Metadata-Version: 1.1
Name: DeepGraph
Version: 0.2.2
Summary: Analyze Data with Pandas-based Networks.
Home-page: https://github.com/deepgraph/deepgraph/
Author: Dominik Traxl
Author-email: dominik.traxl@posteo.org
License: BSD
Download-URL: https://github.com/deepgraph/deepgraph/tarball/v0.2.2
Description: 
        |Anaconda Version| |Anaconda Downloads| |Documentation| |PyPi|
        
        DeepGraph
        =========
        
        DeepGraph is a scalable, general-purpose data analysis package. It implements a
        `network representation <https://en.wikipedia.org/wiki/Network_theory>`_ based
        on `pandas <http://pandas.pydata.org/>`_
        `DataFrames <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html>`_
        and provides methods to construct, partition and plot networks, to interface
        with popular network packages and more.
        
        It is based on a new network representation introduced
        `here <http://arxiv.org/abs/1604.00971>`_. DeepGraph is also capable of
        representing
        `multilayer networks <http://deepgraph.readthedocs.io/en/latest/tutorials/terrorists.html>`_.
        
        
        Main Features
        -------------
        
        This network package is targeted specifically towards
        `Pandas <http://pandas.pydata.org/>`_ users. Utilizing one of Pandas' primary
        data structures, the
        `DataFrame <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html>`_,
        we represent the (super)nodes of a graph by one set of tables, and their
        pairwise relations (i.e. the (super)edges of a graph) by another set of tables.
        DeepGraph's main features are
        
        - `Create edges <https://deepgraph.readthedocs.io/en/latest/api_reference.html#creating-edges>`_:
          Methods that enable an iterative, yet
          vectorized computation of pairwise relations (edges) between nodes using
          arbitrary, user-defined functions on the nodes' properties. The methods
          provide arguments to parallelize the computation and control memory consumption,
          making them suitable for very large data-sets and adjustable to whatever
          hardware you have at hand (from netbooks to cluster architectures).
        
        - `Partition nodes, edges or a graph <https://deepgraph.readthedocs.io/en/latest/api_reference.html#graph-partitioning>`_:
          Methods to partition nodes,
          edges or a graph by the graph’s properties and labels, enabling the
          aggregation, computation and allocation of information on and between
          arbitrary *groups* of nodes. These methods also let you express
          elaborate queries on the information contained in a deep graph.
        
        - `Interfaces to other packages <https://deepgraph.readthedocs.io/en/latest/api_reference.html#graph-interfaces>`_:
          Methods to convert to common
          network representations and graph objects of popular Python network packages
          (e.g., SciPy sparse matrices, NetworkX graphs, graph-tool graphs).
        
        - `Plotting <https://deepgraph.readthedocs.io/en/latest/api_reference.html#plotting-methods>`_:
          A number of useful plotting methods for networks,
          including drawings on geographical map projections.
        
        
        Quick Start
        -----------
        
        DeepGraph can be installed via pip from
        `PyPI <https://pypi.python.org/pypi/deepgraph>`_
        
        ::
        
           $ pip install deepgraph
        
        or if you're using `Conda <http://conda.pydata.org/docs/>`_,
        install with
        
        ::
        
           $ conda install -c conda-forge deepgraph
        
        Then, import and get started with::
        
           >>> import deepgraph as dg
           >>> help(dg)
        
        
        Documentation
        -------------
        
        The official documentation is hosted here:
        http://deepgraph.readthedocs.io
        
        The documentation provides a good starting point for learning how
        to use the library. Expect the docs to continue to expand as time goes on.
        
        
        Development
        -----------
        
        So far the package has only been developed by me, a fact that I would like
        to change very much. So if you feel like contributing in any way, shape or
        form, please feel free to contact me, report bugs, create pull requestes,
        milestones, etc. You can contact me via email: dominik.traxl@posteo.org
        
        
        Bug Reports
        -----------
        
        To search for bugs or report them, please use the bug tracker:
        https://github.com/deepgraph/deepgraph/issues
        
        
        Citing DeepGraph
        ----------------
        
        Please acknowledge and cite the use of this software and its authors when
        results are used in publications or published elsewhere. You can use the
        following BibTex entry
        
        ::
        
           @Article{traxl-2016-deep,
               author      = {Dominik Traxl AND Niklas Boers AND J\"urgen Kurths},
               title       = {Deep Graphs - A general framework to represent and analyze
                              heterogeneous complex systems across scales},
               journal     = {Chaos},
               year        = {2016},
               volume      = {26},
               number      = {6},
               eid         = {065303},
               doi         = {http://dx.doi.org/10.1063/1.4952963},
               eprinttype  = {arxiv},
               eprintclass = {physics.data-an, cs.SI, physics.ao-ph, physics.soc-ph},
               eprint      = {http://arxiv.org/abs/1604.00971v1},
               version     = {1},
               date        = {2016-04-04},
               url         = {http://arxiv.org/abs/1604.00971v1}
           }
        
        Licence
        -------
        
        Distributed with a `BSD license <LICENSE.txt>`_::
        
            Copyright (C) 2017 DeepGraph Developers
            Dominik Traxl <dominik.traxl@posteo.org>
        
        
        .. |Anaconda Version| image:: https://anaconda.org/conda-forge/deepgraph/badges/version.svg
           :target: https://anaconda.org/conda-forge/deepgraph
        
        .. |Anaconda Downloads| image:: https://anaconda.org/conda-forge/deepgraph/badges/downloads.svg
           :target: https://anaconda.org/conda-forge/deepgraph
        
        .. |Anaconda Install| image:: https://anaconda.org/conda-forge/deepgraph/badges/installer/conda.svg
           :target: https://anaconda.org/conda-forge/deepgraph
        
        .. |Documentation| image:: https://readthedocs.org/projects/deepgraph/badge/?version=latest
            :target: http://deepgraph.readthedocs.io/en/latest/?badge=latest
        
        .. |PyPi| image:: https://badge.fury.io/py/DeepGraph.svg
            :target: https://badge.fury.io/py/DeepGraph
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Cython
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
