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Common Graph Algorithms Library

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Copyright (c) 2016 David McDougall

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Description: Common Graph Algorithms Library

Library of graph algorithms which operate directly on python data structures.

This library uses a novel API for representing graphs. Graph vertexes can be any hashable python value and the connectivity between vertexes is represented with a callback function. This callback is named the ‘adjacent’ function. The adjacent function has the following form:

def adjacent(vertex):
‘’’ This function returns all vertexes which the given vertex is connected to. ‘’’ return iterable-of-neighboring-vertexes

Contents:

depth_first_traversal()
A lazy depth first traversal
depth_first_search()
A depth first search
iterative_deepening_depth_first_search()
Searching infinite graphs
a_star()
Fast optimal pathfinding
topological_sort()
Dependency resolution.
strongly_connected_components()
Determines which areas of the graph can reach which other areas.

In the future I would like to implement more algorithms: - Minimum Spanning Tree - Min-cut/Max-flow - Substructure Search

Installation note: This package optionally uses numpy. Numpy is used by some unit tests. Numpy is used to calculate A-stars effective branching factor (EBF). If numpy is not available then EBF is not reported.

Comments and feedback are welcome Send to David McDougall email: dam1784[at]rit.edu

Keywords: development,graph Platform: UNKNOWN Classifier: Development Status :: 3 - Alpha Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: OS Independent Classifier: Intended Audience :: Developers Classifier: Topic :: Software Development Classifier: Topic :: Software Development :: Libraries Classifier: Topic :: Software Development :: Libraries :: Python Modules Classifier: Topic :: Utilities Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.5 Provides-Extra: debug Provides-Extra: test

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