitude Spectrum of y(t)\n    \"\"\"\n    n = len(y) # length of the signal\n    k = arange(n)\n    T = n/Fs\n    frq = k/T # two sides frequency range\n    frq = frq[range(int(n/2))] # one side frequency range\n\n    Y = fft(y)/n # fft computing and normalization\n    Y = Y[range(int(n/2))]\n    ax.plot(frq, abs(Y), color) # plotting the spectrum\n    xlabel('Freq (Hz)')\n    ylabel('Gain')\n\ndef plotSignal(before, after, interval, title, freq):\n    #------------------------------------------------------\n    # Plot the original and filtered sine signals\n    #------------------------------------------------------\n    delay = 0.5 * (4-1) / sample_rate\n    # Plot in time domain\n    fig = figure(2)\n    gs = gridspec.GridSpec(2, 2)\n    ax1 = fig.add_subplot(gs[:,0])\n    ax1.plot(interval, before)\n    # Plot the filtered signal, shifted to compensate for the phase delay.\n    ax1.plot(interval-delay, after, 'r-')\n    plt.xlabel('time (s)')\n    plt.ylabel('Gain')\n    plt.title('Gain vs time')\n    plt.grid(True)\n    plt.xlim(0, 1)\n    grid(True)\n\n    # Plot spectrum\n    ax2 = fig.add_subplot(gs[0,1])\n    plotSpectrum(ax2, before, sample_rate, 'b')\n    plt.title('Gain vs Freq(before)')\n    ax3 = fig.add_subplot(gs[1,1])\n    plotSpectrum(ax3, after, sample_rate ,'r')\n    plt.title('Gain vs Freq(after)')\n    gs.update(wspace=0.5, hspace=0.5)\n    plt.suptitle('IIR %s(%dHz)' %(title,freq))\n    show()\n\n#------------------------------------------------\n# Main starts here\n#------------------------------------------------\nGAIN = int(input('Enter Gain:'))\nlowcut = int(input('Enter low frequency:'))\nhighcut = int(input('Enter high frequency:'))\nsample_rate = 2048 #int(input('Enter sample rate:'))\n\nffs = [10, 100, 1000]  # frequencies of the signal\nif lowcut > 0 :\n    ffs.append(lowcut)\nif lowcut + highcut > 1 :\n    ffs.append((lowcut + highcut)/2)\nffs.append(highcut)\nif highcut/2 > 1:\n    ffs.append(highcut/2)# frequencies of the signal\n\n#------------------------------------------------\n# Signal parameters\n#------------------------------------------------\n# Create sine wave parameters\nTs = 1.0/sample_rate\nsineInterval = arange(0, 1 ,Ts) # time vector\n# Create square wave parameters\nsquareInterval = linspace(0, 1, sample_rate, endpoint=False)\n# The Nyquist rate of the signal.\nnyq_rate = sample_rate / 2.0\n\nchose_filter = 'LPF_' + str(highcut) + 'HZ'\n#------------------------------------------------\n# Generate signals before and after\n#------------------------------------------------\niirFilter = IIRFilter()\nb = iirFilter.IIRFilterConf[chose_filter]['numerator']\na = iirFilter.IIRFilterConf[chose_filter]['denominator']\n\n#------------------------------------------------\n# Plot the IIR filter coefficients and\n# the magnitude response of the filter.\n#------------------------------------------------\nfigure(1)\nw, h = signal.freqz(b, a, 8000)\nplt.plot((w/pi)*nyq_rate, absolute(h), linewidth=2)\nplt.plot([0, 0.5 * sample_rate], [GAIN * sqrt(0.5),GAIN * sqrt(0.5)],'--')\nxlabel('Frequency (Hz)')\nylabel('Gain')\ntitle('Frequency Response')\nylim(-0.05, 1.5*GAIN)\ngrid(True)\nsubplots_adjust(hspace=.5)\n\n\n\nsquareWave = signal.square(2 * pi * ffs[0] * squareInterval)\nfiltered_squarewave = iirFilter.filter(chose_filter, squareWave)\nplotSignal(squareWave, filtered_squarewave, squareInterval, 'Square wave', ffs[0])\n\nfor ff in ffs:\n    sineWave = sin(2*pi*ff*sineInterval)\n    filtered_sinewave = iirFilter.filter(chose_filter, sineWave)\n    plotSignal(sineWave, filtered_sinewave, sineInterval, 'Sine wave', ff)\n", "repo_name": "jinjinfangit/digitalFilter", "sub_path": "SingleIIRFilter.py", "file_name": "SingleIIRFilter.py", "file_ext": "py", "file_size_in_byte": 4295, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "numpy.arange", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.fft", "line_number": 40, "usage_type": "call"}, {"api_name": "pylab.xlabel", "line_number": 43, "usage_type": "call"}, {"api_name": "pylab.ylabel", "line_number": 44, "usage_type": "call"}, {"api_name": "pylab.figure", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "pylab.grid", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "pylab.title", "line_number": 73, "usage_type": "name"}, {"api_name": "pylab.show", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 100, "usage_type": "call"}, {"api_name": "IIRFilter.IIRFilter", "line_number": 108, "usage_type": "call"}, {"api_name": "pylab.figure", "line_number": 116, "usage_type": "call"}, {"api_name": "scipy.signal.freqz", "line_number": 117, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.absolute", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 119, "usage_type": "call"}, {"api_name": "pylab.xlabel", "line_number": 120, "usage_type": "call"}, {"api_name": "pylab.ylabel", "line_number": 121, "usage_type": "call"}, {"api_name": "pylab.title", "line_number": 122, "usage_type": "call"}, {"api_name": "pylab.ylim", "line_number": 123, "usage_type": "call"}, {"api_name": "pylab.grid", "line_number": 124, "usage_type": "call"}, {"api_name": "pylab.subplots_adjust", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.signal.square", "line_number": 129, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 129, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 129, "usage_type": "name"}, {"api_name": "numpy.sin", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 134, "usage_type": "name"}]}
{"seq_id": "24176521338", "text": "from flask import Blueprint, jsonify, request\nfrom http import HTTPStatus\nfrom config.extensions import db\nfrom models.models import Appointment\nfrom utilities.utilities import has_conflict\nfrom datetime import datetime\n\nhome = Blueprint(\"/\", __name__)\n\n\n@home.route(\"/\")\ndef index():\n    return {\"data\": \"OK\"}\n\n\n\n@home.route(\"/appointments\", methods=[\"POST\"])\ndef create_appointment():\n    data = request.get_json()\n    doctor_id = data[\"doctor_id\"]\n    patient_name = data[\"patient_name\"]\n    start_time = datetime.strptime(data[\"start_time\"], \"%Y-%m-%d %H:%M:%S\")\n    duration = data[\"duration\"]\n\n    #if no conflict then move to next step and create the appointment \n    conflict_message = has_conflict(doctor_id, start_time, duration)\n    if conflict_message:\n        return jsonify({\"error\": conflict_message}), HTTPStatus.CONFLICT\n\n    \n    new_appointment = Appointment(\n        doctor_id=doctor_id,\n        patient_name=patient_name,\n        start_time=start_time,\n        duration=duration,\n    )\n    db.session.add(new_appointment)\n    db.session.commit()\n\n    return new_appointment.json(), HTTPStatus.CREATED\n\n\n\n@home.route(\"/appointments/<int:doctor_id>\", methods=[\"GET\"])\ndef get_appointments(doctor_id):\n    start_time = request.args.get(\"start_time\")\n    end_time = request.args.get(\"end_time\")\n\n    if not start_time or not end_time:\n        return (\n            jsonify({\"error\": \"Both start_time and end_time are required\"}),\n            HTTPStatus.BAD_REQUEST,\n        )\n    \n\n\n    start_time = datetime.strptime(start_time, \"%Y-%m-%d %H:%M:%S\")\n    end_time = datetime.strptime(end_time, \"%Y-%m-%d %H:%M:%S\")\n\n    #Returns all appointments  within that time window\n    appointments = Appointment.query.filter(\n        Appointment.doctor_id == doctor_id,\n        Appointment.start_time >= start_time,\n        Appointment.start_time <= end_time,\n    ).all()\n\n    return {\n        \"appointments\": [appointment.json() for appointment in appointments]\n    }, HTTPStatus.OK\n\n\n@home.route(\"/appointments/first_available/<int:doctor_id>\", methods=[\"GET\"])\ndef get_first_available_appointment(doctor_id):\n    start_time = request.args.get(\"start_time\")\n\n    if not start_time:\n        return jsonify({\"error\": \"start_time is required\"}), HTTPStatus.BAD_REQUEST\n\n    start_time = datetime.strptime(start_time, \"%Y-%m-%d %H:%M:%S\")\n\n    #filter for appointments with that specific doctor within that time window\n    appointment = (\n        Appointment.query.filter(\n            Appointment.doctor_id == doctor_id, Appointment.start_time >= start_time\n        )\n        .order_by(Appointment.start_time)\n        .first()\n    )\n\n    if appointment:\n        return {\"appointment\": appointment.json()}, HTTPStatus.OK\n    else:\n        return {\n            \"message\": \"No available appointments found after the specified time\"\n        }, HTTPStatus.NOT_FOUND\n", "repo_name": "arinkulshi/optometrist_availability", "sub_path": "src/api/endpoints.py", "file_name": "endpoints.py", "file_ext": "py", "file_size_in_byte": 2865, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "flask.Blueprint", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}, {"api_name": "utilities.utilities.has_conflict", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 28, "usage_type": "call"}, {"api_name": "http.HTTPStatus.CONFLICT", "line_number": 28, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 28, "usage_type": "name"}, {"api_name": "models.models.Appointment", "line_number": 31, "usage_type": "call"}, {"api_name": "config.extensions.db.session.add", "line_number": 37, "usage_type": "call"}, {"api_name": "config.extensions.db.session", "line_number": 37, "usage_type": "attribute"}, {"api_name": "config.extensions.db", "line_number": 37, "usage_type": "name"}, {"api_name": "config.extensions.db.session.commit", "line_number": 38, "usage_type": "call"}, {"api_name": "config.extensions.db.session", "line_number": 38, "usage_type": "attribute"}, {"api_name": "config.extensions.db", "line_number": 38, "usage_type": "name"}, {"api_name": "http.HTTPStatus.CREATED", "line_number": 40, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 51, "usage_type": "call"}, {"api_name": "http.HTTPStatus.BAD_REQUEST", "line_number": 52, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 52, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "name"}, {"api_name": "models.models.Appointment.query.filter", "line_number": 61, "usage_type": "call"}, {"api_name": "models.models.Appointment.query", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.models.Appointment", "line_number": 61, "usage_type": "name"}, {"api_name": "models.models.Appointment.doctor_id", "line_number": 62, "usage_type": "attribute"}, {"api_name": "models.models.Appointment", "line_number": 62, "usage_type": "name"}, {"api_name": "models.models.Appointment.start_time", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.models.Appointment", "line_number": 63, "usage_type": "name"}, {"api_name": "models.models.Appointment.start_time", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.models.Appointment", "line_number": 64, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 69, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 77, "usage_type": "call"}, {"api_name": "http.HTTPStatus.BAD_REQUEST", "line_number": 77, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 77, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 79, "usage_type": "name"}, {"api_name": "models.models.Appointment.query.filter", "line_number": 83, "usage_type": "call"}, {"api_name": "models.models.Appointment.query", "line_number": 83, "usage_type": "attribute"}, {"api_name": "models.models.Appointment", "line_number": 83, "usage_type": "name"}, {"api_name": "models.models.Appointment.doctor_id", "line_number": 84, "usage_type": "attribute"}, {"api_name": "models.models.Appointment", "line_number": 84, "usage_type": "name"}, {"api_name": "models.models.Appointment.start_time", "line_number": 84, "usage_type": "attribute"}, {"api_name": "models.models.Appointment.start_time", "line_number": 86, "usage_type": "attribute"}, {"api_name": "models.models.Appointment", "line_number": 86, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 91, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 91, "usage_type": "name"}, {"api_name": "http.HTTPStatus.NOT_FOUND", "line_number": 95, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 95, "usage_type": "name"}]}
{"seq_id": "5658814200", "text": "from typing import cast\nfrom xml.etree.ElementTree import Element, parse\n\nfrom py_pdf_term.pdftoxml import PDFnXMLElement, PDFnXMLPath\nfrom py_pdf_term.tokenizers import BaseLanguageTokenizer, Term, Token, Tokenizer\n\nfrom .augmenters import AugmenterCombiner, BaseAugmenter\nfrom .classifiers import BaseTokenClassifier, MeaninglessMarker\nfrom .data import DomainCandidateTermList, PageCandidateTermList, PDFCandidateTermList\nfrom .filters import BaseCandidateTermFilter, BaseCandidateTokenFilter, FilterCombiner\nfrom .splitters import BaseSplitter, SplitterCombiner\nfrom .utils import textnode_fontsize, textnode_ncolor, textnode_text\n\n\nclass CandidateTermExtractor:\n    \"\"\"Term extractor which extracts candidate terms from a XML file.\n\n    Args\n    ----\n        lang_tokenizer_clses:\n            List of language tokenizer classes to tokenize texts. If None, the default\n            language tokenizers are used.\n\n        token_classifier_clses:\n            List of token classifier classes to classify tokens. If None, the default\n            token classifiers are used.\n\n        token_filter_clses:\n            List of token filter classes to filter tokens. If None, the default token\n            filters are used.\n\n        term_filter_clses:\n            List of term filter classes to filter candidate terms. If None, the\n            default term filters are used.\n\n        splitter_clses:\n            List of splitter classes to split candidate terms. If None, the default\n            splitters are used.\n\n        augmenter_clses:\n            List of augmenter classes to augment candidate terms. If None, the default\n            augmenters are used.\n    \"\"\"\n\n    def __init__(\n        self,\n        lang_tokenizer_clses: list[type[BaseLanguageTokenizer]] | None = None,\n        token_classifier_clses: list[type[BaseTokenClassifier]] | None = None,\n        token_filter_clses: list[type[BaseCandidateTokenFilter]] | None = None,\n        term_filter_clses: list[type[BaseCandidateTermFilter]] | None = None,\n        splitter_clses: list[type[BaseSplitter]] | None = None,\n        augmenter_clses: list[type[BaseAugmenter]] | None = None,\n    ) -> None:\n        lang_tokenizers = (\n            list(map(lambda cls: cls(), lang_tokenizer_clses))\n            if lang_tokenizer_clses is not None\n            else None\n        )\n        self._tokenizer = Tokenizer(lang_tokenizers=lang_tokenizers)\n\n        token_filters = (\n            list(map(lambda cls: cls(), token_filter_clses))\n            if token_filter_clses is not None\n            else None\n        )\n        term_filters = (\n            list(map(lambda cls: cls(), term_filter_clses))\n            if term_filter_clses is not None\n            else None\n        )\n        self._filter = FilterCombiner(token_filters, term_filters)\n\n        classifiers = (\n            list(map(lambda cls: cls(), token_classifier_clses))\n            if token_classifier_clses is not None\n            else None\n        )\n        self._marker = MeaninglessMarker(classifiers)\n\n        splitters = (\n            list(map(lambda cls: cls(classifiers), splitter_clses))\n            if splitter_clses is not None\n            else None\n        )\n        self._splitter = SplitterCombiner(splitters)\n\n        augmenters = (\n            list(map(lambda cls: cls(), augmenter_clses))\n            if augmenter_clses is not None\n            else None\n        )\n        self._augmenter = AugmenterCombiner(augmenters)\n\n    def extract_from_domain_files(\n        self, domain: str, pdfnxmls: list[PDFnXMLPath]\n    ) -> DomainCandidateTermList:\n        \"\"\"Extract candidte terms from pairs of PDF and XML files in a domain.\n\n        Args\n        ----\n            domain:\n                Domain name of PDF files.\n            pdfnxmls:\n                List of pairs of paths to PDF and XML files in a domain.\n\n        Returns\n        -------\n            DomainCandidateTermList:\n                List of candidate terms in a domain.\n        \"\"\"\n\n        xmls = list(map(self.extract_from_xml_file, pdfnxmls))\n        return DomainCandidateTermList(domain, xmls)\n\n    def extract_from_xml_file(self, pdfnxml: PDFnXMLPath) -> PDFCandidateTermList:\n        \"\"\"Extract candidate terms from a pair of PDF and XML files.\n\n        Args\n        ----\n            pdfnxml:\n                Pair of paths to a PDF and XML file.\n\n        Returns\n        -------\n            PDFCandidateTermList:\n                List of candidate terms in a PDF file.\n        \"\"\"\n\n        xml_root = parse(pdfnxml.xml_path).getroot()\n        xml_candidates = self._extract_from_xmlroot(pdfnxml.pdf_path, xml_root)\n        return xml_candidates\n\n    def extract_from_domain_elements(\n        self, domain: str, pdfnxmls: list[PDFnXMLElement]\n    ) -> DomainCandidateTermList:\n        \"\"\"Extract candidate terms from pairs of PDF and XML elements in a domain.\n\n        Args\n        ----\n            domain:\n                Domain name of PDF files.\n            pdfnxmls:\n                List of pairs of paths to PDF and XML elements in a domain.\n\n        Returns\n        -------\n            DomainCandidateTermList:\n                List of candidate terms in a domain.\n        \"\"\"\n\n        xmls = list(map(self.extract_from_xml_element, pdfnxmls))\n        return DomainCandidateTermList(domain, xmls)\n\n    def extract_from_xml_element(self, pdfnxml: PDFnXMLElement) -> PDFCandidateTermList:\n        \"\"\"Extract candidate terms from a pair of PDF and XML elements.\n\n        Args\n        ----\n            pdfnxml:\n                Pair of path to a PDF and XML elements.\n\n        Returns\n        -------\n            PDFCandidateTermList:\n                List of candidate terms in a PDF file.\n        \"\"\"\n\n        xml_candidates = self._extract_from_xmlroot(pdfnxml.pdf_path, pdfnxml.xml_root)\n        return xml_candidates\n\n    def extract_from_text(\n        self, text: str, fontsize: float = 0.0, ncolor: str = \"\"\n    ) -> list[Term]:\n        \"\"\"Extract candidate terms from a text. This method is mainly used for testing.\n\n        Args\n        ----\n            text:\n                Text to extract candidate terms.\n            fontsize:\n                Font size of output terms.\n            ncolor:\n                Color of output terms.\n\n        Returns\n        -------\n            list[Term]:\n                List of candidate terms in a text.\n        \"\"\"\n\n        tokens = self._tokenizer.tokenize(text)\n        return self._extract_from_tokens(tokens, fontsize, ncolor)\n\n    def _extract_from_xmlroot(\n        self, pdf_path: str, xml_root: Element\n    ) -> PDFCandidateTermList:\n        page_candidates: list[PageCandidateTermList] = []\n        for page in xml_root.iter(\"page\"):\n            page_candidates.append(self._extract_from_page(page))\n\n        return PDFCandidateTermList(pdf_path, page_candidates)\n\n    def _extract_from_page(self, page: Element) -> PageCandidateTermList:\n        page_num = int(cast(str, page.get(\"id\")))\n\n        candicate_terms: list[Term] = []\n        for textnode in page.iter(\"text\"):\n            text = textnode_text(textnode)\n            fontsize = textnode_fontsize(textnode)\n            ncolor = textnode_ncolor(textnode)\n            tokens = self._tokenizer.tokenize(text)\n            terms = self._extract_from_tokens(tokens, fontsize, ncolor)\n            candicate_terms.extend(terms)\n\n        return PageCandidateTermList(page_num, candicate_terms)\n\n    def _extract_from_tokens(\n        self, tokens: list[Token], fontsize: float, ncolor: str\n    ) -> list[Term]:\n        candicate_terms: list[Term] = []\n        candicate_tokens: list[Token] = []\n        for idx, token in enumerate(tokens):\n            if self._filter.is_partof_candidate(tokens, idx):\n                candicate_tokens.append(token)\n                continue\n\n            terms = self._terms_from_tokens(candicate_tokens, fontsize, ncolor)\n            candicate_terms.extend(terms)\n            candicate_tokens = []\n\n        terms = self._terms_from_tokens(candicate_tokens, fontsize, ncolor)\n        candicate_terms.extend(terms)\n\n        return candicate_terms\n\n    def _terms_from_tokens(\n        self, candicate_tokens: list[Token], fontsize: float, ncolor: str\n    ) -> list[Term]:\n        candidate_term = Term(candicate_tokens, fontsize, ncolor)\n\n        candicate_terms: list[Term] = []\n        splitted_candidates = self._splitter.split(candidate_term)\n        for splitted_candidate in splitted_candidates:\n            augmented_candidates = self._augmenter.augment(splitted_candidate)\n            candicate_terms.extend(augmented_candidates)\n            candicate_terms.append(splitted_candidate)\n\n        return list(\n            map(self._marker.mark, filter(self._filter.is_candidate, candicate_terms))\n        )\n", "repo_name": "kumachan-mis/py-pdf-term", "sub_path": "py_pdf_term/candidates/_candidates/extractor.py", "file_name": "extractor.py", "file_ext": "py", "file_size_in_byte": 8744, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "py_pdf_term.tokenizers.BaseLanguageTokenizer", "line_number": 47, "usage_type": "name"}, {"api_name": "classifiers.BaseTokenClassifier", "line_number": 48, "usage_type": "name"}, {"api_name": "filters.BaseCandidateTokenFilter", "line_number": 49, "usage_type": "name"}, {"api_name": "filters.BaseCandidateTermFilter", "line_number": 50, "usage_type": "name"}, {"api_name": "splitters.BaseSplitter", "line_number": 51, "usage_type": "name"}, {"api_name": "augmenters.BaseAugmenter", "line_number": 52, "usage_type": "name"}, {"api_name": "py_pdf_term.tokenizers.Tokenizer", "line_number": 59, "usage_type": "call"}, {"api_name": "filters.FilterCombiner", "line_number": 71, "usage_type": "call"}, {"api_name": "classifiers.MeaninglessMarker", "line_number": 78, "usage_type": "call"}, {"api_name": "splitters.SplitterCombiner", "line_number": 85, "usage_type": "call"}, {"api_name": "augmenters.AugmenterCombiner", "line_number": 92, "usage_type": "call"}, {"api_name": "py_pdf_term.pdftoxml.PDFnXMLPath", "line_number": 95, "usage_type": "name"}, {"api_name": "data.DomainCandidateTermList", "line_number": 113, "usage_type": "call"}, {"api_name": "data.DomainCandidateTermList", "line_number": 96, "usage_type": "name"}, {"api_name": "py_pdf_term.pdftoxml.PDFnXMLPath", "line_number": 115, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 129, "usage_type": "call"}, {"api_name": "data.PDFCandidateTermList", "line_number": 115, "usage_type": "name"}, {"api_name": "py_pdf_term.pdftoxml.PDFnXMLElement", "line_number": 134, "usage_type": "name"}, {"api_name": "data.DomainCandidateTermList", "line_number": 152, "usage_type": "call"}, {"api_name": "data.DomainCandidateTermList", "line_number": 135, "usage_type": "name"}, {"api_name": "py_pdf_term.pdftoxml.PDFnXMLElement", "line_number": 154, "usage_type": "name"}, {"api_name": "data.PDFCandidateTermList", "line_number": 154, "usage_type": "name"}, {"api_name": "py_pdf_term.tokenizers.Term", "line_number": 173, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 195, "usage_type": "name"}, {"api_name": "data.PageCandidateTermList", "line_number": 197, "usage_type": "name"}, {"api_name": "data.PDFCandidateTermList", "line_number": 201, "usage_type": "call"}, {"api_name": "data.PDFCandidateTermList", "line_number": 196, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 203, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 204, "usage_type": "call"}, {"api_name": "py_pdf_term.tokenizers.Term", "line_number": 206, "usage_type": "name"}, {"api_name": "utils.textnode_text", "line_number": 208, "usage_type": "call"}, {"api_name": "utils.textnode_fontsize", "line_number": 209, "usage_type": "call"}, {"api_name": "utils.textnode_ncolor", "line_number": 210, "usage_type": "call"}, {"api_name": "data.PageCandidateTermList", "line_number": 215, "usage_type": "call"}, {"api_name": "data.PageCandidateTermList", "line_number": 203, "usage_type": "name"}, {"api_name": "py_pdf_term.tokenizers.Token", "line_number": 218, "usage_type": "name"}, {"api_name": "py_pdf_term.tokenizers.Term", "line_number": 220, "usage_type": "name"}, {"api_name": "py_pdf_term.tokenizers.Token", "line_number": 221, "usage_type": "name"}, {"api_name": "py_pdf_term.tokenizers.Term", "line_number": 219, "usage_type": "name"}, {"api_name": "py_pdf_term.tokenizers.Token", "line_number": 237, "usage_type": "name"}, {"api_name": "py_pdf_term.tokenizers.Term", "line_number": 239, "usage_type": "call"}, {"api_name": "py_pdf_term.tokenizers.Term", "line_number": 241, "usage_type": "name"}, {"api_name": "py_pdf_term.tokenizers.Term", "line_number": 238, "usage_type": "name"}]}
{"seq_id": "44085426539", "text": "from __future__ import  absolute_import\nfrom __future__ import  division\nimport torch as t\nfrom data.voc_dataset import VOCBboxDataset\nfrom skimage import transform as sktsf\nfrom torchvision import transforms as tvtsf\nfrom data import util\nimport numpy as np\nfrom utils.config import opt\nimport pandas as pd\nimport scipy.misc\nimport json\nfrom PIL import Image\nfrom PIL import ImageFilter\nimport csv\nfrom .util import read_image\nimport ast\n\ndef inverse_normalize(img):\n    if opt.caffe_pretrain:\n        img = img + (np.array([122.7717, 115.9465, 102.9801]).reshape(3, 1, 1))\n        return img[::-1, :, :]\n    # approximate un-normalize for visualize\n    return (img * 0.225 + 0.45).clip(min=0, max=1) * 255\n\n\ndef pytorch_normalze(img):\n    \"\"\"\n    https://github.com/pytorch/vision/issues/223\n    return appr -1~1 RGB\n    \"\"\"\n    normalize = tvtsf.Normalize(mean=[0.485, 0.456, 0.406],\n                                std=[0.229, 0.224, 0.225])\n    img = normalize(t.from_numpy(img))\n    return img.numpy()\n\n\ndef caffe_normalize(img):\n    \"\"\"\n    return appr -125-125 BGR\n    \"\"\"\n    img = img[[2, 1, 0], :, :]  # RGB-BGR\n    img = img * 255\n    mean = np.array([122.7717, 115.9465, 102.9801]).reshape(3, 1, 1)\n    img = (img - mean).astype(np.float32, copy=True)\n    return img\n\n\ndef preprocess(img, min_size=1200, max_size=1600):\n    \"\"\"Preprocess an image for feature extraction.\n\n    The length of the shorter edge is scaled to :obj:`self.min_size`.\n    After the scaling, if the length of the longer edge is longer than\n    :param min_size:\n    :obj:`self.max_size`, the image is scaled to fit the longer edge\n    to :obj:`self.max_size`.\n\n    After resizing the image, the image is subtracted by a mean image value\n    :obj:`self.mean`.\n\n    Args:\n        img (~numpy.ndarray): An image. This is in CHW and RGB format.\n            The range of its value is :math:`[0, 255]`.\n\n    Returns:\n        ~numpy.ndarray: A preprocessed image.\n\n    \"\"\"\n    C, H, W = img.shape\n    scale1 = min_size / min(H, W)\n    scale2 = max_size / max(H, W)\n    scale = min(scale1, scale2)\n    img = img / 255.\n    img = sktsf.resize(img, (C, H * scale, W * scale), mode='reflect',anti_aliasing=False)\n    # both the longer and shorter should be less than\n    # max_size and min_size\n    if opt.caffe_pretrain:\n        normalize = caffe_normalize\n    else:\n        normalize = pytorch_normalze\n    return normalize(img)\n\n\nclass Transform(object):\n\n    def __init__(self, min_size=1200, max_size=1600):\n        self.min_size = min_size\n        self.max_size = max_size\n\n    def __call__(self, in_data):\n        img, bbox, label = in_data\n        _, H, W = img.shape\n        img = preprocess(img, self.min_size, self.max_size)\n        _, o_H, o_W = img.shape\n        scale = o_H / H\n        bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W))\n\n        # horizontally flip\n        img, params = util.random_flip(\n            img, x_random=True, return_param=True)\n        bbox = util.flip_bbox(\n            bbox, (o_H, o_W), x_flip=params['x_flip'])\n\n        return img, bbox, label, scale\n\ndef make_csv(json_file, csv_file):\n    with open(json_file, 'r') as f:\n        data = json.load(f)\n    cnt = 0\n    root_dir = '../../malaria/images'\n    Data = []\n    for img in data:\n        img_name = img['image']['pathname'][7:]\n        img_path = root_dir+img_name\n        # print(img_path)\n        bounding_boxes, labels = [], []\n        for obj in img['objects']:\n            bb = obj['bounding_box']\n            bbox = (bb['minimum']['r'], bb['minimum']['c'], bb['maximum']['r'], bb['maximum']['c'])\n            bounding_boxes.append(bbox)\n            labels.append(int(obj['category'] != 'red blood cell'))\n        Data.append([img_path, bounding_boxes, labels])\n    df = pd.DataFrame(Data, columns=['img', 'bbox', 'label'])\n    df.to_csv(csv_file)\n    print('data written in csv')\n        \n\n\nclass Dataset:\n    def __init__(self, opt):\n        self.opt = opt\n        # self.db = VOCBboxDataset(opt.voc_data_dir)\n        make_csv('../../malaria/test.json', 'data/test.csv')\n        self.data_frame = pd.read_csv('data/test.csv', header='infer')\n        # print(self.data_frame)\n        self.label_names = ['rbc', 'infected']\n        self.tsf = Transform(opt.min_size, opt.max_size)\n\n    def __getitem__(self, idx):\n        ori_img, bbox, label = self.data_frame.iloc[idx, 1:4] #self.db.get_example(idx)\n        ori_img = read_image(ori_img, color=True)\n        bbox = np.stack(ast.literal_eval(bbox)).astype(np.float32)\n        label = np.stack(ast.literal_eval(label)).astype(np.int32)\n        # print(ori_img, bbox, label)\n        img, bbox, label, scale = self.tsf((ori_img, bbox, label))\n        # TODO: check whose stride is negative to fix this instead copy all\n        # some of the strides of a given numpy array are negative.\n        return img.copy(), bbox.copy(), label.copy(), scale\n\n    def __len__(self):\n        return len(self.data_frame)\n\n\nclass TestDataset:\n    def __init__(self, opt, split='test', use_difficult=False):\n        self.opt = opt\n        make_csv('../../malaria/test.json', 'data/test.csv')\n        self.data_frame = pd.read_csv('data/test.csv', header='infer')\n        # print(self.data_frame)\n        self.label_names = ['rbc', 'infected']\n        # self.db = VOCBboxDataset(opt.voc_data_dir, split=split, use_difficult=use_difficult)\n\n    def __getitem__(self, idx):\n        # ori_img, bbox, label, difficult = self.db.get_example(idx)\n        ori_img, bbox, label = self.data_frame.iloc[idx, 1:4]\n        ori_img = read_image(ori_img, color=True)\n        bbox = np.stack(ast.literal_eval(bbox)).astype(np.float32)\n        label = np.stack(ast.literal_eval(label)).astype(np.int32)\n        img = preprocess(ori_img)\n        difficult = np.array([False]*label.shape[0], dtype=np.bool).astype(np.uint8)\n        return img, ori_img.shape[1:], bbox, label, difficult\n\n    def __len__(self):\n        return len(self.data_frame)\n", "repo_name": "ameypatil10/CS490-RnD-Malaria-Detection", "sub_path": "faster-rcnn/data/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 5938, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "utils.config.opt.caffe_pretrain", "line_number": 20, "usage_type": "attribute"}, {"api_name": "utils.config.opt", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "skimage.transform.resize", "line_number": 74, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 74, "usage_type": "name"}, {"api_name": "utils.config.opt.caffe_pretrain", "line_number": 77, "usage_type": "attribute"}, {"api_name": "utils.config.opt", "line_number": 77, "usage_type": "name"}, {"api_name": "data.util.resize_bbox", "line_number": 96, "usage_type": "call"}, {"api_name": "data.util", "line_number": 96, "usage_type": "name"}, {"api_name": "data.util.random_flip", "line_number": 99, "usage_type": "call"}, {"api_name": "data.util", "line_number": 99, "usage_type": "name"}, {"api_name": "data.util.flip_bbox", "line_number": 101, "usage_type": "call"}, {"api_name": "data.util", "line_number": 101, "usage_type": "name"}, {"api_name": "data.voc_dataset", "line_number": 108, "usage_type": "name"}, {"api_name": "json.load", "line_number": 108, "usage_type": "call"}, {"api_name": "data.voc_dataset", "line_number": 112, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 123, "usage_type": "call"}, {"api_name": "utils.config.opt", "line_number": 131, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 134, "usage_type": "call"}, {"api_name": "utils.config.opt.min_size", "line_number": 137, "usage_type": "attribute"}, {"api_name": "utils.config.opt", "line_number": 137, "usage_type": "name"}, {"api_name": "utils.config.opt.max_size", "line_number": 137, "usage_type": "attribute"}, {"api_name": "util.read_image", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 142, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 143, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 143, "usage_type": "attribute"}, {"api_name": "utils.config.opt", "line_number": 156, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 158, "usage_type": "call"}, {"api_name": "util.read_image", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 167, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 167, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 168, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 170, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 170, "usage_type": "attribute"}]}
{"seq_id": "328947733", "text": "from __future__ import absolute_import\nimport datetime as dt\nfrom nose.tools import *  # noqa PEP8 asserts\nfrom modularodm.exceptions import ValidationValueError, ValidationError\n\nfrom framework.auth import Auth\nfrom framework.exceptions import PermissionsError\nfrom framework.guid.model import Guid\nfrom website.addons.osfstorage import settings as osfstorage_settings\nfrom website.project.model import Comment, NodeLog\nfrom website.project.signals import comment_added\nfrom website.util import permissions\nfrom website import settings\n\n\nfrom tests.base import (\n    OsfTestCase,\n    assert_datetime_equal,\n    capture_signals\n)\nfrom tests.factories import (\n    UserFactory, ProjectFactory, AuthUserFactory, CommentFactory,\n)\n\n\nclass TestCommentViews(OsfTestCase):\n\n    def setUp(self):\n        super(TestCommentViews, self).setUp()\n        self.project = ProjectFactory(is_public=True)\n        self.user = AuthUserFactory()\n        self.project.add_contributor(self.user)\n        self.project.save()\n        self.user.save()\n\n    def test_view_project_comments_updates_user_comments_view_timestamp(self):\n        url = self.project.api_url_for('update_comments_timestamp')\n        res = self.app.put_json(url, {\n            'page': 'node',\n            'rootId': self.project._id\n        }, auth=self.user.auth)\n        self.user.reload()\n\n        user_timestamp = self.user.comments_viewed_timestamp[self.project._id]['node']\n        view_timestamp = dt.datetime.utcnow()\n        assert_datetime_equal(user_timestamp, view_timestamp)\n\n    def test_confirm_non_contrib_viewers_dont_have_pid_in_comments_view_timestamp(self):\n        non_contributor = AuthUserFactory()\n        url = self.project.api_url_for('update_comments_timestamp')\n        res = self.app.put_json(url, {\n            'page': 'node',\n            'rootId': self.project._id\n        }, auth=self.user.auth)\n\n        non_contributor.reload()\n        assert_not_in(self.project._id, non_contributor.comments_viewed_timestamp)\n\n    def test_view_comments_updates_user_comments_view_timestamp_files(self):\n        osfstorage = self.project.get_addon('osfstorage')\n        root_node = osfstorage.get_root()\n        test_file = root_node.append_file('test_file')\n        test_file.create_version(self.user, {\n            'object': '06d80e',\n            'service': 'cloud',\n            osfstorage_settings.WATERBUTLER_RESOURCE: 'osf',\n        }, {\n            'size': 1337,\n            'contentType': 'img/png'\n        }).save()\n\n        url = self.project.api_url_for('update_comments_timestamp')\n        res = self.app.put_json(url, {\n            'page': 'files',\n            'rootId': test_file._id\n        }, auth=self.user.auth)\n        self.user.reload()\n\n        user_timestamp = self.user.comments_viewed_timestamp[self.project._id]['files'][test_file._id]\n        view_timestamp = dt.datetime.utcnow()\n        assert_datetime_equal(user_timestamp, view_timestamp)\n\n\nclass TestCommentModel(OsfTestCase):\n\n    def setUp(self):\n        super(TestCommentModel, self).setUp()\n        self.comment = CommentFactory()\n        self.auth = Auth(user=self.comment.user)\n\n    def test_create(self):\n        comment = Comment.create(\n            auth=self.auth,\n            user=self.comment.user,\n            node=self.comment.node,\n            target=self.comment.target,\n            page='node',\n            is_public=True,\n            content='This is a comment.'\n        )\n        assert_equal(comment.user, self.comment.user)\n        assert_equal(comment.node, self.comment.node)\n        assert_equal(comment.target, self.comment.target)\n        assert_equal(len(comment.node.logs), 2)\n        assert_equal(comment.node.logs[-1].action, NodeLog.COMMENT_ADDED)\n\n    def test_create_comment_content_cannot_exceed_max_length(self):\n        with assert_raises(ValidationValueError):\n            comment = Comment.create(\n                auth=self.auth,\n                user=self.comment.user,\n                node=self.comment.node,\n                target=self.comment.target,\n                is_public=True,\n                content=''.join(['c' for c in range(settings.COMMENT_MAXLENGTH + 1)])\n            )\n\n    def test_create_comment_content_cannot_be_none(self):\n        with assert_raises(ValidationError) as error:\n            comment = Comment.create(\n                auth=self.auth,\n                user=self.comment.user,\n                node=self.comment.node,\n                target=self.comment.target,\n                is_public=True,\n                content=None\n        )\n        assert_equal(error.exception.message, 'Value <content> is required.')\n\n    def test_create_comment_content_cannot_be_empty(self):\n        with assert_raises(ValidationValueError) as error:\n            comment = Comment.create(\n                auth=self.auth,\n                user=self.comment.user,\n                node=self.comment.node,\n                target=self.comment.target,\n                is_public=True,\n                content=''\n        )\n        assert_equal(error.exception.message, 'Value must not be empty.')\n\n    def test_create_comment_content_cannot_be_whitespace(self):\n        with assert_raises(ValidationValueError) as error:\n            comment = Comment.create(\n                auth=self.auth,\n                user=self.comment.user,\n                node=self.comment.node,\n                target=self.comment.target,\n                is_public=True,\n                content='    '\n        )\n        assert_equal(error.exception.message, 'Value must not be empty.')\n\n    def test_create_sends_comment_added_signal(self):\n        with capture_signals() as mock_signals:\n            comment = Comment.create(\n                auth=self.auth,\n                user=self.comment.user,\n                node=self.comment.node,\n                target=self.comment.target,\n                is_public=True,\n                content='This is a comment.'\n            )\n        assert_equal(mock_signals.signals_sent(), set([comment_added]))\n\n    def test_edit(self):\n        self.comment.edit(\n            auth=self.auth,\n            content='edited',\n            save=True\n        )\n        assert_equal(self.comment.content, 'edited')\n        assert_true(self.comment.modified)\n        assert_equal(len(self.comment.node.logs), 2)\n        assert_equal(self.comment.node.logs[-1].action, NodeLog.COMMENT_UPDATED)\n\n    def test_delete(self):\n        self.comment.delete(auth=self.auth, save=True)\n        assert_equal(self.comment.is_deleted, True)\n        assert_equal(len(self.comment.node.logs), 2)\n        assert_equal(self.comment.node.logs[-1].action, NodeLog.COMMENT_REMOVED)\n\n    def test_undelete(self):\n        self.comment.delete(auth=self.auth, save=True)\n        self.comment.undelete(auth=self.auth, save=True)\n        assert_equal(self.comment.is_deleted, False)\n        assert_equal(len(self.comment.node.logs), 3)\n        assert_equal(self.comment.node.logs[-1].action, NodeLog.COMMENT_RESTORED)\n\n    def test_read_permission_contributor_can_comment(self):\n        project = ProjectFactory()\n        user = UserFactory()\n        project.set_privacy('private')\n        project.add_contributor(user, permissions=[permissions.READ])\n        project.save()\n\n        assert_true(project.can_comment(Auth(user=user)))\n\n    def test_get_content_for_not_deleted_comment(self):\n        project = ProjectFactory(is_public=True)\n        comment = CommentFactory(node=project)\n        content = comment.get_content(auth=Auth(comment.user))\n        assert_equal(content, comment.content)\n\n    def test_get_content_returns_deleted_content_to_commenter(self):\n        comment = CommentFactory(is_deleted=True)\n        content = comment.get_content(auth=Auth(comment.user))\n        assert_equal(content, comment.content)\n\n    def test_get_content_does_not_return_deleted_content_to_non_commenter(self):\n        user = AuthUserFactory()\n        comment = CommentFactory(is_deleted=True)\n        content = comment.get_content(auth=Auth(user))\n        assert_is_none(content)\n\n    def test_get_content_public_project_does_not_return_deleted_content_to_logged_out_user(self):\n        project = ProjectFactory(is_public=True)\n        comment = CommentFactory(node=project, is_deleted=True)\n        content = comment.get_content(auth=None)\n        assert_is_none(content)\n\n    def test_get_content_private_project_throws_permissions_error_for_logged_out_users(self):\n        project = ProjectFactory(is_public=False)\n        comment = CommentFactory(node=project, is_deleted=True)\n        with assert_raises(PermissionsError):\n            comment.get_content(auth=None)\n\n    def test_find_unread_is_zero_when_no_comments(self):\n        n_unread = Comment.find_n_unread(user=UserFactory(), node=ProjectFactory())\n        assert_equal(n_unread, 0)\n\n    def test_find_unread_new_comments(self):\n        project = ProjectFactory()\n        user = UserFactory()\n        project.add_contributor(user)\n        project.save()\n        comment = CommentFactory(node=project, user=project.creator)\n        n_unread = Comment.find_n_unread(user=user, node=project)\n        assert_equal(n_unread, 1)\n\n    def test_find_unread_includes_comment_replies(self):\n        project = ProjectFactory()\n        user = UserFactory()\n        project.add_contributor(user)\n        project.save()\n        comment = CommentFactory(node=project, user=user)\n        reply = CommentFactory(node=project, target=Guid.load(comment._id), user=project.creator)\n        n_unread = Comment.find_n_unread(user=user, node=project)\n        assert_equal(n_unread, 1)\n\n    # Regression test for https://openscience.atlassian.net/browse/OSF-5193\n    def test_find_unread_includes_edited_comments(self):\n        project = ProjectFactory()\n        user = AuthUserFactory()\n        project.add_contributor(user)\n        project.save()\n        comment = CommentFactory(node=project, user=project.creator)\n\n        url = project.api_url_for('update_comments_timestamp')\n        payload = {'page': 'node', 'rootId': project._id}\n        res = self.app.put_json(url, payload, auth=user.auth)\n        user.reload()\n        n_unread = Comment.find_n_unread(user=user, node=project)\n        assert_equal(n_unread, 0)\n\n        # Edit previously read comment\n        comment.edit(\n            auth=Auth(project.creator),\n            content='edited',\n            save=True\n        )\n        n_unread = Comment.find_n_unread(user=user, node=project)\n        assert_equal(n_unread, 1)\n\n    def test_find_unread_does_not_include_deleted_comments(self):\n        project = ProjectFactory()\n        user = AuthUserFactory()\n        project.add_contributor(user)\n        project.save()\n        comment = CommentFactory(node=project, user=project.creator, is_deleted=True)\n        n_unread = Comment.find_n_unread(user=user, node=project)\n        assert_equal(n_unread, 0)\n", "repo_name": "sf2ne/Playground", "sub_path": "tests/test_comments.py", "file_name": "test_comments.py", "file_ext": "py", "file_size_in_byte": 10871, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "tests.base.OsfTestCase", "line_number": 26, "usage_type": "name"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 30, "usage_type": "call"}, {"api_name": "tests.factories.AuthUserFactory", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "attribute"}, {"api_name": "tests.base.assert_datetime_equal", "line_number": 46, "usage_type": "call"}, {"api_name": "tests.factories.AuthUserFactory", "line_number": 49, "usage_type": "call"}, {"api_name": "website.addons.osfstorage.settings.WATERBUTLER_RESOURCE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "website.addons.osfstorage.settings", "line_number": 66, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tests.base.assert_datetime_equal", "line_number": 81, "usage_type": "call"}, {"api_name": "tests.base.OsfTestCase", "line_number": 84, "usage_type": "name"}, {"api_name": "tests.factories.CommentFactory", "line_number": 88, "usage_type": "call"}, {"api_name": "framework.auth.Auth", "line_number": 89, "usage_type": "call"}, {"api_name": "website.project.model.Comment.create", "line_number": 92, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 92, "usage_type": "name"}, {"api_name": "website.project.model.NodeLog.COMMENT_ADDED", "line_number": 105, "usage_type": "attribute"}, {"api_name": "website.project.model.NodeLog", "line_number": 105, "usage_type": "name"}, {"api_name": "modularodm.exceptions.ValidationValueError", "line_number": 108, "usage_type": "argument"}, {"api_name": "website.project.model.Comment.create", "line_number": 109, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 109, "usage_type": "name"}, {"api_name": "website.settings.COMMENT_MAXLENGTH", "line_number": 115, "usage_type": "attribute"}, {"api_name": "website.settings", "line_number": 115, "usage_type": "name"}, {"api_name": "modularodm.exceptions.ValidationError", "line_number": 119, "usage_type": "argument"}, {"api_name": "website.project.model.Comment.create", "line_number": 120, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 120, "usage_type": "name"}, {"api_name": "modularodm.exceptions.ValidationValueError", "line_number": 131, "usage_type": "argument"}, {"api_name": "website.project.model.Comment.create", "line_number": 132, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 132, "usage_type": "name"}, {"api_name": "modularodm.exceptions.ValidationValueError", "line_number": 143, "usage_type": "argument"}, {"api_name": "website.project.model.Comment.create", "line_number": 144, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 144, "usage_type": "name"}, {"api_name": "tests.base.capture_signals", "line_number": 155, "usage_type": "call"}, {"api_name": "website.project.model.Comment.create", "line_number": 156, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 156, "usage_type": "name"}, {"api_name": "website.project.signals.comment_added", "line_number": 164, "usage_type": "name"}, {"api_name": "website.project.model.NodeLog.COMMENT_UPDATED", "line_number": 175, "usage_type": "attribute"}, {"api_name": "website.project.model.NodeLog", "line_number": 175, "usage_type": "name"}, {"api_name": "website.project.model.NodeLog.COMMENT_REMOVED", "line_number": 181, "usage_type": "attribute"}, {"api_name": "website.project.model.NodeLog", "line_number": 181, "usage_type": "name"}, {"api_name": "website.project.model.NodeLog.COMMENT_RESTORED", "line_number": 188, "usage_type": "attribute"}, {"api_name": "website.project.model.NodeLog", "line_number": 188, "usage_type": "name"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 191, "usage_type": "call"}, {"api_name": "tests.factories.UserFactory", "line_number": 192, "usage_type": "call"}, {"api_name": "website.util.permissions.READ", "line_number": 194, "usage_type": "attribute"}, {"api_name": "website.util.permissions", "line_number": 194, "usage_type": "name"}, {"api_name": "framework.auth.Auth", "line_number": 197, "usage_type": "call"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 200, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 201, "usage_type": "call"}, {"api_name": "framework.auth.Auth", "line_number": 202, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 206, "usage_type": "call"}, {"api_name": "framework.auth.Auth", "line_number": 207, "usage_type": "call"}, {"api_name": "tests.factories.AuthUserFactory", "line_number": 211, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 212, "usage_type": "call"}, {"api_name": "framework.auth.Auth", "line_number": 213, "usage_type": "call"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 217, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 218, "usage_type": "call"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 223, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 224, "usage_type": "call"}, {"api_name": "framework.exceptions.PermissionsError", "line_number": 225, "usage_type": "argument"}, {"api_name": "website.project.model.Comment.find_n_unread", "line_number": 229, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 229, "usage_type": "name"}, {"api_name": "tests.factories.UserFactory", "line_number": 229, "usage_type": "call"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 229, "usage_type": "call"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 233, "usage_type": "call"}, {"api_name": "tests.factories.UserFactory", "line_number": 234, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 237, "usage_type": "call"}, {"api_name": "website.project.model.Comment.find_n_unread", "line_number": 238, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 238, "usage_type": "name"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 242, "usage_type": "call"}, {"api_name": "tests.factories.UserFactory", "line_number": 243, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 246, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 247, "usage_type": "call"}, {"api_name": "framework.guid.model.Guid.load", "line_number": 247, "usage_type": "call"}, {"api_name": "framework.guid.model.Guid", "line_number": 247, "usage_type": "name"}, {"api_name": "website.project.model.Comment.find_n_unread", "line_number": 248, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 248, "usage_type": "name"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 253, "usage_type": "call"}, {"api_name": "tests.factories.AuthUserFactory", "line_number": 254, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 257, "usage_type": "call"}, {"api_name": "website.project.model.Comment.find_n_unread", "line_number": 263, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 263, "usage_type": "name"}, {"api_name": "framework.auth.Auth", "line_number": 268, "usage_type": "call"}, {"api_name": "website.project.model.Comment.find_n_unread", "line_number": 272, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 272, "usage_type": "name"}, {"api_name": "tests.factories.ProjectFactory", "line_number": 276, "usage_type": "call"}, {"api_name": "tests.factories.AuthUserFactory", "line_number": 277, "usage_type": "call"}, {"api_name": "tests.factories.CommentFactory", "line_number": 280, "usage_type": "call"}, {"api_name": "website.project.model.Comment.find_n_unread", "line_number": 281, "usage_type": "call"}, {"api_name": "website.project.model.Comment", "line_number": 281, "usage_type": "name"}]}
{"seq_id": "13770961116", "text": "#%%\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport sys\nimport torchvision.transforms as transforms\n\nsys.path.append('/home/userfs/j/jrs596/scripts/CocoaReader/utils')\n\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n    \n    def __init__(self, in_channels, out_channels, stride=1, max_groups=32, kernel_size=3):\n        super(Bottleneck, self).__init__()\n        mid_channels = out_channels // self.expansion\n        groups = mid_channels if mid_channels % max_groups == 0 else 1  # Ensure num_groups is a divisor of num_channels\n        \n        self.residual = nn.Sequential(\n            nn.Conv2d(in_channels, mid_channels, kernel_size=1, bias=False),\n            nn.GroupNorm(num_groups=groups, num_channels=mid_channels),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(mid_channels, mid_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size//2, bias=False, groups=groups),\n            nn.GroupNorm(num_groups=groups, num_channels=mid_channels),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(mid_channels, out_channels, kernel_size=1, bias=False),\n            nn.GroupNorm(num_groups=groups, num_channels=out_channels)\n        )\n        \n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_channels != out_channels:\n            self.shortcut = nn.Sequential(\n                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n                nn.GroupNorm(num_groups=groups, num_channels=out_channels)\n            )\n        self.relu = nn.ReLU(inplace=True)\n        \n    def forward(self, x):\n        out_residual = self.residual(x)\n        out_shortcut = self.shortcut(x)\n        \n        # Check if padding is needed\n        if out_residual.size(2) != out_shortcut.size(2) or out_residual.size(3) != out_shortcut.size(3):\n            padding = [0, out_residual.size(3) - out_shortcut.size(3), 0, out_residual.size(2) - out_shortcut.size(2)]\n            out_shortcut = nn.functional.pad(out_shortcut, padding)\n        \n        out = out_residual + out_shortcut\n        out = self.relu(out)\n        return out\n\n\nclass DisNetV1_2(nn.Module):\n    def __init__(self, config):\n        super(DisNetV1_2, self).__init__()\n        self.config_dict = config\n        \n        out_channels = self.config_dict['out_channels']\n        groups = self.config_dict['dim_1'] if self.config_dict['dim_1'] % 32 == 0 else 1  # Ensure num_groups is a divisor of num_channels\n        \n        self.conv1 = nn.Conv2d(3, self.config_dict['dim_1'], kernel_size=self.config_dict['kernel_1'], stride=2, padding=self.config_dict['kernel_1']//2, bias=False)\n        self.gn1 = nn.GroupNorm(num_groups=groups, num_channels=self.config_dict['dim_1'])\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        \n        # Pass num_blocks parameter when creating layers\n        self.layer1 = self._make_layer(self.config_dict['dim_1'], self.config_dict['dim_2'], blocks=self.config_dict['num_blocks_1'], stride=1, kernel_size=self.config_dict['kernel_2'])\n        self.layer2 = self._make_layer(self.config_dict['dim_2'], self.config_dict['dim_3'], blocks=self.config_dict['num_blocks_2'], stride=2, kernel_size=self.config_dict['kernel_3'])\n        \n        self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n        self.fc = nn.Linear(self.config_dict['dim_3'], out_channels)\n        \n    def _make_layer(self, in_channels, out_channels, blocks, stride, kernel_size):\n        layers = []\n        layers.append(Bottleneck(in_channels, out_channels, stride=stride, kernel_size=kernel_size))\n        for _ in range(1, blocks):  # Use blocks parameter to determine the number of Bottleneck blocks\n            layers.append(Bottleneck(out_channels, out_channels, kernel_size=kernel_size))\n        return nn.Sequential(*layers)\n         \n    \n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.gn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n        \n        x = self.layer1(x)\n        x = self.layer2(x)\n        \n        w = self.global_avg_pool(x)\n        w = torch.flatten(w, 1)\n        \n        # x = self.fc(x)\n        \n        return x, w\n\n\n\nclass PositionalEncoding2D(nn.Module):\n    def __init__(self, d_model, max_len=5000):\n        super(PositionalEncoding2D, self).__init__()\n        self.encoding = torch.zeros(max_len, d_model)\n        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n        div_term = torch.exp(torch.arange(0, d_model, 2).float() * -(torch.log(torch.tensor(10000.0)) / d_model))\n        \n        self.encoding[:, 0::2] = torch.sin(position * div_term)\n        \n        # Handling the case when d_model is odd\n        if d_model % 2 == 0:\n            self.encoding[:, 1::2] = torch.cos(position * div_term)\n        else:\n            self.encoding[:, 1::2] = torch.cos(position * div_term)[:,:-1]\n        \n        self.encoding = self.encoding.unsqueeze(0).transpose(0, 1)\n\n    def forward(self, x):\n        x = x.flatten(2).permute(2, 0, 1)\n        y = self.encoding[:x.size(0), :].to(x.device)\n        return x + y\n\nclass TransformerDecoderBlock(nn.Module):\n    def __init__(self, d_model, nhead):\n        super(TransformerDecoderBlock, self).__init__()\n        self.self_attn = nn.MultiheadAttention(d_model, nhead)\n        self.norm1 = nn.LayerNorm(d_model)\n        self.norm2 = nn.LayerNorm(d_model)\n        self.dropout = nn.Dropout(0.1)\n        self.fc = nn.Sequential(\n            nn.Linear(d_model, d_model),\n            nn.ReLU(inplace=True),\n            nn.Dropout(0.1),\n            nn.Linear(d_model, d_model)\n        )\n\n    def forward(self, x, memory):\n        attn_output, _ = self.self_attn(x, x, x)  # Self attention\n        x = x + self.dropout(attn_output)\n        x = self.norm1(x)\n        fc_output = self.fc(x)\n        x = x + self.dropout(fc_output)\n        x = self.norm2(x)\n        return x\n\nclass TransformerDecoder(nn.Module):\n    def __init__(self, feature_dim, num_heads, num_layers, img_size):\n        super(TransformerDecoder, self).__init__()\n        self.feature_dim = feature_dim\n        self.img_size = img_size\n        self.positional_encoding = PositionalEncoding2D(feature_dim)\n        self.decoder_blocks = nn.ModuleList([TransformerDecoderBlock(feature_dim, num_heads) for _ in range(num_layers)])\n        \n        # Upsampling layers\n        self.upsample1 = nn.ConvTranspose2d(feature_dim, feature_dim, kernel_size=3, stride=3, padding=1, output_padding=1)\n        self.upsample2 = nn.ConvTranspose2d(feature_dim, feature_dim, kernel_size=3, stride=2, padding=1, output_padding=1)\n        self.upsample3 = nn.ConvTranspose2d(feature_dim, feature_dim, kernel_size=3, stride=2, padding=1, output_padding=1)\n        \n        self.final_conv = nn.Conv2d(feature_dim, 3, kernel_size=1)  # Assuming the output has 3 channels\n\n    def forward(self, x):\n        # x is the encoded feature map: [batch_size, feature_dim, height, width]\n        batch_size, _, height, width = x.size()\n        x = self.positional_encoding(x)\n        \n        for decoder_block in self.decoder_blocks:\n            x = decoder_block(x, x)  # Pass through each transformer block\n        \n        # Upsampling steps\n        x = self.upsample1(x.permute(1, 2, 0).view(batch_size, self.feature_dim, height, width))\n        x = self.upsample2(x)\n        x = self.upsample3(x)\n        \n        x = self.final_conv(x)\n        return x\n\n\n\nclass DisNet_SRAutoencoder(nn.Module):\n    def __init__(self, config):\n        super(DisNet_SRAutoencoder, self).__init__()\n        \n        # Initialize the encoder\n        self.encoder = DisNetV1_2(config)\n        \n        # Extract necessary parameters from config or define them manually\n        feature_dim = config['dim_3']\n        num_heads = config['num_heads'] \n        num_layers = config['num_decoder_layers']\n        img_size = config['input_size'] \n        \n        # Initialize the transformer decoder\n        self.decoder = TransformerDecoder(feature_dim, num_heads, num_layers, img_size)\n\n\n    def forward(self, x):\n        \n        encoded, encode_pooled  = self.encoder.forward(x)  # Get feature map before the fc layer\n        decoded = self.decoder(encoded)\n\n        return encode_pooled, decoded \n\n\n\n##################################################\n\nclass UNet(nn.Module):\n    def __init__(self, in_channels, out_channels):\n        super(UNet, self).__init__()\n                \n        self.encoder1 = self.conv_block(in_channels, 64)\n        self.pool1 = nn.MaxPool2d(kernel_size=2)\n        self.encoder2 = self.conv_block(64, 128)\n        self.pool2 = nn.MaxPool2d(kernel_size=2)\n        \n        self.bottleneck = self.conv_block(128, 256)\n        \n        self.upconv2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)\n        self.decoder2 = self.conv_block(256, 128)\n        self.upconv1 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2)\n        self.decoder1 = self.conv_block(128, 64)\n        \n        self.final_conv = nn.Conv2d(64, out_channels, kernel_size=1)\n        \n    def conv_block(self, in_channels, out_channels):\n        return nn.Sequential(\n            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        \n    def forward(self, x):\n        enc1 = self.encoder1(x)\n        enc2 = self.encoder2(self.pool1(enc1))\n        bottleneck = self.bottleneck(self.pool2(enc2))\n        \n        dec2 = self.decoder2(torch.cat([enc2, self.upconv2(bottleneck)], 1))\n        dec1 = self.decoder1(torch.cat([enc1, self.upconv1(dec2)], 1))\n        \n        return self.final_conv(dec1)\n\n\nfrom diffusers import UNet2DModel\nfrom torch.nn import functional as F\n\n\n\n\nclass PhytNet_SRAutoencoder(nn.Module):\n    def __init__(self, config):\n        super(PhytNet_SRAutoencoder, self).__init__()\n        \n        # Initialize the encoder (assuming DisNetV1_2 is defined elsewhere)\n        self.encoder = DisNetV1_2(config)\n        \n        # Extract necessary parameters from config or define them manually\n        feature_dim = config['dim_3']\n        num_heads = config['num_heads'] \n        num_layers = config['num_decoder_layers']\n        img_size = config['input_size'] \n        \n        # Initialize the transformer decoder (assuming TransformerDecoder is defined elsewhere)\n        self.decoder = TransformerDecoder(feature_dim, num_heads, num_layers, img_size)\n        \n        # Diffusion parameters\n        self.noise_std = config.get('noise_std', 0.2)  # Default noise standard deviation\n        self.num_diffusion_steps = config.get('num_diffusion_steps', 10)  # Default number of diffusion steps\n        \n        # Add U-Net for refinement\n        # self.refinement_net = UNet(feature_dim, feature_dim)\n        self.refinement_net = UNet2DModel(\n                                    sample_size=(48, 48),\n                                    in_channels=84,\n                                    out_channels=84).float()\n\n    def latent_diffusion(self, encoded):\n        # Add noise to the encoded representations\n        noisy_encoded = encoded + torch.randn_like(encoded) * self.noise_std\n        noisy_encoded = F.pad(noisy_encoded, (1, 2, 1, 2))\n\n        # Iteratively refine the noisy representations using the U-Net\n        for i in range(self.num_diffusion_steps):\n            noisy_encoded = self.refinement_net(sample=noisy_encoded, timestep=i)\n            noisy_encoded = noisy_encoded.sample\n        \n        noisy_encoded = noisy_encoded[:, :, :45, :45]\n\n        return noisy_encoded\n\n    def forward(self, x):\n        encoded, encode_pooled  = self.encoder.forward(x)  # Get feature map before the fc layer\n            \n        # Apply latent diffusion to the encoded representations\n        diffused_encoded = self.latent_diffusion(encoded)\n        \n        decoded = self.decoder(diffused_encoded)\n        return encode_pooled, decoded \n    \n\n", "repo_name": "jrsykes/CocoaReader", "sub_path": "utils/ArchitectureZoo.py", "file_name": "ArchitectureZoo.py", "file_ext": "py", "file_size_in_byte": 12041, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.GroupNorm", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.GroupNorm", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.GroupNorm", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.GroupNorm", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.functional.pad", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.GroupNorm", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.flatten", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 98, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.exp", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.MultiheadAttention", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 124, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 127, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 129, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 131, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 143, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 149, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 153, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 154, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 156, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 176, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 204, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 204, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 209, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 211, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 217, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 220, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 223, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 224, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 225, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 226, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 227, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 247, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 247, "usage_type": "name"}, {"api_name": "diffusers.UNet2DModel", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.randn_like", "line_number": 276, "usage_type": "call"}, {"api_name": "torch.nn.functional.pad", "line_number": 277, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 277, "usage_type": "name"}]}
{"seq_id": "5772493749", "text": "import re\n\nfrom collections import Counter, OrderedDict\n\nfrom poetics.patterning import assign_letters_to_dict\n\n\n########################################################################################################################\n# Tokenizing\n########################################################################################################################\n# Tokenizes text into words.\ndef tokenize(line):\n    tokenized = []\n\n    # Substitutions for various punctuation.\n    subs = {'-': ' ', '‘': \"'\", '’': \"'\"}\n    for chars, substitution in subs.items():\n        line = line.replace(chars, substitution)\n\n    # Loops through expressions/replacements in expressions. Currently deals with getting rid of uneeded apostrophes\n    # while maintaining conjunctions\n    # Final expression removes all non-apostrophe, digit, or word characters left over\n    expressions = {'\\'[^\\w]': ' ', '[^\\w]\\'': ' ', \"\\'\\Z\": ' ', \"\\A\\'\": ' ', '[^\\d\\w\\']': ' '}\n    for expression, replacement in expressions.items():\n        line = re.sub(expression, replacement, line)\n\n    # Split into individual words\n    tokenized.extend([word.lower() for word in line.strip().split()])\n    return tokenized\n\n\n# Generator for getting indexes of tokens in lines.\ndef get_line_indexes(token_list):\n    start_index = 0\n    for index, token in enumerate(token_list):\n        if index == len(token_list) - 1:\n            if token == '\\n':\n                yield(start_index, index)\n            else:\n                yield(start_index, index + 1)\n        elif token == '\\n':\n            yield(start_index, index)\n            start_index = index + 1\n\n\n# Generator for getting indexes of tokens in sentences.\ndef get_sentence_indexes(token_list):\n    terminators = ['.', '!', '?']\n    start_index = 0\n    for index, token in enumerate(token_list):\n        if index == len(token_list) - 1:\n            if index > start_index:\n                yield(start_index, index)\n        elif index == start_index and token == '\\n':\n            start_index += 1\n        elif token in terminators:\n            end_index = index\n            for token2 in token_list[index:]:\n                match = re.match(\"[^\\w\\'\\s]+\", token2)\n                if match:\n                    end_index += 1\n                else:\n                    break\n            if index > start_index:\n                yield(start_index, end_index)\n            start_index = end_index + 1\n\n\n# Generator for getting indexes of tokens in stanzas.\ndef get_stanza_indexes(token_list):\n    start_index = 0\n    for index, token in enumerate(token_list):\n        if index == len(token_list) - 1:\n            if token == '\\n':\n                yield(start_index, index)\n            else:\n                yield(start_index, index + 1)\n        elif index == start_index and token == '\\n':\n            start_index += 1\n        elif token == '\\n':\n            if token_list[index + 1] == '\\n':\n                if index > start_index:\n                    yield (start_index, index)\n                start_index = index + 1\n\n\n# Tokenizes text and gets indexes of tokens belonging to lines, sentences, and stanzas.\ndef full_tokenize(text):\n    # TODO: add exceptions for 'cause, 'em,, 'll, 'nuff, doin', goin', nothin', nothin', ol', somethin'\n\n    # List of abbreviations that should be considered a single token.\n    abbre = ('a\\.m\\.|p\\.m\\.|'\n             '[vV][sS]\\.|'\n             '[eE]\\.[gG]\\.|[iI]\\.[eE]\\.|'\n             'Mt\\.|'\n             'Mont\\.|'\n             'Bros\\.|'\n             '[cC]o\\.|'\n             'Corp\\.|'\n             'Inc\\.|'\n             'Ltd\\.|'\n             'Md\\.|'\n             'Dr\\.|'\n             'Ph\\.|'\n             'Rep\\.|'\n             'Rev\\.|'\n             'Sen\\.|'\n             'St\\.|'\n             'Messrs\\.|'\n             'Jr\\.|'\n             'Mr\\.|'\n             'Mrs\\.|'\n             'Ms\\.|')\n\n    # Pattern that splits tokens.\n    pattern = ('(' + abbre +\n               '[A-Z](?=\\.)|'  # Any capital letter followed by a period.\n               '[\\'](?=[^\\w])|'  # ' followed by a non-word character.\n               '(?<=[^\\w])\\'|'  # ' preceeded by a non-word character.\n               '\\'\\Z|\\A\\'|'  # ' at the start or end of the text.\n               '[^\\w\\']|'  # Anything that isn't a word character or an apostrophe.\n               '\\s+'  # Any number of consecutive spaces.\n               ')')\n\n    tokens = [segment for segment in re.split(pattern, text) if segment]\n\n    line_indexes = [index for index in get_line_indexes(tokens)]\n    sentence_indexes = [index for index in get_sentence_indexes(tokens)]\n    stanza_indexes = [index for index in get_stanza_indexes(tokens)]\n\n    return tokens, line_indexes, sentence_indexes, stanza_indexes\n\n\n########################################################################################################################\n# Conversion\n########################################################################################################################\n# Converts parts of speech tags from tagger to those used by wordnet. Returns None if not relevant\ndef convert_pos(pos):\n    relevant_letters = ['a', 'n', 'r', 'v']\n    pos = pos.replace(\"J\", \"A\").lower()\n    if pos[0] in relevant_letters:\n        return pos[0]\n    else:\n        return None\n\n\n# Title cases titles/names. Does not handle subtitles presently.\n# Future: handle roman numerals.\ndef title_case(text):\n    decap = ['a', 'an', 'and', 'as', 'at', 'but', 'by', 'en', 'for', 'if', 'in', 'of', 'on', 'or', 'the', 'to', 'v',\n             'v.', 'via', 'vs', 'vs.']\n    words = re.split('[\\t ]', text.title())\n    for index, word in enumerate(words[1:]):\n        if word.lower() in decap:\n            words[index + 1] = word.lower()\n\n    for index, word in enumerate(words):\n        match = re.match('\\w+\\'\\w+', word)\n        if match:\n            out_word = [word[0].upper()]\n            out_word.extend([i.lower() for i in word[1:]])\n            words[index] = ''.join(out_word)\n    return ' '.join(words)\n\n\n########################################################################################################################\n# Grouping\n########################################################################################################################\n# Gets groups from a sound set.\ndef get_sound_set_groups(sound_list, tokenized_text, max_feature_distance):\n    # Make a dictionary of sound appearances that are {Sound: [indexes of appearances]}.\n    sound_set = index_unique_strings(sound_list, 2)\n    output_dict = {}\n    # Turn the sets of indexes into groups with a maximum distance between each consequitive member.\n    for key, indexes in sound_set.items():\n        groups = [num for num in get_distance_groups(indexes, max_feature_distance, 3)]\n        # Test to make sure that those index groups correspond to at least two unique words.\n        for index, group in enumerate(groups):\n            words = [tokenized_text[index2].lower() for index2 in group]\n            if len(set(words)) < 2:\n                del(groups[index])\n        # If any groups remain, add them to an output dictionary.\n        if groups:\n            output_dict[key] = groups\n    return output_dict\n\n\n# Yields max-length groups of integers, of min_length or longer, where each consecutive integer in a group is no more\n# than distance apart.\ndef get_distance_groups(num_list, distance, min_length=1):\n    group = []\n    for index, num in enumerate(num_list):\n        # If group is currently empty or the last number in group is within distance of num, append num to group.\n        if not group or num - group[-1] <= distance:\n            group.append(num)\n            # If we're not on the last number in the list, then we move to the next one.\n            # This check necessary because otherwise the last group will never be yielded.\n            if index < len(num_list) - 1:\n                continue\n        # If we aren't within distance and group isn't empty (and so we never hit continue) then we yield group if\n        # group is at least the minimum number of items long.\n        if len(group) >= min_length:\n            yield group\n        # Then we take the current num, and put it in group alone for the next iteration.\n        group = [num]\n\n\n########################################################################################################################\n# Indexing\n########################################################################################################################\n# Takes a list of strings and returns a dict of unique items and indexes of appearances.\n# Handles lists of lists of strings or lists of strings (e.g. [['a', 'b'],['c', 'd']] or ['a', 'b']).\ndef index_unique_strings(input_list, min_count=None):\n    min_count = min_count or 1\n    out = {}\n    if isinstance(input_list[0], list):\n        count = Counter([string for item in input_list for string in item if string])\n        for key in [key for key, value in count.items() if value >= min_count]:\n            # Creates a list of the top-level indexes of the occurances of the present key.\n            indexes = [index for index, item in enumerate(input_list) for string in item if key == string]\n            # Adds the key and its indexes to output.\n            out[key] = indexes\n        return out\n    else:\n        count = Counter([string for string in input_list if string])\n        for key in [key for key, value in count.items() if value >= min_count]:\n            # Creates a list of the top-level indexes of the occurances of the present key.\n            indexes = [index for index, string in enumerate(input_list) if key == string]\n            # Adds the key and its indexes to output.\n            out[key] = indexes\n        return out\n\n\n# Takes a list of features and turns it into a scheme.\ndef feats_to_scheme(features, lower=False, allow_blanks=False, max_unique=None):\n    # If blanks aren't allowed, return None if any are present.\n    if not allow_blanks:\n        blanks = [feature for feature in features if not feature]\n        if len(blanks) > 0:\n            return None\n    # Create an ordered dict of unique features that assigns each unique feature a letter.\n    ordered_features = assign_letters_to_dict(OrderedDict.fromkeys([feature for feature in features\n                                                                    if feature and not feature == ' ']), lower)\n    # Return none if the number of features surpasses max unique.\n    if max_unique:\n        if len(ordered_features) > max_unique:\n            return None\n    # Return joined scheme\n    return ''.join([' ' if not feature or feature == ' ' else ordered_features[feature] for feature in features])\n", "repo_name": "M-R-Epstein/poetics", "sub_path": "poetics/conversions.py", "file_name": "conversions.py", "file_ext": "py", "file_size_in_byte": 10660, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "46", "api": [{"api_name": "re.sub", "line_number": 25, "usage_type": "call"}, {"api_name": "re.match", "line_number": 59, "usage_type": "call"}, {"api_name": "re.split", "line_number": 125, "usage_type": "call"}, {"api_name": "re.split", "line_number": 152, "usage_type": "call"}, {"api_name": "re.match", "line_number": 158, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 217, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 225, "usage_type": "call"}, {"api_name": "poetics.patterning.assign_letters_to_dict", "line_number": 242, "usage_type": "call"}, {"api_name": "collections.OrderedDict.fromkeys", "line_number": 242, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 242, "usage_type": "name"}]}
{"seq_id": "29642133423", "text": "from django.conf.urls import include, url\nfrom django.contrib import admin\nfrom django.views.generic.base import RedirectView\n\nfrom products.views import (\n        ProductCreateView,\n        ProductDetailView,\n        ProductListView,\n        ProductUpdateView,\n        )\n\nurlpatterns = [\n    url(r'^$', ProductListView.as_view(), name='list'), #products:list\n    url(r'^(?P<pk>\\d+)/$', ProductDetailView.as_view(), name='detail'),\n    url(r'^(?P<slug>[\\w-]+)/$', ProductDetailView.as_view(), name='detail_slug'),\n    url(r'^(?P<pk>\\d+)/edit/$', ProductUpdateView.as_view(), name='update'),\n    url(r'^(?P<slug>[\\w-]+)/edit/$', ProductUpdateView.as_view(), name='update_slug'),\n]", "repo_name": "mathemartins/shakaraexpress", "sub_path": "products/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "products.views.ProductListView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "products.views.ProductListView", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "products.views.ProductDetailView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "products.views.ProductDetailView", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "products.views.ProductDetailView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "products.views.ProductDetailView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "products.views.ProductUpdateView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "products.views.ProductUpdateView", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "products.views.ProductUpdateView.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "products.views.ProductUpdateView", "line_number": 17, "usage_type": "name"}]}
