Merge branch 'dev' of https://github.com/USGS-Astrogeology/autocnet into voronoi

language: python

sudo: false

branches:

only:

  - master

python:

  - "3.5"

env:

  global:

    - BINSTAR_USER: jlaura

  matrix:

    - PYTHON_VERSION: 3.5

os:

  - linux

  - osx

before_install:

install:

  # We do this conditionally because it saves us some downloading if the

  # version is the same.

  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then

  - if [ "$TRAVIS_OS_NAME" == "linux" ]; then

      if [ "$PYTHON_VERSION" == 2.7 ]; then

        wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh;

      else

        wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;

      fi

    else

      if ["$PYTHON_VERSION" == 2.7]; then

        curl -o miniconda.sh  https://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh;

      else

        curl -o miniconda.sh  https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh;

      fi

    fi

  - bash miniconda.sh -b -p $HOME/miniconda

  - export PATH="$HOME/miniconda/bin:$PATH"

  - hash -r

  # Useful for debugging any issues with conda

  - conda info -a

  # Create a virtual env and install dependencies

  - conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION nose numpy pillow scipy pandas networkx scikit-image sqlalchemy numexpr dill cython pyyaml

  # Activate the env

  - source activate test-env

  # Install the non-conda packages if required, requirements.txt duplicates are ignored

  - conda install -c https://conda.anaconda.org/jlaura opencv3=3.0.0

  - conda install -c menpo cyvlfeat

  # Install dependencies

  - conda config --add channels conda-forge

  - conda install runipy

  - conda install -c https://conda.anaconda.org/conda-forge gdal h5py

  - pip install -r requirements.txt

  - conda config --add channels menpo

  - conda config --add channels jlaura

  - conda install -c conda-forge numpy

  - conda install -c jlaura plio opencv3=3.0.0

  - conda install -c conda-forge vlfeat

  - conda install -c menpo cyvlfeat

  - pip install pillow pysal

  - conda install scipy networkx numexpr dill cython pyyaml matplotlib runipy

  # Development installation

  - conda install nose coverage

  - conda install nose coverage sh anaconda-client

  - pip install coveralls

  - python runipynbs.py

    # Straight from the menpo team

  - if [["$TRAVIS_OS_NAME" == "osx"]]; then

      curl -o condaci.py https://raw.githubusercontent.com/menpo/condaci/v0.4.8/condaci.py;

    else

      wget https://raw.githubusercontent.com/menpo/condaci/v0.4.8/condaci.py -O condaci.py;

    fi

    # Build autocnet and push to anaconda cloud

  - python condaci.py setup

script:

  - nosetests --with-coverage --cover-package=autocnet

after_success:

  # Upload to anaconda and push to coveralls

  - ~/miniconda/bin/python condaci.py build ./conda

  - coveralls

notifications:

  webhooks:

    urls:

 #     - https://webhooks.gitter.im/e/6cbe4b65fff6764ed80f

    on_success: always  

    on_failure: always  

    on_start: never     

  email:

      recipients:

#          - kberry@usgs.gov

#          - dmlytle@usgs.gov

#          - jwbacker@usgs.gov

#          - krodriguez@usgs.gov

      - https://webhooks.gitter.im/e/6cbe4b65fff6764ed80f

    on_success: always

    on_failure: always

    :target: http://autocnet.readthedocs.org/en/latest/

    :alt: Documentation Status

R&D for automated control network generation.

Automated sparse control network generation to support photogrammetric control of planetary image data.

* Documentation: https://autocnet.readthedocs.org.

Developer TODOs

---------------

* Ensure installation requirements are met

    * Watch for the OpenCV dependency

* Read comparing workflows (https://www.atlassian.com/git/tutorials/comparing-workflows)

Credits

---------

Tools used in rendering this package:

*  Cookiecutter_

*  `cookiecutter-pypackage`_

.. _Cookiecutter: https://github.com/audreyr/cookiecutter

.. _`cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage

Installation Instructions

-------------------------

We suggest using Anaconda Python to install Autocnet within a virtual environment.  These steps will walk you through the process.

1. [Download](https://www.continuum.io/downloads) and install the Python 3.x Miniconda installer.  Respond ``Yes`` when

   prompted to add conda to your BASH profile.

1. (Optional) We like to sequester applications in their own environments to avoid any dependency conflicts.  To do this:

  * ``conda create -n <your_environment_name> python=3 && source activate <your_environment_name>``

1. Bring up a command line and add three channels to your conda config (``~/condarc``):

  * ``conda config --add channels conda-forge``

  * ``conda condig --add channels jlaura``

  * ``conda config --add channels menpo``

1. Finally, install autocnet: ``conda install -c jlaura autocnet-dev``

    fullname = os.path.join(dirname, filename)

    return fullname

import autocnet.examples

import autocnet.camera

import autocnet.cg

import autocnet.control

import autocnet.graph

import autocnet.matcher

import autocnet.transformation

import autocnet.utils

import autocnet.utils

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