ENH: numpy depends, removed majority of the comments, inlined common_deps
Thanks  for the review!
-# Use
-# python -c 'from jinja2 import Template; print(Template(open("meta.yaml").read()).render())'
-# to see templating interpolated
{% set name = "pymvpa2" %}
{% set version = "2.6.1" %}
{% set sha256 = "206fa302fc6292831c50d6ffe9b1a95fc048380b0701c140eb7300dcd017052a" %}
-# Common dependencies between run and build (Yarik hates duplication)
-{% set common_deps = "
- - python
- - numpy \
-" %}
package:
name: {{ name|lower }}
@@ -26,15 +18,21 @@ build:
# TODO: entry_points -- well, we have scripts
requirements:
- build: {{common_deps}}
+ build:
+ - python
- setuptools
- swig
- toolchain
- run: {{common_deps}}
+ - numpy 1.9.* # [not (win and py36)]
+ - numpy 1.11.* # [win and py36]
+ run:
+ - python
- h5py
- joblib
- matplotlib
- nibabel
+ - numpy >=1.9 # [not (win and py36)]
+ - numpy >=1.11 # [win and py36]
- scikit-learn
- scipy
- statsmodels
@@ -56,13 +54,9 @@ about:
home: http://pymvpa.org
license: MIT
license_family: MIT
- # It is strongly encouraged to include a license file in the package,
- # (even if the license doesn't require it) using the license_file entry.
- # See http://conda.pydata.org/docs/building/meta-yaml.html#license-file
license_file: COPYING
summary: 'Python multivariate pattern analysis framework'
- # The remaining entries in this section are optional, but recommended
description: |
PyMVPA eases pattern classification analyses of large datasets, with an
accent on neuroimaging. It provides high-level abstraction of typical
