.travis.yml

language: python
python:
  # We don't actually use the Travis Python, but this keeps it organized.
  - "3.5"
install:
  # We do this conditionally because it saves us some downloading if the
  # version is the same.
  - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
  - bash miniconda.sh -b -p $HOME/miniconda
  - export PATH="$HOME/miniconda/bin:$PATH"
  - hash -r
  - conda config --set always_yes yes --set changeps1 no
  - conda update -q conda

  # Useful for debugging any issues with conda
  - conda info -a

  # Install Python and required packages.
  - conda env create -f environment.yml
  - source activate bayesian-analysis-recipes

  # Ensure that the kernel is installed into the compute environment.
  - python -m ipykernel install --user --name bayesian-analysis-recipes

script:
  # Create the docs directory. This is where we will be publishing from
  # (see the "deploy" section below).
  # - mkdir -p docs
  # All notebooks that need to be converted are inside nbconvert_config.py
  # We have to call on --execute - without doing so, the execution config
  # inside nbconvert_config.py won't run.
  # - jupyter nbconvert --config nbconvert_config.py --execute

  # Build docs using mkdocs
  - mkdocs build


# This is an example to deploy to a branch through Travis.
deploy:
  provider: pages
  skip-cleanup: true
  github-token: $GITHUB_TOKEN  # Set in the settings page of your repository, as a secure variable
  keep-history: true
  # We read the master branch
  on:
    branch: master
  # Take the docs/ directory
  local-dir: site
  # Publish to the gh-pages branch
  target-branch: gh-pages
  verbose: true