.travis.yml

# what language
language: python

# what versions of python
python:
  - "3.6"

# what branches should be evaluated
branches:
    only:
        - master

# commands to prepare the conda install - download the latest conda
# and install it and add to path
before_install:
    - pwd
    - curl -o miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
    - chmod +x miniconda.sh
    - ./miniconda.sh -b
    - export PATH=/home/travis/miniconda3/bin:$PATH
    - conda update --yes conda
    - source $(conda info --root)/etc/profile.d/conda.sh


install:
    - conda create --yes -n test-environment python=$TRAVIS_PYTHON_VERSION
    - conda activate test-environment
# install the dependencies of the cnns4qspr repo
    - conda install --yes pytorch torchvision -c pytorch
    - conda install --yes -c conda-forge biopandas
    - conda install --yes plotly

# go into the environment site-packages, install lie-learn
    - cd $(conda info --root)/envs/test-environment/lib/python3.6/site-packages/
    - git clone https://github.com/AMLab-Amsterdam/lie_learn
    - cd lie_learn
    - python setup.py install
    - cd ..
# installing the forked se3cnn repo from our CNNs4QSPR organization
# also in the site-packages
    - git clone https://github.com/CNNs4QSPR/se3cnn.git
    - cd se3cnn
    - python setup.py install
# installing things relevant to unit testing
    - conda install --yes -c anaconda coverage
    - conda install --yes -c conda-forge coveralls

script:
    - cd /home/travis/build/CNNs4QSPR/cnns4qspr
    - coverage run -m unittest discover

after_success:
    - coverage report
    - coveralls