{"seq_id": "9520626764", "text": "import rospy\nfrom std_msgs.msg import String\nfrom franka_action_lib.msg import SensorData\nfrom proto import sensor_msg_pb2\n\ndef talker():\n    sensor_msg = sensor_msg_pb2.BoundingBox()\n    sensor_msg.name = \"hello\"\n    sensor_msg.id = 0\n    sensor_msg.x = 1\n    sensor_msg.y = 2\n    sensor_msg.w = 3\n    sensor_msg.h = 4\n\n    sensor_data_bytes = sensor_msg.SerializeToString()\n    \n    print('data_in_bytes: type: {}, data: {}'.format(\n       type(sensor_data_bytes), sensor_data_bytes))\n\n    rospy.init_node('talker', anonymous=True)\n    \n    pub = rospy.Publisher('dummy_sensor', SensorData, queue_size=1000)\n\n    f_data = SensorData()\n    f_data.sensorDataInfo = \"BoundingBox\"\n    f_data.type = 5\n    f_data.size = len(sensor_data_bytes)\n    f_data.sensorData = sensor_data_bytes\n\n    rate = rospy.Rate(1)\n    while not rospy.is_shutdown():\n        rospy.loginfo(\"will send message\")\n        pub.publish(f_data)\n        rate.sleep()\n\n    #f_data.size =len10\n\n    # rospy.init_node('dummy_sensor_bytes_publisher', anonymous=True)\n    # rate = rospy.Rate(10) # 10hz\n    # while not rospy.is_shutdown():\n       \n\n    #     print('data_in_bytes',data_in_bytes)\n\n    #     hello_str = \"hello world %s\" % rospy.get_time()\n    #     rospy.loginfo(hello_str)\n    #     pub.publish(f_data)\n    #     rate.sleep()\n\nif __name__ == '__main__':\n    try:\n        talker()\n    except rospy.ROSInterruptException:\n        pass", "repo_name": "iamlab-cmu/robot-interface-public", "sub_path": "catkin_ws/src/franka_action_lib/frankapy/sensor_data_bytes_publisher.py", "file_name": "sensor_data_bytes_publisher.py", "file_ext": "py", "file_size_in_byte": 1412, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "proto.sensor_msg_pb2.BoundingBox", "line_number": 7, "usage_type": "call"}, {"api_name": "proto.sensor_msg_pb2", "line_number": 7, "usage_type": "name"}, {"api_name": "rospy.init_node", "line_number": 20, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 22, "usage_type": "call"}, {"api_name": "franka_action_lib.msg.SensorData", "line_number": 22, "usage_type": "argument"}, {"api_name": "franka_action_lib.msg.SensorData", "line_number": 24, "usage_type": "call"}, {"api_name": "rospy.Rate", "line_number": 30, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 31, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 32, "usage_type": "call"}, {"api_name": "rospy.ROSInterruptException", "line_number": 53, "usage_type": "attribute"}]}
{"seq_id": "37583841865", "text": "import pygame, sys\nfrom pygame.locals import *\n\npygame.init()\n\nFPS = 100 # frames per second setting\nfpsClock = pygame.time.Clock()\n\n# set up the window\nDISPLAYSURF = pygame.display.set_mode((400, 300), 0, 32)\npygame.display.set_caption('Animation')\n\n\npygame.mixer.music.load('megalovania.wav')\npygame.mixer.music.play(-1, 0.0)\n\nsound = pygame.mixer.Sound('tumpunch.wav')\ncolor = (255, 255, 255)\ncatImg = pygame.image.load('smallsans.png')\ncatx = 1\ncaty = 100\ndirection = 'right'\nDISPLAYSURF.fill(color)\nwhile True: # the main game loop\n    DISPLAYSURF.fill(color)\n\n\n    if direction == 'right':\n        catx += 7\n        if catx == 309:\n            direction = 'left'\n            sound.play()\n            color = (255,255,255)\n\n    elif direction == 'left':\n        catx -= 7\n        if catx == 15:\n            direction = 'right'\n            sound.play()\n            color = (0,0,0)\n\n\n    DISPLAYSURF.blit(catImg, (catx, caty))\n\n    for event in pygame.event.get():\n        if event.type == QUIT:\n            pygame.quit()\n            sys.exit()\n\n    pygame.display.update()\n    fpsClock.tick(FPS)", "repo_name": "Ryan-Callahan/2018_2019Programs", "sub_path": "PygamePractice/General stuff/sound.py", "file_name": "sound.py", "file_ext": "py", "file_size_in_byte": 1099, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 50, "usage_type": "attribute"}]}
{"seq_id": "32239158805", "text": "import numpy as np\nimport torch\nimport config\n\nHIDDEN_DIM = config.PARAM['hidden_dim']\nN_LAYERS = config.PARAM['n_layers']\n\n\ndef prep_batches(dataset, batch_size, sequence_length, print_every = None):\n    num_batches = (len(dataset)-1) // sequence_length // batch_size\n    inputs = dataset[:num_batches*batch_size*sequence_length].view(-1, sequence_length)\n    targets = dataset[1:num_batches*batch_size*sequence_length + 1].view(-1, sequence_length)\n\n    # We now have (batches, batchsize, sequence_length) for both inputs and targets\n    # However, we wish to have (i, j) to have the next sequence be (i+1, j) such that states between\n    # batches can be used.\n\n    temp_tuple = ([None] * num_batches, [None] * num_batches)\n    for batch in range(0, num_batches):\n        temp_tuple[0][batch] = inputs[batch:num_batches*batch_size:num_batches].view(batch_size, sequence_length)\n        temp_tuple[1][batch] = targets[batch:num_batches*batch_size:num_batches].view(batch_size, sequence_length)\n        if print_every and batch % print_every == 0:\n            print(\"Preparing batch {}/{}\".format(batch + 1, num_batches))\n\n    inputs, targets = temp_tuple\n    return inputs, targets\n", "repo_name": "simonamtoft/Language-Modelling", "sub_path": "Helpers.py", "file_name": "Helpers.py", "file_ext": "py", "file_size_in_byte": 1184, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "config.PARAM", "line_number": 5, "usage_type": "attribute"}, {"api_name": "config.PARAM", "line_number": 6, "usage_type": "attribute"}]}
{"seq_id": "71528516285", "text": "#!usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n仓位管理\n\n\"\"\"\n\n\nfrom pandas import DataFrame, Series, concat, ExcelWriter, read_excel, set_option\nfrom datetime import datetime, timedelta, date\n\n\nclass Asset():\n    \"\"\"\n    净值计算相关\n    \"\"\"\n    def __init__(self, args):\n        self.args = args\n\n    def get_position_and_asset(self, strategy_name, signal, market_data, if_plot, path_out=\"\", \\\n                                if_atr_fix=False, if_change_position_grade=False):\n        \"\"\"\n        根据交易信号计算每日持仓和净值\n        \"\"\"\n        \"\"\"\n        signal 持仓信号字典（1做多 -1做空 0平仓/空仓）\n        price 收盘价字典\n        \"\"\"\n        if if_plot == True:\n            file_log = open(path_out+strategy_name+\"-log.csv\", \"w\")\n            file_log = open(path_out+strategy_name+\"-log.csv\", \"a\")\n            print('Date,position,position_grade,price,low,high,asset_low,open_asset,maximum_asset,signal,asset', file=file_log)\n        price = market_data['Close']\n        high = market_data['High']\n        low = market_data['Low']\n        asset = {}  # 净值是扣除当日换仓成本后的当日净值\n        asset[signal.index[0]] = self.args.original_asset\n        position = {}  # 这里的仓位是按当日收盘价换仓后的当日持仓\n        position[signal.index[0]] = 0\n        count_change = 0\n        count_abort = 0\n        count_drop = 0\n        winning_days = 0\n        position_grade = 0\n        positive_signal = {}\n        negative_signal = {}\n        withdraw_line = {}\n        cost_line = {}\n        think_of_withdraw_line = {}\n        asset_low = {}\n        maximum_asset = asset[signal.index[0]]\n\n        for i, Date in enumerate(signal.index):\n            # print(i,signal[Date])\n            # print(Date, Series(asset).index[-1])\n            if i > 0:\n                yesterday = signal.index[i - 1]\n                # 计算当日资产最低值\n                # print(yesterday, position[yesterday], signal[yesterday], signal[Date])\n                if signal[yesterday] >= 1:\n                    asset_low[Date] = asset[yesterday] + position[yesterday] * signal[yesterday] * (low[Date] - price[yesterday])\n                elif signal[yesterday] <= -1:\n                    asset_low[Date] = asset[yesterday] + position[yesterday] * signal[yesterday] * (high[Date] - price[yesterday])\n                else:\n                    asset_low[Date] = asset[yesterday]\n                # \n                if signal[Date] == 0:\n                    signal[Date] = signal[yesterday]\n\n                # 当前仓位的历史最大净值\n                maximum_asset = max(maximum_asset, asset[yesterday])\n                think_of_withdraw_line[Date] = open_asset * (1+self.args.begin_withdraw)\n                if maximum_asset > open_asset * (1+self.args.begin_withdraw):\n                    # 净资产已需考虑止盈\n                    withdraw_line[Date] = open_asset + (maximum_asset - open_asset) \\\n                                            * (1 - self.args.withdraw_ratio_critical)  # 止盈线\n                else:\n                    withdraw_line[Date] = open_asset * (1+self.args.begin_withdraw)\n                cost_line[Date] = open_asset * (1-self.args.cost_ratio_critical)\n                \n                # 止损止盈\n                if asset_low[Date] < withdraw_line[Date] and asset[yesterday] > withdraw_line[yesterday] and signal[yesterday] != 0:\n                    count_abort += 1\n                    signal_last = signal[yesterday]  # 记录平仓前的仓位\n                    asset[Date], position[Date] \\\n                    = self.prevent_loss(Date, date_of_opening, withdraw_line[Date])\n                    winning_days = 0\n                    signal[Date] = 0\n                    if if_change_position_grade == True:\n                        position_grade = len(self.args.position_grade_mode)-1\n                        # position_grade += 1\n                elif asset_low[Date] < cost_line[Date] and signal[yesterday] != 0:\n                    count_abort += 1\n                    signal_last = signal[yesterday]  # 记录平仓前的仓位\n                    asset[Date], position[Date] \\\n                    = self.prevent_loss(Date, date_of_opening, cost_line[Date])\n                    winning_days = 0\n                    signal[Date] = 0\n                    if if_change_position_grade == True:\n                        position_grade = len(self.args.position_grade_mode)-1\n                        # position_grade += 1\n\n                else:\n                    # 换仓大业！\n                    # 计算换仓前当日净值\n                    asset_before_changing_position \\\n                    = asset[yesterday] + position[yesterday] * (price[Date] - price[yesterday]) * signal[yesterday]\n                    if signal[yesterday] * (price[Date] - price[yesterday]) > 0:\n                        # 若今天能盈利\n                        winning_days += 1\n                    else:\n                        # 若今天亏损\n                        winning_days = 0\n\n                    # 换仓 vs 不换仓\n                    if signal[Date] == signal[yesterday]:\n                        # 不换仓，加不加仓？\n                        # winning_days > 2 盈3加仓\n                        # winning_days > 4 盈5加仓\n                        if winning_days > 2 and position_grade > 0:\n                            # 若达到连续判胜次数 且现持仓等级不是最高级 ==> 加仓 \n                            winning_days = 0\n                            asset[Date], position[Date], position_grade \\\n                            = self.promote_position(i, price[Date], position_grade, \\\n                                                    asset_before_changing_position, \\\n                                                    signal[Date], asset[yesterday], position[yesterday])\n                        else:\n                            asset[Date] = asset_before_changing_position\n                            position[Date] = position[yesterday]\n                    else:\n                        # signal[Date] ！= signal[yesterday]\n                        if signal[yesterday] == 0:\n                            # 若前一天是空仓，则当天只能开仓或继续空仓，不能反手\n                            open_asset, asset[Date], position[Date], maximum_asset, signal[Date], date_of_opening \\\n                            = self.build_position(i, Date, price[Date], asset[yesterday], \\\n                                                    signal[Date], signal[yesterday], position_grade)\n                            withdraw_line[Date] = open_asset * (1+self.args.begin_withdraw)\n                            asset_low[Date] = open_asset\n                        else:\n                            # 若前一天不是空仓\n                            if signal[Date]*signal[yesterday]<0 and (signal[Date] >= 1 or signal[Date] <= -1): \n                                # 反手(先平，再反向开)\n                                count_change += 1\n                                # 平仓\n                                position_asset = price[Date] * position[yesterday] / self.args.lever_ratio\n                                asset[Date] = asset_before_changing_position - position_asset \\\n                                                + self.closing_position(position_asset, Date, date_of_opening)\n                                \n                                # 反向开仓\n                                open_asset, asset[Date], position[Date], maximum_asset, signal[Date], date_of_opening \\\n                                = self.build_position(i, Date, price[Date], asset[Date], \\\n                                                    signal[Date], signal[yesterday], position_grade)\n                                if maximum_asset > open_asset * (1+self.args.begin_withdraw):\n                                    # 净资产已需考虑止盈\n                                    withdraw_line[Date] = open_asset + (maximum_asset - open_asset) \\\n                                            * (1 - self.args.withdraw_ratio_critical)  # 止盈线\n                                else:\n                                    withdraw_line[Date] = open_asset * (1+self.args.begin_withdraw)\n                                # print(\"换仓, {}, {:.4f}, {:.4f}, {:.4f}\" \\\n                                # .format(i, withdraw_line[Date], think_of_withdraw_line[Date], cost_line[Date]))\n                            else:    \n                                # signal[Date] = 0.5 or -0.5 or 0\n                                # 不动\n                                signal[Date] = signal[yesterday]\n                                asset[Date] = asset_before_changing_position\n                                position[Date] = position[yesterday]\n                    if asset[Date] < asset_min:\n                        asset_min = asset[Date]\n                # print(position[Date],', ',self.args.position_grade_mode[position_grade] ,\\\n                #         ', ',price[Date],', ',low[Date],', ',high[Date],', ',asset_low[Date],\\\n                #         ', ',open_asset,', ',maximum_asset,', ',signal[Date],', ',asset[Date])\n                if if_plot == True:\n                    print(Date,', ',position[Date],', ',self.args.position_grade_mode[position_grade] ,\\\n                            ', ',price[Date],', ',low[Date],', ',high[Date],', ',asset_low[Date],\\\n                            ', ',open_asset,', ',maximum_asset,', ',signal[Date],', ',asset[Date], file=file_log)\n            \n            else:\n                # 第一天 开仓或继续空仓，不可能反手\n                # print(\"开仓： {}  {}  {}  {}  asset: {}\".format(i, Date, signal[Date], price[Date], asset[Date]))\n                if signal[Date] != 0:\n                    open_asset, asset[Date], position[Date], maximum_asset, signal[Date], date_of_opening \\\n                    = self.build_position(i, Date, price[Date], asset[signal.index[0]], \\\n                                            signal[Date], 0, position_grade)\n                else:\n                    open_asset = self.args.original_asset\n                    maximum_asset = self.args.original_asset\n                    position[Date] = 0\n                think_of_withdraw_line[Date] = open_asset * (1+self.args.begin_withdraw)\n                withdraw_line[Date] = open_asset * (1+self.args.begin_withdraw)  #止盈线\n                cost_line[Date] = open_asset * (1-self.args.cost_ratio_critical)  # 止损线\n                asset_low[Date] = open_asset\n                asset_min = open_asset\n        if if_plot == True:\n            file_log.close()\n        for Date in signal.index:\n            # yesterday = signal.index[i - 1]\n            # print(i, \"  \",Date,\"  \",price[Date] - price[yesterday])\n            if signal[Date] >= 1:\n                positive_signal[Date] = 5000\n                negative_signal[Date] = 0\n            elif signal[Date] <= -1:\n                positive_signal[Date] = 0\n                negative_signal[Date] = 5000\n            else:\n                positive_signal[Date] = 0\n                negative_signal[Date] = 0\n        # print(\"止损止盈平仓次数:{}\".format(count_abort))\n        # print(\"主动平仓次数：{}\".format(count_drop))\n        # print(\"换仓次数:{}\".format(count_change))\n        # print('asset_min = ',asset_min)\n        return Series(position), Series(asset), Series(signal), Series(positive_signal), \\\n                Series(negative_signal), Series(withdraw_line), Series(cost_line), \\\n                Series(think_of_withdraw_line), Series(asset_low), \\\n                count_abort, count_change\n\n    def build_position(self, i, Date, price_today, asset_today, signal_today, signal_yesterday, position_grade):\n        \"\"\"\n        开仓（开仓或继续空仓）\n        \"\"\"\n        \"\"\"\n        price_today 今日收盘价\n        asset_today 换仓前资产（保证金）\n        signal_today 当日持仓信号（1做多 -1做空 0平仓/空仓）\n        \"\"\" \n        fee_ratio = self.args.transaction_fee[0]\n        open_asset = self.args.original_asset\n        maximum_asset = self.args.original_asset\n        position_today = 0\n        if -1 < signal_today < 1:\n            # 如果今天无信号，则照搬前一个信号（针对布林带策略开仓后平仓的情形）\n            signal_today = signal_yesterday\n            \n        position_asset = asset_today * self.args.position_grade_mode[position_grade]\n        asset_today -= self.deducting_transaction_fee(position_asset, fee_ratio)\n        position_asset -= self.deducting_transaction_fee(position_asset, fee_ratio)\n        # 开仓资产\n        open_asset = asset_today\n        maximum_asset = asset_today  # 最大资产重新开始记录\n        # 持仓标的物数量（若开仓为正值，若空仓为负值，单位为吨）  \n        position_today = position_asset / price_today * self.args.lever_ratio\n        # print(\"开仓, {}, {}, {:.6f}\".format(i, position_grade, position_today))\n        Date = str(Date)\n        Date = datetime.strptime(Date, '%Y-%m-%d %H:%M:%S')\n        return open_asset, asset_today, position_today, maximum_asset, signal_today, Date.date\n\n    def prevent_loss(self, Date, date_of_opening, leave_asset):\n        \"\"\"\n        平仓\n        \"\"\"\n        asset = self.closing_position(leave_asset, Date, date_of_opening)\n        position_today = 0\n        return asset, position_today\n\n    def promote_position(self, i, price_today, position_grade, asset_before_changing_position, \n                            signal_yesterday, asset_yesterday, position_today):\n        \"\"\"\n        加仓（升级）\n        \"\"\"\n        fee_ratio = self.args.transaction_fee[0]\n        position_asset_before_promotion = position_today * price_today * self.args.lever_ratio\n        position_grade -= 1\n        position_asset_today = asset_before_changing_position * self.args.position_grade_mode[position_grade]\n        if signal_yesterday >= 1 or signal_yesterday <= -1:\n            asset_today = asset_before_changing_position - self.deducting_transaction_fee(position_asset_today, fee_ratio)\n            position_asset_today -= self.deducting_transaction_fee(position_asset_today, fee_ratio)\n        else:\n            asset_today = asset_yesterday\n        position_today = position_asset_today / price_today * self.args.lever_ratio\n        # print(\"加仓, {}, {}, {:.6f}\".format(i,position_grade,position_today))\n        return asset_today, position_today, position_grade\n        \n    def closing_position(self, asset, Date, date_of_opening):\n        \"\"\"\n        平仓\n        \"\"\"\n        if len(self.args.transaction_fee) > 1:\n            # 隔日平仓/当日平仓\n            fee_ratio_another_day = self.args.transaction_fee[0]\n            fee_ratio_in_day = self.args.transaction_fee[1]\n\n            Date = str(Date)\n            Date = datetime.strptime(Date, '%Y-%m-%d %H:%M:%S')\n            if Date.date == date_of_opening:\n                # 日内平仓\n                asset -= self.deducting_transaction_fee(asset, fee_ratio_in_day)\n            else:\n                # 隔日平仓\n                asset -= self.deducting_transaction_fee(asset, fee_ratio_another_day)\n        else:\n            # 平仓日期不影响手续费\n            fee_ratio_another_day = self.args.transaction_fee[0]\n            asset -= self.deducting_transaction_fee(asset, fee_ratio_another_day)\n        return asset\n\n    def deducting_transaction_fee(self, asset, fee_ratio):\n        \"\"\"\n        扣除手续费\n        \"\"\"\n        fee = asset * self.args.lever_ratio *  fee_ratio\n        return fee\n\n\ndef import_contracts_info():\n    \"\"\"\n    导入期货品种合约信息\n    \"\"\"\n    # 单位交易额的保证金费率\n    transcation_fees_at_price = {\n                                    \"rb\":[0.00012],  # 螺纹钢 0.000012每交易额\n                                    \"ni\":[0.0000127],  # 镍 1.2每手每吨=0.0127%%\n                                    \"m\":[0.00000857],  # 豆粕 0.24每手10吨=0.00857%%\n                                    \"sr\":[0.00006],  # 白糖 3.6每手10吨=0.06%%\n                                    \"PTA\":[0.00012],  # PTA 3.6每手5吨=0.12%%\n                                    \"GZ300\":[0.000028,0.000828],  # 沪深300股指期货\n                                    \"GZ500\":[0.000028,0.000828],  # 中证500股指期货\n                                    \"GZ50\":[0.000028,0.000828],  # 上证50股指期货\n                                    \"cffex2\":[0.000005],  # 2年期国债期货 4.6每一百万票面一手=0.005%%\n                                    \"cffex5\":[0.000005],  # 5年期国债期货 4.6每一百万票面一手=0.005%%\n                                    \"cffex10\":[0.000005],  # 10年期国债期货 4.6每一百万票面一手=0.005%%\n                                    \"ma\":[0.00008,0.00024],  # 甲醇 2.4每手10吨每吨=0.08%%\n                                    \"fu\":[0.00006],  # 燃油 0.00006每交易额\n                                    \"i\":[0.000007],  # 铁矿石 0.000007每交易额\n                                    \"hc\":[0.00012],  # 热轧卷板 \n                                    \"rm\":[0.00075],  # 菜粕\n                                    \"j\":[0.000072],  # 焦炭\n                                    \"jm\":[0.000072],  # 焦煤\n                                    \"c\":[0.000012],  # 玉米 0.24每手10吨\n                                    \"sm\":[0.0001, 0.0002],  # 硅锰 3.6每手5吨\n                                }\n\n    # 标的物单位\n    commodity_unit = {\n                                    \"rb\":10,  # 螺纹钢 0.000012每交易额\n                                    \"ni\":1,  # 镍 1.2每手每吨=0.0127%%\n                                    \"m\":10,  # 豆粕 0.24每手10吨=0.00857%%\n                                    \"sr\":10,  # 白糖 3.6每手10吨=0.06%%\n                                    \"PTA\":5,  # PTA 3.6每手5吨=0.12%%\n                                    \"GZ300\":300,  # 沪深300股指期货\n                                    \"GZ500\":300,  # 中证500股指期货\n                                    \"GZ50\":300,  # 上证50股指期货\n                                    \"cffex2\":10000,  # 2年期国债期货 4.6每一百万票面一手\n                                    \"cffex5\":10000,  # 5年期国债期货 4.6每一百万票面一手\n                                    \"cffex10\":10000,  # 10年期国债期货 4.6每一百万票面一手\n                                    \"ma\":10,  # 甲醇 2.4每手10吨每吨=0.08%%\n                                    \"fu\":10,  # 燃油 0.00006每交易额\n                                    \"i\":100,  # 铁矿石 0.000007每交易额\n                                    \"hc\": 10,  # 菜粕\n                                    \"rm\": 10,  # 热轧卷板 \n                                    \"j\":100,  # 焦炭\n                                    \"jm\":60,  # 焦煤\n                                    \"c\":10,  # 玉米\n                                    \"sm\":5,  # 硅锰\n                                }\n\n    # 杠杆比率\n    lever_ratio = {\n                    \"rb\":10,  \n                    \"ni\":10, \n                    \"m\":10,  \n                    \"sr\":10,  \n                    \"PTA\":10,  \n                    \"GZ300\":5,\n                    \"GZ500\":5,\n                    \"GZ50\":5,\n                    \"cffex2\":66,\n                    \"cffex5\":40,  \n                    \"cffex10\":33, \n                    \"ma\":14,  # 甲醇 7%\n                    \"fu\":12.5,  # 燃油 8%\n                    \"i\":12.5,  # 铁矿石 8%\n                    \"hc\":10,  # 热轧卷板 \n                    \"rm\":16,  # 菜粕\n                    \"j\":12.5,  # 焦炭\n                    \"jm\":20,  # 焦煤\n                    \"c\":20,  # 玉米\n                    \"sm\":20,  # 硅锰\n                    }\n\n    # 各品种合约\n    commodities = {\n                    \"rb\":\"RB.SHF\",\n                    \"ni\":\"NI.SHF\",\n                    \"sr\":\"SR.CZC\",\n                    \"PTA\":\"TA.CZC\",\n                    \"m\":\"M.DCE\",\n                    \"GZ300\":\"IF.CFE\",  # 沪深300股指\n                    \"GZ500\":\"IC.CFE\",  # 中证500股指\n                    \"GZ50\":\"IH.CFE\",  # 上证50股指\n                    \"cffex2\":\"TS.CFE\",  # 国债2年期\n                    \"cffex5\":\"TF.CFE\",  # 国债5年期\n                    \"cffex10\":\"T.CFE\",  # 国债10年期\n                    \"ma\":\"MA.CZC\",  # 甲醇\n                    \"fu\":\"FU.SHF\",  # 燃油\n                    \"i\":\"I.DCE\",  # 铁矿石\n                    \"hc\":\"HC.SHF\",  # 热轧卷板\n                    \"rm\":\"RM.CZC\",  # 菜粕\n                    \"j\":\"J.DCE\",  # 焦炭\n                    \"jm\":\"JM.DCE\",  # 焦煤\n                    \"c\":\"C.DCE\",  # 玉米\n                    \"sm\":\"SM.CZC\",  # 硅锰\n                  }\n\n    return transcation_fees_at_price, commodity_unit, lever_ratio, commodities\n    ", "repo_name": "beefsoup18/quant-TestingPlatform", "sub_path": "position_management.py", "file_name": "position_management.py", "file_ext": "py", "file_size_in_byte": 21118, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pandas.Series", "line_number": 212, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 213, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 214, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 244, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 244, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 283, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 283, "usage_type": "name"}]}
{"seq_id": "6138253044", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'items'\nurlpatterns = [\n    path('', views.ItemListView.as_view(), name='index'),\n    path('<int:pk>/', views.ItemDetailView.as_view(), name='detail'),\n    path('create/', views.create_item, name='create'),\n    path('<int:pk>/edit/', views.ItemUpdateView.as_view(), name='edit'),\n    path('generic/', views.GenericItemListView.as_view(), name='generic-index'),\n    path('generic/<int:pk>/', views.GenericItemDetailView.as_view(), name='generic-detail'),\n    path('generic/create/', views.create_generic_item, name='generic-create'),\n    path('generic/<int:pk>/edit/', views.GenericItemUpdateView.as_view(), name='generic-edit'),\n]\n", "repo_name": "freestyletin/budget", "sub_path": "items/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 693, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "20482067419", "text": "#!/usr/bin/python3\n# -*- coding:utf-8 -*-\n\nremark = '''selenium使用笔记'''\n\n#引入相关包\npythonPath = r'D:\\TDdownload\\Document\\Python'\nimport os,sys,time\nsys.path.append(pythonPath)\nimport configure as cfg\nfrom selenium import webdriver\n\ndriver = webdriver.Chrome('exedir')\ndriver.get('url')\n\n#获取浏览器中已经保存的所有cookie\ndriver.get_cookies()\n#获取特定cookie\ndriver.get_cookie('cookiename')\n\n#隐式等待\ndriver.implicitly_wait(10)\n\n#显式等待,需要的时候百度搜索吧，太长了\n\n\nele = driver.find_element_by_id('kw')\n\n#对元素操作\nele.click()\nele.clear()\nele.send_keys('hello')\nele.find_element_by_id('su')\n\n\n\nprint(ele.text)#元素的文本内容\nele.get_attribute('href')#属性值\nele.get_attribute('innerHTML')#html内容\nele.get_attribute('outerHTML')#html内容\n\n#获取输入框中的内容\nele.get_attribute('value')#注意不是用ele.text\n\n\n\n#选择元素的方式\ndriver.find_element_by_id('kw')#找到返回，找不到报错\n\ndriver.find_element_by_name('kw')#如果有多个，返回第一个。找不到报错。\ndriver.find_elements_by_name('kw')#找所有kw的元素，返回一个列表，如果找不到，就是空列表\n\ndriver.find_element_by_class_name('kw')\n\ndriver.find_element_by_tag_name('kw')\n\ndriver.find_element_by_link_text('kw')\n\ndriver.find_element_by_partial_link_text('kw')\n\n\n#iframe和frameset:iframe就是新开一个页面，frameset是里面可以放多个HTML页面\ndriver.switch_to.frame('frame_reference')\n#frame_reference可以是1.frame元素的id或者name。2.frame元素的索引值。3.frame元素对应的webelement,driver.find_element_by_tag_name('iframe')\n\n#切换回到主框架\ndriver.switch_to.default_content()\n\n#如果是嵌套类型的，框架里面的框架里面还有框架，不能直接switch_to最里面一层，需要一步步切换。但是返回主框架可以一步完成\n#如果是返回上一层\ndriver.switch_to.parent_frame()\n\n\n\n#css选择器     h1 {color:red;font-size:14px;}属性之间用分号隔开。每个属性用键值对表示\n#css选择元素的方式\n\n#根据id       #food {color:red;}\ndriver.find_element_by_css_selector('#food')\n\n#根据class   .vegetable {color:red;}\ndriver.find_element_by_css_selector('.vegetable')\n\n#根据tag      p {color:red;}\ndriver.find_element_by_css_selector('p')\n\n#根据tag和class组合     span.vegetable {color:red;}   注意这是并的关系\ndriver.find_element_by_css_selector('span.vegetable.good')#同时具有span这个tag属性，又有vegetable和good这两个class属性\n\n#class中如果有空格，代表有多个属性，而不是带空格的属性\n#仅限class属性，其他的例如spec属性，中间有空格也还是一个属性，是带中间空格的一个属性\n'''\n<span class=\"vegetable good\">黄瓜</span>\n'''\n\n\n\n#后代选择器，即选择元素里面的子元素，可以跨代\ndriver.find_element_by_css_selector('#choose_car option')#与后代之间用空格隔开。\n\n#子元素选择器，即必须是直接子元素，不可跨代\ndriver.find_element_by_css_selector('#choose_car > option')#用大于号如果是多层可以使用ul>ol>li>em，多次使用>,选中的是最后的元素\n\n#选择所有子元素\ndriver.find_element_by_css_selector('#choose_car > *')#用星号表示所有子元素\n\n\n\n#兄弟节点选择器，拥有相同的父节点：注意必须是相同的父节点\n# 1.两个元素连接在一起的，不被其他元素隔开，用+号\ndriver.find_element_by_css_selector('#food + div')\n# 2.必须是连接在一起的，不能被其他元素隔开，用~号\ndriver.find_element_by_css_selector('#food ~ div')\n\n\n#某节点子元素有多个相同属性，但是只需要取其中一个\ndriver.find_element_by_css_selector('#food > p:nth-child(3)')#数字3是第几个子节点，跟p属性无关。是所有属性都算。\n\n\n\n\n\n\n\n#组选择。要选择多个元素，是或的关系，可以用逗号隔开\ndriver.find_element_by_css_selector('p,button')#逗号隔开，就表示p属性和button属性的都选中\n\n#因为或的优先级比较低，所以如下#food > span是一个整体\ndriver.find_element_by_css_selector('#food > span,button')#这是food下面的span属性（前面是一个整体），button属性。\n\n\n'''\n#CSS多种属性组合\n*[style]#所有属性，并且，style属性\np[spec=len2]#p属性，并且，spec属性值为len2\np[spec='len2 len3']#p属性，并且，spec属性值为‘len2 len3’\np[spec*='len2']#p属性，并且，spec属性值包含len2\np[spec^='len2']#p属性，并且，spec属性是以len2开头的\np[spec$='len2']#p属性，并且，spec属性是以len2结尾的\np[class=special][name=p1]#p属性，并且，class属性的值等于special，并且，name属性的值是p1\n\n'''\n\n\n#单选框\nele.click()\n\n#勾选框\nele.click()\nele.is_selected()#返回的是True或者False\n\n\n#复选框，需要用到select方法\nfrom seleniium.webdriver.support.ui import Select\nselect = Select(driver.find_element_by_id('multi'))\nselect.deselect_all()#去掉所有勾选\nselect.select_by_visible_text('雅阁')\nselect.select_by_visible_text('宝马')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__==\"__main__\":\n    pass", "repo_name": "peng211217610/Python", "sub_path": "知识/selenium元素定位.py", "file_name": "selenium元素定位.py", "file_ext": "py", "file_size_in_byte": 5153, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "seleniium.webdriver.support.ui.Select", "line_number": 155, "usage_type": "call"}]}
{"seq_id": "20976658735", "text": "'''\nmysql\n'''\nimport pymysql\n\n'''\nmysql operation\n'''\ndb_table_name = ['user', 'mail', 'edu_expe', 'work_expe', 'diary', 'diary_reply', 'friend', 'friend_group', 'share', 'message_reply']\ndb_user = ['name', 'sex', 'birthday', 'addr', 'passwd', 'mail']\ndb_user_decorate = [1, 1, 0, 1, 1, 1]\ndb_mail = ['email', 'mail_iduser', 'active']\ndb_work_expe = ['work_place', 'work_start_time', 'work_end_time', 'work_job', 'work_iduser']\ndb_work_decorate = [1, 0, 0, 1, 0]\ndb_edu_expe = ['edu_level', 'edu_start_time', 'edu_end_time', 'edu_schoolname', 'edu_degree', 'edu_iduser']\ndb_edu_decorate = [1, 0, 0, 1, 1, 0]\n\ndef connect_mysql():\n    '''\n    connect mysql\n    '''\n    return pymysql.connect(host='127.0.0.1', user='root', passwd='root', db='mydb',charset=\"utf8\")\n\ndef search(conn, sql):\n    '''\n    search by sql\n    '''\n    cur = conn.cursor()\n    cur.execute('SET NAMES utf8;')\n    cur.execute('SET CHARACTER SET utf8;')\n    cur.execute('SET character_set_connection=utf8;')\n    cur.execute(sql)\n    result = [x for x in cur.fetchall()]\n    cur.close()\n    return result\n\ndef other_action(conn, sql):\n    '''\n    update/insert/delete\n    '''\n    cur = conn.cursor()\n    cur.execute('SET NAMES utf8;')\n    cur.execute('SET CHARACTER SET utf8;')\n    cur.execute('SET character_set_connection=utf8;')\n    ret = cur.execute(sql)\n    conn.commit()\n    cur.close()\n    return ret\n\ndef gen_insert_sql(table_name, table, info):\n    print(table)\n    print(info)\n    xy = zip(table, info)\n    want = [x for x in xy if x[1] != '' and x[1] != None]\n    cloumn_list = ', '.join([x[0] for x in want])\n    value_list = ', '.join([str(x[1]) for x in want])\n    sql = 'insert into ' + table_name + ' ( ' + cloumn_list + ' ) values (' + value_list + ')'\n    return sql\ndef decorate(db_decorate, info):\n    for i in range(0, len(info)):\n        if info[i] != '' and db_decorate[i] == 1:\n            info[i] = '\"' + info[i] + '\"'\n\n\n", "repo_name": "my-university-labs/dbsystem", "sub_path": "social_zone/dboperator.py", "file_name": "dboperator.py", "file_ext": "py", "file_size_in_byte": 1914, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pymysql.connect", "line_number": 22, "usage_type": "call"}]}
{"seq_id": "6429641921", "text": "# Imports\nimport os\nimport sys\n\nimport pygame\n\nfrom app.menu.menu import Menu\nfrom app.scene.titleScreen.eventHandlerTitleScreen import EventHandlerTitleScreen\nfrom app.mapData import MapData\nfrom app.settings import *\nfrom app.scene.musicFactory import MusicFactory\nfrom app.scene.drawer import Drawer\n\n\nclass TitleScreen:\n    def __init__(self, screen, gameData=None):\n        self.screen = screen\n\n        self.gameData = gameData\n\n        self.screen.fill((0,0,0))\n        titleImage = pygame.image.load(os.path.join('img', 'title_v1.png'))\n        self.screen.blit(titleImage, (0, 0))\n\n        # Define MainMenu\n        self.menuWidth = 5*SCREEN_WIDTH / 12\n        self.menuTotalHeight = SCREEN_HEIGHT * 0.40\n        self.menuHeight = self.menuTotalHeight\n\n        self.createMenu()\n\n        self.eventHandler = EventHandlerTitleScreen()\n        self.drawer = Drawer()\n\n\n        self.type = TITLE_SCREEN\n        self.sceneRunning = True\n\n        self.nextScene = None\n\n\n    def mainLoop(self):\n        self.sceneRunning = True\n        while self.sceneRunning:\n            self.eventHandler.eventHandle(self.menu.optionList, self.menu.selector)\n            self.menu.spritesMenu.update()  # This would be in the logic\n            self.drawer.draw(self.screen, None, self.menu.spritesMenu, None)  # Drawer in THIS file, below\n\n    def createMenu(self):\n\n        self.setMenuHeight()\n        self.menu = Menu(pygame.Rect(SCREEN_WIDTH / 2, SCREEN_HEIGHT *4/7, self.menuWidth, self.menuHeight),18)\n        if not self.gameData.mapComplete['map1']:\n            self.menu.addOption('Level: the Exploding Phonograph', self.startLvlMusic)\n\n        if not self.gameData.mapComplete['map2']:\n            self.menu.addOption('Level: the Crazy Computer', self.startLvlComm)\n\n        self.menu.addOption('How To Play', self.startInstruction)\n\n        self.menu.addOption('Credit', self.startCredit)\n        self.menu.addOption('Exit', sys.exit)\n\n    def setMenuHeight(self):\n        numLevelComplete = sum(self.gameData.mapComplete.values())\n        totalOptionNum = len(self.gameData.mapComplete)+3\n        self.menuHeight = self.menuTotalHeight*(totalOptionNum-numLevelComplete)/totalOptionNum\n\n    def startLvlMusic(self):\n        self.nextScene = PLATFORM_SCREEN\n        self.sceneRunning = False\n        self.gameData.typeScene = PLATFORM_SCREEN\n\n        self.gameData.mapData = MapData(\"LevelMusic\", \"StartPointMusic\")\n\n\n        if TAG_BP == 1 or  TAG_MARIE == 1: #To try any level rapidly.\n            self.gameData.mapData = MapData(\"LevelCommBoss\", \"StartPointCommBoss\")\n        if TAG_MARIE == 2: #To try any level rapidly.\n            self.gameData.mapData = MapData(\"LevelMusicBoss\", \"StartPointMusicBoss\")\n\n\n    def startLvlComm(self):\n        self.nextScene = PLATFORM_SCREEN\n        self.sceneRunning = False\n        self.gameData.typeScene = PLATFORM_SCREEN\n\n        self.gameData.mapData = MapData(\"LevelComm\", \"StartPointComm\")\n\n    def startCredit(self):\n        self.nextScene = CREDIT_SCREEN\n        self.sceneRunning = False\n        self.gameData.typeScene = CREDIT_SCREEN\n\n    def startInstruction(self):\n        self.nextScene = INSTRUCTION_SCREEN\n        self.sceneRunning = False\n        self.gameData.typeScene = INSTRUCTION_SCREEN\n\n        # self.gameData.mapData = MapData(\"LevelComm\", \"StartPointComm\")", "repo_name": "Benoit-Pouliot/LD36", "sub_path": "app/scene/titleScreen/titleScreen.py", "file_name": "titleScreen.py", "file_ext": "py", "file_size_in_byte": 3323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pygame.image.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "app.scene.titleScreen.eventHandlerTitleScreen.EventHandlerTitleScreen", "line_number": 32, "usage_type": "call"}, {"api_name": "app.scene.drawer.Drawer", "line_number": 33, "usage_type": "call"}, {"api_name": "app.menu.menu.Menu", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 62, "usage_type": "attribute"}, {"api_name": "app.mapData.MapData", "line_number": 74, "usage_type": "call"}, {"api_name": "app.mapData.MapData", "line_number": 78, "usage_type": "call"}, {"api_name": "app.mapData.MapData", "line_number": 80, "usage_type": "call"}, {"api_name": "app.mapData.MapData", "line_number": 88, "usage_type": "call"}]}
{"seq_id": "17525493712", "text": "\"\"\"create authenticated_users table\n\nRevision ID: 906a48cc55d0\nRevises:\nCreate Date: 2017-05-30 19:47:38.457143\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '906a48cc55d0'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n    op.create_table(\n        'authenticated_users',\n        sa.Column('id', sa.Integer, primary_key=True),\n        sa.Column('guild_id', sa.String(255), nullable=False),\n        sa.Column('client_id', sa.String(255), nullable=False),\n        sa.Column('last_timestamp', sa.TIMESTAMP, nullable=False),\n    )\n\n\ndef downgrade():\n    op.drop_table('authenticated_users')\n", "repo_name": "TitanEmbeds/Titan", "sub_path": "webapp/alembic/versions/906a48cc55d0_create_authenticated_users_table.py", "file_name": "906a48cc55d0_create_authenticated_users_table.py", "file_ext": "py", "file_size_in_byte": 677, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 315, "dataset": "github-code", "pt": "46", "api": [{"api_name": "alembic.op.create_table", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.TIMESTAMP", "line_number": 25, "usage_type": "attribute"}, {"api_name": "alembic.op.drop_table", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}]}
{"seq_id": "34655553533", "text": "import csv\nimport os.path\n\nfrom django.core.management.base import BaseCommand\n\nfrom apps.imdb.models import Person\n\n\nclass Command(BaseCommand):\n    help = \"Imorts movies from tsv file\"\n\n    def add_arguments(self, parser):\n        parser.add_argument(\"-f\", \"--file\", type=str, required=True)\n\n    def handle(self, *args, **options):\n        file_name = options.get(\"file\")\n\n        if not os.path.exists(file_name):\n            print(\"No file exists.\")\n\n        with open(file_name, encoding=\"utf-8\") as f:\n            reader = csv.DictReader(\n                f,\n                dialect=csv.excel_tab,\n                fieldnames=[\n                    \"imdb_id\",\n                    \"name\",\n                    \"birth_date\",\n                    \"death_date\",\n                    \"primary_profession\",\n                    \"known_for\",\n                ],\n            )\n\n            for line in reader:\n                person_data = line\n                imdb_id = person_data[\"imdb_id\"]\n                person_data.pop(\"primary_profession\")\n                person_data.pop(\"known_for\")\n\n                if person_data[\"birth_date\"] == \"\\\\N\":\n                    person_data[\"birth_date\"] = None\n                else:\n                    person_data[\"birth_date\"] = f'{person_data[\"birth_date\"]}-01-01'\n\n                if person_data[\"death_date\"] == \"\\\\N\":\n                    person_data[\"death_date\"] = None\n                else:\n                    person_data[\"death_date\"] = f'{person_data[\"death_date\"]}-01-01'\n\n                person, created = Person.objects.get_or_create(\n                    imdb_id=imdb_id, defaults=person_data\n                )\n\n                if not created:\n                    Person.objects.filter(id=person.id).update(**person_data)\n", "repo_name": "ogir-ok/django-ithillel-2022-feb", "sub_path": "apps/imdb/management/commands/load_persons.py", "file_name": "load_persons.py", "file_ext": "py", "file_size_in_byte": 1768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 9, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 18, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 22, "usage_type": "call"}, {"api_name": "csv.excel_tab", "line_number": 24, "usage_type": "attribute"}, {"api_name": "apps.imdb.models.Person.objects.get_or_create", "line_number": 51, "usage_type": "call"}, {"api_name": "apps.imdb.models.Person.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "apps.imdb.models.Person", "line_number": 51, "usage_type": "name"}, {"api_name": "apps.imdb.models.Person.objects.filter", "line_number": 56, "usage_type": "call"}, {"api_name": "apps.imdb.models.Person.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "apps.imdb.models.Person", "line_number": 56, "usage_type": "name"}]}
{"seq_id": "2949198721", "text": "from collections.abc import Iterable\nfrom itertools import repeat\n\n\ndef _ntuple(n, name=\"parse\"):\n    def parse(x):\n        if isinstance(x, Iterable):\n            return tuple(x)\n        return tuple(repeat(x, n))\n\n    parse.__name__ = name\n    return parse\n\n\n_single = _ntuple(1, \"_single\")\n_pair = _ntuple(2, \"_pair\")\n_triple = _ntuple(3, \"_triple\")\n_quadruple = _ntuple(4, \"_quadruple\")\n\nif __name__ == \"__main__\":\n    v = 0\n    print(_single(v))\n\n    v = (0, 0)\n    print(_pair(v))\n", "repo_name": "ShivamPR21/MZT", "sub_path": "moduleZoo/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 487, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "46", "api": [{"api_name": "collections.abc.Iterable", "line_number": 7, "usage_type": "argument"}, {"api_name": "itertools.repeat", "line_number": 9, "usage_type": "call"}]}
{"seq_id": "21818343602", "text": "\"\"\"add citekey and senspec manufacturer columns\n\nRevision ID: 0c66a0480d6a\nRevises: 527670956fe7\nCreate Date: 2021-05-16 15:39:28.445332\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '0c66a0480d6a'\ndown_revision = '527670956fe7'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('research_source', sa.Column('sensspec_from_manufacturer', sa.Boolean(), nullable=True))\n    op.add_column('research_source', sa.Column('zotero_citation_key', sa.String(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('research_source', 'zotero_citation_key')\n    op.drop_column('research_source', 'sensspec_from_manufacturer')\n    # ### end Alembic commands ###\n", "repo_name": "serotracker/iit-backend", "sub_path": "migrations/versions/0c66a0480d6a_add_citekey_and_sensspec_manufacturer_columns.py", "file_name": "0c66a0480d6a_add_citekey_and_sensspec_manufacturer_columns.py", "file_ext": "py", "file_size_in_byte": 901, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "46", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 28, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}]}
{"seq_id": "73706283659", "text": "from aiogram import types\nfrom aiogram.dispatcher import FSMContext\n\nfrom loader import dp\nimport numpy as np\n\n\n# Only messages with digits in them will be passed here\n@dp.message_handler(regexp='^[0-9]*$')\nasync def bot_area(message: types.Message):\n    diameter = message.text\n    area = np.pi * (int(diameter)/2) * (int(diameter)/2)\n    await message.answer(f\"The diameter you entered is {diameter}cm\\n\"\n                         f\"The area is {int(area)}cm^2\")\n\n\n# Handler that activates if the first one did not pass through\n@dp.message_handler()\nasync def bot_wrongFormat(message: types.Message):\n    await message.answer(f\"Wrong format!\"\n                         f\"Please, input digits\")\n", "repo_name": "IlyaSinyugin/Telegram-Pizza-Area-Calculator", "sub_path": "handlers/users/messages.py", "file_name": "messages.py", "file_ext": "py", "file_size_in_byte": 694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "aiogram.types.Message", "line_number": 10, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 12, "usage_type": "attribute"}, {"api_name": "loader.dp.message_handler", "line_number": 9, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 9, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 19, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 19, "usage_type": "name"}, {"api_name": "loader.dp.message_handler", "line_number": 18, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 18, "usage_type": "name"}]}
{"seq_id": "32453944611", "text": "from pathlib import Path\n\nfrom exceptions.client_exceptions import ValidationException\nfrom http_client.core.content_observer import ContentObserver\nfrom http_client.core.workers import TaskWorker\nfrom http_client.core.workers_manager import WorkersManager\nfrom http_client.utils.builders.abstract_builder import AbstractBuilder\nfrom http_client.core.http_client import HTTPClient\nfrom http_client.models.storages.task_queue import TaskQueue\nfrom http_client.core.task_scheduler import TaskScheduler\nfrom http_client.utils.file_saver import FileSaver\nfrom settings import settings\n\n\nclass HTTPClientBuilder(AbstractBuilder):\n    \"\"\"Builds an HTTP-client.\"\"\"\n\n    instance_class = HTTPClient\n    workers_manager: WorkersManager\n\n    def configure_instance(self):\n        \"\"\"Saves the instance of Workers Manager.\"\"\"\n        self.workers_manager = WorkersManagerBuilder.construct()\n\n\nclass WorkersManagerBuilder(AbstractBuilder):\n    \"\"\"Builds a Workers Manager.\"\"\"\n\n    instance_class = WorkersManager\n    task_queue: TaskQueue\n    task_scheduler: TaskScheduler\n    workers: list[TaskWorker]\n\n    def configure_instance(self):\n        \"\"\"Creates a task scheduler, task queue and workers.\"\"\"\n        self.task_scheduler = TaskSchedulerBuilder.construct()\n        self.task_queue = TaskQueue()\n\n        content_observer = ContentObserverBuilder.construct()\n        self.workers = []\n        for _ in range(settings.THREADS_AMOUNT):\n            worker_builder = TaskWorkerBuilder()\n            worker_builder.task_queue = self.task_queue\n            worker_builder.content_observer = content_observer\n            self.workers.append(worker_builder.build())\n\n    @staticmethod\n    def validate_workers_list(value):\n        \"\"\"Validates a workers list.\"\"\"\n        if not value:\n            raise ValidationException(\"The workers list can't be empty.\")\n        elif not all((map(lambda x: isinstance(x, TaskWorker), value))):\n            raise ValidationException(\"Values in workers list must be a `TaskWorker` type.\")\n        if len(value) != settings.settings.THREADS_AMOUNT:\n            raise ValidationException(\n                f\"Workers amount must be {settings.settings.THREADS_AMOUNT}. But it is {len(value)}.\"\n            )\n\n\nclass TaskSchedulerBuilder(AbstractBuilder):\n    \"\"\"Builds a Task Scheduler.\"\"\"\n\n    instance_class = TaskScheduler\n    bytes_amount: int\n\n    def configure_instance(self):\n        \"\"\"Sets bytes amount from the settings.\"\"\"\n        self.bytes_amount = settings.BYTES_AMOUNT\n\n    @staticmethod\n    def validate_bytes_amount(value):\n        \"\"\"Validates `bytes_amount` is greater than 0.\"\"\"\n        if value <= 0:\n            raise ValidationException(\"The bytes amount can't be less than zero.\")\n\n\nclass TaskWorkerBuilder(AbstractBuilder):\n    \"\"\"Builds a TaskWorker instance.\"\"\"\n\n    instance_class = TaskWorker\n    task_queue: TaskQueue\n    content_observer: ContentObserver\n\n    def configure_instance(self):\n        \"\"\"Sets bytes amount from the settings.\"\"\"\n        self.task_queue = TaskQueue()\n        self.content_observer = ContentObserverBuilder.construct()\n\n\nclass FileSaverBuilder(AbstractBuilder):\n    \"\"\"Builds a FileSaver instance.\"\"\"\n\n    instance_class = FileSaver\n    files_directory: Path\n\n    def configure_instance(self):\n        \"\"\"Sets a directory which will contain all downloaded content.\"\"\"\n        self.files_directory = settings.CONTENT_DIRECTORY\n\n\nclass ContentObserverBuilder(AbstractBuilder):\n    \"\"\"Builds a ContentObserve instance.\"\"\"\n\n    instance_class = ContentObserver\n    file_saver: FileSaver\n\n    def configure_instance(self):\n        \"\"\"Sets a directory which will contain all downloaded content.\"\"\"\n        self.file_saver = FileSaverBuilder.construct()\n", "repo_name": "BorisPlaton/multithread_http_client", "sub_path": "src/http_client/utils/builders/builders.py", "file_name": "builders.py", "file_ext": "py", "file_size_in_byte": 3723, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "http_client.utils.builders.abstract_builder.AbstractBuilder", "line_number": 15, "usage_type": "name"}, {"api_name": "http_client.core.http_client.HTTPClient", "line_number": 18, "usage_type": "name"}, {"api_name": "http_client.core.workers_manager.WorkersManager", "line_number": 19, "usage_type": "name"}, {"api_name": "http_client.utils.builders.abstract_builder.AbstractBuilder", "line_number": 26, "usage_type": "name"}, {"api_name": "http_client.core.workers_manager.WorkersManager", "line_number": 29, "usage_type": "name"}, {"api_name": "http_client.models.storages.task_queue.TaskQueue", "line_number": 30, "usage_type": "name"}, {"api_name": "http_client.core.task_scheduler.TaskScheduler", "line_number": 31, "usage_type": "name"}, {"api_name": "http_client.core.workers.TaskWorker", "line_number": 32, "usage_type": "name"}, {"api_name": "http_client.models.storages.task_queue.TaskQueue", "line_number": 37, "usage_type": "call"}, {"api_name": "settings.settings.THREADS_AMOUNT", "line_number": 41, "usage_type": "attribute"}, {"api_name": "settings.settings", "line_number": 41, "usage_type": "name"}, {"api_name": "exceptions.client_exceptions.ValidationException", "line_number": 51, "usage_type": "call"}, {"api_name": "http_client.core.workers.TaskWorker", "line_number": 52, "usage_type": "argument"}, {"api_name": "exceptions.client_exceptions.ValidationException", "line_number": 53, "usage_type": "call"}, {"api_name": "settings.settings.settings", "line_number": 54, "usage_type": "attribute"}, {"api_name": "settings.settings", "line_number": 54, "usage_type": "name"}, {"api_name": "exceptions.client_exceptions.ValidationException", "line_number": 55, "usage_type": "call"}, {"api_name": "settings.settings.settings", "line_number": 56, "usage_type": "attribute"}, {"api_name": "settings.settings", "line_number": 56, "usage_type": "name"}, {"api_name": "http_client.utils.builders.abstract_builder.AbstractBuilder", "line_number": 60, "usage_type": "name"}, {"api_name": "http_client.core.task_scheduler.TaskScheduler", "line_number": 63, "usage_type": "name"}, {"api_name": "settings.settings.BYTES_AMOUNT", "line_number": 68, "usage_type": "attribute"}, {"api_name": "settings.settings", "line_number": 68, "usage_type": "name"}, {"api_name": "exceptions.client_exceptions.ValidationException", "line_number": 74, "usage_type": "call"}, {"api_name": "http_client.utils.builders.abstract_builder.AbstractBuilder", "line_number": 77, "usage_type": "name"}, {"api_name": "http_client.core.workers.TaskWorker", "line_number": 80, "usage_type": "name"}, {"api_name": "http_client.models.storages.task_queue.TaskQueue", "line_number": 81, "usage_type": "name"}, {"api_name": "http_client.core.content_observer.ContentObserver", "line_number": 82, "usage_type": "name"}, {"api_name": "http_client.models.storages.task_queue.TaskQueue", "line_number": 86, "usage_type": "call"}, {"api_name": "http_client.utils.builders.abstract_builder.AbstractBuilder", "line_number": 90, "usage_type": "name"}, {"api_name": "http_client.utils.file_saver.FileSaver", "line_number": 93, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 94, "usage_type": "name"}, {"api_name": "settings.settings.CONTENT_DIRECTORY", "line_number": 98, "usage_type": "attribute"}, {"api_name": "settings.settings", "line_number": 98, "usage_type": "name"}, {"api_name": "http_client.utils.builders.abstract_builder.AbstractBuilder", "line_number": 101, "usage_type": "name"}, {"api_name": "http_client.core.content_observer.ContentObserver", "line_number": 104, "usage_type": "name"}, {"api_name": "http_client.utils.file_saver.FileSaver", "line_number": 105, "usage_type": "name"}]}
{"seq_id": "73505186059", "text": "import os\n\nfrom airPLS import airPLS\nimport nmrglue as ng\nimport numpy as np\nfrom tqdm import tqdm\n\n\ndef read_bruker_h_base(nmr_path, bRaw=False, bMinMaxScale=False):\n    nmr_path = os.path.normpath(nmr_path)\n    if bRaw:\n        dic, fid = ng.fileio.bruker.read(f'{nmr_path}/1')\n        zero_fill_size = dic['acqus']['TD']\n        fid = ng.bruker.remove_digital_filter(dic, fid)\n        fid = ng.proc_base.zf_size(fid, zero_fill_size)\n        fid = ng.proc_base.fft(fid)\n\n    else:\n        dic, fid = ng.fileio.bruker.read_pdata(f'{nmr_path}/1/pdata/1')\n        zero_fill_size = dic['acqus']['TD']\n        offset = (float(dic['acqus']['SW']) / 2) - (float(dic['acqus']['O1']) / float(dic['acqus']['BF1']))\n        start = float(dic['acqus']['SW']) - offset\n        end = -offset\n        step = float(dic['acqus']['SW']) / zero_fill_size\n        ppms = np.arange(start, end, -step)[:zero_fill_size]\n\n        # baseline\n        baseline = airPLS(fid, lambda_=100, porder=1, itermax=15)\n        fid = fid - baseline\n\n        # set solvent peaks to zero\n        q = np.min(np.where(np.round(ppms, 3) == 5.365))\n        w = np.min(np.where(np.round(ppms, 3) == 5.100))\n        t = np.min(np.where(np.round(ppms, 3) == 4.160))\n        y = np.min(np.where(np.round(ppms, 3) == 3.800))\n        u = np.min(np.where(np.round(ppms, 3) == 1.060))\n        i = np.min(np.where(np.round(ppms, 3) == 1.030))\n        o = np.min(np.where(np.round(ppms, 3) == 1.180))\n        p = np.min(np.where(np.round(ppms, 3) == 1.105))\n        a = np.min(np.where(np.round(ppms, 3) == 3.530))\n        s = np.min(np.where(np.round(ppms, 3) == 3.190))\n\n        fid[q:w] = 0\n        fid[t:y] = 0\n        fid[u:i] = 0\n        fid[o:p] = 0\n        fid[a:s] = 0\n\n    # Normalization\n    if bMinMaxScale:\n        fid = fid / np.max(fid)\n    v = np.max(np.where(np.round(ppms, 3) == 10.700))\n    b = v + 32724  # b = np.max(np.where(np.round(ppms,3)==0.300))\n\n    return {'name': nmr_path.split(os.sep)[-1], 'ppm': ppms[v:b], 'fid': fid[v:b], 'bRaw': bRaw}\n\n\ndef read_bruker_hs_base(data_folder, bRaw, bMinMaxScale, bDict):\n    if bDict:\n        spectra = {}\n    else:\n        spectra = []\n    for name in tqdm(os.listdir(data_folder), desc=\"Read Bruker H-NMR files\"):\n        nmr_path = os.path.normpath(os.path.join(data_folder, name))\n        s = read_bruker_h_base(nmr_path, bRaw, bMinMaxScale)\n        if bDict:\n            spectra[s['name']] = s\n        else:\n            spectra.append(s)\n    return spectra\n", "repo_name": "yfWang01/DeepMID", "sub_path": "readBruker.py", "file_name": "readBruker.py", "file_ext": "py", "file_size_in_byte": 2478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "os.path.normpath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "nmrglue.fileio.bruker.read", "line_number": 12, "usage_type": "call"}, {"api_name": "nmrglue.fileio", "line_number": 12, "usage_type": "attribute"}, {"api_name": "nmrglue.bruker.remove_digital_filter", "line_number": 14, "usage_type": "call"}, {"api_name": "nmrglue.bruker", "line_number": 14, "usage_type": "attribute"}, {"api_name": "nmrglue.proc_base.zf_size", "line_number": 15, "usage_type": "call"}, {"api_name": "nmrglue.proc_base", "line_number": 15, "usage_type": "attribute"}, {"api_name": "nmrglue.proc_base.fft", "line_number": 16, "usage_type": "call"}, {"api_name": "nmrglue.proc_base", "line_number": 16, "usage_type": "attribute"}, {"api_name": "nmrglue.fileio.bruker.read_pdata", "line_number": 19, "usage_type": "call"}, {"api_name": "nmrglue.fileio", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 25, "usage_type": "call"}, {"api_name": "airPLS.airPLS", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 52, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 63, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}]}
{"seq_id": "20999539877", "text": "#!/usr/bin/env python\nfrom time import sleep\nfrom kafka import KafkaProducer\nimport json\nfrom datetime import datetime, timedelta\nimport sys\nimport random\nimport pandas as pd\n\n\nrandom.seed(100)\n\nid='producer_1'\nserver='localhost:9092'\ntopic='sensors'\ninterval=0.5 # seconds\nduration=1 # mins\ndays = 1 # days of data\n\ndef formatData(id, interval, duration, data):\n    sampleCount = 96 # one every 15 minutes\n    print(f'Generating {sampleCount*days} samples')\n\n    counter=0\n    date_now = datetime.now()\n    date = (datetime.now() -timedelta(days=1,hours=date_now.hour, minutes=date_now.minute, seconds= date_now.second,microseconds=date_now.microsecond)).isoformat()\n    for numDays in range(days+1,1,-1):\n        for minutes in range(sampleCount,0,-1):\n            now = (datetime.strptime(date,'%Y-%m-%dT%H:%M:%S.%f') - timedelta(days=numDays,minutes=minutes*15)).isoformat()\n            sampledAt = now\n            if (counter == 30 or counter+random.randint(1, 4) == 30):\n                # print(\"lateEventCount\")\n                counter = 0\n                sampledAt = (datetime.strptime(now, '%Y-%m-%dT%H:%M:%S.%f') - timedelta(minutes=random.randint(10,20))).isoformat()\n            counter += 1\n            yield {\n                'id': id,\n                'created_at': now,\n                'sampled_at': sampledAt,\n                # 'value': data[(numDays*96 + minutes)*int(id[-1])][0]\n                'value': data[numDays*96 + minutes+(int(id[-1])-1)*(days*96+sampleCount)][0]\n            }\n\nserializer = lambda v: json.dumps(v).encode('ascii')\n\ndef main(id, server, topic, interval, duration, data):\n    print(f'Creating producer {id} on {server}')\n    producer = KafkaProducer(\n        client_id=f'{id}',\n        bootstrap_servers=[server],\n        value_serializer=serializer,\n        api_version=(0, 10, 1)\n    )\n    print(f'Producer {id} created')\n    readyToSendData = formatData(id, interval, duration,data)\n    print(f'Sending samples')\n    count= 0\n    count = 0\n    for data in readyToSendData:\n        count+=1\n        if count<= 50 : \n            producer.send(topic,data)\n            # print(data)\n        else : \n            # print(data)\n            producer.send(topic, data)\n            sleep(interval)\n    print(f'Producer {id} finished')\n\nif __name__ == '__main__':\n    if len(sys.argv) > 1 and sys.argv[1] == 'help':\n        print(f'Usage: {sys.argv[0]} [id=<id>] [server=<server>] [topic=<topic>] [interval=<interval>] [duration=<duration>] [days=<days>]')\n        print(f'Default values: id={id}, server={server}, topic={topic}, interval={interval}, duration={duration}, days={days}')\n        sys.exit(0)\n    for arg in sys.argv[1:]:\n        if arg.startswith('id='):\n            id = arg[3:]\n        elif arg.startswith('server='):\n            server = arg[7:]\n        elif arg.startswith('topic='):\n            topic = arg[6:]\n        elif arg.startswith('interval='):\n            interval = int(arg[9:])\n        elif arg.startswith('duration='):\n            duration = int(arg[9:])\n        elif arg.startswith('days='):\n            days = int(arg[5:])\n        else:\n            print(f'Unknown argument: {arg}')\n            sys.exit(1)\n\n    data = pd.read_csv('temperature.csv').to_numpy()\n    main(id, server, topic, interval, duration,data)\n", "repo_name": "ZOrfeas/big-data-class-project", "sub_path": "producer/test_producer.py", "file_name": "test_producer.py", "file_ext": "py", "file_size_in_byte": 3281, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "random.seed", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 29, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 34, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "kafka.KafkaProducer", "line_number": 48, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 72, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 74, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 90, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 92, "usage_type": "call"}]}
{"seq_id": "8787337996", "text": "import os\nimport time\nimport logging\nimport requests\nimport re\nfrom selenium import webdriver\nfrom queue import Queue\nimport win32gui\nimport win32con\nimport win32clipboard as w\nimport urllib3\nfrom selenium.webdriver.chrome.options import Options\n\nfrom qq_post import QQSendMessage\n\nurl_kumaoke_all_data = 'http://www.kumaoke.com/tao/index.php?r=p&type=2&cid=2&u=696035'\nurl_kumaoke_two_data = 'http://www.kumaoke.com/tao/index.php?r=p&type=1&cid=2&u=696035'\n\nlogging.basicConfig(level=logging.DEBUG)\nchrome_options = Options()\n# chrome_options.add_argument('--headless')\n# chrome_options.add_argument('--disable-gpu')\ndriver = webdriver.Chrome(r'../chromedriver.exe', chrome_options=chrome_options)\nlogging.getLogger(\"requests\").setLevel(logging.WARNING)\n\nclass Post_data:\n    def __init__(self, name, urls):\n        self.urls = urls\n        self.name = name\n        self.saved = []\n        self.q = []\n        self.data_file = 'TB_data.txt'\n        self.data_path = os.path.join(os.getcwd(), self.data_file)\n        self.all_data = []\n        self.qq = QQSendMessage()\n\n        # if not os.path.exists(data_path):\n        #     os.remove(data_path)\n\n    def save_data_and_post_msg(self):\n        d = self.q[0]\n        if d not in self.all_data and d:\n            logging.info(d)\n            self.send_message(d)\n            self.q.pop(0)\n            self.all_data.append(d)\n            with open(self.data_path, 'w') as f:\n                f.write(d)\n\n    def post_message(self, data):\n        # qq.setImage(data[1] if 'http' in data[1] else 'http:' + data[1])\n        time.sleep(1)\n        self.qq.setText(data)\n        time.sleep(1)\n        self.qq.searchByUser(self.name)\n        time.sleep(1)\n        self.qq.sendByUser(self.name)\n\n    def get_url_data(self, url):\n        \"\"\"\n        :param url:\n        :return:\n        \"\"\"\n        text = requests.get(url).text\n        urls = re.findall(r'<a href=\"/tao/index.php\\?r=(.*?)nav_wrap=p&(.*?)\"', text)\n        images = re.findall(r'<Image class=\"Image\" src=\"(.*?)\" ', text)\n        for i in range(len(images)):\n            logging.info(f'正在抓取第{i}个页面信息')\n            url = f'http://www.kumaoke.com/tao/index.php?r={urls[i][0]}nav_wrap=p&{urls[i][1]}'\n            driver.get(url)\n            driver.find_element_by_class_name('share').click()\n            time.sleep(5)\n            try:\n                driver.find_element_by_xpath('//*[@id=\"layui-layer2\"]/div/div/div[1]/a[2]').click()\n            except Exception as e:\n                logging.error(str(e))\n                time.sleep(5)\n                driver.find_element_by_xpath('//*[@id=\"layui-layer2\"]/div/div/div[1]/a[2]').click()\n            time.sleep(2)\n            data = driver.find_element_by_xpath('//*[@id=\"codeText\"]').text\n            if not data:\n                continue\n            self.q.append(data)\n            self.save_data_and_post_msg()\n\n    def send_message(self, msg):\n        # 窗口名字，就是备注名\n        name = self.name\n        # 将测试消息复制到剪切板中\n        w.OpenClipboard()\n        w.EmptyClipboard()\n        w.SetClipboardData(win32con.CF_UNICODETEXT, msg)\n        w.CloseClipboard()\n        # 获取窗口句柄\n        handle = win32gui.FindWindow(None, name)\n        # 填充消息\n        win32gui.SendMessage(handle, 770, 0, 0)\n        # 回车发送消息\n        win32gui.SendMessage(handle, win32con.WM_KEYDOWN, win32con.VK_RETURN, 0)\n\n    def run(self):\n\n        for url in self.urls:\n            logging.info(f'开始爬取{url}数据')\n            self.get_url_data(url)\n\n\nif __name__ == '__main__':\n    p = Post_data(name='1test', urls=[url_kumaoke_all_data, url_kumaoke_two_data])\n    p.run()\n", "repo_name": "wangzyh/Demo", "sub_path": "Demo/wechat and qq/Get TB data.py", "file_name": "Get TB data.py", "file_ext": "py", "file_size_in_byte": 3691, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "logging.basicConfig", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 19, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 20, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 23, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 23, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 33, "usage_type": "call"}, {"api_name": "qq_post.QQSendMessage", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 43, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 64, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 65, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 76, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "win32clipboard.OpenClipboard", "line_number": 90, "usage_type": "call"}, {"api_name": "win32clipboard.EmptyClipboard", "line_number": 91, "usage_type": "call"}, {"api_name": "win32clipboard.SetClipboardData", "line_number": 92, "usage_type": "call"}, {"api_name": "win32con.CF_UNICODETEXT", "line_number": 92, "usage_type": "attribute"}, {"api_name": "win32clipboard.CloseClipboard", "line_number": 93, "usage_type": "call"}, {"api_name": "win32gui.FindWindow", "line_number": 95, "usage_type": "call"}, {"api_name": "win32gui.SendMessage", "line_number": 97, "usage_type": "call"}, {"api_name": "win32gui.SendMessage", "line_number": 99, "usage_type": "call"}, {"api_name": "win32con.WM_KEYDOWN", "line_number": 99, "usage_type": "attribute"}, {"api_name": "win32con.VK_RETURN", "line_number": 99, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 104, "usage_type": "call"}]}
{"seq_id": "15107665304", "text": "# -*- coding: utf-8 -*-\n\"\"\"async_upnp_client.exceptions module.\"\"\"\n\nimport asyncio\nfrom enum import IntEnum\nfrom typing import Any, Optional\nfrom xml.etree import ElementTree as ET\n\nimport aiohttp\n\n# pylint: disable=too-many-ancestors\n\n\nclass UpnpError(Exception):\n    \"\"\"Base class for all errors raised by this library.\"\"\"\n\n    def __init__(\n        self, *args: Any, message: Optional[str] = None, **_kwargs: Any\n    ) -> None:\n        \"\"\"Initialize base UpnpError.\"\"\"\n        super().__init__(*args, message)\n\n\nclass UpnpContentError(UpnpError):\n    \"\"\"Content of UPnP response is invalid.\"\"\"\n\n\nclass UpnpActionErrorCode(IntEnum):\n    \"\"\"Error codes for UPnP Action errors.\"\"\"\n\n    INVALID_ACTION = 401\n    INVALID_ARGS = 402\n    # (DO_NOT_USE) = 403\n    ACTION_FAILED = 501\n    ARGUMENT_VALUE_INVALID = 600\n    ARGUMENT_VALUE_OUT_OF_RANGE = 601\n    OPTIONAL_ACTION_NOT_IMPLEMENTED = 602\n    OUT_OF_MEMORY = 603\n    HUMAN_INTERVENTION_REQUIRED = 604\n    STRING_ARGUMENT_TOO_LONG = 605\n\n\nclass UpnpActionError(UpnpError):\n    \"\"\"Server returned a SOAP Fault in response to an Action.\"\"\"\n\n    def __init__(\n        self,\n        *args: Any,\n        error_code: Optional[int] = None,\n        error_desc: Optional[str] = None,\n        message: Optional[str] = None,\n        **kwargs: Any,\n    ) -> None:\n        \"\"\"Initialize from response body.\"\"\"\n        if not message:\n            message = f\"Received UPnP error {error_code} ({error_desc})\"\n        super().__init__(*args, message=message, **kwargs)\n        self.error_code = error_code\n        self.error_desc = error_desc\n\n\nclass UpnpXmlParseError(UpnpContentError, ET.ParseError):\n    \"\"\"UPnP response is not valid XML.\"\"\"\n\n    def __init__(self, orig_err: ET.ParseError) -> None:\n        \"\"\"Initialize from original ParseError, to match it.\"\"\"\n        super().__init__(message=str(orig_err))\n        self.code = orig_err.code\n        self.position = orig_err.position\n\n\nclass UpnpValueError(UpnpContentError):\n    \"\"\"Invalid value error.\"\"\"\n\n    def __init__(self, name: str, value: Any) -> None:\n        \"\"\"Initialize.\"\"\"\n        super().__init__(message=f\"Invalid value for {name}: '{value}'\")\n        self.name = name\n        self.value = value\n\n\nclass UpnpSIDError(UpnpContentError):\n    \"\"\"Missing Subscription Identifier from response.\"\"\"\n\n\nclass UpnpXmlContentError(UpnpContentError):\n    \"\"\"XML document does not have expected content.\"\"\"\n\n\nclass UpnpCommunicationError(UpnpError, aiohttp.ClientError):\n    \"\"\"Error occurred while communicating with the UPnP device .\"\"\"\n\n\nclass UpnpResponseError(UpnpCommunicationError):\n    \"\"\"HTTP error code returned by the UPnP device.\"\"\"\n\n    def __init__(\n        self,\n        *args: Any,\n        status: int,\n        headers: Optional[aiohttp.typedefs.LooseHeaders] = None,\n        message: Optional[str] = None,\n        **kwargs: Any,\n    ) -> None:\n        \"\"\"Initialize.\"\"\"\n        if not message:\n            message = f\"Did not receive HTTP 200 but {status}\"\n        super().__init__(*args, message=message, **kwargs)\n        self.status = status\n        self.headers = headers\n\n\nclass UpnpActionResponseError(UpnpActionError, UpnpResponseError):\n    \"\"\"HTTP error code and UPnP error code.\n\n    UPnP errors are usually indicated with HTTP 500 (Internal Server Error) and\n    actual details in the response body as a SOAP Fault.\n    \"\"\"\n\n    def __init__(\n        self,\n        *args: Any,\n        status: int,\n        headers: Optional[aiohttp.typedefs.LooseHeaders] = None,\n        error_code: Optional[int] = None,\n        error_desc: Optional[str] = None,\n        message: Optional[str] = None,\n        **kwargs: Any,\n    ) -> None:\n        \"\"\"Initialize.\"\"\"\n        if not message:\n            message = (\n                f\"Received HTTP error code {status}, UPnP error code\"\n                f\" {error_code} ({error_desc})\"\n            )\n        super().__init__(\n            *args,\n            status=status,\n            headers=headers,\n            error_code=error_code,\n            error_desc=error_desc,\n            message=message,\n            **kwargs,\n        )\n\n\nclass UpnpClientResponseError(aiohttp.ClientResponseError, UpnpResponseError):\n    \"\"\"HTTP response error with more details from aiohttp.\"\"\"\n\n\nclass UpnpConnectionError(UpnpCommunicationError, aiohttp.ClientConnectionError):\n    \"\"\"Error in the underlying connection to the UPnP device.\n\n    This could indicate that the device is offline.\n    \"\"\"\n\n\nclass UpnpConnectionTimeoutError(\n    UpnpConnectionError, aiohttp.ServerTimeoutError, asyncio.TimeoutError\n):\n    \"\"\"Timeout while communicating with the device.\"\"\"\n\n\nclass UpnpServerError(UpnpError):\n    \"\"\"Error with a local server.\"\"\"\n\n\nclass UpnpServerOSError(UpnpServerError, OSError):\n    \"\"\"System-related error when starting a local server.\"\"\"\n\n    def __init___(self, errno: int, strerror: str) -> None:\n        \"\"\"Initialize simplified version of OSError.\"\"\"\n        OSError.__init__(self, errno, strerror)\n", "repo_name": "StevenLooman/async_upnp_client", "sub_path": "async_upnp_client/exceptions.py", "file_name": "exceptions.py", "file_ext": "py", "file_size_in_byte": 4959, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 42, "dataset": "github-code", "pt": "46", "api": [{"api_name": "typing.Any", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 18, "usage_type": "name"}, {"api_name": "enum.IntEnum", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 51, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 52, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.ParseError", "line_number": 62, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 62, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.ParseError", "line_number": 65, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 75, "usage_type": "name"}, {"api_name": "aiohttp.ClientError", "line_number": 90, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 101, "usage_type": "name"}, {"api_name": "aiohttp.typedefs", "line_number": 101, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 102, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 124, "usage_type": "name"}, {"api_name": "aiohttp.typedefs", "line_number": 124, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 126, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 128, "usage_type": "name"}, {"api_name": "aiohttp.ClientResponseError", "line_number": 147, "usage_type": "attribute"}, {"api_name": "aiohttp.ClientConnectionError", "line_number": 151, "usage_type": "attribute"}, {"api_name": "aiohttp.ServerTimeoutError", "line_number": 159, "usage_type": "attribute"}, {"api_name": "asyncio.TimeoutError", "line_number": 159, "usage_type": "attribute"}]}
{"seq_id": "72241886539", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport PIL\nimport tensorflow as tf\n\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential\nimport tensorflow_hub as hub\nimport os\nimport pickle\nimport argparse\n\nimport pathlib\n\n\ndef getDataStats(data_path):\n\n    data_dir = pathlib.Path(data_path).with_suffix('')\n    image_list = list(data_dir.glob('*/*.jpg'))\n    widths = []\n    heights = []\n\n    for img in image_list:\n        _temp = PIL.Image.open(str(img))\n        widths.append(_temp.size[0])\n        heights.append(_temp.size[1])\n\n    AVG_HEIGHT = round(sum(heights)/len(heights))\n    AVG_WIDTH = round(sum(widths)/len(widths))\n    print(f\"AVG_HEIGHT: {AVG_HEIGHT}\\nAVG_WIDTH: {AVG_WIDTH}\")\n    return data_dir\n\ndef createDataset(data_path, img_height, img_width, batch_size=32):\n\n\n    train_ds = tf.keras.utils.image_dataset_from_directory(\n        data_path,\n        validation_split=0.2,\n        subset=\"training\",\n        seed=123,\n        image_size=(img_height, img_width),\n        batch_size=batch_size)\n    \n    val_ds = tf.keras.utils.image_dataset_from_directory(\n        data_path,\n        validation_split=0.2,\n        subset=\"validation\",\n        seed=123,\n        image_size=(img_height, img_width),\n        batch_size=batch_size)\n    \n    return train_ds, val_ds\n\ndef plotResults(history, epochs):\n    \n    acc = history.history['accuracy']\n    val_acc = history.history['val_accuracy']\n\n    loss = history.history['loss']\n    val_loss = history.history['val_loss']\n\n    epochs_range = range(epochs)\n\n    plt.figure(figsize=(8, 8))\n    plt.subplot(1, 2, 1)\n    plt.plot(epochs_range, acc, label='Training Accuracy')\n    plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n    plt.legend(loc='lower right')\n    plt.title('Training and Validation Accuracy')\n\n    plt.subplot(1, 2, 2)\n    plt.plot(epochs_range, loss, label='Training Loss')\n    plt.plot(epochs_range, val_loss, label='Validation Loss')\n    plt.legend(loc='upper right')\n    plt.title('Training and Validation Loss')\n    plt.show()\n\ndef vanillaModel(train, val, input_shape, epochs=50):\n\n    # Model Definition\n    data_augmentation = keras.Sequential(\n        [\n            layers.RandomFlip(\"horizontal\",\n                            input_shape=input_shape),\n            layers.RandomRotation(0.1),\n        ]\n    )\n\n    model = Sequential([\n        data_augmentation,\n        layers.Rescaling(1./255),\n        layers.BatchNormalization(),\n        layers.Conv2D(16, 3, padding='same', activation='relu'),\n        layers.MaxPooling2D(),\n        layers.Conv2D(32, 3, padding='same', activation='relu'),\n        layers.MaxPooling2D(),\n        layers.Conv2D(64, 3, padding='same', activation='relu'),\n        layers.MaxPooling2D(),\n        layers.Conv2D(128, 3, padding='same', activation='relu'),\n        layers.MaxPooling2D(),\n        layers.Dropout(0.2),\n        layers.Flatten(),\n        layers.Dense(128, activation='relu'),\n        layers.Dense(64, activation='relu'),\n        layers.Dense(num_classes, name=\"outputs\")\n    ])\n\n    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])\n\n    epochs=50\n    history = model.fit(\n        train,\n        validation_data=val,\n        epochs=epochs\n        )\n    \n    plotResults(history=history, epochs=epochs)\n\nif __name__==\"__main__\":\n\n    img_height = 224\n    img_width = 224\n    base_learning_rate = 0.0001\n\n    parser = argparse.ArgumentParser(description = \"Training Module\")\n    parser.add_argument(\"-f\", \"--filePath\", help = \"Input File Path\")\n\n    args = parser.parse_args()\n\n    data_dir = getDataStats(args.filePath)  # Eg argument - \"/home/batknight/Downloads/FoodImage/FoodImage\"\n    train_ds, val_ds = createDataset(data_path=data_dir, img_height=img_height, img_width=img_width)\n    class_names = train_ds.class_names\n    num_classes = len(class_names)\n    \n    hub_url = 'https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5'\n    embed = hub.KerasLayer(hub_url)\n\n    data_augmentation = keras.Sequential(\n        [\n            layers.RandomFlip(\"horizontal\",\n                            input_shape=(img_height, img_width, 3)),\n            layers.RandomRotation(0.1),\n        ]\n    )\n\n    model = tf.keras.Sequential([\n        data_augmentation,\n        layers.Rescaling(1./255),\n        embed,\n        tf.keras.layers.Dense(128, activation=\"relu\"),\n        tf.keras.layers.Dense(num_classes, activation=\"softmax\"),\n    ])\n\n    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])\n\n    epochs=25\n    history = model.fit(\n        train_ds,\n        validation_data=val_ds,\n        epochs=epochs\n        )\n    \n    export_module_dir = os.path.join(os.getcwd(), \"finetuned_model_export\")\n    model.save('saved_model/my_model')\n\n    with open('labels.pickle', \"wb\") as f:\n        f.write(pickle.dumps(class_names))\n\n    \n    plotResults(history=history, epochs=epochs)\n    \n\n    \n", "repo_name": "awearytraveler/ml-boilerplate-inference", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 5202, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.utils.image_dataset_from_directory", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.utils.image_dataset_from_directory", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 45, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 82, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.RandomFlip", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 84, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.RandomRotation", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 86, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Rescaling", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 92, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 93, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 94, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 95, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 96, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 97, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 98, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 99, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 100, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 101, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 102, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 103, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 104, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 105, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 106, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 109, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 110, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow_hub.KerasLayer", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 141, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.RandomFlip", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 143, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.RandomRotation", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 145, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Rescaling", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 151, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 153, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 154, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 157, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 168, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 172, "usage_type": "call"}]}
