meta.yaml

{% set version = "0.2.0" %}
package:
  name: enspara
  version: {{ version }}

source:
  path: .
  
build:
  noarch: python
  number: 0
  script: | 
            pip install --progress-bar off .[dev]
            python setup.py build_ext --inplace
            python -m pip install . -vv

requirements:
  channels:
    - defaults
    - conda-forge
  host:
    - python
    - pip
    - nose
    - numpy>=1.13
    - mdtraj>=1.7
    - cython>=0.24
    - pytables>=3.2
    - matplotlib>=1.5.1
    - psutil>=5.2.2
    - pandas
    - scikit-learn>=0.21.0
  run:
    - python>=3.7
    - numpy>=1.13
    - mdtraj>=1.7
    - cython>=0.24
    - pytables>=3.2
    - matplotlib>=1.5.1
    - psutil>=5.2.2
    - pandas
    - scikit-learn>=0.21.0

test:
  requires:
    - python
    - pip
    - nose
    - numpy>=1.13
    - mdtraj>=1.7
    - cython>=0.24
    - pytables>=3.2
    - matplotlib>=1.5.1
    - psutil>=5.2.2
    - pandas
    - scikit-learn>=0.21.0
    - enspara
  imports:
    - enspara

about:
  home: https://github.com/bowman-lab/enspara
  license: GPLv3
  summary: 'MSMs at Scale'
  description: |
    Enspara is primarily a python library, but it also contains a few applications for very common Markov-state model related tasks.

    MSM estimation and manipulation
    Turn-key applications for clustering and analysis
    Transition path theory
    Information theory
    Ragged ndarrays
  dev_url: https://github.com/bowman-lab/enspara
  doc_url: https://enspara.readthedocs.io/en/latest/
  doc_source_url: https://github.com/bowman-lab/enspara/tree/master/docs