codeparrot/github-jupyter-parsed · Datasets at Hugging Face

repo_name stringlengths 6 77	path stringlengths 8 215	license stringclasses 15 values	cells list	types list
peterwittek/ipython-notebooks	Parameteric and Bilevel Polynomial Optimization Problems.ipynb	gpl-3.0	[ "Relaxations of parametric and bilevel polynomial optimization problems\nSuppose we are interested in finding the global optimum of the following constrained polynomial optimization problem:\n$$ \\min_{x\\in\\mathbb{R}^n}f(x)$$\nsuch that\n$$ g_i(x) \\geq 0, i=1,\\ldots,r$$\nHere $f$ and $g_i$ are polynomials in $x...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
Vizzuality/gfw	docs/Update_GFW_Layers_Vault.ipynb	mit	[ "Create Layer Config Backup\nThis notebook outlines how to run a process to create a remote backup of gfw layers.\nRough process:\n\nRun this notebook from the gfw/data folder\nWait...\nCheck _metadata.json files in the production and staging folders for changes\nIf everything looks good, make a PR\n\nFirst, instal...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
pastas/pasta	examples/notebooks/02_fix_parameters.ipynb	mit	[ "Time Series Analysis with Pastas\nDeveloped by Mark Bakker, TU Delft\nRequired files to run this notebook (all available from the data subdirectory):\n\nHead files: head_nb1.csv, B58C0698001_1.csv, B50H0026001_1.csv, B22C0090001_1.csv, headwell.csv\nPricipitation files: rain_nb1.csv, neerslaggeg_HEIBLOEM-L_967.txt...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
UCSBarchlab/PyRTL	ipynb-examples/introduction-to-hardware.ipynb	bsd-3-clause	[ "Introduction to Hardware Design\nThis code works through the hardware design process with the the\naudience of software developers more in mind. We start with the simple\nproblem of designing a fibonacci sequence calculator (http://oeis.org/A000045).", "import pyrtl", "A normal old python function to return t...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
LucaCanali/Miscellaneous	Pyspark_SQL_Magic_Jupyter/IPython_Pyspark_SQL_Magic.ipynb	apache-2.0	[ "IPython magic functions for Pyspark\nExamples of shortcuts for executing SQL in Spark", "#\n# IPython magic functions to use with Pyspark and Spark SQL\n# The following code is intended as examples of shorcuts to simplify the use of SQL in pyspark\n# The defined functions are:\n#\n# %sql <statement> - r...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
pramitchoudhary/Experiments	notebook_gallery/other_experiments/build-models/model-selection-and-tuning/current-solutions/TPOT/TPOT-demo.ipynb	unlicense	[ "from IPython import display\nURL = \"https://github.com/rhiever/tpot\"\ndisplay.IFrame(URL, 1000, 1000)\n", "TPOT uses a genetic algorithm (implemented with DEAP library) to pick an optimal pipeline for a regression task.\nWhat is a pipeline?\nPipeline is composed of preprocessors:\n* take polynomial transformat...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
efoley/deep-learning	transfer-learning/Transfer_Learning.ipynb	mit	[ "Transfer Learning\nMost of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies	ex15-Trend and Anomaly Analyses of Long-term Tempro-Spatial Dataset.ipynb	mit	[ "%load_ext load_style\n%load_style talk.css", "Trend and Anomaly Analyses of Long-term Tempro-Spatial Dataset\nTrend and anomaly analyses are widely used in atmospheric and oceanographic research for detecting long term change.\nAn example is presented in this notebook of a numerical analysis of Sea Surface Tempe...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
spacedrabbit/PythonBootcamp	Advanced Modules/Collections Module.ipynb	mit	[ "from collections import Counter\n\nCounter('with a string')\n\nCounter('with a string'.split())\n\nc = Counter([1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 5, 6, 100, 'test'])\n\nc\n\nc.viewitems()\n\nfor k, v in c.iteritems(): \n print \"key:\", k, \"value:\", v\n\nc.most_common() # descending order of most common\...	[ "code", "markdown", "code", "markdown", "code" ]
tensorflow/docs-l10n	site/en-snapshot/io/tutorials/orc.ipynb	apache-2.0	[ "Copyright 2021 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
phoebe-project/phoebe2-docs	2.2/tutorials/requiv_crit_detached.ipynb	gpl-3.0	[ "Critical Radii: Detached Systems\nSetup\nLet's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).", "!pip install -I \"phoebe>=2.2,<2.3\"", "As always, let's do imports and...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
dipanjank/ml	text_classification_and_clustering/step_3_classification_of_full_dataset.ipynb	gpl-3.0	[ "<h1 align=\"center\">Level and Group Classification on Train and Test Datasets</h1>\n\nWe have two classification tasks:\n\nPredict the level, which ranges from 1-16.\nPredict the group of a given text, given this mapping from levels to group:\nLevels 1-3 = Group A1\nLevels 4-6 = Group A2\nLevels 7-9 = Group B1\nL...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mtasende/Machine-Learning-Nanodegree-Capstone	notebooks/prod/n08_simple_q_learner_fast_learner_full_training.ipynb	mit	[ "In this notebook a simple Q learner will be trained and evaluated. The Q learner recommends when to buy or sell shares of one particular stock, and in which quantity (in fact it determines the desired fraction of shares in the total portfolio value). One initial attempt was made to train the Q-learner with multipl...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
cpatrickalves/simprev	notebooks/CalculoEstoqueMetodoProb.ipynb	gpl-3.0	[ "Sugestão de metodologia para cálculo de Intervalos de Confiança\nConforme mencionado na LDO de 2018, o modelo oficial do governo se define como determinístico: \n“[...] ou seja, a partir da fixação de um conjunto de variáveis, o modelo determina de maneira única seus resultados [...]\nComo se trabalha com probabil...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
Z0m6ie/Zombie_Code	Data_Science_Course/Michigan Data Analysis Course/0 Introduction to Data Science in Python/Week4/Week+4.ipynb	mit	[ "You are currently looking at version 1.0 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the Jupyter Notebook FAQ course resource.\n\nDistributions in Pandas", "import pandas as pd\nimport numpy as np\n\nfor i in range(5):\n coinf...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
koverholt/notebooks	dask/create-cluster.ipynb	bsd-3-clause	[ "Create a Dask cluster using Coiled\nFirst, we'll create a Dask cluster with Coiled:", "import coiled\ncluster = coiled.Cluster(n_workers=10)", "Let's point the distributed client to the Dask cluster on Coiled and output the link to the dashboard:", "from dask.distributed import Client\nclient = Client(cluste...	[ "markdown", "code", "markdown", "code", "markdown" ]
dsacademybr/PythonFundamentos	Cap04/Notebooks/DSA-Python-Cap04-Exercicios-Solucao.ipynb	gpl-3.0	[ "<font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 4</font>\nDownload: http://github.com/dsacademybr", "# Versão da Linguagem Python\nfrom platform import python_version\nprint('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())", "Versão da Linguagem Python\nfrom...	[ "markdown", "code", "markdown", "code", "markdown" ]
mne-tools/mne-tools.github.io	0.20/_downloads/05c57a644672d33707fd1264df7f5617/plot_time_frequency_global_field_power.ipynb	bsd-3-clause	[ "%matplotlib inline", "Explore event-related dynamics for specific frequency bands\nThe objective is to show you how to explore spectrally localized\neffects. For this purpose we adapt the method described in [1]_ and use it on\nthe somato dataset. The idea is to track the band-limited temporal evolution\nof spat...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
daviddesancho/mdtraj	examples/WebGL-Viewer.ipynb	lgpl-2.1	[ "Interactive WebGL trajectory widget\nNote: this feature requires a 'running' notebook, connected to a live kernel. It will not work with a staticly rendered display. For an introduction to the IPython interactive widget system and its capabilities, see this talk by Brian Granger\nhttp://player.vimeo.com/video/7983...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ajhenrikson/phys202-2015-work	assignments/assignment03/NumpyEx03.ipynb	mit	[ "Numpy Exercise 3\nImports", "import numpy as np\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport antipackage\nimport github.ellisonbg.misc.vizarray as va", "Geometric Brownian motion\nHere is a function that produces standard Brownian motion using NumPy. This is also known ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
kgourgou/stochastic-simulations-class	ipython_notebooks/langevin.ipynb	mit	[ "# Importing some python libraries.\nimport numpy as np\nfrom numpy.random import randn\nimport matplotlib.pyplot as pl\nimport seaborn as sns\n%matplotlib inline\n# Fixing figure sizes\nfrom pylab import rcParams\nrcParams['figure.figsize'] = 10,5\n\nimport sympy as sp\n\npale_red = sns.xkcd_rgb['pale red'] \nd...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
modin-project/modin	examples/tutorial/jupyter/execution/omnisci_on_native/local/exercise_1.ipynb	apache-2.0	[ "<center><h2>Scale your pandas workflows by changing one line of code</h2>\nExercise 1: How to use Modin\nGOAL: Learn how to import Modin to accelerate and scale pandas workflows.\nModin is a drop-in replacement for pandas that distributes the computation \nacross all of the cores in your machine or in a cluster.\n...	[ "markdown", "code", "markdown", "code", "markdown" ]
abhipr1/DATA_SCIENCE_INTENSIVE	Week_1/DATA_WRANGLING/WORKING_WITH_DATA_IN_FILES/data_wrangling_xml/data_wrangling_xml/.ipynb_checkpoints/sliderule_dsi_xml_exercise-checkpoint.ipynb	apache-2.0	[ "XML example and exercise\n\n\nstudy examples of accessing nodes in XML tree structure \nwork on exercise to be completed and submitted\n\n\n\nreference: https://docs.python.org/2.7/library/xml.etree.elementtree.html\ndata source: http://www.dbis.informatik.uni-goettingen.de/Mondial", "from xml.etree import Elem...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
4dsolutions/Python5	About_Decorators.ipynb	mit	[ "Decorators\n\nI use UFO as a decorator not because I want or need people to believe in UFOs, but because the science fiction idea of being abducted is you stay the same but for something lasting the UFO did to you.\nIn the case of decorator syntax that's useful because to \"decorate\" (\"abduct\") is to \n\nfeed a...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
