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Mastering Python Data Visualization

You're reading from  Mastering Python Data Visualization

Product type Book
Published in Oct 2015
Publisher
ISBN-13 9781783988327
Pages 372 pages
Edition 1st Edition
Languages

Table of Contents (16) Chapters

Mastering Python Data Visualization
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
A Conceptual Framework for Data Visualization Data Analysis and Visualization Getting Started with the Python IDE Numerical Computing and Interactive Plotting Financial and Statistical Models Statistical and Machine Learning Bioinformatics, Genetics, and Network Models Advanced Visualization Go Forth and Explore Visualization Index

Maximum flow and minimum cut


A flow network is a directed graph from a source to a destination with capacities assigned along each edge. Just as we can model a street map as a directed graph in order to find the shortest path from one place to another, we can also interpret a directed graph as a "flow network". Some examples of flow networks are liquid flowing through pipes, current passing through electrical networks, and data transferring through communication networks. The following is an example graph flow diagram:

The edges of the G graph are expected to have a capacity that indicates how much flow the edge can support. If this capacity is not present, then it is assumed to have infinite capacity. The maximum flow of the flow network G here is 4.

In the NetworkX package, the maximum_flow_value(Graph, from, to) function evaluates the maximum flow of a graph, as shown in the following code:

import networkx as nx
G = nx.DiGraph()
G.add_edge('p','y', capacity=5.0)
G.add_edge('p','s', capacity...
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