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Python Data Visualization Cookbook

You're reading from   Python Data Visualization Cookbook As a developer with knowledge of Python you are already in a great position to start using data visualization. This superb cookbook shows you how in plain language and practical recipes, culminating with 3D animations.

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Product type Paperback
Published in Nov 2013
Publisher Packt
ISBN-13 9781782163367
Length 280 pages
Edition 1st Edition
Languages
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Author (1):
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Igor Milovanovic Igor Milovanovic
Author Profile Icon Igor Milovanovic
Igor Milovanovic
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Table of Contents (15) Chapters Close

Python Data Visualization Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Preparing Your Working Environment FREE CHAPTER 2. Knowing Your Data 3. Drawing Your First Plots and Customizing Them 4. More Plots and Customizations 5. Making 3D Visualizations 6. Plotting Charts with Images and Maps 7. Using Right Plots to Understand Data 8. More on matplotlib Gems Index

Drawing streamlines of vector flow


Stream plots are used to visualize a flow in vector fields. Examples from Science and Nature include fields of magnetic and gravitational forces and movement of liquid materials.

A vector field can be visualized in such a way where we assign a line and one or more arrows to every point. The intensity can be represented by the line length, and the direction by an arrow pointing in a particular direction.

Usually, the intensity of the force is visualized with the length of a particular streamline, but the density can also be used for the same purpose.

Getting ready

To visualize vector fields, we will use matplotlib's matplotlib.pyplot.streamplot function. This function creates plots from streamlines of a flow, uniformly filling the domain. The velocities field is interpolated and streamlines are integrated. The original source for this function is to visualize wind patterns or liquid flow; hence, we don't need strict vector lines but a uniform representation...

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