sebastiandres/mat281	laboratorios/lab01-PythonNumerico/PythonNumerico.ipynb	cc0-1.0	[ "<header class=\"w3-container w3-teal\">\n<img src=\"images/utfsm.png\" alt=\"\" height=\"100px\" align=\"left\"/>\n<img src=\"images/mat.png\" alt=\"\" height=\"100px\" align=\"right\"/>\n</header>\n<br/><br/><br/><br/><br/>\nMAT281\nAplicaciones de la Matemática en la Ingeniería\nLaboratorio 1: Python Numérico\nI...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
melissawm/lpwithnotebooks	exemplo/IDEB.ipynb	gpl-3.0	[ "Exemplo: Análise do IDEB\nNeste notebook, vamos analisar dados relativos ao IDEB calculado por município no Brasil. Os dados estão no arquivo", "arquivo = \"IDEB por Município Rede Federal Séries Finais (5ª a 8ª).xml\"", "obtido no site <a href=\"http://dados.gov.br\">dados.gov.br</a>\nComo nosso arquivo é um ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
ethen8181/machine-learning	networkx/max_influence/max_influence.ipynb	mit	[ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Submodular-Optimization-&-Influence-Maximization\" data-toc-modified-id=\"Submodular-Optimization-&-Influence-Maximization-1\"><span class=\"toc-item-num\">1  </span>Submodula...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
jwjohnson314/data-803	notebooks/Regularization and Model Tuning.ipynb	mit	[ "Regularization\nRegularization is the name for a technique developed at different times and in different ways in statistics and machine learning for improving the predictive quality of a model. The idea is to make a model simpler than it might otherwise be by either making the coefficients small, making the coeffi...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
InsightLab/data-science-cookbook	2019/02-python-bibliotecas-manipulacao-dados/pandas_basico.ipynb	mit	[ "Pandas\nImportando o Pandas e o NumPy", "import pandas as pd\nimport numpy as np", "Series\nUma Series é um objeto semelhante a uma vetor que possui um vetor de dados e um vetor de labels associadas chamado index.\nSua documentação completa se encontra em: http://pandas.pydata.org/pandas-docs/stable/generated/...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
gaufung/Data_Analytics_Learning_Note	Data_Analytics_in_Action/pandasIO.ipynb	mit	[ "Pandas 数据读写\nAPI\n读取 \| 写入 \n--- \| ---\nread_csv \| to_csv\nread_excel \| to_excel\nread_hdf \| to_hdf\nread_sql \| to_sql\nread_json \| to_json\nread_html \| to_html\nread_stata \| to_stata\nread_clipboard \| to_clipboard\nread_pickle \| to_pickle\nCVS 文件读写\ncsv 文件内容\nwhite,read,blue,green,animal\n1,5,2,3,cat\n2,7,8,5,dog\...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
jljones/portfolio	ds/Webscraping_Craigslist_multi.ipynb	apache-2.0	[ "Webscraping Craigslist for Housing Listings in the East Bay\nJennifer Jones", "%pylab inline\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport requests\nfrom bs4 import BeautifulSoup as bs4", "Craigslist houses for sale\nLook on the Craigslist website, select relevant search cri...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
kaushik94/tardis	docs/research/code_comparison/plasma_compare/plasma_compare.ipynb	bsd-3-clause	[ "Plasma comparison", "from tardis.simulation import Simulation\nfrom tardis.io.config_reader import Configuration\nfrom IPython.display import FileLinks", "The example tardis_example can be downloaded here\ntardis_example.yml", "config = Configuration.from_yaml('tardis_example.yml')\nsim = Simulation.from_con...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
neurodata/ndmg	tutorials/Overview.ipynb	apache-2.0	[ "Ndmg Tutorial: Running Inside Python\nThis tutorial provides a basic overview of how to run ndmg manually within Python. <br>\nWe begin by checking for dependencies,\nthen we set our input parameters,\nthen we smiply run the pipeline.\nRunning the pipeline is quite simple: call ndmg_dwi_pipeline.ndmg_dwi_worker wi...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
Ccaccia73/semimonocoque	01_SemiMonoCoque.ipynb	mit	[ "Semi-Monocoque Theory", "from pint import UnitRegistry\nimport sympy\nimport networkx as nx\n#import numpy as np\nimport matplotlib.pyplot as plt\n#import sys\n%matplotlib inline\n#from IPython.display import display", "Import Section class, which contains all calculations", "from Section import Section", ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
tensorflow/neural-structured-learning	workshops/kdd_2020/adversarial_regularization_mnist.ipynb	apache-2.0	[ "Copyright 2020 Google LLC", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or ag...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
google/starthinker	colabs/google_api_to_bigquery.ipynb	apache-2.0	[ "Google API To BigQuery\nExecute any Google API function and store results to BigQuery.\nLicense\nCopyright 2020 Google LLC,\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\nhttps://www.apac...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
tpin3694/tpin3694.github.io	machine-learning/f1_score.ipynb	mit	[ "Title: F1 Score\nSlug: f1_score\nSummary: How to evaluate a Python machine learning using F1 score. \nDate: 2017-09-15 12:00\nCategory: Machine Learning\nTags: Model Evaluation\nAuthors: Chris Albon\n<a alt=\"F1 Score\" href=\"https://machinelearningflashcards.com\">\n <img src=\"f1_score/F1_Score_print.png\" ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
savioabuga/arrows	arrows.ipynb	mit	[ "arrows: Yet Another Twitter/Python Data Analysis\nGeospatially, Temporally, and Linguistically Analyzing Tweets about Top U.S. Presidential Candidates with Pandas, TextBlob, Seaborn, and Cartopy\nHi, I'm Raj. For my internship this summer, I've been using data science and geospatial Python libraries like xray, num...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
flaviocordova/udacity_deep_learn_project	gan_mnist/Intro_to_GANs_Solution.ipynb	mit	[ "Generative Adversarial Network\nIn this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\nGANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exp...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
kit-cel/wt	sigNT/tutorial/approximation.ipynb	gpl-2.0	[ "Content and Objective\n\nShow approximations by using gaussian approximation\nAdditionally, applying Gram-Schmidt for \"orthonormalizing\" a set of functions", "# importing\nimport numpy as np\nimport scipy.signal\nimport scipy as sp\n\nimport sympy as sym\nfrom sympy.plotting import plot\n", "definitions", ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
jsjol/GaussianProcessRegressionForDiffusionMRI	notebooks/show_ODFs.ipynb	bsd-3-clause	[ "%load_ext autoreload\n%autoreload 2\n\nimport os\nimport sys\nmodule_path = os.path.abspath(os.path.join('..'))\nif module_path not in sys.path:\n sys.path.append(module_path)\n\nimport numpy as np\nimport json\nimport matplotlib.pyplot as plt\n\nfrom dipy.reconst import mapmri\nimport dipy.reconst.dti as dti\n...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
esa-as/2016-ml-contest	ar4/ar4_submission2_VALIDATION.ipynb	apache-2.0	[ "Facies classification using machine learning techniques\nCopy of <a href=\"https://home.deib.polimi.it/bestagini/\">Paolo Bestagini's</a> \"Try 2\", augmented, by Alan Richardson (Ausar Geophysical), with an ML estimator for PE in the wells where it is missing (rather than just using the mean).\nIn the following, ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
nicolas998/wmf	Examples/Ejemplo_Hidrologia_Maximos.ipynb	gpl-3.0	[ "Realiza el análisis hidrológico de la cuenca de Danta", "%matplotlib inline\nfrom wmf import wmf \nimport numpy as np\nimport pylab as pl\nimport datetime as dt\nimport os\nimport pandas as pd\nimport pickle\nimport plot_y_tablas as pyt\nfrom scipy import stats as stat\n\nfrom IPython.display import HTML\n\nHTML...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
julienchastang/unidata-python-workshop	notebooks/Python_Ecosystem/Scientific_Python_Ecosystem_Overview.ipynb	mit	[ "The Scientific Python Ecosystem\nPython\nPython is a interpreted, high-level programming language that is meant to be easily understandable and usable for a multitude of purposes. It is composed of libraries that contain useful tools for you to do quick and efficient data analysis and visualization. These librarie...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
nehal96/Deep-Learning-ND-Exercises	Sentiment Analysis/Handwritten Digit Recognition with TFLearn and MNIST/handwritten-digit-recognition-with-tflearn.ipynb	mit	[ "Handwritten Number Recognition with TFLearn and MNIST\nIn this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9. \nThis kind of neural network is used in a variety of real-world applications including: recognizing phone numbers and sorting postal mail by address. To build the ne...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
gregorjerse/rt2	2015_2016/lab13/Extending values on vertices.ipynb	gpl-3.0	[ "Extending values on vertices to a discrete gradient vector field\nDuring extension algorithm one has to compute lover_link for every vertex in the complex. So let us implement search for the lower link first. It requires quite a lot of code: first we find a star, then link and finally lower link for the given simp...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
DJCordhose/ai	notebooks/workshops/tss/cnn-standard-architectures.ipynb	mit	[ "Training on an Advanced Standard CNN Architecture\n\nhttps://keras.io/applications/\nThe 9 Deep Learning Papers You Need To Know About: https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html\n\nNeural Network Architectures\n\ntop-1 rating on ImageNet: https://sta...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
darioizzo/d-CGP	doc/sphinx/notebooks/symbolic_regression_3.ipynb	gpl-3.0	[ "Multi-objective memetic approach\nIn this third tutorial we consider an example with two dimensional input data and we approach its solution using a multi-objective approach where, aside the loss, we consider the formula complexity as a second objective.\nWe will use a memetic approach to learn the model parameter...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mercybenzaquen/foundations-homework	foundations_hw/08/Homework8_benzaquen_congress_data.ipynb	mit	[ "!pip install pandas\n\n!pip install matplotlib\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline", "Open your dataset up using pandas in a Jupyter notebook", "df = pd.read_csv(\"congress.csv\", error_bad_lines=False)", "Do a .head() to get a feel for your data", "df.head()\n\n#bio...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
