{
    "info": {
        "author": "Louis Abraham",
        "author_email": "louis.abraham@yahoo.fr",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "Topic :: Scientific/Engineering"
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        "description": "[![PyPI\nversion](https://badge.fury.io/py/tomaster.svg)](https://badge.fury.io/py/tomaster)\n[![Downloads](https://pepy.tech/badge/tomaster)](https://pepy.tech/project/tomaster)\n\n[![Build\nStatus](https://travis-ci.org/louisabraham/tomaster.svg?branch=master)](https://travis-ci.org/louisabraham/tomaster)\n\n# tomaster: Topological Mode Analysis on Steroids\n\n`tomaster` implements algorithms for topological mode analysis.\n\nThe code is simple to read because it is written in pure Python.\n\nThe performance is good thanks to jit compilation with\n[numba](https://numba.pydata.org/).\n\n# Usage\n\n``` pycon\n>>> from tomaster import tomato\n>>> from sklearn import datasets\n>>> X, y = datasets.make_moons(n_samples=1000, noise=0.05, random_state=1337)\n>>> clusters, _ = tomato(X, k=5, n_clusters=2)\n\n>>> import matplotlib.pyplot as plt\n>>> plt.scatter(*X.T, c=clusters)\n>>> plt.show()\n```\n\n![](https://raw.githubusercontent.com/louisabraham/tomaster/master/examples/moons.png)\n\n# Installation\n\n    pip install tomaster\n\n# Testing\n\n    pytest\n\n# API\n\n    def tomato(\n        points,\n        *,\n        k,\n        tau=None,\n        n_clusters=None,\n        relative_tau: bool = True,\n        keep_cluster_labels: bool = False,\n    ):\n        \"\"\"ToMATo clustering\n\n        Parameters\n        ----------\n\n        points : np.ndarray\n            Array of shape (n, dim)\n        k : int\n            Number of nearest neighbors to build the graph with\n        tau : float or None\n            Prominence threshold. Must not be specified if `n_clusters` is given.\n        relative_tau : bool\n            If `relative_tau` is set to `True`, `tau` will be multiplied by the standard deviation of the densities, making easier to have a unique value of `tau` for multiple datasets.       \n        n_clusters : int or None\n            Target number of clusters. Must not be specified if `tau` is given.\n        keep_cluster_labels : bool\n            If False, converts the labels to make them contiguous and start from 0.\n\n        Returns\n        -------\n\n        clusters : np.ndarray\n            Array of shape (n,) containing the cluster indexes.\n        tau : float\n            Prominence threshold. Only present if `n_clusters` was given.\n\n        \"\"\"\n\n    def tomato_img(\n        img: np.ndarray, *, spatial_weight: float = 0, lab_space: bool = True, **kwargs\n    ):\n        \"\"\"ToMATo for images\n\n        Parameters\n        ----------\n\n        img : np.ndarray\n            Image of shape (h, w) or (h, w, 3)\n        spatial_weight : float\n            Importance of the pixel positions in the distance function\n        lab_space : bool\n            If True, converts color images to the CIE L*a*b color space (<https://en.wikipedia.org/wiki/CIELAB_color_space>)\n\n        see tomato() for other arguments.\n\n        Returns\n        -------\n\n        clusters : np.ndarray\n            Array of shape (h, w) containing the cluster indexes.\n        \"\"\"\n\n# References\n\n  - Chazal, Fr\u00e9d\u00e9ric, Leonidas J. Guibas, Steve Y. Oudot, and Primoz\n    Skraba. \"Persistence-based clustering in riemannian manifolds.\"\n    Journal of the ACM (JACM) 60, no. 6 (2013): 41.\n    [\\[pdf\\]](https://geometrica.saclay.inria.fr/data/Steve.Oudot/clustering/jacm_oudot.pdf)\n\n  - Reference implementations:\n    <https://geometrica.saclay.inria.fr/data/ToMATo/>,\n    <https://geometrica.saclay.inria.fr/data/Steve.Oudot/clustering/>\n\n\n",
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