Metadata-Version: 2.1
Name: mogutda
Version: 0.3.2
Summary: Topological Data Analysis in Python
Home-page: https://github.com/stephenhky/MoguTDA
Author: Kwan-Yuet Ho
Author-email: stephenhky@yahoo.com.hk
License: MIT
Description: ## Introduction
        
        `mogutda` contains Python codes that demonstrate the numerical calculation
        of algebraic topology in an application to topological data analysis
        (TDA). Its core code is the numerical methods concerning implicial complex,
        and the estimation of homology and Betti numbers.
        
        Topological data analysis aims at studying the shapes of the data, and
        draw some insights from them. A lot of machine learning algorithms deal
        with distances, which are extremely useful, but they miss the
        information the data may carry from their geometry.
        
        ## History
        
        The codes in this package were developed as a demonstration of a few posts of my blog.
        It was not designed to be a Python package but a pedagogical collection of codes.
        (See: [PyTDA](https://github.com/stephenhky/PyTDA).)
        However, the codes and the blog posts have been unexpectedly popular. Therefore,
        I modularized the code into the package [`mogu`](https://pypi.org/project/mogu/).
        (or corresponding repository: [MoguNumerics](https://github.com/stephenhky/MoguNumerics))
        However, `mogu` is simply a collection of unrelated numerical routines with a lot of
        dependencies, but the part of TDA can be quite independent.
        
        In order to provide other researchers and developers an independent package, which is compact (without
        unecessary alternative packages to load), and efficient, I decided to modularize
        the codes of TDA separately, and name this package `mogutda`.
        
        ## Prerequisite
        
        It runs under Python 3.5, 3.6, 3.7, and 3.8.
        
        Release 0.1.5 can work under `numpy`>0.16.0, but previous
        release will constitute error under the new `numpy`.
        
        ## Simple Tutorial: Simplicial Complex
        
        You can install by:
        
        ```
        pip install -U mogutda
        ```
        
        To establish a simplicial complex for a torus, type
        
        ```
        import numpy as np
        from mogutda import SimplicialComplex
        
        torus_sc = [(1,2,4), (4,2,5), (2,3,5), (3,5,6), (5,6,1), (1,6,2), (6,7,2), (7,3,2),
                    (1,3,4), (3,4,6), (4,6,7), (4,5,7), (5,7,1), (7,3,1)]
        torus_c = SimplicialComplex(simplices=torus_sc)
        ```
        
        To retrieve its Betti numbers, type:
        
        ```
        print(torus_c.betti_number(0))   # print 1
        print(torus_c.betti_number(1))   # print 2
        print(torus_c.betti_number(2))   # print 1
        ```
        
        ## Demo Codes and Blog Entries
        
        Codes in this repository are demo codes for a few entries of my blog,
        [Everything about Data Analytics](https://datawarrior.wordpress.com/),
        and the corresponding entries are:
        
        * [Starting the Journey of Topological Data Analysis (TDA)](https://datawarrior.wordpress.com/2015/08/03/tda-1-starting-the-journey-of-topological-data-analysis-tda/) (August 3, 2015)
        * [Constructing Connectivities](https://datawarrior.wordpress.com/2015/09/14/tda-2-constructing-connectivities/) (September 14, 2015)
        * [Homology and Betti Numbers](https://datawarrior.wordpress.com/2015/11/03/tda-3-homology-and-betti-numbers/) (November 3, 2015)
        * [Topology in Physics and Computing](https://datawarrior.wordpress.com/2015/11/10/mathanalytics-6-topology-in-physics-and-computing/) (November 10, 2015)
        * [Persistence](https://datawarrior.wordpress.com/2015/12/20/tda-4-persistence/) (December 20, 2015)
        * [Topological Phases](https://datawarrior.wordpress.com/2016/10/06/topological-phases/) (October 6, 2016)
        * [moguTDA: Python package for Simplicial Complex](https://datawarrior.wordpress.com/2018/07/02/mogutda-python-package-for-simplicial-complex/) (July 2, 2018)
        
        ## Wolfram Demonstration
        Richard Hennigan put a nice Wolfram Demonstration online explaining what
        the simplicial complexes are, and how homologies are defined:
        