phoebe-project/phoebe2-docs	development/tutorials/datasets_advanced.ipynb	gpl-3.0	[ "Advanced: Datasets\nDatasets tell PHOEBE how and at what times to compute the model. In some cases these will include the actual observational data, and in other cases may only include the times at which you want to compute a synthetic model.\nIf you're not already familiar with the basic functionality of adding ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
qinwf-nuan/keras-js	notebooks/layers/wrappers/TimeDistributed.ipynb	mit	[ "import numpy as np\nfrom keras.models import Model\nfrom keras.layers import Input\nfrom keras.layers.core import Dense\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.wrappers import TimeDistributed\nfrom keras import backend as K\nimport json\nfrom collections import OrderedDict\n\ndef format_d...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
tuanavu/coursera-university-of-washington	machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb	mit	[ "Training Logistic Regression via Stochastic Gradient Ascent\nThe goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:\n\nExtract features from Amazon product reviews.\nConvert an SFrame into a NumPy array.\nWrite a function to compute the derivative of ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
jegibbs/phys202-2015-work	assignments/assignment11/OptimizationEx01.ipynb	mit	[ "Optimization Exercise 1\nImports", "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.optimize as opt", "Hat potential\nThe following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the \"hat potential\":\n$$ V(x) = -a ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
chrinide/optunity	notebooks/basic-cross-validation.ipynb	bsd-3-clause	[ "Basic: cross-validation\nThis notebook explores the main elements of Optunity's cross-validation facilities, including:\n\nstandard cross-validation\nusing strata and clusters while constructing folds\nusing different aggregators\n\nWe recommend perusing the <a href=\"http://docs.optunity.net/user/cross_validation...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
Kaggle/learntools	notebooks/game_ai/raw/tut2.ipynb	apache-2.0	[ "Introduction\nEven if you're new to Connect Four, you've likely developed several game-playing strategies. In this tutorial, you'll learn to use a heuristic to share your knowledge with the agent. \nGame trees\nAs a human player, how do you think about how to play the game? How do you weigh alternative moves?\n...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
charliememory/AutonomousDriving	CarND-Advanced-Lane-Lines/src/.ipynb_checkpoints/camera_calibration-checkpoint.ipynb	gpl-3.0	[ "%%HTML\n<style> code {background-color : pink !important;} </style>", "Camera Calibration with OpenCV\nRun the code in the cell below to extract object points and image points for camera calibration.", "import numpy as np\nimport cv2\nimport glob\nimport matplotlib.pyplot as plt\n%matplotlib qt\n\n# prepare ob...	[ "code", "markdown", "code", "markdown", "code" ]
johnpfay/environ859	06_WebGIS/Notebooks/GeocodingWithOSM.ipynb	gpl-3.0	[ "Geocoding using the Open Street Map API\nHere we explore an example of using an Application Programming Interface, or API. Briefly, an API is a set of commands we can send over the internet to a remote server, spurring the server to process these commands and return a response. In this example, we'll explore how w...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
statsmodels/statsmodels.github.io	v0.13.0/examples/notebooks/generated/variance_components.ipynb	bsd-3-clause	[ "Variance Component Analysis\nThis notebook illustrates variance components analysis for two-level\nnested and crossed designs.", "import numpy as np\nimport statsmodels.api as sm\nfrom statsmodels.regression.mixed_linear_model import VCSpec\nimport pandas as pd", "Make the notebook reproducible", "np.random....	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
agrc/Presentations	UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb	mit	[ "Ditch the Cursor\n\nEditing Feature Classes with Spatialy-Enabled DataFrames\nArcPy Is Great, But...\n\nProblem one: row[0]\n```python\ndef update_year_built(layer, year_fields):\n with arcpy.da.UpdateCursor(layer, year_fields) as cursor:\n for row in cursor:\n if row[0] is None or row[0] < 1 ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
blink1073/oct2py	example/octavemagic_extension.ipynb	mit	[ "octavemagic: Octave inside IPython\nInstallation\nThe octavemagic extension provides the ability to interact with Octave. It is provided by the oct2py package,\nwhich may be installed using pip or easy_install.\nTo enable the extension, load it as follows:", "%load_ext oct2py.ipython", "Overview\nLoading the ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
broundy/udacity	nanodegrees/deep_learning_foundations/unit_2/project_2/dlnd_image_classification.ipynb	unlicense	[ "Image Classification\nIn this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
maciejkula/triplet_recommendations_keras	triplet_keras.ipynb	apache-2.0	[ "Recommendations in Keras using triplet loss\nAlong the lines of BPR [1]. \n[1] Rendle, Steffen, et al. \"BPR: Bayesian personalized ranking from implicit feedback.\" Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2009.\nThis is implemented (more efficiently) in Li...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
biof-309-python/BIOF309-2016-Fall	Week_06/Week 06 - 02 - Conditionals.ipynb	mit	[ "Conditions\nSource: This material adapted from the Python for Biologists website.\nConditions, True and False\nA condition is simply a bit of code that can produce a true or false answer. The easiest way to understand how conditions work in Python is try out a few examples. The following example prints out the res...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
CalPolyPat/phys202-2015-work	assignments/assignment03/NumpyEx04.ipynb	mit	[ "Numpy Exercise 4\nImports", "import numpy as np\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport seaborn as sns", "Complete graph Laplacian\nIn discrete mathematics a Graph is a set of vertices or nodes that are connected to each other by edges or lines. If those edges don't have directionality, th...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
survey-methods/samplics	docs/source/tutorial/replicate_weights.ipynb	mit	[ "Replicate weights\nReplicate weights are usually created for the purpose of variance (uncertainty) estimation. One common use case for replication-based methods is the estimation of non-linear parameters fow which Taylor-based approximation may not be accurate enough. Another use case is when the number of primary...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
basnijholt/orbitalfield	Phase-diagrams.ipynb	bsd-2-clause	[ "Phase diagram for multiple angles\nStart a ipcluster from the Cluster tab in Jupyter or use the command:\nipcluster start \nin a terminal.", "from ipyparallel import Client\ncluster = Client()\ndview = cluster[:]\ndview.use_dill()\nlview = cluster.load_balanced_view()\nlen(dview)", "This next cell is for inter...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
wheeler-microfluidics/mr-box-peripheral-board.py	mr_box_peripheral_board/notebooks/Streaming plot demo.ipynb	mit	[ "Table of Contents\n<p><div class=\"lev1 toc-item\"><a href=\"#Embedded-in-Jupyter-notebook\" data-toc-modified-id=\"Embedded-in-Jupyter-notebook-1\"><span class=\"toc-item-num\">1  </span>Embedded in Jupyter notebook</a></div><div class=\"lev1 toc-item\"><a href=\"#Using-GTK\" data-toc-modified-id=\"Usin...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
quantumlib/ReCirq	docs/qaoa/binary_paintshop.ipynb	apache-2.0	[ "Copyright 2021 The Cirq Developers", "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
tensorflow/docs-l10n	site/zh-cn/hub/tutorials/bangla_article_classifier.ipynb	apache-2.0	[ "Copyright 2019 The TensorFlow Hub Authors.\nLicensed under the Apache License, Version 2.0 (the \"License\");", "# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the Lic...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
usantamaria/iwi131	ipynb/23-ProcesamientoDeTexto/Texto.ipynb	cc0-1.0	[ "\"\"\"\nIPython Notebook v4.0 para python 2.7\nLibrerías adicionales: Ninguna.\nContenido bajo licencia CC-BY 4.0. Código bajo licencia MIT. (c) Sebastian Flores.\n\"\"\"\n\n# Configuracion para recargar módulos y librerías \n%reload_ext autoreload\n%autoreload 2\n\nfrom IPython.core.display import HTML\n\nHTML(op...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
kdheepak/psst	docs/notebooks/interactive_visuals/Demo.ipynb	mit	[ "How NetworkModel Works", "import pandas as pd\nimport numpy as np\n\nfrom psst.network.graph import (\n NetworkModel, NetworkViewBase, NetworkView\n)\n\nfrom psst.case import read_matpower\ncase = read_matpower('../cases/case118.m')", "I. Creating a NetworkModel", "# Create the model from a PSSTCase, opti...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
JAmarel/LiquidCrystals	ElectroOptics/MinimizeAttempt.ipynb	mit	[ "import numpy as np\nfrom scipy.integrate import quad, dblquad\n%matplotlib inline\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import minimize\n\nthetamin = 25.6np.pi/180\nthetamax = 33.7np.pi/180\nt = 110*-6 #Cell Thickness", "Data", "tempsC = np.array([26, 27, 29, 31, 33, 35, 37])\nvoltages = np...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
bgruening/EDeN	examples/Sequence_example.ipynb	gpl-3.0	[ "Example\nConsider sequences that are increasingly different. EDeN allows to turn them into vectors, whose similarity is decreasing.", "%matplotlib inline", "Build an artificial dataset: starting from the string 'abcdefghijklmnopqrstuvwxyz', generate iteratively strings by swapping two characters at random. In ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
barjacks/pythonrecherche	Kursteilnehmer/Sven Millischer/06 /01 Rückblick For-Loop-Übungen.ipynb	mit	[ "10 For-Loop-Rückblick-Übungen\nIn den Teilen der folgenden Übungen habe ich den Code mit \"XXX\" ausgewechselt. Es gilt in allen Übungen, den korrekten Code auszuführen und die Zelle dann auszuführen. \n1.Drucke alle diese Prim-Zahlen aus:", "primes = [2, 3, 5, 7]\nfor prime in primes:\n print(prime)", "2.D...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
NelisW/ComputationalRadiometry	03-Introduction-to-Radiometry.ipynb	mpl-2.0	[ "3 Brief Introduction to Radiometry\nThis notebook forms part of a series on computational optical radiometry \nThe date of this document and module versions used in this document are given at the end of the file.\nFeedback is appreciated: neliswillers at gmail dot com.\nOverview", "from IPython.display import ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
