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        "author": "Martin Campos",
        "author_email": "tinotinocampos@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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            "Programming Language :: Python :: 2.7"
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        "description": "# Cubix\n\nCubix is a simple, 100% python written, module for computing persistent homology in alternative way. Given a data cloud *S* of **R**^n, it builds a simplicial cubic complex covering *S* and makes a filtration over this complex using a kernel density estimator (KDE) of *S*.  For a formal definition of the method and the simplicial cubic homology implemented, we redirect the reader to the paper 'Filtraciones en homolog\u00eda persistente mediante estimadores kernel de densidad' ---writen in Spanish--- available on the Github repository.\n\n## Dependencies\nCubix has very few dependences:\n* [numpy](https://pypi.org/project/numpy/) for numerical treatment\n* [scipy](https://pypi.org/project/scipy/) as it uses *scipy.stats.gaussian_kde()* as kernel function\n* [matplotlib](https://pypi.org/project/matplotlib/) for plotting\n\nAll of them are available in PyPI.\n\n## Install\nYou can easily install Cubix via *pip*:\n```\npip install cubix\n``` \n\n## Basic usage\nFirst of all, you must import the module:\n```python\nimport cubix \n``` \nThe second step is choosing the data cloud to analyze. Cubix class *Cloud* for these objects. You can create your cloud importing points from a CSV file just like: \n```python\nX = cubex.Cloud(csv=\"input_file.csv\")\n``` \nIf you have your *N* points of **R**^n stored in a numpy array (let's call it *array*) with shape *n x N* you can make a *Cloud* with them with:\n```python\nX = cubex.Cloud(data=array)\n``` \nAlternatively, Cubix has methods to generate random data clouds with some particular shapes: the spheres *S\u2070* (in **R**), *S\u00b9* (in **R**\u00b2) and *S\u00b2* (in **R**\u00b3) , the torus *T\u00b2* (in **R**\u00b3), the real projective spaces **RP**\u00b2 (in **R**\u2074), and de wedge sum of two spheres *S\u00b9vS\u00b9* (in  **R**\u00b2). These are subclasses of *Cloud* so you can easily instantiate a 2000-point cloud with *S\u00b2* shape like:\n```python\nX = cubex.S2(center=(2,1,4), r=5, err=0.1, N=2000)\n``` \nFor more information about the arguments accepted to instantiate this classes, please read the documentation of each one.\n\n*Cloud* class have some useful methods for plotting (when possible) and exporting data. Take a look at those 3 methods: \n```python\nX.plot()\nX.kde_plot()\nX.export_to_csv(\"output.csv\")\n``` \n\nOnce you have your cloud *X*, you can calculate the persistence homology of it. You just have to create a variable of the class *PersistentHomology* this way:\n```python\nh = X.persistent_homology()\n``` \n Of course, this will run the algorithm with default values. Arguments accepted by *persistence_homology* are:\n * *n* - precision of the cubic complex covering the cloud (number of cubes per direction of **R**^n). Default: 10.\n * *margin* - parameter to make the cubic complex bigger than the space occupied by the cloud. Ex: with *margin=0.1* the cubic complex will take a 10% more of space. Default: 0.1 \n * *pruning* - parameter to cut off the last (the most insignificant) cubes of the filtration in order to make the algorithm faster. Ex: *pruning=0.9* will keep only the 90% most significant cubes. Default: 0 (don't cut off).\n * *verbose* - If *True*, print by standard error the progress of the calculation. Default: False.\n\nFinally, you can see the results in three ways: a persistence diagram, a bar code or just explicitly printing out all born and death times:\n```python\nh.persitence_diagram()\nh.bar_code()\nh.detail()\n```\n\nFor more information, please check the documentation in the source code.\n\n\n",
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        "summary": "Persistent homology for data clouds using KDE",
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