        * [Simplicial Homology of the Alpha Complex](http://demonstrations.wolfram.com/SimplicialHomologyOfTheAlphaComplex/)
        
        ## News
        
        * 04/10/2021: `mogutda` 0.3.2 released.
        * 11/28/2020: `mogutda` 0.3.1 released.
        * 08/16/2020: `mogutda` 0.3.0 released.
        * 04/28/2020: `mogutda` 0.2.1 released.
        * 01/16/2020: `mogutda` 0.2.0 released.
        * 02/20/2019: `mogutda` 0.1.5 released.
        * 12/31/2018: `mogutda` 0.1.4 released.
        * 07/18/2018: `mogutda` 0.1.3 released.
        * 07/02/2018: `mogutda` 0.1.2 released.
        * 06/13/2018: `mogutda` 0.1.1 released.
        * 06/11/2018: `mogutda` 0.1.0 released.
        
        ## Other TDA Packages
        
        It is recommended that for real application, you should use the following packages
        for efficiency, because my codes serve the pedagogical purpose only.
        
        ### C++
        * [Dionysus](http://www.mrzv.org/software/dionysus/)
        * [PHAT](https://bitbucket.org/phat-code/phat)
        
        ### Python
        * [Dionysus](http://www.mrzv.org/software/dionysus/python/overview.html)
        
        ### R
        * [TDA](https://cran.r-project.org/web/packages/TDA/index.html) (article: [\[arXiv\]](http://arxiv.org/abs/1411.1830))
        
        ## Contributions
        
        If you want to contribute, feel free to fork the repository, and submit
        a pull request whenever you are ready.
        
        If you spot any bugs or issues, go to the [Issue](https://github.com/stephenhky/MoguTDA) page.
        
        I may not approve pull request immediately if your suggested change is big.
        If you want to incorporate something big, please discuss with me first.
        
        ## References
        * Afra J. Zomorodian. *Topology for Computing* (New York, NY: Cambridge University Press, 2009). [\[Amazon\]](https://www.amazon.com/Computing-Cambridge-Monographs-Computational-Mathematics/dp/0521136091)
        * Afra J. Zomorodian. "Topological Data Analysis," *Proceedings of Symposia in Applied Mathematics* (2011). [\[link\]](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.261.1298)
        * Afra Zomorodian, Gunnar Carlsson, “Computing Persistent Homology,” *Discrete Comput. Geom.* 33, 249-274 (2005). [\[pdf\]](http://geometry.stanford.edu/papers/zc-cph-05/zc-cph-05.pdf)
        * Gunnar Carlsson, “Topology and Data”, *Bull. Amer. Math. Soc.* 46, 255-308 (2009). [\[link\]](http://www.ams.org/journals/bull/2009-46-02/S0273-0979-09-01249-X/)
        * Jeffrey Ray, Marcello Trovati, "A Survey of Topological Data Analysis (TDA) Methods Implemented in Python," in *Advances in Intelligent Networking and Collaborative Systems*, Springer (2018).
        * P. Y. Lum, G. Singh, A. Lehman, T. Ishkanov, M. Vejdemo-Johansson, M. Alagappan, J. Carlsson, G. Carlsson, “Extracting insights from the shape of complex data using topology”, *Sci. Rep.* 3, 1236 (2013). [\[link\]](http://www.nature.com/srep/2013/130207/srep01236/full/srep01236.html)
        * Robert Ghrist, “Barcodes: The persistent topology of data,” *Bull. Amer. Math. Soc.* 45, 1-15 (2008). [\[pdf\]](http://www.ams.org/journals/bull/2008-45-01/S0273-0979-07-01191-3/S0273-0979-07-01191-3.pdf)
        
        ## Links
        
        * PyPI: [https://pypi.org/project/mogutda/](https://pypi.org/project/mogutda/)
        * Documentation: [https://mogutda.readthedocs.io/](https://mogutda.readthedocs.io/)
        * Bug Reports: [https://github.com/stephenhky/MoguTDA/issues](https://github.com/stephenhky/MoguTDA/issues)
        
Keywords: mogutda numerics topology data
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Description-Content-Type: text/markdown