{"seq_id": "72753977098", "text": "from datetime import datetime\n\n\"\"\"\n\"\"\"\nclass MedicalVisit(object):\n\n    def __init__(self, visit_date=datetime.now, visit_reason=\"Sour Stomach\", \n                 patient_comments=\"It hurts when I do this!\", doctor_comments=\"Then don't do that!\"):\n        self.visit_date = visit_date\n        self.visit_reason = visit_reason\n        self.patient_comments = patient_comments\n        self.doctor_comments = doctor_comments", "repo_name": "zkastl/ADB_MedicalApp", "sub_path": "app/MedicalVisit.py", "file_name": "MedicalVisit.py", "file_ext": "py", "file_size_in_byte": 421, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "datetime.datetime.now", "line_number": 7, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "name"}]}
{"seq_id": "44152851540", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# # Support-vector machine\n# \n# ## I want to classify something well!\n# > I can classify real objects in real world by my eyes and hands.\n# > How about entangible things like numbers, positions, attributes, types\n# \n# ### Which position is the best in a specific Dimension?\n# \n# 1. How could we devide two points in a 1D by a point?\n# Imagine that if there are different two points in a line.\n# Which position of a new point is proper to divide the two points below?\n# \n# ![1d.PNG](./assets/images/1d.PNG)\n# \n# 2. How could we devide two points in a 2D by a line?\n# \n# ![2d.PNG](./assets/images/2d.PNG)\n# \n# \n# 3. How could we devide two points in a 3D by a plane?\n# \n# ![3d.PNG](./assets/images/3d.PNG)\n# \n# ### Where is the best position of the shape? \n# \n# 1. The place Where it is the middle of the two points.\n# \n# \n# ## Support vector classification\n# More formally, a support-vector machine **constructs a hyperplane** or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks like outliers detection.[3] \n# \n# > Intuitively, a good separation is achieved by *he hyperplane that has the largest distance to the nearest training-data point of any class (so-called functional margin), since in general the larger the margin, the lower the generalization error of the classifier.[4]\n# \n# The objective of the support vector machine algorithm is to find a hyperplane(N-1 D Subspace) in an N-dimensional space(N — the number of features) that distinctly classifies the data points. (https://en.wikipedia.org/wiki/Support-vector_machine)\n# \n# ### Hyperplane \n# In geometry, a hyperplane is a subspace whose dimension is **one less** than that of its ambient space. If a space is 3-dimensional then its hyperplanes are the 2-dimensional planes, while if the space is 2-dimensional, its hyperplanes are the 1-dimensional lines. (https://en.wikipedia.org/wiki/Hyperplane)\n# \n# ### Maximum-margin \n# Maximum-margin hyperplane and margins for an SVM trained with samples from two classes. Samples on the margin are called the support vectors. (https://en.wikipedia.org/wiki/Support-vector_machine)\n# \n# ![SVM_margin.PNG](./assets/images/SVM_margin.PNG)\n# \n# \n# ### Problem: Not linearly separable in that space (Curved like below)\n# 1. Kernal function is to keep the computational load reasonable, the mappings used by SVM schemes are designed to ensure that dot products of pairs of input data vectors may be computed easily in terms of the variables in the original space\n# \n# ![kernal_function.PNG](./assets/images/1920px-Kernel_Machine.svg.PNG)\n# \n# 2.  a set of vectors is an orthogonal (and thus minimal) set of vectors that defines a hyperplane. The vectors defining the hyperplanes can be chosen to be linear combinations with parameters {\\displaystyle \\alpha _{i}}\\alpha _{i} of images of feature vectors {\\displaystyle x_{i}}x_{i} that occur in the data bas\n# \n# ### Hinge Loss function\n# To extend SVM to cases in which the data are not linearly separable, the hinge loss function is helpful\n# \n# ![hinge_loss.PNG](./assets/images/hinge_loss.PNG)\n# \n# The goal of the optimization then is to minimize\n# \n# ![optimization_function.PNG](./assets/images/optimization_function.PNG)\n# \n\n# In[1]:\n\n\n\"\"\"svm_model.py: This model is the implementation of Classification of KDD datasets.\n\"\"\"\n\n__author__ = 'Youngseok Joung'\n__copyright__ = \"Copyright 2021, The Cogent Project\"\n__credits__ = [\"Youngseok Joung\"]\n__license__ = \"GPL\"\n__version__ = \"1.0.1\"\n__maintainer__ = \"Youngseok Joung\"\n__email__ = \"none\"\n__status__ = \"Production\"\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split as splitter\nfrom sklearn.svm import SVC\nimport cProfile\nimport pstats\nimport os\nimport sys\nfrom sklearn.metrics import classification_report, confusion_matrix, roc_curve, roc_auc_score\nfrom sklearn.preprocessing import LabelEncoder\nfrom matplotlib import pyplot\nimport pickle\nfrom joblib import dump, load\n\n\n# In[2]:\n\n\ndef labelEncoding(model_name, data):\n    \"\"\"labelEncoding function replace the categorical valeu with a numberic value from 0 and the number of classes -1.\n    Also, the label encoder object is saved as a file using Pickle package to be recalled after classification.\n\n    :param model_name: model name used in this project (e.g. \"SVM\")\n    :param data: categorical datasets\n    :return: label encoded data, lable encoder object\n    \"\"\" \n    \n    for column in data.columns:\n        # If the data type of the cell is 'object'(Categorical), it will be transformed as a numerical \n        if data[column].dtype == type(object):\n            le_file_path = 'result/' + model_name + '/' + model_name + '_' + column + '_encoder.pkl'\n            if os.path.exists(le_file_path):\n                pkl_file = open(le_file_path, 'rb')\n                le = pickle.load(pkl_file) \n                pkl_file.close()\n                data[column] = le.transform(data[column])            \n            else:\n                le = LabelEncoder()\n                data[column] = le.fit_transform(data[column])\n                #exporting the departure encoder\n                output = open(le_file_path, 'wb')\n                pickle.dump(le, output)\n                output.close()\n            if column == 'result':\n                le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n                print(le_name_mapping)\n    return data, le\n\n\n# In[3]:\n\n\ndef Preprocessing(model_name, data):\n    \"\"\"Preprocessing function first separate datasets as input features(x) and class type 'result'(y) respectively.\n    And split dataset into train and test using splitter.\n    \n    :param model_name: model name used in this project (e.g. \"SVM\")\n    :param data: categorical datasets\n    :return: x_train, x_test, y_train, y_test\n    \"\"\"\n    y = data.result\n    x = data.drop('result', axis=1)\n    \n    # Preprocessing: Split 7:3 Train: Test\n    x_train, x_test, y_train, y_test = splitter(x, y, test_size=0.3)\n    \n    return x_train, x_test, y_train, y_test\n\n\n# In[4]:\n\n\ndef train_and_test(model_name, x_train, x_test, y_train, y_test):\n    \"\"\"train_and_test function train the proposed model with the train dataset\n    And test it with test dataset\n    Additionally it will finalize the model to be used in the product. \n    \n    :param model_name: model name used in this project (e.g. \"SVM\")\n    :param x_train: train input features\n    :param y_train: train label\n    :param x_test: test input features\n    :param y_test: test label    \n    :return: model, y_pred is prediced lables from the model\n    \"\"\"\n\n    # Profile: Start \n    profile = cProfile.Profile()\n    profile.enable()\n    \n    # train and test\n    model = SVC()\n    model.fit(x_train, y_train)\n    y_pred = model.predict(x_test)\n\n    # Profile: End \n    profile.disable()\n    profile.dump_stats('output.prof')\n    stream = open('result/' + model_name + '/' + model_name + '_profiling.txt', 'w')\n    stats = pstats.Stats('output.prof', stream=stream)\n    stats.sort_stats('cumtime')\n    stats.print_stats()\n    os.remove('output.prof')\n    \n    # Freezing model for production \n    dump(model, 'result/' + model_name + '/' + model_name + '_model.joblib') \n    \n    return model, y_pred\n\n\n# In[5]:\n\n\ndef report(model_name, y_test, y_pred, le=None):\n    \"\"\"report function evaluates the quality of the output of a classifier on this data set.\n    We can get the value of Precision, Recall,, F1-Score, Support, accuracy by Lables \n    And it can get Multiclass AUC score multiclass using roc_auc_score_multiclass function\n    Additionally, it draws Bar graph about comparison between labels in each metrics (precision, recall, f1-score, AUC)\n    All are saved as a file\n    \n    :param model_name: model name used in this project (e.g. \"SVM\")\n    :param y_test: test label    \n    :param y_pred: test label    \n    :param le: None or Label encoder    \n    :return: _confusion_matrix, _classification_report, _auc_dict, _classification_report_dict\n    \"\"\"\n    \n    # Estimation: Confusion Matrix & classification-report \n    _confusion_matrix = confusion_matrix(y_test, y_pred)\n    _classification_report = classification_report(y_test, y_pred, target_names=le.classes_, output_dict=False)\n    _classification_report_dict = classification_report(y_test, y_pred, target_names=le.classes_, output_dict=True)\n\n    # For Multiclass AUC\n    _auc_dict = roc_auc_score_multiclass(y_test, y_pred)\n    _auc_dict = dict((le.classes_[key], value) for (key, value) in _auc_dict.items())\n#     _auc = roc_auc_score(y_test, y_pred, multi_class='ovr')\n#     _fpr, _tpr, _thresholds = roc_curve(y_test, y_pred)\n\n    with open('result/' + model_name + '/' + model_name + '_output.txt', 'w') as f:\n        f.write(\"\\n---Confusion Matrix---\\n\")\n        f.write(np.array2string(_confusion_matrix, separator=', '))\n        f.write(\"\\n---Classification Report---\\n\")\n        f.write(_classification_report)\n        f.write(\"\\n---ROC AUC Score---\\n\")\n        f.write(str(_auc_dict))\n#         f.write(_auc)\n    \n    print('\\n-----Confusion Matrix-----\\n')\n    print(_confusion_matrix)\n    print('\\n-----Classification Report-----\\n')\n    print(_classification_report)\n    print('\\n-----AUC Dictionary-----\\n')\n    print(str(_auc_dict))\n    \n    metrix = ['precision', 'recall', 'f1-score']\n#     metrix = ['precision', 'recall', 'f1-score', 'support']\n    xKeys = le.classes_\n    for met in metrix:\n        xValues = []\n        for target_name in le.classes_:\n            xValues += [_classification_report_dict[target_name][met]]\n\n        pyplot.title(met)\n        pyplot.bar(range(len(xValues)), list(xValues), align='center')\n        pyplot.xticks(range(len(xKeys)), list(xKeys))\n        pyplot.show()\n    \n    pyplot.title('AUC')\n    pyplot.bar(range(len(_auc_dict)), list(_auc_dict.values()), align='center')\n    pyplot.xticks(range(len(_auc_dict)), list(_auc_dict.keys()))\n    pyplot.show()\n    \n#     # plot the roc curve for the model\n#     # pyplot.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n#     pyplot.plot(_fpr, _tpr, marker='.', label=model_name)\n#     # axis labels\n#     pyplot.xlabel('False Positive Rate')\n#     pyplot.ylabel('True Positive Rate')\n#     # show the legend\n#     pyplot.legend()\n#     # show the plot\n#     pyplot.show()\n    \n    return _confusion_matrix, _classification_report, _auc_dict, _classification_report_dict\n    \n\n\n# In[6]:\n\n\ndef roc_auc_score_multiclass(y_test, y_pred, average = \"macro\"):\n    \"\"\"roc_auc_score_multiclass function evaluate the multiclass output as a ROC AUC score.\n    \n    :param y_test: test label    \n    :param y_pred: test label    \n    :param average: \"macro\" or Label encoder    \n    :return: _confusion_matrix, _classification_report, _auc_dict, _classification_report_dict\n    \"\"\"\n    \n    #creating a set of all the unique classes using the actual class list\n    unique_class = set(y_test)\n    roc_auc_dict = {}\n    for per_class in unique_class:\n        #creating a list of all the classes except the current class \n        other_class = [x for x in unique_class if x != per_class]\n\n        #marking the current class as 1 and all other classes as 0\n        new_y_test = [0 if x in other_class else 1 for x in y_test]\n        new_y_pred = [0 if x in other_class else 1 for x in y_pred]\n\n        #using the sklearn metrics method to calculate the roc_auc_score\n        roc_auc = roc_auc_score(new_y_test, new_y_pred, average = average)\n        roc_auc_dict[per_class] = roc_auc\n\n    return roc_auc_dict\n\n\n# In[7]:\n\n\ndef production(model_name, data):\n    \"\"\"production function receive real network traffic data from the product \n    And classify it with saved label encoder and the model\n    \n    :param model_name: model name    \n    :param data: real dataset     \n    :return: pred_label, real_label\n    \"\"\"\n    real_data, le = labelEncoding(model_name, data)\n    real_y = real_data.result\n    real_x = real_data.drop('result', axis=1)\n#     print(real_y)\n#     print(real_x)\n\n    clf = load('result/' + model_name + '/' + model_name + '_model.joblib')\n    yy_pred = clf.predict(real_x)\n    pred_label = le.inverse_transform(yy_pred)\n    real_label = le.inverse_transform(real_y)\n\n    return pred_label, real_label\n\n\n# # Run main program\n\n# In[8]:\n\n\nif __name__ == \"__main__\":\n    \n    \"\"\"Receive Input datasets\"\"\"\n    model_name = 'svm_kdd'\n    # model_name = 'svm_nsl_kdd'\n    dataset_name = 'kdd_prediction'\n    # dataset_name = 'kdd_prediction_NSL'\n\n    data = pd.read_csv('./dataset/' + dataset_name + '.csv', delimiter=',', dtype={'protocol_type': str, 'service': str, 'flag': str, 'result': str})\n#     print(data.head)\n\n\n# In[9]:\n\n\n\"\"\"Label Encoding for categorical datasets\"\"\"\ndata, le = labelEncoding(model_name, data)\n\n\n# In[10]:\n\n\n\"\"\"Pre-processing\"\"\"\nx_train, x_test, y_train, y_test = Preprocessing(model_name, data)\n\n\n# In[11]:\n\n\n\"\"\"Train and Test\"\"\"\nmodel, y_pred = train_and_test(model_name, x_train, x_test, y_train, y_test)\n\n\n# In[12]:\n\n\n\"\"\"Report\"\"\"\ncm, cr, auc, _ = report(model_name, y_test, y_pred, le)\n\n\n# # Test in Product \n\n# In[13]:\n\n\n\"\"\"Production\"\"\"\n\nreal_data = pd.read_csv('./dataset/kdd_prediction.csv', delimiter=',', dtype={'protocol_type': str, 'service': str, 'flag': str, 'result': str})\nreal_data = real_data.head(1)\n\npred_label, real_label = production(model_name, real_data)\nprint(pred_label, real_label)\n\n\n# In[ ]:\n\n\n\n\n", "repo_name": "youngseok-joung/youngseok-joung.github.io", "sub_path": "my_book/_build/jupyter_execute/books/ml/anomaly_detection/svm_model.py", "file_name": "svm_model.py", "file_ext": "py", "file_size_in_byte": 13429, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "os.path.exists", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 113, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 117, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 144, "usage_type": "call"}, {"api_name": "cProfile.Profile", "line_number": 166, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 170, "usage_type": "call"}, {"api_name": "pstats.Stats", "line_number": 178, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 181, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 184, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 207, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 208, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.array2string", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 290, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 313, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 334, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 373, "usage_type": "call"}]}
{"seq_id": "5352103237", "text": "import os\nimport logging\nfrom json import loads, dumps\nfrom datetime import timedelta\nfrom argparse import ArgumentParser\n\nfrom redis import Redis\nfrom flask import Response, Flask, request\n\napp = Flask(__name__)\nlog = logging.getLogger(__name__)\n\nparser = ArgumentParser()\nparser.add_argument(\"-a\", \"--address\",\n                    action=\"store\", dest=\"address\",\n                    type=str, required=True,\n                    help=\"Address for api\")\nparser.add_argument(\"-p\", \"--port\",\n                    action=\"store\", dest=\"port\",\n                    type=str, required=True,\n                    help=\"Port for api\")\nparser.add_argument(\"-c\", \"--crt\",\n                    action=\"store\", dest=\"cert\",\n                    type=str, required=False,\n                    help=\"Path to certificate for this API\")\nparser.add_argument(\"-k\", \"--key\",\n                    action=\"store\", dest=\"key\",\n                    type=str, required=False,\n                    help=\"Path to key of certificate used by this API\")\n\nparser.add_argument(\"-rp\", \"--redis-port\",\n                    action=\"store\", dest=\"redis-port\",\n                    type=str, required=True,\n                    help=\"Port for Redis client\")\n\nargs = vars(parser.parse_args())\n\napi_address = args[\"address\"]\napi_port = args[\"port\"]\napi_cert = args[\"cert\"]\napi_key = args[\"key\"]\n\nredis_port = args[\"redis-port\"]\nr = Redis(port=redis_port, charset=\"utf-8\", decode_responses=True)\n\n\n@app.route(\"/hash\", methods=['POST'])\ndef create_redis_hash():\n    data = loads(request.data)\n    success = r.hmset(data[\"key\"], data[\"pairs\"])\n    if data.get(\"expire\") is not None:\n      expiration = timedelta(**data.get(\"expire\"))\n      r.expire(data[\"key\"], expiration)\n    \n    response_body = {\"success\": success}\n    response_body[data[\"key\"]] = r.hgetall(data[\"key\"])\n  \n    return Response(dumps(response_body), status=200, mimetype=\"application/json\")\n\n\n@app.route(\"/hash\", methods=['PUT'])\ndef update_redis_hash():\n    data = loads(request.data)\n    success = r.hmset(data[\"key\"], data[\"pairs\"])\n    if data.get(\"expire\") is not None:\n      expiration = timedelta(**data.get(\"expire\"))\n      r.expire(data[\"key\"], expiration)\n    \n    if data.get(\"newkey\") is not None:\n      r.rename(data[\"key\"], data[\"newkey\"])\n\n    response_body = {\"success\": success}\n    if data.get(\"newkey\") is not None:\n      response_body[data[\"newkey\"]] = r.hgetall(data[\"newkey\"])\n    else:\n      response_body[data[\"key\"]] = r.hgetall(data[\"key\"])\n  \n    return Response(dumps(response_body), status=200, mimetype=\"application/json\")\n\n\n@app.route(\"/hash\", methods=['GET'])\ndef get_redis_hash():\n    response_body = {\"success\": True}\n    key = request.headers.get(\"key\")\n    response_body[key] = r.hgetall(key)\n\n    return Response(dumps(response_body), status=200, mimetype=\"application/json\")\n\n\n@app.route(\"/key\", methods=['DELETE'])\ndef delete_redis_key():\n    status = 200\n    key = request.headers.get(\"key\")\n    success = r.delete(key)\n\n    if not success:\n        status = 404\n    response_body = {\"success\": bool(success)}\n\n    return Response(dumps(response_body), status=status, mimetype=\"application/json\")\n\n\n@app.route(\"/list\", methods=['POST'])\ndef create_redis_list():\n    data = loads(request.data)\n    strat = data.get(\"strategy\")\n    if strat is not None and strat == \"left\":\n      length = r.lpush(data[\"key\"], *data[\"values\"])\n    else:\n      length = r.rpush(data[\"key\"], *data[\"values\"])\n   \n    response_body = {\"length\": length}\n    response_body[data[\"key\"]] = r.lrange(data[\"key\"], 0, -1)\n  \n    return Response(dumps(response_body), status=200, mimetype=\"application/json\")\n\n\n@app.route(\"/list\", methods=['GET'])\ndef get_entire_list():\n    response_body = {\"success\": True}\n    key = request.headers.get(\"key\")\n    response_body[key] = r.lrange(key, 0, -1) \n\n    return Response(dumps(response_body), status=200, mimetype=\"application/json\")\n\n\n@app.route(\"/list/<idx>\", methods=['GET'])\ndef get_list_at_idx(idx):\n    response_body = {\"success\": True}\n    key = request.headers.get(\"key\")\n    response_body[key] = {}\n    response_body[key][str(idx)] = r.lindex(key, idx) \n\n    return Response(dumps(response_body), status=200, mimetype=\"application/json\")\n\n\n@app.route(\"/set\", methods=['POST'])\ndef create_add_set():\n    data = loads(request.data)\n    length = r.sadd(data[\"key\"], *data[\"values\"])\n   \n    response_body = {\"length\": length}\n    response_body[data[\"key\"]] = list(r.smembers(data[\"key\"]))\n  \n    return Response(dumps(response_body), status=200, mimetype=\"application/json\")\n\n\n@app.route(\"/set/<n_items>\", methods=['GET'])\ndef get_n_items_set(n_items):\n    response_body = {\"success\": True}\n    key = request.headers.get(\"key\")\n   \n    response_body = {key: list(r.srandmember(key, n_items))}\n  \n    return Response(dumps(response_body), status=200, mimetype=\"application/json\")\n\n\n@app.route(\"/set\", methods=['GET'])\ndef get_set():\n    response_body = {\"success\": True}\n    key = request.headers.get(\"key\")\n   \n    response_body = {key: list(r.smembers(key))}\n  \n    return Response(dumps(response_body), status=200, mimetype=\"application/json\")\n\n\ndef start_api(address, port, clnt_cert=None, clnt_key=None):\n    if clnt_cert is None or clnt_key is None:\n      app.run(host=address, port=port, debug=False)\n    else:\n      app.run(host=address, port=port, \n              ssl_context=(clnt_cert, clnt_key), debug=False)\n\nif api_cert is None or api_key is None: \n  start_api(api_address, api_port)\nelse:\n  start_api(api_address, api_port, api_cert, api_key)\n\n", "repo_name": "Benardi/redis-basics", "sub_path": "api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 5534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 44, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 58, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 58, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 78, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 87, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 100, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 105, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 115, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 124, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 130, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 130, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 130, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 134, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 134, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 139, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 139, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 145, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 151, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 155, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 161, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 161, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 161, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 165, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 165, "usage_type": "call"}]}
{"seq_id": "8233749453", "text": "import argparse\nimport os\nimport sys\n\nimport numpy as np\nimport torch\nimport torchvision\nfrom matplotlib import pyplot as plt\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"..\"))\nfrom losses import L1, L2\nfrom models import FCGenerator, PixelGenerator\nfrom utils import *\nfrom utils.common import human_format\nfrom utils.data import get_data\nfrom utils.image import to_patches\n\ntorch.backends.cudnn.benchmark = True\n\nz_batch = None\n\ndef OTS(netG, psi, opt_psi, data, loss_func, args, it, device):\n    primals = []\n    duals = []\n    memory = []\n    for ots_iter in range(args.max_ots_iters):\n        opt_psi.zero_grad()\n\n        # print(f\"{ots_iter}: {human_format(torch.cuda.memory_allocated(0))}\")\n\n        global z_batch\n        if z_batch is None:\n            z_batch = torch.randn(args.batch_size, args.nz, device=device)\n\n        y_fake = netG(z_batch).detach()\n\n        phi, idx = loss_func.score(y_fake, data, psi)\n\n        dual_loss = torch.mean(phi) + torch.mean(psi)\n        primal_loss = loss_func.loss(y_fake, data[idx])\n        loss_back = -1 * dual_loss\n        # loss_back = -torch.mean(psi[idx])  # equivalent to loss\n        loss_back.backward()\n\n        opt_psi.step()\n\n        duals.append(dual_loss.item())\n        primals.append(primal_loss.item())\n\n        memory.append((z_batch, idx))\n        if len(memory) > args.memory_size:\n            memory = memory[1:]\n\n        if ots_iter % 100 == 0:\n            # Empirical stopping criterion\n            # np_indices = torch.cat(memory_idx).reshape(-1).cpu().numpy()\n            # n, min_f, max_f = args.early_end\n            # histo = np.histogram(np_indices, bins=n, range=(0, len(data) - 1), density=True)[0]\n            # histo /= histo.sum()\n            # if histo.min() >= min_f/n and histo.max() <= max_f/n:\n            #     break\n            print(f\"Iteration {it}, OTS: {ots_iter}, \" +\n                  f\"loss_dual: {duals[-1]:.2f}, \" +\n                  f\"loss_primal: {primals[-1]:.2f}, \" +\n                  # f\"histogram ({histo.min()}: {histo.max()}), goal ({min_f/n}, {max_f/n}), \"\n                  f\"memory-size: {len(memory)}, \" +\n                  (f\"Cuda-memory {human_format(torch.cuda.memory_allocated(0))}\" if device == torch.device(\"cuda\") else \"\"))\n\n    return memory, primals, duals\n\n\ndef FIT(netG, opt_g, data, memory, loss_func):\n    primals = []\n    for z_batch, data_idx in memory:\n        opt_g.zero_grad()\n\n        y_fake = netG(z_batch)  # G_t(z)\n\n        primal_loss = loss_func.loss(data[data_idx], y_fake)\n\n        primal_loss.backward()\n        opt_g.step()\n\n        primals.append(primal_loss.item())\n\n    return primals\n\n\ndef train_images(args):\n    os.makedirs(output_dir, exist_ok=True)\n    data = get_data(args.data_path, args.im_size, gray=False, limit_data=args.limit_data, center_crop=args.center_crop).to(device)\n    output_dim = args.c * args.im_size**2\n    data = data.view(len(data), output_dim)\n\n    loss_func = L1() if args.distance == \"L1\" else L2()\n\n    if args.arch == \"FC\":\n        netG = FCGenerator(args.nz, output_dim, args.n_hidden).to(device)\n    else:\n        netG = PixelGenerator(args.nz, args.im_size).to(device)\n    opt_g = torch.optim.Adam(netG.parameters(), lr=0.001)\n\n    psi = torch.randn(len(data), requires_grad=True, device=device)\n    opt_psi = torch.optim.Adam([psi], lr=0.01)\n\n    debug_fixed_z = torch.randn(args.batch_size, args.nz, device=device)\n    print(f\"- , {torch.cuda.memory_allocated(0)}\")\n\n    all_primals = []\n    all_duals = []\n    for it in range(args.n_iters):\n        memory, primals, duals = OTS(netG, psi, opt_psi, data, loss_func, args, it, device)\n        g_loss = FIT(netG, opt_g, data, memory, loss_func)\n        print(it, \"FIT loss:\", np.mean(g_loss))\n\n        all_duals += duals\n        all_primals += primals\n        plt.plot(range(len(all_primals)), all_primals, label=\"Primal\", color='r')\n        plt.title(f\"Last: {all_primals[-1]:.4f}\" )\n        plt.plot(range(len(all_duals)), all_duals, label=\"Dual\", color='b')\n        plt.title(f\"Last: {all_duals[-1]:.4f}\" )\n        plt.legend()\n        plt.savefig(f\"{output_dir}/plot.png\")\n        plt.clf()\n\n        y_output = netG(debug_fixed_z).view(-1, args.c, args.im_size, args.im_size)\n        torchvision.utils.save_image(y_output, f\"{output_dir}/fixed_z_fake_%d.png\" % it, normalize=True)\n        y_output = netG(torch.randn(args.batch_size, args.nz, device=device)).view(-1, args.c, args.im_size, args.im_size)\n        torchvision.utils.save_image(y_output, f\"{output_dir}/fake_%d.png\" % it, normalize=True)\n\n\ndef train_patches(args):\n    p = args.p\n    s = args.s\n    d = args.im_size\n    c = args.c\n    os.makedirs(output_dir, exist_ok=True)\n    data = get_data(args.data_path, args.im_size, gray=False, limit_data=10000).to(device)\n    data = to_patches(data, d, c, p, s)\n    print(f\"Got {len(data)} patches\")\n\n    loss_func = L1() if args.distance == \"L2\" else L2()\n\n    # netG = get_generator(args.nz, args.n_hidden, output_dim=c*d**2).to(device)\n    netG = PixelGenerator(args.nz, args.im_size).to(device)\n\n    opt_g = torch.optim.Adam(netG.parameters(), lr=1e-4)\n\n    def patch_wrapper(x):\n        return to_patches(netG(x), d, c, p, s)\n\n    psi = torch.zeros(len(data), requires_grad=True, device=device)\n    opt_psi = torch.optim.Adam([psi], lr=1e-1)\n\n    debug_fixed_z = torch.randn(args.batch_size, args.nz, device=device)\n    for it in range(args.n_iters):\n        memory = OTS(patch_wrapper, psi, opt_psi, data, loss_func, args, it, device)\n        g_loss = FIT(patch_wrapper, opt_g, data, memory, loss_func)\n\n        print(it, \"FIT loss:\", np.mean(g_loss))\n        y_output = netG(debug_fixed_z).view(-1, args.c, args.im_size, args.im_size)\n        torchvision.utils.save_image(y_output, f\"{output_dir}/fake_%d.png\" % it, normalize=True)\n\n\nif __name__ == '__main__':\n    device = torch.device(\"cuda\")\n    args = argparse.Namespace()\n    args.data_path = \"/mnt/storage_ssd/datasets/FFHQ/FFHQ/FFHQ\"\n    args.c = 3\n    args.limit_data = 10000\n    args.center_crop = 90\n\n    args.im_size = 64\n    args.nz = 64\n\n    args.arch = \"Pixels\"\n    args.batch_size = 64\n    args.n_hidden = 128\n\n    # Empirical stopping criterion\n    args.memory_size = 2000\n    args.early_end = (1000, 200, 320)\n\n    args.n_iters = 100\n    args.max_ots_iters = 2000\n\n    args.distance = \"L2\"  # W1 or W2 or hybrid: W1 better looking, W2 faster\n\n    output_dir = f\"{os.path.basename(args.data_path)}_I-{args.im_size}\"\n    train_images(args)\n\n\n\n\n", "repo_name": "ariel415el/DiscreteDualWGAN", "sub_path": "optimization/main_generator.py", "file_name": "main_generator.py", "file_ext": "py", "file_size_in_byte": 6466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 67, "usage_type": "call"}, {"api_name": "utils.common.human_format", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.cuda.memory_allocated", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 90, "usage_type": "call"}, {"api_name": "utils.data.get_data", "line_number": 91, "usage_type": "call"}, {"api_name": "losses.L1", "line_number": 95, "usage_type": "call"}, {"api_name": "losses.L2", "line_number": 95, "usage_type": "call"}, {"api_name": "models.FCGenerator", "line_number": 98, "usage_type": "call"}, {"api_name": "models.PixelGenerator", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 101, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.cuda.memory_allocated", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 127, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 127, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 128, "usage_type": "call"}, {"api_name": "torchvision.utils.save_image", "line_number": 129, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 129, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 137, "usage_type": "call"}, {"api_name": "utils.data.get_data", "line_number": 138, "usage_type": "call"}, {"api_name": "utils.image.to_patches", "line_number": 139, "usage_type": "call"}, {"api_name": "losses.L1", "line_number": 142, "usage_type": "call"}, {"api_name": "losses.L2", "line_number": 142, "usage_type": "call"}, {"api_name": "models.PixelGenerator", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 147, "usage_type": "attribute"}, {"api_name": "utils.image.to_patches", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 160, "usage_type": "call"}, {"api_name": "torchvision.utils.save_image", "line_number": 162, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 162, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 166, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}]}
{"seq_id": "7155885244", "text": "import cv2 as cv\nimport numpy as np\nimport time\nimport requests\nimport imutils\n\n\ndef empty(a):\n\tpass\n\ncv.namedWindow(\"TrackBar\")\ncv.resizeWindow(\"TrackBar\", 640, 240)\ncv.createTrackbar(\"H Min\", \"TrackBar\", 0, 255, empty)\ncv.createTrackbar(\"S Min\", \"TrackBar\", 0, 255, empty)\ncv.createTrackbar(\"V Min\", \"TrackBar\", 0, 255, empty)\n\ncv.createTrackbar(\"H Max\", \"TrackBar\", 0, 255, empty)\ncv.createTrackbar(\"S Max\", \"TrackBar\", 0, 255, empty)\ncv.createTrackbar(\"V Max\", \"TrackBar\", 0, 255, empty)\n\nif __name__ == \"__main__\":\n\n    url = \"http://192.168.1.16:8080/shot.jpg\"\n    frame_width = 640\n    frame_height = 480\n    while True:\n        img_resp = requests.get(url)\n        img_arr = np.array(bytearray(img_resp.content), dtype=np.uint8)\n        img = cv.imdecode(img_arr, -1)\n        img = imutils.resize(img, width=frame_width, height=frame_height)\n\n        # face = cv.imread('Images/Cube_Face.jpg')\n\n        half = cv.resize(img, (0, 0), fx = 0.5, fy = 0.5)\n        img_HSV = cv.cvtColor(half, cv.COLOR_BGR2HSV)\n        h_min = cv.getTrackbarPos(\"H Min\", \"TrackBar\")\n        h_max = cv.getTrackbarPos(\"H Max\", \"TrackBar\")\n        s_min = cv.getTrackbarPos(\"S Min\", \"TrackBar\")\n        s_max = cv.getTrackbarPos(\"S Max\", \"TrackBar\")\n        v_min = cv.getTrackbarPos(\"V Min\", \"TrackBar\")\n        v_max = cv.getTrackbarPos(\"V Max\", \"TrackBar\")\n\n        # print(h_min,h_max,s_min,s_max,v_min,v_max)\n        print(h_min,s_min,v_min,h_max,s_max,v_max)\n\n        lower = np.array([h_min, s_min, v_min])\n        upper = np.array([h_max, s_max, v_max])\n        mask = cv.inRange(img_HSV, lower, upper)\n        #img_result = cv.bitwise_and(half, half, mask=mask)\n\n        cv.imshow(\"Original\", half)\n        # cv.imshow(\"HSV\", img_HSV)\n        cv.imshow(\"Mask\", mask)\n        # cv.imshow(\"Result\", img_result)\n\n\n        cv.waitKey(1)", "repo_name": "u4atharva/my-code-arsenal", "sub_path": "Computer Vision/KociembaDemo/venv/CalibrateColors.py", "file_name": "CalibrateColors.py", "file_ext": "py", "file_size_in_byte": 1826, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "cv2.namedWindow", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.resizeWindow", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.imdecode", "line_number": 29, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cv2.getTrackbarPos", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 57, "usage_type": "call"}]}
{"seq_id": "22470173430", "text": "# Blake Van Dyken\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nT_cup_start = 84 # celcius\nT_room_start = 19 # celcius\nr_start = 0.025\n\n# Forward Euler equation\ndef forwardEulerEquation(cupTemp, roomTemp, h):\n    return cupTemp + (h * -(r_start) * (cupTemp - roomTemp))\n\n# just Euler equation\ndef EulerEquation(r, T_cup, T_surround):\n    return -r *(T_cup - T_surround)\n\n# trapezoidal eq\ndef trapezoidalEuler(step, thresh):\n    cup_values = []\n    threshold = thresh\n    h_values = np.arange(0, 301, step)\n    \n    y_n = forwardEulerEquation(T_cup_start, T_room_start, 0) # should be 82.375\n    \n    for h in (h_values):\n        k_curr = EulerEquation(r_start, y_n, T_room_start)\n        \n        k = 1\n        while (True):\n            k_next = EulerEquation(r_start, y_n, T_room_start) # get next y_k+1 item\n            \n            if (k_next - k_curr < threshold): # compare successive values\n                cup_values.append(y_n)\n                y_next = y_n + (h/2)*(k_curr+k_next) # make next y_n using trapezoidal eq\n                y_n = y_next\n                break\n            else:\n                y_n = forwardEulerEquation(y_n, T_room_start, h)\n                k+=1 # inc k\n    \n    print(cup_values, \"\\nh = \", step, \"\\n\")\n    return cup_values  \n\n   \ndef main():\n    fig,ax = plt.subplots(4, 2)\n    fig.delaxes(ax[3, 1])\n    \n    fig.suptitle(\"Trapezoidal Euler Method graphs\")\n    \n    step = .05\n    \n    ax[0, 0].set_ylabel(\"temperature of cup (Celcius)\")\n    ax[0, 0].set_xlabel(\"time elapsed (seconds)\")\n    ax[0, 0].set_title(\"h = 30\")\n    ax[0, 0].plot(trapezoidalEuler(30, step), \"o\")\n    \n    ax[1, 0].set_ylabel(\"temperature of cup (Celcius)\")\n    ax[1, 0].set_xlabel(\"time elapsed (seconds)\")\n    ax[1, 0].set_title(\"h = 15\")\n    ax[1, 0].plot(trapezoidalEuler(15, step), \"o\")\n    \n    ax[2, 0].set_ylabel(\"temperature of cup (Celcius)\")\n    ax[2, 0].set_xlabel(\"time elapsed (seconds)\")\n    ax[2, 0].set_title(\"h = 10\")\n    ax[2, 0].plot(trapezoidalEuler(10, step), \"o\")\n    \n    ax[3, 0].set_ylabel(\"temperature of cup (Celcius)\")\n    ax[3, 0].set_xlabel(\"time elapsed (seconds)\")\n    ax[3, 0].set_title(\"h = 5\")\n    ax[3, 0].plot(trapezoidalEuler(5, step), \"o\")\n    \n    ax[0, 1].set_ylabel(\"temperature of cup (Celcius)\")\n    ax[0, 1].set_xlabel(\"time elapsed (seconds)\")\n    ax[0, 1].set_title(\"h = 1\")\n    ax[0, 1].plot(trapezoidalEuler(1, step), \"o\")\n    \n    ax[1, 1].set_ylabel(\"temperature of cup (Celcius)\")\n    ax[1, 1].set_xlabel(\"time elapsed (seconds)\")\n    ax[1, 1].set_title(\"h = .5\")\n    ax[1, 1].plot(trapezoidalEuler(.5, step), \"o\")\n    \n    ax[2, 1].set_ylabel(\"temperature of cup (Celcius)\")\n    ax[2, 1].set_xlabel(\"time elapsed (seconds)\")\n    ax[2, 1].set_title(\"h = .25\")\n    ax[2, 1].plot(trapezoidalEuler(.25, step), \"o\")\n    \n    # set the spacing between subplots\n    plt.subplots_adjust(left=0.1,\n                        bottom=0.1, \n                        right=0.9, \n                        top=0.9, \n                        wspace=0.4, \n                        hspace=0.4)\n    plt.show()\nmain()", "repo_name": "blakevd/Theory", "sub_path": "EulerEquations/TrapezoidalEuler.py", "file_name": "TrapezoidalEuler.py", "file_ext": "py", "file_size_in_byte": 3063, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.arange", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}]}
{"seq_id": "3653946228", "text": "import pandas as pd\r\nimport pypokedex\r\nimport PIL.Image, PIL.ImageTk\r\nimport tkinter as tk\r\nimport urllib3\r\nfrom io import BytesIO\r\n\r\n# filepath of German spreadsheet\r\ngerman_sheet = pd.read_csv(\"german-names.csv\")\r\n\r\n# builds array of German names for Pokémon\r\ngerman = []\r\ni = 0\r\nx = True\r\nwhile(x is True):\r\n    try:\r\n        german.append(german_sheet.iloc[i][2])\r\n        i = i+1\r\n    except:\r\n        x = False\r\n\r\n# main window\r\nwindow = tk.Tk()\r\nwindow.geometry(\"600x500\")\r\nwindow.title(\"English-German Pokédex\")\r\n# alt background color #910904\r\nbg_color = \"#0047ab\"\r\nfont_color = \"#dde2f0\"\r\nwindow.config(padx=10, pady=10, background=bg_color)\r\n\r\n\r\n\r\n# title label\r\nheader = tk.Label(window, text=\"Englisch-Deutscher Pokédex\", background=bg_color, fg=font_color)\r\nheader.config(font=(\"Arial\", 32))\r\nheader.pack(padx=10, pady=10)\r\n\r\n# image window\r\npokeimg = tk.Label(window)\r\npokeimg.config(font=(\"Arial\", 20), background=bg_color, fg=font_color, text=\"Image\")\r\npokeimg.pack()\r\n\r\n# ID - English name\r\npokeinfo = tk.Label(window)\r\npokeinfo.config(font=(\"Arial\", 20), background=bg_color, fg=font_color, text=\"ID - English\")\r\npokeinfo.pack(padx=10, pady=10)\r\n\r\n# German name\r\nlang1name = tk.Label(window)\r\nlang1name.config(font=(\"Arial\", 30), background=bg_color, fg=font_color, text=\"German\")\r\nlang1name.pack(padx=6, pady=6)\r\n\r\n# Pokémon type(s)\r\npoketypes = tk.Label(window)\r\npoketypes.config(font=(\"Arial\", 12), background=bg_color, fg=font_color, text=\"Type 1 - Type 2\")\r\npoketypes.pack(padx=2, pady=2)\r\n\r\n# load function when lookup is performed\r\ndef load_pokemon():\r\n    # clears instructions text\r\n    instructions.destroy()\r\n    isgerman = False\r\n    a = 0\r\n\r\n# lookup in German (this is broken right now)\r\n#    for x in german:\r\n#        if (text_box.get(1.0, \"end-1c\").lower().title() == x):\r\n#            pokemon = pypokedex.get(name=str(a))\r\n#            isgerman = True\r\n#        a = a + 1\r\n\r\n    # looks up Pokémon in dex\r\n#    if (isgerman is False):\r\n    pokemon = pypokedex.get(name=text_box.get(1.0, \"end-1c\"))\r\n\r\n    # pulls image from URL\r\n    http = urllib3.PoolManager()\r\n    response = http.request('GET', pokemon.sprites.front.get('default'))\r\n    image = PIL.Image.open(BytesIO(response.data))\r\n\r\n    # displays image\r\n    img = PIL.ImageTk.PhotoImage(image)\r\n    pokeimg.config(image=img)\r\n    pokeimg.image = img\r\n\r\n    # displays pokemon info (German text is from array built from spreadsheet)\r\n    pokeinfo.config(text=f\"{pokemon.dex} - {pokemon.name}\".title())\r\n    lang1name.config(text=f\"{german[pokemon.dex-1]}\".title())\r\n    poketypes.config(text=\" - \".join([t for t in pokemon.types]).title())\r\n\r\n    # clears text box\r\n    text_box.delete(\"1.0\",\"end\")\r\n\r\n# Instruction text that disappears after first lookup\r\ninstructions = tk.Label(window, text=\"Enter name or ID of a Pokémon to begin\".upper())\r\ninstructions.config(font=(\"Roboto\", 16), background=\"black\", fg=font_color)\r\ninstructions.pack(padx=6, pady=6)\r\n\r\n# text box for user input\r\ntext_box = tk.Text(window, height=1)\r\ntext_box.config(font=(\"Arial\", 20), background=\"#0968ed\", fg=font_color)\r\ntext_box.pack(padx=10, pady=10)\r\n\r\nbutton = tk.Button(window, text=\"Load Pokémon\", command=load_pokemon,  activeforeground=\"#ccd2e0\", activebackground=\"#0968ed\", background=\"#0c4da8\", fg=font_color)\r\nbutton.config(font=(\"Arial\", 20))\r\nbutton.pack(padx=10, pady=10)\r\n\r\nwindow.mainloop()\r\n\r\n", "repo_name": "arzansa/pokedex", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3389, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 23, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 34, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 39, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 44, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 49, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 54, "usage_type": "call"}, {"api_name": "pypokedex.get", "line_number": 74, "usage_type": "call"}, {"api_name": "urllib3.PoolManager", "line_number": 77, "usage_type": "call"}, {"api_name": "PIL.Image.Image.open", "line_number": 79, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 79, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 79, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 79, "usage_type": "call"}, {"api_name": "PIL.Image.ImageTk.PhotoImage", "line_number": 82, "usage_type": "call"}, {"api_name": "PIL.Image.ImageTk", "line_number": 82, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 82, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 95, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 100, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 104, "usage_type": "call"}]}
{"seq_id": "23214155157", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='ClientInfo',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', primary_key=True, serialize=False, auto_created=True)),\n                ('client_name', models.CharField(verbose_name='客户端名称', max_length=150)),\n                ('score', models.IntegerField(verbose_name='分数')),\n                ('create_time', models.DateTimeField(verbose_name='创建时间')),\n                ('update_time', models.DateTimeField(verbose_name='更新时间')),\n                ('is_valid', models.CharField(verbose_name='是否有效', max_length=1)),\n            ],\n            options={\n                'db_table': 'client_score',\n                'verbose_name': '分数信息',\n                'verbose_name_plural': '分数信息',\n            },\n        ),\n    ]\n", "repo_name": "only-ove/ScoreRank", "sub_path": "scorerank/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 1039, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "8658196269", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.ticker import FuncFormatter\nfrom seaborn import heatmap\n\nfrom pyaerocom.mathutils import exponent\n\n\ndef _format_annot_heatmap(annot, annot_fmt_rows, annot_fmt_exceed):\n    \"\"\"\n    Process annotation formatting info for :func:`df_to_heatmap`\n\n    Parameters\n    ----------\n    annot : ndarray\n        2D pre-existing annotation information.\n    annot_fmt_rows : list\n        annotation formatting strings for each row of the input table. This\n        parameter is only considered if `annot_fmt_rowwise` is True. See also\n        :func:`_format_annot_heatmap`.\n    annot_fmt_exceed : list, optional\n        how to format annotated values that exceed a certain threshold.\n        The list contains 2 entries, 1. the threshold values, 2. how values\n        exceeding this threshold should be formatted. .\n\n    Returns\n    -------\n    ndarray\n        updated annotation information (input `annot`)\n    list\n        row formatting info.\n\n\n    \"\"\"\n    _annot = []\n    if not isinstance(annot_fmt_rows, list):\n        annot_fmt_rows = []\n        for row in annot:\n            mask = row[~np.isnan(row)]\n            if len(mask) == 0:  # all NaN\n                annot_fmt_rows.append(\"\")\n                continue\n            mask = mask[mask != 0]\n            exps = exponent(mask)\n            minexp = exps.min()\n            if minexp < -3:\n                annot_fmt_rows.append(\".1E\")\n            elif minexp < 0:\n                annot_fmt_rows.append(f\".{-minexp + 1}f\")\n            elif minexp in [0, 1]:\n                annot_fmt_rows.append(\".1f\")\n            else:\n                annot_fmt_rows.append(\".0f\")\n    if isinstance(annot_fmt_exceed, list):\n        exceed_val, exceed_fmt = annot_fmt_exceed\n    else:\n        exceed_val, exceed_fmt = None, None\n\n    for i, row in enumerate(annot):\n        rowfmt = annot_fmt_rows[i]\n        if rowfmt == \"\":\n            row_fmt = [\"\"] * len(row)\n        else:\n            row_fmt = []\n            for val in row:\n                if np.isnan(val):\n                    valstr = \"\"\n                else:\n                    if exceed_val is not None and val > exceed_val:\n                        valstr = format(val, exceed_fmt)\n                    else:\n                        valstr = format(val, rowfmt)\n\n                row_fmt.append(valstr)\n        _annot.append(row_fmt)\n    annot = np.asarray(_annot)\n    return annot, annot_fmt_rows\n\n\ndef df_to_heatmap(\n    df,\n    cmap=None,\n    center=None,\n    low=0.3,\n    high=0.3,\n    vmin=None,\n    vmax=None,\n    color_rowwise=False,\n    normalise_rows=False,\n    normalise_rows_how=None,\n    normalise_rows_col=None,\n    norm_ref=None,\n    sub_norm_before_div=True,\n    annot=True,\n    num_digits=None,\n    ax=None,\n    figsize=(12, 12),\n    cbar=False,\n    cbar_label=None,\n    cbar_labelsize=None,\n    xticklabels=None,\n    xtick_rot=45,\n    yticklabels=None,\n    ytick_rot=45,\n    xlabel=None,\n    ylabel=None,\n    title=None,\n    labelsize=12,\n    annot_fontsize=None,\n    annot_fmt_rowwise=False,\n    annot_fmt_exceed=None,\n    annot_fmt_rows=None,  # explicit formatting strings for rows\n    cbar_ax=None,\n    cbar_kws=None,\n    **kwargs,\n):\n    \"\"\"Plot a pandas dataframe as heatmap\n\n    Parameters\n    ----------\n    df : DataFrame\n        table data\n    cmap : str, optional\n        string specifying colormap to be used\n    center : float, optional\n        value that is mapped to center colour of colormap (e.g. 0)\n    low : float, optional\n        Extends lower range of the table values so that when mapped to  the\n        colormap, it’s entire range isn’t used. E.g. 0.3 roughly corresponds\n        to colormap crop of 30% at the lower end.\n    high : float, optional\n        Extends upper range of the table values so that when mapped to the\n        colormap, it’s entire range isn’t used. E.g. 0.3 roughly corresponds\n        to colormap crop of 30% at the upper end.\n    vmin : float, optional\n        lower end of value range to be plotted. If specified, input arg `low`\n        will be ignored.\n    vmax : float, optional\n        upper end of value range to be plotted. If specified, input arg `low`\n        will be ignored.\n    color_rowwise : bool, optional\n        if True, the color mapping is applied row by row, else, for the whole\n        table. Defaults to False.\n    normalise_rows : bool, optional\n        if True, the table is normalised in a rowwise manner either using the\n        mean value in each row (if argument ``normalise_rows_col`` is\n        unspecified) or using the value in a specified column. Defaults to\n        False.\n    normalise_rows_how : str, optional\n        aggregation string for row normalisation. Choose from mean or median.\n        Only relevant if input arg ``normalise_rows==True``.\n    normalise_rows_col : int, optional\n        if provided and if arg. ``normalise_rows==True``, then the\n        corresponding table column is used for normalisation rather than\n        the mean value of each row.\n    norm_ref : float or ndarray, optional\n        reference value(s) used for rowwise normalisation. Only relevant if\n        normalise_rows is True. If specified, normalise_rows_how and\n        normalise_rows_col will be ignored.\n    sub_norm_before_div : bool, optional\n        if True, the rowwise normilisation is applied by subtracting the\n        normalisation value for each row before dividing by it. This can be\n        useful to visualise positive or negative departures from the mean or\n        median.\n    annot : bool or list or ndarray, optional\n        if True, the table values are printed into the heatmap. Defaults to\n        True, in which case the values are computed based on the table content.\n        If list or ndarray, the shape needs to be the same as input table shape\n        (no of rows and cols), in which case the values of that 2D frame are\n        used.\n    num_digits : int, optional\n        number of digits printed in heatmap annotation.\n    ax : axes, optional\n        matplotlib axes instance used for plotting, if None, an axes will be\n        created\n    figsize : tuple, optional\n        size of figure for plot\n    cbar : bool, optional\n        if True, a colorbar is included\n    cbar_label : str, optional\n        label of colorbar (if colorbar is included, see cbar option)\n    cbar_labelsize : int, optional\n        size of colorbar label\n    xticklabels : list, optional\n        List of x axis labels.\n    xtick_rot : int, optional\n        rotation of x axis labels, defaults to 45 degrees.\n    yticklabels : list, optional\n        List of string labels.\n    ytick_rot : int, optional\n        rotation of y axis labels, defaults to 45 degrees.\n    xlabel : str, optional\n        x axis label\n    ylabel : str, optional\n        y axis label\n    title : str, optional\n        title of heatmap\n    labelsize : int, optional\n        fontsize of labels, default to 12\n    annot_fontsize : int, optional\n        fontsize of annotated text.\n    annot_fmt_rowwise : bool\n        rowwise formatting of annotation values, based on row value ranges.\n        Defaults to False.\n    annot_fmt_exceed : list, optional\n        how to format annotated values that exceed a certain threshold.\n        The list contains 2 entries, 1. the threshold values, 2. how values\n        exceeding this thrshold should be formatted. This parameter is only\n        considered if `annot_fmt_rowwise` is True. See also\n        :func:`_format_annot_heatmap`.\n    annot_fmt_rows : list\n        annotation formatting strings for each row of the input table. This\n        parameter is only considered if `annot_fmt_rowwise` is True. See also\n        :func:`_format_annot_heatmap`.\n    cbar_ax : Axes, optional\n        axes instance for colorbar, parsed to :func:`seaborn.heatmap`.\n    cbar_kws : dict, optional\n        keywords for colorbar formatting, , parsed to :func:`seaborn.heatmap`.\n    **kwargs\n        further keyword args parsed to :func:`seaborn.heatmap`\n\n    Raises\n    ------\n    ValueError\n        if input `annot` is list or ndarray and has a different shape than\n        the input `df`.\n\n    Returns\n    -------\n    Axes\n        plot axes instance\n    list or None\n        annotation information for rows\n\n    \"\"\"\n    if cmap is None:\n        cmap = \"bwr\"\n    if cbar_label is None:\n        cbar_label = \"\"\n    if normalise_rows_how is None:\n        normalise_rows_how = \"median\"\n\n    if ax is None:\n        _, ax = plt.subplots(1, 1, figsize=figsize)\n    if cbar_kws is None:\n        cbar_kws = {}\n    if annot_fontsize is None:\n        annot_fontsize = labelsize - 4\n    if not \"annot_kws\" in kwargs:\n        kwargs[\"annot_kws\"] = {}\n    kwargs[\"annot_kws\"][\"size\"] = annot_fontsize\n    df_hm = df\n    if normalise_rows:\n        if norm_ref is None:\n            if normalise_rows_col is not None:\n                if isinstance(normalise_rows_col, str):\n                    try:\n                        normalise_rows_col = df.columns.to_list().index(normalise_rows_col)\n                    except ValueError:\n                        raise ValueError(f\"Failed to localise column {normalise_rows_col}\")\n                norm_ref = df.values[:, normalise_rows_col]\n            else:\n                if normalise_rows_how == \"mean\":\n                    norm_ref = df.mean(axis=1)\n                elif normalise_rows_how == \"median\":\n                    norm_ref = df.median(axis=1)\n                else:\n                    raise ValueError(\n                        f\"Invalid input for normalise_rows_how ({normalise_rows_how}). \"\n                        f\"Choose mean or median\"\n                    )\n\n        if sub_norm_before_div:\n            df_hm = df.subtract(norm_ref, axis=0).div(norm_ref, axis=0)\n        else:\n            df_hm = df.div(norm_ref, axis=0)\n        cbar_kws[\"format\"] = FuncFormatter(lambda x, pos: f\"{x:.0%}\")\n\n    if color_rowwise:\n        df_hm = df_hm.div(abs(df_hm).max(axis=1), axis=0)\n\n    cbar_kws[\"label\"] = cbar_label\n\n    if \"norm\" in kwargs:\n        norm = kwargs[\"norm\"]\n        vmin = norm.boundaries[0]\n        vmax = norm.boundaries[-1]\n    else:\n        if vmin is None:\n            vmin = df_hm.min().min()  # * (1 - low)\n        if vmax is None:\n            vmax = df_hm.max().max()  # * (1 + high)\n        vmin -= abs(vmin) * low\n        vmax += abs(vmax) * high\n\n    if annot is True:\n        annot = df.values\n    elif isinstance(annot, list):\n        annot = np.asarray(annot)\n    else:\n        fmt = \"\"\n\n    if isinstance(annot, np.ndarray):\n        if not annot.shape == df.values.shape:\n            raise ValueError(\n                \"Invalid input for annot: needs to have same shape as input dataframe\"\n            )\n        elif np.any([isinstance(x, str) for x in annot.flatten()]):\n            fmt = \"\"\n        elif annot_fmt_rowwise:\n            annot, annot_fmt_rows = _format_annot_heatmap(annot, annot_fmt_rows, annot_fmt_exceed)\n            fmt = \"\"\n\n        elif num_digits is None or num_digits > 5:\n            fmt = \".4g\"\n        else:\n            fmt = f\".{num_digits}f\"\n\n    ax = heatmap(\n        df_hm,\n        cmap=cmap,\n        center=center,\n        annot=annot,\n        ax=ax,\n        # changes this from df_hm to df because the annotation and colorbar didn't work.\n        cbar=cbar,\n        cbar_ax=cbar_ax,\n        cbar_kws=cbar_kws,\n        fmt=fmt,\n        vmin=vmin,\n        vmax=vmax,\n        xticklabels=True,\n        yticklabels=True,\n        **kwargs,\n    )\n\n    ax.figure.axes[-1].yaxis.label.set_size(labelsize)\n    if title is not None:\n        ax.set_title(title, fontsize=labelsize + 2)\n\n    if yticklabels is None:\n        yticklabels = ax.get_yticklabels()\n\n    ax.set_yticklabels(yticklabels, rotation=ytick_rot, fontsize=labelsize - 2)\n\n    if xticklabels is None:\n        xticklabels = ax.get_xticklabels()\n\n    ax.set_xticklabels(xticklabels, rotation=xtick_rot, ha=\"right\", fontsize=labelsize - 2)\n\n    if xlabel is None:\n        xlabel = \"\"\n\n    ax.set_xlabel(xlabel, fontsize=labelsize)\n\n    if ylabel is None:\n        ylabel = \"\"\n    ax.set_ylabel(ylabel, fontsize=labelsize)\n    if cbar_labelsize is not None:\n        ax.figure.axes[-1].yaxis.label.set_size(cbar_labelsize)\n\n    return ax, annot_fmt_rows\n", "repo_name": "metno/pyaerocom", "sub_path": "pyaerocom/plot/heatmaps.py", "file_name": "heatmaps.py", "file_ext": "py", "file_size_in_byte": 12254, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.isnan", "line_number": 39, "usage_type": "call"}, {"api_name": "pyaerocom.mathutils.exponent", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.ticker.FuncFormatter", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 303, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 308, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 319, "usage_type": "call"}]}
{"seq_id": "7651765046", "text": "import os\nimport sqlite3\n\n\nclass Prompt:\n    prompt = \"SQLSn3k %s> \"\n\n    def __init__(self, db, pwd):\n        self.db = db\n        self.pwd = pwd\n\n    def __repr__(self):\n        info_string = \"\"\n        if self.pwd:\n            info_string += f'{self.pwd}'\n        if self.db:\n            info_string += f'/{self.db}'\n        return self.prompt % (info_string)\n\n    def update(self, db, pwd=None):\n        if pwd:\n            self.pwd = pwd\n        self.db = db\n\n\nclass DBUser():\n    def __init__(self, to_connect=None):\n        self.db = None\n        self.db_name = None\n        if to_connect:\n            self.db = self.connect(to_connect)\n            if self.db:\n                self.db_name = to_connect.split('/')[-1]\n        self.home = os.getenv(\"HOME\")\n        self.set_pwd()\n        self.prompt = Prompt(self.db_name, self.pwd)\n\n    def set_pwd(self):\n        if self.home:\n            self.pwd = os.getcwd().replace(self.home, '~')\n        else:\n            self.pwd = os.getcwd()\n\n    def connect(self, path):\n        try:\n            return sqlite3.connect(path)\n        except Exception as e:\n            print(e)\n            return None\n\n    def disconnect(self):\n        try:\n            self.db.commit()\n            self.db.close()\n            self.db\n        except Exception as e:\n            print(e)\n\n    def update(self):\n        self.set_pwd()\n        self.prompt.update(self.db_name, self.pwd)\n", "repo_name": "bwmabe/sqlsn3k", "sub_path": "old/db_user.py", "file_name": "db_user.py", "file_ext": "py", "file_size_in_byte": 1419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.getenv", "line_number": 34, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 40, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "32771620160", "text": "import pygame\nimport numpy as np\nfrom copy import deepcopy\nfrom settings import *\nfrom pieces import *\n\n\nclass Board:\n    def __init__(self, screen):\n        self.screen = screen\n        self.reset()\n\n    def reset(self):\n        self.turn = \"w\"\n        self.selected_piece = None\n        self.selected_square = None\n        self.selected_piece_prev = None\n        self.highlighted_squares = []\n        self.board = self.get_board()\n        self.game_over = False\n        # starting position of all the pieces on the board\n        # (0, 0) top left\n        # initiate the piece objects that is imported from pieces.py\n        self.w_pieces = {\n            \"rook1\": Rook((0, 7), \"w\", 1),\n            \"rook2\": Rook((7, 7), \"w\", 2),\n            \"knight1\": Knight((1, 7), \"w\", 1),\n            \"knight2\": Knight((6, 7), \"w\", 2),\n            \"bishop1\": Bishop((2, 7), \"w\", 1),\n            \"bishop2\": Bishop((5, 7), \"w\", 2),\n            \"queen1\": Queen((3, 7), \"w\", 1),\n            \"king1\": King((4, 7), \"w\", 1),\n            \"pawn1\": Pawn((0, 6), \"w\", 1),\n            \"pawn2\": Pawn((1, 6), \"w\", 2),\n            \"pawn3\": Pawn((2, 6), \"w\", 3),\n            \"pawn4\": Pawn((3, 6), \"w\", 4),\n            \"pawn5\": Pawn((4, 6), \"w\", 5),\n            \"pawn6\": Pawn((5, 6), \"w\", 6),\n            \"pawn7\": Pawn((6, 6), \"w\", 7),\n            \"pawn8\": Pawn((7, 6), \"w\", 8),\n        }\n        self.b_pieces = {\n            \"rook1\": Rook((0, 0), \"b\", 1),\n            \"rook2\": Rook((7, 0), \"b\", 2),\n            \"knight1\": Knight((1, 0), \"b\", 1),\n            \"knight2\": Knight((6, 0), \"b\", 2),\n            \"bishop1\": Bishop((2, 0), \"b\", 1),\n            \"bishop2\": Bishop((5, 0), \"b\", 2),\n            \"queen1\": Queen((3, 0), \"b\", 1),\n            \"king1\": King((4, 0), \"b\", 1),\n            \"pawn1\": Pawn((0, 1), \"b\", 1),\n            \"pawn2\": Pawn((1, 1), \"b\", 2),\n            \"pawn3\": Pawn((2, 1), \"b\", 3),\n            \"pawn4\": Pawn((3, 1), \"b\", 4),\n            \"pawn5\": Pawn((4, 1), \"b\", 5),\n            \"pawn6\": Pawn((5, 1), \"b\", 6),\n            \"pawn7\": Pawn((6, 1), \"b\", 7),\n            \"pawn8\": Pawn((7, 1), \"b\", 8),\n        }\n        self.pieces = {\"w\": self.w_pieces, \"b\": self.b_pieces}\n        self.piece_board = self.get_piece_board()\n\n    def reverse_board(self):\n        self.board = np.flip(self.board, 0)\n        self.piece_board = self.board.astype(str)\n\n        # update pieces pos\n        for pieces in self.b_pieces.values():\n            pieces.pos = (abs(7 - pieces.pos[0]), abs(7 - pieces.pos[1]))\n\n        for pieces in self.w_pieces.values():\n            pieces.pos = (abs(7 - pieces.pos[0]), abs(7 - pieces.pos[1]))\n\n        self.pieces = {\"w\": self.w_pieces, \"b\": self.b_pieces}\n\n    def get_board(self):\n        board = np.zeros((8, 8), dtype=int)\n        board[::2, 1::2] = 1\n        board[1::2, ::2] = 1\n        board = np.flip(board, 0)\n        return board\n\n    def get_piece_board(self):\n        piece_board = self.board.astype(str)\n        for _, pieces in self.pieces.items():\n            for piece in pieces.values():\n                piece_board[piece.pos[0]][piece.pos[1]] = piece.name\n        return piece_board\n\n    def draw_board(self):\n        # draw the board\n        for i in range(8):\n            for j in range(8):\n                if self.board[i, j] == 1:\n                    self.draw_square(\"w\", (i, j))\n                else:\n                    self.draw_square(\"b\", (i, j))\n        self.draw_borders()\n\n    def draw_square(self, color, pos):\n        if color == \"b\":\n            pygame.draw.rect(\n                self.screen,\n                BLACK_SQUARE,\n                (pos[0] * SQUARE_SIZE, pos[1] * SQUARE_SIZE, SQUARE_SIZE, SQUARE_SIZE),\n            )\n        elif color == \"w\":\n            pygame.draw.rect(\n                self.screen,\n                WHITE_SQUARE,\n                (pos[0] * SQUARE_SIZE, pos[1] * SQUARE_SIZE, SQUARE_SIZE, SQUARE_SIZE),\n            )\n        else:\n            pygame.draw.rect(\n                self.screen,\n                SELECTED_SQUARE_COLOR,\n                (pos[0] * SQUARE_SIZE, pos[1] * SQUARE_SIZE, SQUARE_SIZE, SQUARE_SIZE),\n            )\n\n    def draw_piece(self, color, piece, pos):\n        self.screen.blit(\n            pygame.image.load(piece.get_piece_img(color, piece)),\n            (\n                pos[0] * SQUARE_SIZE + PIECE_PADDING,\n                pos[1] * SQUARE_SIZE + PIECE_PADDING,\n            ),\n        )\n\n    def draw_borders(self):\n        for i in range(WIDTH // SQUARE_SIZE + 1):\n            pygame.draw.line(\n                self.screen, BORDER_COLOR, (i * SQUARE_SIZE, 0), (i * SQUARE_SIZE, 640)\n            )\n            pygame.draw.line(\n                self.screen, BORDER_COLOR, (0, i * SQUARE_SIZE), (640, i * SQUARE_SIZE)\n            )\n\n    def update_board(self):\n        for i in range(8):\n            for j in range(8):\n                if self.board[i, j] == 1:\n                    self.draw_square(\"w\", (i, j))\n                if self.board[i, j] == 0:\n                    self.draw_square(\"b\", (i, j))\n        for square in self.highlighted_squares if self.highlighted_squares else []:\n            self.draw_square(\"h\", square)\n        self.highlighted_squares = []\n        self.update_and_draw_pieces()\n        self.draw_borders()\n\n    def update_and_draw_pieces(self):\n        \"\"\"\n        update all pieces on the board\n        \"\"\"\n        for color, pieces in self.pieces.items():\n            for piece in pieces.values():\n                self.piece_board[piece.pos[0]][piece.pos[1]] = piece.name\n                self.draw_piece(color, piece, piece.pos)\n\n    def highlight_selected_piece(self):\n        self.highlighted_squares.append(self.selected_piece.pos)\n\n    def highlight_valid_moves(self, moves):\n        for move in moves:\n            self.highlighted_squares.append(move)\n\n    def get_clicked_square(self, pos):\n        if pos[0] < 640 and pos[1] < 640:\n            return (pos[0] // SQUARE_SIZE, pos[1] // SQUARE_SIZE)\n\n    def get_selected_square(self, pos):\n        \"\"\"\n        returns selected piece at pos\n        if no pieces at pos, return pos of clicked square\n        \"\"\"\n        for color, pieces in self.pieces.items():\n            for piece in pieces.values():\n                if piece.pos == pos and self.turn == color:\n                    return piece\n        return pos\n\n    # undo previous move\n    def take_back(self):\n        if REVERSE_BOARD:\n            self.reverse_board()\n        self.update_board()\n        # iterate through board and update pieces\n        for i in range(len(self.previous_board)):\n            for j in range(len(self.previous_board[i])):\n                s = self.previous_board[i][j]\n                if self.piece_board[i][j] != s:\n                    if s[0] == \"b\":\n                        for piece in self.b_pieces.values():\n                            if piece.name == s:\n                                piece.pos = (i, j)\n                    elif s[0] == \"w\":\n                        for piece in self.w_pieces.values():\n                            if piece.name == s:\n                                piece.pos = (i, j)\n                    else:\n                        continue\n        self.piece_board = self.previous_board\n        self.turn = \"w\" if self.turn == \"b\" else \"b\"\n        self.update_board()\n\n    def check_moves(self, moves):\n        \"\"\"\n        make a copy of self as a new board\n        \"\"\"\n        rejected_moves = []\n        for i in range(len(moves)):\n            _piece_board = deepcopy(self.piece_board)\n            _board = deepcopy(self.board)\n            _pieces = deepcopy(self.pieces)\n            _piece = _pieces[self.turn].get(\n                self.selected_piece.type.lower() + str(self.selected_piece.num)\n            )\n            _piece_board, _piece = self._do_move(_piece, moves[i], _piece_board, _board)\n            if self.is_in_check(self.turn, _piece_board, _pieces):\n                rejected_moves.append(moves[i])\n        moves = [move for move in moves if move not in rejected_moves]\n        return moves\n\n    def _do_move(self, piece, move, piece_board, board):\n        \"\"\"\n        returns a new board with the move applied\n        \"\"\"\n        piece_board[piece.pos[0]][piece.pos[1]] = board[piece.pos[0]][piece.pos[1]]\n        piece_board[move[0]][move[1]] = piece.name\n        piece.pos = move\n        return piece_board, piece\n\n    def get_king(self, color, pieces):\n        for piece in pieces[color].values():\n            if piece.type == \"King\":\n                return piece\n\n    def get_enemy(self, color):\n        return \"b\" if color == \"w\" else \"w\"\n\n    def get_all_possible_moves(self, color, piece_board, pieces):\n        result = []\n        for piece in pieces[color].values():\n            moves = piece.valid_moves(piece_board)\n            result += moves\n        return result\n\n    def is_in_check(self, color, piece_board, pieces):\n        king = self.get_king(color, pieces)\n        enemy = self.get_enemy(color)\n        return king.pos in self.get_all_possible_moves(enemy, piece_board, pieces)\n\n    def checkmate(self, color):\n        if self.check_moves(\n            self.get_all_possible_moves(color, self.piece_board, self.pieces)\n        ) == [] and self.is_in_check(color, self.piece_board, self.pieces):\n            return True\n        return False\n\n    def stalemate(self, color):\n        if self.check_moves(\n            self.get_all_possible_moves(color, self.piece_board, self.pieces)\n        ) == [] and not self.is_in_check(color, self.piece_board, self.pieces):\n            return True\n        return False\n\n    def get_winner(self):\n        if self.checkmate(\"w\"):\n            return \"Black\"\n        if self.checkmate(\"b\"):\n            return \"White\"\n        return None\n\n    def get_draw(self):\n        if self.stalemate(\"w\") and self.stalemate(\"b\"):\n            return True\n        return False\n\n    # check if the game is over\n    def _game_over(self):\n        return True if self.get_winner() or self.get_draw() else False\n\n    def move(self, pos):\n        self.previous_board = np.copy(self.piece_board)\n        if self.board[self.selected_piece.pos[0]][self.selected_piece.pos[1]] == 1:\n            self.piece_board[self.selected_piece.pos[0]][\n                self.selected_piece.pos[1]\n            ] = \"1\"\n        else:\n            self.piece_board[self.selected_piece.pos[0]][\n                self.selected_piece.pos[1]\n            ] = \"0\"\n        if self.turn == \"b\":\n            self.turn = \"w\"\n            for key, value in self.w_pieces.items():\n                if value.pos == pos:\n                    del self.w_pieces[key]\n                    break\n        else:\n            self.turn = \"b\"\n            for key, value in self.b_pieces.items():\n                if value.pos == pos:\n                    del self.b_pieces[key]\n                    break\n        try:\n            self.selected_piece.first = False\n        except:\n            pass\n        self.selected_piece.pos = pos\n        self.piece_board[pos[0]][pos[1]] = self.selected_piece.name\n        self.update_board()\n        self.highlighted_squares = []\n        if self._game_over():\n            self.game_over = True\n            return\n        self.selected_piece_prev = None\n        self.selected_piece = None\n        self.selected_square = None\n        if REVERSE_BOARD:\n            self.reverse_board()\n\n    def handle_click(self):\n        position = self.get_clicked_square(pygame.mouse.get_pos())\n        self.selected_square = self.get_selected_square(position)\n        self.selected_piece = (\n            self.selected_square\n            if self.selected_square in self.b_pieces.values()\n            or self.selected_square in self.w_pieces.values()\n            else self.selected_piece\n        )\n        # deselect piece\n        if (\n            self.selected_piece == self.selected_piece_prev\n            and self.selected_piece == self.selected_square\n        ):\n            self.selected_piece = None\n        if (\n            self.selected_piece\n            and self.selected_piece.color == self.turn\n            and self.selected_piece\n        ):\n            self.highlight_selected_piece()\n            moves = self.check_moves(self.selected_piece.valid_moves(self.piece_board))\n            self.highlight_valid_moves(moves)\n        if self.selected_piece and self.selected_square in moves:\n            self.move(self.selected_square)\n        self.update_board()\n        self.selected_piece_prev = self.selected_piece\n", "repo_name": "SYChai271/chess", "sub_path": "board.py", "file_name": "board.py", "file_ext": "py", "file_size_in_byte": 12448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "numpy.flip", "line_number": 64, "usage_type": "call"}, {"api_name": "pieces.pos", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pieces.pos", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 80, "usage_type": "call"}, {"api_name": "pieces.values", "line_number": 86, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 102, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 108, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 114, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 131, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 134, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pieces.values", "line_number": 156, "usage_type": "call"}, {"api_name": "pieces.values", "line_number": 177, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 212, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 213, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 284, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 323, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 323, "usage_type": "attribute"}]}
{"seq_id": "41213469441", "text": "from collections import defaultdict, deque\r\n\r\ndef makeGraph(edges):\r\n    # W: Residual Capacities\r\n    G, W = defaultdict(list), defaultdict(dict)\r\n    for f,t,w in edges:\r\n        # adjacency lists\r\n        G[f].append(t)\r\n        G[t].append(f)\r\n        # would need to take care of duplicate edges in case of erroneous input\r\n        W[f][t] = w\r\n        W[t].setdefault(f, 0) # In case a backward edge already exits\r\n    return G, W\r\n\r\ndef find_path(queue, parents, visited, G, W, sink):\r\n    while queue:\r\n        v = queue.popleft()\r\n        for vt in G.get(v, []):\r\n            if W[v][vt] > 0: # residual capacity > 0 indicates reachability \r\n                if vt not in visited:\r\n                    parents[vt] = v\r\n                    if vt == sink: return True\r\n                    visited.add(vt)\r\n                    queue.append(vt)\r\n    return False\r\n\r\n# helper method to find the min_flow (bottleneck) of an augmented path\r\ndef find_bottleneck(parents, sink, W):\r\n    mn = float(\"inf\")\r\n    while sink in parents:\r\n        mn = min(mn, W[parents[sink]][sink])\r\n        sink = parents[sink]\r\n    return mn\r\n\r\n# updating residual capacity for edges\r\n# subtract min_flow from forward edges and add the same to reverse edges\r\ndef update_capacity(parents, sink, W):\r\n    mn = find_bottleneck(parents, sink, W)\r\n    while sink in parents:\r\n        W[parents[sink]][sink] -= mn # decrementing residual capacity for path\r\n        W[sink][parents[sink]] += mn # adding to reverse edges\r\n        sink = parents[sink]\r\n    return mn\r\n\r\n# using Ford-Fulkerson's Algorithm for max flow (Edmonds Karp's implementation)\r\ndef ford_fulkerson(edges, src, sink):\r\n    G, W = makeGraph(edges)\r\n    max_flow = 0\r\n    while True:\r\n        queue = deque()\r\n        parents = {}\r\n        visited = set([src])\r\n        for v in G[src]:\r\n            if W[src][v] > 0: \r\n                parents[v] = src\r\n                visited.add(v)\r\n                queue.append(v)\r\n        if not find_path(queue, parents, visited, G, W, sink): break\r\n        max_flow += update_capacity(parents, sink, W)\r\n    return max_flow\r\n\r\nif __name__ == \"__main__\":\r\n    edges = \\\r\n        [\r\n            ['A','B',3],['A','D',3],['C','A',3],['B','C',4],\r\n            ['C','E',2],['E','B',1],['C','D',1],['D','E',2],\r\n            ['D','F',6],['F','G',9],['E','G',1]\r\n        ]\r\n\r\n    print(ford_fulkerson(edges, 'A', 'G'))\r\n", "repo_name": "elllot/Algorithms", "sub_path": "Graphs/MaxFlow.py", "file_name": "MaxFlow.py", "file_ext": "py", "file_size_in_byte": 2393, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "collections.defaultdict", "line_number": 5, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 50, "usage_type": "call"}]}
{"seq_id": "183493927", "text": "from course_dashboard.tests.base import BaseCourseDashboardTestCase\nfrom django.core.urlresolvers import reverse\n\nclass ProblemStatsViewTestCase(BaseCourseDashboardTestCase):\n    def test_index(self):\n        self._generate_modules_tree(self.course,\n                                    'chapter', 'sequential',\n                                    'vertical', 'problem')\n        response = self.get_response(\"course-dashboard:problem-stats:index\",\n                                     self.course)\n        self.assertEqual(200, response.status_code)\n\n    def test_get_stats(self):\n        url = reverse(\"course-dashboard:problem-stats:get-stats\",\n                      kwargs={\"course_id\": self.get_course_id(self.course),\n                              \"problem_id\": self.problem_module.location.name})\n        response = self.client.get(url, {'problem_id' : self.problem_module.location.name})\n        self.assertEqual(200, response.status_code)\n\n    def test_invalid_course_key_raises_404(self):\n        # The staff instructor bypasses the valid course id checks\n        self.instructor.is_staff = True\n        self.instructor.save()\n        invalid_course_id = 'not/a/validcourseid'\n\n        response_index = self.client.get(\n            reverse(\"course-dashboard:problem-stats:index\",\n                    kwargs={'course_id': invalid_course_id})\n        )\n        response_stats = self.client.get(\n            reverse(\"course-dashboard:problem-stats:get-stats\",\n                    kwargs={\"course_id\": invalid_course_id,\n                            \"problem_id\": self.problem_module.location.name}),\n            {'problem_id' : self.problem_module.location.name})\n\n        self.assertEqual(404, response_index.status_code)\n        self.assertEqual(404, response_stats.status_code)\n", "repo_name": "openfun/fun-apps", "sub_path": "course_dashboard/problem_stats/tests/test_views.py", "file_name": "test_views.py", "file_ext": "py", "file_size_in_byte": 1785, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "41", "api": [{"api_name": "course_dashboard.tests.base.BaseCourseDashboardTestCase", "line_number": 4, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 14, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 27, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 31, "usage_type": "call"}]}
