Metadata-Version: 2.1
Name: graphtools
Version: 1.5.1
Summary: graphtools
Home-page: https://github.com/KrishnaswamyLab/graphtools
Author: Scott Gigante, Daniel Burkhardt, and Jay Stanley, Yale University
Author-email: scott.gigante@yale.edu
License: GNU General Public License Version 2
Download-URL: https://github.com/KrishnaswamyLab/graphtools/archive/v1.5.1.tar.gz
Description: ==========
        graphtools
        ==========
        
        .. image:: https://img.shields.io/pypi/v/graphtools.svg
            :target: https://pypi.org/project/graphtools/
            :alt: Latest PyPi version
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            :target: https://anaconda.org/conda-forge/graphtools/
            :alt: Latest Conda version
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            :target: https://travis-ci.com/KrishnaswamyLab/graphtools
            :alt: Travis CI Build
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            :target: https://graphtools.readthedocs.io/
            :alt: Read the Docs
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            :alt: Code style: black
        
        Tools for building and manipulating graphs in Python.
        
        Installation
        ------------
        
        graphtools is available on `pip`. Install by running the following in a terminal::
        
            pip install --user graphtools
        
        Alternatively, graphtools can be installed using `Conda <https://conda.io/docs/>`_ (most easily obtained via the `Miniconda Python distribution <https://conda.io/miniconda.html>`_)::
        
            conda install -c conda-forge graphtools
        
        Or, to install the latest version from github::
        
            pip install --user git+git://github.com/KrishnaswamyLab/graphtools.git
        
        Usage example
        -------------
        
        The `graphtools.Graph` class provides an all-in-one interface for k-nearest neighbors, mutual nearest neighbors, exact (pairwise distances) and landmark graphs.
        
        Use it as follows::
        
            from sklearn import datasets
            import graphtools
            digits = datasets.load_digits()
            G = graphtools.Graph(digits['data'])
            K = G.kernel
            P = G.diff_op
            G = graphtools.Graph(digits['data'], n_landmark=300)
            L = G.landmark_op
        
        Help
        ----
        
        If you have any questions or require assistance using graphtools, please contact us at https://krishnaswamylab.org/get-help
        
Keywords: graphs,big-data,signal processing,manifold-learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Mathematics
Provides-Extra: test
Provides-Extra: doc
