.travis.yml

language:  python

os: linux

python: "2.7"

before_install:
        - sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
        - sudo apt-get -qq update
        - wget http://sourceforge.net/projects/boost/files/boost/1.55.0/boost_1_55_0.tar.bz2
        - tar --bzip2 -xf boost_1_55_0.tar.bz2
        - cd boost_1_55_0
        - ./bootstrap.sh --help
        - sudo ./bootstrap.sh --with-libraries="atomic"
        # Way too much output, so just display the last 50 lines.
        - sudo ./b2 install | tail -n 50
        - cd ..

        # configuration for python interface
        # install mini anaconda
        - wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
        - bash miniconda.sh -b -p $HOME/miniconda
        - export PATH="$HOME/miniconda/bin:$PATH"
        - hash -r
        - conda config --set always_yes yes --set changeps1 no
        - conda update -q conda
        # Useful for debugging any issues with conda
        - conda info -a
        # Replace dep1 dep2 ... with your dependencies
        - conda install python=$TRAVIS_PYTHON_VERSION numpy matplotlib h5py

install:
        - sudo apt-get install -y libfftw3-dev cmake
        - sudo apt-get install -y python-numpy python-matplotlib python-h5py libboost-python-dev
        - sudo apt-get install -qq g++-4.8
        - sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 50

        # install Boost.Numpy for python interface
        - cd python; git clone https://github.com/ndarray/Boost.NumPy.git
        - cd Boost.NumPy; cmake ./
        - make -j 8
        - sudo make install
        - cd ../../
        - export LD_LIBRARY_PATH=$LD_LIBRARY:/usr/local/lib64

script:
        # make znn binary
       - make --jobs=3 --keep-going
       - make clean
       # make python core
       - cd python/core/; make --jobs=3 --keep-going; cd ../../

       # check patch using single precision, this check will create the net_current.h5 file for testing loading
       - cd python
       # test affinity training patch, network initialization, network with even field of view
       - python train.py -c ../testsuit/affinity/config.cfg -d single -k yes
       # test boundary map training, patch matching, network initialization
       - python train.py -c ../testsuit/boundary/config.cfg -d single -k yes
       # second check to test the network loading
       - python train.py -c ../testsuit/boundary/config.cfg -d single -k yes
       # check the double precision
       # compile the core with double precision
       - cd core; make double -j 4
       # return to `python`
       - cd ..
       - python train.py -c ../testsuit/boundary/config.cfg -d double -k yes
       # test forward pass
       - python forward.py -c ../testsuit/forward/config.cfg
       # return to root directory
       - cd ..

notifications:
        email: false