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README.rst

peartree 🍐🌳

https://img.shields.io/travis/kuanb/peartree.svg?branch=master

peartree is a library for converting GTFS feed schedules into a representative directed network graph. The tool uses Partridge to convert the target operator schedule data into Pandas dataframes and then NetworkX to hold the manipulated schedule data as a directed multigraph.

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

pip install peartree

Usage

See a full notebook at this gist to see a simple, step-by-step iPython Notebook pulling in an AC Transit GTFS feed and converting it to a NetworkX graph.

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

Combining a peartree transit network and an OpenStreetMap walk network

Generating comparative acyclic route graphs

Coalescing transit network graphs and spectral clustering methods

Exploratory graph analysis with betweenness and load centrality

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