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        "author": "Christopher Oballe",
        "author_email": "coballejr@gmail.com",
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            "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 :: Mathematics"
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        "description": "# bayes_tda\n\nThis module contains classes to implement a marked Poisson process model for Bayesian inference with persistence diagrams. The model relies on mixed Gaussian assumptions. For a full description of the model, please refer to [https://arxiv.org/abs/1901.02034](https://arxiv.org/abs/1901.02034).\n\n\n# Installation\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install bayes_tda.\n\n```bash\npip install bayes_tda\n```\n# Classes\n\n| Class name| Description |Methods |\n|--|--|--|\n| WedgeGaussian |Gaussian density restricted to upper half of $\\mathbb{R}^2$.| eval|\n|Prior| Mixed Gaussian prior intensity.| eval\n|Posterior|Mixed Gaussian posterior intensity.| eval\n\n\n\n# Usage\n```python\nfrom bayes_tda import *\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = [0,0] # a point in birth-persistence coordinates\nwg = WedgeGaussian(mu = [0,0], sigma = 1) # Gaussian densities restricted to the upper half plane\nd = wg.eval(x) # evaluates the Gaussian density at x\n\nmeans = np.array([[0,0],[6,6]])\nss = [1,1]\nws = [1,1]\n\npri  = Prior(weights = ws,mus = means, sigmas = ss)\nd_pri = pri.eval(x)\n\nb = np.linspace(0,10,50)\np = np.linspace(0,10,50)\n\nB,P = np.meshgrid(b,p)\n\nZ = list()\nfor ind in range(len(P)):\n    l = list()\n    for i in range(len(P)):\n        l.append(pri.eval([B[ind][i],P[ind][i]]))\n    Z.append(l)\n        \nplt.style.use('seaborn-white')\nplt.contourf(B,P,Z, 20, cmap = 'twilight')\nplt.colorbar()\nplt.show()\n\nnoise = Prior(weights = [0], mus = [[30,30]], sigmas = [10])\npost = Posterior(prior = pri,clutter = noise,Dy = [[1,5],[5,1]], sy = 1)\npeval = post.eval(x)\n\nZ = list()\nfor ind in range(len(P)):\n    l = list()\n    for i in range(len(P)):\n        l.append(post.eval([B[ind][i],P[ind][i]]))\n    Z.append(l)\n        \nplt.style.use('seaborn-white')\nplt.contourf(B,P,Z, 20, cmap = 'twilight')\nplt.colorbar()\nplt.show()\n```\n## Reporting Bugs\nReport any bugs by opening an issue at [https://github.com/coballejr/bayes_tda/](https://github.com/coballejr/bayes_tda/).\n\n\n## License\n[MIT](https://choosealicense.com/licenses/mit/)",
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        "keywords": "Persistent Homology,Topological Data Analysis,Bayesian,Point process,Poisson process",
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