{"seq_id": "32327406831", "text": "#!/usr/bin/env python\n#\n# Author: Qiming Sun <osirpt.sun@gmail.com>\n#\n\nimport unittest\nimport numpy\nimport pyscf.pbc.gto as pbcgto\nimport pyscf.pbc.scf as pscf\ncell = pbcgto.Cell()\ncell.atom = '''\nHe 0 0 1\nHe 1 0 1\n'''\ncell.basis = '3-21g'\ncell.a = numpy.eye(3) * 2\ncell.gs = [7] * 3\ncell.verbose = 5\ncell.output = '/dev/null'\ncell.build()\nnao = cell.nao_nr()\n\nclass KnowValues(unittest.TestCase):\n    def test_krhf_smearing(self):\n        mf = pscf.KRHF(cell, cell.make_kpts([2,1,1]))\n        nkpts = len(mf.kpts)\n        pscf.addons.smearing_(mf, 0.1, 'fermi')\n        mo_energy_kpts = numpy.array([numpy.arange(nao)*.2+numpy.cos(i+.5)*.1\n                                      for i in range(nkpts)])\n        occ = mf.get_occ(mo_energy_kpts)\n        self.assertAlmostEqual(mf.entropy, 6.1656394960533021/2, 9)\n\n        mf.smearing_method = 'gauss'\n        occ = mf.get_occ(mo_energy_kpts)\n        self.assertAlmostEqual(mf.entropy, 0.94924016074521311/2, 9)\n\n    def test_kuhf_smearing(self):\n        mf = pscf.KUHF(cell, cell.make_kpts([2,1,1]))\n        nkpts = len(mf.kpts)\n        pscf.addons.smearing_(mf, 0.1, 'fermi')\n        mo_energy_kpts = numpy.array([numpy.arange(nao)*.2+numpy.cos(i+.5)*.1\n                                      for i in range(nkpts)])\n        mo_energy_kpts = numpy.array([mo_energy_kpts,\n                                      mo_energy_kpts+numpy.cos(mo_energy_kpts)*.02])\n        occ = mf.get_occ(mo_energy_kpts)\n        self.assertAlmostEqual(mf.entropy, 6.1803390081500869/2, 9)\n\n        mf.smearing_method = 'gauss'\n        occ = mf.get_occ(mo_energy_kpts)\n        self.assertAlmostEqual(mf.entropy, 0.9554526863670467/2, 9)\n\n    def test_rhf_smearing(self):\n        mf = pscf.RHF(cell)\n        pscf.addons.smearing_(mf, 0.1, 'fermi')\n        mo_energy = numpy.arange(nao)*.2+numpy.cos(.5)*.1\n        mf.get_occ(mo_energy)\n        self.assertAlmostEqual(mf.entropy, 3.0922723199786408, 9)\n\n        mf.smearing_method = 'gauss'\n        occ = mf.get_occ(mo_energy)\n        self.assertAlmostEqual(mf.entropy, 0.4152467504725415, 9)\n\n    def test_uhf_smearing(self):\n        mf = pscf.UHF(cell)\n        pscf.addons.smearing_(mf, 0.1, 'fermi')\n        mo_energy = numpy.arange(nao)*.2+numpy.cos(.5)*.1\n        mo_energy = numpy.array([mo_energy, mo_energy+numpy.cos(mo_energy)*.02])\n        mf.get_occ(mo_energy)\n        self.assertAlmostEqual(mf.entropy, 3.1007387905421022, 9)\n\n        mf.smearing_method = 'gauss'\n        occ = mf.get_occ(mo_energy)\n        self.assertAlmostEqual(mf.entropy, 0.42189309944541731, 9)\n\n\nif __name__ == '__main__':\n    print(\"Full Tests for pbc.scf.addons\")\n    unittest.main()\n", "repo_name": "mibumi/pyscf", "sub_path": "pyscf/pbc/scf/test/test_addons.py", "file_name": "test_addons.py", "file_ext": "py", "file_size_in_byte": 2643, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "41", "api": [{"api_name": "pyscf.pbc.gto.Cell", "line_number": 10, "usage_type": "call"}, {"api_name": "pyscf.pbc.gto", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 16, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pyscf.pbc.scf.KRHF", "line_number": 25, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf", "line_number": 25, "usage_type": "name"}, {"api_name": "pyscf.pbc.scf.addons.smearing_", "line_number": 27, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf.addons", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pyscf.pbc.scf", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 28, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf.KUHF", "line_number": 38, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf", "line_number": 38, "usage_type": "name"}, {"api_name": "pyscf.pbc.scf.addons.smearing_", "line_number": 40, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf.addons", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pyscf.pbc.scf", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 44, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf.RHF", "line_number": 53, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf", "line_number": 53, "usage_type": "name"}, {"api_name": "pyscf.pbc.scf.addons.smearing_", "line_number": 54, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf.addons", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pyscf.pbc.scf", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 55, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf.UHF", "line_number": 64, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf", "line_number": 64, "usage_type": "name"}, {"api_name": "pyscf.pbc.scf.addons.smearing_", "line_number": 65, "usage_type": "call"}, {"api_name": "pyscf.pbc.scf.addons", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pyscf.pbc.scf", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 67, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 78, "usage_type": "call"}]}
{"seq_id": "5056011882", "text": "# summarize the region count file\n\nimport os\nimport sys\nimport re\nimport time\nimport subprocess\nimport glob\nimport numpy\nfrom subprocess import call\nfrom scipy.stats import binom\n\ngenus = 'Sulfurovum' #the genus, which can be substituted\ninFile = str(genus+'_vent_count_per_cluster.txt')\ncluster_file = str(genus+'_gene_clusters_summary_cleaned.txt')\naxial_lim = int(sys.argv[1]) #min number of genome in axial; default to be 12\nmcr_lim = int(sys.argv[2]) #min number of genome in MCR; default to be 8\n\ngenus=cluster_file.split('_')[0]\n\naxial_enriched={}\naxial_enriched_list = []\nmcr_enriched_list = []\ncluster_dict={}\ncategory_dict={}\ncog_dict={}\n\ncluster = open(cluster_file,'r+')\n\ninFile = open(inFile,'r+') \nfor line in inFile.readlines()[1:]:\n\tparsed = line.split('\\t')\n\t(cluster_id,axial_count,mcr_count) = (parsed[0],float(parsed[1]),float(parsed[2]))\n\tif (axial_count >= axial_lim-1 or mcr_count >= mcr_lim-1):\n\t\taxial_enriched[cluster_id]=binom.cdf(axial_count, axial_count+mcr_count, float(13)/float(22))\n\nscore={}\n\t\t\nfor cluster_id in axial_enriched:\n\tfor seq in cluster_dict:\n\t\tif cluster_dict[seq][0] == cluster_id:\n\t\t\tinitial = cluster_dict[seq]\n\t\t\tinitial.append(axial_enriched[cluster_id])\n\t\t\taxial_enriched_list.append(initial)\n\t\t\tif initial[0] not in score:\n\t\t\t\tscore[initial[0]] = axial_enriched[cluster_id]\n\t\t\t\n\nfor line in axial_enriched_list:\n\taxial_out.write('\\t'.join(str(e) for e in line) + '\\n')\n\t\naxial_out.close()\n\nout2 = open('%s_Axial_enriched_compressed.txt'%(genus),'w')\nout2.write('cluster id\tEnrichment\tCategory\tCOG' + '\\n')\nfor line in score:\n\tcat = category_dict[line]\n\tcat_split = cat.split('|')\n\tcog = cog_dict[line]\n\tfor category in cat_split:\n\t\tout2.write(line+'\\t'+str(score[line])+'\\t'+category+'\\t'+cog+ '\\n')\n", "repo_name": "carleton-spacehogs/pangenome-selection", "sub_path": "aux_files/compare_mcr_axial.py", "file_name": "compare_mcr_axial.py", "file_ext": "py", "file_size_in_byte": 1753, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "scipy.stats.binom.cdf", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.stats.binom", "line_number": 35, "usage_type": "name"}]}
{"seq_id": "34814446600", "text": "# -*- coding: utf-8 -*-\nfrom django.shortcuts import render\nfrom django.conf import settings\nfrom .models import Article, Tag, CourseVideo, CourseVideoList, Course, CourseList,User,Comment,proUser,Category,Jingdong,Ouwang\nfrom .models import DoupanTop\nfrom django.core.paginator import Paginator, InvalidPage, EmptyPage, PageNotAnInteger\n\n# Create your views here.\n\n\ndef login12306(request):\n    return render(request, 'login12306.html', locals())\n\ndef douban(request):\n    jingdong_list = DoupanTop.objects.all()\n    totalNum = 0\n    for i in jingdong_list:\n        totalNum += 1\n    paginator = Paginator(jingdong_list, 25) # 每页显示25条\n\n    page = request.GET.get('page')\n    try:\n        jingdong_list = paginator.page(page)\n    except PageNotAnInteger:\n        # 如果请求的页数不是整数，返回第一页。\n        jingdong_list = paginator.page(1)\n    except EmptyPage:\n        # 如果请求的页数不在合法的页数范围内，返回结果的最后一页。\n        jingdong_list = paginator.page(paginator.num_pages)\n    return render(request, 'douban.html', locals())\n\ndef jingdong(request):\n    jingdong_list = Jingdong.objects.all().order_by('productname')  \n    totalNum = 0\n    for i in jingdong_list:\n    \n        totalNum += 1\n    #jingdong_list,thisPage1,thisPage2,thisPage3,thisPage4,thisPage5 = getPage(request, jingdong_list)\n    paginator = Paginator(jingdong_list, 25) # 每页显示25条\n\n    page = request.GET.get('page')\n    try:\n        jingdong_list = paginator.page(page)\n    except PageNotAnInteger:\n        # 如果请求的页数不是整数，返回第一页。\n        jingdong_list = paginator.page(1)\n    except EmptyPage:\n        # 如果请求的页数不在合法的页数范围内，返回结果的最后一页。\n        jingdong_list = paginator.page(paginator.num_pages)\n    return render(request, 'jingdong.html', locals())\n\n\n\ndef ouwang(request):\n    jingdong_list = Ouwang.objects.all().order_by('title')  \n    totalNum = 0\n    for i in jingdong_list:\n    \n        totalNum += 1\n    #jingdong_list,thisPage1,thisPage2,thisPage3,thisPage4,thisPage5 = getPage(request, jingdong_list)\n    paginator = Paginator(jingdong_list, 25) # 每页显示25条\n\n    page = request.GET.get('page')\n    try:\n        jingdong_list = paginator.page(page)\n    except PageNotAnInteger:\n        # 如果请求的页数不是整数，返回第一页。\n        jingdong_list = paginator.page(1)\n    except EmptyPage:\n        # 如果请求的页数不在合法的页数范围内，返回结果的最后一页。\n        jingdong_list = paginator.page(paginator.num_pages)\n    return render(request, 'ouwang.html', locals())\n\n# 分页代码\ndef getPage(request, article_list):\n    paginator = Paginator(article_list, 20)\n    try:\n        page = int(request.GET.get('page', 1))\n        thisPage1 = 1\n        thisPage2 = 2\n        thisPage3 = 3\n        thisPage4 = 4\n        thisPage5 = 5\n        if page == 1:\n            thisPage1 = 1 \n            thisPage2 = 2\n            thisPage3 = 3\n            thisPage4 = 4\n            thisPage5 = 5\n        elif page == 2:\n            thisPage1 = 2 \n            thisPage2 = 3\n            thisPage3 = 4\n            thisPage4 = 5\n            thisPage5 = 6\n        else:\n            thisPage1 = page\n            thisPage2 = page+1\n            thisPage3 = page+2\n            thisPage4 = page+3\n            thisPage5 = page+4\n        article_list = paginator.page(page)\n    except (EmptyPage, InvalidPage, PageNotAnInteger):\n        article_list = paginator.page(1)\n    return article_list,thisPage1,thisPage2,thisPage3,thisPage4,thisPage5\n#logger = logging.getLogger('first_app.views')\n\ndef global_setting(request):\n    # 站点基本信息\n    SITE_URL = settings.SITE_URL\n    SITE_NAME = settings.SITE_NAME\n    SITE_DESC = settings.SITE_DESC\n    # 文章归档数据\n    #archive_list = Article.objects.distinct_date()\n    #BASE_DIR = settings.BASE_DIR\n    return locals()\n\n\ndef index(request):\n    courseVideo_title_id1 = CourseVideo.objects.get(title='python基础')\n    title_id1 = courseVideo_title_id1.id\n    python_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id1)[0:5]\n\n    courseVideo_title_id2 = CourseVideo.objects.get(title='爬虫')\n    title_id2 = courseVideo_title_id2.id\n    crawl_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id2)\n    \n    courseVideo_title_id3 = CourseVideo.objects.get(title='Django')\n    title_id3 = courseVideo_title_id3.id\n    Django_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id3)\n    \n    courseVideo_title_id4 = CourseVideo.objects.get(title='Tornado')\n    title_id4 = courseVideo_title_id4.id\n    Tornado_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id4)\n    \n    courseVideo_title_id5 = CourseVideo.objects.get(title='C#')\n    title_id5 = courseVideo_title_id5.id\n    C_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id5)[0:5]\n    \n    \n    courseVideo_title_id6 = CourseVideo.objects.get(title='Mybatis')\n    title_id6 = courseVideo_title_id6.id\n    M_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id6)[0:5]\n    username = request.session.get('username' , 'admin')\n    print('index'+ username)\n    return render(request, 'index.html', locals())\n\n\ndef remen(request):\n    try:\n        strCom = '站长推荐'\n        tagName = request.GET.get('tagName', None)\n        if tagName:\n            tagName = str(tagName)\n            tagg = Tag.objects.get(name=tagName)\n\n            print(tagg)\n            article_list = Article.objects.filter(tag=tagg)\n            tag_list = Tag.objects.all()\n            print(tagName)\n            \n        else:\n            # 最新文章数据\n            article_list = Article.objects.all()\n            tag_list = Tag.objects.all()\n            #print(article_list)\n        article_list_recommend = Article.objects.filter(is_recommend=1)[0:6]\n        article_list = getPage(request, article_list)\n    except Exception as e:\n        #logger.error(e) \n        print(e)\n    return render(request, 'remen.html', locals())\n\n    \ndef userLead(request):\n    try:\n        try:\n            username = request.GET.get('username', '')\n            tagName = request.GET.get('tagName', None)\n        except Exception as ee:\n            print(ee)\n        print(username)\n        user_list = User.objects.all().order_by('id')\n        tag_list = Tag.objects.all()\n        strCom = '用户排行榜'\n        if username == '':\n            username = 'admin'\n            article_list = Article.objects.all()\n        else:\n            userr = User.objects.filter(username=username)[0]\n            article_list = Article.objects.filter(user=userr)\n            for i in article_list:\n                print(i.title)\n        if tagName:\n            tagName = str(tagName)\n            tagg = Tag.objects.get(name=tagName)\n\n            print(tagg)\n            article_list = Article.objects.filter(tag=tagg)\n            tag_list = Tag.objects.all()\n            print(tagName)\n        article_list = getPage(request, article_list)\n    except Exception as e:\n        #logger.error(e) \n        print(e)\n    return render(request, 'userLead.html', locals())\n\n# 分页代码\ndef getPage(request, article_list):\n    paginator = Paginator(article_list, 6)\n    try:\n        page = int(request.GET.get('page', 1))\n        article_list = paginator.page(page)\n    except (EmptyPage, InvalidPage, PageNotAnInteger):\n        article_list = paginator.page(1)\n    return article_list\n\n# 文章详情\ndef article(request):\n    try:\n        id = request.GET.get('id', None)\n        #commentName = request.POST.get(\"commentName\",\"\")\n        commentName = request.session.get('username' , 'admin')\n        \n        print('评论名'+commentName)\n        commentId = request.POST.get('commentId','')\n        print(commentId)\n        commentTextarea = request.POST.get('commentTextarea', '')\n        print(commentTextarea)\n        print(request.path)\n        tagName = request.GET.get('tagName', None)\n        tag_list = Tag.objects.all()\n        try:\n            # 获取文章信息\n            article = Article.objects.get(pk=id)\n            article.click_count += 1\n            article.save()\n            commentList = Comment.objects.filter(article=article)\n            if commentName!='' and commentTextarea!='':\n                comment = Comment.objects.create(username=commentName,content=commentTextarea,article=article)\n                comment.save()\n                \n        except Article.DoesNotExist:\n            return render(request, 'failure.html', {'reason': '没有找到对应的文章'})\n        \n        \n    except Exception as e:\n        print(e)\n    return render(request, 'article.html', locals())\n\n\ndef course(request):\n    try:\n        id = request.GET.get('id', None)\n        contentId = request.GET.get('v', None) \n        print('id是'+ id)\n        print('v是'+ contentId)\n        try:\n            course = Course.objects.get(pk=id)\n            \n            \n            course.click_count = course.click_count  + 1\n \n            course.save()\n            courseList = CourseList.objects.filter(course_title=id)\n            \n            content = CourseList.objects.filter(course_title=id).filter(index=contentId)\n            contentCatalog = content[0].catalog\n\n            contentContent = content[0].content\n            courseID = id\n        except Exception:\n            return render(request, 'failure.html', {'reason': '没有找到对应的教程list'})\n    except Exception as e:\n        print(e)\n    \n    return render(request, 'course.html', locals())\n\ndef courseVideo(request):\n    try:\n        idd = request.GET.get('id', None) \n        \n        vvideo = request.GET.get('v', None) \n        try:\n            thisVideo = CourseVideoList.objects.filter(courseVideo_title=idd).filter(index=vvideo)\n            courseVideoTitle = CourseVideo.objects.get(pk=idd)\n            courseVideoList = CourseVideoList.objects.filter(courseVideo_title=idd)\n            print(courseVideoTitle.title)\n            print(thisVideo[0].videoUrl)\n            video = str(thisVideo[0].videoUrl)\n        except CourseVideoList.DoesNotExist:\n            return render(request, 'failure.html', {'reason': '没有找到对应的视频'})\n    except Exception as e:\n        print(e)\n    return render(request, 'courseVideo.html', locals())\n\ndef my(request):\n    \n    \n    return render(request, 'my.html')\n\ndef author(request):\n    \n    return render(request, 'author.html')\n\n\ndef comment(request):\n    \n    print('comment')\n    \ndef login(request):\n    try:\n\n        name = request.POST.get(\"username\",\"\")\n        print('名'+name)\n \n        pasw = request.POST.get('password', '')\n        print(pasw)\n\n        try:\n            user = proUser.objects.filter(name=name,pasw=pasw)\n            if user:\n                print('登陆成功')\n                request.session['username'] = name\n                courseVideo_title_id1 = CourseVideo.objects.get(title='python基础')\n                title_id1 = courseVideo_title_id1.id\n                python_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id1)[0:5]\n        \n                courseVideo_title_id2 = CourseVideo.objects.get(title='爬虫')\n                title_id2 = courseVideo_title_id2.id\n                crawl_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id2)\n            \n                courseVideo_title_id3 = CourseVideo.objects.get(title='Django')\n                title_id3 = courseVideo_title_id3.id\n                Django_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id3)\n                    \n                courseVideo_title_id4 = CourseVideo.objects.get(title='Tornado')\n                title_id4 = courseVideo_title_id4.id\n                Tornado_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id4)\n                \n                courseVideo_title_id5 = CourseVideo.objects.get(title='C#')\n                title_id5 = courseVideo_title_id5.id\n                C_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id5)[0:5]\n                \n                \n                courseVideo_title_id6 = CourseVideo.objects.get(title='Mybatis')\n                title_id6 = courseVideo_title_id6.id\n                M_Video_list = CourseVideoList.objects.filter(courseVideo_title_id=title_id6)[0:5]\n               \n                username = request.session.get('username' , 'admin')\n                print('index'+ username)\n                return render(request, 'index.html', locals())\n            else:\n                print('falie')\n        except Article.DoesNotExist:\n            return render(request, 'failure.html', {'reason': '没有找到对应的文章'})\n        \n        \n    except Exception as e:\n        print(e)\n    return render(request, 'login.html',locals())\n    \n\ndef articleAdd(request):\n    \n    username = request.session.get('username' , 'admin')\n    try:\n        \n        title = request.POST.get('title', '')\n        desc = request.POST.get('desc', '')\n        content = request.POST.get('content', '')\n        strcategory = request.POST.get('category', '')\n        strtag = request.POST.get('tag', '')\n        if title != '':\n            print(title)\n            print(desc)\n            print(content)\n            print(strcategory)\n            print(strtag)\n            strcategory = int(strcategory)\n            strtag = int(strtag)\n            user = User.objects.filter(username=username)[0]\n            category = Category.objects.filter(pk=strcategory)[0]\n            tag = Tag.objects.filter(pk=strtag)[0]\n            \n            article = Article.objects.create(title=title, desc=desc, content=content,category=category,user=user)\n            article.tag.set(tag)\n            article.save()\n            istrue = '添加成功'\n        else:\n            print('title为空')\n            \n    except Exception as e:\n        print(e)\n    return render(request, 'articleAdd.html',locals())\n\n", "repo_name": "myacai/Django-IT-Education", "sub_path": "pythonWeb/education_project/first_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 14098, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "models.DoupanTop.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "models.DoupanTop.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.DoupanTop", "line_number": 15, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 19, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 24, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 27, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Jingdong.objects.all", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Jingdong.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Jingdong", "line_number": 33, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 39, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 44, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 47, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Ouwang.objects.all", "line_number": 55, "usage_type": "call"}, {"api_name": "models.Ouwang.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.Ouwang", "line_number": 55, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 61, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 66, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 69, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 72, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 76, "usage_type": "call"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 103, "usage_type": "name"}, {"api_name": "django.core.paginator.InvalidPage", "line_number": 103, "usage_type": "name"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 103, "usage_type": "name"}, {"api_name": "django.conf.settings.SITE_URL", "line_number": 110, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 110, "usage_type": "name"}, {"api_name": "django.conf.settings.SITE_NAME", "line_number": 111, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 111, "usage_type": "name"}, {"api_name": "django.conf.settings.SITE_DESC", "line_number": 112, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 112, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 120, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 120, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 120, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 122, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 122, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 122, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 124, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 124, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 126, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 126, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 126, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 128, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 128, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 128, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 130, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 130, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 130, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 132, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 132, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 134, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 134, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 134, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 136, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 136, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 136, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 138, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 138, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 138, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 141, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 141, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 141, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 143, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 143, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 143, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 146, "usage_type": "call"}, {"api_name": "models.Tag.objects.get", "line_number": 155, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 155, "usage_type": "name"}, {"api_name": "models.Article.objects.filter", "line_number": 158, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 158, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 158, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 159, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 159, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 159, "usage_type": "name"}, {"api_name": "models.Article.objects.all", "line_number": 164, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 164, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 164, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 165, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 165, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 165, "usage_type": "name"}, {"api_name": "models.Article.objects.filter", "line_number": 167, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 167, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 167, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 172, "usage_type": "call"}, {"api_name": "models.User.objects.all", "line_number": 183, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 183, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 183, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 184, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 184, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 184, "usage_type": "name"}, {"api_name": "models.Article.objects.all", "line_number": 188, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 188, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 188, "usage_type": "name"}, {"api_name": "models.User.objects.filter", "line_number": 190, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 190, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 190, "usage_type": "name"}, {"api_name": "models.Article.objects.filter", "line_number": 191, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 191, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 191, "usage_type": "name"}, {"api_name": "models.Tag.objects.get", "line_number": 196, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 196, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 196, "usage_type": "name"}, {"api_name": "models.Article.objects.filter", "line_number": 199, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 199, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 199, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 200, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 200, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 200, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 206, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 210, "usage_type": "call"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 214, "usage_type": "name"}, {"api_name": "django.core.paginator.InvalidPage", "line_number": 214, "usage_type": "name"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 214, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 232, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 232, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 232, "usage_type": "name"}, {"api_name": "models.Article.objects.get", "line_number": 235, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 235, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 235, "usage_type": "name"}, {"api_name": "models.Comment.objects.filter", "line_number": 238, "usage_type": "call"}, {"api_name": "models.Comment.objects", "line_number": 238, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 238, "usage_type": "name"}, {"api_name": "models.Comment.objects.create", "line_number": 240, "usage_type": "call"}, {"api_name": "models.Comment.objects", "line_number": 240, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 240, "usage_type": "name"}, {"api_name": "models.Article.DoesNotExist", "line_number": 243, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 243, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 244, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 249, "usage_type": "call"}, {"api_name": "models.Course.objects.get", "line_number": 259, "usage_type": "call"}, {"api_name": "models.Course.objects", "line_number": 259, "usage_type": "attribute"}, {"api_name": "models.Course", "line_number": 259, "usage_type": "name"}, {"api_name": "models.CourseList.objects.filter", "line_number": 265, "usage_type": "call"}, {"api_name": "models.CourseList.objects", "line_number": 265, "usage_type": "attribute"}, {"api_name": "models.CourseList", "line_number": 265, "usage_type": "name"}, {"api_name": "models.CourseList.objects.filter", "line_number": 267, "usage_type": "call"}, {"api_name": "models.CourseList.objects", "line_number": 267, "usage_type": "attribute"}, {"api_name": "models.CourseList", "line_number": 267, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 273, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 277, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 285, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 285, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 285, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 286, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 286, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 286, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 287, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 287, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 287, "usage_type": "name"}, {"api_name": "models.CourseVideoList.DoesNotExist", "line_number": 291, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 291, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 292, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 295, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 300, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 304, "usage_type": "call"}, {"api_name": "models.proUser.objects.filter", "line_number": 321, "usage_type": "call"}, {"api_name": "models.proUser.objects", "line_number": 321, "usage_type": "attribute"}, {"api_name": "models.proUser", "line_number": 321, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 325, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 325, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 325, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 327, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 327, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 327, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 329, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 329, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 329, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 331, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 331, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 331, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 333, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 333, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 333, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 335, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 335, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 335, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 337, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 337, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 337, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 339, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 339, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 339, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 341, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 341, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 341, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 343, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 343, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 343, "usage_type": "name"}, {"api_name": "models.CourseVideo.objects.get", "line_number": 346, "usage_type": "call"}, {"api_name": "models.CourseVideo.objects", "line_number": 346, "usage_type": "attribute"}, {"api_name": "models.CourseVideo", "line_number": 346, "usage_type": "name"}, {"api_name": "models.CourseVideoList.objects.filter", "line_number": 348, "usage_type": "call"}, {"api_name": "models.CourseVideoList.objects", "line_number": 348, "usage_type": "attribute"}, {"api_name": "models.CourseVideoList", "line_number": 348, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 352, "usage_type": "call"}, {"api_name": "models.Article.DoesNotExist", "line_number": 355, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 355, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 356, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 361, "usage_type": "call"}, {"api_name": "models.User.objects.filter", "line_number": 382, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 382, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 382, "usage_type": "name"}, {"api_name": "models.Category.objects.filter", "line_number": 383, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 383, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 383, "usage_type": "name"}, {"api_name": "models.Tag.objects.filter", "line_number": 384, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 384, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 384, "usage_type": "name"}, {"api_name": "models.Article.objects.create", "line_number": 386, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 386, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 386, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 395, "usage_type": "call"}]}
{"seq_id": "71766663820", "text": "#!/usr/bin/python3\nimport requests\n\n\ndef top_ten(subreddit):\n    \"\"\" Program that queries Reddit API and prints the titles of\n    first 10 hot posts listed for a given subreddit.\n    \"\"\"\n    url = \"https://www.reddit.com/r/{}/hot/.json\".format(subreddit)\n    headers = {\n        \"User-Agent\": \"0x16-api_advanced:project:\\\n        v1.0.0 (by /u/firdaus_cartoon_jr)\"\n    }\n    params = {\n        \"limit\": 10\n    }\n    r = requests.get(url, headers=headers, params=params,\n                     allow_redirects=False)\n\n    if (r.status_code is 302):\n        print(\"None\")\n        return\n    if (r.status_code is 404):\n        print(\"None\")\n        return\n    else:\n        r = r.json()\n        for post in r['data']['children']:\n            print(post['data']['title'])\n", "repo_name": "eemanDigital/alx-system_engineering-devops", "sub_path": "0x16-api_advanced/1-top_ten.py", "file_name": "1-top_ten.py", "file_ext": "py", "file_size_in_byte": 766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "21521795191", "text": "# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this file,\n# You can obtain one at http://mozilla.org/MPL/2.0/.\n\nimport sys\nfrom setuptools import setup, find_packages\n\nversion = '0.1.4'\n\ndeps = ['mozautoeslib', 'requests']\n\nif sys.version < '2.6' or sys.version >= '3.0':\n    print >>sys.stderr, '%s requires Python >= 2.6 and < 3.0' % _PACKAGE_NAME\n    sys.exit(1)\n\nsetup(name='bzcache',\n      version=version,\n      description=\"Pulse-based bugzilla cache\",\n      long_description=\"\"\"\\\n\"\"\",\n      classifiers=[], # Get strings from http://pypi.python.org/pypi?%3Aaction=list_classifiers\n      keywords='',\n      author='Jonathan Griffin',\n      author_email='jgriffin@mozilla.com',\n      url='https://github.com/jonallengriffin/bzcache',\n      license='MPL',\n      packages=find_packages(exclude=['ez_setup', 'examples', 'tests']),\n      include_package_data=True,\n      zip_safe=False,\n      install_requires=deps,\n      )\n", "repo_name": "jonallengriffin/bzcache", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1014, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "sys.version", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 14, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 16, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 27, "usage_type": "call"}]}
{"seq_id": "1426957187", "text": "import boto3\nimport urllib3\n\nurllib3.disable_warnings()\n\n\n\ns3 = boto3.resource(\n     's3',\n     endpoint_url='https://minio.labo.local:9002',\n     aws_access_key_id='sSJSyiiLqVD3wX6gk5HJ', # ログイン名\n     aws_secret_access_key='mtcEbUYKSEPKuQkNhOzmVWBnJjOBMLOQzlthbTxe', # ログインパスワード\n     verify=False\n)\n\n# バケット内の全オブジェクトを表示\nBUCKET_NAME = 'loki'\nFILE_NAME = 'testfile.txt'\nOBJECT_NAME = FILE_NAME\n\n\n# オブジェクトをアップロード\nbucket = s3.Bucket(BUCKET_NAME)\nwith open(FILE_NAME, 'rb') as f:\n    bucket.upload_fileobj(f, OBJECT_NAME)\n\n\n", "repo_name": "takara9/marmot-minio", "sub_path": "client/put.py", "file_name": "put.py", "file_ext": "py", "file_size_in_byte": 605, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 4, "usage_type": "call"}, {"api_name": "boto3.resource", "line_number": 8, "usage_type": "call"}]}
{"seq_id": "4744340515", "text": "import os\nimport secrets\nfrom discord.ext import commands\n\n\nbot = commands.Bot(command_prefix='$')\n\n\n@bot.event\nasync def on_ready():\n    bot.the_number = secrets.randbits(32)\n\n@bot.event\nasync def on_message(message):\n    if message.author.bot == True:\n        return\n    \n    try:\n        n = int(message.content)\n        if n == bot.the_number:\n            await message.reply('correct')\n            await bot.close()\n        else:\n            await message.reply('incorrect')\n    except ValueError:\n        pass\n\n\nif __name__ == '__main__':\n    tk = os.getenv('GUESSLARGENUMBER_TOKEN')\n    if tk:\n        bot.run(tk)\n    else:\n        print('GUESSLARGENUMBER_TOKEN not set. quitting...')", "repo_name": "s-viour/GtLN", "sub_path": "guesslargenumber/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 691, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 6, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 6, "usage_type": "name"}, {"api_name": "secrets.randbits", "line_number": 11, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 30, "usage_type": "call"}]}
{"seq_id": "12632265176", "text": "import openpyxl\n\ndef get_urls(file_path):\n\n    # Load the Excel file\n    workbook = openpyxl.load_workbook(file_path)\n\n    # Choose a specific sheet by name\n    sheet_name = 'Sheet1'\n    sheet = workbook[sheet_name]\n\n    # Create a dictionary to store the rows from the sheet\n    url_dict =dict()\n    b=0\n    \n    # Iterate through rows and columns to read data\n    for row in sheet.iter_rows(values_only=True):\n        if b==0:\n            b+=1\n        elif row[0]%1==0.0:\n            url_dict[str(int(row[0]))] = row[1]\n        else:\n            url_dict[str(row[0])] = row[1]\n        \n    # Close the workbook when you're done\n    workbook.close()\n\n    return url_dict\n", "repo_name": "kushagrapatidar/Internship_Assignments", "sub_path": "Blackcoffer/Solution/xl_reader.py", "file_name": "xl_reader.py", "file_ext": "py", "file_size_in_byte": 672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 6, "usage_type": "call"}]}
{"seq_id": "4750748207", "text": "from django.contrib import admin\nfrom django.urls import path, include  # add this\nfrom django.contrib.auth.views import LogoutView\n\n\nurlpatterns = [\n    #URLs para redirigir el logout\n    path('admin/logout/', LogoutView.as_view(next_page='/SDB/admin/'), name='admin_logout'),\n\n    #URLs para el panel de administracion\n    path('admin/', admin.site.urls),\n\n    #URLs para la autenticacion del Login\n    path(\"\", include(\"apps.authentication.urls\")),\n\n    #URLs para los tipos de ordenes\n    path(\"\", include(\"apps.ordenes.urls\")),\n    \n    #URLs para el sistema de despacho, Home y otras funciones\n    # Leave `Home.Urls` as last the last line\n    path(\"\", include(\"apps.home.urls\"))\n]\n\n\n\n\n", "repo_name": "DevBufalinda/SDB", "sub_path": "core/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 692, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 21, "usage_type": "call"}]}
{"seq_id": "16415220328", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 24 20:46:51 2019\n\n@author: Francesco\n\"\"\"\n\nfrom pgmpy.models import BayesianModel\nfrom pgmpy.factors.discrete import TabularCPD\nimport SQL\nimport numpy as np\n\n\ndef numberOfSons(conn, symIdList):\n    mat = np.zeros((len(symIdList), 2))\n    for i in range (0, len(symIdList)):\n        mat[i][0] = symIdList[i][0]         \n        mat[i][1] = SQL.numOfRelatedDiseases(conn, symIdList[i][0])\n    return mat\n   \n    \ndef buildNet(data, conn):\n    model = BayesianModel(data)\n    checkedSymp = list()    #Lista dei sintomi già visitati ed aggiunti alla rete\n    checkedDis = list()     #Lista delle malattie già aggiunte alla rete\n    \n    \n    #Costruzione dei nodi parents della rete\n    for t in data:\n        if t[0] not in checkedSymp:\n            cpd = TabularCPD(variable = t[0],variable_card = 2,values=[[0.5, 0.5]])\n            checkedSymp.append(t[0])\n        model.add_cpds(cpd)\n    #Costruzione dei nodi figli, collegandoli ai rispettivi parent\n    for t in data:\n        if t[1] not in checkedDis:\n            sym_list = SQL.symList(conn, t[1])      #Ricavo la lista di sintomi collegati alla malattia\n            sym_list_length = len(sym_list)\n            mat = numberOfSons(conn, sym_list)\n            arr = []\n            for i in range(0, len(mat)):\n                arr.append(mat[i][1])\n            print(arr)\n            cpd = TabularCPD(variable = t[1],\n                             variable_card = sym_list_length,\n                             values = np.full((1, sym_list_length), 1/sym_list_length),\n                             evidence = sym_list,\n                             evidence_card = arr\n                             )\n            break\n            checkedDis.append(t[1])\n        model.add_cpds(cpd)\n    return model\n\ndef check_model(model):\n    return model.check_model()\n", "repo_name": "FrancescoCel/ICon", "sub_path": "doc/source/BN.py", "file_name": "BN.py", "file_ext": "py", "file_size_in_byte": 1852, "program_lang": "python", "lang": "it", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "SQL.numOfRelatedDiseases", "line_number": 18, "usage_type": "call"}, {"api_name": "pgmpy.models.BayesianModel", "line_number": 23, "usage_type": "call"}, {"api_name": "pgmpy.factors.discrete.TabularCPD", "line_number": 31, "usage_type": "call"}, {"api_name": "SQL.symList", "line_number": 37, "usage_type": "call"}, {"api_name": "pgmpy.factors.discrete.TabularCPD", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "10182363808", "text": "import json\nimport re\nfrom datetime import datetime\nfrom lex_response import LexResponse\nimport boto3\nimport json\n\nCITIES = set([\"manhattan\"])\nCUISINES = set([\"japanese\", \"italian\",\n                \"mexican\", \"chinese\",\n                \"indpak\", \"french\",\n                \"thai\", \"irish\"])\n\nOPEN_TIME = datetime.strptime(\"09:00\", \"%H:%M\")\nCLOSE_TIME = datetime.strptime(\"22:00\", \"%H:%M\")\n\nCURRENT_TIME = datetime.strptime(datetime.now().strftime(\"%H:%M\"), \"%H:%M\")\n\n\ndef lambda_handler(event, context):\n\n    response = LexResponse(event)\n\n    print(event)\n\n    if response.intent == \"DiningSuggestionsIntent\":\n        if response.event[\"invocationSource\"] == \"DialogCodeHook\":\n            return dining_suggestion_validate(response)\n        print(\"first if\")\n        if response.event[\"invocationSource\"] == \"FulfillmentCodeHook\":\n            print(\"Second if\")\n            return dining_suggestion(response)\n\n    if response.intent == \"GreetingIntent\":\n        return greeting_intent(response)\n\n    if response.intent == \"ThankYouIntent\":\n        return thankyou_intent(response)\n\n    return response.confirm_intent(message=\"Do you want Dining Suggestions?\")\n\n\ndef get_slots(response):\n    return response.event[\"currentIntent\"][\"slots\"][\"Location\"],  response.event[\"currentIntent\"][\"slots\"][\"Cuisine\"], response.event[\"currentIntent\"][\"slots\"][\"Dine_Time\"], response.event[\"currentIntent\"][\"slots\"][\"Party_Size\"], response.event[\"currentIntent\"][\"slots\"][\"Contact\"]\n\n\ndef slots_validation_response(validation_response, message, slotToElicit):\n    return {\"isValid\": validation_response, \"slotToElicit\": slotToElicit} if not message else {\"isValid\": validation_response, \"slotToElicit\": slotToElicit, \"message\": {\"contentType\": \"PlainText\", \"content\": message}}\n\n\ndef slots_validation(city, cuisine, time, people, email):\n\n    if city is not None and city.lower() not in CITIES:\n        return slots_validation_response(False, f\"We do not serve this city try {', '.join(list(CITIES))}\", \"Location\")\n\n    if cuisine is not None and cuisine.lower() not in CUISINES:\n        return slots_validation_response(False, f\"Choose one of the cuisines {', '.join(list(CUISINES))}\", \"Cuisine\")\n\n    # MM-DD-YYYY HH:MM\n    if time is not None:\n        if len(time) != 5:\n            return slots_validation_response(False, f\"Please specify time in following format, with am or pm.\", \"Dine_Time\")\n        else:\n            try:\n                response_time = datetime.strptime(time, \"%H:%M\")\n                if response_time < OPEN_TIME or response_time > CLOSE_TIME or response_time < CURRENT_TIME:\n                    raise Exception(\"A error occured!\")\n            except Exception as ex:\n                return slots_validation_response(False, f\"We serve between {OPEN_TIME.strftime('%H:%M')} and {CLOSE_TIME.strftime('%H:%M')}. Also time should be in the future\", \"Dine_Time\")\n\n    if people is not None and (int(people) >= 20 or int(people) <= 0):\n        return slots_validation_response(False, f\"Choose people in between 1 and 16\", \"Party_Size\")\n\n    email_regex = re.compile(\n        r'([A-Za-z0-9]+[.-_])*[A-Za-z0-9]+@[A-Za-z0-9-]+(\\.[A-Z|a-z]{2,})+')\n\n    if email is not None and not email_regex.fullmatch(email):\n        print(\"email invalid\")\n        return slots_validation_response(False, \"Please enter a valid email address.\", \"Contact\")\n\n    print(slots_validation_response(True, None, None))\n    return slots_validation_response(True, None, None)\n\n\ndef dining_suggestion_validate(response):\n    city, cuisine, time, people, email = get_slots(response)\n    slot_validation_res = slots_validation(city, cuisine, time, people, email)\n\n    if not slot_validation_res[\"isValid\"]:\n        return response.elecit_slot(slotToElicit=slot_validation_res[\"slotToElicit\"], message=slot_validation_res[\"message\"])\n    print(f\"#### {response.delegate()}\")\n    return response.delegate()\n\n\ndef dining_suggestion(response):\n    city, cuisine, time, people, email = get_slots(response)\n\n    print(city, cuisine, time, people, email)\n    message = {\n        \"Location\": {\n            \"Datatype\": \"String\",\n            \"StringValue\": response.slots[\"Location\"]\n        },\n        \"Cuisine\": {\n            \"Datatype\": \"String\",\n            \"StringValue\": response.slots[\"Cuisine\"]\n        },\n        \"Dine_Time\": {\n            \"Datatype\": \"String\",\n            \"StringValue\": response.slots[\"Dine_Time\"]\n        },\n        \"Party_Size\": {\n            \"Datatype\": \"String\",\n            \"StringValue\": str(response.slots[\"Party_Size\"])\n        },\n        \"Contact\": {\n            \"Datatype\": \"String\",\n            \"StringValue\": response.slots[\"Contact\"]\n        },\n\n    }\n\n    print(message)\n\n    send_message_to_sqs(message)\n    print(response.close(message=\"Expect an email shortly, Thanks!\",\n          fulfillmentState=\"Fulfilled\"))\n    return response.close(message=\"Expect an email shortly, Thanks!\", fulfillmentState=\"Fulfilled\")\n\n\ndef send_message_to_sqs(message):\n    sqs_client = boto3.client(\"sqs\", region_name=\"us-east-1\")\n\n    response = sqs_client.send_message(\n        QueueUrl=\"\",  # TODO\n        MessageBody=json.dumps(message)\n    )\n\n\ndef greeting_intent(response):\n    print(response.elecit_intent(message=\"Try saying dining suggestions!\"))\n    return response.elecit_intent(message=\"Try saying dining suggestions!\")\n\n\ndef thankyou_intent(response):\n    return response.elecit_intent(message=\"Your Welcome! Come back soon!\")\n", "repo_name": "EashanKaushik/chatBot", "sub_path": "lambda_functions/lf1.py", "file_name": "lf1.py", "file_ext": "py", "file_size_in_byte": 5430, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 17, "usage_type": "call"}, {"api_name": "lex_response.LexResponse", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 74, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 132, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 136, "usage_type": "call"}]}
{"seq_id": "5277700383", "text": "from flask import Blueprint, request, jsonify\n\nfrom cafe.exceptions.service_exception import ServiceException\nfrom cafe.services.user_services import UserService\nfrom cafe.utils.json_utils import json_response\n\nuser_blueprint = Blueprint(\n    'users',\n    __name__,\n    url_prefix='/users')\n\n\n@user_blueprint.route('/', methods=['GET'])\ndef get_all_users_route():\n\n    is_lazy = False\n    args = request.args.to_dict()\n    user_service = UserService()\n\n    try:\n        if 'lazy' in request.args:\n            args.pop('lazy', None)\n            is_lazy = True\n\n        response = user_service.\\\n            get_without_filters_service(\n                is_lazy=is_lazy)\n\n    except ServiceException as err:\n        return json_response(**err.serialize)\n    return jsonify(response)\n", "repo_name": "GajovskiMaxime/cafe-back", "sub_path": "cafe/api/user_controller.py", "file_name": "user_controller.py", "file_ext": "py", "file_size_in_byte": 780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.args.to_dict", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "cafe.services.user_services.UserService", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "cafe.exceptions.service_exception.ServiceException", "line_number": 29, "usage_type": "name"}, {"api_name": "cafe.utils.json_utils.json_response", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 31, "usage_type": "call"}]}
{"seq_id": "9318160962", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom bs4 import BeautifulSoup\nimport time\nimport numpy as np\nimport pandas as pd\nimport csv\nimport os \ndir_path = os.path.dirname(os.path.realpath(__file__))\nseoul = []\n\nwith open(dir_path + '/data/api/seoul0.csv') as f:\n    seoul_csv = csv.reader(f)\n    for sl in seoul_csv:\n        seoul.append(sl)\n\nseoul.pop(0)\ncount = 0\n\n#\nfor sl in seoul:\n    if count == 3: break;\n    count += 1\n    print(sl)\n\n    s_name = sl[0].replace('(주)','')\n    s_add_arr = sl[1].split()\n    s_add_gu = s_add_arr[1]\n    s_add_dong = s_add_arr[2]\n    s_add_ro = ''\n\n    if len(sl[2]) != 0 : s_add_ro = sl[2].split()[2]\n\n    # print('자치구 :',s_add_gu,'\\n동 :',s_add_dong)\n    # print(s_add_ro)\n\n    l = 0\n    for index,s in enumerate(s_add_ro) :\n        if str(s).isnumeric() :\n            l = index\n            break\n\n    s_add_ro = s_add_ro[:l]\n    # print(s_add_ro)\n    # print()\n\n    service_key = webdriver.chrome.service.Service('../drivers/chromedriver')\n    #\n    # # try :\n    print(count, s_add_gu+\" \"+s_add_dong+\" \"+s_name)\n    url = f'https://map.naver.com/v5/search/{s_add_gu+\"%20\"+s_add_dong+\"%20\"+s_name}/place'\n    driver = webdriver.Chrome(service=service_key)\n    driver.implicitly_wait(2)\n    driver.get(url)  # url 가져오기\n    time.sleep(7)\n    #\n    # 첫번째 아이프레임 찾기 (검색 후 목록)\n    search_iframe = driver.find_elements(By.CSS_SELECTOR,'#searchIframe')\n    #\n    driver.switch_to.frame('searchIframe')  # (2) 특정 프레임으로 전환\n    time.sleep(10)\n    search_ul = driver.find_elements(By.TAG_NAME,'ul')\n    search_li_list = search_ul[0].find_elements(By.TAG_NAME,'li')\n\n    search_li_div = search_li_list[0].find_elements(By.TAG_NAME,'div')\n    search_li_div[1].click()\n\n    # 가장 상위의 html로 이동\n    driver.switch_to.default_content()\n    time.sleep(5)\n\n    search_iframe = driver.find_elements(By.CSS_SELECTOR, '#entryIframe')\n\n    # 두번째 아이프레임 찾기 (가게 클릭 후 아이프레임)\n    driver.switch_to.frame('entryIframe')\n    time.sleep(5)\n\n    # body = search_iframe.find_element(By.XPATH,'html/body')\n    soup = BeautifulSoup(driver.page_source, 'html.parser')\n    body = soup.find('body',class_='place_on_pcmap')\n\n    search_add = body.find('span',class_='_2yqUQ').get_text()\n    search_add_ro = search_add.split('로')[0].split('구')[-1].replace(' ','')+'로'\n    print(search_add_ro)\n    if search_add_ro == s_add_ro :\n        print(search_add_ro,s_add_ro)\n    else :\n        print(search_add_ro, s_add_ro)\n    #\n    # driver.close()\n\n#     # except:\n#     #     print('에러 발생',s_name)\n", "repo_name": "codnjs3575/Final_Team04", "sub_path": "3.산출물/code/크롤링/naver.py", "file_name": "naver.py", "file_ext": "py", "file_size_in_byte": 2659, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 9, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 47, "usage_type": "call"}, {"api_name": "selenium.webdriver.chrome", "line_number": 47, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 47, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 52, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 52, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 58, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 58, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 62, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 62, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 63, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 63, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 65, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 65, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 70, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 72, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 72, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 76, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 79, "usage_type": "call"}]}
{"seq_id": "713956454", "text": "from PCA.PCAUI import PCAUI\nimport sys\nimport numpy as np\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtCore import *\nfrom PCA.Figure_Canvas import Figure_Canvas\nfrom PCA.PCA import PCA, scale\n\nclass LogicalUI(QMainWindow, PCAUI):\n    def __init__(self, parent=None):\n        super(LogicalUI, self).__init__(parent)\n        self.setupUi(self)\n        self.showMaximized()\n        self.start.clicked.connect(self.on_start_clicked)\n        self.selectData.clicked.connect(self.on_selectData_clicked)\n        self.selectNew.clicked.connect(self.on_selectNew_clicked)\n\n    @pyqtSlot()\n    def on_start_clicked(self):\n        \"\"\"\n        运行PCA过程，并保存降维后数据文件\n        :return: None\n        \"\"\"\n        try:\n            dataPath = self.dataPath.text()  # 获取数据集路径\n            newPath = self.newPath.text()  # 获取降维后文件的路径\n            ratio = float(self.ratio.text())  # 降维比例\n            data = np.loadtxt(dataPath, skiprows=1).T  # 导入数据\n            data_scaled = scale(data)  # Z-Score数据集\n            pca = PCA(ratio=ratio)  # 实例化数据集对象\n            pca.fit(data_scaled)  # 训练PCA\n            vec = np.array(pca.eig_vec).T  # 获取变换矩阵A\n\n            # 将变换矩阵A输出到表格控件中\n            self.tableWidget.setRowCount(vec.shape[0])\n            self.tableWidget.setColumnCount(vec.shape[1])\n            for i in range(vec.shape[0]):\n                for j in range(vec.shape[1]):\n                    self.tableWidget.setItem(j, i, QTableWidgetItem(str(vec[i][j])))\n\n            # 将PCA输出结果构建为html，展示到文字控件中\n            s = ''\n            for i in range((pca.cov.shape[0])):\n                s += '<tr>'\n                for j in range((pca.cov.shape[1])):\n                    s += '<td>' + str(round(pca.cov[i][j], 3)) + '</td>'\n                s += '</tr>'\n            s = '<center><table border=\"1\">' + s + '</table></center>'\n            s = '<h3>协方差矩阵：</h3>' + s + '<h3>累计贡献率</h3>' + '<center>' +\\\n                str(np.around(pca.cumulative, decimals=4)) + '</center>'\n            s = s + '<h3>特征根：</h3>' + '<center>' + str(np.around(pca.eig_val, decimals=4)) + '</center>'\n\n            s1 = ''\n            for i in range(pca.n_components.shape[0]):\n                s1 += '<tr>'\n                for j in range(pca.n_components.shape[1]):\n                    s1 += '<td>' + str(round(pca.n_components[i][j], 3)) + '</td>'\n                s1 += '</tr>'\n            s1 = '<center><table border=\"1\">' + s1 + '</table></center>'\n\n            s = s + '<h3>主分量：</h3>' + s1\n            self.cov.setText(s)\n\n            tranform_data = pca.transform(data_scaled).T  # 对原始数据进行降维\n            np.savetxt(newPath, tranform_data)  # 将降维数据保存\n\n            fc = Figure_Canvas()\n            fc.draw_pict(tranform_data.T)\n            graphicscene = QtWidgets.QGraphicsScene()\n            graphicscene.addWidget(fc)\n            self.pcaPict.setScene(graphicscene)\n            self.pcaPict.show()\n\n        except Exception as e:\n            print(e)\n            QMessageBox.critical(self, \"输入错误\", self.tr(\"请输入正确的参数！\"))  # 出现参数错误，弹出对话框提示\n\n    @pyqtSlot()\n    def on_selectData_clicked(self):\n        \"\"\"\n        点击工具按钮，选择路径。\n        :return: None\n        \"\"\"\n        try:\n            pth, _ = QFileDialog.getOpenFileName(self, '选择文件')\n            print(pth)\n            if pth:\n                self.dataPath.setText(pth)\n        except Exception as e:\n            print(e)\n\n    @pyqtSlot()\n    def on_selectNew_clicked(self):\n        \"\"\"\n        点击工具按钮，选择文件路径\n        :return: None\n        \"\"\"\n        try:\n            pth, _ = QFileDialog.getOpenFileName(self, '选择文件')\n            print(pth)\n            if pth:\n                self.newPath.setText(pth)\n        except Exception as e:\n            print(e)\n", "repo_name": "zmddzf/information_intelligence", "sub_path": "PCA/PCAMain.py", "file_name": "PCAMain.py", "file_ext": "py", "file_size_in_byte": 4050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "41", "api": [{"api_name": "PCA.PCAUI.PCAUI", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 29, "usage_type": "call"}, {"api_name": "PCA.PCA.scale", "line_number": 30, "usage_type": "call"}, {"api_name": "PCA.PCA.PCA", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 66, "usage_type": "call"}, {"api_name": "PCA.Figure_Canvas.Figure_Canvas", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGraphicsScene", "line_number": 70, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 70, "usage_type": "name"}]}
{"seq_id": "8679837598", "text": "from bs4 import BeautifulSoup\nimport urllib.parse\nimport urllib.request\nimport os, sys\n\nurl = sys.argv[1]\nheaders = {}\nheaders['User-Agent'] = \"Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:48.0) Gecko/20100101 Firefox/100.0\"\nreq = urllib.request.Request(url, headers = headers)\ncontent = BeautifulSoup(urllib.request.urlopen(req).read())\n\nclear_links = []\nfor link in content.find_all(\"a\"):\n    tmp = link.get(\"href\") #urllib.parse.quote(link.get(\"href\"))\n    print(tmp)\n    if tmp.endswith(\".pdf\"):\n        clear_links.append(link)\n        os.system(\"wget '{0}'\".format(tmp))\n", "repo_name": "rf-peixoto/Studies", "sub_path": "Code/Python/pdf_downloader.py", "file_name": "pdf_downloader.py", "file_ext": "py", "file_size_in_byte": 573, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 33, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sys.argv", "line_number": 6, "usage_type": "attribute"}, {"api_name": "urllib.parse.request.Request", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 9, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 9, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.parse.request.urlopen", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 10, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 10, "usage_type": "name"}, {"api_name": "os.system", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "42380324151", "text": "import usb\n\ndev = usb.core.find(idVendor=0x0922, idProduct=0x8003, find_all = False)\n\n\nreattach = False\nif dev.is_kernel_driver_active(0):\n    reattach = True\n    dev.detach_kernel_driver(0)\n\ndev.set_configuration()\ncfg = dev.get_active_configuration()\n\ninterface_number = cfg[(0,0)].bInterfaceNumber\nalternate_settting = usb.control.get_interface(dev, interface_number)\nintf = usb.util.find_descriptor(cfg, bInterfaceNumber = interface_number, bAlternateSetting = alternate_settting)\n\n# ep = usb.util.find_descriptor(intf,custom_match = lambda e: usb.util.endpoint_direction(e.bEndpointAddress) == usb.util.ENDPOINT_OUT)\n# ep.write(\"test\\n\\r\")\n\n# This is needed to release interface, otherwise attach_kernel_driver fails\n# due to \"Resource busy\"\nusb.util.dispose_resources(dev)\n\n# It may raise USBError if there's e.g. no kernel driver loaded at all\nif reattach:\n    dev.attach_kernel_driver(0)\n", "repo_name": "duke-iml/ece490-s2016", "sub_path": "apc2016/Sensors/scaleTest.py", "file_name": "scaleTest.py", "file_ext": "py", "file_size_in_byte": 896, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "usb.core.find", "line_number": 3, "usage_type": "call"}, {"api_name": "usb.core", "line_number": 3, "usage_type": "attribute"}, {"api_name": "usb.control.get_interface", "line_number": 15, "usage_type": "call"}, {"api_name": "usb.control", "line_number": 15, "usage_type": "attribute"}, {"api_name": "usb.util.find_descriptor", "line_number": 16, "usage_type": "call"}, {"api_name": "usb.util", "line_number": 16, "usage_type": "attribute"}, {"api_name": "usb.util.dispose_resources", "line_number": 23, "usage_type": "call"}, {"api_name": "usb.util", "line_number": 23, "usage_type": "attribute"}]}
{"seq_id": "74889479178", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2022/4/28 23:14\n# @Author  : Yizheng Dai\n# @Email   : 387942239@qq.com\n# @File    : finetuning_argparse.py.py\n\nimport argparse\n\ndef get_argparse():\n    ## Required parameters\n    parser = argparse.ArgumentParser()\n\n    ## Required parameters\n    parser.add_argument(\"--task_name\", default=None, type=str, required=False,\n                        help=\"name of the task.\")\n    parser.add_argument(\"--data_dir\", default=None, type=str, required=False,\n                        help=\"The input data dir should contain the training files for the CoNLL-2003 NER task.\")\n    parser.add_argument(\"--config_dir\", default=None, type=str, required=True,\n                        help=\"The parameter config path.\")\n    parser.add_argument(\"--model_type\", default=None, type=str, required=False,\n                        help=\"Model type selected in the list\")\n    parser.add_argument(\"--output_dir\", default=None, type=str, required=False,\n                        help=\"The output dir where the model predictions and checkpoints will be written.\")\n\n    ## other parameters\n    parser.add_argument(\"--labels\", default=\"\", type=str,\n                        help=\"Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.\")\n    parser.add_argument(\"--max_seq_length\", default=256, type=int,\n                        help=\"The maximum total input sequence length after tokenization.Sequence longer than this will be truncated, sequences shorter will be padded.\")\n    parser.add_argument(\"--do_train\", action=\"store_true\",\n                        help=\"Whether to run training.\")\n    parser.add_argument(\"--do_eval\", action=\"store_true\",\n                        help=\"Whether to run eval on the dev set.\")\n    parser.add_argument(\"--do_predict\", action=\"store_true\",\n                        help=\"Whether to run predictions on the dev set.\")\n    parser.add_argument(\"--do_lower_case\", default=False,\n                        help=\"Set this flag if you are using an uncased model.\")\n\n    ## dropout\n    parser.add_argument(\"--overwrite_output_dir\", default=True,\n                        help=\"Overwrite the content of the output directory.\")\n    parser.add_argument(\"--overwrite_cache\", default=False,\n                        help=\"Overwrite the cached training and evaluation sets.\")\n    parser.add_argument(\"--seed\", default=42, type=int,\n                        help=\"random seed for initialization\")\n    parser.add_argument(\"--log_dir\", default='log/', type=str, required=False,\n                        help=\"The log directions.\")\n\n\n    return parser", "repo_name": "daiyizheng/entity-extraction", "sub_path": "src/ml_models/Hmm/finetuning_argparse.py", "file_name": "finetuning_argparse.py", "file_ext": "py", "file_size_in_byte": 2616, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "46", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "30461450939", "text": "from binary_number import BinaryNumber\nimport matplotlib.pyplot as plt\n\n# np.ndarray(shape=(2,2), dtype=float, order='F')\nperiodic_sequences = 0\namount = 100000\nnumbers = []\nratios = []\noccurrences = []\nall_numbers = []\nperiodic_sequences_list = []\n# teorema de midy, estudar?\n\n# testar comecando de outros offsets\nfor i in range(amount*5):\n    number = i/amount\n    binary_number = BinaryNumber(number)\n    result = binary_number.is_periodic_sequence()\n    all_numbers.append(number)\n    occurrences.append(binary_number.periodic_occurrences)\n    periodic_sequences_list.append(periodic_sequences)\n    ratio = (periodic_sequences/i) if i != 0 else 0\n    ratios.append(ratio)\n    if result: \n        periodic_sequences += 1\n        numbers.append(number)\n    # if i!=0:\n    #     if binary_number.periodic_occurrences>3:\n    #         print('-')\n    #         print('occurrences', binary_number.periodic_occurrences)\n    #         print('fraction', binary_number.fraction_part())\n    #         print('number', number)\n    #         print('ratio', periodic_sequences/i) \n\nplt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\nplt.rcParams[\"figure.autolayout\"] = True\n\nplt.title(\"Relações entre dizimas periódicas em binário\")\n\nplt.subplot(311)\nplt.plot(all_numbers, ratios)\nplt.ylabel(f\"Dízimas/total(final={ratio})\", rotation=0, loc='top')\n\nplt.subplot(312)\nplt.plot(all_numbers, periodic_sequences_list)\nplt.ylabel('Dízimas', rotation=0, loc='top')\n\nplt.subplot(313)\nplt.plot(all_numbers, occurrences, 'b-')\nplt.xlabel('Número analisado')\nplt.ylabel('Repetições \\nnos primeiros digitos', rotation=0, loc='top')\n\nplt.show()", "repo_name": "Hikari-desuyoo/periodic-sequences", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1624, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "binary_number.BinaryNumber", "line_number": 17, "usage_type": "call"}, {"api_name": "binary_number.is_periodic_sequence", "line_number": 18, "usage_type": "call"}, {"api_name": "binary_number.periodic_occurrences", "line_number": 20, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 35, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 36, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}]}
{"seq_id": "11025988341", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -\n\nimport logging\nfrom config import MONGO_URL, MONGO_DATABASE, REDIS_HOST, REDIS_PORT, REDIS_DB, REDIS_SOCKET_PATH, DISABLE_CACHING\n#from pymongo import Connection     # deprecated\nfrom pymongo import MongoClient\nimport pickle\nimport redis\n\nimport time\nimport sys\nif sys.platform == \"win32\":\n    profiler_timer = time.clock     # On Windows, the best timer is time.clock()\nelse:\n    profiler_timer = time.time      # On most other platforms the best timer is time.time()\n\n# if REDIS_HOST is not None:\nredispool = redis.ConnectionPool(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB)\n\n\nclass DBBase(object):\n\n    connection = MongoClient(MONGO_URL)     # Single connection for all instanses\n    # Use ConnectionPool for Redis connection\n    redispool = redispool\n    # if REDIS_HOST is not None:\n    #     redispool = redis.ConnectionPool(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB)\n    db_start = profiler_timer()\n    '''\n    db = db\n    redis = redis\n    '''\n\n    #connection = Connection(MONGO_URL)\n    \"\"\"\n    db = connection[MONGO_DATABASE]\n    if REDIS_HOST is not None:\n        redis = redis.Redis(connection_pool=redispool)\n    else:\n        redis = redis.Redis(db=REDIS_DB, unix_socket_path=REDIS_SOCKET_PATH)\n    db_stop = profiler_timer()\n    \"\"\"\n\n    def __init__(self, key=None, cached=False):\n        db_start = profiler_timer()\n        #connection = MongoClient(MONGO_URL)\n        self.db = self.connection[MONGO_DATABASE]\n        if REDIS_HOST is not None:\n            self.redis = redis.Redis(connection_pool=self.redispool)\n        else:\n            self.redis = redis.Redis(unix_socket_path=REDIS_SOCKET_PATH, db=REDIS_DB)\n        db_stop = profiler_timer()\n        logging.error(\"  db.delay=%f\", db_stop - db_start)\n\n        if DISABLE_CACHING:\n            self.cached = False\n        else:\n            self.cached = cached\n        self.name = self.__class__.__name__\n        self.collection = self.db[self.name]\n        #self.document = self.get_by_key(key)\n        self.document = None\n        if key is not None:\n            self.document = {\"_id\": key}\n        #logging.info(\"  + self.document=%s\", repr(self.document))\n        #logging.info(\"DBBase.__init__(%s, %s) as %s\", str(key), str(cached), str(self.name))\n\n    def __repr__(self):\n        return \"DB:'{0}' collection:'{0}' document:'{1}'\".format(self.__class__.__name__, repr(self.document))\n\n    @classmethod\n    def get(cls, key, **kwargs):\n        rep = cls(key, **kwargs)\n        op_start = profiler_timer()\n        rep.document = rep.find(key)\n        op_stop = profiler_timer()\n        logging.error(\"  op.delay=%f\", op_stop - op_start)\n        return rep\n\n    def find(self, key):\n        logging.info(\"db.base.find(%s, %s)\" % (self.name, key))\n        if key is None:\n            return None\n\n        if self.cached:\n            prefix = self.__class__.__name__\n            s = self.redis.get('%s.%s' % (prefix, key))\n            if s is not None:\n                try:\n                    s = pickle.loads(s)\n                    s[\"__cache__\"] = 'from cache'\n                except:\n                    self.redis.delete('%s.%s' % (prefix, key))\n                    s = None\n                    logging.error('== error in pickle')\n                return s\n            else:\n                s = self.collection.find_one({\"_id\": key})\n                if s is not None:\n                    self.redis.set('%s.%s' % (prefix, key), pickle.dumps(s))\n                    s[\"__cache__\"] = 'from db'\n                    return s\n            return None\n        else:\n            s = self.collection.find_one({\"_id\": key})\n            if s is not None:\n                s[\"__cache__\"] = 'disabled'\n            return s\n\n    def find_all(self, keys):\n        '''\n            Возвращает словарь значений, где ключ-это значение из keys\n        '''\n        if keys is None:\n            return {}\n        if not isinstance(keys, list):\n            return {}\n        if len(keys) == 0:\n            return {}\n\n        #out = dict([(key, None) for key in keys])\n        #return out\n\n        out = {}\n        if self.cached:\n            prefix = self.__class__.__name__\n\n            # Сначала пробуем получить все значения из кеша\n            cache = self.redis.mget(('%s.%s' % (prefix, key) for key in keys))\n            # logging.info(\"  mget result len=%d\", len(cache))\n            misskeys = []\n            for i in range(len(keys)):\n                s = cache[i]\n                key = keys[i]\n                if s is not None:\n                    try:\n                        s = pickle.loads(s)\n                        s[\"__cache__\"] = 'from cache'\n                        out[key] = s\n                    except:\n                        self.redis.delete('%s.%s' % (prefix, key))\n                        misskeys.append(key)\n                        logging.error('== error in pickle')\n                else:\n                    misskeys.append(key)\n\n            # Все значения, которые содержат None попытаемся загрузить из базы\n            #logging.warning('== miss hits [%s]' % misskeys)\n            if len(misskeys) != 0:\n                for s in self.collection.find({\"_id\": {\"$in\": misskeys}}):\n                    key = s[\"_id\"]\n                    self.redis.set('%s.%s' % (prefix, key), pickle.dumps(s))\n                    s[\"__cache__\"] = 'from db'\n                    out[key] = s\n\n        else:\n            out = {}\n            logging.info(' self name = %s' % self.name)\n            logging.info(' coll name = %s' % self.collection.name)\n            logging.info('== keys = %s' % repr(keys))\n            # syss = self.collection.find({\"_id\": {\"$in\": keys}}).count()\n            syss = self.collection.find({\"_id\": {\"$in\": [\"dGVzdC0wMQ\"]}}).count()\n            logging.info('== syss = %s' % repr(syss))\n            for s in self.collection.find({\"_id\": {\"$in\": keys}}):\n                logging.info('== s = %s' % repr(s))\n                s[\"__cache__\"] = 'disabled'\n                out[s[\"_id\"]] = s\n\n        for key in keys:\n            if key not in out:\n                out[key] = None\n        return out\n\n    def reset_cache(self):\n        prefix = self.__class__.__name__\n        logging.info('== RESET CACHE = %s.%s' % (repr(prefix), repr(self.key)))\n        self.redis.delete('%s.%s' % (prefix, self.key))\n\n    def update(self, param):\n        self.reset_cache()\n        self.collection.update({\"_id\": self.key}, param)\n\n    @property\n    def isNone(self):\n        return self.document is None\n\n    @property\n    def key(self):\n        if self.document is None:\n            return None\n        return self.document[\"_id\"]\n\n    def insert(self, document):\n        self.document = document\n        self.collection.save(self.document)\n\n    @staticmethod\n    def tokey(key):\n        return key.replace(\".\", \"#\")\n\n    @staticmethod\n    def fromkey(key):\n        return key.replace(\"#\", \".\")\n", "repo_name": "baden/api.navi.cc", "sub_path": "src/db/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 7058, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "46", "api": [{"api_name": "sys.platform", "line_number": 13, "usage_type": "attribute"}, {"api_name": "time.clock", "line_number": 14, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 16, "usage_type": "attribute"}, {"api_name": "redis.ConnectionPool", "line_number": 19, "usage_type": "call"}, {"api_name": "config.REDIS_HOST", "line_number": 19, "usage_type": "name"}, {"api_name": "config.REDIS_PORT", "line_number": 19, "usage_type": "name"}, {"api_name": "config.REDIS_DB", "line_number": 19, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 24, "usage_type": "call"}, {"api_name": "config.MONGO_URL", "line_number": 24, "usage_type": "argument"}, {"api_name": "config.MONGO_DATABASE", "line_number": 48, "usage_type": "name"}, {"api_name": "config.REDIS_HOST", "line_number": 49, "usage_type": "name"}, {"api_name": "redis.Redis", "line_number": 50, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 52, "usage_type": "call"}, {"api_name": "config.REDIS_SOCKET_PATH", "line_number": 52, "usage_type": "name"}, {"api_name": "config.REDIS_DB", "line_number": 52, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 54, "usage_type": "call"}, {"api_name": "config.DISABLE_CACHING", "line_number": 56, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 82, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 91, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 96, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 101, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 138, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 144, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 153, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 159, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 160, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 161, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 164, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 166, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 177, "usage_type": "call"}]}
{"seq_id": "8132056687", "text": "from google.cloud import storage\n\n# Create a client object\nclient = storage.Client()\n\n# List all the buckets in your project\nbuckets = list(client.list_buckets())\nprint(\"Buckets:\")\nfor bucket in buckets:\n    print(bucket.name)", "repo_name": "AnalystBean/Data_Engineering_Projects", "sub_path": "Google_Cloud_Connection_Example.py", "file_name": "Google_Cloud_Connection_Example.py", "file_ext": "py", "file_size_in_byte": 226, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "google.cloud.storage.Client", "line_number": 4, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 4, "usage_type": "name"}]}