getsmarter/bda	module_2/M2_NB1_SourcesOfData.ipynb	mit	[ "<div align=\"right\">Python 3.6 Jupyter Notebook</div>\n\nSources of data\nYour completion of the notebook exercises will be graded based on your ability to do the following:\n\nApply: Are you able to execute code (using the supplied examples) that performs the required functionality on supplied or generated data ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
sysid/nbs	cnn/tw_vgg16.ipynb	mit	[ "Using Convolutional Neural Networks\nThis is running on theano!\nWelcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.\nIntroduction to this week's task: 'Dogs...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
deepmind/dm_control	tutorial.ipynb	apache-2.0	[ "dm_control tutorial\n\n\n<p><small><small>Copyright 2020 The dm_control Authors.</small></p>\n<p><small><small>Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at <a href=\"http://www.apache.org/l...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
huongttlan/statsmodels	examples/notebooks/statespace_sarimax_internet.ipynb	bsd-3-clause	[ "SARIMAX: Model selection, missing data\nThe example mirrors Durbin and Koopman (2012), Chapter 8.4 in application of Box-Jenkins methodology to fit ARMA models. The novel feature is the ability of the model to work on datasets with missing values.", "%matplotlib inline\n\nimport numpy as np\nimport pandas as pd\...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
UDST/activitysim	activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb	bsd-3-clause	[ "Estimating Mandatory Tour Frequency\nThis notebook illustrates how to re-estimate a single model component for ActivitySim. This process \nincludes running ActivitySim in estimation mode to read household travel survey files and write out\nthe estimation data bundles used in this notebook. To review how to do so...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
GoogleCloudPlatform/vertex-ai-samples	notebooks/community/managed_notebooks/pricing_optimization/pricing-optimization.ipynb	apache-2.0	[ "Pricing Optimization\nTable of contents\n\nOverview\nDataset\nObjective\nCosts\nCreate a BigQuery dataset\nLoad the dataset from Cloud Storage\nData analysis\nPreprocess the data for training\nTrain the model using BigQuery ML\nGenerate forecasts from the model\nInterpret the results to choose the best price\nClea...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
slundberg/shap	notebooks/image_examples/image_classification/Multi-input Gradient Explainer MNIST Example.ipynb	mit	[ "Multi-input Gradient Explainer MNIST Example\nHere we demonstrate how to use GradientExplainer when you have multiple inputs to your Keras/TensorFlow model. To keep things simple but also mildly interesting we feed two copies of MNIST into our model, where one copy goes into a conv-net layer and the other copy goe...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
ES-DOC/esdoc-jupyterhub	notebooks/cccma/cmip6/models/canesm5/atmoschem.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Atmoschem\nMIP Era: CMIP6\nInstitute: CCCMA\nSource ID: CANESM5\nTopic: Atmoschem\nSub-Topics: Transport, Emissions Concentrations, Gas Phase Chemistry, Stratospheric Heterogeneous Chemistry, Tropospheric Heterogeneous Chemistry, Photo Chemistry. \nProperties: 84 (39 required)\nMode...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
kubeflow/pipelines	components/gcp/dataflow/launch_template/sample.ipynb	apache-2.0	[ "Name\nData preparation by using a template to submit a job to Cloud Dataflow\nLabels\nGCP, Cloud Dataflow, Kubeflow, Pipeline\nSummary\nA Kubeflow Pipeline component to prepare data by using a template to submit a job to Cloud Dataflow.\nDetails\nIntended use\nUse this component when you have a pre-built Cloud Dat...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
eric-svds/flask-with-docker	app/.ipynb_checkpoints/my_notebook-checkpoint.ipynb	gpl-2.0	[ "Sample PCA analysis with Iris dataset\nThe following are required for this notebook:\n- pip install matplotlib\n- pip install scikit-learn\nThis notebook plots (and pickles) the Iris data set before and after Principal Component Analysis. Output is intended to be imported by a Flask application and passed to an HT...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
sbenthall/bigbang	examples/obsolete_notebooks/SummerSchoolCompareWordRankings.ipynb	agpl-3.0	[ "from bigbang.archive import Archive\nfrom bigbang.archive import load as load_archive\nimport os\nimport pandas as pd\nimport numpy as np\n\n\nietf_path = \"../archives/\"\nncuc_path = \"../archives/http:/lists.ncuc.org/pipermail\"\n\npaths = [os.path.join(ietf_path,\"6lo.csv\"),\n os.path.join(ietf_path,\"...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
antoniomezzacapo/qiskit-tutorial	community/aqua/chemistry/LiH_with_qubit_tapering_and_uccsd.ipynb	apache-2.0	[ "# import common packages\nfrom collections import OrderedDict\nimport itertools\nimport logging\n\nimport numpy as np\nimport scipy\n\nfrom qiskit_aqua import (get_algorithm_instance, get_optimizer_instance, \n get_variational_form_instance, get_initial_state_instance, Operator)\nfrom qiskit...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ondrolexa/sg2	14_Simultaneous_deformation.ipynb	mit	[ "Simultaneous deformation", "%pylab inline\n\nfrom sg2lib import *", "Naive concept of simultaneous deformation\nHere we try to split simple shear and pure shear to several incremental steps and mutually superposed those increments to simulate simultaneous deformation. We will use following deformation gradient...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
QuantStack/quantstack-talks	2019-05-22-pydata-frankfurt/notebooks/bqplot.ipynb	bsd-3-clause	[ "bqplot https://github.com/bloomberg/bqplot\nA Jupyter - d3.js bridge\nbqplot is a jupyter interactive widget library bringing d3.js visualization to the Jupyter notebook.\n\nApache Licensed\n\nbqplot implements the abstractions of Wilkinson’s “The Grammar of Graphics” as interactive Jupyter widgets.\nbqplot provid...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
kubernetes-client/python	examples/notebooks/intro_notebook.ipynb	apache-2.0	[ "Managing kubernetes objects using common resource operations with the python client\nSome of these operations include;\n\n\ncreate_xxxx : create a resource object. Ex create_namespaced_pod and create_namespaced_deployment, for creation of pods and deployments respectively. This performs operations similar to kubec...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
tpin3694/tpin3694.github.io	statistics/t-tests.ipynb	mit	[ "Title: T-Tests\nSlug: t-tests\nSummary: T-tests in Python. \nDate: 2016-02-08 12:00\nCategory: Statistics\nTags: Basics\nAuthors: Chris Albon \nPreliminaries", "from scipy import stats\nimport numpy as np", "Create Data", "# Create a list of 20 observations drawn from a random distribution \n# with mean 1 ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
fedor1113/LineCodes	Decoder.ipynb	mit	[ "Decode line codes in png graphs\nAssumptions (format):\n\nThe clock is given and it is a red line on the top.\nThe signal line is black\n...", "# Makes sure to install PyPNG image handling module\nimport sys\n!{sys.executable} -m pip install pypng\n\nimport png\n\nr = png.Reader(\"ex.png\")\nt = r.asRGB()\n\nimg...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
jorisvandenbossche/DS-python-data-analysis	notebooks/case4_air_quality_processing.ipynb	bsd-3-clause	[ "<p><font size=\"6\"><b> CASE - air quality data of European monitoring stations (AirBase)</b></font></p>\n\n\n© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvandenbossche@gmail.c&#111...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
LFPy/LFPy	examples/LFPy-example-02.ipynb	gpl-3.0	[ "%matplotlib inline", "Example 2: Extracellular response of synaptic input\nThis is an example of LFPy running in a Jupyter notebook. To run through this example code and produce output, press <shift-Enter> in each code block below.\nFirst step is to import LFPy and other packages for analysis and plotting:...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
teoguso/sol_1116	cumulant-to-pdf.ipynb	mit	[ "Best report ever\nEverything you see here is either markdown, LaTex, Python or BASH.\nThe spectral function\nIt looks like this:\n\\begin{equation}\n A(\\omega) = \\mathrm{Im}\|G(\\omega)\|\n\\end{equation}\nGW vs Cumulant\nMathematically very different:\n\\begin{equation}\n G^{GW} (\\omega) = \\frac1{ \\omega - \\e...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
NLP-Deeplearning-Club/Classic-ML-Methods-Algo	ipynbs/appendix/ensemble/voting.ipynb	mit	[ "投票\n投票是最简单最基本的集成方式,核心思想也很朴素:大家伙投票决定结果.\n其原理是结合了多个不同的机器学习分类器,并且采用多数表决(硬投票)或者平均预测概率(软投票)的方式来预测分类标签.这样的分类器可以用于一组同样表现良好的模型,以便平衡它们各自的弱点.\n使用sklearn做投票\nsklearn提供了用于投票的接口sklearn.ensemble.VotingClassifier.下面的例子可以大体了解如何使用投票接口", "import numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
tensorflow/docs-l10n	site/en-snapshot/addons/tutorials/optimizers_cyclicallearningrate.ipynb	apache-2.0	[ "Copyright 2021 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
mne-tools/mne-tools.github.io	dev/_downloads/5b9edf9c05aec2b9bb1f128f174ca0f3/40_cluster_1samp_time_freq.ipynb	bsd-3-clause	[ "%matplotlib inline", "Non-parametric 1 sample cluster statistic on single trial power\nThis script shows how to estimate significant clusters\nin time-frequency power estimates. It uses a non-parametric\nstatistical procedure based on permutations and cluster\nlevel statistics.\nThe procedure consists of:\n\next...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
jasonding1354/pyDataScienceToolkits_Base	Scikit-learn/.ipynb_checkpoints/(3)linear_regression-checkpoint.ipynb	mit	[ "内容概要\n\n如何使用pandas读入数据\n如何使用seaborn进行数据的可视化\nscikit-learn的线性回归模型和使用方法\n线性回归模型的评估测度\n特征选择的方法\n\n作为有监督学习，分类问题是预测类别结果，而回归问题是预测一个连续的结果。\n1. 使用pandas来读取数据\nPandas是一个用于数据探索、数据处理、数据分析的Python库", "import pandas as pd\n\n# read csv file directly from a URL and save the results\ndata = pd.read_csv('http://www-bcf.usc.edu/~...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
atlury/deep-opencl	cs480/23 Linear Dimensionality Reduction.ipynb	lgpl-3.0	[ "$\\newcommand{\\xv}{\\mathbf{x}}\n\\newcommand{\\Xv}{\\mathbf{X}}\n\\newcommand{\\yv}{\\mathbf{y}}\n\\newcommand{\\Yv}{\\mathbf{Y}}\n\\newcommand{\\zv}{\\mathbf{z}}\n\\newcommand{\\av}{\\mathbf{a}}\n\\newcommand{\\Wv}{\\mathbf{W}}\n\\newcommand{\\wv}{\\mathbf{w}}\n\\newcommand{\\betav}{\\mathbf{\\beta}}\n\\newcomm...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

