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Python Data Visualization Cookbook (Second Edition)

You're reading from   Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

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 the Right Plots to Understand Data 8. More on matplotlib Gems 9. Visualizations on the Clouds with Plot.ly Index

Drawing streamlines of vector flow


Stream plots are used to visualize flow in vector fields. Examples from science and nature include fields of magnetic and gravitational forces or movement of liquid materials.

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 direction by arrow pointing in particular direction.

Usually, the intensity of the force is visualized with the length of a particular streamline, but 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 uniform representation of the vector field.

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