{"seq_id": "9893046148", "text": "import os, sys, logging\nfrom configparser import ConfigParser, DEFAULTSECT\nfrom empbase.attach.attachments import EmpAlarm\nfrom empbase.config.defaults import ATTACHMENT_DIRS, DEFAULT_CONFIGS, \\\n                                    DEFAULT_CFG_FILES, SAVE_CFG_FILE\n\nCATEGORY_MAP = {\"Loops\"   : \"plug_\",\n                \"Signals\" : \"plug_\",\n                \"Alarms\"  : \"alarm_\"}\n\ndef isCommentBlank(line, comments=\"#;\"):\n    l = line.strip()\n    if l == '': return True\n    if l[0] in comments: return True\n    if l.split(None,1)[0].lower() == 'rem' and l[0] in 'rR': return True\n    return False\n    \n\nclass EmpConfigParser(ConfigParser):\n    \"\"\"At its heart, this config parser is a SafeConfigParser. The\n    only added functionality is the default configurations automatically\n    added and automatic config file validation. Oh, and there are some\n    functions for getting certain values quicker.\n    \n    EmpConfigParser also is given all the default config variables for \n    all the attachments on EMP. This means they can retrieve their \n    variables and they can be adjusted by the user at run time.\n    \"\"\"\n    def __init__(self, configfile=None):\n        \"\"\" Check the config file and include the defaults. \"\"\"\n        self.CONFIG_FILES = DEFAULT_CFG_FILES\n\n        if configfile is not None:\n            if not os.path.exists(configfile):\n                raise IOError(\"Configuration File does not exist.\")\n            \n            self.CONFIG_FILES.append(configfile)\n\n        ## now set up the parent class using defaults ##\n        ConfigParser.__init__(self)\n        #reset internal configparser variable...\n        if sys.version_info[0]==3 and sys.version_info[1]<2:            \n            # i know thats bad form, but this is what happens when\n            # you dont think ahead \n            self._sections=DEFAULT_CONFIGS\n        else:\n            #this is in python3.2+ only\n            self.read_dict(DEFAULT_CONFIGS) \n\n    def validateInternals(self):\n        \"\"\" Check all the values you are overwriting from the config \n        file. \n        \"\"\"\n        self.read(self.CONFIG_FILES)\n        \n        ### ### VALIDATION ### ### \n        #then check if the update speed is valid, must be >= 1 minute\n        if self.getfloat(\"Daemon\",\"update-speed\") < 1.0:\n            self.set(\"Daemon\",\"update-speed\", 1.0)\n            \n        #first check logging capabilities.\n        if self.getboolean(\"Logging\",\"logging-on\"):              \n            self.__try_setup_path(self.get(\"Logging\",\"log-file\"))\n\n    def __try_setup_path(self,path):\n        if os.path.exists(path):\n            return os.access(path, os.W_OK)\n        else:\n            try:\n                dirs, _ = os.path.split(path)\n                if not os.path.exists(dirs):\n                    os.makedirs(os.path.abspath(dirs))\n            except: \n                return False\n            return True\n                      \n    def getAttachmentVars(self, module):\n        \"\"\"Returns a TinyCfgPrsr of the attachment's variables that were in \n        saved to smtg's config file. TinyCfgPrsr provides some utility functions\n        for retrieving the types of variable that were stored there.\n        \"\"\"\n        if type(module) is not str: \n            raise TypeError(\"Plugin Name must be a string\")\n        \n        savedir = self.get(\"Daemon\", \"save-dir\")\n        attach_name=\"plug_\"+module\n        try: return TinyCfgPrsr(dict(self.items(attach_name)))\n        except:\n            attach_name=\"alarm_\"+module\n            try: return TinyCfgPrsr(dict(self.items(attach_name)),savedir)\n            except: return TinyCfgPrsr({},savedir)\n        \n    def getAttachmentDirs(self):\n        \"\"\" Gets the directories that attachments can be found in.\"\"\"\n        #TODO: ?? allow to be changable via cfg file ??\n        return ATTACHMENT_DIRS\n    \n    def getRegistryFile(self):\n        \"\"\" The registry file to be read in by the Registry object. \"\"\"\n        return self.get(\"Daemon\",\"registry-file\")\n    \n    def defaultAttachmentVars(self, module, defaults, category):\n        \"\"\"Called by SmtgPluginManager and SmtgAlertManager to load the default\n        configurations into the database for use later.\n        \"\"\"\n        section = CATEGORY_MAP[category]+module\n        if not self.has_section(section):\n            self.add_section(section)\n\n        for option in defaults.keys():\n            if not self.has_option(section, option):\n                self.set(section, option, defaults[option])\n    \n    def getlist(self, section, option, default=[]):\n        \"\"\" Utility function for getting a list from the config file. \n        The format for saving them is easy too. \"\"\"\n        try:\n            val = self.get(section, option)\n            return str(val).split(\",\")\n        except: \n            return default\n    \n    def save(self, attachments):\n        \"\"\" Save the configurations to the local user's configuration. \"\"\"\n        if SAVE_CFG_FILE is not None:\n            # update the plug-in variables before saving.\n            for attach in attachments:\n                try:\n                    attach.plugin_object.save() #make the plugin save before pulling the configs\n                    \n                    if isinstance(attach.plugin_object, EmpAlarm):\n                        for key in attach.plugin_object.config.keys():\n                            self.set(\"alarm_\"+attach.module, str(key), str(attach.plugin_object.config[key]))\n                    else:\n                        for key in attach.plugin_object.config.keys():\n                            self.set(\"plug_\"+attach.module,key,str(attach.plugin_object.config[key]))\n                        #else, it doesn't matter \n                except Exception as e: \n                    logging.exception(e)\n            \n            # make sure it exists and can be written to.\n            logging.debug(\"Trying to save cfg to: %s\"%SAVE_CFG_FILE)\n            if self.__try_setup_path(SAVE_CFG_FILE):\n                fp=None\n                try: fp = open(SAVE_CFG_FILE, mode=\"r+\")\n                except:# new file \n                    fp = open(SAVE_CFG_FILE, mode=\"w+\")\n                    fp.write(\"#\\n# EMP CONFIGS\\n#\")\n                self.update_myfile(fp)\n                logging.debug(\"Saved to cfg file!\")\n            else:\n                logging.error(\"Couldn't write to cfg file, it cant be created.\")\n\n    def update_myfile(self, fp, add_missing=True):\n        \"\"\" This function was suppose to be released as a standard in \n        python3.2 when they overhauled the configparser module, but they\n        didn't. See:\n                     http://bugs.python.org/issue1410680\n        I included it as a different name, in case they do end up adding it\n        later... sigh... I would have thought this would be a more important\n        feature request. Obviously the python gods thought otherwise.\n        \n        Oh, I changed a lot since it was messing with variable order and adding\n        random newlines and spacing. I also pushed a couple checks into new \n        functions so I could adjust things later.\n        \"\"\"\n        from io import StringIO\n        sections = {}\n        current = StringIO()\n        replacement = [current]\n        sect = None\n        opt = None\n        written = []\n        # Default to \" = \" to match write(), but use the most recent\n        # separator found if the file has any options.\n        vi = \" = \"\n        while True:\n            line = fp.readline()\n            if not line:\n                break\n            # Comment or blank line?\n            if isCommentBlank(line):\n                current.write(line)\n                continue\n            # Continuation line?\n            if line[0].isspace() and sect is not None and opt:\n                if ';' in line:\n                    # ';' is a comment delimiter only if it follows\n                    # a spacing character\n                    pos = line.find(';')\n                    if line[pos-1].isspace():\n                        comment = line[pos-1:]\n                        # Get rid of the newline, and put in the comment.\n                        current.seek(-1, 1)\n                        current.write(comment + \"\\n\")\n                continue\n            # A section header or option header?\n            else:\n                # Is it a section header?\n                mo = self.SECTCRE.match(line)\n                if mo:\n                    # Remember the most recent section with this name,\n                    # so that any missing options can be added to it.\n                    if sect:\n                        sections[sect] = current\n                    sect = mo.group('header')\n                    current = StringIO()\n                    replacement.append(current)\n                    if sect in self.sections():\n                        current.write(line)\n                    # So sections can't start with a continuation line:\n                    opt = None\n                # An option line?\n                else:\n                    mo = self.OPTCRE.match(line)\n                    if mo:\n                        opt, vi, value = mo.group('option', 'vi', 'value')\n                        comment = \"\"\n                        if vi in ('=', ':') and ';' in value:\n                            # ';' is a comment delimiter only if it follows\n                            # a spacing character\n                            pos = value.find(';')\n                            if value[pos-1].isspace():\n                                comment = value[pos-1:]\n                        opt = opt.rstrip().lower()\n                        if self.has_option(sect, opt):\n                            value = self.get(sect, opt)\n                            # Fix continuations.\n                            value = value.replace(\"\\n\", \"\\n\\t\")\n                            current.write(\"%s%s%s %s\\n\" % (opt, vi, value,\n                                                          comment))\n                            written.append((sect, opt))\n        if sect:\n            sections[sect] = current\n        if add_missing:\n            # Add any new sections.\n            sects = [DEFAULTSECT]\n            sects.extend(self.sections())\n            sects.sort()\n            for sect in sects:\n                if sect == DEFAULTSECT:\n                    opts = self._defaults.keys()\n                else:\n                    # Must use _section here to avoid defaults.\n                    opts = self._sections[sect].keys()\n                #opts.sort() <-- dont sort my keys\n                if sect in sections:\n                    output = sections[sect] or current\n                else:\n                    output = current\n                    #only write the non default section headers.\n                    if sect is not DEFAULTSECT:\n                        output.write(\"\\n[%s]\\n\" % (sect,))\n                    sections[sect] = None\n                for opt in opts:\n                    if opt != \"__name__\" and not (sect, opt) in written:\n                        value = self.get(sect, opt)\n                        # Fix continuations.\n                        value = value.replace(\"\\n\", \"\\n\\t\")\n                        output.write(\"%s%s%s\\n\" % (opt, vi, value))\n                        written.append((sect, opt))\n                #output.write(\"\\n\")\n        # Copy across the new file.\n        fp.seek(0)\n        fp.truncate()\n        for sect in replacement:\n            if sect is not None:\n                fp.write(sect.getvalue())\n        # fp.write(\"\\n\")\n        fp.close()\n\n\n\nclass TinyCfgPrsr():\n    \"\"\" A Utility class to make handling configuration variables easier for\n    attachment construction. They mimic the functionality of a standard \n    config parser, but minus a lot of un-needed functions and variables. It\n    also just holds one section as a dictionary.\n    \"\"\"\n    \n    BOOL_CHOICES = {\"0\":False,\"1\":True,\"no\":False,\"yes\":True,\"true\":True,\n                \"false\":False,\"on\":True,\"off\":False, \"\":False}\n    \n    def __init__(self, dictionary, savedir=\"\"):\n        self._savedir = savedir\n        self._value = dictionary\n    \n    def keys(self):\n        \"\"\" Returns a list of the variable names.\"\"\"\n        return list(self._value.keys())\n    \n    def __getitem__(self, i):\n        \"\"\" Runs TinyCfgPrsr.get(i, None) to get the string of whatever \n        the variable is holding.\n        \"\"\"\n        return self.get(i,None)\n    \n    def get(self, key, default):\n        \"\"\" Values are held as strings, so this will return the value \n        as a string.\n        \"\"\"\n        if key in self._value:\n            return self._value[key]\n        else: return default\n    \n    def getint(self, key, default):\n        \"\"\" Gets the value held and casts it as an integer, may throw a \n        ValueError if there is a problem casting the value.\n        \"\"\"\n        if key in self._value:\n            return int(self._value[key])\n        else: return default\n\n    def getfloat(self, key, default):\n        \"\"\" Gets the value held and casts it as a float, may throw a \n        ValueError if there is a problem casting the value.\n        \"\"\"\n        if key in self._value:\n            return float(self._value[key])\n        else: return default\n\n    def getlist(self, key, default):\n        \"\"\" Gets the value held and parses it as a list. In the emp.cfg files\n        lists are held like this:\n            [section]\n            islist = value1,value2,value3\n            notlist = value\n        This means that any list given to TinyCfgPrsr will turn it into this \n        string.\n        \"\"\"\n        if key in self._value and self._value[key] != '':\n            return self._value[key].split(',')\n        else: return default\n    \n    def getboolean(self, key, default):\n        \"\"\" Gets the value held and casts it as a boolean, may throw a \n        ValueError if there is a problem casting the value.\n        \"\"\"\n        if key in self._value:\n            res=self._value[key].lower()\n            if res in TinyCfgPrsr.BOOL_CHOICES:\n                return TinyCfgPrsr.BOOL_CHOICES[res]\n            else: return False\n        else: return default\n    \n    def getgroups(self, grouptag=\"_g*\"):\n        \"\"\"If you want a group of variables TinyCfgPrsr can help you get \n        them quickly. Heres an example of what groups are:\n            [mysection]\n            myinfo = 3843.398\n            name_g*1 = bob \n            address_g*1 = 123 mystreet rd\n            address_2_g*1 = 12345, New Port, State\n            name_g*2 = mary\n            phone_g*2 = 128739174\n        When you call getgroups() you will get back a TinyCfgPrsr with the\n        value 'myinfo' since it doesnt belong to a group, and a dictionary\n        with two values '1' and '2' (these can be different strings, just\n        using numbers for demonstration). These values map to two \n        TinyCfgPrsrs with the values on the other side of _g*. \n            (eg. name, address, address_2 are the keys in the first \n            TinyCfgPrsr and name, phone are the keys in the other.) \n        \"\"\"\n        defaults = {}\n        groups = {}\n        for key in self._value.keys():\n            if grouptag in key:\n                try: \n                    name,group = self.__parsekey(key, grouptag)\n                    if group in groups:\n                        groups[group].update({name:self._value[key]})\n                    else: groups[group] = {name:self._value[key]}\n                except: defaults[key] = self._value[key]\n        \n        tinygroups = {}\n        for key in groups.keys():\n            tinygroups[key] = TinyCfgPrsr(groups[key])\n        \n        return TinyCfgPrsr(defaults), tinygroups\n    \n    def __parsekey(self, key, tag):\n        lst = key.split(tag)\n        if len(lst) != 2: raise Exception(\"doesn't have tag\")\n        else: return lst[0], lst[1]\n    \n    def __cvrtList(self, val):\n        return \",\".join(str(n) for n in val)\n    \n    def set(self, key, value):\n        \"\"\" Converts the value into a string and then saves it in the\n        internal configuration index. Can be retrieved by using the \n        get methods. Can only save integers, floats, booleans, strings,\n        and lists of any of the above. Objects and dictionaries are\n        expressly prohibited. (for now)\n        \"\"\"\n        if type(value) is list:\n            value = self.__cvrtList(value)\n        elif type(value) is dict:\n            raise Exception(\"Can't save dictionaries in the config file.\")\n        elif type(value) is object:\n            raise Exception(\"Can't save objects in the config file.\")\n        else: value = str(value)\n        self._value[key] = value\n\n    def setgroup(self, cfgs, groupname, grouptag=\"_g*\"):\n        \"\"\" Saves a group of variables as a 'group'. Make sure the \n        group name you give is unique from the others, as TinyCfgPrsr\n        will not do it for you. If you want a different separator between \n        the variable name and the group-name than'_g*' you can, but make \n        sure you 'getgroups' with the same separator.\n        \n        cfgs can be a dictionary that you make of variable names to values\n        (of any valid value), or another TinyCfgParser (as retrieved from\n        'getgroups').\n        \"\"\"\n        val = {}\n        if type(cfgs) is dict: val = cfgs\n        elif type(cfgs) is TinyCfgPrsr: val = cfgs._value\n        else: raise Exception(\"invalid value, must be dictionary or TinyCfgPrsr\")\n        \n        for key in val.keys():\n            newkey = key+grouptag+groupname\n            self.set(newkey, val[key])\n    \n    def getMyEmpSaveDir(self, id):\n        \"\"\" All attachments should save their information in the save directory\n        on the local machine and should only work in their own directory.\n        An example would be on linux:\n                                    ~/.emp/save/2ejqo2/...\n        Where '2ejqo2' is the id of the Attachment.\n        \"\"\"\n        return self._savedir+\"/\"+id+\"/\"\n        \n    \n    ", "repo_name": "dstar4138/emp_draft", "sub_path": "src/empbase/config/empconfigparser.py", "file_name": "empconfigparser.py", "file_ext": "py", "file_size_in_byte": 17965, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "41", "api": [{"api_name": "configparser.ConfigParser", "line_number": 19, "usage_type": "name"}, {"api_name": "empbase.config.defaults.DEFAULT_CFG_FILES", "line_number": 31, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser.__init__", "line_number": 40, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 40, "usage_type": "name"}, {"api_name": "sys.version_info", "line_number": 42, "usage_type": "attribute"}, {"api_name": "empbase.config.defaults.DEFAULT_CONFIGS", "line_number": 45, "usage_type": "name"}, {"api_name": "empbase.config.defaults.DEFAULT_CONFIGS", "line_number": 48, "usage_type": "argument"}, {"api_name": "os.path.exists", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 67, "usage_type": "call"}, {"api_name": "os.W_OK", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "empbase.config.defaults.ATTACHMENT_DIRS", "line_number": 96, "usage_type": "name"}, {"api_name": "empbase.config.defaults.SAVE_CFG_FILE", "line_number": 125, "usage_type": "name"}, {"api_name": "empbase.attach.attachments.EmpAlarm", "line_number": 131, "usage_type": "argument"}, {"api_name": "logging.exception", "line_number": 139, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 142, "usage_type": "call"}, {"api_name": "empbase.config.defaults.SAVE_CFG_FILE", "line_number": 142, "usage_type": "name"}, {"api_name": "empbase.config.defaults.SAVE_CFG_FILE", "line_number": 143, "usage_type": "argument"}, {"api_name": "empbase.config.defaults.SAVE_CFG_FILE", "line_number": 145, "usage_type": "argument"}, {"api_name": "empbase.config.defaults.SAVE_CFG_FILE", "line_number": 147, "usage_type": "argument"}, {"api_name": "logging.debug", "line_number": 150, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 152, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 169, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 207, "usage_type": "call"}, {"api_name": "configparser.DEFAULTSECT", "line_number": 237, "usage_type": "name"}, {"api_name": "configparser.DEFAULTSECT", "line_number": 241, "usage_type": "name"}, {"api_name": "configparser.DEFAULTSECT", "line_number": 252, "usage_type": "name"}]}
{"seq_id": "70910473724", "text": "from itertools import product\nfrom .text import lowers\nfrom .modifier import Result\n\nkeypad = {\n    2:'abc',\n    3:'def',\n    4:'ghi',\n    5:'jkl',\n    6:'mno',\n    7:'pqrs',\n    8:'tuv',\n    9:'wxyz',\n}\npadback = dict()\nfor (k, letters) in keypad.items():\n    for l in letters:\n        padback[l] = k\n\ndef to_phone(word):\n    return [padback[c.lower()]  if c.lower() in lowers else c for c in word]\n\ndef from_phone(sequence):\n    strings = [keypad[i] for i in sequence]\n    for combo in product(*strings):\n        yield Result(''.join(combo), sequence)\n\ndef from_word(word):\n    return from_phone(to_phone(word))\n", "repo_name": "dplepage/toolbot", "sub_path": "puztool/phone.py", "file_name": "phone.py", "file_ext": "py", "file_size_in_byte": 614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "text.lowers", "line_number": 21, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 25, "usage_type": "call"}, {"api_name": "modifier.Result", "line_number": 26, "usage_type": "call"}]}
{"seq_id": "12933981070", "text": "import os, sys\nimport chainer\nfrom matplotlib import pylab as plt\nimport numpy as np\nnp.random.seed(555)\nfrom image_normalizer import ImageNormalizer\nfrom skimage import io, transform\nimport numbers\nimport pandas as pd\n\n\nclass DatasetPreProcessor(chainer.dataset.DatasetMixin):\n    def __init__(self, args):\n        self.args = args\n        self.image_normalizer = ImageNormalizer()\n        self.pairs = self.read_paths()\n        self.counter = 0\n        self.image_size_in_batch = [None, None]  # height, width\n\n    def __len__(self):\n        return len(self.pairs)\n\n    def read_paths(self):\n        path_label_pairs = []\n        for image_path, label in self.__path_label_pair_generator():\n            path_label_pairs.append((image_path, label))\n        return path_label_pairs\n\n    def __path_label_pair_generator(self):\n        with open(self.args.image_pointer_path, 'r') as f_image:\n            for image_file_name in f_image:\n                image_file_name = image_file_name.rstrip()\n                image_full_path = os.path.join(self.args.image_dir_path, image_file_name)\n\n                label_file_name = image_file_name.replace('.png', '_L.npz')\n                label_full_path = os.path.join(self.args.image_dir_path, label_file_name)\n                if os.path.isfile(image_full_path) and os.path.isfile(label_full_path):\n                    yield image_full_path, np.load(label_full_path)['data']\n                else:\n                    assert False, \"error occured at path_label_pair_generator(file is not fined).\"\n\n    def __init_batch_counter(self):\n        if self.args.train and self.counter==self.args.training_params.batch_size:\n            self.counter = 0\n            self.image_size_in_batch = [None, None]\n\n    def __set_image_size_in_batch(self, image):\n        if self.counter==1:\n            resized_h, resized_w = image.shape[:2]\n            self.image_size_in_batch = [resized_h, resized_w]\n\n    def load_data(self, indices):\n        xs = []\n        ys = []\n\n        for idx in indices:\n            batch_inputs = self.get_example(idx)\n            xs.append(batch_inputs[0])\n            ys.append(batch_inputs[1])\n        return np.array(xs, dtype=np.float32), np.array(ys, np.int32),\n\n    def get_example(self, index):\n        self.counter += 1\n        if self.args.debug_mode:\n            if self.counter>15:\n                assert False, 'stop test'\n\n        path, gt = self.pairs[index]\n        image = io.imread(path)\n\n        if image is None:\n            raise RuntimeError(\"invalid image: {}\".format(path))\n\n        if self.args.debug_mode:\n            plt.figure()\n            io.imshow(image)\n            plt.show()\n\n        h, w, ch = image.shape\n\n        image, ms, ds = self.resize_image(image)\n        gt = gt[ds[0]:(gt.shape[0]-ms[0]+ds[0]),\n                ds[1]:(gt.shape[1]-ms[1]+ds[1])]\n\n        image, ms, ds = self.resize_image(image, 0.25)\n        gt = gt[::4, ::4]\n        gt = gt[ds[0]:(gt.shape[0]-ms[0]+ds[0]),\n                ds[1]:(gt.shape[1]-ms[1]+ds[1])]\n\n        if self.args.debug_mode:\n            plt.figure()\n            io.imshow(image)\n            plt.show()\n\n            color_data = pd.read_csv(self.args.label_path)\n            restoration([gt], color_data, './', index, 32)\n\n        # オーグメンテーション\n        if self.args.aug_params.do_augment:\n            image, gt = self.augment_image(image, gt)\n\n        # バッチごとにデータサイズを統一する\n        self.__set_image_size_in_batch(image)\n\n        # 画像の正規化\n        image = self.image_normalizer.GCN(image)\n\n        if self.args.debug_mode:\n            show_image = image.astype(np.uint8)\n            plt.figure()\n            io.imshow(show_image)\n            plt.show()\n\n        # Chainerの入力に合わせてメモリオーダーを変更\n        image = image.transpose(2, 0, 1)\n        # バッチカウンターの初期化\n        self.__init_batch_counter()\n\n        batch_inputs = image.astype(np.float32), np.array(gt, dtype=np.int32)\n        return batch_inputs\n\n    def augment_image(self, image, gt):\n        orig_h, orig_w, _ = image.shape\n        if self.args.aug_params.params.do_scale and self.counter==1:\n            image, ms, ds = self.scaling(image)\n            h, w, ch = image.shape\n            inv_scale = orig_h//h, orig_w//w\n            gt = gt[::inv_scale[0], ::inv_scale[1]]\n\n        if self.args.aug_params.params.do_flip:\n            image, gt = self.flip(image, gt)\n\n        if self.args.aug_params.params.change_britghtness:\n            image = self.random_brightness(image)\n\n        if self.args.aug_params.params.change_contrast:\n            image = self.random_contrast(image)\n\n        return image, gt\n\n    def resize_image(self, image, scale=None):\n        xh, xw = image.shape[:2]\n\n        if scale is None:\n            # スケールの定義\n            h_scale = (xh//chainer.config.user_multiple)*chainer.config.user_multiple/xh\n            w_scale = (xw//chainer.config.user_multiple)*chainer.config.user_multiple/xw\n            scale = h_scale, w_scale\n        elif isinstance(scale, numbers.Number):\n            scale = scale, scale\n        elif isinstance(scale, tuple) and len(scale)>2:\n            raise InvalidArgumentError\n\n        new_sz = (int(xh*scale[0]), int(xw*scale[1]))\n        image = transform.resize(image, new_sz, mode='constant')\n\n        xh, xw = image.shape[:2]\n        m0, m1 = xh % chainer.config.user_multiple, xw % chainer.config.user_multiple\n        d0, d1 = np.random.randint(m0+1), np.random.randint(m1+1)\n\n        image = image[d0:(image.shape[0] - m0 + d0), d1:(image.shape[1] - m1 + d1)]\n\n        if len(image.shape)==2:\n            return image.reshape((image.shape[0], image.shape[1], 1))\n        else:\n            return image, (m0, m1), (d0, d1)\n\n    def flip(self, image, gt):\n        do_flip_xy = np.random.randint(0, 2)\n        do_flip_x = np.random.randint(0, 2)\n        do_flip_y = np.random.randint(0, 2)\n\n        if do_flip_xy: # X,Y軸の反転\n            image = image[::-1, ::-1, :]\n            gt = gt[::-1, ::-1]\n        elif do_flip_x: # X軸の反転\n            image = image[::-1, :, :]\n            gt = gt[::-1, :]\n        elif do_flip_y: # Y軸の反転\n            image = image[:, ::-1, :]\n            gt = gt[:, ::-1]\n        return image, gt,\n\n    def scaling(self, image):\n        do_scale = np.random.randint(0, 2)\n        if do_scale:\n            scale = self.args.aug_params.params.scale[ \\\n                np.random.randint(0,len(self.args.aug_params.params.scale))]\n            return self.resize_image(image, scale)\n        else:\n            return image, None, None\n\n    def random_brightness(self, image, max_delta=63, seed=None):\n        brightness_flag = np.random.randint(0, 2)\n        if brightness_flag:\n            delta = np.random.uniform(-max_delta, max_delta)\n            return image + delta\n        else:\n            return image\n\n    def random_contrast(self, image, lower=0.2, upper=1.8, seed=None):\n        contrast_flag = np.random.randint(0, 2)\n        if contrast_flag:\n            factor = np.random.uniform(-lower, upper)\n            im_mean = image.mean(axis=2)\n            return ((image.transpose(2, 0, 1) - im_mean)*factor + im_mean).transpose(1,2,0).astype(np.uint8)\n        else:\n            return image\n", "repo_name": "ghmagazine/chainerbook", "sub_path": "ch6/mini_batch_loader.py", "file_name": "mini_batch_loader.py", "file_ext": "py", "file_size_in_byte": 7318, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "46", "api": [{"api_name": "numpy.random.seed", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 5, "usage_type": "attribute"}, {"api_name": "chainer.dataset", "line_number": 12, "usage_type": "attribute"}, {"api_name": "image_normalizer.ImageNormalizer", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 60, "usage_type": "attribute"}, {"api_name": "skimage.io.imread", "line_number": 69, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pylab.figure", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 75, "usage_type": "name"}, {"api_name": "skimage.io.imshow", "line_number": 76, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pylab.figure", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 91, "usage_type": "name"}, {"api_name": "skimage.io.imshow", "line_number": 92, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 93, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 109, "usage_type": "attribute"}, {"api_name": "matplotlib.pylab.figure", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 110, "usage_type": "name"}, {"api_name": "skimage.io.imshow", "line_number": 111, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 112, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 119, "usage_type": "attribute"}, {"api_name": "chainer.config", "line_number": 146, "usage_type": "attribute"}, {"api_name": "chainer.config", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numbers.Number", "line_number": 149, "usage_type": "attribute"}, {"api_name": "skimage.transform.resize", "line_number": 155, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 155, "usage_type": "name"}, {"api_name": "chainer.config", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 170, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 185, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 194, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 196, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 202, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 204, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 206, "usage_type": "attribute"}]}
{"seq_id": "71559621261", "text": "import io\nimport os\nimport re\nfrom collections import namedtuple\nfrom typing import Union, List\nfrom urllib.parse import urljoin, urlsplit\n\nimport requests\n\nfrom .file import File\nfrom .job import Job\nfrom .service import Service\n\nVersion = namedtuple('Version', ['client', 'server', 'API'])\n\n\nclass SlivkaClient:\n    \"\"\"\n    A Client class which will connect to the specified address.\n    You can provide a single argument *url* either a string or an\n    instance of ``urllib3.util.url.Url`` of you can specify individual\n    arguments that will be passed to the ``urllib3.util.url.Url``. This\n    method does not check the validity of the URL, its up to the user to\n    make sure that URL is correct and points to an existing server.\n    \"\"\"\n\n    def __init__(self, url: str):\n        if not re.match(r'(\\w+:)?//', url):\n            url = 'http://' + url\n        self._url = urlsplit(url, scheme='http').geturl()\n        self._services = None\n\n    def get_url(self) -> str:\n        \"\"\"Get the URL the client will connect to.\n\n        :rtype: urllib3.util.url.Url\n        \"\"\"\n        return self._url\n\n    url = property(get_url)\n\n    def get_version(self) -> Version:\n        from . import __version__\n        response = requests.get(urljoin(self.url, 'api/version'))\n        response.raise_for_status()\n        resp_json = response.json()\n        return Version(\n            client=__version__,\n            server=resp_json['slivkaVersion'],\n            API=resp_json['APIVersion']\n        )\n\n    version = property(get_version)\n\n    def get_services(self) -> List['Service']:\n        \"\"\"Return the list of services.\n\n        :rtype: list[slivka_client.Service]\n        \"\"\"\n        if self._services is None:\n            self.reload_services()\n        return self._services\n\n    services = property(get_services)\n\n    def get_service(self, name):\n        try:\n            return next(s for s in self.services if s.id == name)\n        except StopIteration:\n            raise KeyError(name)\n\n    __getitem__ = get_service\n\n    def reload_services(self):\n        \"\"\"Force reloading the services list from the server.\"\"\"\n        response = requests.get(urljoin(self.url, 'api/services'))\n        response.raise_for_status()\n        self._services = [\n            Service.from_response(self.url, service)\n            for service in response.json()['services']\n        ]\n\n    def upload_file(self,\n                    file: Union[str, io.BufferedIOBase],\n                    title: str = \"\") -> File:\n        \"\"\"Upload the file to the server and obtain its handler.\n\n        *file* can be either a stream open for reading or a path.\n        If path is provided, the file will be opened in binary mode.\n        Optionally, the file title can be specified.\n\n        :return: handler to the file on the server.\n        :rtype: slivka_client.File\n        \"\"\"\n        if isinstance(file, (str, bytes, os.PathLike)):\n            file = open(file, 'rb')\n        response = requests.post(\n            url=urljoin(self.url, 'api/files'),\n            files={'file': (title, file)}\n        )\n        response.raise_for_status()\n        return File.from_response(self.url, response.json())\n\n    def get_file(self, file_id: str) -> File:\n        \"\"\"Create a file handler from file id.\"\"\"\n        parts = file_id.split('/', 1)\n        if len(parts) > 1:\n            path = f\"api/jobs/{parts[0]}/files/{parts[1]}\"\n        else:\n            path = f\"api/files/{parts[0]}\"\n        response = requests.get(urljoin(self.url, path))\n        response.raise_for_status()\n        return File.from_response(self.url, response.json())\n\n    def get_job(self, job_id: str) -> Job:\n        \"\"\"Create a job handler from job id \"\"\"\n        response = requests.get(urljoin(self.url, f\"api/jobs/{job_id}\"))\n        response.raise_for_status()\n        return Job.from_response(self.url, response.json())\n\n    def __repr__(self):\n        return 'SlivkaClient(%s)' % self._url\n", "repo_name": "bartongroup/slivka-python-client", "sub_path": "slivka_client/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 3939, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "collections.namedtuple", "line_number": 14, "usage_type": "call"}, {"api_name": "re.match", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.parse.urlsplit", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 44, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 55, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 76, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 76, "usage_type": "call"}, {"api_name": "service.Service.from_response", "line_number": 79, "usage_type": "call"}, {"api_name": "service.Service", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 84, "usage_type": "name"}, {"api_name": "io.BufferedIOBase", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.PathLike", "line_number": 95, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 97, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 98, "usage_type": "call"}, {"api_name": "file.File.from_response", "line_number": 102, "usage_type": "call"}, {"api_name": "file.File", "line_number": 102, "usage_type": "name"}, {"api_name": "file.File", "line_number": 85, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 111, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 111, "usage_type": "call"}, {"api_name": "file.File.from_response", "line_number": 113, "usage_type": "call"}, {"api_name": "file.File", "line_number": 113, "usage_type": "name"}, {"api_name": "file.File", "line_number": 104, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 117, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 117, "usage_type": "call"}, {"api_name": "job.Job.from_response", "line_number": 119, "usage_type": "call"}, {"api_name": "job.Job", "line_number": 119, "usage_type": "name"}, {"api_name": "job.Job", "line_number": 115, "usage_type": "name"}]}
{"seq_id": "35405565878", "text": "import numpy as np\nfrom scipy.stats import norm\n\ndef compute_div_pv(interest, dividends):\n    div_pv = 0.\n    for div in dividends:\n        div_pv += div[0] * np.exp(-1*interest*div[1]/12)\n    return div_pv\n\ndef black_scholes(S, K, r, sigma, tao):\n    d1 = (np.log(S / K) + (r + sigma**2 / 2) * tao) / (sigma * np.sqrt(tao))\n    d2 = d1 - sigma * np.sqrt(tao)\n    call = S * norm.cdf(d1) - K * np.exp(-1*r*tao) * norm.cdf(d2)\n    put = K * np.exp(-1*r*tao) * norm.cdf(-1*d2) - S * norm.cdf(-1*d1)\n    return call, put\n\ndef main():\n    spot_price = 75\n    strike = 65\n    interest = 6 * 10**(-2)\n    tao = 6 / 12\n    sigma = 0.35\n\n    dividends = [[1,1], [1,6]]  # (cash div, paied in months)\n\n    div_pv = compute_div_pv(interest, dividends)\n\n    net_spot = spot_price - div_pv\n    c, p = black_scholes(S=net_spot,\n                         K=strike,\n                         r=interest,\n                         sigma=sigma,\n                         tao=tao)\n\n    print(\" - Black-Scholes Option pricing Model - \")\n    print(\"call - %.4f\" % c)\n    print(\"put  - %.4f\" % p)\n\n    \n\"\"\"\n - Black-Scholes Option pricing Model - \ncall - 12.8136\nput  - 2.8580\n\"\"\"\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "Jlin8801/Financial-engineering", "sub_path": "作業四/review_version.py", "file_name": "review_version.py", "file_ext": "py", "file_size_in_byte": 1195, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "numpy.exp", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 12, "usage_type": "call"}, {"api_name": "scipy.stats.norm.cdf", "line_number": 13, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 14, "usage_type": "call"}, {"api_name": "scipy.stats.norm.cdf", "line_number": 14, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 14, "usage_type": "name"}]}
{"seq_id": "40786114703", "text": "from django.contrib import admin\nfrom . models import GolfCourse, Hole\n\n\nclass HoleInline(admin.TabularInline):\n    model = Hole\n    max_num = 18\n    can_delete = False\n    exclude = ['hole_id']\n\n\nclass GolfCourseAdmin(admin.ModelAdmin):\n\n    inlines = [HoleInline]\n    fieldsets = [(None, {'fields': ['course_name']}),\n                 ('Course Total Par', {'fields': ['course_total_par']}), ]\n    exclude = ['course_id']\n    actions_on_top = False\n\n\nadmin.site.register (GolfCourse, GolfCourseAdmin)\n", "repo_name": "gtvanderkin/WakeGolfTour", "sub_path": "WGT_Website/golf_course/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 502, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.contrib.admin.TabularInline", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 5, "usage_type": "name"}, {"api_name": "models.Hole", "line_number": 6, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 12, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 21, "usage_type": "call"}, {"api_name": "models.GolfCourse", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "14264390512", "text": "import requests\nimport json\nimport logging, time, os, datetime\n\nfrom bs4 import BeautifulSoup\nfrom django.http import JsonResponse\nfrom fake_useragent import UserAgent\nfrom logging import config, Formatter, getLogger\nfrom logging.handlers import RotatingFileHandler\nfrom selenium import webdriver\nfrom selenium.webdriver import Firefox, Chrome, ChromeOptions\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom selenium.webdriver.firefox.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\n\nfrom .data import api_text\nfrom .models import Product\nfrom .serializers import ProductSerializer\n\ndef create_logger():\n    logger = logging.getLogger('OZON_api')\n    log_file = 'ozon_api.log'\n    log_formatter = logging.Formatter('%(asctime)s %(name)s - %(levelname)s:%(message)s')\n    handler = RotatingFileHandler(log_file, mode='a', maxBytes=1*1024*1024, backupCount=60)\n    handler.setFormatter(log_formatter)\n    logger.addHandler(handler)\n\n    return logger\n\ndef open_browser():\n    profile = webdriver.FirefoxProfile()\n    \"\"\"\n    ПОМЕНЯТЬ ПУТЬ К РАСШИРЕНИЮ\n    \"\"\"\n    #profile.add_extension(extension='/usr/ozon/ozon_api/doz4@hotmail.com.xpi')\n    #profile.install_addon('/root/.mozilla/extensions/doz4@hotmail.com.xpi', temporary=True)    \n\n    cap = DesiredCapabilities.FIREFOX\n    cap['marionette'] = True\n\n    options = Options()\n    options.headless = True\n    options.add_argument(\"--window-size=1920,1080\")\n    options.add_argument(\"--start-maximized\")\n\n    \"\"\"\n    ПОМЕНЯТЬ ПУТЬ К ДРАЙВЕРУ\n    \"\"\"\n    browser = Firefox(executable_path='/usr/ozon/ozon_api/driver', service_log_path=os.path.devnull, options=options, firefox_profile=profile, capabilities=cap)\n    # browser = Firefox(executable_path='/home/element/Projects/ozon_api/ozon_api/driver', service_log_path=os.path.devnull, options=options, firefox_profile=profile, capabilities=cap)\n    browser.install_addon('/root/.mozilla/extensions/doz4@hotmail.com.xpi', temporary=True)\n    # browser.install_addon('/home/element/Projects/ozon_api/ozon_api/doz4@hotmail.com.xpi', temporary=True)\n\n    browser.maximize_window()        \n    time.sleep(1)\n    browser.get('about:addons')\n    time.sleep(1)\n    browser.find_element_by_xpath(\"//button[@title='Extensions']\").click()\n    time.sleep(2)\n    browser.find_element_by_xpath(\"//div[@class='addon-name-container']\").click()\n    time.sleep(2)\n\n    prefs = browser.find_element_by_xpath(\"//button[@class='tab-button'][text()='Preferences']\")\n \n    prefs.click()\n    time.sleep(0.5)\n    prefs.send_keys(Keys.TAB)\n    time.sleep(0.5)\n    prefs.send_keys(Keys.TAB)\n    time.sleep(0.5)\n    prefs.send_keys(Keys.TAB)\n    time.sleep(0.5)\n    prefs.send_keys(Keys.TAB)\n\n    opts = browser.find_element_by_id('addon-inline-options')\n    opts.send_keys(Keys.TAB)\n    opts.send_keys('************************')\n    opts.send_keys(Keys.TAB)\n    opts.send_keys(Keys.TAB)\n    opts.send_keys(Keys.TAB)\n    opts.send_keys(Keys.SPACE)\n    opts.send_keys(Keys.TAB)\n    opts.send_keys(Keys.SPACE)\n    opts.send_keys(Keys.TAB)\n    opts.send_keys(\"1\")\n    opts.send_keys(Keys.TAB)\n    opts.send_keys(Keys.SPACE)\n    return browser\n\nclass GetProducts(APIView):\n    def get(self, request):\n        products = Product.objects.all().order_by('id_product')\n        serializer = ProductSerializer(products, many=True)\n        context = serializer.data\n        return JsonResponse(context, safe=False)\n", "repo_name": "pymaster13/ozon_api", "sub_path": "api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3796, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 30, "usage_type": "call"}, {"api_name": "selenium.webdriver.FirefoxProfile", "line_number": 37, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 37, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities.FIREFOX", "line_number": 44, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities", "line_number": 44, "usage_type": "name"}, {"api_name": "selenium.webdriver.firefox.options.Options", "line_number": 47, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 73, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 73, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 75, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 75, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 76, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 77, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 77, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 79, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 79, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 82, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 82, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 84, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 84, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 85, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 85, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 86, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 86, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.SPACE", "line_number": 87, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 87, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 88, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 88, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.SPACE", "line_number": 89, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 89, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 90, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 90, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 92, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 92, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.SPACE", "line_number": 93, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 93, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 96, "usage_type": "name"}, {"api_name": "models.Product.objects.all", "line_number": 98, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 98, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 98, "usage_type": "name"}, {"api_name": "serializers.ProductSerializer", "line_number": 99, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 101, "usage_type": "call"}]}
{"seq_id": "5661426250", "text": "import os\nimport time\nfrom PIL import Image, ImageOps\nimport random\nimport numpy as np\n\nimport torch\nimport torch.optim as optim\nimport torch.nn as nn\nimport torchvision\n\nfrom models.snmpnet.snmpnet import Projector\nfrom utils.logger import AverageMeter\nfrom utils import utils\n\n_BASE_PATH = '/home/spaul/windows/TTT-UCDR/src/data'\n\n\nclass Jigsaw_Online:\n\n\tdef __init__(self, te_query, te_gallery, model, checkpoint, img_transforms, args, device, jig_classes=30, bias_whole_image=0.8):\n\n\t\tself.permutations = self.__retrieve_permutations(jig_classes)\n\t\tself.grid_size = 3\n\t\tself.bias_whole_image = bias_whole_image # biases the training procedure to show the whole image more often\n\t\n\t\tself._image_transformer = img_transforms['train']['image']\n\t\tself._augment_tile = img_transforms['train']['tile']\n\t\tself._eval_transformer = img_transforms['eval']\n\t\t\n\t\tdef make_grid(x):\n\t\t\treturn torchvision.utils.make_grid(x, self.grid_size, padding=0)\n\t\tself.returnFunc = make_grid\n\n\t\tself.te_query = te_query\n\t\tself.te_gallery = te_gallery\n\t\tself.model = model\n\t\tself.projector = Projector(args, args.jigsaw_version, 31).to(device)\n\t\tself.checkpoint = checkpoint\n\t\tself.args = args\n\t\tself.device = device\n\n\t\tself.backbone_params = list(self.model.base_model.parameters())[:-2]\n\t\tself.classifier_params = self.model.base_model.last_linear.parameters()\n\n\t\tself.opt_net = optim.SGD(self.backbone_params, momentum=0, weight_decay=0, lr=self.args.lr_net)\n\t\tself.opt_clf = optim.SGD(list(self.classifier_params) + list(self.projector.parameters()), momentum=self.args.momentum, \n\t\t\t\t\t\t\t\t weight_decay=5e-4, nesterov=bool(self.args.nesterov), lr=self.args.lr_clf)\n\n\t\tself.jig_loss = nn.CrossEntropyLoss()\n\t\tself.loss_meter = AverageMeter()\n\n\tdef get_tile(self, img, n):\n\t\tw = float(img.size[0]) / self.grid_size\n\t\ty = int(n / self.grid_size)\n\t\tx = n % self.grid_size\n\t\ttile = img.crop([x * w, y * w, (x + 1) * w, (y + 1) * w])\n\t\ttile = self._augment_tile(tile)\n\t\treturn tile\n\t\t\n\tdef get_tiled_img(self, img):\n\t\t\n\t\tn_grids = self.grid_size ** 2\n\t\ttiles = [None] * n_grids\n\t\tfor n in range(n_grids):\n\t\t\ttiles[n] = self.get_tile(img, n)\n\n\t\torder = np.random.randint(len(self.permutations) + 1) # added 1 for class 0: unsorted\n\t\tif self.bias_whole_image:\n\t\t\tif self.bias_whole_image > random.random():\n\t\t\t\torder = 0\n\t\t\n\t\tif order == 0:\n\t\t\tdata = tiles\n\t\telse:\n\t\t\tdata = [tiles[self.permutations[order - 1][t]] for t in range(n_grids)]\n\t\t\t\n\t\tdata = torch.stack(data, 0)\n\t\tdata = self.returnFunc(data)\n\t\t\n\t\treturn [data, order]\n\n\tdef __retrieve_permutations(self, classes):\n\t\tall_perm = np.load(os.path.join(_BASE_PATH, 'permutations_%d.npy' % (classes)))\n\t\t# from range [1,9] to [0,8]\n\t\tif all_perm.min() == 1:\n\t\t\tall_perm = all_perm - 1\n\n\t\treturn all_perm\n\n\tdef adapt_single_sample(self, img):\n\n\t\tif self.args.online=='std':\n\t\t\t# print('000')\n\t\t\tself.model.load_state_dict(self.checkpoint['model_state_dict'])\n\t\t\tself.projector.reset()\n\n\t\tself.model.train()\n\t\tself.projector.train()\n\n\t\timg_domain = img.split('/')[-3]\n\t\tif img_domain=='sketch' or img_domain=='quickdraw':\n\t\t\tsample = ImageOps.invert(Image.open(img)).convert(mode='RGB')\n\t\telse:\n\t\t\tsample = Image.open(img).convert(mode='RGB')\n\n\t\tfor _ in range(self.args.num_iter):\n\t\t\tinputs = [self.get_tiled_img(self._image_transformer(sample)) for _ in range(self.args.batch_size_train)]\n\t\t\t# extract img and label from list of tuples\n\t\t\tinputs_batch, labels_batch = zip(*inputs)\n\t\t\tinputs_batch, labels_batch = torch.stack(inputs_batch).to(self.device), torch.LongTensor(list(labels_batch)).to(self.device)\n\n\t\t\tself.opt_net.zero_grad()\n\t\t\tself.opt_clf.zero_grad()\n\n\t\t\t_, im_feat = self.model(inputs_batch)\n\t\t\tif self.args.jigsaw_version==1:\n\t\t\t\tim_feat = self.model.base_model.last_linear(im_feat)\n\n\t\t\tpred_var = self.projector(im_feat)\n\t\t\tloss = self.jig_loss(pred_var, labels_batch)\n\t\t\tloss.backward()\n\n\t\t\tself.opt_net.step()\n\t\t\tself.opt_clf.step()\n\n\t\t\tself.loss_meter.update(loss.item(), inputs_batch.size(0))\n\n\tdef test_single_sample(self, img):\n\n\t\tself.model.eval()\n\n\t\timg_domain = img.split('/')[-3]\n\t\timg_clss = img.split('/')[-2]\n\n\t\tif img_domain=='sketch' or img_domain=='quickdraw':\n\t\t\tsample = ImageOps.invert(Image.open(img)).convert(mode='RGB')\n\t\telse:\n\t\t\tsample = Image.open(img).convert(mode='RGB')\n\n\t\twith torch.no_grad():\n\t\t\tinputs = self._eval_transformer(sample).unsqueeze(0).to(self.device)\n\n\t\t\t_, im_feat = self.model(inputs)\n\t\t\tim_em = self.model.base_model.last_linear(im_feat)\n\n\t\treturn im_em.cpu().data.numpy(), np.expand_dims(np.array(img_clss), axis=0)\n\n\tdef save_model(self, path_cp, model_save_name):\n\n\t\tutils.save_checkpoint({\n\t\t\t\t\t\t\t'iter':self.args.num_iter, \n\t\t\t\t\t\t\t'model_state_dict':self.model.state_dict(),\n\t\t\t\t\t\t\t'projector_state_dict':self.projector.state_dict()\n\t\t\t\t\t\t\t}, directory=path_cp, save_name=model_save_name, last_chkpt='')\n\n\tdef train(self):\n\n\t\t# Start counting time\n\t\tstart = time.time()\n\n\t\tnp.random.shuffle(self.te_query['te'])\n\n\t\tprint('\\nQuery set size: ', len(self.te_query['te']))\n\t\tfor i in range(len(self.te_query['te'])):\n\n\t\t\tself.adapt_single_sample(self.te_query['te'][i])\n\t\t\tsk_em, cls_sk = self.test_single_sample(self.te_query['te'][i])\n\n\t\t\t# Accumulate sketch embedding\n\t\t\tif i == 0:\n\t\t\t\tself.acc_sk_em = sk_em\n\t\t\t\tself.acc_cls_sk = cls_sk\n\t\t\telse:\n\t\t\t\tself.acc_sk_em = np.concatenate((self.acc_sk_em, sk_em), axis=0)\n\t\t\t\tself.acc_cls_sk = np.concatenate((self.acc_cls_sk, cls_sk), axis=0)\n\n\t\t\tif (i+1) % self.args.log_interval == 0:\n\t\t\t\tprint('[Train] [{0}/{1}]\\t'\n\t\t\t\t\t  'jigsaw loss: {rn.val:.4f} ({rn.avg:.4f})\\t'\n\t\t\t\t\t  .format(i+1, len(self.te_query['te']), rn=self.loss_meter))\n\n\t\t\t# if (i+1)==50:\n\t\t\t# \tbreak\n\n\t\tif self.args.include_gallery:\n\t\t\tnp.random.shuffle(self.te_gallery['te_unseen_cls'])\n\n\t\tprint('\\nGallery set size: ', len(self.te_gallery['te_unseen_cls']))\n\t\tfor i in range(len(self.te_gallery['te_unseen_cls'])):\n\n\t\t\tif self.args.include_gallery:\n\t\t\t\tself.adapt_single_sample(self.te_gallery['te_unseen_cls'][i])\n\t\t\t\n\t\t\tim_em, cls_im = self.test_single_sample(self.te_gallery['te_unseen_cls'][i])\n\n\t\t\t# Accumulate image embedding\n\t\t\tif i == 0:\n\t\t\t\tself.acc_im_em = im_em\n\t\t\t\tself.acc_cls_im = cls_im\n\t\t\telse:\n\t\t\t\tself.acc_im_em = np.concatenate((self.acc_im_em, im_em), axis=0)\n\t\t\t\tself.acc_cls_im = np.concatenate((self.acc_cls_im, cls_im), axis=0)\n\n\t\t\tif (i+1) % self.args.log_interval == 0:\n\t\t\t\tprint('[Train] [{0}/{1}]\\t'\n\t\t\t\t\t  'jigsaw loss: {rn.val:.4f} ({rn.avg:.4f})\\t'\n\t\t\t\t\t  .format(i+1, len(self.te_gallery['te_unseen_cls']), rn=self.loss_meter))\n\n\t\t\t# if (i+1)==50:\n\t\t\t# \tbreak\n\t\t\n\t\tif self.args.dataset=='DomainNet':\n\t\t\tprint('\\nSeen gallery set size: ', len(self.te_gallery['te_seen_cls']))\n\t\t\tfor i in range(len(self.te_gallery['te_seen_cls'])):\n\n\t\t\t\tim_seen_em, cls_seen_im = self.test_single_sample(self.te_gallery['te_seen_cls'][i])\n\n\t\t\t\t# Accumulate image embedding\n\t\t\t\tif i == 0:\n\t\t\t\t\tacc_im_seen_em = im_seen_em\n\t\t\t\t\tacc_cls_seen_im = cls_seen_im\n\t\t\t\telse:\n\t\t\t\t\tacc_im_seen_em = np.concatenate((acc_im_seen_em, im_seen_em), axis=0)\n\t\t\t\t\tacc_cls_seen_im = np.concatenate((acc_cls_seen_im, cls_seen_im), axis=0)\n\n\t\t\t\t# if (i+1)==50:\n\t\t\t\t# \tbreak\n\n\t\t\tself.acc_im_em_gzs = np.concatenate((self.acc_im_em, acc_im_seen_em), axis=0)\n\t\t\tself.acc_cls_im_gzs = np.concatenate((self.acc_cls_im, acc_cls_seen_im), axis=0)\n\n\t\t\tprint('\\nSeen + Unseen Gallery Emb Dim:{}'.format(self.acc_im_em_gzs.shape))\n\n\t\tprint('\\nQuery Emb Dim:{}; Gallery Emb Dim:{}.'.format(self.acc_sk_em.shape, self.acc_im_em.shape))\n\n\t\tend = time.time()\n\t\telapsed = end-start\n\t\tprint(f\"Time Taken:{elapsed//60:.0f}m{elapsed%60:.0f}s.\\n\")\n\n\n# Jigsaw Puzzle Loss for test time training\ndef jigsaw_ttt(loader, model, jig_classes, args):\n\n\tif args.jigsaw_version==1:\n\t\tjig_classifier = nn.Linear(args.semantic_emb_size, jig_classes).cuda()\n\t\tclassifier_params = list(model.base_model.last_linear.parameters()) + list(jig_classifier.parameters())\n\telif args.jigsaw_version==2:\n\t\tjig_classifier = nn.Linear(2048, jig_classes).cuda()\n\t\tclassifier_params = list(jig_classifier.parameters())\n\telse:\n\t\traise ValueError('Jigsaw version not supported')\n\t\n\t# get list of parameters in model.base_model except model.base_model.last_linear\n\tbackbone_params = list(model.base_model.parameters())[:-2]\n\tjigen_loss = nn.CrossEntropyLoss()\n\n\topt_net = optim.SGD(backbone_params, momentum=0, weight_decay=0, lr=args.lr_net)\n\topt_clf = optim.SGD(classifier_params, momentum=0.9, nesterov=True, weight_decay=5e-4, lr=args.lr_clf)\n\n\tmodel.train()\n\tjig_loss = AverageMeter()\n\n\tfor epoch in range(args.epochs):\n\n\t\t# Start counting time\n\t\tstart = time.time()\n\n\t\tfor i, (data, order, cl) in enumerate(loader):\n\t\t\t\n\t\t\tdata = data.float().cuda()\n\t\t\tdata1, data2 = torch.split(data, [3, 3], dim=1)\n\n\t\t\torder = order.long().cuda()\n\n\t\t\topt_net.zero_grad()\n\t\t\topt_clf.zero_grad()\n\n\t\t\t_, im_feat = model(data2)\n\t\t\tif args.jigsaw_version==1:\n\t\t\t\tim_feat = model.base_model.last_linear(im_feat)\n\t\t\tjig = jig_classifier(im_feat)\n\n\t\t\tloss = jigen_loss(jig, order)\n\t\t\tloss.backward()\n\n\t\t\topt_net.step()\n\t\t\topt_clf.step()\n\n\t\t\tjig_loss.update(loss.item(), data2.size(0))\n\n\t\t\tif (i+1) % args.log_interval == 0:\n\t\t\t\tprint('[Train] Epoch: [{0}][{1}/{2}]\\t'\n\t\t\t\t\t  'Jig loss: {jig.val:.4f} ({jig.avg:.4f})\\t'\n\t\t\t\t\t  .format(epoch+1, i+1, len(loader), jig=jig_loss))\n\n\t\tend = time.time()\n\t\telapsed = end-start\n\t\tprint(f\"Time Taken:{elapsed//60:.0f}m{elapsed%60:.0f}s.\\n\")\n\t\n\treturn model", "repo_name": "mvp18/TTT-UCDR", "sub_path": "src/algos/Jigsaw/jigsaw.py", "file_name": "jigsaw.py", "file_ext": "py", "file_size_in_byte": 9342, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "46", "api": [{"api_name": "torchvision.utils.make_grid", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.snmpnet.snmpnet.Projector", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "utils.logger.AverageMeter", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 68, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "PIL.ImageOps.invert", "line_number": 103, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 103, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 103, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 103, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 105, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 111, "usage_type": "call"}, {"api_name": "PIL.ImageOps.invert", "line_number": 137, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 137, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 137, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 137, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 139, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "utils.utils.save_checkpoint", "line_number": 151, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 151, "usage_type": "name"}, {"api_name": "time.time", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 231, "usage_type": "call"}, {"api_name": "time.time", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 246, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 249, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 256, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 256, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 258, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 259, "usage_type": "name"}, {"api_name": "utils.logger.AverageMeter", "line_number": 262, "usage_type": "call"}, {"api_name": "time.time", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.split", "line_number": 272, "usage_type": "call"}, {"api_name": "time.time", "line_number": 297, "usage_type": "call"}]}
{"seq_id": "30953112845", "text": "from game_scripts.utilities import draw_button2\nimport random\nimport pygame\npygame.init()\n\n\n# COLORS\nblack = (0,0,0)\nwhite = (255,255,255)\npink = (255, 153, 255)\ndark_grey = (40,40,40)\ngrey = (60,60,60)\nlight_grey = (100,100,100)\nyellow = (255,204,0)\ngreen = (51,204,51)\npurple = (153,0,255)\norange = (255, 153, 0)\nred = (255, 57, 57)\nblue = (0, 64, 255)\nturquoise = (0, 255, 255)\n\n# FONTS\nfont_h1 = pygame.font.Font('resources/fonts/Gameplay.ttf', 30)\nfont_h2 = pygame.font.Font('resources/fonts/Gameplay.ttf', 25)\nfont_h3 = pygame.font.Font('resources/fonts/Gameplay.ttf', 20)\n\ntetris_panel_width = 400  \ntetris_panel_height = 800\ntetris_panel_X = 250\ntetris_panel_Y = 25\ntetris_panel = pygame.Rect(tetris_panel_X, tetris_panel_Y, tetris_panel_width, tetris_panel_height)\nhold_panel_width = 200\nhold_panel_height = 270\nhold_panel_X = 25\nhold_panel_Y = 50\nnext_panel_width = 210\nnext_panel_height = 500\nnext_panel_X = 670\nnext_panel_Y = 50\n\nBLOCKSIZE = 40\nTEMPLATEWIDTH = 5\nTEMPLATEHEIGHT = 5\nWINDOWWIDTH = 900\nWINDOWHEIGHT = 850\nROWS = 20\nCOLUMNS = 10\nBLANK = '.'\n\n\n# TETRIS UI FUNCTIONS\ndef draw_tetris_panels(screen, score=None, lines=None) :\n\ttetris_panel_border = pygame.Rect(tetris_panel_X-2, tetris_panel_Y-2, tetris_panel_width+4, tetris_panel_height+4)\n\ttetris_panel = pygame.Rect(tetris_panel_X, tetris_panel_Y, tetris_panel_width, tetris_panel_height)\n\n\thold_panel_border = pygame.Rect(hold_panel_X-2, hold_panel_Y-2, hold_panel_width+4, hold_panel_height+4)\n\thold_panel = pygame.Rect(hold_panel_X, hold_panel_Y, hold_panel_width, hold_panel_height)\n\thold_space = pygame.Rect(hold_panel_X+10, hold_panel_Y+60, hold_panel_width-20, hold_panel_height-70)\n\n\tscore_panel_width = 150\n\tscore_panel_height = 300\n\tscore_panel_X = 50\n\tscore_panel_Y = 500\n\tscore_panel_border = pygame.Rect(score_panel_X-2, score_panel_Y-2, score_panel_width+4, score_panel_height+4)\n\tscore_panel = pygame.Rect(score_panel_X, score_panel_Y, score_panel_width, score_panel_height)\n\tscore_space = pygame.Rect(score_panel_X+10, score_panel_Y+60, score_panel_width-20, 50)\n\tlines_space = pygame.Rect(score_panel_X+10, score_panel_Y+220, score_panel_width-20, 50)\n\n\tnext_panel_border = pygame.Rect(next_panel_X-2, next_panel_Y-2, next_panel_width+4, next_panel_height+4)\n\tnext_panel = pygame.Rect(next_panel_X, next_panel_Y, next_panel_width, next_panel_height)\n\tnext_space = pygame.Rect(next_panel_X+10, next_panel_Y+60, next_panel_width-20, next_panel_height-70)\n\n\tpause_button_size = 100\n\tpause_button_X = 725\n\tpause_button_Y = 650\n\tpause_button_border = pygame.Rect(pause_button_X-2, pause_button_Y-2, pause_button_size+4, pause_button_size+4)\n\tpause_button = pygame.Rect(pause_button_X, pause_button_Y, pause_button_size, pause_button_size)\n\tpause_button_simbol_1 = pygame.Rect(pause_button_X+30, pause_button_Y+15, 10, 70)\n\tpause_button_simbol_2 = pygame.Rect(pause_button_X+60, pause_button_Y+15, 10, 70)\n\n\t# \t- TETRIS\n\t# Panel\n\tpygame.draw.rect(screen, pink, tetris_panel_border)\n\tpygame.draw.rect(screen, dark_grey, tetris_panel)\n\n\n\t# \t- HOLD\n\t# Panel\n\tpygame.draw.rect(screen, pink, hold_panel_border)\n\tpygame.draw.rect(screen, dark_grey, hold_panel)\n\tpygame.draw.rect(screen, black, hold_space, border_radius = 10)\n\n\t# Text\n\thold_string = 'HOLD'\n\thold_text = font_h2.render(hold_string, True, white)\n\tscreen.blit(hold_text, (hold_panel_X+65, hold_panel_Y+15))\n\n\n\t# \t- SCORE / LINES\n\t# Panel\n\tpygame.draw.rect(screen, pink, score_panel_border)\n\tpygame.draw.rect(screen, dark_grey, score_panel)\n\tpygame.draw.rect(screen, black, score_space, border_radius = 10)\n\tpygame.draw.rect(screen, black, lines_space, border_radius = 10)\n\n\t# Text\n\tscore_string = 'SCORE'\n\tlines_string = 'LINES'\n\tscore_text = font_h2.render(score_string, True, white)\n\tlines_text = font_h2.render(lines_string, True, white)\n\tscreen.blit(score_text, (score_panel_X+30, score_panel_Y+15))\n\tscreen.blit(lines_text, (score_panel_X+35, score_panel_Y+175))\n\n\tif score != None and lines != None :\n\t\tscore = font_h3.render(str(score).center(7), True, white)\n\t\tlines = font_h3.render(str(lines).center(7), True, white)\n\t\tscreen.blit(score, (score_panel_X+30, score_panel_Y+70))\n\t\tscreen.blit(lines, (score_panel_X+30, score_panel_Y+230))\n\n\n\t# \t- NEXT\n\t# Panel\n\tpygame.draw.rect(screen, pink, next_panel_border)\n\tpygame.draw.rect(screen, dark_grey, next_panel)\n\tpygame.draw.rect(screen, black, next_space, border_radius = 10)\n\n\t# Text\n\tnext_string = 'NEXT'\n\tnext_text = font_h2.render(next_string, True, white)\n\tscreen.blit(next_text, (next_panel_X+70, next_panel_Y+15))\n\n\n\t# \t- PAUSE\n\t\t# Button\n\tpygame.draw.rect(screen, pink, pause_button_border, border_radius = 20)\n\tpygame.draw.rect(screen, black, pause_button, border_radius = 20)\n\tpygame.draw.rect(screen, white, pause_button_simbol_1)\n\tpygame.draw.rect(screen, white, pause_button_simbol_2)\n\ndef draw_tetris_board(screen) :\n\tcover_above_board1 = pygame.Rect(tetris_panel_X, 0, tetris_panel_width, 23)\n\tcover_above_board2 = pygame.Rect(tetris_panel_X, 23, tetris_panel_width, 2)\n\tpygame.draw.rect(screen, black, cover_above_board1)\n\tpygame.draw.rect(screen, pink, cover_above_board2)\n\tfor i in range(ROWS):\n\t\t\tpygame.draw.line(screen, light_grey, (tetris_panel_X, tetris_panel_Y+ i*BLOCKSIZE), (tetris_panel_X + tetris_panel_width, tetris_panel_Y + i * BLOCKSIZE))\n\t\t\tfor j in range(COLUMNS):\n\t\t\t\t\tpygame.draw.line(screen, light_grey, (tetris_panel_X + j * BLOCKSIZE, tetris_panel_Y), (tetris_panel_X + j * BLOCKSIZE, tetris_panel_Y + tetris_panel_height))\n\ndef draw_tetris_menu(screen, mouse, record_1_string, record_2_string, record_3_string, record_4_string, record_5_string) :\n\t# TETRIS LABEL\n\ttetris_title_string = 'TETRIS'\n\ttetris_label_width = 300\n\ttetris_label_height = 110\n\ttetris_label_X = 300\n\ttetris_label_Y = 75\n\t\n\tpygame.draw.rect(screen, pink, [tetris_label_X-10, tetris_label_Y-10, tetris_label_width+20, tetris_label_height+20])\n\tpygame.draw.rect(screen, pink, [tetris_label_X+99-10, tetris_label_Y-10, tetris_label_height+20, tetris_label_width-100+20])\n\n\tpygame.draw.rect(screen, purple, [tetris_label_X-8, tetris_label_Y-8, tetris_label_width+16, tetris_label_height+16])\n\tpygame.draw.rect(screen, purple, [tetris_label_X+99-8, tetris_label_Y-8, tetris_label_height+16, tetris_label_width-100+16])\n\n\tpygame.draw.rect(screen, pink, [tetris_label_X-2, tetris_label_Y-2, tetris_label_width+4, tetris_label_height+4])\n\tpygame.draw.rect(screen, pink, [tetris_label_X+99-2, tetris_label_Y-2, tetris_label_height+4, tetris_label_width-100+4])\n\n\tpygame.draw.rect(screen, black, [tetris_label_X, tetris_label_Y, tetris_label_width, tetris_label_height])\n\tpygame.draw.rect(screen, black, [tetris_label_X+99, tetris_label_Y, tetris_label_height, tetris_label_width-100])\n\n\ttetris_font = pygame.font.Font('resources/fonts/UniformBold.ttf', 100)\n\ttetris_text = tetris_font.render(tetris_title_string, True, white)\n\tscreen.blit(tetris_text, (tetris_label_X+18, tetris_label_Y))\n\n\t# START BUTTON\n\tplay_string = '    PLAY'\n\tplay_button_X = 325\n\tplay_button_Y = 350\n\tdraw_button2(screen, play_button_X, play_button_Y, play_string, mouse)\n\n\t# HIGH SCORES TABLE\n\thighscores_string = 'HIGH SCORES'\n\thighscores_text = font_h1.render(highscores_string, True, white)\n\thighscores_panel_width = 340\n\thighscores_panel_height = 320\n\thighscores_panel_X = 280\n\thighscores_panel_Y = 470\n\thighscores_panel_border = pygame.Rect(highscores_panel_X-2, highscores_panel_Y-2, highscores_panel_width+4, highscores_panel_height+4)\n\thighscores_panel = pygame.Rect(highscores_panel_X, highscores_panel_Y, highscores_panel_width, highscores_panel_height)\n\trecord_1 = pygame.Rect(highscores_panel_X+15, highscores_panel_Y+60, highscores_panel_width-30, 47) \n\trecord_2 = pygame.Rect(highscores_panel_X+15, highscores_panel_Y+109, highscores_panel_width-30, 47)\n\trecord_3 = pygame.Rect(highscores_panel_X+15, highscores_panel_Y+158, highscores_panel_width-30, 47)\n\trecord_4 = pygame.Rect(highscores_panel_X+15, highscores_panel_Y+207, highscores_panel_width-30, 47)\n\trecord_5 = pygame.Rect(highscores_panel_X+15, highscores_panel_Y+256, highscores_panel_width-30, 47)\n\n\trecord1_text = font_h2.render(record_1_string, True, white)\n\trecord2_text = font_h2.render(record_2_string, True, white)\n\trecord3_text = font_h2.render(record_3_string, True, white)\n\trecord4_text = font_h2.render(record_4_string, True, white)\n\trecord5_text = font_h2.render(record_5_string, True, white)\n\n\tpygame.draw.rect(screen, pink, highscores_panel_border)\n\tpygame.draw.rect(screen, black, highscores_panel)\n\n\tpygame.draw.rect(screen, grey, record_1)\n\tpygame.draw.rect(screen, light_grey, record_2)\n\tpygame.draw.rect(screen, grey, record_3)\n\tpygame.draw.rect(screen, light_grey, record_4)\n\tpygame.draw.rect(screen, grey, record_5)\n\n\tscreen.blit(highscores_text, (highscores_panel_X+60, highscores_panel_Y+10))\n\tscreen.blit(record1_text, (highscores_panel_X+50, highscores_panel_Y+65,))\n\tscreen.blit(record2_text, (highscores_panel_X+50, highscores_panel_Y+114,))\n\tscreen.blit(record3_text, (highscores_panel_X+50, highscores_panel_Y+163))\n\tscreen.blit(record4_text, (highscores_panel_X+50, highscores_panel_Y+212))\n\tscreen.blit(record5_text, (highscores_panel_X+50, highscores_panel_Y+261))\n\ndef draw_tetris_pause(screen, mouse) :\n\t# Cover background\n\tpygame.draw.rect(screen, light_grey, tetris_panel)\n\n\t# Menu\n\tpause_string = 'PAUSE'\n\tpause_menu_size = 350\n\tpause_menu_X = 275\n\tpause_menu_Y = 220\n\tpause_menu_border = pygame.Rect(pause_menu_X-2, pause_menu_Y-2, pause_menu_size+4, pause_menu_size+4)\n\tpause_menu = pygame.Rect(pause_menu_X, pause_menu_Y, pause_menu_size, pause_menu_size)\n\tpygame.draw.rect(screen, pink, pause_menu_border)\n\tpygame.draw.rect(screen, black, pause_menu)\n\tpause_label = font_h1.render(pause_string, True, white)\n\tscreen.blit(pause_label, (pause_menu_X+125, pause_menu_Y+20))\n\n\t# Buttons\n\tresume_string = '  RESUME'\n\tcontrols_string = 'CONTROLS'\n\tquit_string = '     QUIT'\n\tresume_button_X = 325\n\tresume_button_Y = 300\n\tcontrols_button_X = 325\n\tcontrols_button_Y = 390\n\tquit_button_X = 325\n\tquit_button_Y = 480\n\tdraw_button2(screen, resume_button_X, resume_button_Y, resume_string, mouse)\n\tdraw_button2(screen, controls_button_X, controls_button_Y, controls_string, mouse)\n\tdraw_button2(screen, quit_button_X, quit_button_Y, quit_string, mouse)\n\n\t# Pause button\n\tpause_button_size = 100\n\tpause_button_X = 725\n\tpause_button_Y = 650\n\tpause_button_border = pygame.Rect(pause_button_X-2, pause_button_Y-2, pause_button_size+4, pause_button_size+4)\n\tpause_button = pygame.Rect(pause_button_X, pause_button_Y, pause_button_size, pause_button_size)\n\tpause_button_simbol_1 = pygame.Rect(pause_button_X+30, pause_button_Y+15, 10, 70)\n\tpause_button_simbol_2 = pygame.Rect(pause_button_X+60, pause_button_Y+15, 10, 70)\n\tpygame.draw.rect(screen, purple, pause_button_border, border_radius = 20)\n\tpygame.draw.rect(screen, black, pause_button, border_radius = 20)\n\tpygame.draw.rect(screen, pink, pause_button_simbol_1)\n\tpygame.draw.rect(screen, pink, pause_button_simbol_2)\n\ndef draw_tetris_game_over(screen, mouse, score) :\n\tgame_over_string = 'GAME OVER'\n\tgame_over_menu_width = 600\n\tgame_over_menu_height = 350\n\tgame_over_menu_X = 150\n\tgame_over_menu_Y = 220\n\tgame_over_menu_border = pygame.Rect(game_over_menu_X-2, game_over_menu_Y-2, game_over_menu_width+4, game_over_menu_height+4)\n\tgame_over_menu = pygame.Rect(game_over_menu_X, game_over_menu_Y, game_over_menu_width, game_over_menu_height)\n\n\tpygame.draw.rect(screen, pink, game_over_menu_border)\n\tpygame.draw.rect(screen, black, game_over_menu)\n\n\t# Score\n\tscore_string = 'SCORE : '+str(score)\n\tscore_label = font_h1.render(score_string, True, white)\n\tscreen.blit(score_label, (game_over_menu_X+175, game_over_menu_Y+100))\n\n\t# Buttons\n\trestart_string = 'RESTART'\n\trestart_button_X = 180\n\trestart_button_Y = 470\n\tquit_string = 'QUIT'\n\tquit_button_X = 470\n\tquit_button_Y = 470\n\n\tdraw_button2(screen, restart_button_X, restart_button_Y, restart_string, mouse)\n\tdraw_button2(screen, quit_button_X, quit_button_Y, quit_string, mouse)\n\n# TETRIS GAME FUNCTIONS\n\nS_SHAPE_TEMPLATE = [['.....',\n                     '.....',\n                     '..OO.',\n                     '.OO..',\n                     '.....'],\n                    ['.....',\n                     '..O..',\n                     '..OO.',\n                     '...O.',\n                     '.....']]\n\nZ_SHAPE_TEMPLATE = [['.....',\n                     '.....',\n                     '.OO..',\n                     '..OO.',\n                     '.....'],\n                    ['.....',\n                     '..O..',\n                     '.OO..',\n                     '.O...',\n                     '.....']]\n\nI_SHAPE_TEMPLATE = [['..O..',\n                     '..O..',\n                     '..O..',\n                     '..O..',\n                     '.....'],\n                    ['.....',\n                     '.....',\n                     'OOOO.',\n                     '.....',\n                     '.....']]\n\nO_SHAPE_TEMPLATE = [['.....',\n                     '.....',\n                     '.OO..',\n                     '.OO..',\n                     '.....']]\n\nJ_SHAPE_TEMPLATE = [['.....',\n                     '.O...',\n                     '.OOO.',\n                     '.....',\n                     '.....'],\n                    ['.....',\n                     '..OO.',\n                     '..O..',\n                     '..O..',\n                     '.....'],\n                    ['.....',\n                     '.....',\n                     '.OOO.',\n                     '...O.',\n                     '.....'],\n                    ['.....',\n                     '..O..',\n                     '..O..',\n                     '.OO..',\n                     '.....']]\n\nL_SHAPE_TEMPLATE = [['.....',\n                     '...O.',\n                     '.OOO.',\n                     '.....',\n                     '.....'],\n                    ['.....',\n                     '..O..',\n                     '..O..',\n                     '..OO.',\n                     '.....'],\n                    ['.....',\n                     '.....',\n                     '.OOO.',\n                     '.O...',\n                     '.....'],\n                    ['.....',\n                     '.OO..',\n                     '..O..',\n                     '..O..',\n                     '.....']]\n\nT_SHAPE_TEMPLATE = [['.....',\n                     '..O..',\n                     '.OOO.',\n                     '.....',\n                     '.....'],\n                    ['.....',\n                     '..O..',\n                     '..OO.',\n                     '..O..',\n                     '.....'],\n                    ['.....',\n                     '.....',\n                     '.OOO.',\n                     '..O..',\n                     '.....'],\n                    ['.....',\n                     '..O..',\n                     '.OO..',\n                     '..O..',\n                     '.....']]\n\nPIECES = {'S': S_SHAPE_TEMPLATE,\n          'Z': Z_SHAPE_TEMPLATE,\n          'J': J_SHAPE_TEMPLATE,\n          'L': L_SHAPE_TEMPLATE,\n          'I': I_SHAPE_TEMPLATE,\n          'O': O_SHAPE_TEMPLATE,\n          'T': T_SHAPE_TEMPLATE}\n\nCOLORS = [green, red, blue, orange, turquoise, yellow, purple]\n\n\ndef get_piece():\n    shape = random.choice(list(PIECES.keys()))\n    piece = {'shape': shape,\n\t\t\t'rotation': random.randint(0, len(PIECES[shape]) - 1),\n\t\t\t'x': int(COLUMNS / 2) - int(TEMPLATEWIDTH / 2),\n\t\t\t'y': -4, # start it above the board\n\t\t\t'color': COLORS[list(PIECES.keys()).index(shape)]}\n    return piece\n\ndef get_empty_board():\n\t# returns a 10x20 array filled with blanks (.)\n    board = []\n    for i in range(COLUMNS):\n        board.append([BLANK] * ROWS)\n    return board\n\ndef is_valid_position(board, piece, ad_X=0, ad_Y=0):\n\tfor x in range(TEMPLATEWIDTH):\n\t\tfor y in range(TEMPLATEHEIGHT):\n\t\t\tabove_board = y + piece['y'] + ad_Y < 0\n\t\t\tif (above_board and (0 <= (x + piece['x'] + ad_X) < COLUMNS)) or (PIECES[piece['shape']][piece['rotation']][y][x] == BLANK):\n\t\t\t\tcontinue\n\t\t\tif not (0 <= (x + piece['x'] + ad_X) < COLUMNS and (y + piece['y'] + ad_Y) < ROWS):\n\t\t\t\treturn False\n\t\t\tif board[x + piece['x'] + ad_X][y + piece['y'] + ad_Y] != BLANK:\n\t\t\t\treturn False\n\treturn True\n\ndef add_piece_to_board(board, piece):\n    for x in range(TEMPLATEWIDTH):\n        for y in range(TEMPLATEHEIGHT):\n            if PIECES[piece['shape']][piece['rotation']][y][x] != BLANK:\n                board[x + piece['x']][y + piece['y']] = piece['color']\n\ndef draw_board_blocks(screen, board):\n    for x in range(COLUMNS):\n        for y in range(ROWS):\n            draw_block(screen, x, y, board[x][y])\n\ndef draw_block(screen, blockx, blocky, color, x_axis=None, y_axis=None):\n\t# blockx and blocky are variables for the board pieces\n\t# x_axis and y_axis are variables for drawing the next and hold pieces\n    if color == BLANK:\n        return\n    if x_axis == None and y_axis == None:\n        x_axis, y_axis = tetris_panel_X + (blockx * BLOCKSIZE), tetris_panel_Y + (blocky * BLOCKSIZE) \n    pygame.draw.rect(screen, color, (x_axis, y_axis, BLOCKSIZE , BLOCKSIZE))\n\ndef draw_piece(screen, piece, pixelx=None, pixely=None):\n    piece_shape = PIECES[piece['shape']][piece['rotation']]\n    if pixelx == None and pixely == None:\n        # if pixelx & pixely are specified, use the location stored in the piece data structure\n        pixelx, pixely = tetris_panel_X + (piece['x'] * BLOCKSIZE), tetris_panel_Y + (piece['y'] * BLOCKSIZE)\n\n    # draw each of the boxes that make up the piece\n    for x in range(TEMPLATEWIDTH):\n        for y in range(TEMPLATEHEIGHT):\n            if piece_shape[y][x] != BLANK:\n                draw_block(screen, None, None, piece['color'], pixelx + (x * BLOCKSIZE), pixely + (y * BLOCKSIZE))\n\ndef draw_ui_pieces(screen, next_piece1, next_piece2, hold_piece):\n\tif next_piece1 != None : draw_piece(screen, next_piece1,  pixelx=next_panel_X+15, pixely=next_panel_Y+90)\n\tif next_piece2 != None : draw_piece(screen, next_piece2,  pixelx=next_panel_X+15, pixely=next_panel_Y+270)\n\tif hold_piece != None : draw_piece(screen, hold_piece,  pixelx=hold_panel_X+20, pixely=hold_panel_Y+80)\n\ndef remove_complete_lines(board):\n\tremoved_lines = 0\n\tscore = 0 \n\tcomplete_line = True\n\ty = ROWS - 1\n\twhile y >= 0:\n\t\tfor x in range(COLUMNS):\n\t\t\tif board[x][y] == BLANK:\n\t\t\t\tcomplete_line = False\n\t\t\t\tbreak\n\n\t\tif complete_line:\n\t\t\t# Remove the line and pull blocks down\n\t\t\tfor pull_blocks_down in range(y, 0, -1):\n\t\t\t\tfor x in range(COLUMNS):\n\t\t\t\t\tboard[x][pull_blocks_down] = board[x][pull_blocks_down-1]\n\t\t\t\n\t\t\tfor x in range(COLUMNS):\n\t\t\t\tboard[x][0] = BLANK\n\t\t\t\n\t\t\tremoved_lines += 1\n\t\telse:\n\t\t\tcomplete_line = True\n\t\t\ty -= 1\n\n\tif removed_lines == 1 : score = 10\n\tif removed_lines == 2 : score = 50\n\tif removed_lines == 3 : score = 250\n\tif removed_lines == 4 : score = 1000\t\t\n\n\treturn [score, removed_lines]", "repo_name": "45NGC/Arcade-Machine", "sub_path": "game_scripts/tetris.py", "file_name": "tetris.py", "file_ext": "py", "file_size_in_byte": 18767, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 78, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 84, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 101, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 101, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 102, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 104, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 123, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 124, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 135, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 135, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 136, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 137, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 138, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 141, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 142, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 143, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 144, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 146, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 148, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 148, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 158, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 159, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 161, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 162, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 162, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 164, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 165, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 167, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 168, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 170, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 170, "usage_type": "attribute"}, {"api_name": "game_scripts.utilities.draw_button2", "line_number": 178, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 187, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 188, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 189, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 190, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 191, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 192, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 193, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 201, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 202, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 202, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 204, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 205, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 206, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 206, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 207, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 207, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 208, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 208, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 219, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 219, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 226, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 227, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 228, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 228, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 229, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 229, "usage_type": "attribute"}, {"api_name": "game_scripts.utilities.draw_button2", "line_number": 243, "usage_type": "call"}, {"api_name": "game_scripts.utilities.draw_button2", "line_number": 244, "usage_type": "call"}, {"api_name": "game_scripts.utilities.draw_button2", "line_number": 245, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 251, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 252, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 253, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 254, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 255, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 255, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 256, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 256, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 257, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 257, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 258, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 258, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 266, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 267, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 269, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 269, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 270, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 270, "usage_type": "attribute"}, {"api_name": "game_scripts.utilities.draw_button2", "line_number": 285, "usage_type": "call"}, {"api_name": "game_scripts.utilities.draw_button2", "line_number": 286, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 404, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 406, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 449, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 449, "usage_type": "attribute"}]}
