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    "info": {
        "author": "Carlos Ronchi and Lun Zhang",
        "author_email": "carloshvronchi@gmail.com",
        "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 :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "perscode\n===\n\nVectorization methods for persistence diagrams based in the paper [Persistence Codebooks for\nTopological Data Analysis](https://arxiv.org/abs/1802.04852).\n\n## Usage\n\n```python\nimport perscode\nimport numpy as np\n\n# generate diagrams\ndiagrams = [np.random.rand(100,2) for _ in range(20)]\nfor diagram in diagrams:\n    diagram[:,1] += diagram[:,0]\n\n# N is the size of the vectors\n# normalize is a Bool to whether or not normalize the output vector\npbow = perscode.PBoW(N = 3, normalize = False)\nwpbow = perscode.wPBoW(N = 3)\n# n_subsample is an int or None. If none all points will be used when calculating GMMs.\nspbow = perscode.sPBoW(N = 10, n_subsample = None)\n\n# vectorize diagrams\npbow_diagrams  = pbow.transform(diagrams)\nwpbow_diagrams = wpbow.transform(diagrams)\nspbow_diagrams = spbow.transform(diagrams)\n\n# for PVLAD and stable PVLAD\npvlad = perscode.PVLAD(N = 3)\nspvlad = perscode.sPVLAD(N = 3)\n\npvlad_diagrams = pvlad.transform(diagrams)\nspvlad_diagrams = spvlad.transform(diagrams)\n```\n\n## TODO\n- [x] Implement options to pass cluster centers as arguments in wPBoW and sPBoW.\n- [x] Implement PVLAD\n- [x] Implement sPVLAD\n- [ ] Implement PFV\n- [x] Implement optional weighted subsampling to wPBoW, sPBoW, sPVLAD classes.\n\n\n",
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        "home_page": "https://github.com/chronchi/perscode",
        "keywords": "persistent homology,persistence codebooks,persistence diagrams,topological data analysis,algebraic topology",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "perscode",
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        "summary": "Representation of persistence diagrams using persistence codebooks",
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