peterwittek/ipython-notebooks

Parameteric and Bilevel Polynomial Optimization Problems.ipynb

gpl-3.0

[ "Relaxations of parametric and bilevel polynomial optimization problems\nSuppose we are interested in finding the global optimum of the following constrained polynomial optimization problem:\n$$ \\min_{x\\in\\mathbb{R}^n}f(x)$$\nsuch that\n$$ g_i(x) \\geq 0, i=1,\\ldots,r$$\nHere $f$ and $g_i$ are polynomials in $x...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

Vizzuality/gfw

docs/Update_GFW_Layers_Vault.ipynb

mit

[ "Create Layer Config Backup\nThis notebook outlines how to run a process to create a remote backup of gfw layers.\nRough process:\n\nRun this notebook from the gfw/data folder\nWait...\nCheck _metadata.json files in the production and staging folders for changes\nIf everything looks good, make a PR\n\nFirst, instal...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

pastas/pasta

examples/notebooks/02_fix_parameters.ipynb

mit

[ "Time Series Analysis with Pastas\nDeveloped by Mark Bakker, TU Delft\nRequired files to run this notebook (all available from the data subdirectory):\n\nHead files: head_nb1.csv, B58C0698001_1.csv, B50H0026001_1.csv, B22C0090001_1.csv, headwell.csv\nPricipitation files: rain_nb1.csv, neerslaggeg_HEIBLOEM-L_967.txt...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

UCSBarchlab/PyRTL

ipynb-examples/introduction-to-hardware.ipynb

bsd-3-clause

[ "Introduction to Hardware Design\nThis code works through the hardware design process with the the\naudience of software developers more in mind. We start with the simple\nproblem of designing a fibonacci sequence calculator (http://oeis.org/A000045).", "import pyrtl", "A normal old python function to return t...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

LucaCanali/Miscellaneous

Pyspark_SQL_Magic_Jupyter/IPython_Pyspark_SQL_Magic.ipynb

apache-2.0

[ "IPython magic functions for Pyspark\nExamples of shortcuts for executing SQL in Spark", "#\n# IPython magic functions to use with Pyspark and Spark SQL\n# The following code is intended as examples of shorcuts to simplify the use of SQL in pyspark\n# The defined functions are:\n#\n# %sql <statement> - r...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

pramitchoudhary/Experiments

notebook_gallery/other_experiments/build-models/model-selection-and-tuning/current-solutions/TPOT/TPOT-demo.ipynb

unlicense

[ "from IPython import display\nURL = \"https://github.com/rhiever/tpot\"\ndisplay.IFrame(URL, 1000, 1000)\n", "TPOT uses a genetic algorithm (implemented with DEAP library) to pick an optimal pipeline for a regression task.\nWhat is a pipeline?\nPipeline is composed of preprocessors:\n* take polynomial transformat...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

efoley/deep-learning

transfer-learning/Transfer_Learning.ipynb

mit

[ "Transfer Learning\nMost of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

royalosyin/Python-Practical-Application-on-Climate-Variability-Studies

ex15-Trend and Anomaly Analyses of Long-term Tempro-Spatial Dataset.ipynb

mit

[ "%load_ext load_style\n%load_style talk.css", "Trend and Anomaly Analyses of Long-term Tempro-Spatial Dataset\nTrend and anomaly analyses are widely used in atmospheric and oceanographic research for detecting long term change.\nAn example is presented in this notebook of a numerical analysis of Sea Surface Tempe...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...

spacedrabbit/PythonBootcamp

Advanced Modules/Collections Module.ipynb

mit

[ "from collections import Counter\n\nCounter('with a string')\n\nCounter('with a string'.split())\n\nc = Counter([1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 5, 6, 100, 'test'])\n\nc\n\nc.viewitems()\n\nfor k, v in c.iteritems(): \n print \"key:\", k, \"value:\", v\n\nc.most_common() # descending order of most common\...

[ "code", "markdown", "code", "markdown", "code" ]

tensorflow/docs-l10n

site/en-snapshot/io/tutorials/orc.ipynb

apache-2.0

[ "Copyright 2021 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

phoebe-project/phoebe2-docs

2.2/tutorials/requiv_crit_detached.ipynb

gpl-3.0

[ "Critical Radii: Detached Systems\nSetup\nLet's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).", "!pip install -I \"phoebe>=2.2,<2.3\"", "As always, let's do imports and...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

dipanjank/ml

text_classification_and_clustering/step_3_classification_of_full_dataset.ipynb

gpl-3.0

[ "<h1 align=\"center\">Level and Group Classification on Train and Test Datasets</h1>\n\nWe have two classification tasks:\n\nPredict the level, which ranges from 1-16.\nPredict the group of a given text, given this mapping from levels to group:\nLevels 1-3 = Group A1\nLevels 4-6 = Group A2\nLevels 7-9 = Group B1\nL...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

mtasende/Machine-Learning-Nanodegree-Capstone

notebooks/prod/n08_simple_q_learner_fast_learner_full_training.ipynb

mit

[ "In this notebook a simple Q learner will be trained and evaluated. The Q learner recommends when to buy or sell shares of one particular stock, and in which quantity (in fact it determines the desired fraction of shares in the total portfolio value). One initial attempt was made to train the Q-learner with multipl...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

cpatrickalves/simprev

notebooks/CalculoEstoqueMetodoProb.ipynb

gpl-3.0

[ "Sugestão de metodologia para cálculo de Intervalos de Confiança\nConforme mencionado na LDO de 2018, o modelo oficial do governo se define como determinístico: \n“[...] ou seja, a partir da fixação de um conjunto de variáveis, o modelo determina de maneira única seus resultados [...]\nComo se trabalha com probabil...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

Z0m6ie/Zombie_Code

Data_Science_Course/Michigan Data Analysis Course/0 Introduction to Data Science in Python/Week4/Week+4.ipynb

mit

[ "You are currently looking at version 1.0 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the Jupyter Notebook FAQ course resource.\n\nDistributions in Pandas", "import pandas as pd\nimport numpy as np\n\nfor i in range(5):\n coinf...

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

koverholt/notebooks

dask/create-cluster.ipynb

bsd-3-clause

[ "Create a Dask cluster using Coiled\nFirst, we'll create a Dask cluster with Coiled:", "import coiled\ncluster = coiled.Cluster(n_workers=10)", "Let's point the distributed client to the Dask cluster on Coiled and output the link to the dashboard:", "from dask.distributed import Client\nclient = Client(cluste...

[ "markdown", "code", "markdown", "code", "markdown" ]

dsacademybr/PythonFundamentos

Cap04/Notebooks/DSA-Python-Cap04-Exercicios-Solucao.ipynb

gpl-3.0

[ "<font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 4</font>\nDownload: http://github.com/dsacademybr", "# Versão da Linguagem Python\nfrom platform import python_version\nprint('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())", "Versão da Linguagem Python\nfrom...

[ "markdown", "code", "markdown", "code", "markdown" ]

mne-tools/mne-tools.github.io

0.20/_downloads/05c57a644672d33707fd1264df7f5617/plot_time_frequency_global_field_power.ipynb

bsd-3-clause

[ "%matplotlib inline", "Explore event-related dynamics for specific frequency bands\nThe objective is to show you how to explore spectrally localized\neffects. For this purpose we adapt the method described in [1]_ and use it on\nthe somato dataset. The idea is to track the band-limited temporal evolution\nof spat...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

daviddesancho/mdtraj

examples/WebGL-Viewer.ipynb

lgpl-2.1

[ "Interactive WebGL trajectory widget\nNote: this feature requires a 'running' notebook, connected to a live kernel. It will not work with a staticly rendered display. For an introduction to the IPython interactive widget system and its capabilities, see this talk by Brian Granger\nhttp://player.vimeo.com/video/7983...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

ajhenrikson/phys202-2015-work

assignments/assignment03/NumpyEx03.ipynb

mit

[ "Numpy Exercise 3\nImports", "import numpy as np\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport antipackage\nimport github.ellisonbg.misc.vizarray as va", "Geometric Brownian motion\nHere is a function that produces standard Brownian motion using NumPy. This is also known ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

kgourgou/stochastic-simulations-class

ipython_notebooks/langevin.ipynb

mit

[ "# Importing some python libraries.\nimport numpy as np\nfrom numpy.random import randn\nimport matplotlib.pyplot as pl\nimport seaborn as sns\n%matplotlib inline\n# Fixing figure sizes\nfrom pylab import rcParams\nrcParams['figure.figsize'] = 10,5\n\nimport sympy as sp\n\npale_red = sns.xkcd_rgb['pale red'] \nd...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

modin-project/modin

examples/tutorial/jupyter/execution/omnisci_on_native/local/exercise_1.ipynb

apache-2.0

[ "<center><h2>Scale your pandas workflows by changing one line of code</h2>\nExercise 1: How to use Modin\nGOAL: Learn how to import Modin to accelerate and scale pandas workflows.\nModin is a drop-in replacement for pandas that distributes the computation \nacross all of the cores in your machine or in a cluster.\n...