{"seq_id": "41495814799", "text": "r\"\"\"Extracts import dependencies from a Jsonnet file\n\n    > python get_jsonnet_dependencies.py\n    > local thing = import \"thing.jsonnet\";\n    thing.jsonnet\n    > local amoguise = import @\"C:\\Users\\sus\\Documents\\amoguise.libsonnet\";\n    C:\\Users\\sus\\Documents\\amoguise.libsonnet\n    > local a = import \"a.jsonnet\"; local b = importstr \"b.txt\";\n    a.jsonnet\n    b.txt\n\nNote: Imports spanning multiple lines won't be found by this script.\n\n\"\"\"\nimport sys\nimport fileinput\nimport re\nfrom typing import Optional\n\n# - Unescaping escape sequences\n\nESCAPE_PATTERN = re.compile(\n    r'''\n    \\\\[\"'/\\\\bfnrt]  # simple escapes\n    |\\\\u[0-9a-fA-F]{4}  # unicode escape\n    ''',\n    flags=re.VERBOSE,\n)\n\n_SIMPLE_ESCAPES = {\n    \"b\": \"\\b\",\n    \"f\": \"\\f\",\n    \"n\": \"\\n\",\n    \"r\": \"\\r\",\n    \"t\": \"\\t\",\n}\n\ndef _replace_escape(match):\n    string = match[0]\n    if string[1] == \"u\":\n        return chr(int(string[2:], base=16))\n    return _SIMPLE_ESCAPES.get(string[1], string[1])\n\n# - Extracting import strings\n\nIMPORT_PATTERN = re.compile(\n    r'''\n        \\b\n        (?P<func>\n            import(?:str)?  # find import or importstr\n        )\n        \\s*\n        (?P<at>@)?  # check if verbatim string\n        (?P<quote>['\"])\n        (?P<string>\n            (?(at)\n                (?:\n                    (?P=quote){2}  # doubled quotes get turned into one quote\n                    |(?!(?P=quote)).\n                )*\n                |(?:\n                    \\\\.  # escaped char\n                    |(?!\\\\|(?P=quote)).\n                )*\n            )\n        )\n        (?P=quote)\n        (?(at)\n            (?!(?P=quote))  # makes sure @\"\"\" doesnt match\n        )\n    ''',\n    flags=re.VERBOSE,\n)\n\ndef extract_imports(string: str):\n    \"\"\"Yields all import strings found\n\n    - string is the string to search\n\n        >>> list(extract_imports(\"import 'sus'\"))\n        ['sus']\n        >>> list(extract_imports(\"import 'sus' import @'s\\e\\t''us'\"))\n        ['sus', \"s\\\\e\\t'us\"]\n\n    \"\"\"\n    for match in IMPORT_PATTERN.finditer(string):\n        string = match[\"string\"]\n        if match[\"at\"]:  # Verbatim string\n            yield string.replace(match[\"quote\"]*2, match[\"quote\"])\n        else:\n            yield ESCAPE_PATTERN.sub(_replace_escape, string)\n\n# - Command line\n\ndef main(argv: Optional[list[str]] = None):\n    \"\"\"Command line entry point\n\n    - argv => sys.argv[1:]\n\n    \"\"\"\n    if argv is None:\n        argv = sys.argv[1:]\n\n    for line in fileinput.input(argv):\n        # UX improvement by allowing commented imports during development\n        if line.strip().startswith(\"#\") or line.strip().startswith(\"//\"):\n            continue\n        for string in extract_imports(line):\n            print(string)\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "GeeTransit/get-jsonnet-dependencies", "sub_path": "get_jsonnet_dependencies.py", "file_name": "get_jsonnet_dependencies.py", "file_ext": "py", "file_size_in_byte": 2740, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "re.compile", "line_number": 22, "usage_type": "call"}, {"api_name": "re.VERBOSE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 46, "usage_type": "call"}, {"api_name": "re.VERBOSE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 95, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 102, "usage_type": "attribute"}, {"api_name": "fileinput.input", "line_number": 104, "usage_type": "call"}]}
{"seq_id": "7209903166", "text": "import pygame\nimport random\nimport math\npygame.init()\n\n\n#CLASS OF VARIABLES\nclass DrawInformation:\n\tBLACK = 0, 0, 0\n\tWHITE = 183, 196, 208\n\tGREEN = 0, 255, 0\n\tRED = 255, 0, 0\n\tBACKGROUND_COLOR = WHITE\n\n\tGRADIENTS = [\n\t\t(238, 227, 203),\n\t\t(215, 192, 174),\n\t\t(150, 126, 118)\n\t]\n\n\tFONT = pygame.font.SysFont('arial', 30)\n\tLARGE_FONT = pygame.font.SysFont('arial', 40)\n\n\tSIDE_PAD = 120\n\tTOP_PAD = 150\n\n\tdef __init__(self, width, height, lst):\n\t\tself.width = width\n\t\tself.height = height\n\n\t\tself.window = pygame.display.set_mode((width, height))\n\t\tpygame.display.set_caption(\"Xenon\")\n\t\tself.set_list(lst)\n\n\tdef set_list(self, lst):\n\t\tself.lst = lst\n\t\tself.min_val = min(lst)\n\t\tself.max_val = max(lst)\n\n\t\tself.block_width = round((self.width - self.SIDE_PAD) / len(lst))\n\t\tself.block_height = math.floor((self.height - self.TOP_PAD) / (self.max_val - self.min_val))\n\t\tself.start_x = self.SIDE_PAD // 2\n\n\ndef draw(draw_info, algo_name, ascending):\n\tdraw_info.window.fill(draw_info.BACKGROUND_COLOR)\n\n\n\ttitle = draw_info.LARGE_FONT.render(f\"{algo_name}\", 1, draw_info.GREEN)\n\tdraw_info.window.blit(title, (draw_info.width/2 - title.get_width()/2 , 5))\n\n\n\tcontrols = draw_info.FONT.render(\"R - Reset | SPACE - Start Sorting\", 1, draw_info.BLACK)\n\tdraw_info.window.blit(controls, (draw_info.width/2 - controls.get_width()/2 , 45))\n\n\t\n\tsorting = draw_info.FONT.render(\"I - Insertion Sort | B - Bubble Sort |S -Selection Sort\", 1, draw_info.BLACK)\n\tdraw_info.window.blit(sorting, (draw_info.width/2 - sorting.get_width()/2 , 75))\n\n\t\n\t\n\n\tdraw_list(draw_info)\n\tpygame.display.update()\n\n\ndef draw_list(draw_info, color_positions={}, clear_bg=False):\n\tlst = draw_info.lst\n #clearing only the portion of screen which is being used for sorting.\n\tif clear_bg:\n\t\tclear_rect = (draw_info.SIDE_PAD//2, draw_info.TOP_PAD, \n\t\t\t\t\t\tdraw_info.width - draw_info.SIDE_PAD, draw_info.height - draw_info.TOP_PAD)\n\t\t#redrawing\n\t\tpygame.draw.rect(draw_info.window, draw_info.BACKGROUND_COLOR, clear_rect)\n\n #Finding the leftTop(x,y)for each block\n\tfor i, val in enumerate(lst):\n\t\tx = draw_info.start_x + i * draw_info.block_width\n\t\ty = draw_info.height - (val - draw_info.min_val) * draw_info.block_height\n\n\t\tcolor = draw_info.GRADIENTS[i % 3]\n  #manually recoloring blocks that is being sorted.\n\t\tif i in color_positions:\n\t\t\tcolor = color_positions[i] \n\n\t\tpygame.draw.rect(draw_info.window, color, (x, y, draw_info.block_width, draw_info.height))\n\n\tif clear_bg:#if we have redrawn some portion make sure that we update. \n\t\tpygame.display.update()\n\n\ndef generate_starting_list(n, min_val, max_val):\n\tlst = []\n\n\tfor _ in range(n):\n\t\tval = random.randint(min_val, max_val)\n\t\tlst.append(val)\n\n\treturn lst\n\n\ndef selection_sort(draw_info,ascending=True):\n  lst=draw_info.lst\n  size = len(lst)\n  for i in range(size-1):\n      smallIndex = i\n      for j in range(i, size):\n          if lst[j] < lst[smallIndex]:\n              smallIndex = j\n      lst[i], lst[smallIndex] = lst[smallIndex], lst[i]\n      draw_list(draw_info, {i: draw_info.GREEN, j: draw_info.RED}, True) \n      yield True\n  return lst\n\n\ndef bubble_sort(draw_info, ascending=True):\n\tlst = draw_info.lst\n\n\tfor i in range(len(lst) - 1):\n\t\tfor j in range(len(lst) - 1 - i):\n\t\t\tnum1 = lst[j]\n\t\t\tnum2 = lst[j + 1]\n\n\t\t\tif (num1 > num2 and ascending) or (num1 < num2 and not ascending):\n\t\t\t\tlst[j], lst[j + 1] = lst[j + 1], lst[j]\n\t\t\t\t#in this block itself we are going to manually draw the blocks sorted.Redraw entire blocks and color present blocks\n\t\t\t\tdraw_list(draw_info, {j: draw_info.GREEN, j + 1: draw_info.RED}, True)\n\t\t\t\tyield True\n\t\t\t\t#pausing the bubble sort function everytime a swap happens.Wait for calling this next time\n\t\t\t\t#this helps to retain control over reset and exit while the program runs.\n\n\n\treturn lst\n\ndef insertion_sort(draw_info, ascending=True):\n\tlst = draw_info.lst\n\n\tfor i in range(1, len(lst)):\n\t\tcurrent = lst[i]\n\n\t\twhile True:\n\t\t\tascending_sort = i > 0 and lst[i - 1] > current and ascending\n\t\t\tdescending_sort = i > 0 and lst[i - 1] < current and not ascending\n\n\t\t\tif not ascending_sort and not descending_sort:\n\t\t\t\tbreak\n\n\t\t\tlst[i] = lst[i - 1]\n\t\t\ti = i - 1\n\t\t\tlst[i] = current\n\t\t\tdraw_list(draw_info, {i - 1: draw_info.GREEN, i: draw_info.RED}, True)\n\t\t\tyield True\n\n\treturn lst\n\n\ndef main():\n\trun = True\n\tclock = pygame.time.Clock()\n\n\tn = 50\n\tmin_val = 0\n\tmax_val = 100\n\n\tlst = generate_starting_list(n, min_val, max_val)\n\tdraw_info = DrawInformation(1000, 800, lst)\n\tsorting = False\n\tascending = True\n\n\tsorting_algorithm = bubble_sort\n\tsorting_algo_name = \"Bubble Sort\"\n\tsorting_algorithm_generator = None\n\n\twhile run:\n\t\tclock.tick(60)\n\n  #Continously calling the generatoru next method if sorting is true.\n\t\tif sorting:\n\t\t\ttry:\n\t\t\t\tnext(sorting_algorithm_generator)\n\t\t\texcept StopIteration:\n\t\t\t\tsorting = False\n\t\telse:\n\t\t\tdraw(draw_info, sorting_algo_name, ascending)\n\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\trun = False\n\n\t\t\tif event.type != pygame.KEYDOWN:\n\t\t\t\tcontinue\n\n\t\t\tif event.key == pygame.K_r:\n\t\t\t\tlst = generate_starting_list(n, min_val, max_val)\n\t\t\t\tdraw_info.set_list(lst)\n\t\t\t\tsorting = False\n\t\t\telif event.key == pygame.K_SPACE and sorting == False:\n\t\t\t\tsorting = True\n\t\t\t\t#storing the generator object when the sorting algorithm is called.\n\t\t\t\tsorting_algorithm_generator = sorting_algorithm(draw_info, ascending)\n\t\t\telif event.key == pygame.K_i and not sorting:\n\t\t\t\tsorting_algorithm = insertion_sort\n\t\t\t\tsorting_algo_name = \"Insertion Sort\"\n\t\t\telif event.key == pygame.K_b and not sorting:\n\t\t\t\tsorting_algorithm = bubble_sort\n\t\t\t\tsorting_algo_name = \"Bubble Sort\"\n\t\t\telif event.key == pygame.K_s and not sorting:\n\t\t\t\tsorting_algorithm = selection_sort\n\t\t\t\tsorting_algo_name = \"Selection Sort\"\n\n\n\tpygame.quit()\n\n\nif __name__ == \"__main__\":\n\tmain()\n", "repo_name": "vaishakhRaveendran/Visualisation", "sub_path": "program.py", "file_name": "program.py", "file_ext": "py", "file_size_in_byte": 5753, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.font.SysFont", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 32, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 86, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 89, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 159, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 186, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 186, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 187, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 190, "usage_type": "attribute"}, {"api_name": "pygame.K_r", "line_number": 193, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pygame.K_i", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pygame.K_b", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 207, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 212, "usage_type": "call"}]}
{"seq_id": "1125327612", "text": "#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\nimport argparse\nfrom ete3 import Tree\n\n# 创建解析器\nparser = argparse.ArgumentParser(description=\"Remove branch lengths from a Newick tree.\")\n\n# 添加输入文件参数\nparser.add_argument(\"--input\", \"-i\", required=True, help=\"Input tree file in Newick format\")\n\n# 添加输出文件参数\nparser.add_argument(\"--output\", \"-o\", required=True, help=\"Output tree file without branch lengths\")\n\n# 解析命令行参数\nargs = parser.parse_args()\n\n# 从输入文件中读取Newick格式的树\nwith open(args.input, 'r') as f:\n    tree_str = f.read()\n\n# 将树解析为ete3对象\nt = Tree(tree_str)\n\n# 去掉树中的分支长度\nfor node in t.traverse():\n    node.dist = 0\n\n# 将结果写入输出文件\nwith open(args.output, 'w') as f:\n    f.write(t.write())\n\nprint(\"分支长度已经去掉，并已写入文件:\", args.output)\n", "repo_name": "hebuzailikaideshihou/C_genome", "sub_path": "RemoveTreebranch.py", "file_name": "RemoveTreebranch.py", "file_ext": "py", "file_size_in_byte": 879, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "ete3.Tree", "line_number": 23, "usage_type": "call"}]}
{"seq_id": "11155272518", "text": "import paho.mqtt.client as mqtt\nimport requests\n\n# MQTT broker details\nbroker = \"test.mosquitto.org\"\nport = 1883\ntopic = \"http/post\"\n\n# HTTP endpoint details\nurl = \"https://httpbin.org/post\"\nheaders = {\"Content-Type\": \"application/json\"}\npayload = {\"key\": \"Hello MQTT from Python! I am George! I am a student at the University of Patras! I am 23 years old!\"}\n\n# MQTT callback functions\ndef on_connect(client, userdata, flags, rc):\n    print(\"Connected to MQTT broker\")\n    client.publish(topic, str(payload))\n\ndef on_publish(client, userdata, mid):\n    print(\"Message published to MQTT topic\")\n    send_http_request()\n\n# Send HTTP request\ndef send_http_request():\n    response = requests.request(\"POST\", url, headers=headers, data=str(payload))\n    print(\"HTTP response: \" + str(response.status_code))\n    \n\n# Create MQTT client\nclient = mqtt.Client()\n\n# Set MQTT callback functions\nclient.on_connect = on_connect\nclient.on_publish = on_publish\n\n# Connect to MQTT broker\nclient.connect(broker, port, 60)\n\n# Enable network traffic handling\nclient.loop_forever()", "repo_name": "Giwrgosalib/ex3", "sub_path": "ergasia.py", "file_name": "ergasia.py", "file_ext": "py", "file_size_in_byte": 1060, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "requests.request", "line_number": 25, "usage_type": "call"}, {"api_name": "paho.mqtt.client.Client", "line_number": 30, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 30, "usage_type": "name"}]}
{"seq_id": "20045719741", "text": "__all__ = [ 'WidgetException', 'Widget', 'WidgetSettings', 'StringWdg', 'Html', 'Url', 'ClassWdg', 'MethodWdg', 'WidgetSettingSaveCbk' ]\n\nimport types, string, urllib, random\n\ntry:\n    import urlparse\nexcept:\n    from urllib import parse as urlparse\n\ntry:\n    from cStringIO import StringIO as Buffer\nexcept:\n    from io import StringIO as Buffer\n\nimport six\nbasestring = six.string_types\n\n\nfrom pyasm.common import *\nfrom pyasm.biz import Project\nfrom pyasm.security import *\nfrom pyasm.search import *\n\nfrom .web_container import *\nfrom .web_state import *\n\nimport sys\n\nif sys.version_info[0] < 3:\n    IS_Pv3 = False\nelse:\n    IS_Pv3 = True\n\n\nclass WidgetException(Exception):\n    pass\n\n\nclass Widget(object):\n    '''Base class for all display widgets'''\n\n    def _get_my_widgets(self):\n        if self._widgets == None:\n            self._widgets = []\n        return self._widgets\n    def _set_my_widgets(self, widgets):\n        self._widgets = widgets\n    widgets = property(_get_my_widgets, _set_my_widgets)\n\n    def _get_my_named_widgets(self):\n        if self._named_widgets == None:\n            self._named_widgets = {}\n        return self._named_widgets\n    def _set_my_named_widgets(self, named_widgets):\n        self._named_widgets = named_widgets\n    named_widgets = property(_get_my_named_widgets, _set_my_named_widgets)\n\n    def _get_my_sobjects(self):\n        if self._sobjects == None:\n            self._sobjects = []\n        return self._sobjects\n    def _set_my_sobjects(self, sobjects):\n        self._sobjects = sobjects\n    sobjects = property(_get_my_sobjects, _set_my_sobjects)\n\n    def _get_my_options(self):\n        if self._options == None:\n            self._options = {}\n        return self._options\n    def _set_my_options(self, options):\n        self._options = options\n    options = property(_get_my_options, _set_my_options)\n\n\n    # This is said to reduce memory, but it doesn't seem to\n    #__slots__ = ['name', 'title', 'search','_sobjects','current_index','_widgets','named_widgets','typed_widgets','is_top','security_denied','_options','state']\n\n\n    def __init__(self, name=\"\"):\n        self.name = name\n        self.title = ''\n        self.search = None\n        self._sobjects = None\n        self.current_index = -1\n        self._widgets = None\n        self._named_widgets = None\n        self.typed_widgets = None\n        self.is_top = False\n\n        self.security_denied = False\n\n        #self.options = {}\n        self._options = None\n        self.state = None\n\n        # make sure the init function catches security exceptions\n        try:\n            # quick test to see if the class_init function exists\n            \"\"\"\n            if hasattr(self, \"class_init\"):\n                # do a class initialization if necessary\n                inits = Container.get(\"Widget:class_init\")\n                if inits == None:\n                    inits = {}\n                    Container.put(\"Widget:class_init\", inits )\n\n                class_name = Common.get_full_class_name(self)\n                if class_name not in inits:\n                    inits[class_name] = \"\"\n                    self.class_init()\n            \"\"\"\n\n            # call the main initialization function\n            self.init()\n\n        except SecurityException as e:\n            self.__cripple_widget(e)\n\n\n    # DEPRECATED\n    \"\"\"\n    DO NOT REMOVE\n    def class_init(self):\n        '''this function is called once per class.  It is used initialize\n        global features of this class as opposed to a particular instance\n        of a widget'''\n        # Note: this is should not be uncommented.  The code above uses\n        # the existence of this function to optimize\n        pass\n    \"\"\"\n\n    def _use_bootstrap(self):\n        return True\n        from pyasm.biz import ProjectSetting\n        ui_library = ProjectSetting.get_value_by_key(\"feature/ui_library\") or \"bootstrap_material\"\n        if ui_library not in ['bootstrap', 'bootstrap_material']:\n            return False\n        \n    \n    def init_dynamic(arg_dict):\n        '''it returns a dynamically instantiated widget'''\n        raise WidgetException(\"must override [init_dynamic] method\")\n    \n    init_dynamic = staticmethod(init_dynamic)\n\n\n    def init(self):\n        '''initialize all of the widgets.  Classes override this method\n        for initialization'''\n        pass\n\n\n    def __cripple_widget(self, e):\n        '''function that basically makes this widget useless for display\n        purposes.  This ensures that even if a function is overridden,\n        it will not display correctly'''\n\n        def do_search():\n            pass\n        self.do_search = do_search\n\n        def get_display():\n            widget = StringWdg(\"Security Denied: '%s' with error: '%s'<br/>\" \\\n                % (self.__class__.__name__, str(e)) )\n            return widget\n        self.get_display = get_display;\n\n\n    def set_state(self, state):\n        self.state = state\n\n    def get_state(self):\n        return self.state\n\n\n\n    def check_access(cls, group, key, access, value=None, is_match=False, default=\"edit\"):\n        '''convenience function to check the security level to the access\n        manager'''\n        security = Environment.get_security()\n        return security.check_access(group, key, access, value, is_match, default=default)\n    check_access = classmethod(check_access)\n\n\n    def get_access(cls, group, key, default=None):\n        security = Environment.get_security()\n        return security.get_access(group, key, default=None)\n    get_access = classmethod(get_access)\n\n    def get_top(self):\n        '''return the top element.  In the case of widget, this is the instance\n        itself'''\n        return self\n\n\n    def get_name(self):\n        '''returns the name of the widget'''\n        return self.name\n\n    def get_class_name(self):\n        '''returns the class that this sobject is an instance of'''\n        return Common.get_full_class_name(self)\n  \n    def set_title(self, title):\n        self.title = title\n\n    def set_name(self, name):\n        self.name = name\n\n    def set_as_top(self):\n        self.is_top = True\n\n\n    def add_widget(self,widget,name=None,wdgtype=None,index=None,use_state=False):\n        '''add a child widget to this widget'''\n        if not widget:\n            return\n        self._add_widget(widget,name,wdgtype,index=index)\n\n\n    def add(self,widget,name=None,wdgtype=None,index=None,use_state=False):\n        '''Convenience function to add a child widget. Does the same\n        as add widget'''\n        if not widget:\n            return\n        self._add_widget(widget,name,wdgtype,index=index)\n\n\n\n    def _add_widget(self,widget,name=None,wdgtype=None,index=None,use_state=False):\n        '''actually adds the child widget.  This is a protected member\n        and allows the adding of widgets internally.  Subclasses can\n        override add_widget to add to an internal widget instead'''\n\n        assert widget != None\n\n        if isinstance(widget, basestring):\n            widget = StringWdg(widget)\n        elif isinstance(widget, (int, float)):\n            widget = StringWdg(str(widget))\n        elif not isinstance(widget,Widget):\n            widget = StringWdg(str(widget))\n\n\n        if name:\n            # find if the old widget is in the dictionary\n            if self.named_widgets == None:\n                self.named_widgets = {}\n\n\n            if name in self.named_widgets:\n\n                # replace the old widget with the new one\n                self.__replace_widget(widget, name)\n            else:\n                # or just append a new on if it is not in the dictionary\n                if index == None:\n                    self.widgets.append(widget)\n                else:\n                    self.widgets.insert(index, widget)\n\n\n            self.named_widgets[name] = widget\n    \n\n        # if there is no name, just append\n        else:\n            if index == None:\n                self.widgets.append(widget)\n            else:\n                self.widgets.insert(index, widget)\n\n\n\n        if wdgtype != None and wdgtype != \"\":\n\n            if self.typed_widgets == None:\n                self.typed_widgets = {}\n\n            if wdgtype not in self.typed_widgets:\n                self.typed_widgets[wdgtype] = []\n            self.typed_widgets[wdgtype].append(widget)\n\n\n        if use_state:\n            self.state = WebState.get().get_current()\n\n\n    def __replace_widget(self, widget, name):\n        try:\n            old_widget = self.named_widgets[name]\n            index = self.widgets.index(old_widget)\n        except ValueError:\n            pass\n        else:\n            self.widgets[index] = widget\n\n    def set_widget(self, widget, name):\n        assert name != None and name.strip() != ''\n        self.__replace_widget(widget, name) \n        self.named_widgets[name] = widget\n\n    def get_widget(self, name):\n        return self.named_widgets.get(name)\n\n\n    def get_widgets_by_type(self,wdgtype):\n        if wdgtype in self.typed_widgets:\n            return self.typed_widgets[wdgtype]\n        else:\n            return []\n\n\n    def get_widgets(self):\n        return self.widgets\n\n\n    def set_parent(self, parent):\n        return parent.add(self)\n\n\n\n    \"\"\"\n    def add_command(self, command):\n        '''deprecated: use CommandDelegator'''\n        self.commands.append(command)\n    \"\"\"\n\n    def set_search(self, search):\n        '''sets the search that will be performed in the search phase'''\n        self.search = search\n\n    def set_sobjects(self,sobjects,search=None):\n        '''store the sobjects that this widget will act upon'''\n        #print(\"set_sobjects\")\n        if search:\n            self.search = search\n        self.sobjects = sobjects\n        self.current_index = 0\n\n        # DEPRECATED: is this really necessary???\n        # set all of the sobjects to all of the child widgets\n        for widget in (self.widgets):\n            widget.set_sobjects(sobjects,search)\n\n\n    def set_sobject(self,sobject,search=None):\n        '''convenience function to set only one sobject'''\n        if sobject:\n            self.set_sobjects([sobject],search)\n\n    def is_sobjects_explicitly_set(self):\n        return self.current_index != -1\n\n    def get_sobjects(self):\n        return self.sobjects\n\n\n    def set_current_index(self, index):\n        '''sets the index of the current sobject'''\n        self.current_index = index\n\n        for widget in (self.widgets):\n            widget.set_current_index(index)\n    \n    def get_current_index(self):\n        return self.current_index\n\n    def get_current_sobject(self):\n        if self.current_index == -1 or len(self.sobjects) == 0:\n            return None\n        else:\n            return self.sobjects[self.current_index]\n\n\n\n    def alter_search(self, search):\n        '''Alter any search that comes through this widget'''\n        for widget in self.widgets:\n            widget.alter_search( search )\n\n    def do_search(self):\n        '''Perform any searches that were created in the init function.\n        Returns a list of SObjects'''\n        # if no search is defined in this class, then skip this\n        if self.search != None:\n            # give the opportunity for each of the lower widgets to alter the\n            # search\n            self.alter_search( self.search )\n\n            # actually do the search and notify all of the children\n            self.sobjects = self.search.get_sobjects()\n\n        # go through each widget and perform their searches\n        for widget in self.widgets:\n            widget.do_search()\n\n\n        # set the sobjects from this search\n        # FIXME: this has the effect of overriding any lower searches\n        # that may exist.  This is probably not desired behaviour\n        # and should be fixed\n        if self.search != None:\n            self.set_sobjects( self.sobjects, self.search )\n\n\n\n    def check_security(self):\n        '''give the widget a callback that allows it to check security.\n        This function will return if the widget is in a state that\n        is acceptable to the security manager'''\n        # execute all of the security checks of the lower widgets.  If\n        # any fail with a security exception\n        for widget in self.widgets:\n            try:\n                widget.check_security()\n            except SecurityException as e:\n                self.__cripple_widget(e)\n\n\n    def explicit_display(self, cls=None):\n        if not cls:\n            child = self.get_display()\n        else:\n            child = cls.get_display(self)\n        if child:\n            widget = None\n            if isinstance(child,basestring):\n                widget = StringWdg(child)\n                widget.explicit_display()\n            elif isinstance(child,Widget):\n                widget = child.get_display()\n                if widget:\n                    if isinstance(widget,basestring):\n                        widget = StringWdg(widget)\n                    widget.explicit_display()\n            elif isinstance(child,Html):\n                widget = StringWdg(child.getvalue())\n                widget.explicit_display()\n            \n            del(child)\n            del(widget)\n\n\n    def get_display(self):\n        '''Draw all of the contents'''\n        for widget in self.widgets:\n            #try:\n            #    widget.explicit_display()\n            #except SecurityException:\n            #    # do nothing for now\n            #    pass\n            widget.explicit_display()\n\n   \n\n\n    def get_buffer(self, cls=None):\n        '''create a new buffer and execute the get_display of this widget.\n        This is useful for caching the display of a widget for multiple\n        reuse'''\n        # have to bake the widget in a new buffer\n        WebContainer.push_buffer()\n        try:\n            self.explicit_display(cls)\n        finally:\n            buffer = WebContainer.pop_buffer()\n        return buffer\n\n    def get_buffer_display(self, cls=None):\n        '''get a new buffer's value and execute the get_display of this widget.\n        This is useful for caching the display of a widget for multiple\n        reuse'''\n        buffer = self.get_buffer(cls)\n        value = buffer.getvalue()\n        buffer.clear()\n        return value\n\n    def render(self, cls=None):\n        '''get a new buffer's value and execute the get_display of this widget.\n        This is useful for caching the display of a widget for multiple\n        reuse'''\n        return self.get_buffer(cls).getvalue()\n\n\n\n\n    def get_class_display(cls, self):\n        '''Explicitly use the get_display function of a provided class for a\n        particular instance.  This is rarely used and should only be used\n        carefully.  It is intended to break infinite loops when trying to\n        build a composite widget while maintaining an internal widget's\n        interface.  For example see: pyasm.wdget.FilterCheckboxWdg where\n        a span is wrapped around a checkbox, but the widget needs to have the\n        internal checkbox's interface.\n        '''\n        return self.get_buffer(cls).getvalue()\n    get_class_display = classmethod(get_class_display)\n\n    def get_buffer_on_exception(cls, last_buffer=1):\n        '''resets everything currently in the stack of buffers.  This is mainly\n        used when an exception occurs to retrieve all of the widgets that\n        has been assembled so far, but was not returned because\n        of the exception'''\n        widget = Widget()\n        num_buffers = WebContainer.get_num_buffers()\n\n        # NOTE: this is weird ... last buffer is dependent on something.\n        # Not sure what.  For example, it depends on the level of TabWdg\n        # However, this appears to be working\n\n        for i in range(num_buffers, last_buffer, -1):\n            buffer = WebContainer.pop_buffer()\n            widget.add(buffer)\n        return widget\n\n\n\n\n    def is_ajax(cls, check_name=True):\n        ''' return True if it is an Ajax wdg on construction\n        NOTE: there is a is_from_ajax() in AjaxWdg'''\n        # Since TACTIC is 99.9% ajax now, this is probably not relevant\n        web = WebContainer.get_web()\n        ajax_class = web.get_form_value(\"widget\")\n        is_ajax = False\n        if web.get_form_value(\"ajax\") == \"true\":\n            if check_name:\n                module, class_name = Common.breakup_class_path(ajax_class)\n                if class_name == cls.__name__:\n                    is_ajax = True\n            else:\n                is_ajax = True\n\n        return is_ajax\n\n    is_ajax = classmethod(is_ajax)\n    \n    def has_restriction(cls):\n        ''' check if the user is restricted to see this secure wdg based on class name '''\n        security = Environment.get_security()\n        key = cls.__name__\n        if security.check_access(\"public_wdg\", key, access='deny', is_match=True):\n            return True\n        return False\n    has_restriction = classmethod(has_restriction)\n\n    def has_access(cls):\n        ''' check if the user has access to this secure wdg based on class name '''\n        security = Environment.get_security()\n        key = cls.__name__\n        if security.check_access(\"secure_wdg\", key, access='view'):\n            return True\n        return False\n\n    has_access = classmethod(has_access)\n\n    def dump(self):\n        print(self)\n        for widget in self.widgets:\n            print(\"child: \\t\", widget)\n\n        for widget in self.widgets:\n            widget.dump()\n\n\n    def generate_unique_id(base='', wdg='HtmlWdg', is_random=False):\n        ref_count = Container.get(\"%s:ref_count\" %wdg)\n        unique_code = Container.get(\"%s:unique_code\" %wdg)\n        if ref_count == None:\n            ref_count = 0\n            unique_code = ''.join([ Common.randchoice('abcdefghijklmno') for i in range(0, 6)])\n            Container.put(\"%s:unique_code\" %wdg, unique_code)\n      \n        Container.put(\"%s:ref_count\" %wdg, ref_count+1)\n        if is_random:\n            base = '%s_%s' %(base, unique_code) \n       \n        if not base:\n            base = \"unique\"\n        \n            \n        return \"%s_%s\" % (base, ref_count)\n    generate_unique_id = staticmethod(generate_unique_id)\n\n    \n# Determine if there is a TACTIC database in this AppServer\nDATABASE = Environment.has_tactic_database()\n\nclass WidgetSettings(SObject):\n    '''sobject for all storing settings for a widget.  Note that this class\n    respects whether there is a tactic database.  If not, widget settings will\n    fail gracefully.  This is used in SimpleAppServer'''\n\n    SEARCH_TYPE = \"sthpw/wdg_settings\"\n\n\n    #\n    # Simple explicit functions with no manipulation of data\n    #\n\n    def get_value_by_key(key, auto_create=True, default=\"\", is_json=False):\n        if not DATABASE:\n            return default\n\n        settings = WidgetSettings.get_by_key(key)\n        if not settings:\n            return default\n\n        value_str = settings.get_value(\"data\")\n\n\n\n        if is_json:\n            if not value_str:\n                value_str = default\n            else:\n                try:\n                    value_str = jsonloads(value_str)\n                except Exception as e:\n                    print(\"WARNING: \", e)\n                    return default\n\n\n        return value_str\n    get_value_by_key = staticmethod(get_value_by_key)\n\n\n    def set_value_by_key(key, value, default=\"\"):\n        '''set and commit the value'''\n        if not DATABASE:\n            return default\n\n        if isinstance(value, list) or isinstance(value, dict):\n            value = jsondumps(value)\n\n        settings = WidgetSettings.get_by_key(key, auto_create=True)\n        settings.set_value(\"data\", value)\n        settings.commit(triggers=False)\n        return value\n    set_value_by_key = staticmethod(set_value_by_key)\n\n\n\n\n    #\n    # older methods ... need to clean up ... to many assumptions\n    #\n\n    def get_key_values(key, auto_create=True):\n        '''return the values as an array: WARNING: this will split on ||\n        to get an array value'''\n        if not DATABASE:\n            return []\n\n        settings = WidgetSettings.get_by_key(key, auto_create=auto_create)\n        if not settings:\n            return []\n        value_str = settings.get_value(\"data\")\n        if not value_str:\n            return []\n        else:\n            return value_str.split(\"||\")\n    get_key_values = staticmethod(get_key_values)\n\n\n\n\n    def set_key_values(key, values):\n        if not DATABASE:\n            return\n\n        assert(key)\n        settings = WidgetSettings.get_by_key(key)\n        if not settings:\n            return \n\n        tmp_array = []\n        for x in values:\n            if not x:\n                continue\n            if not isinstance(x, basestring):\n                x = str(x)\n            tmp_array.append(x)\n            \n        values_str = \"||\".join(tmp_array)\n        \n        # if the current value is different from stored value, then update\n        if values_str == settings.get_value('data'):\n            return\n        \n        settings.set_value(\"data\", values_str)\n        settings.commit(triggers=False)\n    set_key_values = staticmethod(set_key_values)\n\n\n\n    def get_wdg_value(widget, widget_name):\n        '''boilerplate function which gets the value of a widget'''\n        # ensure uniqueness of a key for the settings\n        class_name = Common.get_full_class_name(widget)\n        value = WidgetSettings.get_key_value(class_name, widget_name)\n        return value\n\n    get_wdg_value = staticmethod(get_wdg_value)\n\n\n\n    # DEPRECATED\n    def get_key_value(key, widget_name):\n\n        #print('DEPRECATED: WidgetSettings.get_key_value()')\n\n        # web form value overrides all others\n        web = WebContainer.get_web()\n        value = web.get_form_value(widget_name,raw=True)\n\n        # make it so the database doesn't need to be present\n        if not DATABASE:\n            return value\n\n        settings_key = \"%s|%s\" % (key,widget_name)\n        settings = WidgetSettings.get_by_key(settings_key)\n        if not settings:\n            return value\n\n        # if value is empty then get it from the database\n        if value == None:\n            # get the widget settings object for this filter\n            value = settings.get_value(\"data\")\n        # if the value is different from the settings then replace in\n        # the database\n        elif settings.get_value(\"data\") != value:\n            settings.set_value(\"data\",value)\n            settings.commit()\n\n        return value\n    get_key_value = staticmethod(get_key_value)\n\n\n\n    def get_by_key(key, auto_create=False):\n        login = WebContainer.get_user_name()\n        dict_key = '%s:%s' %(WidgetSettings.SEARCH_TYPE, login) \n        settings_dict = Container.get(dict_key)\n        setting = None\n        if not settings_dict:\n            \n            settings_dict = {}\n            Container.put(dict_key, settings_dict)\n                   \n            search = Search(WidgetSettings.SEARCH_TYPE)\n            search.add_filter(\"project_code\", Project.get_project_code())\n            search.add_filter(\"login\", login)\n            settings = search.get_sobjects()\n            for setting in settings:\n                settings_dict[setting.get_value('key')] = setting\n                \n        setting = settings_dict.get(key)\n            \n        # autocreate if it does not exist\n        if auto_create and setting == None:\n            setting = WidgetSettings.create(key)\n            \n            settings_dict[setting.get_value('key')] = setting\n\n        return setting\n    get_by_key = staticmethod(get_by_key)\n\n\n\n    def create(key):\n        login = WebContainer.get_login().get_login() \n\n        settings = SObjectFactory.create(WidgetSettings.SEARCH_TYPE)\n        settings.set_value(\"key\",key)\n        settings.set_value(\"login\",login)\n        settings.set_value(\"data\",\"\")\n        settings.set_value(\"project_code\", Project.get_project_code())\n        settings.commit(triggers=False) \n\n        return settings\n    create = staticmethod(create)\n\n\nfrom pyasm.command import Command\nclass WidgetSettingSaveCbk(Command):\n    '''Callback to save a WidgetSetting'''\n    def execute(self):\n        data = self.kwargs.get('data')\n        key = self.kwargs.get('key')\n        value = WidgetSettings.set_value_by_key(key, data)\n        self.info['data'] = value\n\n    def is_undoable(cls):\n        return False\n    is_undoable = classmethod(is_undoable)\n\n\nclass StringWdg(Widget):\n    def __init__(self,string=\"\"):\n        self.widgets = []\n        self.string = string\n        self.name = None\n\n    def do_search(self):\n        pass\n    \n    def check_security(self):\n        pass\n    \n    def get_display(self):\n        html = WebContainer.get_buffer()\n\n        # write directly to the StringIO\n        if not IS_Pv3:\n            if isinstance(self.string, unicode):\n                self.string = Common.process_unicode_string(self.string)\n        html.get_buffer().write(self.string)\n    \nclass ClassWdg(Widget):\n    def __init__(self, class_type=None):\n        self.widgets = []\n        if IS_Pv3:\n            assert isinstance(class_type, type)\n        else:\n            assert isinstance(class_type, types.TypeType)\n\n        self.class_type = class_type\n    \n    def do_search(self):\n        pass\n    \n    def check_security(self):\n        pass\n    \n    def get_display(self):    \n        return self.class_type()\n        \n    def get_class_name(self):\n        return '%s.%s'%(self.class_type.__module__, self.class_type.__name__)\n    \nclass MethodWdg(Widget):\n    def __init__(self, method =''):\n        self.widgets = []\n        assert isinstance(method, types.MethodType)\n        self.method = method\n    def do_search(self):\n        pass\n    \n    def check_security(self):\n        pass\n    \n    def get_display(self):    \n        return self.method()\n        \n    def get_class_name(self):\n        return '%s.%s'%(self.method.im_class.__module__, \\\n            self.method.im_class.__name__)\n\n    def get_function_name(self):\n        return self.method.im_func.func_name\n\nclass Html(Base):\n    '''String buffer class for html code'''\n\n    def __init__(self):\n        self._buffer = Buffer()\n\n    def __del__(self):\n        self.clear()\n\n    def write(self, html):\n        if isinstance(html, basestring):\n            self._buffer.write(html)\n        #elif isinstance(html, int):\n        elif isinstance(html, (int, float)):\n            self._buffer.write( str(html) )\n        elif isinstance(html,Html):\n            self._buffer.write(html.getvalue())\n        elif not html:\n            pass\n        else:\n            raise WidgetException(\"Cannot handle [%s] type\" % html)\n\n\n    def writeln(self, html):\n        self.write(html)\n        self._buffer.write(\"\\n\")\n\n\n    def getvalue(self):\n        '''This exists to maintain backwards compatibility when this class\n        used the StringIO class directly.'''\n        return self._buffer.getvalue()\n\n    def get_display(self):\n        return self._buffer.getvalue()\n\n    def to_string(self):\n        return self._buffer.getvalue()\n\n    def get_buffer(self):\n        return self._buffer\n\n    def clear(self):\n        self._buffer.close()\n\n\n\n\n\nclass Url(Base):\n    '''class which builds up a url'''\n\n    def __init__(self, base=\"\"):\n        self.base = base\n        self.options = {}\n\n    def set_base(self, base):\n        self.base = base\n\n    def append_to_base(self, extra):\n        if extra.startswith(\"/\"):\n            self.base += extra\n        else:\n            self.base += \"/%s\" % extra\n\n\n\n    def set_option(self, name, value):\n        self.options[name] = value\n\n\n    def get_info(self):\n        url = self.to_string()\n        urlparts = urlparse.urlsplit(url)\n        return urlparts\n\n    def get_protocol(self):\n        return self.get_info()[0]\n\n    def get_host(self):\n        return self.get_info()[1]\n\n\n    def get_selector(self):\n        return self.get_info()[2]\n\n\n\n    def to_string(self):\n\n        options_list = []\n        for name,value in self.options.items():\n\n            if isinstance(value, list):\n                for item in value:\n                    if not isinstance(value, basestring):\n                        value = str(value)\n                    encoded = urlparse.quote_plus(value.encode('utf'))\n                    options_list.append(\"%s=%s\" % (name,encoded) )\n            else:\n                if not isinstance(value, basestring):\n                    value = str(value)\n                encoded = urlparse.quote_plus(value.encode('utf'))\n                options_list.append(\"%s=%s\" % (name,encoded) )\n\n        options_str = \"&\".join(options_list)\n\n        if options_str != \"\":\n            url = \"%s?%s\" % (self.base, options_str)\n        else:\n            url = self.base\n\n        return url\n\n\n    def add_web_state(self):\n        '''add web state paramenters to this url'''\n        state = WebState.get()\n        state.add_state_to_url(self)\n\n\n    def get_protocol(self):\n        parts = self.base.split(\"://\")\n        return parts[0]\n\n\n\n    def get_url(self):\n        return self.to_string()\n\n\n    def get_base(self):\n        return self.base\n\n\n\n\n", "repo_name": "Southpaw-TACTIC/TACTIC", "sub_path": "src/pyasm/web/widget.py", "file_name": "widget.py", "file_ext": "py", "file_size_in_byte": 29039, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 469, "dataset": "github-code", "pt": "46", "api": [{"api_name": "six.string_types", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pyasm.biz.ProjectSetting.get_value_by_key", "line_number": 135, "usage_type": "call"}, {"api_name": "pyasm.biz.ProjectSetting", "line_number": 135, "usage_type": "name"}, {"api_name": "{'ProjectSetting': 'pyasm.biz.ProjectSetting'}", "line_number": 505, "usage_type": "call"}, {"api_name": "pyasm.biz.Project.get_project_code", "line_number": 753, "usage_type": "call"}, {"api_name": "pyasm.biz.Project", "line_number": 753, "usage_type": "name"}, {"api_name": "pyasm.biz.Project.get_project_code", "line_number": 779, "usage_type": "call"}, {"api_name": "pyasm.biz.Project", "line_number": 779, "usage_type": "name"}, {"api_name": "pyasm.command.Command", "line_number": 787, "usage_type": "name"}, {"api_name": "types.TypeType", "line_number": 827, "usage_type": "attribute"}, {"api_name": "types.MethodType", "line_number": 846, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 868, "usage_type": "call"}, {"api_name": "urllib.parse.urlsplit", "line_number": 937, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 937, "usage_type": "name"}, {"api_name": "urllib.parse.quote_plus", "line_number": 961, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 961, "usage_type": "name"}, {"api_name": "urllib.parse.quote_plus", "line_number": 966, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 966, "usage_type": "name"}]}
{"seq_id": "1736815722", "text": "from datetime import timedelta\nfrom typing import Any, Dict, List, Tuple, Union\n\nfrom django.core.exceptions import ValidationError\nfrom django.db import models\n\nfrom origin_common.constants import (\n    ADJUSTMENTS,\n    BUSINESS_DAY_CONVENTIONS,\n    CURRENCIES,\n    DAY_COUNTS,\n    FUNDING_BASES,\n    PAYMENT_FREQUENCIES,\n    TENORS,\n)\nfrom origin_common.constants.adjustments import Adjustment\nfrom origin_common.constants.base import Constant, Constants\nfrom origin_common.constants.business_day_conventions import BusinessDayConvention\nfrom origin_common.constants.currencies import Currency\nfrom origin_common.constants.day_counts import DayCount\nfrom origin_common.constants.funding_bases import FundingBasis\nfrom origin_common.constants.payment_frequencies import PaymentFrequency\nfrom origin_common.constants.tenors import Tenor\n\n\nclass ConstantDescriptor:\n    \"\"\"\n    The descriptor for the constant attribute on the model instance.\n    Returns a Constant when accessed so you can do stuff like::\n\n        >>> instance.constant.label\n\n    Assigns a phone number object on assignment so you can do::\n\n        >>> instance.constant = Constant(...)\n    or\n        >>> instance.constant = 'foobar'\n    \"\"\"\n\n    def __init__(self, field: \"ConstantField\"):\n        self.field = field\n\n    def __get__(\n        self, instance: models.Model = None, cls=None\n    ) -> Union[Constant, \"ConstantDescriptor\"]:\n        if instance is None:\n            return self\n        return instance.__dict__[self.field.name]\n\n    def __set__(self, instance: models.Model, value: Any):\n        instance.__dict__[self.field.name] = self.field.to_python(value)\n\n\nclass ConstantField(models.Field):\n    attr_class: type(Constant) = None\n    base_type: type = str\n    constants: Constants = None\n    descriptor_class = ConstantDescriptor\n    description: str = \"A single immutable constant\"\n    type_mappings: Dict[type, models.Field] = {\n        str: models.CharField,\n        int: models.IntegerField,\n        timedelta: models.DurationField,\n    }\n\n    def __init__(self, *args, **kwargs):\n        if \"choices\" not in kwargs:\n            kwargs[\"choices\"] = self.constants.to_django_choices()\n        if \"max_length\" not in kwargs and self.base_type == str:\n            kwargs[\"max_length\"] = max(len(x) for x, _ in kwargs[\"choices\"])\n        super().__init__(*args, **kwargs)\n\n    def deconstruct(self) -> Tuple[str, str, List, Dict]:\n        name, path, args, kwargs = super().deconstruct()\n        if tuple(kwargs[\"choices\"]) == self.constants.to_django_choices():\n            del kwargs[\"choices\"]\n        return name, path, args, kwargs\n\n    def get_internal_type(self) -> str:\n        return self.type_mappings[self.base_type].__name__\n\n    def to_python(self, value: Union[None, base_type]) -> Union[Constant, None]:\n        try:\n            constant = self.constants[value]\n        except KeyError:\n            if value in {None, \"\"}:\n                # for nullable/blank values\n                return value\n            raise ValidationError(f\"Invalid input: '{value}' is not a valid constant.\")\n        else:\n            constant.make_immutable()\n            return constant\n\n    def get_prep_value(\n        self, value: Union[Constant, base_type, None]\n    ) -> Union[base_type, None]:\n        if isinstance(value, Constant):\n            return value.value\n        return value\n\n    def value_from_object(self, obj):\n        value = super().value_from_object(obj)\n        if isinstance(value, Constant):\n            return value.value\n        return value\n\n    def contribute_to_class(self, cls, name, private_only=False):\n        super().contribute_to_class(cls, name, private_only)\n        setattr(cls, self.name, self.descriptor_class(self))\n\n    def _get_flatchoices(self):\n        flatchoices = super()._get_flatchoices()\n        flatchoices.extend((constant, constant.label) for constant in self.constants)\n        return flatchoices\n\n    flatchoices = property(_get_flatchoices)\n\n\nclass AdjustmentField(ConstantField):\n    attr_class = Adjustment\n    constants = ADJUSTMENTS\n\n\nclass BusinessDayConventionField(ConstantField):\n    attr_class = BusinessDayConvention\n    constants = BUSINESS_DAY_CONVENTIONS\n\n\nclass CurrencyField(ConstantField):\n    attr_class = Currency\n    constants = CURRENCIES\n\n\nclass DayCountField(ConstantField):\n    attr_class = DayCount\n    constants = DAY_COUNTS\n\n\nclass FundingBasisField(ConstantField):\n    attr_class = FundingBasis\n    constants = FUNDING_BASES\n\n\nclass PaymentFrequencyField(ConstantField):\n    attr_class = PaymentFrequency\n    constants = PAYMENT_FREQUENCIES\n    base_type = int\n\n\nclass TenorField(ConstantField):\n    attr_class = Tenor\n    constants = TENORS\n    base_type = timedelta\n", "repo_name": "youbond/python-utils", "sub_path": "origin_common/constants/django/model_fields.py", "file_name": "model_fields.py", "file_ext": "py", "file_size_in_byte": 4733, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.db.models.Model", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 45, "usage_type": "name"}, {"api_name": "origin_common.constants.base.Constant", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 50, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 50, "usage_type": "name"}, {"api_name": "django.db.models.Field", "line_number": 54, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 54, "usage_type": "name"}, {"api_name": "origin_common.constants.base.Constant", "line_number": 55, "usage_type": "argument"}, {"api_name": "origin_common.constants.base.Constants", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.Field", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 63, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 62, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}, {"api_name": "django.db.models.DurationField", "line_number": 63, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 82, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 89, "usage_type": "call"}, {"api_name": "origin_common.constants.base.Constant", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 95, "usage_type": "name"}, {"api_name": "origin_common.constants.base.Constant", "line_number": 95, "usage_type": "name"}, {"api_name": "origin_common.constants.base.Constant", "line_number": 97, "usage_type": "argument"}, {"api_name": "typing.Union", "line_number": 96, "usage_type": "name"}, {"api_name": "origin_common.constants.base.Constant", "line_number": 103, "usage_type": "argument"}, {"api_name": "origin_common.constants.adjustments.Adjustment", "line_number": 120, "usage_type": "name"}, {"api_name": "origin_common.constants.ADJUSTMENTS", "line_number": 121, "usage_type": "name"}, {"api_name": "origin_common.constants.business_day_conventions.BusinessDayConvention", "line_number": 125, "usage_type": "name"}, {"api_name": "origin_common.constants.BUSINESS_DAY_CONVENTIONS", "line_number": 126, "usage_type": "name"}, {"api_name": "origin_common.constants.currencies.Currency", "line_number": 130, "usage_type": "name"}, {"api_name": "origin_common.constants.CURRENCIES", "line_number": 131, "usage_type": "name"}, {"api_name": "origin_common.constants.day_counts.DayCount", "line_number": 135, "usage_type": "name"}, {"api_name": "origin_common.constants.DAY_COUNTS", "line_number": 136, "usage_type": "name"}, {"api_name": "origin_common.constants.funding_bases.FundingBasis", "line_number": 140, "usage_type": "name"}, {"api_name": "origin_common.constants.FUNDING_BASES", "line_number": 141, "usage_type": "name"}, {"api_name": "origin_common.constants.payment_frequencies.PaymentFrequency", "line_number": 145, "usage_type": "name"}, {"api_name": "origin_common.constants.PAYMENT_FREQUENCIES", "line_number": 146, "usage_type": "name"}, {"api_name": "origin_common.constants.tenors.Tenor", "line_number": 151, "usage_type": "name"}, {"api_name": "origin_common.constants.TENORS", "line_number": 152, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 153, "usage_type": "name"}]}
{"seq_id": "1525849931", "text": "# Nombre: Luis Alejandro Martinez\n# Matricula: 2019-7725\n\nimport requests\nimport json\nimport os\nfrom os import system\n\nurl = \"http://173.249.49.169:88/api/test/consulta/\"\n\nsigno = (\"capricornio\", \"Acuario\", \"Piscis\", \"Aries\", \"Tauro\", \"Geminis\",\n         \"Cancer\", \"Leo\", \"Virgo\", \"Libra\", \"Escorpio\", \"Saegitario\")\nfechas = (20, 19, 20, 20, 20, 21, 22, 23, 22, 22, 22, 21)\n\n\ndef generate_html(names, last_name, b_day, b_place, zodiac_sign, photo_url):\n    os.makedirs(names, exist_ok=True)\n\n    html = f\"\"\"\n    <!DOCTYPE html>\n<html lang=\"en\">\n\n<head>\n    <meta charset=\"UTF-8\">\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n    <title>Personal Information</title>\n    <link rel=\"stylesheet\" href=\"style.css\">\n</head>\n\n<body>\n    <h1>Personal Information</h1>\n    <div class=\"container\">\n        <div class=\"data\">\n            <img src=\"{photo_url}\" alt=\"foto\">\n            <span>Nombre: {names}</span>\n            <span>Apellido:{last_name}</span>\n            <span>Fecha de Nacimiento: {b_day}</span>\n            <span>Lugar de Nacimiento: {b_place}</span>\n            <span>Zodiao: { zodiac_sign}</span>\n        </div>\n    </div>\n</body>\n\n</html>\n\"\"\"\n    html_style = \"\"\"\n* {\n\tmargin: 0;\n\tpadding: 0;\n}\n\nhtml,\nbody {\n\theight: 100%;\n}\n\nbody {\n\tbackground: lightblue;\n}\nh1 {\n\ttext-align: center;\n}\n.container {\n\tposition: absolute;\n\ttop: 50%;\n\tleft: 50%;\n\ttransform: translate(-50%, -50%);\n\tmax-width: 600px;\n\tborder: 2px solid white;\n\tborder-radius: 15px;\n}\n\nimg {\n\twidth: 150px;\n\tmargin: 0 auto;\n}\n\n.data {\n\tdisplay: grid;\n\tmax-width: 400px;\n\tpadding: 10px;\n\tmargin: auto 10px;\n\ttext-align: center;\n}\n\nspan {\n\tborder: 2px solid white;\n\tpadding: 5px 5px;\n\tborder-radius: 15px;\n\tmargin: 10px;\n\tfont-weight: bolder;\n}\n\n\"\"\"\n\n    file = open(f\"{names}/{names}.html\", \"w\")\n    file.write(html)\n    file.close()\n\n    file2 = open(f\"{names}/style.css\", \"w\")\n    file2.write(html_style)\n    file2.close()\n\n    print(\"Sean ha gereado su archivo html en la carpeta donde esta el ejecutable .py\")\n\n\ndef get_information():\n    system('cls')\n    print(\"Programa que acpeta una cedula y genera un archivo html cons la informacion\")\n\n    id = input(\"Digite su numero de cedula: \")\n    try:\n        response = requests.get(url + id)\n        data = response.json()\n\n        if data[\"Ok\"] == True:\n            names = data[\"Nombres\"]\n\n            if type(data[\"Apellido2\"]) == str:\n                last_name = data[\"Apellido1\"] + \" \" + data[\"Apellido2\"]\n            else:\n                last_name = data[\"Apellido1\"]\n\n            b_day = data[\"FechaNacimiento\"].split()[0]\n            b_place = data[\"LugarNacimiento\"]\n\n            photo_url = data[\"Foto\"]\n\n            day = int(b_day.split(\"-\")[2])\n            month = int(b_day.split(\"-\")[1])\n\n            mes = month - 1\n            if day > fechas[mes]:\n                mes + 1\n            if mes == 12:\n                mes = 0\n\n            zodiac_sign = signo[mes]\n\n            print(f\"\"\"\n            Nombre = {names}\n            Apellidos = {last_name}\n            Fecha de nacimiento = {b_day}\n            Lugar de Nacimiento = {b_place}\n            Zodiaco = {zodiac_sign}\n            \"\"\")\n\n            generate_html(names, last_name, b_day,\n                          b_place, zodiac_sign, photo_url)\n\n        else:\n            print(\"La cedula no existe!\")\n\n    except requests.ConnectionError:\n        print(\"No tiene conexion!\")\n\n\nget_information()\n\ninput(\"Presiona cualquier tecla para salir...\")\n\n\n# Nombre: Luis Alejandro Martinez\n# Matricula: 2019-7725\n", "repo_name": "Louislam09/programming-fund-with-py", "sub_path": "parcial_1/parcial.py", "file_name": "parcial.py", "file_ext": "py", "file_size_in_byte": 3534, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "os.system", "line_number": 108, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 113, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 154, "usage_type": "attribute"}]}
{"seq_id": "14000923841", "text": "# import the different functionnalities necessary for your work\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mne import Epochs, pick_types, events_from_annotations\nfrom mne.io import concatenate_raws, read_raw_edf\nfrom mne.datasets import eegbci\nfrom mne.decoding import CSP\nfrom mne.channels import read_layout\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.model_selection import ShuffleSplit, cross_val_score\n\n\ndef load_data(subject):\n\n    \"\"\"Load data from a subject designated by his identifier.Input:subject: int between 1 and 109Output:raw: mne.Raw, structure containing EEG dataevents: numpy array (n_events, 3)first column:\n    date of event in samplesecond column: duration of eventthrid column: event code\"\"\"\n    assert 1 <= subject <= 109\n    # dictionnary to specify the label and code of each event of interest\n    event_id = dict(left=0, right=1, hands=2, feet=3)\n    # list of dictionnaries to specify the different tasks of interest\n    task = [\n        dict(T1=event_id[\"left\"], T2=event_id[\"right\"]),\n        dict(T1=event_id[\"hands\"], T2=event_id[\"feet\"]),\n    ]\n    # list of dictionnaries to specify the different runs to load for one subject\n    runs = [\n        dict(id=4, task=task[0]),\n        dict(id=6, task=task[1]),\n        dict(id=8, task=task[0]),\n        dict(id=10, task=task[1]),\n        dict(id=12, task=task[0]),\n        dict(id=14, task=task[1]),\n    ]\n    # load and concatenate the different files from the specified subject\n    # download the files if necessary\n    raws = list()\n    events_list = list()\n    for run in runs:\n        # localize the file, download it if necessary\n        filename = eegbci.load_data(subject, run[\"id\"])\n        # load its contain\n        raw = read_raw_edf(filename[0], preload=True)\n        events, _ = events_from_annotations(raw, event_id=run[\"task\"])\n        # accumulate the data\n        raws.append(raw)\n        events_list.append(events)\n        # concatenate all data in two structures : one for EEG, one for the events\n        raw, events = concatenate_raws(raws, events_list=events_list)\n        # strip channel names of \".\" characters\n        raw.rename_channels(lambda x: x.strip(\".\"))\n        # delete annotations\n        indices = [x for x in range(len(raw.annotations))]\n        indices.reverse()\n        for i in indices:\n            raw.annotations.delete(i)\n        return raw, events\n\n\nraw_1, events_1 = load_data(1)\nraw_2, events_2 = load_data(2)\n\nraw_filtre_1 = raw_1.copy()\nraw_filtre_2 = raw_2.copy()\n\nraw_filtre_1 = raw_1.filter(l_freq=8, h_freq=9)\n\nraw_filtre_1.plot()\n\nplt.show()\n", "repo_name": "samyhaff/DATASIM_TP", "sub_path": "AMULT/TP4/tp.py", "file_name": "tp.py", "file_ext": "py", "file_size_in_byte": 2664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "mne.datasets.eegbci.load_data", "line_number": 41, "usage_type": "call"}, {"api_name": "mne.datasets.eegbci", "line_number": 41, "usage_type": "name"}, {"api_name": "mne.io.read_raw_edf", "line_number": 43, "usage_type": "call"}, {"api_name": "mne.events_from_annotations", "line_number": 44, "usage_type": "call"}, {"api_name": "mne.io.concatenate_raws", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}]}
{"seq_id": "34288005379", "text": "\"\"\"\nExample to introduce how to read a camera connected to your computer and show FPS\n\"\"\"\n\n# Import the required packages\nimport cv2\nimport argparse\nimport time\n\n# We first create the ArgumentParser object\n# The created object 'parser' will have the necessary information\n# to parse the command-line arguments into data types.\nparser = argparse.ArgumentParser()\n\n# We add 'index_camera' argument using add_argument() including a help.\nparser.add_argument(\"index_camera\", help=\"index of the camera to read from\", type=int)\nargs = parser.parse_args()\n\n# We create a VideoCapture object to read from the camera (pass 0):\ncapture = cv2.VideoCapture(args.index_camera)\n\n# Get some properties of VideoCapture (frame width, frame height and frames per second (fps)):\nframe_width = capture.get(cv2.CAP_PROP_FRAME_WIDTH)\nframe_height = capture.get(cv2.CAP_PROP_FRAME_HEIGHT)\nfps = capture.get(cv2.CAP_PROP_FPS)\n\n# Print these values:\nprint(\"CV_CAP_PROP_FRAME_WIDTH: '{}'\".format(frame_width))\nprint(\"CV_CAP_PROP_FRAME_HEIGHT : '{}'\".format(frame_height))\nprint(\"CAP_PROP_FPS : '{}'\".format(fps))\n\n# Check if camera opened successfully\nif capture.isOpened()is False:\n    print(\"Error opening the camera\")\n\n# Read until the video is completed, or 'q' is pressed\nwhile capture.isOpened():\n    # Capture frame-by-frame from the camera\n    ret, frame = capture.read()\n\n    if ret is True:\n        # Calculate time before processing the frame:\n        processing_start = time.time()\n\n        # Display the captured frame:\n        cv2.imshow('Input frame from the camera', frame)\n\n        # Convert the frame captured from the camera to grayscale:\n        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n        # Display the grayscale frame:\n        cv2.imshow('Grayscale input camera', gray_frame)\n\n        # Press q on keyboard to exit the program:\n        if cv2.waitKey(20) & 0xFF == ord('q'):\n            break\n\n        # Calculate time after processing the frame:\n        processing_end = time.time()\n\n        # Calculate the difference:\n        processing_time_frame = processing_end - processing_start\n\n        # FPS = 1 / time_per_frame\n        # Show the number of frames per second:\n        print(\"fps: {}\".format(1.0 / processing_time_frame))\n\n    # Break the loop\n    else:\n        break\n \n# Release everything:\ncapture.release()\ncv2.destroyAllWindows()\n", "repo_name": "PacktPublishing/Mastering-OpenCV-4-with-Python", "sub_path": "Chapter03/01-chapter-content/read_camera_fps.py", "file_name": "read_camera_fps.py", "file_ext": "py", "file_size_in_byte": 2358, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 354, "dataset": "github-code", "pt": "46", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 25, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 55, "usage_type": "call"}, {"api_name": "time.time", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 74, "usage_type": "call"}]}
{"seq_id": "10506086602", "text": "import cv2\nimport imutils\nimport argparse\nimport numpy as np\n\n# construct the argument parser and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-v\", \"--video\", help=\"path to the video file\")\nap.add_argument(\"-a\", \"--min-area\", type=int, default=500, help=\"minimum area size\")\nargs = vars(ap.parse_args())\n \n# if the video argument is None, then we are reading from webcam\nif args.get(\"video\", None) is None:\n\tcamera = cv2.VideoCapture(0)\n\ttime.sleep(0.25)\n \n# otherwise, we are reading from a video file\nelse:\n\tcamera = cv2.VideoCapture(args[\"video\"])\n\nroi = cv2.imread('hand2.jpg')  # the skin sample\n\nhsv = cv2.cvtColor(roi,cv2.COLOR_BGR2HSV)\n\nwhile(camera.isOpened()):\n\n    ret, target = camera.read()\n\n    r = 800.0 / target.shape[1]\n    dim = (800, int(target.shape[0] * r))\n \n    target = cv2.resize(target, dim, interpolation = cv2.INTER_AREA)\n\n    hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV)\n\n    # calculating object histogram\n    roihist = cv2.calcHist([hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] )\n    # normalize histogram and apply backprojection\n    cv2.normalize(roihist,roihist,0,255,cv2.NORM_MINMAX)\n    dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256],1)\n    # Now convolute with circular disc\n    disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))\n    cv2.filter2D(dst,-1,disc,dst)\n    # threshold and binary AND\n    ret,thresh = cv2.threshold(dst,50,255,0)\n    thresh = cv2.erode(thresh, None, iterations=4)\n    thresh = cv2.dilate(thresh, None, iterations=4)\n\n    cnts = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n    cnts = cnts[0] if imutils.is_cv2() else cnts[1]\n    if (len(cnts)> 1):\n        c = max(cnts, key=cv2.contourArea)\n        cv2.drawContours(target,[c],-1,(0,255,255), 2)\n    \n    thresh = cv2.merge((thresh,thresh,thresh))\n    \n    res = cv2.bitwise_and(target,thresh)\n\n    cv2.imshow('frame',target)\n    cv2.imshow('result',res)\n\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\ncamera.release()\ncv2.destroyAllWindows()\n", "repo_name": "MagnusCardell/STO-detect", "sub_path": "skin_detection/skin_contour_detector.py", "file_name": "skin_contour_detector.py", "file_ext": "py", "file_size_in_byte": 2046, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "46", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cv2.calcHist", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.normalize", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.NORM_MINMAX", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.calcBackProject", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.MORPH_ELLIPSE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.filter2D", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "imutils.is_cv2", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 66, "usage_type": "call"}]}
{"seq_id": "28063549880", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport re\nimport django.core.validators\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('tablemanager', '0018_input_ds_modify_time'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='workspace',\n            name='name',\n            field=models.SlugField(help_text='Name of workspace', max_length=255, validators=[django.core.validators.RegexValidator(re.compile('^[a-z0-9_]+$'), 'Slug can only contain lowercase letters, numbers and underscores', 'invalid')]),\n            preserve_default=True,\n        ),\n    ]\n", "repo_name": "dbca-wa/borgcollector", "sub_path": "tablemanager/migrations/0019_auto_20160120_1314.py", "file_name": "0019_auto_20160120_1314.py", "file_ext": "py", "file_size_in_byte": 676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "46", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.core.validators.RegexValidator", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.core", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 19, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "36317335999", "text": "import os\nimport re\nfrom math import sqrt\nimport pandas as pd\nimport ast\nimport MeCab\nfrom gensim import corpora\nfrom gensim import matutils\nfrom gensim import models\nfrom sklearn.svm import LinearSVC\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\n\n\ndirname_temp = os.path.dirname(__file__)\ntemplates = '/sample/'\ndirname = dirname_temp + templates\n# This CSV has already processed Morphological analysis(['Name',...],[Classification]).\ndata_csv = 'sample.csv'\nid2word_txt = 'sample1.txt'  # ID-Word-Frequent\nbasename_stopwords = 'sample2.txt'  # Stop word\nbasename_mecab = 'sample.dic'  # User dictionary of Mecab\nfilename_data_csv = os.path.join(dirname, data_csv)\nfilename_id2word_txt = os.path.join(dirname, id2word_txt)\nfilename_mecab = os.path.join(dirname, basename_mecab)\n\n\ninput_data = []  # Please input sentece contains adverse events or not.\n\n\n# Convert from sparse gensim format to dense list of numbers\ndef vec2dense(vec, num_terms) -> list:\n    return list(matutils.corpus2dense([vec], num_terms=num_terms).T[0])\n\n\ndf = pd.read_csv(filename_data_csv)\n\nwords_list = [ast.literal_eval(d) for d in df['names']]\n\nresult_list = []\nfor results in df['classified']:\n    result_list.append(results)\n\ndocs = {}\nfor key, value in zip(result_list, words_list):\n    docs[key] = value\nnames = docs.keys()\n\n\n# Create a dictionary\ndct = corpora.Dictionary(docs.values())\ndct.filter_extremes(no_below=20, no_above=0.05)\ndct.save_as_text(filename_id2word_txt)\n\n# BoW(Bag of Words)\nbow_docs = {}\nbow_docs_all_zeros = {}\nfor name in names:\n    sparse = dct.doc2bow(docs[name])\n    bow_docs[name] = sparse\n    dense = vec2dense(sparse, num_terms=len(dct))\n\n# LSI model\nlsi_docs = {}\nnum_topics: int = 2\nlsi_model = models.LsiModel(bow_docs.values(), id2word=dct.load_from_text(\n    filename_id2word_txt), num_topics=num_topics)\n\nfor name in names:\n    vec = bow_docs[name]\n    sparse = lsi_model[vec]\n    dense = vec2dense(sparse, num_topics)\n    lsi_docs[name] = sparse\n\n# Normalize a vector\nunit_vecs = {}\nfor name in names:\n    vec = vec2dense(lsi_docs[name], num_topics)\n    norm = sqrt(sum(num**2 for num in vec))\n    unit_vec = [num / norm for num in vec]\n    unit_vecs[name] = unit_vec\n\n\n# Binary classification\nall_data = [unit_vecs[name] for name in names if re.match('NonAE', name)]\nall_data.extend([unit_vecs[name]\n                for name in names if re.match('AE', name)])\n\nall_labels = [0 for name in names if re.match('NonAE', name)]\nall_labels.extend([1 for name in names if re.match('AE', name)])\n\nX_train, X_test, y_train, y_test = train_test_split(\n    all_data, all_labels)\n\n# Train SVM classifier\nclassifier = LinearSVC()\nclassifier.fit(X_train, y_train)\n\n# Prediction\npredict_label = classifier.predict(X_test)\ntarget_names = [\"class 'NonAE'\", \"class 'AE'\"]\nclassification_report(y_test, predict_label, target_names=target_names)\n\n\n# Confirmation of unknown data\ndef make_flame() -> list:\n\n    keywords = []\n    for row in input_data:\n        keyword = ''.join(row)\n        keywords.append(keyword)\n\n    df = pd.DataFrame(keywords)\n    df = df.rename(columns={0: 'texts'})\n\n    return df\n\n\nmake_flames = make_flame()\n\n\ndef mecab_text(text) -> list:\n\n    mecab = MeCab.Tagger(\n        r'-d /mecab/-u {}'.format(filename_mecab))\n    mecab.parse('')\n    node = mecab.parseToNode(text)\n\n    wordlist = []\n    while node:\n        if node.feature.split(',')[0] == '名詞':\n            wordlist.append(node.surface)\n        node = node.next\n    return wordlist\n\n\nmake_flames['words'] = make_flames['texts'].apply(mecab_text)\n\n\ndef inputdata_decision() -> str:\n\n    pre: list = ['NOT contains', 'contains']\n\n    for wakati_input_texts in make_flames['words']:\n        dct = corpora.Dictionary([wakati_input_texts])\n        tuple_sparse = dct.doc2bow(wakati_input_texts)\n        sparse_lsi: list = lsi_model[tuple_sparse]\n        list_dense: list = vec2dense(sparse_lsi, num_topics)\n        list_norm = sqrt(sum(num**2 for num in list_dense))\n        unit_vec_unlisted: list = [num / list_norm for num in list_dense]\n\n        # Result\n        Prediction_result: list = classifier.predict([unit_vec_unlisted])\n\n    return print(f'Result:This description {pre[int(Prediction_result)]} adverse events.')\n", "repo_name": "shimoooon/AdverseEventsClassification", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4266, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "gensim.matutils.corpus2dense", "line_number": 33, "usage_type": "call"}, {"api_name": "gensim.matutils", "line_number": 33, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 36, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 38, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 51, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 51, "usage_type": "name"}, {"api_name": "gensim.models.LsiModel", "line_number": 66, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 66, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 79, "usage_type": "call"}, {"api_name": "re.match", "line_number": 85, "usage_type": "call"}, {"api_name": "re.match", "line_number": 87, "usage_type": "call"}, {"api_name": "re.match", "line_number": 89, "usage_type": "call"}, {"api_name": "re.match", "line_number": 90, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 92, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 113, "usage_type": "call"}, {"api_name": "MeCab.Tagger", "line_number": 124, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 145, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 145, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 149, "usage_type": "call"}]}
