semvec/pypi-packages huggingface dataset:
peartree 0.6.4
peartree is a library for converting GTFS to directed graphs.
License: MIT license
Keywords: peartree

=============
peartree 🍐🌳
=============

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        :target: https://gitter.im/peartree_transit

.. image:: https://img.shields.io/pypi/v/peartree.svg
        :target: https://pypi.python.org/pypi/peartree

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        :target: https://travis-ci.org/kuanb/peartree

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  :target: https://codecov.io/gh/kuanb/peartree

peartree is a library for converting `GTFS <https://developers.google.com/transit/gtfs/>`_ feed schedules into a representative directed network graph. The tool uses `Partridge <https://github.com/remix/partridge>`__ to convert the target operator schedule data into `Pandas <https://github.com/pandas-dev/pandas>`__ dataframes and then `NetworkX <https://networkx.github.io/>`_ to hold the manipulated schedule data as a directed multigraph.

.. image:: https://raw.githubusercontent.com/kuanb/peartree/master/examples/example.gif

Above, an example of multiple Bay Area transit operators being incrementally loaded into peartree.

Installation
------------

.. code:: console

    pip install peartree

Usage
-----

See a full notebook at `this gist <https://gist.github.com/kuanb/c54d0ae7ee353cac3d56371d3491cf56>`_ to see a simple, step-by-step iPython Notebook pulling in an AC Transit GTFS feed and converting it to a NetworkX graph.

.. code:: python

    import peartree as pt

    path = 'path/to/actransit_gtfs.zip'

    # Automatically identify the busiest day and
    # read that in as a Partidge feed
    feed = pt.get_representative_feed(path)

    # Set a target time period to
    # use to summarize impedance
    start = 7*60*60  # 7:00 AM
    end = 10*60*60  # 10:00 AM

    # Converts feed subset into a directed
    # network multigraph
    G = pt.load_feed_as_graph(feed, start, end)

Examples
--------

I've yet to produce a full how-to guide for this library, but will begin to populate this section with any blog posts or notebooks that I or others produce, that include workflows using peartree.

`Calculating betweeness centrality with Brooklyn bus network <http://kuanbutts.com/2017/12/13/betweenness-centrality-peartree/>`_

`Combining a peartree transit network and an OpenStreetMap walk network <http://kuanbutts.com/2018/12/24/peartree-with-walk-network/>`_

`Generating comparative acyclic route graphs <http://kuanbutts.com/2018/03/15/comparative-routes-mpl/>`_

`Coalescing transit network graphs and spectral clustering methods <http://kuanbutts.com/2018/04/01/spectral-cluster-transit/>`_

`Exploratory graph analysis with betweenness and load centrality <http://simplistic.me/playing-with-gtfs-iii-geo-graphs.html>`_