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    "info": {
        "author": "Michael Wilber",
        "author_email": "mwilber@mjwilber.org",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "License :: OSI Approved :: zlib/libpng License",
            "Operating System :: MacOS",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Cython",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering :: Visualization"
        ],
        "description": "Stochastic Neighbor and Crowd Kernel (SNaCK) embedding\n======================================================\nQuick and dirty visualization of large-scale datasets via concept embeddings\n\nInstallation\n============\nThe following platforms are supported:\n- Python 2.7 on Linux\n   - Binary packages available on Conda\n   - Source packages available from Pip\n- Python 2.7 on OSX\n   - Binary packages for Yosemite available on Conda\n   - Source packages available from Pip (Homebrew-GCC required)\n\nLinux: Install from Conda\n-------------------------\nJust run:\n    $ conda install -c https://conda.anaconda.org/gcr snack\n\nMac OS X: Install from Conda\n----------------------------\nTODO.\n\nLinux: Install from Pip\n-----------------------\nJust run:\n    $ pip install snack\n\nYou need to install Python 2.7, Numpy, and Cython. You also need a\nworking compiler, CBLAS, and the Python development headers, which are\ninstallable from your distribution's package manager.\n\nTo install SNaCK on a clean Ubuntu Trusty x64 system, run:\n\n    # sudo aptitude install \\\n      build-essential       \\\n      python-dev            \\\n      libblas3              \\\n      libblas-dev           \\\n      python-virtualenv\n    $ virtualenv venv; source venv/bin/activate\n    $ pip install numpy\n    $ pip install cython\n    $ pip install snack\n\nOS X: Install from Pip and Homebrew\n-----------------------------------\nIf you are on Mac OS X, you must install the real \"not-clang\" version\nof gcc because it has OpenMP support. At the time of writing, clang\ndoes not support OpenMP, and Apple has unhelpfully symlinked clang to\n`/usr/bin/gcc`. This is not sufficient.\n\nUsing Apple-provided GCC is NOT supported. If `gcc-5 --version`\ncontains the string `clang` anywhere in its output, you do not have\nthe correct version of gcc.\n\nUsing Apple-provided Python is NOT supported.\n\nThe recommended installation method on OS X is with Homebrew:\n\n    $ brew install gcc\n    $ brew install python\n    $ virtualenv venv; source venv/bin/activate\n    $ pip install numpy\n    $ pip install cython\n    $ pip install snack\n\nYou may need to edit `setup.py` and change `GCC_VERSION` to point to\nthe correct version, if you are not using `/usr/local/bin/gcc-5`.\n\n\nJust build without installing\n-----------------------------\n\nTo simply build Snack without installing it, run:\n\n    $ python setup.py build_ext --inplace\n\nThis builds `snack/_snack.so`. You can move the `snack` folder to your\nproject's directory and then `import snack`. This should work as long\nas the `snack` folder is inside your current directory.\n\nHow to use\n----------\n\nExamples\n--------\n\nSee also\n--------\n",
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        "home_page": "http://vision.cornell.edu/se3/projects/concept-embeddings/",
        "keywords": "snack embedding tsne visualization triplets tste",
        "license": "",
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        "name": "snack",
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        "summary": "Stochastic Neighbor and Crowd Kernel (SNaCK) embeddings: Quick and dirty visualization of large-scale datasets via concept embeddings",
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