[ "markdown", "code", "markdown", "code", "markdown" ]

abhipr1/DATA_SCIENCE_INTENSIVE

Week_1/DATA_WRANGLING/WORKING_WITH_DATA_IN_FILES/data_wrangling_xml/data_wrangling_xml/.ipynb_checkpoints/sliderule_dsi_xml_exercise-checkpoint.ipynb

apache-2.0

[ "XML example and exercise\n\n\nstudy examples of accessing nodes in XML tree structure \nwork on exercise to be completed and submitted\n\n\n\nreference: https://docs.python.org/2.7/library/xml.etree.elementtree.html\ndata source: http://www.dbis.informatik.uni-goettingen.de/Mondial", "from xml.etree import Elem...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

4dsolutions/Python5

About_Decorators.ipynb

mit

[ "Decorators\n\nI use UFO as a decorator not because I want or need people to believe in UFOs, but because the science fiction idea of being abducted is you stay the same but for something lasting the UFO did to you.\nIn the case of decorator syntax that's useful because to \"decorate\" (\"abduct\") is to \n\nfeed a...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

sebastiandres/mat281

laboratorios/lab01-PythonNumerico/PythonNumerico.ipynb

cc0-1.0

[ "<header class=\"w3-container w3-teal\">\n<img src=\"images/utfsm.png\" alt=\"\" height=\"100px\" align=\"left\"/>\n<img src=\"images/mat.png\" alt=\"\" height=\"100px\" align=\"right\"/>\n</header>\n<br/><br/><br/><br/><br/>\nMAT281\nAplicaciones de la Matemática en la Ingeniería\nLaboratorio 1: Python Numérico\nI...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

melissawm/lpwithnotebooks

exemplo/IDEB.ipynb

gpl-3.0

[ "Exemplo: Análise do IDEB\nNeste notebook, vamos analisar dados relativos ao IDEB calculado por município no Brasil. Os dados estão no arquivo", "arquivo = \"IDEB por Município Rede Federal Séries Finais (5ª a 8ª).xml\"", "obtido no site <a href=\"http://dados.gov.br\">dados.gov.br</a>\nComo nosso arquivo é um ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

ethen8181/machine-learning

networkx/max_influence/max_influence.ipynb

mit

[ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Submodular-Optimization-&-Influence-Maximization\" data-toc-modified-id=\"Submodular-Optimization-&-Influence-Maximization-1\"><span class=\"toc-item-num\">1  </span>Submodula...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

jwjohnson314/data-803

notebooks/Regularization and Model Tuning.ipynb

mit

[ "Regularization\nRegularization is the name for a technique developed at different times and in different ways in statistics and machine learning for improving the predictive quality of a model. The idea is to make a model simpler than it might otherwise be by either making the coefficients small, making the coeffi...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

InsightLab/data-science-cookbook

2019/02-python-bibliotecas-manipulacao-dados/pandas_basico.ipynb

mit

[ "Pandas\nImportando o Pandas e o NumPy", "import pandas as pd\nimport numpy as np", "Series\nUma Series é um objeto semelhante a uma vetor que possui um vetor de dados e um vetor de labels associadas chamado index.\nSua documentação completa se encontra em: http://pandas.pydata.org/pandas-docs/stable/generated/...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

gaufung/Data_Analytics_Learning_Note

Data_Analytics_in_Action/pandasIO.ipynb

mit

[ "Pandas 数据读写\nAPI\n读取 | 写入 \n--- | ---\nread_csv | to_csv\nread_excel | to_excel\nread_hdf | to_hdf\nread_sql | to_sql\nread_json | to_json\nread_html | to_html\nread_stata | to_stata\nread_clipboard | to_clipboard\nread_pickle | to_pickle\nCVS 文件读写\ncsv 文件内容\nwhite,read,blue,green,animal\n1,5,2,3,cat\n2,7,8,5,dog\...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

jljones/portfolio

ds/Webscraping_Craigslist_multi.ipynb

apache-2.0

[ "Webscraping Craigslist for Housing Listings in the East Bay\nJennifer Jones", "%pylab inline\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport requests\nfrom bs4 import BeautifulSoup as bs4", "Craigslist houses for sale\nLook on the Craigslist website, select relevant search cri...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

kaushik94/tardis

docs/research/code_comparison/plasma_compare/plasma_compare.ipynb

bsd-3-clause

[ "Plasma comparison", "from tardis.simulation import Simulation\nfrom tardis.io.config_reader import Configuration\nfrom IPython.display import FileLinks", "The example tardis_example can be downloaded here\ntardis_example.yml", "config = Configuration.from_yaml('tardis_example.yml')\nsim = Simulation.from_con...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

neurodata/ndmg

tutorials/Overview.ipynb

apache-2.0

[ "Ndmg Tutorial: Running Inside Python\nThis tutorial provides a basic overview of how to run ndmg manually within Python. <br>\nWe begin by checking for dependencies,\nthen we set our input parameters,\nthen we smiply run the pipeline.\nRunning the pipeline is quite simple: call ndmg_dwi_pipeline.ndmg_dwi_worker wi...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

Ccaccia73/semimonocoque

01_SemiMonoCoque.ipynb

mit

[ "Semi-Monocoque Theory", "from pint import UnitRegistry\nimport sympy\nimport networkx as nx\n#import numpy as np\nimport matplotlib.pyplot as plt\n#import sys\n%matplotlib inline\n#from IPython.display import display", "Import Section class, which contains all calculations", "from Section import Section", ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

tensorflow/neural-structured-learning

workshops/kdd_2020/adversarial_regularization_mnist.ipynb

apache-2.0

[ "Copyright 2020 Google LLC", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or ag...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

google/starthinker

colabs/google_api_to_bigquery.ipynb

apache-2.0

[ "Google API To BigQuery\nExecute any Google API function and store results to BigQuery.\nLicense\nCopyright 2020 Google LLC,\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\nhttps://www.apac...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

tpin3694/tpin3694.github.io

machine-learning/f1_score.ipynb

mit

[ "Title: F1 Score\nSlug: f1_score\nSummary: How to evaluate a Python machine learning using F1 score. \nDate: 2017-09-15 12:00\nCategory: Machine Learning\nTags: Model Evaluation\nAuthors: Chris Albon\n<a alt=\"F1 Score\" href=\"https://machinelearningflashcards.com\">\n <img src=\"f1_score/F1_Score_print.png\" ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

savioabuga/arrows

arrows.ipynb

mit

[ "arrows: Yet Another Twitter/Python Data Analysis\nGeospatially, Temporally, and Linguistically Analyzing Tweets about Top U.S. Presidential Candidates with Pandas, TextBlob, Seaborn, and Cartopy\nHi, I'm Raj. For my internship this summer, I've been using data science and geospatial Python libraries like xray, num...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

flaviocordova/udacity_deep_learn_project

gan_mnist/Intro_to_GANs_Solution.ipynb

mit

[ "Generative Adversarial Network\nIn this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\nGANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exp...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

kit-cel/wt

sigNT/tutorial/approximation.ipynb

gpl-2.0

[ "Content and Objective\n\nShow approximations by using gaussian approximation\nAdditionally, applying Gram-Schmidt for \"orthonormalizing\" a set of functions", "# importing\nimport numpy as np\nimport scipy.signal\nimport scipy as sp\n\nimport sympy as sym\nfrom sympy.plotting import plot\n", "definitions", ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

jsjol/GaussianProcessRegressionForDiffusionMRI

notebooks/show_ODFs.ipynb

bsd-3-clause

[ "%load_ext autoreload\n%autoreload 2\n\nimport os\nimport sys\nmodule_path = os.path.abspath(os.path.join('..'))\nif module_path not in sys.path:\n sys.path.append(module_path)\n\nimport numpy as np\nimport json\nimport matplotlib.pyplot as plt\n\nfrom dipy.reconst import mapmri\nimport dipy.reconst.dti as dti\n...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

esa-as/2016-ml-contest

ar4/ar4_submission2_VALIDATION.ipynb

apache-2.0

[ "Facies classification using machine learning techniques\nCopy of <a href=\"https://home.deib.polimi.it/bestagini/\">Paolo Bestagini's</a> \"Try 2\", augmented, by Alan Richardson (Ausar Geophysical), with an ML estimator for PE in the wells where it is missing (rather than just using the mean).\nIn the following, ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

nicolas998/wmf

Examples/Ejemplo_Hidrologia_Maximos.ipynb

gpl-3.0

[ "Realiza el análisis hidrológico de la cuenca de Danta", "%matplotlib inline\nfrom wmf import wmf \nimport numpy as np\nimport pylab as pl\nimport datetime as dt\nimport os\nimport pandas as pd\nimport pickle\nimport plot_y_tablas as pyt\nfrom scipy import stats as stat\n\nfrom IPython.display import HTML\n\nHTML...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

julienchastang/unidata-python-workshop

notebooks/Python_Ecosystem/Scientific_Python_Ecosystem_Overview.ipynb

mit

[ "The Scientific Python Ecosystem\nPython\nPython is a interpreted, high-level programming language that is meant to be easily understandable and usable for a multitude of purposes. It is composed of libraries that contain useful tools for you to do quick and efficient data analysis and visualization. These librarie...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

nehal96/Deep-Learning-ND-Exercises

Sentiment Analysis/Handwritten Digit Recognition with TFLearn and MNIST/handwritten-digit-recognition-with-tflearn.ipynb

mit

[ "Handwritten Number Recognition with TFLearn and MNIST\nIn this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9. \nThis kind of neural network is used in a variety of real-world applications including: recognizing phone numbers and sorting postal mail by address. To build the ne...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

gregorjerse/rt2

2015_2016/lab13/Extending values on vertices.ipynb

gpl-3.0

[ "Extending values on vertices to a discrete gradient vector field\nDuring extension algorithm one has to compute lover_link for every vertex in the complex. So let us implement search for the lower link first. It requires quite a lot of code: first we find a star, then link and finally lower link for the given simp...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

DJCordhose/ai

notebooks/workshops/tss/cnn-standard-architectures.ipynb

mit

[ "Training on an Advanced Standard CNN Architecture\n\nhttps://keras.io/applications/\nThe 9 Deep Learning Papers You Need To Know About: https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html\n\nNeural Network Architectures\n\ntop-1 rating on ImageNet: https://sta...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

darioizzo/d-CGP

doc/sphinx/notebooks/symbolic_regression_3.ipynb

gpl-3.0

[ "Multi-objective memetic approach\nIn this third tutorial we consider an example with two dimensional input data and we approach its solution using a multi-objective approach where, aside the loss, we consider the formula complexity as a second objective.\nWe will use a memetic approach to learn the model parameter...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

mercybenzaquen/foundations-homework

foundations_hw/08/Homework8_benzaquen_congress_data.ipynb

mit

[ "!pip install pandas\n\n!pip install matplotlib\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline", "Open your dataset up using pandas in a Jupyter notebook", "df = pd.read_csv(\"congress.csv\", error_bad_lines=False)", "Do a .head() to get a feel for your data", "df.head()\n\n#bio...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...

