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    "info": {
        "author": "Nathaniel Saul",
        "author_email": "nathaniel.saul@wsu.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Education",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Healthcare Industry",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "# Scikit-TDA\n\nThere is a growing need for an ecosystem of TDA libraries that is approachable to non-researchers. This project aims to provide a curated library for Python tools that are widely usable and easily approachable. Each is easy to install through traditional Python mechanisms, portable to all platforms, requires no dependencies outside of what is available on Pypi, has comprehensive documentation , is open source, provides an issue tracker and is responsive to questions, and exposes an intuitive API for developers familiar with the Python scientific computing ecosystem.\n\nEach project can stand alone, or be used as part of the scikit-tda bundle. This project curates the group of packages and houses extensive documentation and examples on how each package can be used together.\n\nScikit-TDA is a home for compatible TDA libraries intended for non-researchers. We provide detailed documentation and unified APIs so that using TDA can be used in the wild. The TDA ecosystem is rapidly growing. Below is the list of current projects, either built or in development, to be included in scikit-tda.\n\n- [Ripser](https://pypi.org/project/ripser/) - Data to diagrams in one line\n- [Persim](https://pypi.org/project/persim/) - Easy Persistence Images\n- [UMAP](https://pypi.org/project/umap-learn/) - Mathematically justified dimensionality reduction\n- [Kepler Mapper](https://pypi.org/project/kmapper/) - Mapper framework integrated into sklearn\n\n\nThe following packages are currently in development:\n\n- Cechmate - Custom filtrations builder\n- Diagrams - Comparison & Visualization of diagrams\n- TaDAsets - Data sets designed for TDA\n\n\nTo install all these libraries\n```\n    pip install scikit-tda\n```\n\n## Contributions\n\nThis project is entirely a work in progress and still in the coneptual phase. We hope to assemble an ecosystem of TDA libraries, complete with documentation and examples, that is approachable to people outside the field of Algebraic Topology.  If you would like to contribute and have ideas for how to do so, please reach out!\n\n\n",
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        "keywords": "topology data analysis,algebraic topology,unsupervised learning,persistent homology,persistence images,persistence diagrams,uniform manifold approximation and projection,sheaf theory,mapper,data visualization",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "scikit-tda",
        "package_url": "https://pypi.org/project/scikit-tda/",
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        "summary": "Topological Data Analysis for humans",
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