{"seq_id": "11347967895", "text": "#!/usr/bin/python3\n\"\"\"HBNBCommand Module\"\"\"\nimport cmd\nimport re\nimport json\nfrom models.base_model import BaseModel\nfrom models import storage\n\n\nclass HBNBCommand(cmd.Cmd):\n    \"\"\"Class for the command interpreter.\"\"\"\n    prompt = \"(hbnb) \"\n\n    def do_EOF(self, line):\n        \"\"\"\n        Handles the End Of File (EOF) character.\n\n        Args:\n            line (str): The command line string.\n\n        Returns:\n            bool: True to exit the program.\n\n        \"\"\"\n        print()\n        return True\n\n    def do_create(self, line):\n        \"\"\"\n        Creates an instance of a class.\n\n        Args:\n            line (str): The command line string.\n\n        \"\"\"\n        if not line:\n            print(\"** class name missing **\")\n        elif line not in storage.classes():\n            print(\"** class doesn't exist **\")\n        else:\n            instance = storage.classes()[line]()\n            instance.save()\n            print(instance.id)\n\n    def do_show(self, line):\n        \"\"\"\n        Prints the string representation of an instance.\n\n        Args:\n            line (str): The command line string.\n\n        \"\"\"\n        if not line:\n            print(\"** class name missing **\")\n        else:\n            words = line.split(' ')\n            if words[0] not in storage.classes():\n                print(\"** class doesn't exist **\")\n            elif len(words) < 2:\n                print(\"** instance id missing **\")\n            else:\n                key = f\"{words[0]}.{words[1]}\"\n                if key not in storage.all():\n                    print(\"** no instance found **\")\n                else:\n                    print(storage.all()[key])\n\n    def do_quit(self, line):\n        \"\"\"\n        Exits the program.\n\n        Args:\n            line (str): The command line string.\n\n        Returns:\n            bool: True to exit the program.\n\n        \"\"\"\n        return True\n\n    def emptyline(self):\n        \"\"\"Doesn't do anything on ENTER.\"\"\"\n        pass\n\n    def do_destroy(self, line):\n        \"\"\"\n        Deletes an instance based on the class name and ID.\n\n        Args:\n            line (str): The command line string.\n\n        \"\"\"\n        if not line:\n            print(\"** class name missing **\")\n        else:\n            words = line.split(' ')\n            if words[0] not in storage.classes():\n                print(\"** class doesn't exist **\")\n            elif len(words) < 2:\n                print(\"** instance id missing **\")\n            else:\n                key = f\"{words[0]}.{words[1]}\"\n                if key not in storage.all():\n                    print(\"** no instance found **\")\n                else:\n                    del storage.all()[key]\n                    storage.save()\n\n    def do_all(self, line):\n        \"\"\"\n        Prints the string representation of instances\n\n        Args:\n            line (str): The command line string.\n\n        \"\"\"\n        if line:\n            words = line.split(' ')\n            if words[0] not in storage.classes():\n                print(\"** class doesn't exist **\")\n            else:\n                instance_list = [str(obj) for key, obj in storage.all().items()\n                                 if type(obj).__name__ == words[0]]\n                print(instance_list)\n        else:\n            instance_list = [str(obj) for key, obj in storage.all().items()]\n            print(instance_list)\n\n    def do_count(self, line):\n        \"\"\"\n        Counts the instances of a class.\n\n        Args:\n            line (str): The command line string.\n\n        \"\"\"\n        words = line.split(' ')\n        if not words[0]:\n            print(\"** class name missing **\")\n        elif words[0] not in storage.classes():\n            print(\"** class doesn't exist **\")\n        else:\n            matches = [\n                k for k in storage.all() if k.startswith(\n                    words[0] + '.')]\n            print(len(matches))\n\n    def do_update(self, line):\n        \"\"\"\n        Updates an instance by adding or updating an attribute.\n\n        Args:\n            line (str): The command line string.\n\n        \"\"\"\n        if not line:\n            print(\"** class name missing **\")\n            return\n\n        rex = r'^(\\S+)(?:\\s(\\S+)(?:\\s(\\S+)(?:\\s((?:\"[^\"]*\")|(?:(\\S)+)))?)?)?'\n        match = re.search(rex, line)\n        class_name = match.group(1)\n        uid = match.group(2)\n        attribute = match.group(3)\n        value = match.group(4)\n        if not match:\n            print(\"** class name missing **\")\n        elif class_name not in storage.classes():\n            print(\"** class doesn't exist **\")\n        elif uid is None:\n            print(\"** instance id missing **\")\n        else:\n            key = f\"{class_name}.{uid}\"\n            if key not in storage.all():\n                print(\"** no instance found **\")\n            elif not attribute:\n                print(\"** attribute name missing **\")\n            elif not value:\n                print(\"** value missing **\")\n            else:\n                cast = None\n                if not re.search('^\".*\"$', value):\n                    if '.' in value:\n                        cast = float\n                    else:\n                        cast = int\n                else:\n                    value = value.replace('\"', '')\n                attributes = storage.attributes()[class_name]\n                if attribute in attributes:\n                    value = attributes[attribute](value)\n                elif cast:\n                    try:\n                        value = cast(value)\n                    except ValueError:\n                        pass  # fine, stay a string then\n                setattr(storage.all()[key], attribute, value)\n                storage.all()[key].save()\n\n    def default(self, line):\n        \"\"\"Called when the entered command is not recognized.\"\"\"\n        self.preprocess_command(line)\n\n    def preprocess_command(self, line):\n        \"\"\"\n        Preprocesses the command entered by the user.\n\n        Args:\n            line (str): The command entered by the user.\n\n        Returns:\n            str: The processed command.\n\n        \"\"\"\n        match = re.search(r\"^(\\w*)\\.(\\w+)(?:\\(([^)]*)\\))$\", line)\n        if not match:\n            return line\n\n        class_name, method, args = match.group(\n            1), match.group(2), match.group(3)\n        match_uid_and_args = re.search('^\"([^\"]*)\"(?:, (.*))?$', args)\n\n        if match_uid_and_args:\n            uid = match_uid_and_args.group(1)\n            attr_or_dict = match_uid_and_args.group(2)\n        else:\n            uid = args\n            attr_or_dict = False\n\n        attr_and_value = \"\"\n        if method == \"update\" and attr_or_dict:\n            match_dict = re.search('^({.*})$', attr_or_dict)\n            if match_dict:\n                self.update_instance_dict(class_name, uid, match_dict.group(1))\n                return \"\"\n            match_attr_and_value = re.search(\n                '^(?:\"([^\"]*)\")?(?:, (.*))?$', attr_or_dict)\n            if match_attr_and_value:\n                attr_and_value = (match_attr_and_value.group(\n                    1) or \"\") + \" \" + (match_attr_and_value.group(2) or \"\")\n\n        command = f\"{method} {class_name} {uid} {attr_and_value}\"\n        self.onecmd(command)\n        return command\n\n    def update_instance_dict(self, class_name, uid, s_dict):\n        \"\"\"\n        Updates an instance with values from a dictionary.\n\n        Args:\n            class_name (str): The name of the class.\n            uid (str): The ID of the instance.\n            s_dict (str): The dictionary representation of the attributes.\n\n        \"\"\"\n        s = s_dict.replace(\"'\", '\"')\n        d = json.loads(s)\n        if not class_name:\n            print(\"** class name missing **\")\n        elif class_name not in storage.classes():\n            print(\"** class doesn't exist **\")\n        elif uid is None:\n            print(\"** instance id missing **\")\n        else:\n            key = f\"{class_name}.{uid}\"\n            if key not in storage.all():\n                print(\"** no instance found **\")\n            else:\n                attributes = storage.attributes()[class_name]\n                for attribute, value in d.items():\n                    if attribute in attributes:\n                        value = attributes[attribute](value)\n                    setattr(storage.all()[key], attribute, value)\n                storage.all()[key].save()\n\n\nif __name__ == '__main__':\n    HBNBCommand().cmdloop()\n", "repo_name": "Ri2023/AirBnB_clone", "sub_path": "console.py", "file_name": "console.py", "file_ext": "py", "file_size_in_byte": 8443, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "cmd.Cmd", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.storage.classes", "line_number": 38, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 38, "usage_type": "name"}, {"api_name": "models.storage.classes", "line_number": 41, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 41, "usage_type": "name"}, {"api_name": "models.storage.classes", "line_number": 57, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 57, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 63, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 63, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 66, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 66, "usage_type": "name"}, {"api_name": "models.storage.classes", "line_number": 97, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 97, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 103, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 103, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 106, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 106, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 107, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 107, "usage_type": "name"}, {"api_name": "models.storage.classes", "line_number": 119, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 119, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 122, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 122, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 126, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 126, "usage_type": "name"}, {"api_name": "models.storage.classes", "line_number": 140, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 140, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 144, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 144, "usage_type": "name"}, {"api_name": "re.search", "line_number": 161, "usage_type": "call"}, {"api_name": "models.storage.classes", "line_number": 168, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 168, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 174, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 174, "usage_type": "name"}, {"api_name": "re.search", "line_number": 182, "usage_type": "call"}, {"api_name": "models.storage.attributes", "line_number": 189, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 189, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 197, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 197, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 198, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 198, "usage_type": "name"}, {"api_name": "re.search", "line_number": 215, "usage_type": "call"}, {"api_name": "re.search", "line_number": 221, "usage_type": "call"}, {"api_name": "re.search", "line_number": 232, "usage_type": "call"}, {"api_name": "re.search", "line_number": 236, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 257, "usage_type": "call"}, {"api_name": "models.storage.classes", "line_number": 260, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 260, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 266, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 266, "usage_type": "name"}, {"api_name": "models.storage.attributes", "line_number": 269, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 269, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 273, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 273, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 274, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 274, "usage_type": "name"}]}
{"seq_id": "29642205975", "text": "#!/usr/bin/env python3\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport talib\n\nimport time\n\n\"\"\"\n    ===Ta-Lib Install===\n    Links:\n        https://visualstudio.microsoft.com/visual-cpp-build-tools/\n        https://github.com/afnhsn/TA-Lib_x64\n        https://github.com/mrjbq7/ta-lib/issues/127#issuecomment-280222942\n\n    1. Install Visual C++ Build Tools\n    2. DL afnhsn/TA-Lib_x64/TA-Lib_x64.zip (disregard exe)\n    3. Move the \"TA-Lib_x64.zip/TA-Lib_x64/ta-lib\" folder to C:\\\n    4. Open Developer Command Prompt for VS[2019]\n    5. CD to C:\\ta-lib\\c\\make\\cdr\\win32\\msvc\n    6. Build the Library with: nmake\n    7. pip install ta-lib\n\n\"\"\"\n\n\nplt.rcParams[\"figure.figsize\"] = (20,20)\n\n# nn inputs\n# [fastema-slowema,rsi,price vs bbands]\n\ndef loadcsv(filename):\n    df = pd.read_csv(filename)\n    return df\n\ndef ema(df, column=\"close\", period=100):\n    ema = df[column].ewm(period).mean()\n    #emadata.plot()\n    return ema\n\ndef rsi(df, column=\"close\", period=30):\n    # pylint: disable=no-member\n    rsi = talib.RSI(df[column], timeperiod=period)\n    return rsi\n\ndef bbands(df, column=\"close\", period=20):\n    # pylint: disable=no-member\n    # https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/bollinger-bands\n    upperBB, middleBB, lowerBB = talib.BBANDS(df[column], timeperiod=period, nbdevup=2, nbdevdn=2, matype=0)\n    return [upperBB,middleBB,lowerBB]\n\ndef neuraldfinput(df, column=\"close\"):\n    slowemaS = ema(df,column)\n    fastemaS = ema(df,column,period=30)\n    rsiS = rsi(df)\n    bbandsS = bbands(df,column) ## nninput should be delta between price and bbands\n\n    deltaemalist = []\n    for i in range(len(slowemaS)):\n        deltaemalist.append(fastemaS[i]-slowemaS[i])\n\n    deltaemaS = pd.Series(deltaemalist)\n\n    outputdf = pd.DataFrame(columns = [\"close\", \"deltaema\", \"rsi\", \"upperbb\", \"middlebb\", \"lowerbb\"])\n    outputdf[\"close\"],outputdf[\"deltaema\"],outputdf[\"rsi\"],outputdf[\"upperbb\"],outputdf[\"middlebb\"],outputdf[\"lowerbb\"] = df[column],deltaemaS,rsiS,bbandsS[0],bbandsS[1],bbandsS[2]\n    return outputdf\n\n\n#tester fxns\ndef simpleplotter(datapoints=500):\n    df = loadcsv(\"BTCUSDT-1h-data.csv\")\n    df[\"close\"].head(datapoints).plot()\n    ema(df).head(datapoints).plot()\n    ema(df, period=30).head(datapoints).plot()\n    #rsi(df).head(200).plot()\n    for i in bbands(df):\n        i.head(datapoints).plot()\n\ndef avgpctpricemovement(df):\n    sum = 0\n    prevprice = df[\"close\"][0]\n    for price in df[\"close\"]:\n        sum += (price - prevprice)/prevprice\n\n    avg = sum / len(df[\"close\"])\n    return avg\n\ndef nndatatest():\n    indf = loadcsv(\"BTCUSDT-1h-data.csv\")\n    outdf = neuraldfinput(indf)\n    outdf.head(500).plot()\n    return 0\n\ndef lastmindelta():\n    df = loadcsv(\"BTCUSDT-1m-data.csv\")\n    stamplist = list(df[\"close_time\"])\n    laststamp = int(stamplist[-1])/1000\n    delta = int(time.time()) - int(laststamp)\n    print(delta)\n\nif __name__ == \"__main__\":\n    #nndatatest()\n    lastmindelta()", "repo_name": "cheesemid/zautocrypto", "sub_path": "code/data/zdataprocessor.py", "file_name": "zdataprocessor.py", "file_ext": "py", "file_size_in_byte": 3013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 28, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 34, "usage_type": "call"}, {"api_name": "talib.RSI", "line_number": 44, "usage_type": "call"}, {"api_name": "talib.BBANDS", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}, {"api_name": "time.time", "line_number": 99, "usage_type": "call"}]}
{"seq_id": "71362057741", "text": "import cv2\nimport numpy as np\nimport os\nimport glob\n\ndef createImgMatch():\n    imgPath = './pics'\n    listImg = glob.glob(imgPath+'/*.jpg')\n    crop = []\n    for i in listImg:\n        img = cv2.imread(i)\n        h,w,c = img.shape\n        newWidth = 800\n        newHight = newWidth*h//w\n        img = cv2.resize(img,(newWidth,newHight),interpolation = cv2.INTER_AREA)\n        img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n        _,binary = cv2.threshold(img_gray,110,255,cv2.THRESH_BINARY)\n\n        cnts, hier = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n        \n        for j,c in enumerate(cnts):\n            rect = cv2.minAreaRect(c) # return (x,y), (w,h), angle \n            x,y,w,h = cv2.boundingRect(c)      \n            x=x+w//4\n            y=y+h//4               \n            if h/w>1 or (1<w/h<1.6):\n                crop.append(img[y:y+h-2*(h//4), x:x+w-2*(w//4)]) \n                \n                # crop.append(binary[y:y+h, x:x+w]) \n                # box = cv2.boxPoints(rect)\n                # box = np.intp(box) \n                # cv2.drawContours(img, [box], 0, (0,255,0),2)\n                \n    # try:\n    for i in crop:\n        cv2.imshow(str(crop.index(i)),i)\n        cv2.imwrite(str(crop.index(i))+'.jpg',i)\n    # except:\n    #     pass\n\n# cap = cv2.VideoCapture(1)\n# imgPath = './crop'\n# listImg = glob.glob(imgPath+'/*.jpg')\n# # print(listImg)\n# listImg = [cv2.cvtColor(cv2.imread(i),cv2.COLOR_BGR2GRAY) for i in listImg]\n# while True:\n#     _,frame = cap.read()\n#     img = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n#     ret,binary = cv2.threshold(img,150,255,cv2.THRESH_BINARY)\n\n#     cnts, hier = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n#     crop = []\n#     for _,c in enumerate(cnts):\n#         rect = cv2.minAreaRect(c) # return (x,y), (w,h), angle \n#         x,y,w,h = cv2.boundingRect(c)       \n        \n#         if h/w>1 or (1<w/h<1.6):\n#             crop.append(img[y:y+h, x:x+w])\n\n#     ref = []\n#     for k in crop:\n#         for i in listImg:\n#             ref.append([cv2.matchShapes(k,i,cv2.CONTOURS_MATCH_I3,0),listImg.index(i),crop.index(k)])\n        \n#     temp=0\n#     res = ''\n#     for i in ref:\n#         # print(type(i[1]))\n#         if i[0]>temp:\n#             if i[1]==0 or i[1]==1:\n#                 res = 'Plus'+str(i[2])\n#             elif i[1]==2 or i[1]==3:\n#                 res = 'Revert'+str(i[2])\n#             elif i[1]==4 or i[1]==5:\n#                 res = 'Stop'+str(i[2])\n#             temp=i[0]\n#     cv2.imshow('frame',frame)\n#     print(res)\n#     if cv2.waitKey(25) & 0xFF == ord('q'):\n#         break\ncreateImgMatch()    \n# for i in listImg:\n#     cv2.imshow(str(listImg.index(i)),i)\n# cv2.waitKey(0)\ncv2.destroyAllWindows()", "repo_name": "gritsp/detect_object", "sub_path": "v1/test02.py", "file_name": "test02.py", "file_ext": "py", "file_size_in_byte": 2747, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "46", "api": [{"api_name": "glob.glob", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.minAreaRect", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 86, "usage_type": "call"}]}
{"seq_id": "24916150285", "text": "#\n# SONA Event class\n# Author: Meng Du\n# August 2017\n#\n\nfrom datetime import datetime\nfrom dateutil import tz, parser\n\n\nclass SonaEvent(object):\n    def __init__(self, slot_id, sona_study_name, date, time, participant, location, researcher):\n        self.id = slot_id\n        self.sona_study_name = sona_study_name\n        self.participant = participant\n        self.location = location\n        self.researcher = researcher\n        self.calendar_study_name = self.sona_study_name\n        self.start_time, self.end_time = self.string2time(date + ' ' + time)\n\n    @classmethod\n    def string2time(cls, string):\n        if len(string) == 0:\n            return datetime.max\n        divide = string.index(' - ')\n        string1 = string[:divide]\n        string2 = string[divide:]\n        time1 = datetime.strptime(string1, '%A, %B %d, %Y %I:%M %p')\n        time1 = time1.replace(tzinfo=tz.tzlocal())\n        time2 = datetime.strptime(string2, ' - %I:%M %p')\n        time2 = time2.replace(year=time1.year, month=time1.month, day=time1.day, tzinfo=tz.tzlocal())\n        return time1, time2\n\n    def __str__(self):\n        return self.calendar_study_name + ' (id: ' + self.id + ')' + \\\n               '\\n\\tSONA study name: ' + self.sona_study_name + \\\n               '\\n\\tTimeslot: ' + self.start_time + \\\n               '\\n\\tParticipant: ' + self.participant + \\\n               '\\n\\tLocation: ' + self.location + \\\n               '\\n\\tResearcher: ' + self.researcher\n\n    def __eq__(self, other):\n        if isinstance(other, SonaEvent):\n            return self.id == other.id and self.start_time == other.start_time and self.end_time == other.end_time and \\\n                   self.participant == other.participant\n        if isinstance(other, dict):\n            # a Google Calendar event dict\n            try:\n                start_time = parser.parse(other['start']['dateTime'])\n                end_time = parser.parse(other['end']['dateTime'])\n                same = ('description' not in other or self.id == other['description']) and \\\n                       self.start_time == start_time and \\\n                       self.end_time == end_time and \\\n                       self.participant in other['summary'] and \\\n                       self.researcher.split()[0] in other['summary'] and \\\n                       other['summary'].startswith(self.calendar_study_name)\n                return same\n            except KeyError:\n                return False\n        return NotImplemented\n\n    def __ne__(self, other):\n        result = self.__eq__(other)\n        return result if result is NotImplemented else (not result)\n\n    def quick_add_summary(self):\n        return self.calendar_study_name + ' on ' + str(self.start_time) + ' at ' + self.location\n\n    def calendar_summary(self):\n        return self.calendar_study_name + ' / ' + self.researcher.split()[0] + ' (' + self.participant + ')'\n\n    def insert2calendar(self, service, google_calendar_id, color_id='1'):\n        return service.events().insert(calendarId=google_calendar_id, body={\n            'summary': self.calendar_summary(),\n            'location': self.location,\n            'description': self.id,\n            'start': {\n                'dateTime': self.start_time.isoformat()\n            },\n            'end': {\n                'dateTime': self.end_time.isoformat()\n            },\n            'colorId': color_id\n        }).execute()\n\n    def match_keywords(self, keywords):\n        # check if every word in the keyword string can be found in the event information\n        for keyword in keywords.split():\n            if keyword in self.sona_study_name or keyword in self.calendar_study_name or \\\n               keyword in self.participant or keyword in self.location or keyword in self.researcher:\n                continue\n            else:\n                return False\n        return True\n", "repo_name": "meng-du/SONA2Calendar", "sub_path": "sona_event.py", "file_name": "sona_event.py", "file_ext": "py", "file_size_in_byte": 3859, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "46", "api": [{"api_name": "datetime.datetime.max", "line_number": 24, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 29, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 31, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 31, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 49, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 49, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 50, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 50, "usage_type": "name"}]}
{"seq_id": "43558256408", "text": "from flask import Flask\nfrom flask_socketio import SocketIO, emit\nimport json\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'vnkdjnfjknfl1232#'\nsocketio = SocketIO(app)\n\n\nwith open(\"data.json\",\"r\") as f:\n    message_object = json.load(f)\n\ndef messageReceived(methods=['GET', 'POST']):\n    print('message was received!!!')\n\n@socketio.on('get_data')\ndef handle_my_custom_event(data, methods=['GET', 'POST']):\n    emit('data_update',json.dumps(message_object))\n    # socketio.emit('my response', json, callback=messageReceived)\n\n@socketio.on('new_message')\ndef new_message(data,methods=['GET','POST']):\n    message_object[\"message\"].append(data)\n    socketio.emit('data_update',json.dumps(message_object))\n    with open(\"data.json\",\"w\") as f:\n        json.dump(message_object,f)\n\nif __name__ == '__main__':\n    socketio.run(app)", "repo_name": "dl8sd11/ICED_APP", "sub_path": "107_1/Expo_Demo/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 829, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask_socketio.SocketIO", "line_number": 7, "usage_type": "call"}, {"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_socketio.emit", "line_number": 18, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 18, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 26, "usage_type": "call"}]}
{"seq_id": "6201174622", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 20 20:40:31 2022\n\n@author: BlueSpark\n\"\"\"\nimport requests\n\nurl=r\"http://127.0.0.1:81/forecast\"\nbuildList=str([1447])\ndays_in_future=3\nuse_center=False\n# test = requests.get(url, params={\"buildList\":buildList, \"days_in_future\":days_in_future,\"use_center\":use_center})\n\nurl_post = r\"http://127.0.0.1:81//historical/csv\"\nfile_energy =r\"C:\\MLBootCamp\\Springboard\\Flask_API\\Flask_API_Forecast\\test\\building_energy_1447.csv\"\nwith open(file_energy,'rb') as payload:\n    file= {\"file\":payload}\n    test2 = requests.post(url_post, params={\"file_type\":\"0\"},files =file)", "repo_name": "winder1991/Springboard", "sub_path": "Capstone-API/test/requests_url.py", "file_name": "requests_url.py", "file_ext": "py", "file_size_in_byte": 605, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "requests.post", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "7125361780", "text": "from airflow import DAG\nfrom airflow.operators.python import PythonOperator\nfrom airflow.providers.http.operators.http import SimpleHttpOperator\nfrom pendulum import datetime, duration\nimport json\n\n# Define body of POST request for the API call to trigger another DAG\ndate = \"{{ execution_date }}\"\nrequest_body = {\"execution_date\": date}\njson_body = json.dumps(request_body)\n\n\ndef print_task_type(**kwargs):\n    \"\"\"\n    Example function to call before and after downstream DAG.\n    \"\"\"\n    print(f\"The {kwargs['task_type']} task has completed.\")\n    print(request_body)\n\n\ndefault_args = {\n    \"owner\": \"airflow\",\n    \"depends_on_past\": False,\n    \"email_on_failure\": False,\n    \"email_on_retry\": False,\n    \"retries\": 1,\n    \"retry_delay\": duration(minutes=5),\n}\n\nwith DAG(\n    \"api-dag\",\n    start_date=datetime(2021, 1, 1),\n    max_active_runs=1,\n    schedule=\"@daily\",\n    catchup=False,\n) as dag:\n    start_task = PythonOperator(\n        task_id=\"starting_task\",\n        python_callable=print_task_type,\n        op_kwargs={\"task_type\": \"starting\"},\n    )\n\n    api_trigger_dependent_dag = SimpleHttpOperator(\n        task_id=\"api_trigger_dependent_dag\",\n        http_conn_id=\"airflow-api\",\n        endpoint=\"/api/v1/dags/dependent-dag/dagRuns\",\n        method=\"POST\",\n        headers={\"Content-Type\": \"application/json\"},\n        data=json_body,\n    )\n\n    end_task = PythonOperator(\n        task_id=\"end_task\",\n        python_callable=print_task_type,\n        op_kwargs={\"task_type\": \"ending\"},\n    )\n\n    start_task >> api_trigger_dependent_dag >> end_task\n", "repo_name": "astronomer/docs", "sub_path": "code-samples/dags/cross-dag-dependencies/rest_api_example_traditional.py", "file_name": "rest_api_example_traditional.py", "file_ext": "py", "file_size_in_byte": 1562, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 40, "dataset": "github-code", "pt": "40", "api": [{"api_name": "json.dumps", "line_number": 10, "usage_type": "call"}, {"api_name": "pendulum.duration", "line_number": 27, "usage_type": "call"}, {"api_name": "airflow.DAG", "line_number": 30, "usage_type": "call"}, {"api_name": "pendulum.datetime", "line_number": 32, "usage_type": "call"}, {"api_name": "airflow.operators.python.PythonOperator", "line_number": 37, "usage_type": "call"}, {"api_name": "airflow.providers.http.operators.http.SimpleHttpOperator", "line_number": 43, "usage_type": "call"}, {"api_name": "airflow.operators.python.PythonOperator", "line_number": 52, "usage_type": "call"}]}
{"seq_id": "26748045458", "text": "import gym\nimport gym_warehouse\n\n# node/tile\nclass Tile:\n    def __init__(self, parent, position):\n        self.position = position\n        self.parent = parent\n        self.g = None\n        self.f = None\n\n# heuristic function: euclidian\ndef h (tile1, tile2):\n    return abs(tile1[0]-tile2[0])**2 + abs(tile1[1]-tile2[1])**2\n\n# function to find positions\ndef find(target, s):\n    for x, y in enumerate(s):\n        try:\n            i = y.index(target)\n        except ValueError:\n            continue\n        yield x, i\n    \n# a* search\ndef findPath(pos, target_pos, s):\n    # initialize the start\n    start_tile = Tile(None, pos)\n    start_tile.g = start_tile.f = 0\n    target_tile = Tile(None, target_pos)\n    target_tile.g = target_tile.f = 0\n    \n    frontier = []\n    expanded = []\n\n    # start from the robot's position\n    frontier.append(start_tile)\n    \n    while len(frontier)>0:\n\n        # get the current tile (with the min cost)\n        current_tile = frontier[0]\n        current_index = 0\n        for index, tile in enumerate(frontier):\n            if tile.f < current_tile.f:\n                current_tile = tile\n                current_index = index\n\n        # pop current and add it to the expanded\n        frontier.pop(current_index)\n        expanded.append(current_tile)\n\n        # target reached?\n        if current_tile.position==target_tile.position:\n            path = []\n            current = current_tile\n            # build path\n            while current.parent is not None:\n                path.append(current.position)\n                current = current.parent\n\n            return path[::-1] # return reversed path\n\n        # check reachable tiles\n        children = []\n        # down\n        if current_tile.position[0] + 1 < len(s):\n            down_object = s[current_tile.position[0] + 1][current_tile.position[1]]\n            down_pos = [current_tile.position[0] + 1, current_tile.position[1]]\n            if down_object != '*':\n                down_tile = Tile(current_tile, down_pos)\n                children.append(down_tile)\n\n        # right\n        if current_tile.position[1] + 1 < len(s[0]):\n            right_object = s[current_tile.position[0]][current_tile.position[1]+1]\n            right_pos = [current_tile.position[0], current_tile.position[1] + 1]\n            if right_object != '*':\n                right_tile = Tile(current_tile, right_pos)\n                children.append(right_tile)\n        \n        # up\n        if current_tile.position[0] - 1 >= 0:\n            up_object = s[current_tile.position[0] - 1][current_tile.position[1]]\n            up_pos = [current_tile.position[0] - 1, current_tile.position[1]]\n            if up_object != '*':\n                up_tile = Tile(current_tile, up_pos)\n                children.append(up_tile)\n\n        # left\n        if current_tile.position[1] - 1 >= 0:\n            left_object = s[current_tile.position[0]][current_tile.position[1] - 1]\n            left_pos = [current_tile.position[0], current_tile.position[1] - 1]\n            if left_object != '*':\n                left_tile = Tile(current_tile, left_pos)\n                children.append(left_tile)\n\n        for child in children:\n            # child expanded?\n            in_expanded = False\n            for exp_child in expanded:\n                if child.position==exp_child.position:\n                    in_expanded = True\n            \n            child.g = current_tile.g + 1\n            child.f = child.g + h(target_tile.position, child.position)\n\n            # child already in frontier?\n            in_frontier = False\n            for front_child in frontier:\n                if child.position== front_child.position and child.g >= front_child.g:\n                    in_frontier = True\n\n            if not in_expanded and not in_frontier:\n                frontier.append(child)\n    \n    return False\n\ndef run_agent():\n    # create the Gym environment\n    env = gym.make('singlerobot-warehouse-v0')\n\n    # sense\n    s = env.look()\n    \n    # think\n    # find the positions of the robot and the target\n    robot = 'a'\n    pos = list(next(find(robot, s), None))\n    target = 'A'\n    target_pos = list(next(find(target, s), None))\n\n    # find path\n    path = findPath(pos, target_pos, s)\n\n    if not path:\n        return False\n    \n    # act\n    t = 0\n    while True:\n        env.render()\n        \n        action = env.ACTION_WAIT\n        if path[t][0] == pos[0] + 1:\n            action = env.ACTION_DOWN\n        elif path[t][1] == pos[1]  + 1:\n            action = env.ACTION_RIGHT\n        elif path[t][0] == pos[0] - 1:\n            action = env.ACTION_UP\n        elif path[t][1] == pos[1]  - 1:\n            action = env.ACTION_LEFT\n\n        # move\n        ob, rew, done = env.step(action)\n\n        # update robot's position\n        pos = path[t]\n\n        # step\n        t += 1\n        \n        if done:\n            break\n\n    env.close()\n\n\nif __name__ == \"__main__\":\n    run_agent()\n\n\n", "repo_name": "schismic/gym_basic_robots", "sub_path": "singlerobot.py", "file_name": "singlerobot.py", "file_ext": "py", "file_size_in_byte": 4938, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "41", "api": [{"api_name": "gym.make", "line_number": 121, "usage_type": "call"}]}
{"seq_id": "73716994698", "text": "import importlib\n\nfrom mtl.meta.param import partition\nfrom mtl.util import calc\nfrom mtl.util import session_manager\nfrom mtl.util import tensorboard_manager\nimport numpy as np\nimport torch\n\n\nclass Task(session_manager.SessionManager):\n  \"\"\"Basic network training manager.\"\"\"\n\n  def __init__(self, opt, ds, dataloaders):\n    super().__init__(opt, ds, dataloaders)\n    self.opt = opt\n    self.ds = ds\n    self.dataloaders = dataloaders\n    self.is_training = True\n    splits = ['train', 'valid']\n\n    # Task reference information\n    self.num_tasks = len(ds['train'])\n    self.task_idx_ref = list(map(int, opt.task_choice.split('-')))\n\n    self.targets = ds['train'][0].targets\n    task_ds_size = [len(d) for d in ds['train']]\n    self.task_ds_size = task_ds_size\n    self.task_proportion = [t / sum(task_ds_size) for t in task_ds_size]\n\n    self.task_num_out = [d.num_out for d in ds['train']]\n    self.task_trained_steps = [0 for _ in range(self.num_tasks)]\n    self.global_trained_steps = 0\n\n    # Initialize running averages for logging\n    self.log = {}\n    for k in self.to_track:\n      self.log[k] = [\n          {s: 0 for s in splits} for _ in range(self.num_tasks)\n      ]\n      self.log['%s_history' % k] = [\n          {s: [] for s in splits} for _ in range(self.num_tasks)\n      ]\n\n    self.score = 0\n\n    # Set up dataset iterators\n    self.iters = {s: [] for s in splits}\n    for split in self.iters:\n      for task_idx in range(self.num_tasks):\n        self.iters[split] += [iter(self.dataloaders[split][task_idx])]\n\n    # Set up metaparameters\n    self.setup_metaparams(opt, ds)\n    if opt.metaparam_load:\n      del self.to_load[self.to_load.index('meta')]\n      metaparam_path = '%s/%s/snapshot' % (opt.exp_root_dir, opt.metaparam_load)\n      print('Loading parameters from... (%s)' % metaparam_path)\n      self.load(metaparam_path, groups=['meta'])\n\n    # Set up model and optimization and loss\n    self.setup_model(opt, ds)\n    self.setup_optimizer(self.model.net_parameters, True)\n    self.loss_fn = torch.nn.CrossEntropyLoss()\n\n    # Check for a fixed curriculum\n    if opt.curriculum == 'fixed':\n      print('Loading fixed curriculum.')\n      self.fixed_curriculum = ds['train'][0].load_fixed_curriculum()\n\n    # Load pretrained model weights, restore previous checkpoint\n    if opt.pretrained:\n      tmp_path = '%s/%s/snapshot_model' % (opt.exp_root_dir, opt.pretrained)\n      print('Loading pretrained model weights from:', tmp_path)\n      pretrained = torch.load(tmp_path)\n      self.model.load_state_dict(pretrained['model'], strict=False)\n\n    self.restore(opt.restore_session, self.to_load)\n\n  def tensorboard_setup(self):\n    self.to_track = ['network_loss', 'accuracy', 'decathlon']\n    self.task_names = [d.task_name for d in self.ds['train']]\n    self.tb = tensorboard_manager.TBManager(self.exp_dir, self.task_names,\n                                            self.to_track)\n\n  def checkpoint_ref_setup(self):\n    # Define key/value pairs for checkpoint management\n    self.checkpoint_ref = {\n        'model': ['model'],\n        'meta': ['metaparams'],\n        'extra': ['log', 'score', 'task_trained_steps', 'global_trained_steps']\n    }\n    self.to_load = list(self.checkpoint_ref.keys())\n\n  def setup_metaparams(self, opt, ds):\n    if opt.metaparam == 'partition':\n      self.metaparams = partition.Metaparam(opt)\n      self.masks = None\n      self.checkpoint_ref['model'] += ['masks']\n\n    else:\n      self.metaparams = None\n\n  def setup_model(self, opt, ds):\n    model = importlib.import_module('mtl.models.' + opt.model)\n\n    if opt.metaparam == 'partition':\n      if opt.restore_session is not None:\n        # Restore session snapshot\n        print('Restoring previous masks... (%s/snapshot)' % opt.restore_session)\n        checkpoint = torch.load('%s/snapshot_model' % opt.restore_session)\n        self.masks = checkpoint['masks']\n\n      self.model = model.initialize(\n          opt, ds, metaparams=self.metaparams, masks=self.masks)\n      self.masks = [self.model.resnet.mask_ref, self.model.grad_mask_ref]\n\n    else:\n      self.model = model.initialize(opt, ds)\n\n  def cuda(self):\n    self.model.cuda()\n\n  def set_train_mode(self, train_flag):\n    self.is_training = train_flag\n    if train_flag:\n      self.model.train()\n    else:\n      self.model.eval()\n\n  def get_log_vals(self, split, task_idx):\n    return {k: self.log[k][task_idx][split] for k in self.to_track}\n\n  def update_log(self, k, v):\n    tmp_log = self.log[k][self.task_idx]\n    if self.step == 0:\n      tmp_v = v\n    else:\n      tmp_v = calc.running_avg(tmp_log[self.split], v)\n    tmp_log[self.split] = tmp_v\n\n  def sample_task(self):\n    task_idxs = np.arange(self.num_tasks)\n    curr = self.opt.curriculum\n\n    if self.split == 'train':\n      if curr == 'fixed':\n        # Fixed training curriculum\n        return self.fixed_curriculum[self.step % len(self.fixed_curriculum)]\n\n      elif curr == 'uniform':\n        # Uniformly iterate through tasks\n        return self.step % self.num_tasks\n\n      elif curr == 'proportional':\n        # Sample tasks proportional to dataset size\n        return np.random.choice(task_idxs, p=self.task_proportion)\n\n      elif curr == 'train_accuracy':\n        # Sample tasks based on relative training accuracies\n        train_acc = np.array([d['train'] for d in self.log['accuracy']])\n        train_acc = np.log(1 - train_acc + self.opt.curriculum_bias)\n        task_dist = calc.softmax(train_acc / self.opt.temperature)\n        return np.random.choice(task_idxs, p=task_dist)\n\n      else:\n        # Undefined, raise error\n        raise ValueError('Undefined task curriculum: %s' % curr)\n\n    else:\n      # Validation (order doesn't matter, just hit all validation samples)\n      return self.opt.valid_iter_ref[self.step % self.opt.iters['valid']]\n\n  def get_next_sample(self, split, task_idx):\n    reset_iter = False\n    try:\n      sample = self.iters[split][task_idx].next()\n    except StopIteration:\n      reset_iter = True\n\n    if reset_iter:\n      self.iters[split][task_idx] = iter(self.dataloaders[split][task_idx])\n      sample = self.iters[split][task_idx].next()\n\n    inp = sample['img'].cuda()\n    label = sample['label'].view(-1).cuda()\n    idxs = sample['index']\n\n    return inp, label, idxs\n\n  def run(self, split, step):\n    self.step = step\n    self.split = split\n\n    # Sample task\n    task_idx = self.sample_task()\n    self.task_idx = task_idx\n    self.curr_task = self.ds['train'][task_idx].task_name\n\n    # Get samples + model output\n    inp, label, idxs = self.get_next_sample(split, task_idx)\n    pred = self.model(inp, task_idx, split, step)\n\n    # Calculate loss\n    _, class_preds = torch.max(pred, 1)\n    accuracy = class_preds.eq(label).float().mean()\n    dec_score, _ = calc.decathlon_score(\n        accuracy.data, task_idxs=[self.task_idx_ref[task_idx]])\n    self.net_loss = self.loss_fn(pred, label)\n\n    # Track accuracy and cache predictions\n    if split == 'valid':\n      self.valid_accuracy_track[task_idx] += [accuracy.data.item()]\n      for batch_idx, tmp_idx in enumerate(idxs):\n        self.prediction_ref[task_idx][\n            tmp_idx.item()] = class_preds.data[batch_idx].item()\n\n    self.update_log('accuracy', accuracy.data.item())\n    self.update_log('network_loss', self.net_loss.data.item())\n    self.update_log('decathlon', dec_score)\n    self.score = np.array([d['valid'] for d in self.log['accuracy']]).mean()\n\n    self.global_trained_steps += 1\n    self.task_trained_steps[task_idx] += 1\n\n  def update_weights(self):\n    opt = self.opt\n    t_idx = self.task_idx\n\n    # Set up optimizer and learning rate\n    net_optimizer = self.__dict__['optim_%d' % t_idx]\n    lr = opt.learning_rate\n    for p in net_optimizer.param_groups:\n      p['lr'] = lr\n\n    # Calculate gradients\n    net_optimizer.zero_grad()\n    self.net_loss.backward()\n\n    if opt.clip_grad:\n      p = self.model.net_parameters[t_idx]\n      torch.nn.utils.clip_grad_norm(p, opt.clip_grad)\n\n    if opt.metaparam == 'partition' and opt.mask_gradient:\n      # Loop through parameters and multiply by appropriate mask\n      for m, bw_masks in self.model.bw_ref:\n        bw_mask = bw_masks[t_idx].cuda()\n        m.weight.grad *= bw_mask\n        if 'bias' in m._parameters and m.bias is not None:\n          m.bias.grad *= bw_mask[:, 0]\n\n    # Do weight update\n    net_optimizer.step()\n", "repo_name": "google/multi-task-architecture-search", "sub_path": "train/multiclass.py", "file_name": "multiclass.py", "file_ext": "py", "file_size_in_byte": 8335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "46", "api": [{"api_name": "mtl.util.session_manager.SessionManager", "line_number": 11, "usage_type": "attribute"}, {"api_name": "mtl.util.session_manager", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 75, "usage_type": "call"}, {"api_name": "mtl.util.tensorboard_manager.TBManager", "line_number": 83, "usage_type": "call"}, {"api_name": "mtl.util.tensorboard_manager", "line_number": 83, "usage_type": "name"}, {"api_name": "mtl.meta.param.partition.Metaparam", "line_number": 97, "usage_type": "call"}, {"api_name": "mtl.meta.param.partition", "line_number": 97, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 111, "usage_type": "call"}, {"api_name": "mtl.util.calc.running_avg", "line_number": 139, "usage_type": "call"}, {"api_name": "mtl.util.calc", "line_number": 139, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 162, "usage_type": "call"}, {"api_name": "mtl.util.calc.softmax", "line_number": 163, "usage_type": "call"}, {"api_name": "mtl.util.calc", "line_number": 163, "usage_type": "name"}, {"api_name": "numpy.random.choice", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 164, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 205, "usage_type": "call"}, {"api_name": "mtl.util.calc.decathlon_score", "line_number": 207, "usage_type": "call"}, {"api_name": "mtl.util.calc", "line_number": 207, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.nn.utils.clip_grad_norm", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 242, "usage_type": "attribute"}]}
{"seq_id": "35039440668", "text": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport requests\nimport logging\n\nimport configuration as conf\n\n\nclass QueryAPI:\n    def __init__(self):\n        self._station_info_url = conf.station_info_url\n        self._station_status_url = conf.station_status_url\n\n    @staticmethod\n    def _get_info(url):\n        try:\n            response = requests.get(url).json()\n        except Exception as e:\n            logging.error(f\"Could not retrieve station information\")\n            raise e\n\n        data = response['data']['stations']\n        return data\n\n    def _get_locations(self):\n        \"\"\"\n        Unpacking locations from the data retrieve using the private method _get_station_info\n        :return:\n        \"\"\"\n        data = QueryAPI._get_info(self._station_info_url)\n\n        # Building a dataframe containing the locations of the Vélib stations\n        locations_df = pd.DataFrame(columns=['NAME', 'STATION_ID', 'LAT', 'LON'])\n\n        # Keys of interest\n        keys = ['name', 'station_id', 'lat', 'lon']\n\n        for d in data:\n            new_row = [d.get(k) for k in keys]\n            locations_df.loc[len(locations_df)] = new_row\n\n        # Locations sorted by ID\n        result = locations_df.sort_values(by='STATION_ID')\n\n        if not result.empty:\n            logging.info(f\"Retrieved locations of {len(result)} Vélib stations successfully\")\n            return result\n\n        logging.error(f\"Couldn't retrieve locations correctly\")\n\n    def _get_stations_status(self):\n        station_status = QueryAPI._get_info(self._station_status_url)\n\n        # Building a dataframe containing the locations of the Vélib stations\n        columns = ['STATION_ID', 'NUM_DOCKS_AVAILABLE', 'NUM_MECHANICAL', 'NUM_E_BIKES']\n        station_status_df = pd.DataFrame(columns=columns)\n\n        # Keys of interest\n        keys = ['station_id', 'num_docks_available']\n\n        for d in station_status:\n            new_row = [d.get(k) for k in keys]\n\n            num_bikes_by_type = d['num_bikes_available_types']\n\n            for elt, key in zip(num_bikes_by_type, ['mechanical', 'ebike']):\n                new_row.append(elt[key])\n\n            station_status_df.loc[len(station_status_df)] = new_row\n\n        # Locations sorted by name\n        result = station_status_df.sort_values(by='STATION_ID')\n\n        if not result.empty:\n            logging.info(f\"Retrieved stations status with length {len(result)} successfully\")\n            return result\n\n        logging.error(f\"Couldn't retrieve status correctly\")\n\n    @staticmethod\n    def _compute_freq_available(row):\n        \"\"\"\n        Computing the frequency of available bikes in a station (whatever their type)\n        :param row:\n        :return:\n        \"\"\"\n        cols = ['NUM_DOCKS_AVAILABLE', 'NUM_MECHANICAL', 'NUM_E_BIKES']\n        x = sum([row.__getattr__(col) for col in cols])\n\n        if not x:\n            return None\n\n        f = row.NUM_DOCKS_AVAILABLE / x\n        return round(f, 4)\n\n    def get_data(self):\n        \"\"\"\n        'Main' method to call to retrieve data about the stations themselves and their respective status information\n        :return:\n        \"\"\"\n        # Stations status\n        stations_status_df = self._get_stations_status()\n\n        # Locations\n        locations_df = self._get_locations()\n\n        # Merging both dataframes to get the most exhaustive data we can\n        data = pd.merge(stations_status_df, locations_df, on='STATION_ID')\n\n        # Adding the frequency of available docks\n        data['FREQUENCY_AVAILABLE_DOCKS'] = data.apply(lambda row: QueryAPI._compute_freq_available(row), axis=1)\n\n        # Ordering by station name\n        data.sort_values(by='NAME', inplace=True)\n\n        return data\n", "repo_name": "SK8gh/VelibMap", "sub_path": "mapping.py", "file_name": "mapping.py", "file_ext": "py", "file_size_in_byte": 3718, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "configuration.station_info_url", "line_number": 11, "usage_type": "attribute"}, {"api_name": "configuration.station_status_url", "line_number": 12, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 108, "usage_type": "call"}]}
{"seq_id": "11438190203", "text": "from django.conf import settings\nfrom django.contrib.auth import get_user_model\nfrom django.db import models\n\nUser = get_user_model()\n\n\nclass Group(models.Model):\n    \"\"\"Модель создает таблицу с описанием группы поста.\"\"\"\n    title = models.CharField(max_length=200, verbose_name=\"Группа\")\n    slug = models.SlugField(max_length=200, unique=True, verbose_name=\"Путь\")\n    description = models.TextField(verbose_name=\"Описание\")\n\n    class Meta:\n        \"\"\"Класс задает удобочитаемое имя.\"\"\"\n        verbose_name = 'Группа'\n        verbose_name_plural = 'Группы'\n\n    def __str__(self):\n        return self.title\n\n\nclass Post(models.Model):\n    \"\"\"Модель создает таблицу поста с его наименованием и содержанием.\"\"\"\n    text = models.TextField(verbose_name='Пост')\n    pub_date = models.DateTimeField(auto_now_add=True, verbose_name=\"Дата\")\n    author = models.ForeignKey(\n        User,\n        on_delete=models.CASCADE,\n        related_name='posts', verbose_name='Автор')\n    group = models.ForeignKey(\n        Group,\n        on_delete=models.SET_NULL,\n        blank=True, null=True,\n        related_name='posts', verbose_name='Группа')\n    image = models.ImageField(\n        verbose_name='Картинка',\n        upload_to='posts/',\n        blank=True\n    )\n\n    class Meta:\n        \"\"\"Класс описывает порядок сортировки и задает удобочитаемое имя.\"\"\"\n        ordering = ['-pub_date']\n        verbose_name = 'Пост'\n        verbose_name_plural = 'Посты'\n\n    def __str__(self):\n        return self.text[:settings.OUTPUT_ELEMENTS_OF_POSTS]\n\n\nclass Comment(models.Model):\n    \"\"\"Модель создает таблицу комментарий к посту.\"\"\"\n    post = models.ForeignKey(\n        Post,\n        on_delete=models.CASCADE,\n        related_name='comments',\n        verbose_name='Пост')\n    author = models.ForeignKey(\n        User,\n        on_delete=models.CASCADE,\n        related_name='comments', verbose_name='Автор')\n    text = models.TextField(verbose_name='Комментарий')\n    created = models.DateTimeField(auto_now_add=True, verbose_name=\"Дата\")\n\n    class Meta:\n        \"\"\"Класс описывает порядок сортировки и задает удобочитаемое имя.\"\"\"\n        ordering = ['-created']\n        verbose_name = 'Комментарий'\n        verbose_name_plural = 'Комментарии'\n\n\nclass Follow(models.Model):\n    \"\"\"Модель создает таблицу подписки пользователя на авторов.\"\"\"\n    user = models.ForeignKey(User, on_delete=models.CASCADE,\n                             related_name='follower',\n                             verbose_name='Пользователь')\n    author = models.ForeignKey(User, on_delete=models.CASCADE,\n                               blank=True, null=True,\n                               related_name='following',\n                               verbose_name='Автор')\n\n    class Meta:\n        \"\"\"Класс описывает порядок сортировки и задает удобочитаемое имя.\"\"\"\n        verbose_name = 'Подписка'\n        verbose_name_plural = 'Подписки'\n        constraints = [\n            models.UniqueConstraint(\n                fields=[\"user\", \"author\"],\n                name=\"unique_user_author\"\n            ),\n            models.CheckConstraint(\n                check=~models.Q(user=models.F('author')),\n                name='user_cannot_follow_yourself'\n            )\n        ]\n", "repo_name": "bodra84/hw05_final", "sub_path": "yatube/posts/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3742, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 5, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.settings.OUTPUT_ELEMENTS_OF_POSTS", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 52, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 56, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 59, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 63, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 63, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 64, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 64, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 73, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 73, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 75, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 75, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 75, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 78, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 78, "usage_type": "attribute"}, {"api_name": "django.db.models.UniqueConstraint", "line_number": 88, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 88, "usage_type": "name"}, {"api_name": "django.db.models.CheckConstraint", "line_number": 92, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 93, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 93, "usage_type": "name"}, {"api_name": "django.db.models.F", "line_number": 93, "usage_type": "call"}]}
{"seq_id": "15065679480", "text": "from crispy_forms.bootstrap import FormActions\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout, Row, Div, Field\nfrom django import forms\n\nfrom apps.myapp.models.pedido import Pedido\n\n\nfrom django_google_maps import widgets as map_widgets\nfrom django_google_maps import fields as map_fields\n\nfrom backend_apps.utils.forms import smtSave, btnCancel, btnReset\n\n\nclass PedidoForm(forms.ModelForm):\n    class Meta:\n        model = Pedido\n        exclude = (\"created_at\", \"updated_at\", \"atendido\",)\n        fields = ['address', 'geolocation']\n        widgets = {\n            \"address\": map_widgets.GoogleMapsAddressWidget,\n        }\n\n    def __init__(self, *args, **kwargs):\n        super(PedidoForm, self).__init__(*args, **kwargs)\n\n\n\n\n        self.helper = FormHelper()\n        self.helper.form_method = 'post'\n        self.helper.form_class = 'js-validate form-vertical'\n        self.helper.layout = Layout(\n            Row(\n                Div(Field('address', css_class='input-required'),\n                    css_class='col-md-4'),\n                Div(Field('parent', ),\n                    css_class='col-md-4'),\n                Div(Field('permission', ),\n                    css_class='col-md-4'),\n            ),\n            Row(\n                Div(Field('pos', css_class='input-required input-integer mask-pint'),\n                    css_class='col-md-4'),\n                Div(Field('icon', ),\n                    css_class='col-md-4'),\n                Div(Field('url', css_class='input-required'),\n                    css_class='col-md-4'),\n            ),\n            Row(\n                FormActions(\n                    smtSave(),\n                    btnCancel(),\n                    btnReset(),\n                ),\n            ),\n        )", "repo_name": "pacifi/Dijkstra", "sub_path": "delivery_app/apps/myapp/forms/pedido.py", "file_name": "pedido.py", "file_ext": "py", "file_size_in_byte": 1779, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "django.forms.ModelForm", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 15, "usage_type": "name"}, {"api_name": "apps.myapp.models.pedido.Pedido", "line_number": 17, "usage_type": "name"}, {"api_name": "django_google_maps.widgets.GoogleMapsAddressWidget", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django_google_maps.widgets", "line_number": 21, "usage_type": "name"}, {"api_name": "crispy_forms.helper.FormHelper", "line_number": 30, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Layout", "line_number": 33, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Row", "line_number": 34, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 35, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 35, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 37, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 37, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 39, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 39, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Row", "line_number": 42, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 43, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 43, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 45, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 45, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 47, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 47, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Row", "line_number": 50, "usage_type": "call"}, {"api_name": "crispy_forms.bootstrap.FormActions", "line_number": 51, "usage_type": "call"}, {"api_name": "backend_apps.utils.forms.smtSave", "line_number": 52, "usage_type": "call"}, {"api_name": "backend_apps.utils.forms.btnCancel", "line_number": 53, "usage_type": "call"}, {"api_name": "backend_apps.utils.forms.btnReset", "line_number": 54, "usage_type": "call"}]}
{"seq_id": "36688216840", "text": "import pandas as pd\nimport numpy as np\nfrom pyliftover import LiftOver\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom numba import jit\n\nfrom NucFrames.monte_carlo_utils import random_sample\n\ndef read_loop_frame(loop_file):\n  \"\"\"\n  Utility to read Rao et al loops file.\n  \"\"\"\n  df = pd.read_csv(loop_file,\n                   sep=\"\\t\",\n                   dtype={'chr1': str,\n                          'x1': np.int32,\n                          'x2': np.int32,\n                          'chr2': str,\n                          'y1': np.int32,\n                          'y2': np.int32})\n\n  df[\"x\"] = df[[\"x1\", \"x2\"]].mean(axis=1)\n  df[\"y\"] = df[[\"y1\", \"y2\"]].mean(axis=1)\n\n  lo = LiftOver('mm9', 'mm10')\n  df[\"x\"] = df.apply(lambda x: row_liftover(x, \"chr1\", \"x\", lo), axis=1)\n  df[\"y\"] = df.apply(lambda x: row_liftover(x, \"chr2\", \"y\", lo), axis=1)\n  df[\"chr1\"] = df.apply(lambda x: x[\"chr1\"][3:], axis=1)\n  df[\"chr2\"] = df.apply(lambda x: x[\"chr2\"][3:], axis=1)\n\n  df = df[[\"chr1\", \"x\", \"chr2\", \"y\"]]\n\n  df.rename(columns={\"x\": \"a\", \"y\": \"b\"}, inplace=True)\n\n  return (df)\n\n\ndef row_liftover(row, chr_col, col, lo):\n  \"\"\"\n    Utility to convert bps in rows of a dataframe.\n    Probably should return nan if exception.\n    Assumes that chr_col is of the form chr2, not just 2.\n    \"\"\"\n  bp_arr = lo.convert_coordinate(row[chr_col], row[col])\n  bp = int(bp_arr[0][1])\n  return (bp)\n\n\n##############################################################################\n# Monte Carlo stuff\n##############################################################################\ndef monte_carlo(dists_count, seps, loop_total, idx_a, idx_b, B=100000, plot=False, n_cells=None):\n  \"\"\"\n  Only on cis (obviously), assume square matrix.\n  \"\"\"\n  max_idx = dists_count.shape[0] - 1\n\n  if n_cells is None:\n    n_cells = np.max(dists_count)\n\n  potential_max = len(seps) * n_cells\n\n  sample_starts, sample_ends = random_sample(seps, max_idx, B)\n  #sample_starts, sample_ends = sanity_check(idx_a, idx_b, max_idx, B)\n\n  sample_sums = mk_sum(dists_count, sample_starts, sample_ends, plot=plot)\n  truth_count = np.sum(sample_sums >= loop_total)\n\n  if plot:\n    print(\"Max from background: {}, loop value: {}, theoretical max: {}\".format(\n            np.max(sample_sums), loop_total, potential_max))\n\n    sns.distplot(sample_sums, kde=False, norm_hist=False)\n    plt.axvline(loop_total, label=\"loop total\")\n    plt.legend()\n    plt.show()\n\n  pval = (1 + truth_count) / (1 + B)\n  return(truth_count, pval)\n\ndef mk_sum(dists_count, sample_starts, sample_ends, plot=False):\n  sample_values = np.zeros_like(sample_starts)\n  _mk_sum(dists_count, sample_values, sample_starts, sample_ends)\n  highest = np.sum(np.max(sample_values, axis=0))\n  lowest = np.sum(np.min(sample_values, axis=0))\n  if plot:\n    print(\"Highest possible sum from sampling: {}\".format(highest))\n    print(\"Lowest possible sum from sampling: {}\".format(lowest))\n    plt.axvline(highest, color='r', label=\"Highest possible from sampling\")\n    plt.axvline(lowest, color='r', label=\"Lowest possible from sampling\")\n\n  return(np.sum(sample_values, axis=1))\n\n@jit(nopython=True, nogil=True)\ndef _mk_sum(dists_count, sample_values, sample_starts, sample_ends):\n  for sample_idx in range(sample_starts.shape[0]):\n    for sep_idx in range(sample_starts.shape[1]):\n      idx_a = sample_starts[sample_idx, sep_idx]\n      idx_b = sample_ends[sample_idx, sep_idx]\n      sample_values[sample_idx, sep_idx] = dists_count[idx_a, idx_b]\n\ndef sanity_check(idx_a, idx_b, max_idx, B):\n  \"\"\"\n  Start with the known loop configuration, randomly shuffle positions from there.\n  \"\"\"\n  samples_a = []\n  samples_b = []\n\n  for i in range(idx_a.shape[0]):\n    a = idx_a[i]\n    b = idx_b[i]\n\n    # loop_changes = np.random.randint(-a, max_idx - b, B)\n    val = np.min([a, max_idx - b - 1])\n\n    loop_changes = np.random.binomial(n=2 * val, p=0.5, size=B)\n    loop_changes = loop_changes - val\n\n    loop_changes[(loop_changes + b) >= max_idx] = max_idx - 1\n    loop_changes[loop_changes < 0] = 0\n\n    starts = a + loop_changes.copy()\n    ends = b + loop_changes\n\n    samples_a.append(starts)\n    samples_b.append(ends)\n\n  samples_a = np.array(samples_a, dtype=np.int32).T\n  samples_b = np.array(samples_b, dtype=np.int32).T\n  return(samples_a, samples_b)\n\ndef circular_permute(idx_a, idx_b, max_idx):\n  samples_a = []\n  samples_b = []\n\n  seps = np.abs(idx_a - idx_b)\n\n  for i in range(100 * max_idx):\n    idx_a = idx_a + 1\n    idx_b = idx_b + 1\n\n    if np.sum(idx_b > max_idx) > 0:\n        pass\n    else:\n      overlap = np.argwhere(idx_a == max_idx)\n\n      idx_a[overlap] = 0\n      idx_b[overlap] = seps[overlap]\n\n      samples_a.append(idx_a)\n      samples_b.append(idx_b)\n  return (np.array(samples_a), np.array(samples_b))\n\n\ndef loop_monte_carlo(idx_a, idx_b, background, B=10000):\n  \"\"\"\n  Assess how unlikely a single loop is, across all cells.\n  Background is the summed along cells pseudocontact matrix for that chromosome.\n  \"\"\"\n\n  sep = np.abs(idx_b - idx_a)\n  random_starts = np.random.randint(0, background.shape[0] - sep, B)\n  random_ends = random_starts + sep\n\n  target_total = background[idx_a, idx_b]\n\n  random_totals = background[random_starts, random_ends]\n\n  truth_count = np.sum(target_total <= random_totals)\n\n  p = (1 + truth_count) / (1 + B)\n\n  return(p)\n\n\nif __name__ == \"__main__\":\n  loop_file = \"/home/lpa24/dev/cam/data/rao_et_al_data/GSE63525_CH12-LX_HiCCUPS_looplist_with_motifs.txt\"\n  df = read_loop_frame(loop_file)\n  print(df)\n", "repo_name": "latkins/single_cell_loop_analysis", "sub_path": "loop_helpers.py", "file_name": "loop_helpers.py", "file_ext": "py", "file_size_in_byte": 5470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pyliftover.LiftOver", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 60, "usage_type": "call"}, {"api_name": "NucFrames.monte_carlo_utils.random_sample", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 72, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.zeros_like", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 93, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 130, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 170, "usage_type": "call"}]}
{"seq_id": "39785278393", "text": "import mxnet as mx\nfrom mxnet import gluon\nimport gluoncv\nimport numpy as np\nimport cv2\n\n#for tiny target batchnorm and dropout may cause much difference between train-stage and test-stage\n#this fcn is based on vgg1 without batchnorm/dropout\nclass FCN(gluon.Block):\n    def __init__(self, num_class):\n        super(FCN,self).__init__()\n        backbone = gluon.model_zoo.vision.vgg11(pretrained=True)\n        self.downlayer_2 = gluon.nn.Sequential()\n        for layer in backbone.features[0:3]:\n            self.downlayer_2.add(layer)\n        \n        self.downlayer_8 = gluon.nn.Sequential()\n        for layer in backbone.features[3:11]:\n            self.downlayer_8.add(layer)\n            \n        self.downlayer_32 = gluon.nn.Sequential()\n        for layer in backbone.features[11:-4]:\n           # print layer.name\n            self.downlayer_32.add(layer)\n\n        self.uplayer_8 = gluon.nn.Sequential()\n        self.uplayer_8.add(\n            gluon.nn.Conv2D(channels=256,kernel_size=3,strides=1,padding=1,activation=\"relu\"),\n            gluon.nn.Conv2D(channels=256,kernel_size=3,strides=1,padding=1,activation=\"relu\")\n        )\n        self.uplayer_8.initialize(init=mx.initializer.Xavier())\n        for layer in self.uplayer_8:\n            layer.weight.lr_mult = 10.0\n\n\n        self.uplayer_2 = gluon.nn.Sequential()\n        self.uplayer_2.add(\n            gluon.nn.Conv2D(channels=64,kernel_size=3,strides=1,padding=1,activation=\"relu\"),\n            gluon.nn.Conv2D(channels=64,kernel_size=3,strides=1,padding=1,activation=\"relu\")\n        )\n        self.uplayer_2.initialize(init=mx.initializer.Xavier())\n        for layer in self.uplayer_2:\n            layer.weight.lr_mult = 10.0\n\n        self.uplayer_1 = gluon.nn.Sequential()\n        self.uplayer_1.add(\n            gluon.nn.Conv2D(channels=32,kernel_size=3,strides=1,padding=1,activation=\"relu\"),\n            gluon.nn.Conv2D(channels=num_class,kernel_size=3,strides=1,padding=1,activation=None)\n        )\n        self.uplayer_1.initialize(init=mx.initializer.Xavier())\n        for layer in self.uplayer_1:\n            layer.weight.lr_mult = 10.0\n\n\n        return\n\n    def forward(self, x):\n        out = x\n        \n        for layer in self.downlayer_2:\n            out = layer(out)\n        d2 = out\n        \n        for layer in self.downlayer_8:\n            out = layer(out)\n        d8 = out\n        \n        for layer in self.downlayer_32:\n            out = layer(out)\n        d32 = out\n        _,_,H,W = out.shape\n        #print 'forward 2', out.shape\n        out = mx.nd.contrib.BilinearResize2D(out,height=H*4,width=W*4)\n        out = self.uplayer_8(out)\n        #out = out + d8\n        out = mx.nd.concat(out,d8,dim=1)\n        _,_,H,W = out.shape\n        \n        out = mx.nd.contrib.BilinearResize2D(out,height=H*4,width=W*4)\n        out = self.uplayer_2(out)\n        #out = out  + d2\n        out = mx.nd.concat(out,d2,dim=1)\n        _,_,H,W = out.shape\n        out = mx.nd.contrib.BilinearResize2D(out,height=H*2,width=W*2)\n        out = self.uplayer_1(out)\n        \n\n        return out\n\n\nclass FCN_WITH_VGG(gluon.nn.HybridBlock):\n    def __init__(self,num_class,**kwargs):\n        super(FCN_WITH_VGG,self).__init__(**kwargs)\n        pretrained = gluon.model_zoo.vision.vgg11(pretrained=True)\n        if 0:\n            data = mx.sym.var(\"data\")\n            internals = pretrained(data).get_internals()\n            print(internals)\n        self.feature_extractor = gluoncv.nn.feature.FeatureExpander(network=pretrained, outputs=['pool4_fwd'], \\\n                                                                    num_filters=[])\n        self.feature_extractor.collect_params().setattr(\"lr_mult\", 0.01)\n        self.tail = gluon.nn.HybridSequential()\n        self.tail.add(\n            gluon.nn.Conv2D(num_class,kernel_size=1,strides=1,padding=0,dilation=1,weight_initializer=mx.init.Xavier()),\n            gluon.nn.Activation(\"sigmoid\")\n        )\n        self.tail.collect_params().initialize()\n        return\n    def hybrid_forward(self, F, x, *args, **kwargs):\n        y = self.feature_extractor(x)\n        y = self.tail(y)\n        return y\n\ndef get_net(num_class):\n    return FCN_WITH_VGG(num_class)\n\nif 0:\n    ctx = mx.gpu()\n    fcn = get_net(4)\n    #fcn.initialize()\n    fcn.hybridize(static_shape = True, static_alloc = True)\n    fcn.collect_params().reset_ctx(ctx)\n    x = mx.nd.random.uniform(0,1,(2,3,64*2,64*2),ctx=ctx)\n    print('input: ',x.shape)\n    y = fcn(x)\n    print('output: ',y.shape, ' y range:', y.min(),\",\",y.max())\n\n", "repo_name": "z01nl1o02/ClassicNets", "sub_path": "networks/fcn.py", "file_name": "fcn.py", "file_ext": "py", "file_size_in_byte": 4511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "mxnet.gluon.Block", "line_number": 9, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 9, "usage_type": "name"}, {"api_name": "mxnet.gluon.model_zoo.vision.vgg11", "line_number": 12, "usage_type": "call"}, {"api_name": "mxnet.gluon.model_zoo", "line_number": 12, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 12, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Sequential", "line_number": 13, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 13, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 13, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Sequential", "line_number": 17, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 17, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 17, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Sequential", "line_number": 21, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 21, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 21, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Sequential", "line_number": 26, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 26, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 26, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Conv2D", "line_number": 28, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 28, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 28, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Conv2D", "line_number": 29, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 29, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 29, "usage_type": "name"}, {"api_name": "mxnet.initializer.Xavier", "line_number": 31, "usage_type": "call"}, {"api_name": "mxnet.initializer", "line_number": 31, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.nn.Sequential", "line_number": 36, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 36, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 36, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Conv2D", "line_number": 38, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 38, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 38, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Conv2D", "line_number": 39, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 39, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 39, "usage_type": "name"}, {"api_name": "mxnet.initializer.Xavier", "line_number": 41, "usage_type": "call"}, {"api_name": "mxnet.initializer", "line_number": 41, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.nn.Sequential", "line_number": 45, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 45, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 45, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Conv2D", "line_number": 47, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 47, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 47, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Conv2D", "line_number": 48, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 48, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 48, "usage_type": "name"}, {"api_name": "mxnet.initializer.Xavier", "line_number": 50, "usage_type": "call"}, {"api_name": "mxnet.initializer", "line_number": 50, "usage_type": "attribute"}, {"api_name": "mxnet.nd.contrib.BilinearResize2D", "line_number": 73, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 73, "usage_type": "attribute"}, {"api_name": "mxnet.nd.concat", "line_number": 76, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 76, "usage_type": "attribute"}, {"api_name": "mxnet.nd.contrib.BilinearResize2D", "line_number": 79, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 79, "usage_type": "attribute"}, {"api_name": "mxnet.nd.concat", "line_number": 82, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 82, "usage_type": "attribute"}, {"api_name": "mxnet.nd.contrib.BilinearResize2D", "line_number": 84, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 84, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.nn", "line_number": 91, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 91, "usage_type": "name"}, {"api_name": "mxnet.gluon.model_zoo.vision.vgg11", "line_number": 94, "usage_type": "call"}, {"api_name": "mxnet.gluon.model_zoo", "line_number": 94, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 94, "usage_type": "name"}, {"api_name": "mxnet.sym.var", "line_number": 96, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 96, "usage_type": "attribute"}, {"api_name": "gluoncv.nn.feature.FeatureExpander", "line_number": 99, "usage_type": "call"}, {"api_name": "gluoncv.nn", "line_number": 99, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.nn.HybridSequential", "line_number": 102, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 102, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 102, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Conv2D", "line_number": 104, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 104, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 104, "usage_type": "name"}, {"api_name": "mxnet.init.Xavier", "line_number": 104, "usage_type": "call"}, {"api_name": "mxnet.init", "line_number": 104, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.nn.Activation", "line_number": 105, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn", "line_number": 105, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 105, "usage_type": "name"}, {"api_name": "mxnet.gpu", "line_number": 118, "usage_type": "call"}, {"api_name": "mxnet.nd.random.uniform", "line_number": 123, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 123, "usage_type": "attribute"}]}
{"seq_id": "6300123495", "text": "from models.db import db\nfrom datetime import datetime\nfrom dataclasses import dataclass\n\n@dataclass\nclass FAQ(db.Model):\n    __tablename__= \"frequently_asked_questions\"\n\n    id: int\n    FAQ_Id: str\n    U_Id: str\n    Qn_body: str\n    Post_Date: str\n    Resp_by: str\n    Resp_Body: str\n    updated_at: str\n\n    id =db.Column(db.Integer, nullable=False, unique=False)\n    FAQ_Id = db.Column(db.String(5), primary_key=True)\n    U_Id = db.Column(db.String(5), db.ForeignKey(\"users.U_Id\"))\n    Qn_body = db.Column(db.Text, nullable=False, unique = True)\n    Post_Date = db.Column(db.String(30), default=datetime.now)\n    Resp_by = db.Column(db.String(2), db.ForeignKey(\"admins.A_Id\"))\n    Resp_Body = db.Column(db.Text, nullable=True)\n    updated_at = db.Column(db.String(30), onupdate=datetime.now())", "repo_name": "NoShadiah/Skills_Connect", "sub_path": "backend/models/FAQs/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 796, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "models.db.db.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "models.db.db", "line_number": 6, "usage_type": "name"}, {"api_name": "models.db.db.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "models.db.db", "line_number": 18, "usage_type": "name"}, {"api_name": "models.db.db.Integer", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.db.db.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "models.db.db", "line_number": 19, "usage_type": "name"}, {"api_name": "models.db.db.String", "line_number": 19, "usage_type": "call"}, {"api_name": "models.db.db.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "models.db.db", "line_number": 20, "usage_type": "name"}, {"api_name": "models.db.db.String", "line_number": 20, "usage_type": "call"}, {"api_name": "models.db.db.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "models.db.db.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "models.db.db", "line_number": 21, "usage_type": "name"}, {"api_name": "models.db.db.Text", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.db.db.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "models.db.db", "line_number": 22, "usage_type": "name"}, {"api_name": "models.db.db.String", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}, {"api_name": "models.db.db.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "models.db.db", "line_number": 23, "usage_type": "name"}, {"api_name": "models.db.db.String", "line_number": 23, "usage_type": "call"}, {"api_name": "models.db.db.ForeignKey", "line_number": 23, "usage_type": "call"}, {"api_name": "models.db.db.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "models.db.db", "line_number": 24, "usage_type": "name"}, {"api_name": "models.db.db.Text", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.db.db.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "models.db.db", "line_number": 25, "usage_type": "name"}, {"api_name": "models.db.db.String", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 5, "usage_type": "name"}]}
{"seq_id": "10561712891", "text": "import json\nimport os\nimport pathlib\nfrom json import JSONDecodeError\nfrom typing import Optional, Tuple\n\nimport numpy as np\nimport pandas as pd\n\nfrom retrieve_results.experiment_organiser import ExperimentOrganiser\nfrom retrieve_results.experiment_processing import ExperimentProcessor\n\n\ndef plot_metrics_for_one_folder(\n    folder_name: str,\n    annotate: bool = True,\n    override_fitness_values: Optional[Tuple[int, int]] = None,\n):\n    experiment_organiser = ExperimentOrganiser()\n    date = experiment_organiser.get_date_from_folder_name(folder_name)\n    config_mapping, config_dict_csv = experiment_organiser.get_config_data(date)\n    config_names = list(config_mapping.keys())\n    experiment_tags_list = list(config_mapping.values())\n\n    centroid_name = experiment_organiser.get_centroid_name(folder_name)\n\n    formula = experiment_organiser.get_formula_from_folder_name(folder_name)\n    experiment_tag = folder_name[15 + len(formula) + 1 :]\n\n    config_match_index = np.argwhere(\n        np.array(experiment_tags_list) == experiment_tag\n    ).reshape(-1)\n    config_filepath = (\n        experiment_organiser.repo_location\n        / \"configs\"\n        / config_names[config_match_index[0]]\n    )\n    experiment_processor = ExperimentProcessor(\n        experiment_label=folder_name,\n        config_filepath=config_filepath,\n        centroid_filename=centroid_name,\n        fitness_limits=(6.5, 10),\n        save_structure_images=False,\n        filter_for_experimental_structures=False,\n    )\n    if override_fitness_values is not None:\n        experiment_processor.experiment_parameters.fitness_min_max_values = (\n            override_fitness_values\n        )\n    experiment_processor.plot(annotate=annotate)\n    # experiment_processor.process_symmetry()\n\n\ndef update_configs_csv(\n    path_to_strip: str = \"/Users/marta/Documents/MSc Artificial Intelligence/Thesis/csp-elites/configs/\",\n):\n    path_to_configs = pathlib.Path(__file__).parent.parent / \"configs\"\n\n    sub_folders = [\n        name\n        for name in os.listdir(f\"{path_to_configs}\")\n        if os.path.isdir(path_to_configs / name)\n    ]\n\n    all_configs = []\n    for sub_folder in sub_folders:\n        path = path_to_configs / sub_folder\n        new_configs = [\n            os.path.join(path, o)\n            for o in os.listdir(path)\n            if os.path.isfile(os.path.join(path, o))\n            and (\".pbs\" not in o)(\".sh\" not in o)\n        ]\n        all_configs += new_configs\n\n    config_names = [\n        config.lstrip(path_to_strip).rstrip(\".json\") for config in all_configs\n    ]\n\n    data = {}\n\n    for i, config in enumerate(all_configs):\n        try:\n            with open(config, \"r\") as file:\n                config_data = json.load(file)\n        except (JSONDecodeError, UnicodeDecodeError) as e:\n            continue\n        data[config_names[i]] = config_data\n\n    df = pd.DataFrame(data)\n    df = df.transpose()\n    df = pd.concat([df, df[\"cvt_run_parameters\"].apply(pd.Series)], axis=1)\n    df.drop(columns=\"cvt_run_parameters\", inplace=True)\n    df.to_csv(path_to_configs / \"list_of_configs.csv\")\n    print()\n\n\nif __name__ == \"__main__\":\n    update_configs_csv()\n", "repo_name": "mwolinska/QD4CSP", "sub_path": "retrieve_results/handy_method_wrappers.py", "file_name": "handy_method_wrappers.py", "file_ext": "py", "file_size_in_byte": 3156, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "typing.Optional", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 17, "usage_type": "name"}, {"api_name": "retrieve_results.experiment_organiser.ExperimentOrganiser", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "retrieve_results.experiment_processing.ExperimentProcessor", "line_number": 38, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 57, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "json.load", "line_number": 85, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 86, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 90, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 92, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 92, "usage_type": "attribute"}]}
{"seq_id": "4265964319", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('',views.log_in),\n    path('sign_up',views.sign_up),\n    path('log_in',views.relogin),\n    path('signup',views.signup),\n    path('home',views.home),\n    path('login',views.login),\n    path('log_out',views.log_out)\n]\n", "repo_name": "Yousef-labadi/IMS", "sub_path": "app1/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 291, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}]}
{"seq_id": "20595337571", "text": "#   a212_rsa_encrypt.py\nimport rsa as rsa\n\nwhile True:\n    key = input(\"Enter the Encryption Key: \")\n    while key.isdigit():\n        mod_value = input(\"Enter the Modulus: \")\n        while mod_value.isdigit():\n            plaintext = input(\"Enter a message to encrypt: \")\n            encrypted_msg = rsa.encrypt(int(key), int(mod_value), plaintext)\n            print(\"Encrypted Message:\", encrypted_msg)\n            exit()\n        if not mod_value.isdigit():\n            print(\"Please enter a valid Modulus\")\n\n    if not key.isdigit():\n        print(\"Please input a valid key\")\n\n", "repo_name": "AlejandroE25/AP-CSP", "sub_path": "2.1.2/rsa_encrypt.py", "file_name": "rsa_encrypt.py", "file_ext": "py", "file_size_in_byte": 579, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "rsa.encrypt", "line_number": 10, "usage_type": "call"}]}