phoebe-project/phoebe2-docs

development/tutorials/datasets_advanced.ipynb

gpl-3.0

[ "Advanced: Datasets\nDatasets tell PHOEBE how and at what times to compute the model. In some cases these will include the actual observational data, and in other cases may only include the times at which you want to compute a synthetic model.\nIf you're not already familiar with the basic functionality of adding ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

qinwf-nuan/keras-js

notebooks/layers/wrappers/TimeDistributed.ipynb

mit

[ "import numpy as np\nfrom keras.models import Model\nfrom keras.layers import Input\nfrom keras.layers.core import Dense\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.wrappers import TimeDistributed\nfrom keras import backend as K\nimport json\nfrom collections import OrderedDict\n\ndef format_d...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

tuanavu/coursera-university-of-washington

machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb

mit

[ "Training Logistic Regression via Stochastic Gradient Ascent\nThe goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:\n\nExtract features from Amazon product reviews.\nConvert an SFrame into a NumPy array.\nWrite a function to compute the derivative of ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

jegibbs/phys202-2015-work

assignments/assignment11/OptimizationEx01.ipynb

mit

[ "Optimization Exercise 1\nImports", "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.optimize as opt", "Hat potential\nThe following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the \"hat potential\":\n$$ V(x) = -a ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

chrinide/optunity

notebooks/basic-cross-validation.ipynb

bsd-3-clause

[ "Basic: cross-validation\nThis notebook explores the main elements of Optunity's cross-validation facilities, including:\n\nstandard cross-validation\nusing strata and clusters while constructing folds\nusing different aggregators\n\nWe recommend perusing the <a href=\"http://docs.optunity.net/user/cross_validation...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

Kaggle/learntools

notebooks/game_ai/raw/tut2.ipynb

apache-2.0

[ "Introduction\nEven if you're new to Connect Four, you've likely developed several game-playing strategies. In this tutorial, you'll learn to use a heuristic to share your knowledge with the agent. \nGame trees\nAs a human player, how do you think about how to play the game? How do you weigh alternative moves?\n...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

charliememory/AutonomousDriving

CarND-Advanced-Lane-Lines/src/.ipynb_checkpoints/camera_calibration-checkpoint.ipynb

gpl-3.0

[ "%%HTML\n<style> code {background-color : pink !important;} </style>", "Camera Calibration with OpenCV\nRun the code in the cell below to extract object points and image points for camera calibration.", "import numpy as np\nimport cv2\nimport glob\nimport matplotlib.pyplot as plt\n%matplotlib qt\n\n# prepare ob...

[ "code", "markdown", "code", "markdown", "code" ]

johnpfay/environ859

06_WebGIS/Notebooks/GeocodingWithOSM.ipynb

gpl-3.0

[ "Geocoding using the Open Street Map API\nHere we explore an example of using an Application Programming Interface, or API. Briefly, an API is a set of commands we can send over the internet to a remote server, spurring the server to process these commands and return a response. In this example, we'll explore how w...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

statsmodels/statsmodels.github.io

v0.13.0/examples/notebooks/generated/variance_components.ipynb

bsd-3-clause

[ "Variance Component Analysis\nThis notebook illustrates variance components analysis for two-level\nnested and crossed designs.", "import numpy as np\nimport statsmodels.api as sm\nfrom statsmodels.regression.mixed_linear_model import VCSpec\nimport pandas as pd", "Make the notebook reproducible", "np.random....

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

agrc/Presentations

UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb

mit

[ "Ditch the Cursor\n\nEditing Feature Classes with Spatialy-Enabled DataFrames\nArcPy Is Great, But...\n\nProblem one: row[0]\n```python\ndef update_year_built(layer, year_fields):\n with arcpy.da.UpdateCursor(layer, year_fields) as cursor:\n for row in cursor:\n if row[0] is None or row[0] < 1 ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

blink1073/oct2py

example/octavemagic_extension.ipynb

mit

[ "octavemagic: Octave inside IPython\nInstallation\nThe octavemagic extension provides the ability to interact with Octave. It is provided by the oct2py package,\nwhich may be installed using pip or easy_install.\nTo enable the extension, load it as follows:", "%load_ext oct2py.ipython", "Overview\nLoading the ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

broundy/udacity

nanodegrees/deep_learning_foundations/unit_2/project_2/dlnd_image_classification.ipynb

unlicense

[ "Image Classification\nIn this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

maciejkula/triplet_recommendations_keras

triplet_keras.ipynb

apache-2.0

[ "Recommendations in Keras using triplet loss\nAlong the lines of BPR [1]. \n[1] Rendle, Steffen, et al. \"BPR: Bayesian personalized ranking from implicit feedback.\" Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2009.\nThis is implemented (more efficiently) in Li...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

biof-309-python/BIOF309-2016-Fall

Week_06/Week 06 - 02 - Conditionals.ipynb

mit

[ "Conditions\nSource: This material adapted from the Python for Biologists website.\nConditions, True and False\nA condition is simply a bit of code that can produce a true or false answer. The easiest way to understand how conditions work in Python is try out a few examples. The following example prints out the res...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

CalPolyPat/phys202-2015-work

assignments/assignment03/NumpyEx04.ipynb

mit

[ "Numpy Exercise 4\nImports", "import numpy as np\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport seaborn as sns", "Complete graph Laplacian\nIn discrete mathematics a Graph is a set of vertices or nodes that are connected to each other by edges or lines. If those edges don't have directionality, th...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

survey-methods/samplics

docs/source/tutorial/replicate_weights.ipynb

mit

[ "Replicate weights\nReplicate weights are usually created for the purpose of variance (uncertainty) estimation. One common use case for replication-based methods is the estimation of non-linear parameters fow which Taylor-based approximation may not be accurate enough. Another use case is when the number of primary...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

basnijholt/orbitalfield

Phase-diagrams.ipynb

bsd-2-clause

[ "Phase diagram for multiple angles\nStart a ipcluster from the Cluster tab in Jupyter or use the command:\nipcluster start \nin a terminal.", "from ipyparallel import Client\ncluster = Client()\ndview = cluster[:]\ndview.use_dill()\nlview = cluster.load_balanced_view()\nlen(dview)", "This next cell is for inter...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

wheeler-microfluidics/mr-box-peripheral-board.py

mr_box_peripheral_board/notebooks/Streaming plot demo.ipynb

mit

[ "Table of Contents\n<p><div class=\"lev1 toc-item\"><a href=\"#Embedded-in-Jupyter-notebook\" data-toc-modified-id=\"Embedded-in-Jupyter-notebook-1\"><span class=\"toc-item-num\">1  </span>Embedded in Jupyter notebook</a></div><div class=\"lev1 toc-item\"><a href=\"#Using-GTK\" data-toc-modified-id=\"Usin...

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

quantumlib/ReCirq

docs/qaoa/binary_paintshop.ipynb

apache-2.0

[ "Copyright 2021 The Cirq Developers", "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

tensorflow/docs-l10n

site/zh-cn/hub/tutorials/bangla_article_classifier.ipynb

apache-2.0

[ "Copyright 2019 The TensorFlow Hub Authors.\nLicensed under the Apache License, Version 2.0 (the \"License\");", "# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the Lic...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

usantamaria/iwi131

ipynb/23-ProcesamientoDeTexto/Texto.ipynb

cc0-1.0

[ "\"\"\"\nIPython Notebook v4.0 para python 2.7\nLibrerías adicionales: Ninguna.\nContenido bajo licencia CC-BY 4.0. Código bajo licencia MIT. (c) Sebastian Flores.\n\"\"\"\n\n# Configuracion para recargar módulos y librerías \n%reload_ext autoreload\n%autoreload 2\n\nfrom IPython.core.display import HTML\n\nHTML(op...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...

kdheepak/psst

docs/notebooks/interactive_visuals/Demo.ipynb

mit

[ "How NetworkModel Works", "import pandas as pd\nimport numpy as np\n\nfrom psst.network.graph import (\n NetworkModel, NetworkViewBase, NetworkView\n)\n\nfrom psst.case import read_matpower\ncase = read_matpower('../cases/case118.m')", "I. Creating a NetworkModel", "# Create the model from a PSSTCase, opti...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

JAmarel/LiquidCrystals

ElectroOptics/MinimizeAttempt.ipynb

mit

[ "import numpy as np\nfrom scipy.integrate import quad, dblquad\n%matplotlib inline\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import minimize\n\nthetamin = 25.6*np.pi/180\nthetamax = 33.7*np.pi/180\nt = 1*10**-6 #Cell Thickness", "Data", "tempsC = np.array([26, 27, 29, 31, 33, 35, 37])\nvoltages = np...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

bgruening/EDeN

examples/Sequence_example.ipynb

gpl-3.0

[ "Example\nConsider sequences that are increasingly different. EDeN allows to turn them into vectors, whose similarity is decreasing.", "%matplotlib inline", "Build an artificial dataset: starting from the string 'abcdefghijklmnopqrstuvwxyz', generate iteratively strings by swapping two characters at random. In ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

barjacks/pythonrecherche

Kursteilnehmer/Sven Millischer/06 /01 Rückblick For-Loop-Übungen.ipynb

mit

[ "10 For-Loop-Rückblick-Übungen\nIn den Teilen der folgenden Übungen habe ich den Code mit \"XXX\" ausgewechselt. Es gilt in allen Übungen, den korrekten Code auszuführen und die Zelle dann auszuführen. \n1.Drucke alle diese Prim-Zahlen aus:", "primes = [2, 3, 5, 7]\nfor prime in primes:\n print(prime)", "2.D...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

NelisW/ComputationalRadiometry

03-Introduction-to-Radiometry.ipynb

mpl-2.0

[ "3 Brief Introduction to Radiometry\nThis notebook forms part of a series on computational optical radiometry \nThe date of this document and module versions used in this document are given at the end of the file.\nFeedback is appreciated: neliswillers at gmail dot com.\nOverview", "from IPython.display import ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

getsmarter/bda

module_2/M2_NB1_SourcesOfData.ipynb

mit

[ "<div align=\"right\">Python 3.6 Jupyter Notebook</div>\n\nSources of data\nYour completion of the notebook exercises will be graded based on your ability to do the following:\n\nApply: Are you able to execute code (using the supplied examples) that performs the required functionality on supplied or generated data ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

sysid/nbs

cnn/tw_vgg16.ipynb

mit

[ "Using Convolutional Neural Networks\nThis is running on theano!\nWelcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.\nIntroduction to this week's task: 'Dogs...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

deepmind/dm_control

tutorial.ipynb

apache-2.0

[ "dm_control tutorial\n\n\n<p><small><small>Copyright 2020 The dm_control Authors.</small></p>\n<p><small><small>Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at <a href=\"http://www.apache.org/l...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

huongttlan/statsmodels

examples/notebooks/statespace_sarimax_internet.ipynb

bsd-3-clause

[ "SARIMAX: Model selection, missing data\nThe example mirrors Durbin and Koopman (2012), Chapter 8.4 in application of Box-Jenkins methodology to fit ARMA models. The novel feature is the ability of the model to work on datasets with missing values.", "%matplotlib inline\n\nimport numpy as np\nimport pandas as pd\...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

