Metadata-Version: 2.1
Name: graphtools
Version: 1.5.3
Summary: graphtools
Home-page: https://github.com/KrishnaswamyLab/graphtools
Download-URL: https://github.com/KrishnaswamyLab/graphtools/archive/v1.5.3.tar.gz
Author: Scott Gigante, Daniel Burkhardt, and Jay Stanley, Yale University
Author-email: scott.gigante@yale.edu
License: GNU General Public License Version 2
Keywords: graphs,big-data,signal processing,manifold-learning
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
License-File: LICENSE

==========
graphtools
==========

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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