{"seq_id": "3898367686", "text": "import pandas as pd\nimport ast\nimport json\n\nfrom geo.helpers import *\n\ntry:\n    data = pd.read_csv('RU.txt', delimiter='\\t', names=[\"geonameid\", \"name\", \"asciiname\", \"alternatenames\", \"latitude\", \"longtitude\", \"feature class\",\n                                                      \"feature code\", \"country code\", \"cc2\", \"admin1 code\", \"admin2 code\", \"admin3 code\", \"admin4 code\",\n                                                      \"population\", \"elevation\", \"dem\", \"timezone\", \"modification date\"],\n                                            low_memory=False)\n\n    data.fillna(\"\", inplace=True)\nexcept FileNotFoundError:\n    print(\"File RU.txt not found\")\n    exit()\n\n\ndef getbygeonameid(geonameid):\n    \"\"\" Returns city info by its geonameid\"\"\"\n    try:\n        info = data.loc[data['geonameid'] == int(geonameid)]\n        d = info.to_dict(orient='records')\n    except ValueError:\n        d = {}\n\n    j = json.dumps(d, indent=4, separators=(',', ': '), ensure_ascii=False)\n    return (j)\n\n\ndef get_city_name_suggestions(city_name, cities):\n    \"\"\" Returns list of suggested city names \"\"\"\n\n    contains_name = cities.loc[data['alternatenames'].str.contains(city_name, na=False)]\n    suggestions = []\n    for c in contains_name['alternatenames']:\n        if c[-1] not in suggestions:\n            suggestions.append(c[-1])\n    return ({f'{city_name} not found. Suggestions': suggestions})\n     \n\ndef getcities(city1, city2):\n    \"\"\" \n    Returns all info about two cities by names\n        feature class == 'P' defines city \n    \"\"\"\n\n    if not city1 or not city2:\n        return {}\n\n    info1 = data.loc[data['alternatenames'].str.contains(city1, na=False) & \n                    (data['feature class'] =='P')].sort_values(by=['population'], ascending=False)\n\n    info2 = data.loc[data['alternatenames'].str.contains(city2, na=False) & \n                    (data['feature class'] =='P')].sort_values(by=['population'], ascending=False)\n\n    city1_check = check_city(city1, info1)\n    city2_check = check_city(city2, info2)\n\n    if not city1_check:\n        lst = get_city_name_suggestions(city1, info1)\n        j = json.dumps(lst, indent=4, separators=(',', ': '), ensure_ascii=False, default=str)\n        return j \n    if not city2_check:\n        lst = get_city_name_suggestions(city2, info2)\n        j = json.dumps(lst, indent=4, separators=(',', ': '), ensure_ascii=False, default=str)\n        return j \n\n    if len(info1) < 0 or len(info2) < 0:\n        return {}\n    else:\n        city1_info = info1.iloc[0]\n        city2_info = info2.iloc[0]\n\n        d1 = city1_info.to_dict()\n        d2 = city2_info.to_dict()\n    \n        lst = []\n        lst.append(d1)\n        lst.append(d2)\n\n        lst.append(lat_diff(city1_info, city2_info))\n        lst.append(tz_diff(city1_info, city2_info))\n\n        j = json.dumps(lst, indent=4, separators=(',', ': '), ensure_ascii=False, default=str)\n        return j\n\ndef cities_list_info(cities_list):\n    \"\"\" Returns all info for each city in cities_list \"\"\"\n\n    results = []\n    for city in cities_list:\n        info = data.loc[data['alternatenames'].str.contains(city, na=False) & \n                    (data['feature class'] =='P')].sort_values(by=['population'], ascending=False)\n\n        city_check = check_city(city, info)\n        \n        if not city_check:\n            results.append(get_city_name_suggestions(city, info))\n            continue\n\n        city1_info = info.iloc[0]\n        results.append(city1_info.to_dict())\n    \n    j = json.dumps(results, indent=4, separators=(',', ': '), ensure_ascii=False, default=str)\n    return j\n", "repo_name": "kirpastuhov/infotecs_test_2", "sub_path": "geo/geo_methods.py", "file_name": "geo_methods.py", "file_ext": "py", "file_size_in_byte": 3585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 66, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 85, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 105, "usage_type": "call"}]}
{"seq_id": "28169990268", "text": "# Databricks notebook source\n# MAGIC %md\n# MAGIC ##Ingest Races File \n\n# COMMAND ----------\n\n# MAGIC %run \"../includes/configuration\"\n\n# COMMAND ----------\n\n# MAGIC %run \"../includes/common_functions\"\n\n# COMMAND ----------\n\ndbutils.widgets.text(\"p_data_source\", \"\")\nv_data_source = dbutils.widgets.get(\"p_data_source\")\n\n# COMMAND ----------\n\ndbutils.widgets.text(\"p_file_date\", \"2021-03-21\")\nv_file_date = dbutils.widgets.get(\"p_file_date\")\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ####Step 1 - Read the CSV using the spark data frame reader\n\n# COMMAND ----------\n\nfrom pyspark.sql.types import StructField, StructType, IntegerType, StringType, DoubleType\n\n# COMMAND ----------\n\nraces_schema = StructType(fields = [StructField(\"raceId\",IntegerType(),False),\n                                  StructField(\"year\",IntegerType(),True),\n                                  StructField(\"round\",IntegerType(),True),\n                                  StructField(\"circuitid\",IntegerType(),True),\n                                  StructField(\"name\",StringType(),True),\n                                  StructField(\"date\",StringType(),True),\n                                  StructField(\"time\",StringType(),True),\n                                  StructField(\"url\",StringType(),True)\n                                  ])\n\n# COMMAND ----------\n\nraces_df = spark.read \\\n.option(\"header\",True) \\\n.schema(races_schema) \\\n.csv(f\"{raw_folder_path}/{v_file_date}/races.csv\")\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ####Step 2 - Add Ingestion date and race_timestamp to the date\n# MAGIC Select only the required columns\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import to_timestamp, col, concat, lit\n\n# COMMAND ----------\n\nraces_timestamp_df = races_df.withColumn(\"ingestion_date\",current_timestamp()) \\\n.withColumn(\"race_timestamp\", to_timestamp(concat(col('date'),lit(\" \"), col('time')),'yyyy-MM-dd HH:mm:ss')) \\\n.withColumn(\"data_source\", lit(v_data_source)) \\\n.withColumn(\"file_date\", lit(v_file_date))\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ####Step 3 - Select only the required columns & Rename as required\n\n# COMMAND ----------\n\nraces_selected_df = races_timestamp_df.select(col(\"raceid\").alias(\"race_id\"),col(\"year\").alias(\"race_year\"),                                  col(\"round\"),col(\"circuitid\").alias(\"circuit_id\"),col(\"name\"),col(\"ingestion_date\"),col(\"race_timestamp\"),col(\"data_source\"),col(\"file_date\"))\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ####Step 4 - Wtrite data to datalake as parquet\n\n# COMMAND ----------\n\nraces_selected_df.write.mode(\"overwrite\").partitionBy(\"race_year\").format(\"delta\").saveAsTable(\"f1_processed.races\")\n                                                                  \n\n# COMMAND ----------\n\n\n\n# COMMAND ----------\n\ndbutils.notebook.exit(\"Success\")\n", "repo_name": "kevinbarvaliya/Azure_F1_racing_DATABRICKS_project", "sub_path": "Databricks Code/Ingest/2.ingest_races_file.py", "file_name": "2.ingest_races_file.py", "file_ext": "py", "file_size_in_byte": 2800, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "pyspark.sql.types.StructType", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.types.IntegerType", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 35, "usage_type": "call"}, {"api_name": "pyspark.sql.types.IntegerType", "line_number": 35, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 36, "usage_type": "call"}, {"api_name": "pyspark.sql.types.IntegerType", "line_number": 36, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 37, "usage_type": "call"}, {"api_name": "pyspark.sql.types.IntegerType", "line_number": 37, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 38, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 38, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 39, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 39, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 40, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 40, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 41, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 41, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.to_timestamp", "line_number": 64, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.concat", "line_number": 64, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 64, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 64, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 65, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 66, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 75, "usage_type": "call"}]}
{"seq_id": "70692265082", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 30 11:05:41 2023\n\n@author: nicolas\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom numpy import linalg as LA\nimport numpy as np\n\nimport time\nimport skimage as ski\n\n\ndef find_surface(arm, x, y, z, Rx=180, Ry=0, Rz=0, absolute_epsilon=1, relative_epsilon=0, verbose=True):\n    torques = []\n    arm.set_position(\n        x=x,\n        y=y,\n        z=z,\n        roll=Rx,\n        pitch=Ry,\n        yaw=Rz,\n        speed=100,\n        is_radian=0,\n        wait=True,\n        radius=None,\n        relative=False,\n    )\n    initial_torques = np.array(arm.joints_torque[0:6])\n    arm.set_position(\n        x=x,\n        y=y,\n        z=z - 1,\n        roll=Rx,\n        pitch=Ry,\n        yaw=Rz,\n        speed=50,\n        is_radian=0,\n        wait=True,\n        radius=None,\n        relative=False,\n    )\n    test1_torques = np.array(arm.joints_torque[0:6])\n    arm.set_position(\n        x=x,\n        y=y,\n        z=z - 2,\n        roll=Rx,\n        pitch=Ry,\n        yaw=Rz,\n        speed=50,\n        is_radian=0,\n        wait=True,\n        radius=None,\n        relative=False,\n    )\n    test2_torques = np.array(arm.joints_torque[0:6])\n    arm.set_position(\n        x=x,\n        y=y,\n        z=z - 3,\n        roll=Rx,\n        pitch=Ry,\n        yaw=Rz,\n        speed=50,\n        is_radian=0,\n        wait=True,\n        radius=None,\n        relative=False,\n    )\n    test3_torques = np.array(arm.joints_torque[0:6])\n    mins = np.min([test1_torques, test2_torques, test3_torques, initial_torques], axis=0)\n    maxs = np.max([test1_torques, test2_torques, test3_torques, initial_torques], axis=0)\n    d = np.abs(maxs - mins)\n    print(d)\n    boundaries = [mins - relative_epsilon*d - absolute_epsilon, maxs + relative_epsilon*d + absolute_epsilon]\n    print(boundaries)\n    actual_torques = np.array(arm.joints_torque[0:6])\n    arm.set_position(\n        x=x,\n        y=y,\n        z=z - 100,\n        roll=Rx,\n        pitch=Ry,\n        yaw=Rz,\n        speed=1,\n        is_radian=0,\n        wait=False,\n        radius=None,\n        relative=False,\n    )\n    while (boundaries[0] < actual_torques).all() and (actual_torques < boundaries[1]).all():\n        actual_torques = np.array(arm.joints_torque[0:6])\n        pos = arm.position_aa\n        torques.append(actual_torques)\n        if verbose:\n            print(\"i'm waiting for collision\")\n    if verbose:\n        print(\"I collided\")\n    arm.set_state(4)\n    time.sleep(1)\n    arm.set_state(0)\n    arm.set_position(\n        x=x,\n        y=y,\n        z=z,\n        roll=Rx,\n        pitch=Ry,\n        yaw=Rz,\n        speed=100,\n        is_radian=0,\n        wait=True,\n        radius=None,\n        relative=False,\n    )\n    return pos, torques\n\n\n\nclass absolute_coords:\n    def __init__(self, x0, y0, z0, x1, y1, z1, x2, y2, z2):\n        self.x0, self.y0, self.z0 = x0, y0, z0\n        self.x1, self.y1, self.z1 = x1, y1, z1\n        self.x2, self.y2, self.z2 = x2, y2, z2\n        self.mat = np.array(\n            [\n                [self.x1 - self.x0, self.x2 - self.x0],\n                [self.y1 - self.y0, self.y2 - self.y0],\n                [self.z1 - self.z0, self.z2 - self.z0],\n            ]\n        )\n\n    def convert(self, points):\n        return np.dot(self.mat, points) + np.array([[self.x0], [self.y0], [self.z0]])\n\n\ndef optimize_path(input_data, threshold):\n    data = input_data / np.max(input_data)\n    plt.figure()\n\n    for seg in data:\n        plt.plot(seg.T[0], seg.T[1])\n        plt.gca().invert_yaxis()\n\n    data = list(data)\n    new_data = [[data[0]]]\n    id_group = 0\n    id_ = 0\n    for id_line in range(len(data)):\n        if len(data) > 1:\n            dmin = LA.norm(data[0][1] - data[1][0])\n            id_min = 0\n            for id_seg in range(len(data)):\n                d = LA.norm(new_data[id_group][id_][1] - data[id_seg][0])\n                if d <= dmin:\n                    id_min = id_seg\n                    dmin = d\n        else:\n            id_min = 0\n        id_ += 1\n        if dmin > threshold:\n            id_group += 1\n            id_ = 0\n            new_data.append([])\n        new_data[id_group].append(data[id_min])\n        data.pop(id_min)\n\n    compression = len(new_data) / len(input_data)\n    return new_data, compression\n\n\ndef sort_edges(edges):\n    \"\"\"Sort edges so as to minimize useless movements.\n    \n    Argument:\n\n    edges -- a list of edges, each of shape [nb_of_points, 2]\n    \"\"\"\n    edges = edges.copy()\n    sorted_edges = [edges.pop(0)]\n    for _ in range(len(edges) - 1):\n\n        def dist_to_last_edge(other_edge):\n            return min(\n                LA.norm(sorted_edges[-1][-1] - other_edge[0]),\n                LA.norm(sorted_edges[-1][-1] - other_edge[-1]),\n            )\n\n        def better_flipped(other_edge):\n            return LA.norm(sorted_edges[-1][-1] - other_edge[-1]) < LA.norm(\n                sorted_edges[-1][-1] - other_edge[0]\n            )\n\n        edges = sorted(edges, key=dist_to_last_edge)\n\n        edge_to_add = edges.pop(0)\n        if better_flipped(edge_to_add):\n            edge_to_add = edge_to_add[::-1]\n\n        sorted_edges.append(edge_to_add)\n\n    return sorted_edges\n\n\ndef image_thresholding(image):\n    image = ski.restoration.denoise_bilateral(\n        image, sigma_color=0.5, sigma_spatial=2, channel_axis=-1\n    )\n    image = ski.exposure.equalize_adapthist(image)\n    image = ski.color.rgb2gray(image)\n    hist = plt.hist(image.ravel(), bins=256)\n    histo = np.array(hist[0])\n    maximum = histo.argmax() / 256\n    thresholds = ski.filters.threshold_multiotsu(image)\n    edge_image = ski.feature.canny(image, low_threshold=0.1, high_threshold=0.2)\n    hough_lines = ski.transform.probabilistic_hough_line(\n        edge_image, line_length=6, line_gap=2, threshold=20\n    )\n    hough_lines = np.array(hough_lines)\n    return edge_image, hough_lines\n", "repo_name": "Kaduo/mira-inauguration", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 5862, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 154, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 157, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 188, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 189, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 193, "usage_type": "name"}, {"api_name": "skimage.restoration.denoise_bilateral", "line_number": 209, "usage_type": "call"}, {"api_name": "skimage.restoration", "line_number": 209, "usage_type": "attribute"}, {"api_name": "skimage.exposure.equalize_adapthist", "line_number": 212, "usage_type": "call"}, {"api_name": "skimage.exposure", "line_number": 212, "usage_type": "attribute"}, {"api_name": "skimage.color.rgb2gray", "line_number": 213, "usage_type": "call"}, {"api_name": "skimage.color", "line_number": 213, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 215, "usage_type": "call"}, {"api_name": "skimage.filters.threshold_multiotsu", "line_number": 217, "usage_type": "call"}, {"api_name": "skimage.filters", "line_number": 217, "usage_type": "attribute"}, {"api_name": "skimage.feature.canny", "line_number": 218, "usage_type": "call"}, {"api_name": "skimage.feature", "line_number": 218, "usage_type": "attribute"}, {"api_name": "skimage.transform.probabilistic_hough_line", "line_number": 219, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 219, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 222, "usage_type": "call"}]}
{"seq_id": "33123886289", "text": "import os\nimport sys\nimport argparse\nimport json\nclass Disabler:\n\tmodifiers = []\n\tdef __init__(self, disable):\n\t\tif not disable:\n\t\t\treturn\n\t\tfor part in disable:\n\t\t\tself.modifiers.extend(part.strip('\"').split())\n\tdef disable(self, spec):\n\t\treturn spec.get('disable') in self.modifiers\ndef mergeStrings(args):\n\tresult = []\n\tstringParts = []\n\tfor arg in args:\n\t\tif arg.startswith('\"') or len(stringParts)>0 :\n\t\t\tstringParts.append(arg)\n\t\telse:\n\t\t\tresult.append(arg)\n\t\tif arg.endswith('\"'):\n\t\t\tresult.append(' '.join(stringParts))\n\t\t\tstringParts = []\n\treturn result\n\ndef parseArguments(args):\n\twith open(\"disableCategories.json\",'r') as file:\n\t\tdisableCategories = json.load(file);\n\targs = mergeStrings(args)\n\tparser = argparse.ArgumentParser(description='Exports tactical information from  the specified server to the DCS Ka-50 ABRIS system.')\n\tparser.add_argument('-path', nargs=1, default=os.getcwd(),help=\"Optional path to the ABRIS folder, use working directory if not specified\")\n\tfor category in disableCategories:\n\t\tparser.add_argument(f'-{category}',dest='disable', action='append_const', const=category)\n\tparser.add_argument('prog', nargs=1)\n\tparser.add_argument('specPath', nargs='+',help=\"Path to abrisspec.json file\")\n\treturn parser.parse_args(args)\n\nclass ConsoleCommands:\n\tdisabler = None\n\tdatabasePath = ''\n\tdef __init__(self, args):\n\t\tparsed = parseArguments(args)\n\t\tself.disabler = Disabler(parsed.disable)\n\t\tif isinstance(parsed.path,list):\n\t\t\tparsed.path = ' '.join(parsed.path)\n\t\toutPath = parsed.path.strip('\"')\n\t\tself.databasePath = os.path.join(outPath, \"Database\\\\\")\n\t\tself.specPath = ' '.join(parsed.specPath).strip('\"')\n\t\t\n", "repo_name": "gronank/ExportToABRIS", "sub_path": "parsecommands.py", "file_name": "parsecommands.py", "file_ext": "py", "file_size_in_byte": 1647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "40", "api": [{"api_name": "json.load", "line_number": 29, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}]}
{"seq_id": "3347151720", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan  12 21:05:31 2020\n\n@author: esl1h\nPython3\n\"\"\"\nimport matplotlib.pyplot as plt\n\nplt.plot ( [ 1, 2, 3, 4 ] )\nplt.ylabel ( 'some numbers' )\nplt.show ( )\n\nx = [ 21, 22, 23, 4, 5, 6, 77, 8, 9, 10, 31, 32, 33, 34, 35, 36, 37, 18, 49, 50, 100 ]\nnum_bins = 5\nplt.hist ( x, num_bins, facecolor='blue' )\nplt.show ( )\n", "repo_name": "Esl1h/python_learning", "sub_path": "matplot-tests.py", "file_name": "matplot-tests.py", "file_ext": "py", "file_size_in_byte": 377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}]}
{"seq_id": "22298269028", "text": "from collective.cart.shipping.interfaces import IShippingMethod\nfrom collective.cart.shipping.interfaces import IShippingMethodAnnotations\nfrom zope.component import adapts\nfrom zope.interface import implements\n\n\nclass ShippingMethodAdapter(object):\n\n    adapts(IShippingMethodAnnotations)\n    implements(IShippingMethod)\n\n    def __init__(self, context):\n        self.context = context\n\n    def shipping_cost(self, weight, price):\n        base_charge = self.context.base_charge\n        weight_charge = self.context.weight_charge\n        fuel_rate = self.context.fuel_rate\n        insurance_base = self.context.insurance_base\n        insurance_rate = self.context.insurance_rate\n        risk_rate = self.context.risk_rate\n        cost = (base_charge + weight * weight_charge) \\\n            * (100 + fuel_rate) / 100 \\\n            * (100 + risk_rate) / 100 + insurance_base\\\n            + price * (insurance_rate) / 100\n        return cost\n", "repo_name": "taito-zz/collective.cart.shipping", "sub_path": "collective/cart/shipping/adapter/shipping.py", "file_name": "shipping.py", "file_ext": "py", "file_size_in_byte": 939, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "zope.component.adapts", "line_number": 9, "usage_type": "call"}, {"api_name": "collective.cart.shipping.interfaces.IShippingMethodAnnotations", "line_number": 9, "usage_type": "argument"}, {"api_name": "zope.interface.implements", "line_number": 10, "usage_type": "call"}, {"api_name": "collective.cart.shipping.interfaces.IShippingMethod", "line_number": 10, "usage_type": "argument"}]}
{"seq_id": "34755212490", "text": "#!/usr/bin/env python3\n\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\nimport json\nfrom rdflib import Graph, Namespace, RDF, URIRef\nfrom rdflib.namespace import FOAF\nfrom urllib.parse import parse_qsl, urlparse\n\nDQ = Namespace('http://purl.org/data-quality#')\nMD = Namespace('http://www.w3.org/ns/md#')\n\n# read settings\nwith open('settings.json') as f:\n    settings = json.load(f)\n\n# prepare the endpoint\ndef prepareEndpoint():\n    # create graph\n    g = Graph()\n\n    # read main page\n    r = g.parse(settings['repositoryUrl'])\n\n    # add items\n    for itemTypeLOD in [DQ.measure, DQ.result, DQ.context, FOAF.person]:\n        for s in r.subjects(RDF.type, itemTypeLOD):\n            g.parse(s)\n\n    # remove the artificially crated triples indicating microdata\n    g.remove((None, MD.item, RDF.nil))\n\n    return g\n\n# run the endpoint\ndef runEndpoint(g):\n    class RequestHandler(BaseHTTPRequestHandler):\n        def _perform_sparql(self, data):\n            # test whether a query has been provided\n            if 'query' not in data:\n                # send header\n                self.send_response(400)\n                self.send_header('Content-type', 'text/html')\n                self.end_headers()\n\n                # send error message\n                self.wfile.write('Please provide a query'.encode('utf-8'))\n            else:\n                # perform the query\n                query = data['query']\n                try:\n                    result = g.query(query)\n                except Exception as e:\n                    # send header\n                    self.send_response(400)\n                    self.send_header('Content-type', 'text/html')\n                    self.end_headers()\n\n                    # send error message\n                    self.wfile.write('The query raised an error: {}'.format(e).encode('utf-8'))\n                else:\n                    # send header\n                    self.send_response(200)\n                    self.send_header('Content-type', 'text/html')\n                    self.end_headers()\n\n                    # sned the content\n                    self.wfile.write(result.serialize(format='json'))\n\n        def do_GET(self):\n            data = dict(parse_qsl(urlparse(self.path).query))\n            self._perform_sparql(data)\n\n        def do_POST(self):\n            length = int(self.headers['Content-Length'])\n            data = {key.decode('utf-8'): val.decode('utf-8') for key, val in dict(parse_qsl(self.rfile.read(length))).items()}\n            self._perform_sparql(data)\n\n    # start the server\n    server_address = ('', settings['port'])\n    httpd = HTTPServer(server_address, RequestHandler)\n    httpd.serve_forever()\n\n# prepare and run the endpoint\ndata = prepareEndpoint()\nrunEndpoint(data)\n", "repo_name": "GIScience/measures-rest-sparql", "sub_path": "endpoint.py", "file_name": "endpoint.py", "file_ext": "py", "file_size_in_byte": 2760, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "rdflib.Namespace", "line_number": 9, "usage_type": "call"}, {"api_name": "rdflib.Namespace", "line_number": 10, "usage_type": "call"}, {"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "rdflib.Graph", "line_number": 19, "usage_type": "call"}, {"api_name": "rdflib.namespace.FOAF.person", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rdflib.namespace.FOAF", "line_number": 25, "usage_type": "name"}, {"api_name": "rdflib.RDF.type", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rdflib.RDF", "line_number": 26, "usage_type": "name"}, {"api_name": "rdflib.RDF.nil", "line_number": 30, "usage_type": "attribute"}, {"api_name": "rdflib.RDF", "line_number": 30, "usage_type": "name"}, {"api_name": "http.server.BaseHTTPRequestHandler", "line_number": 36, "usage_type": "name"}, {"api_name": "urllib.parse.parse_qsl", "line_number": 70, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 70, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qsl", "line_number": 75, "usage_type": "call"}, {"api_name": "http.server.HTTPServer", "line_number": 80, "usage_type": "call"}]}
{"seq_id": "18122982778", "text": "import os\nimport sys\nimport itertools\nfrom matplotlib import pyplot as plt\n\nsys.path.append('../../')\nfrom analysis import analyse_file, gen_pair_graphs\n\n#####################\n##     GROUP 1     ##\n#####################\n\nfor (deg, rept) in itertools.product([30,37],[1,2,3]):\n    # deg = 30\n    deg = 37\n    rept = 3\n    csv_filename = f'{deg}deg_{rept}_repeat'\n    groups_filename = 'group1'\n    output_folder = 'lysis_curve_3_repeats/v4'\n\n    df = analyse_file(\n        opticalDense_path = f'./csv/{csv_filename}.csv',\n        triplicates_path  = f'./groups/{groups_filename}.csv',\n        # blanks            = ['A3'] # for deg30-rept1\n        blanks            = ['A3', 'A9'] # for all else\n    )\n    figs = []\n\n    ## WT ##\n    title = f'Lysis Curve - {deg}°C\\nWT'\n    cols = [ c for c in df.columns if 'WT' in c ]\n    fig,axs = gen_pair_graphs(df, cols, title, xticks=range(13))\n    figs += [fig]\n    ## WT ##\n\n    wt_cols = [ c for c in df.columns if 'WT' in c and 'OE' not in c ]\n    wt_oe_cols = [ c for c in df.columns if 'WT' in c and 'OE' in c ]\n\n    ## cured ##\n    title = f'Lysis Curve - {deg}°C\\ncured'\n    cols = wt_cols + [ c for c in df.columns if 'cured' in c and 'zaga' not in c ]\n    fig,axs = gen_pair_graphs(df, cols, title, xticks=range(13))\n    figs += [fig]\n    ## cured ##\n\n    ## zaga ##\n    title = f'Lysis Curve - {deg}°C\\ncured Δzaga'\n    cols = wt_cols + [ c for c in df.columns if 'zaga' in c ]\n    fig,axs = gen_pair_graphs(df, cols, title, xticks=range(13))\n    figs += [fig]\n    ## zaga ##\n\n    ## struc ##\n    title = f'Lysis Curve - {deg}°C\\nΔstruc'\n    cols = wt_cols + [ c for c in df.columns if 'struc' in c ]\n    fig,axs = gen_pair_graphs(df, cols, title, xticks=range(13))\n    figs += [fig]\n    ## struc ##\n\n\n    # save\n    for i,fig in enumerate(figs):\n        path = f'./figs/{output_folder}/{csv_filename}'\n        os.makedirs(path, exist_ok=True)\n        fig.savefig(f'{path}/{i}.png')\n\n    # show\n    # plt.show()", "repo_name": "LooLzzz/lysis-curves", "sub_path": "OE 56/1/Lysis_Curve_30_37.py", "file_name": "Lysis_Curve_30_37.py", "file_ext": "py", "file_size_in_byte": 1968, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.path.append", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 13, "usage_type": "call"}, {"api_name": "analysis.analyse_file", "line_number": 21, "usage_type": "call"}, {"api_name": "analysis.gen_pair_graphs", "line_number": 32, "usage_type": "call"}, {"api_name": "analysis.gen_pair_graphs", "line_number": 42, "usage_type": "call"}, {"api_name": "analysis.gen_pair_graphs", "line_number": 49, "usage_type": "call"}, {"api_name": "analysis.gen_pair_graphs", "line_number": 56, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 64, "usage_type": "call"}]}
{"seq_id": "20460119490", "text": "# https://nalinc.github.io/blog/2018/skin-detection-python-opencv/\n\n# Required modules\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport os\nimport sys\nfrom colorama import init, Fore\n\nif(len(sys.argv) != 2):\n     print(\"ERROR: The format should be <<< python HSV_color_space.py <image file> >>>\")\n     exit()\n\nfilename = sys.argv[1]\n\nmin_HSV = np.array([0, 58, 30], dtype = \"uint8\")\nmax_HSV = np.array([33, 255, 255], dtype = \"uint8\")\n# Geting the image\nimage = cv2.imread('skin_Images/' + filename)\nimageHSV = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\nskinRegionHSV = cv2.inRange(imageHSV, min_HSV, max_HSV)\n\nskinHSV = cv2.bitwise_and(image, image, mask = skinRegionHSV)\n\n# Get the current working directory \n#   and create result folder for processed images\nresult_path = os.getcwd() + \"/result_Images/HSV_color_space\"\nif not os.path.exists(result_path):\n    os.makedirs(result_path)\n\ncv2.imwrite(result_path+\"/hsv_\"+filename, np.hstack([image, skinHSV]))\n\nprint(Fore.BLUE + \"The \" + filename + Fore.GREEN + \" image is saved to\" + Fore.CYAN + \" result_Images/HSV_color_space.\" + Fore.LIGHTWHITE_EX)\n", "repo_name": "abogutalan/skin-detection", "sub_path": "HSV_color_space.py", "file_name": "HSV_color_space.py", "file_ext": "py", "file_size_in_byte": 1118, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 25, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 33, "usage_type": "call"}, {"api_name": "colorama.Fore.BLUE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 35, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "colorama.Fore.CYAN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "colorama.Fore.LIGHTWHITE_EX", "line_number": 35, "usage_type": "attribute"}]}
{"seq_id": "31954169224", "text": "#! /usr/bin/env python3\n\nimport os\nimport sys\nimport pytest\nimport xarray as xr\nimport numpy as np\n\nsys.path.append(os.path.abspath(os.path.join(\"notebooks\", \"utils\")))\nsys.path.append(os.path.abspath(\"tests\"))\nfrom utils_units import _clean_units, conv_units\nfrom xr_ds_ex import xr_ds_ex\n\nnyrs = 3\nvar_const = False\n\n\n@pytest.mark.parametrize(\n    \"units_in, units_out\",\n    [\n        # basic example, straight from dictionary\n        (\"years\", \"common_years\"),\n        # ensure 'gC' in 'degC' doesn't get converted\n        (\"degC\", \"degC\"),\n        # matches within expressions\n        (\"gN leaf/m^2\", \"g leaf/m^2\"),  # LNC\n        (\"gC/m^2/s\", \"g/m^2/s\"),  # AR\n        (\"meq/m^3\", \"mmol/m^3\"),  # ALK\n        (\n            \"(centimeter^2)(meq/m^3 cm/s)\",\n            \"(centimeter^2)(mmol/m^3 cm/s)\",\n        ),  # ALK_RIV_FLUX integral\n        (\"neq/cm3\", \"nmol/cm3\"),  # ABIO_ALK_SURF\n        (\"degC*cm/s\", \"degC*cm/s\"),  # T_FLUX_EXCH_INTRF\n        (\"days since 0001-01-01 00:00:00\", \"days since 0001-01-01 00:00:00\"),  # time\n        # multiple matches\n        (\"gC/gN\", \"g/g\"),  # LEAFCN\n    ],\n)\ndef test_clean_units(units_in, units_out):\n    assert _clean_units(units_in) == units_out\n\n\n@pytest.mark.parametrize(\"apply_chunk\", [True, False])\n@pytest.mark.parametrize(\"add_encoding\", [True, False])\ndef test_conv_units(apply_chunk, add_encoding):\n    da = xr_ds_ex()[\"var_ex\"]\n    da.attrs[\"units\"] = \"kg\"\n    da.attrs[\"long_name\"] = \"var_ex\"\n    if apply_chunk:\n        da = da.chunk({\"time\": 12})\n    if add_encoding:\n        da.encoding[\"_FillValue\"] = None\n\n    da_out = conv_units(da, \"g\")\n\n    assert da_out.attrs[\"units\"] == \"g\"\n    assert da_out.encoding == da.encoding\n    assert da_out.chunks == da.chunks\n    assert np.all(da_out.values == 1000.0 * da.values)\n", "repo_name": "marbl-ecosys/HiRes-CESM-analysis", "sub_path": "tests/test_utils_units.py", "file_name": "test_utils_units.py", "file_ext": "py", "file_size_in_byte": 1781, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "40", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "utils_units._clean_units", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 18, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 18, "usage_type": "attribute"}, {"api_name": "xr_ds_ex.xr_ds_ex", "line_number": 47, "usage_type": "call"}, {"api_name": "utils_units.conv_units", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 60, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 44, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 45, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 45, "usage_type": "attribute"}]}
{"seq_id": "18367224167", "text": "\"\"\"\r\n@file      uart_monitor.py\r\n@brief     Monitoring and statistics for UART incoming messages\r\n@author    Moosa Baransi\r\n@version   1.0\r\n@date      09/07/2013\r\n\"\"\"\r\n\r\nimport logging\r\nimport threading\r\n\r\nfrom gm_common import *\r\n\r\nCAN_HS_IDX = 0\r\nCAN_SW_IDX = 1\r\n\r\n# Monitoring UART Tx/Rx\r\nclass UartMonitor:\r\n    def __init__(self):\r\n        # monitoring lists\r\n        self.expected_uart_responses = []       # stores expected UART responses when sending data from UART to DUT\r\n        self.expected_hs_can_ids = []           # expected High Speed CAN IDs to be monitored\r\n        self.expected_sw_can_ids = []           # expected Single Wire CAN IDs to be monitored\r\n        self.pending_can_ids = []               # pending CAN IDs to be monitored/filtered\r\n        self.message_counter = {}               # holds each message type with its corresponding number of Rx messages\r\n        self.is_can_enabled = [False, False]    # determines if HS/SW CAN streaming is enabled/disabled\r\n        self.logger = logging.getLogger(UART_LOGGER_NAME)\r\n        # lockers\r\n        self.expected_uart_lock = threading.Lock()\r\n        self.can_enable_lock = threading.Lock()\r\n        self.can_monitor_lock = threading.Lock()\r\n        self.can_pending_lock = threading.Lock()\r\n\r\n    def is_expected_response(self, response):\r\n        self.expected_uart_lock.acquire()\r\n        res = True if response in self.expected_uart_responses else False\r\n        self.expected_uart_lock.release()\r\n        return res\r\n\r\n    def add_expected_response(self, result):\r\n        self.expected_uart_lock.acquire()\r\n        self.expected_uart_responses.append(result)\r\n        self.expected_uart_lock.release()\r\n\r\n    def add_can_id_to_monitor(self, can_id, is_sw_port):\r\n        self.can_monitor_lock.acquire()\r\n        if (is_sw_port):\r\n            self.expected_sw_can_ids.append(can_id)\r\n        else:\r\n            self.expected_hs_can_ids.append(can_id)\r\n        self.can_monitor_lock.release()\r\n\r\n    def remove_can_id_from_monitor(self, can_id, is_sw_port):\r\n        self.can_monitor_lock.acquire()\r\n        if (is_sw_port):\r\n            self.expected_sw_can_ids.remove(can_id)\r\n        else:\r\n            self.expected_hs_can_ids.remove(can_id)\r\n        self.can_monitor_lock.release()\r\n\r\n    def clear_can_monitor_list(self, is_sw_port):\r\n        self.can_monitor_lock.acquire()\r\n        if (is_sw_port):\r\n            del self.expected_sw_can_ids[:]\r\n        else:\r\n            del self.expected_hs_can_ids[:]\r\n        self.can_monitor_lock.release()\r\n\r\n    def is_monitored_can_id(self, can_id, is_sw_port):\r\n        self.can_monitor_lock.acquire()\r\n        res = False\r\n        if (is_sw_port):\r\n            res = True if can_id in self.expected_sw_can_ids else False\r\n        else:\r\n            res = True if can_id in self.expected_hs_can_ids else False\r\n        self.can_monitor_lock.release()\r\n        return res\r\n\r\n    def add_pending_can_id(self, can_id):\r\n        self.can_pending_lock.acquire()\r\n        self.pending_can_ids.append(can_id)\r\n        self.can_pending_lock.release()\r\n\r\n    def pop_pending_can_id(self):\r\n        self.can_pending_lock.acquire()\r\n        res = self.pending_can_ids.pop(0)\r\n        self.can_pending_lock.release()\r\n        return res\r\n\r\n    def is_can_stream_enabled(self, is_sw_port):\r\n        res = False\r\n        self.can_enable_lock.acquire()\r\n        if is_sw_port:\r\n            res = self.is_can_enabled[CAN_SW_IDX]\r\n        else:\r\n            res = self.is_can_enabled[CAN_HS_IDX]\r\n\r\n        self.can_enable_lock.release()\r\n        return res\r\n\r\n    def configCanStrean(self, is_hs_enabled, is_sw_enabled):\r\n        self.can_enable_lock.acquire()\r\n        self.is_can_enabled[CAN_HS_IDX] = is_hs_enabled\r\n        self.is_can_enabled[CAN_SW_IDX] = is_sw_enabled\r\n        self.can_enable_lock.release()\r\n", "repo_name": "seftiz/V2X-ATE", "sub_path": "tests/gm/lan/uart_monitor.py", "file_name": "uart_monitor.py", "file_ext": "py", "file_size_in_byte": 3831, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "40", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 29, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 30, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 31, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 32, "usage_type": "call"}]}
{"seq_id": "25174651066", "text": "from flask import Flask, redirect, url_for, session, request, jsonify\nfrom flask_oauthlib.client import OAuth\nfrom flask import render_template, flash, Markup\n\nfrom github import Github\n\nimport pprint\nimport os\nimport sys\nimport traceback\n\nclass GithubOAuthVarsNotDefined(Exception):\n    '''raise this if the necessary env variables are not defined '''\n\nif os.getenv('GITHUB_CLIENT_ID') == None or \\\n        os.getenv('GITHUB_CLIENT_SECRET') == None or \\\n        os.getenv('APP_SECRET_KEY') == None: \n    raise GithubOAuthVarsNotDefined('''\n      Please define environment variables:\n         GITHUB_CLIENT_ID\n         GITHUB_CLIENT_SECRET\n         APP_SECRET_KEY\n      ''')\n\napp = Flask(__name__)\n\napp.debug = False\n\napp.secret_key = os.environ['APP_SECRET_KEY']\noauth = OAuth(app)\n\n# This code originally from https://github.com/lepture/flask-oauthlib/blob/master/example/github.py\n# Edited by P. Conrad for SPIS 2016 to add getting Client Id and Secret from\n# environment variables, so that this will work on Heroku.\n\n\ngithub = oauth.remote_app(\n    'github',\n    consumer_key=os.environ['GITHUB_CLIENT_ID'],\n    consumer_secret=os.environ['GITHUB_CLIENT_SECRET'],\n    # Scopes are defined here: https://developer.github.com/apps/building-oauth-apps/understanding-scopes-for-oauth-apps/\n    request_token_params={'scope': 'read:user'}, \n    base_url='https://api.github.com/',\n    request_token_url=None,\n    access_token_method='POST',\n    access_token_url='https://github.com/login/oauth/access_token',\n    authorize_url='https://github.com/login/oauth/authorize'\n)\n\n\n@app.context_processor\ndef inject_logged_in():\n    return dict(logged_in=('github_token' in session))\n\n\n@app.route('/')\ndef home():\n    return render_template('home.html')\n\n@app.route('/login')\ndef login():\n    print(\"url_for('authorized')=\",url_for('authorized'))\n    scheme='https' # http for localhost, https for heroku\n    return github.authorize(callback=url_for('authorized',\n                                             _external=True,\n                                             _scheme=scheme))\n\n@app.route('/logout')\ndef logout():\n    session.clear()\n    flash('You were logged out')\n    return redirect(url_for('home'))\n\n\n#@app.route('/logout')\n#def logout():\n#    session.pop('github_token', None)\n#    return redirect(url_for('index'))\n\n\n@app.route('/login/authorized')\ndef authorized():\n    resp = github.authorized_response()\n\n    print(\"Here is the resp object:\")\n    pprint.pprint(resp);\n    \n    if resp is None:\n        session.clear()\n        login_error_message = 'Access denied: reason=%s error=%s full=%s' % (\n            request.args['error'],\n            request.args['error_description'],\n            pprint.pformat(request.args)\n        )        \n        flash(login_error_message, 'error')\n        return redirect(url_for('home'))    \n\n    try:\n        session['github_token'] = (resp['access_token'], '')\n        session['user_data']=github.get('user').data\n        github_userid = session['user_data']['login']\n    except Exception as e:\n        session.clear()\n        message = 'Unable to login: ' + str(type(e)) + str(e)\n        flash(message,'error')\n        return redirect(url_for('home'))\n    \n    flash('You were successfully logged in')\n\n    return redirect(url_for('home'))    \n    \n\n\n@app.route('/page1')\ndef renderPage1():\n    if 'user_data' in session:\n        user_data_pprint = pprint.pformat(session['user_data'])\n    else:\n        user_data_pprint = '';\n    return render_template('page1.html',dump_user_data=user_data_pprint)\n\n@app.route('/page2')\ndef renderPage2():\n    return render_template('page2.html')\n\n\n@github.tokengetter\ndef get_github_oauth_token():\n    return session.get('github_token')\n\n\nif __name__ == '__main__':\n    app.run(port=6789,debug=True)\n", "repo_name": "pconrad-flask/flask-github-oauth-example-01", "sub_path": "webapp.py", "file_name": "webapp.py", "file_ext": "py", "file_size_in_byte": 3784, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "os.getenv", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask_oauthlib.client.OAuth", "line_number": 30, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 62, "usage_type": "call"}, {"api_name": "github.authorize", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.session.clear", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 72, "usage_type": "call"}, {"api_name": "github.authorized_response", "line_number": 83, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.session.clear", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "pprint.pformat", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 100, "usage_type": "name"}, {"api_name": "github.get", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.session.clear", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 116, "usage_type": "name"}, {"api_name": "pprint.pformat", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 129, "usage_type": "name"}, {"api_name": "github.tokengetter", "line_number": 127, "usage_type": "attribute"}]}
{"seq_id": "25524254225", "text": "import torch\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\nimport math as m\n\nfrom src.helpers.get_data import load_names, train_test_split, build_char_dataset, plot_loss\nfrom src.models.language.mlp_torch import Linear, BatchNorm1d, Tanh\n\n\n# https://www.youtube.com/watch?v=t3YJ5hKiMQ0\n# https://github.com/karpathy/nn-zero-to-hero/blob/master/lectures/makemore/makemore_part5_cnn1.ipynb\n\n\nclass Embedding:\n  \"\"\"\n  Embeds characters into a vector space.\n  Each character is represented by a vector of size dim_emb.\n  \"\"\"\n  def __init__(self, vocab_size, n_emb):\n    # before: c = torch.randn((vocab_size, n_emb))\n    self.weight = torch.randn((vocab_size, n_emb))\n    \n  def __call__(self, iz):\n    self.out = self.weight[iz]\n    return self.out\n  \n  def parameters(self):\n    return [self.weight]\n\n\nclass Flatten:\n  \"\"\"\n  Flattens the input into a vector.\n  'Appends' each character in the context (block_size) to a single vector.\n  Each character is represented by a vector of size dim_emb.\n  \"\"\"\n  def __call__(self, x):\n    # input: (num_samples, len_context, dim_emb)\n    # output: (num_samples, len_context*dim_emb)\n    self.out = x.view(x.shape[0], -1)\n    return self.out\n  \n  def parameters(self):\n    return []\n  \n\nclass FlattenConsecutive:\n  \"\"\"\n  Flattens consecutive characters into a single vector.\n  We want to group (fuse) n=2 consecutive characters at a time (what makes it a wavenet).\n  \"\"\"\n  def __init__(self, size_group=2):\n    self.size_group = size_group\n    \n  def __call__(self, x):\n    # input: (num_samples, len_context, dim_emb)\n    # output: (num_samples, num_groups, dim_emb*size_group)\n    # num_groups = len_context // size_group\n    num_samples, len_context, dim_emb = x.shape\n    # if len_context//self.size_group == 1, then we don't need to group characters\n    x = x.view(num_samples, len_context//self.size_group, dim_emb*self.size_group)\n    if x.shape[1] == 1:\n      x = x.squeeze(1)\n    self.out = x\n    return self.out\n  \n  def parameters(self):\n    return []\n  \n\nclass Sequential:\n  \"\"\"A sequential container for layers = model.\"\"\"\n  def __init__(self, layers):\n    self.layers = layers\n  \n  def __call__(self, x):\n    # forward pass\n    # feed input through all layers\n    for layer in self.layers:\n      # print(f'layer: {layer.__class__.__name__}')\n      x = layer(x)\n      # print(f'  output shape: {x.shape}')\n    self.out = x\n    return self.out\n  \n  def parameters(self):\n    # get parameters of all layers and stretch them out into one list\n    return [p for layer in self.layers for p in layer.parameters()]\n  \n\nclass Wavenet:\n  \"\"\"\n  Predicts the next character based on a couple of previous characters.\n  Wavenet = convolutional neural network.\n  \"\"\"\n  def __init__(self, len_context=8, n_hidden=128, dim_emb=24, epochs=1000):\n    # the dimensionality of the character embedding vectors\n    self.dim_emb = dim_emb\n    # the number of neurons in the hidden layer of the MLP\n    self.n_hidden = n_hidden\n    # the number of characters in a context\n    self.len_context = len_context\n    # load data\n    words, self.token, self.vocab_size, self.chr_to_int, self.int_to_chr = load_names()\n    x, y = build_char_dataset(words, self.len_context, self.chr_to_int)\n    x_train, x_test, y_train, y_test = train_test_split(x, y)\n    # init model\n    self.model, self.parameters = self.init_model()\n    # train model\n    losses_log = self.train(x_train, y_train, epochs=epochs)\n    self.plot_loss(losses_log, epochs=epochs)\n    # test model\n    self.evaluate_model(x_train, y_train, x_test, y_test)\n    self.sample_predictions()\n\n  \n  def init_model(self, size_group=2):\n    vocab_size = self.vocab_size\n    dim_emb = self.dim_emb\n    n_hidden = self.n_hidden\n    # Standard MLP\n    # model = Sequential([\n    #   Embedding(vocab_size, dim_emb),\n    #   FlattenConsecutive(8), Linear(dim_emb * self.blocksize, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(),\n    #   Linear(n_hidden, vocab_size),\n    # ])\n    # Hierarchical network\n    # test if len_context is a power of size_group\n    test = m.log(self.len_context, size_group)\n    if test != int(test):\n      # we group size_group consecutive characters, out of len_context characters\n      # Groups of 2 characters, \n      # then groups of 2 of groups of 2 characters, \n      # then groups of 2 of groups of 2 of groups of 2 characters, …\n      raise ValueError(f'len_context must be a power of {size_group}')\n    # build model\n    layer_list = [\n      Embedding(vocab_size, dim_emb),\n      FlattenConsecutive(size_group),\n      Linear(dim_emb * size_group, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh()]\n    for _ in range(int(test)-1):\n      layer_list += [\n        FlattenConsecutive(size_group),\n        Linear(n_hidden*size_group, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh()\n      ]\n    layer_list.append(Linear(n_hidden, vocab_size))\n    model = Sequential(layer_list)\n    # explicit example\n    # if self.len_context == 8:\n    #   model = Sequential([\n    #     # (num_samples, len_context)\n    #     Embedding(vocab_size, dim_emb), # (vocab_size, n_emb)[(num_samples, len_context)] \n    #     # (num_samples, len_context, dim_emb)\n\n    #     FlattenConsecutive(size_group), \n    #     # (num_samples, len_context//size_group, dim_emb*size_group)\n    #     Linear(dim_emb * size_group, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(), \n    #     # (num_samples, len_context//size_group, n_hidden)\n\n    #     FlattenConsecutive(size_group),\n    #     # (num_samples, len_context//(size_group*size_group), n_hidden*size_group)\n    #     Linear(n_hidden*size_group, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(),\n    #     # (num_samples, len_context//(size_group**2), n_hidden)\n\n    #     FlattenConsecutive(size_group), \n    #     # (num_samples, len_context//(size_group**2), n_hidden*size_group)\n    #     Linear(n_hidden*size_group, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(),\n    #     # (num_samples, len_context//(size_group**3), n_hidden)\n\n    #     Linear(n_hidden, vocab_size),\n    #     # (num_samples, len_context//(size_group**3), vocab_size)\n    #   ])\n    # parameter init\n    with torch.no_grad():\n      model.layers[-1].weight *= 0.1 # last layer make less confident\n    # parameters\n    parameters = model.parameters()\n    print('Parameters in model:', sum(p.nelement() for p in parameters)) # number of parameters in total\n    for p in parameters:\n      p.requires_grad = True\n    return model, parameters\n  \n  def train(self, x_train, y_train, epochs, batch_size=32):\n    # same optimization as last time\n    losses_log = []\n    for i in range(epochs):\n      # minibatch construct\n      batch = torch.randint(0, x_train.shape[0], (batch_size,))\n      # (batch_size, len_context), (batch_size,)\n      x_batch, y_batch = x_train[batch], y_train[batch] \n      # forward pass\n      logits = self.model(x_batch)\n      loss = F.cross_entropy(logits, y_batch) # loss function\n      # backward pass\n      for p in self.parameters:\n        p.grad = None\n      loss.backward()\n      # update\n      lr = 0.1 if i < 150000 else 0.01 # step learning rate decay\n      for p in self.parameters:\n        p.data += -lr * p.grad\n      # track stats\n      if i % 10000 == 0: # print every once in a while\n        print(f'{i:7d}/{epochs:7d}: {loss.item():.4f}')\n      losses_log.append(loss.log10().item())\n    return losses_log\n\n  def evaluate_model(self, x_train, y_train, x_test, y_test):\n    # put layers into eval mode (needed for batchnorm especially)\n    for layer in self.model.layers:\n      layer.training = False\n    # evaluate the loss\n    @torch.no_grad() # this decorator disables gradient tracking inside pytorch\n    def split_loss(split):\n      x, y = {\n        'train': (x_train, y_train),\n        'test': (x_test, y_test),\n      }[split]\n      logits = self.model(x)\n      loss = F.cross_entropy(logits, y)\n      print(split, loss.item())\n    split_loss('train')\n    split_loss('test')\n  \n  def sample_predictions(self, num_samples=20):\n    samples = []\n    for _ in range(num_samples):\n      out = []\n      len_context = [0] * self.len_context # initialize with all ...\n      while True:\n        # forward pass the neural net\n        logits = self.model(torch.tensor([len_context]))\n        probs = F.softmax(logits, dim=1)\n        # sample from the distribution\n        ix = torch.multinomial(probs, num_samples=1).item()\n        # shift the len_context window and track the samples\n        len_context = len_context[1:] + [ix]\n        out.append(ix)\n        # if we sample the special '.' token, break\n        if ix == 0:\n          break\n      samples.append(''.join(self.int_to_chr[i] for i in out)) # decode and print the generated word\n    print('samples:\\n', *samples)\n    return samples", "repo_name": "BurgerAndreas/nn-from-scratch", "sub_path": "nn-from-scratch/src/models/language/wavenet.py", "file_name": "wavenet.py", "file_ext": "py", "file_size_in_byte": 8776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "torch.randn", "line_number": 21, "usage_type": "call"}, {"api_name": "src.helpers.get_data.load_names", "line_number": 104, "usage_type": "call"}, {"api_name": "src.helpers.get_data.build_char_dataset", "line_number": 105, "usage_type": "call"}, {"api_name": "src.helpers.get_data.train_test_split", "line_number": 106, "usage_type": "call"}, {"api_name": "math.log", "line_number": 129, "usage_type": "call"}, {"api_name": "src.models.language.mlp_torch.Linear", "line_number": 140, "usage_type": "call"}, {"api_name": "src.models.language.mlp_torch.BatchNorm1d", "line_number": 140, "usage_type": "call"}, {"api_name": "src.models.language.mlp_torch.Tanh", "line_number": 140, "usage_type": "call"}, {"api_name": "src.models.language.mlp_torch.Linear", "line_number": 144, "usage_type": "call"}, {"api_name": "src.models.language.mlp_torch.BatchNorm1d", "line_number": 144, "usage_type": "call"}, {"api_name": "src.models.language.mlp_torch.Tanh", "line_number": 144, "usage_type": "call"}, {"api_name": "src.models.language.mlp_torch.Linear", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 193, "usage_type": "name"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 220, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 233, "usage_type": "name"}, {"api_name": "torch.multinomial", "line_number": 235, "usage_type": "call"}]}
{"seq_id": "35598836845", "text": "\"\"\"Search server index (main) view.\"\"\"\n\nimport threading\nimport heapq\nimport queue\nimport flask\nimport search\nimport requests\n\n\ndef get_hits(search_url, thread_result):\n    \"\"\"Return the hits detail from urls into a queue.\"\"\"\n    return thread_result.put(requests.get(search_url, timeout=10))\n\n\ndef keyfunc(hits):\n    \"\"\"Define a key function for the heapq.merge.\"\"\"\n    return hits['score']\n\n\ndef get_doc(doc_id):\n    \"\"\"Get doc detail from the database.\"\"\"\n    connection = search.model.get_db()  # connect to database\n    cur = connection.execute(\n        \"SELECT title AS doc_title, summary AS doc_summary, url AS doc_url \"\n        \"FROM Documents WHERE docid = ?\", (doc_id, )\n    )\n    return cur.fetchone()\n\n\n@search.app.route('/', methods=['GET'])\ndef show_index():\n    \"\"\"Display / route.\"\"\"\n    queries = flask.request.args.get(\"q\", type=str)\n    weight = flask.request.args.get(\"w\", default=0.5)\n    context = {\n        \"query\": queries,\n        \"weight\": weight,\n        \"results\": []\n    }\n\n    if queries:\n        query = \"+\".join(queries.split())\n        threads = []\n        thread_result = queue.Queue()\n        search_index = search.app.config['SEARCH_INDEX_SEGMENT_API_URLS']\n\n        # Create threads for the urls\n        for _, search_url in enumerate(search_index):\n            search_url = search_url + \"?q=\" + query + \"&w=\" + str(weight)\n            # Use threads to save the results from search indexes into a queue\n            thread = threading.Thread(target=get_hits,\n                                      args=(search_url, thread_result))\n            threads.append(thread)\n            thread.start()\n\n        # Close threads\n        for thread in threads:\n            thread.join()\n\n        results_list = []  # will have three sublists from the three urls\n        while not thread_result.empty():\n            hits_detail = thread_result.get().json()\n            results_list.append(hits_detail['hits'])\n\n        results_list = heapq.merge(*results_list, key=keyfunc, reverse=True)\n        count = 10  # number of hit info shown on the page\n        for result in results_list:\n            context['results'].append(get_doc(result['docid']))\n            count -= 1\n            if count == 0:\n                break\n\n    return flask.render_template(\"index.html\", **context)\n", "repo_name": "imerlwx/search-engine", "sub_path": "search_server/search/views/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 2301, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "search.model.get_db", "line_number": 23, "usage_type": "call"}, {"api_name": "search.model", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request.args.get", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request.args.get", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "attribute"}, {"api_name": "queue.Queue", "line_number": 45, "usage_type": "call"}, {"api_name": "search.app", "line_number": 46, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 52, "usage_type": "call"}, {"api_name": "heapq.merge", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 74, "usage_type": "call"}, {"api_name": "search.app.route", "line_number": 31, "usage_type": "call"}, {"api_name": "search.app", "line_number": 31, "usage_type": "attribute"}]}
{"seq_id": "39032344707", "text": "import re\nimport os\nimport requests\nimport pathlib\nACCOUNT = \"your id, not username or email\"\nPASSWORD = \"your password\"\nclass PixivGet(object):\n    def __init__(self):\n        if not os.path.isdir(\"pixiv\"):\n            os.makedirs(\"pixiv\")\n        self.account = ACCOUNT\n        self.password = PASSWORD\n        self.login_headers = {\n            \"Referer\":\"https://accounts.pixiv.net/login?lang=zh&source=pc&view_type=page&ref=wwwtop_accounts_index\",\n            \"User-Agent\":\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10.13; rv:59.0) Gecko/20100101 Firefox/59.0\",\n            \"Host\":\"accounts.pixiv.net\",\n            \"Content-Type\": \"application/x-www-form-urlencoded; charset=UTF-8\",\n            \"Connection\": \"keep-alive\"\n        }\n        self.return_to = \"http://www.pixiv.net/\"\n        self.post_key = []\n        self.base_url = \"https://accounts.pixiv.net/login?lang=zh&source=pc&view_type=page&ref=wwwtop_accounts_index\"\n        self.login_url = \"https://accounts.pixiv.net/api/login?lang=zh\"\n        self.sess = requests.Session()\n        self.login()\n    def login(self):\n        login_html = self.sess.get(self.base_url)\n        pattern = re.compile('<input type=\"hidden\".*?value=\"(.*?)\">', re.S)\n        result = re.search(pattern, login_html.text)\n        self.post_key = result.group(1)\n        login_data = {\n            \"pixiv_id\":self.account,\n            \"password\":self.password,\n            \"post_key\":self.post_key,\n            \"return_to\":self.return_to\n        }\n        self.sess.post(self.login_url, data=login_data, headers=self.login_headers)\n    def img_download(self,img_link,page_link):\n        header = {\n            \"Referer\":page_link,\n            \"User-Agent\":\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10.13; rv:59.0) Gecko/20100101 Firefox/59.0\"\n        }\n        try:\n            download_path = str(pathlib.Path(os.getcwd()) / \"pixiv\" / img_link.split(\"/\")[-1])\n            if not os.path.exists(download_path):\n                img = self.sess.get(img_link, headers=header)\n                with open(download_path, \"wb\") as f:\n                    f.write(img.content)\n                    f.close()\n            return download_path\n        except Exception as e:\n            print(e)\n            return \"ERIRI\"\n    def pixiv_auto_get(self,ID):\n        pic_index_link = \"https://www.pixiv.net/member_illust.php?mode=medium&illust_id=%s\"%ID\n        headers = {\n            \"Referer\":\"https://www.pixiv.net\",\n            \"User-Agent\":\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10.13; rv:59.0) Gecko/20100101 Firefox/59.0\",\n        }\n        pic_html = self.sess.get(pic_index_link)\n        single_image_pattern = re.compile('<div class=\"_illust_modal.*?<img alt=\"(.*?)\".*?data-src=\"(.*?)\".*?</div>', re.S)\n        single_img_result = re.search(single_image_pattern, pic_html.text)\n        if single_img_result:\n            img_name = single_img_result.group(1)\n            img_source_url = single_img_result.group(2)\n            pic_path = self.img_download(img_source_url, pic_index_link)\n            if not pic_path.startswith(\"ERIRI\"):\n                return [pic_path]\n            else:\n                return None\n        else:\n            pic_index_link = pic_index_link.replace(\"medium\", \"manga\")\n            pic_html = self.sess.get(pic_index_link)\n            total_num_pattern = re.compile('<span class=\"total\">(\\d*)</span></div>', re.S)\n            total_num = re.search(total_num_pattern, pic_html.text)\n            if total_num:\n                total_path = []\n                img_url_pattern = re.compile('<div class=\"item-container.*?<img src=\".*?\".*?data-src=\"(.*?)\".*?</div>', re.S)\n                url_result = re.findall(img_url_pattern, pic_html.text)\n                try:\n                    for item in url_result:\n                        pic_path = self.img_download(img_source_url, pic_index_link)\n                        total_path.append(pic_path)\n                    return total_path\n                except Exception as e:\n                    print(\"===\")\n                    print(e)\n                    return None\n            else:\n                return None\ndef pixiv_auto_get(pixiv_id):\n    pixiv = PixivGet()\n    pixiv.login()\n    return pixiv.pixiv_auto_get(str(pixiv_id))\nif __name__==\"__main__\":\n    pixiv = PixivGet()\n    pixiv.login()\n    pixiv.pixiv_auto_get(\"65637109\")\n", "repo_name": "DongSky/M1saka_bot", "sub_path": "pixiv_auto_get.py", "file_name": "pixiv_auto_get.py", "file_ext": "py", "file_size_in_byte": 4339, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "40", "api": [{"api_name": "os.path.isdir", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 24, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 28, "usage_type": "call"}, {"api_name": "re.S", "line_number": 28, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 29, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 44, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 61, "usage_type": "call"}, {"api_name": "re.S", "line_number": 61, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 62, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 74, "usage_type": "call"}, {"api_name": "re.S", "line_number": 74, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 75, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 78, "usage_type": "call"}, {"api_name": "re.S", "line_number": 78, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 79, "usage_type": "call"}]}
{"seq_id": "17082001200", "text": "import pickle\nimport logging\nimport email\nimport email.utils\nimport time\nimport datetime\nimport pytz\nimport os\nfrom apscheduler.triggers.cron import CronTrigger\nfrom apscheduler.schedulers.blocking import BlockingScheduler\n\nfrom conf.settings import BASE_DIR,PROJECT_DIR,FILES_DIR\nfrom libs.line import lineNotify\n\nlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)\nlogging.getLogger('apscheduler').setLevel(logging.DEBUG)\n\n\nlogger = logging.getLogger(__name__)\n\nscheduler = BlockingScheduler()\n\nclass Email:\n    savePath = os.path.join(FILES_DIR, 'bkrecord.pkl')\n    debug = False\n\n    def __init__(self):\n        self.msgs = []\n        self.filter_subject_text = '通知'\n\n    def loaditems(self):\n        if os.path.exists(self.savePath):\n            with open(self.savePath, 'rb') as file:\n                lstItems = pickle.load(file)\n        else:\n            lstItems = []\n        return lstItems\n\n    def saveItems(self, lstItems):\n        file = open(self.savePath, 'wb')\n        pickle.dump(lstItems, file)\n        file.close()\n\n\n    def clean_date(self,datetext):\n        \"\"\"\n        :param datetext: 'Tue, 13 Oct 2020 14:02:44 +0800 (CST)'\n        :return: datetime.datetime(2020, 10, 13, 22, 40, 18, 441335, tzinfo=<DstTzInfo 'Asia/Taipei' LMT+8:06:00 STD>)\n        \"\"\"\n        date = datetime.datetime.fromtimestamp(time.mktime(email.utils.parsedate(datetext)))\n        return date.replace(tzinfo=pytz.timezone('Asia/Taipei'))\n\n    def get_message_all(self, num=10):\n        messages = []\n        return messages\n\n    def filter_email(self):\n        for m in self.ret:\n            if self.filter_subject_text in m.get('Subject'):\n                self.msgs.append(m)\n\n    def send_line(self):\n        \"\"\"發送TG\"\"\"\n        print('發送TG')\n        for i in self.msgs:\n            print(i)\n\n    def run(self):\n        # 檢查有沒有新出現的\n        items_old = self.loaditems()\n\n        msgs = self.get_message_all()\n\n        self.saveItems(msgs)\n\n        print(\"收到的信件 %s\" % [i['Subject'] for i in msgs])\n        #     # items_old = []\n        # 去除重複 字典跟字典\n        diff_items = msgs.copy()\n        # print(len(diff_items))\n        for e in items_old:\n            if e in diff_items:\n                diff_items.remove(e)\n\n        print(\"過濾的信件 %s\" % [i['Subject'] for i in diff_items])\n\n        self.ret = diff_items\n        logger.debug(self.ret)\n        return self.ret\n\n    def main(self):\n        self.run()\n        self.filter_email()  # 符合\"過濾條件\"\n        self.send_line()\n\n\ndef mail():\n    Email().main()\n\nif __name__ == '__main__':\n    logger.info(\"mail is start\")\n    scheduler.add_job(mail, CronTrigger.from_crontab('*/1 * * * *'), id='mail_run')\n    scheduler.start()", "repo_name": "Maliaotw/ef", "sub_path": "app/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2796, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "40", "api": [{"api_name": "logging.basicConfig", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "apscheduler.schedulers.blocking.BlockingScheduler", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "conf.settings.FILES_DIR", "line_number": 24, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 34, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute"}, {"api_name": "time.mktime", "line_number": 50, "usage_type": "call"}, {"api_name": "email.utils.parsedate", "line_number": 50, "usage_type": "call"}, {"api_name": "email.utils", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 51, "usage_type": "call"}, {"api_name": "apscheduler.triggers.cron.CronTrigger.from_crontab", "line_number": 102, "usage_type": "call"}, {"api_name": "apscheduler.triggers.cron.CronTrigger", "line_number": 102, "usage_type": "name"}]}
{"seq_id": "70737315002", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 23 21:48:20 2020\n\n@author: Shankha\n\"\"\"\n\n\n\n\"\"\"%%%%Data Analytics Framework%%%\"\"\"\n# =============================================================================\n# 1.Poblem Statement ?\n# # Predict salary Level to deliver Subsidy\n# 2.Problem Coseptualization ?\n# Develope  Classifier to Predict the Salary \n# 3.Solution conceptualization?\n# Binary classifier\n# 4.Method Identification \n#\n# 5.Solution Realization \n# Simplify the data sysem by reducing the number of veriables without \n#sacrifising the accuracy\n# =============================================================================\n\n##importing libraries and data\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n#importing train test split for data partition \nfrom sklearn.model_selection import train_test_split\n# importing logistic regression for binary classification\nfrom sklearn.linear_model import LogisticRegression\n## importing performance metrices\nfrom sklearn.metrics import accuracy_score,confusion_matrix\n\nsns.set_style(\"darkgrid\")\n\n### Importing the data set\nincome=pd.read_csv(\"income.csv\")\n\n### copying data to workwith\n\ndf=income.copy()\ndf1=income.copy()\n\n##tocheck the datatype\n\nprint(df.info())\n\n## Scanning missing values\n\nprint(df.isnull().sum())\n\n\n\n##*** We have no missing values in our dataframe\n\n# =============================================================================\n# ### BAsic Descriptive statistics of dataframe\n# =============================================================================\n\nsummery_numerical=df.describe()\nsummery_categorical=df.describe(include=\"O\")\n\n##** checking for the catagories available under eatch categorical vaeiable\n\nprint(pd.unique(df[\"JobType\"]))\n\nprint(pd.unique(df[\"occupation\"]))\n\n##** implement unique catagory matrix\nUnique={}\nfor i in  df.select_dtypes(\"object\").columns:\n    Unique[i]=pd.unique(df[i])\n\n##** missing data as \" ?\" found as catagories\n\ndf=pd.read_csv(\"income.csv\", na_values=[\" ?\",\"??\",\"???\"])\n\n##** again scanning for the missing values\n\n\nprint(df.isnull().sum())\n\nnull_data=df[df.isnull().any(axis=1)]\n\n# =============================================================================\n####################### Conclusion from missing data ##################### \n\"\"\" we have 1816 rows of missing in occupation and 1809 rows of missing in JobType\n    Two categrical variables have missing data Occupation and JobType\n    1816-1809=7 data in JobType more missing because occupation is never worked\n\"\"\"\n# \n# =============================================================================\n# Assumption before droping the data \n# #as compared to the total no of data points rows containing null values are less\n# #devicing imputation method based on the data willbe very  difficult \n# =============================================================================\n\ndf.dropna(axis=0,inplace=True)\nprint(\"df after droping missing value\",df.isnull().sum())\n# =============================================================================\n#  Assumption before imputation (filling null values)(not recomended )\n# =============================================================================\n#if we drop all these coloumns we will be miissing out a lot of data \n# ##Filling missing values\n# exploratory data analysis to find out the type of imputation \n# only Jobtype and Occupation hasx the missing data\n## as both of them are categorical variables so we will impute mode replacing the missing data \n# =============================================================================\n\n\n## \nprint(\"job_type=\",df[\"JobType\"].mode()[0])\ndf1[\"JobType\"].fillna(df[\"JobType\"].mode()[0],inplace=True)\ndf1[\"occupation\"].fillna(df[\"occupation\"].mode()[0],inplace=True)\n\n#** check if the na values are handled succesfully\nprint(\"df1 after imputing all values\",df1.isnull().sum()) ## Success\n\n\n\n\"\"\"Exploratory Data Analysis\"\"\"\n\n\n\"\"\"Frequency Table , Probability Table and Correlation\"\"\"\n\n# =============================================================================\n# ## pearson coeef shows linear corelation or linear degree of association but can not indicate anything \n# ## about nonlinearity \n# ## so calculation of pearson as well as spearman can help us conclude the degree of association\n# =============================================================================\ncorelation_spearman=df.corr(method=\"spearman\")\ncorelation_pearson=df.corr(method=\"pearson\")\n#### now with imputed dataset\ncorelation_spearman1=df1.corr(method=\"spearman\")\ncorelation_pearson1=df1.corr(method=\"pearson\")\n# =============================================================================\n# ##** no two variale has strong linear correlation as coef is near to 0\n# ##** as for some pair of variable spearman coef is greater than pearson implies there might be some \n# ## non linear association although its very low\n# ** comparing coefitient showed that impting missing valus didnt rally impacted much\n# =============================================================================\n\n### Frequency distribution of gender \n\ngender_dist=pd.crosstab(index=df[\"gender\"],columns=\"count\")\n\n\"\"\"gender vs salary status \"\"\"\ngender_salarystatus=pd.crosstab(index=df[\"gender\"],columns=df[\"SalStat\"],normalize=\"index\")\n\n#** compared to men more number  of women has salary under 50k\n\"\"\" joint probability of \"\"\"\n\njoint_age_SalStat=pd.crosstab(index=df[\"SalStat\"],columns=df[\"age\"],normalize=\"index\")\n\n##$** most people from every age group has salary lesser than 50k\n##**all people from  some age group has lalary status lesser than 50k ex\n\"\"\"** People from age group 44 to 54 has higher probability of salary status more than 50k\"\"\"\n\n\n\"\"\"(conditional prob) probability of salarystatus given education type \"\"\"\nsalStat_edu=pd.crosstab(index=df[\"EdType\"],columns=df[\"SalStat\"],normalize=\"index\",margins=True )\n\n\"\"\" conclusion Bachelor degree ,Hs grads , masters and some college degree holders has\"\"\"\n\"\"\"the highesy probability of getting salary more than 50k\"\"\"\n \n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\"\"\" Visualisating the descriptive statistics\"\"\"\n\n\n\"\"\" Frequency Distriution of  Salary Status to know the stability of target variable\"\"\"\nplt.figure(1)\nplt.title('freq dist of Salary Status ')\nsns.countplot(x=\"SalStat\",data=df)\n\n#** amnongst the sample set maximum datapoints belongs to Salary < 50k ;\n\n\"\"\" Frequency Distriution of age group\"\"\"\nplt.figure(2)\nplt.title('freq dist of age ')\nplt.xlabel(\"age\")\nsns.distplot(df[\"age\"],bins=10, kde=False)\n\n##** People with age group 40-45 has highest frequency\n\n\"\"\"Group boxplot to see the relationship between age and salstat \"\"\"\nplt.figure(3)\nplt.title(\" age vs salary status\")\nsns.boxplot(x=df[\"age\"],y=df[\"SalStat\"])\n\n\"\"\"Salary Status vs catagorical variables\"\"\"\n##** How Salary status is varing across the job type\n\nplt.figure(4)\nsns.countplot(y=\"JobType\",data=df,hue=\"SalStat\")\n\n## TO get how slary stat varries with jobtype we have to create two way table \n#** created earlier\nsalStat_Job=pd.crosstab(index=df[\"JobType\"],columns=df[\"SalStat\"],normalize=\"index\",margins=True )\nsalStat_Job=salStat_Job.applymap(lambda x:x*100)\n##** maximum people works for  private firm\n##  self employed people have the highest probabilirty to earn more than 50k\n##** that is why this is a important veriable for classifying salary status\nplt.figure(5)\nsns.countplot(y=\"EdType\",data=df,hue=\"SalStat\")\nsalStat_edu=salStat_edu.applymap(lambda x:x*100)\n##**\n#** maximum people have passed the HS\n##doctorate holders have the highest probability to earn >50k\n##** that is why this is a important veriable for classifying salary status\n\n\n\"frequency distribution of capital loss and capital gain\"\n\nplt.figure(6)\n\nsns.histplot(df[\"capitalgain\"])\n##** very few people have invested in the stock so their captal gain is 0 \nplt.figure(7)\nsns.histplot(x=\"capitalloss\",data=df)\n##** Veryfew people didnt eigther inested or they havnt lost anything \n\nplt.figure(8)\nsns.histplot(x=\"capitalgain\",data=df,hue=\"SalStat\")\n##** people who have invested and have capital gain have salary >50k\n\n\"hours spent per week vs Salary Status\"\nplt.figure(9)\nsns.boxplot(y=df[\"hoursperweek\"],x\n            =df[\"SalStat\"],palette=\"hls\")\n##** people who have spent 45 hours  on an avarage are more likely to earn >50k\n\n## as the mean of >50K plot and < 50k plot varring a lot and the median values are varring so \n## we can say there is a association  \n\n#%%\n# Before moving forward copying the dataset for future use\\\n\ndf_new=df.copy()\n\n#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n#converting cartagorical target veriable to numerical variable\n\n## method 1 using dictionary maping\n\ndf[\"SalStat\"]=df[\"SalStat\"].map({\" less than or equal to 50,000\":0,\" greater than 50,000\":1})\n\n# using one hot encription \n\n\ndf=pd.get_dummies(data=df,drop_first=True)\n\n#%%\n\n# =============================================================================\n\"\"\"                     Logitic Regression         \"\"\"\n#  =============================================================================\n\n\"\"\"Seperating the dependent and independent variabl\"\"\"\n\nx_data=df.drop(\"SalStat\",axis=1)#feature set\n\nx_data=x_data.values\n\ny_data=df[\"SalStat\"].values\n\n\n#spliting the dataset into Test set and Train set\nX_train,X_test,Y_train,Y_test=train_test_split(x_data,y_data,test_size=0.3,random_state=0)\n\n\"\"\"logistic regression classifier\"\"\"\n\n## may need to terae through different values to find best fit \n\nmodel=LogisticRegression(max_iter=900)\n\n## training the model \n\nmodel.fit(X_train,Y_train)\n\n# coeficients \n\nmodel.coef_\n\n#interception \n\nmodel.intercept_\n\n## prediction of the model using the test data \n\ny_pred=model.predict(X_test)\n\n### Accuracy metrices from the model \n\nAccuracy_score=accuracy_score(Y_test,y_pred) \n\nConfusion_matrix=confusion_matrix(Y_test,y_pred)\n\n#%%\n\n\n\"\"\" misclassified samples \"\"\"\n\n  \nprint(\"misclassified samples = \", (y_pred !=Y_test).sum())\n\n## through exploratory data analysis we  can reduce the feature set droping some insignificant variable\n\n## to improve the model accuracy\n\n# Again Training logistic regression model with the new data with  reduced features set \n\n##** droping insignificant data from the feature set \ndf_new=df_new.drop([\"JobType\",\"race\",\"gender\",\"nativecountry\"],axis=1)\n\ndf_new[\"SalStat\"]=df_new[\"SalStat\"].map({\" less than or equal to 50,000\":0,\" greater than 50,000\":1})\n\n# using one hot encription \n\ndf_new=pd.get_dummies(df_new,drop_first=True)\n\nX_new_data=df_new.drop(\"SalStat\",axis=1).values\n\n\nY_new_data=df[\"SalStat\"].values\n\n## splitinfg the data set\n\nX_train_new,X_test_new,Y_train_new,Y_test_new=train_test_split(X_new_data,Y_new_data,test_size=0.3,random_state=0)\n\n#training the model with new data \n\n\nmodel_new=LogisticRegression(max_iter=900)\n\n## traininig the model \n\nmodel_new.fit(X_train_new,Y_train_new)\n\n## model coefficients \n\n\nmodel_new.coef_\n\n## model intersection \n\nmodel_new.intercept_\n\n\"\"\" Prediction using the test set \"\"\"\n\nY_pred_new=model_new.predict(X_test_new)\n\n\"\"\" Model Evaluation \"\"\"\nconfusion_matrix_new=confusion_matrix(Y_test_new,Y_pred_new)\n\nAccuracy_score_new=accuracy_score(Y_test_new,Y_pred_new)\n\nprint(\" Accuracy = {} \\n Accuacy improvement in % ={}\".format(Accuracy_score_new,(Accuracy_score_new-Accuracy_score)*100))\n\n\"\"\" Accuracy Score drops alittle but with all those insignificant variables removed model is more robust\"\"\"\n\n#%%\n\"\"\"                   KNN classification                                     \"\"\"\n\n##** Building knn classifier for the same problem statement with the same dtat set \n\nfrom sklearn.neighbors import KNeighborsClassifier\n\n##**KNN Classifier instance with differnt k\nAccuracy_score_KNN={}\nfor i in range(1,20):\n    \n    KNN=KNeighborsClassifier(n_neighbors=i,metric=\"minkowski\")\n    \n    ##** Training the Model \n    \n    KNN.fit(X_train_new,Y_train_new)\n    \n    ##** Predicting the valus\n    Y_pred_knn=KNN.predict(X_test_new)\n    \n    ##** Aaccuracy Score \n    \n    Accuracy_score_KNN[i]=accuracy_score(Y_test_new,Y_pred_knn)\n    ", "repo_name": "kingshankha/Fundementals-of-data-Science-Workflow-", "sub_path": "CLASSIFICATION/Classification Problem.py", "file_name": "Classification Problem.py", "file_ext": "py", "file_size_in_byte": 12089, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "40", "api": [{"api_name": "seaborn.set_style", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.unique", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.unique", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.unique", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 149, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 152, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 157, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "seaborn.boxplot", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 199, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "seaborn.histplot", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "seaborn.histplot", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "seaborn.histplot", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "seaborn.boxplot", "line_number": 233, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 255, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 273, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 279, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 299, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 301, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 324, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 333, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 338, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 358, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 360, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 377, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 388, "usage_type": "call"}]}