UDST/activitysim

activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb

bsd-3-clause

[ "Estimating Mandatory Tour Frequency\nThis notebook illustrates how to re-estimate a single model component for ActivitySim. This process \nincludes running ActivitySim in estimation mode to read household travel survey files and write out\nthe estimation data bundles used in this notebook. To review how to do so...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

GoogleCloudPlatform/vertex-ai-samples

notebooks/community/managed_notebooks/pricing_optimization/pricing-optimization.ipynb

apache-2.0

[ "Pricing Optimization\nTable of contents\n\nOverview\nDataset\nObjective\nCosts\nCreate a BigQuery dataset\nLoad the dataset from Cloud Storage\nData analysis\nPreprocess the data for training\nTrain the model using BigQuery ML\nGenerate forecasts from the model\nInterpret the results to choose the best price\nClea...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

slundberg/shap

notebooks/image_examples/image_classification/Multi-input Gradient Explainer MNIST Example.ipynb

mit

[ "Multi-input Gradient Explainer MNIST Example\nHere we demonstrate how to use GradientExplainer when you have multiple inputs to your Keras/TensorFlow model. To keep things simple but also mildly interesting we feed two copies of MNIST into our model, where one copy goes into a conv-net layer and the other copy goe...

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

ES-DOC/esdoc-jupyterhub

notebooks/cccma/cmip6/models/canesm5/atmoschem.ipynb

gpl-3.0

[ "ES-DOC CMIP6 Model Properties - Atmoschem\nMIP Era: CMIP6\nInstitute: CCCMA\nSource ID: CANESM5\nTopic: Atmoschem\nSub-Topics: Transport, Emissions Concentrations, Gas Phase Chemistry, Stratospheric Heterogeneous Chemistry, Tropospheric Heterogeneous Chemistry, Photo Chemistry. \nProperties: 84 (39 required)\nMode...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

kubeflow/pipelines

components/gcp/dataflow/launch_template/sample.ipynb

apache-2.0

[ "Name\nData preparation by using a template to submit a job to Cloud Dataflow\nLabels\nGCP, Cloud Dataflow, Kubeflow, Pipeline\nSummary\nA Kubeflow Pipeline component to prepare data by using a template to submit a job to Cloud Dataflow.\nDetails\nIntended use\nUse this component when you have a pre-built Cloud Dat...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

eric-svds/flask-with-docker

app/.ipynb_checkpoints/my_notebook-checkpoint.ipynb

gpl-2.0

[ "Sample PCA analysis with Iris dataset\nThe following are required for this notebook:\n- pip install matplotlib\n- pip install scikit-learn\nThis notebook plots (and pickles) the Iris data set before and after Principal Component Analysis. Output is intended to be imported by a Flask application and passed to an HT...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

sbenthall/bigbang

examples/obsolete_notebooks/SummerSchoolCompareWordRankings.ipynb

agpl-3.0

[ "from bigbang.archive import Archive\nfrom bigbang.archive import load as load_archive\nimport os\nimport pandas as pd\nimport numpy as np\n\n\nietf_path = \"../archives/\"\nncuc_path = \"../archives/http:/lists.ncuc.org/pipermail\"\n\npaths = [os.path.join(ietf_path,\"6lo.csv\"),\n os.path.join(ietf_path,\"...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

antoniomezzacapo/qiskit-tutorial

community/aqua/chemistry/LiH_with_qubit_tapering_and_uccsd.ipynb

apache-2.0

[ "# import common packages\nfrom collections import OrderedDict\nimport itertools\nimport logging\n\nimport numpy as np\nimport scipy\n\nfrom qiskit_aqua import (get_algorithm_instance, get_optimizer_instance, \n get_variational_form_instance, get_initial_state_instance, Operator)\nfrom qiskit...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

ondrolexa/sg2

14_Simultaneous_deformation.ipynb

mit

[ "Simultaneous deformation", "%pylab inline\n\nfrom sg2lib import *", "Naive concept of simultaneous deformation\nHere we try to split simple shear and pure shear to several incremental steps and mutually superposed those increments to simulate simultaneous deformation. We will use following deformation gradient...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

QuantStack/quantstack-talks

2019-05-22-pydata-frankfurt/notebooks/bqplot.ipynb

bsd-3-clause

[ "bqplot https://github.com/bloomberg/bqplot\nA Jupyter - d3.js bridge\nbqplot is a jupyter interactive widget library bringing d3.js visualization to the Jupyter notebook.\n\nApache Licensed\n\nbqplot implements the abstractions of Wilkinson’s “The Grammar of Graphics” as interactive Jupyter widgets.\nbqplot provid...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

kubernetes-client/python

examples/notebooks/intro_notebook.ipynb

apache-2.0

[ "Managing kubernetes objects using common resource operations with the python client\nSome of these operations include;\n\n\ncreate_xxxx : create a resource object. Ex create_namespaced_pod and create_namespaced_deployment, for creation of pods and deployments respectively. This performs operations similar to kubec...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

tpin3694/tpin3694.github.io

statistics/t-tests.ipynb

mit

[ "Title: T-Tests\nSlug: t-tests\nSummary: T-tests in Python. \nDate: 2016-02-08 12:00\nCategory: Statistics\nTags: Basics\nAuthors: Chris Albon \nPreliminaries", "from scipy import stats\nimport numpy as np", "Create Data", "# Create a list of 20 observations drawn from a random distribution \n# with mean 1 ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

fedor1113/LineCodes

Decoder.ipynb

mit

[ "Decode line codes in png graphs\nAssumptions (format):\n\nThe clock is given and it is a red line on the top.\nThe signal line is black\n...", "# Makes sure to install PyPNG image handling module\nimport sys\n!{sys.executable} -m pip install pypng\n\nimport png\n\nr = png.Reader(\"ex.png\")\nt = r.asRGB()\n\nimg...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

jorisvandenbossche/DS-python-data-analysis

notebooks/case4_air_quality_processing.ipynb

bsd-3-clause

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

LFPy/LFPy

examples/LFPy-example-02.ipynb

gpl-3.0

[ "%matplotlib inline", "Example 2: Extracellular response of synaptic input\nThis is an example of LFPy running in a Jupyter notebook. To run through this example code and produce output, press <shift-Enter> in each code block below.\nFirst step is to import LFPy and other packages for analysis and plotting:...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

teoguso/sol_1116

cumulant-to-pdf.ipynb

mit

[ "Best report ever\nEverything you see here is either markdown, LaTex, Python or BASH.\nThe spectral function\nIt looks like this:\n\\begin{equation}\n A(\\omega) = \\mathrm{Im}|G(\\omega)|\n\\end{equation}\nGW vs Cumulant\nMathematically very different:\n\\begin{equation}\n G^{GW} (\\omega) = \\frac1{ \\omega - \\e...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

NLP-Deeplearning-Club/Classic-ML-Methods-Algo

ipynbs/appendix/ensemble/voting.ipynb

mit

[ "投票\n投票是最简单最基本的集成方式,核心思想也很朴素:大家伙投票决定结果.\n其原理是结合了多个不同的机器学习分类器,并且采用多数表决(硬投票)或者平均预测概率(软投票)的方式来预测分类标签.这样的分类器可以用于一组同样表现良好的模型,以便平衡它们各自的弱点.\n使用sklearn做投票\nsklearn提供了用于投票的接口sklearn.ensemble.VotingClassifier.下面的例子可以大体了解如何使用投票接口", "import numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

tensorflow/docs-l10n

site/en-snapshot/addons/tutorials/optimizers_cyclicallearningrate.ipynb

apache-2.0

[ "Copyright 2021 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

mne-tools/mne-tools.github.io

dev/_downloads/5b9edf9c05aec2b9bb1f128f174ca0f3/40_cluster_1samp_time_freq.ipynb

bsd-3-clause

[ "%matplotlib inline", "Non-parametric 1 sample cluster statistic on single trial power\nThis script shows how to estimate significant clusters\nin time-frequency power estimates. It uses a non-parametric\nstatistical procedure based on permutations and cluster\nlevel statistics.\nThe procedure consists of:\n\next...

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

jasonding1354/pyDataScienceToolkits_Base

Scikit-learn/.ipynb_checkpoints/(3)linear_regression-checkpoint.ipynb

mit

[ "内容概要\n\n如何使用pandas读入数据\n如何使用seaborn进行数据的可视化\nscikit-learn的线性回归模型和使用方法\n线性回归模型的评估测度\n特征选择的方法\n\n作为有监督学习，分类问题是预测类别结果，而回归问题是预测一个连续的结果。\n1. 使用pandas来读取数据\nPandas是一个用于数据探索、数据处理、数据分析的Python库", "import pandas as pd\n\n# read csv file directly from a URL and save the results\ndata = pd.read_csv('http://www-bcf.usc.edu/~...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

atlury/deep-opencl

cs480/23 Linear Dimensionality Reduction.ipynb

lgpl-3.0

[ "$\\newcommand{\\xv}{\\mathbf{x}}\n\\newcommand{\\Xv}{\\mathbf{X}}\n\\newcommand{\\yv}{\\mathbf{y}}\n\\newcommand{\\Yv}{\\mathbf{Y}}\n\\newcommand{\\zv}{\\mathbf{z}}\n\\newcommand{\\av}{\\mathbf{a}}\n\\newcommand{\\Wv}{\\mathbf{W}}\n\\newcommand{\\wv}{\\mathbf{w}}\n\\newcommand{\\betav}{\\mathbf{\\beta}}\n\\newcomm...

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]