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Mastering matplotlib

You're reading from Mastering matplotlib

Product type Book

Published in Jun 2015

Publisher

ISBN-13 9781783987542

Pages 292 pages

Edition 1st Edition

Languages

Concepts

Data Visualization

Authors (2):

Duncan M. McGreggor

Duncan M McGreggor

View More author details

Table of Contents (16) Chapters

Mastering matplotlib

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

1. Getting Up to Speed

2. The matplotlib Architecture

3. matplotlib APIs and Integrations

4. Event Handling and Interactive Plots

5. High-level Plotting and Data Analysis

6. Customization and Configuration

7. Deploying matplotlib in Cloud Environments

8. matplotlib and Big Data

9. Clustering for matplotlib

Index

Configuration

We've just covered some examples of matplotlib customization. Hand in hand with this topic is that of configuration—the tweaking of predefined values to override default behaviors. The matplotlib module offers two ways to override the default values for the configuration settings—you can either run the control files, or run the control parameters that are stored in-memory to make changes to a running instance.

The run control for matplotlib

While commonly expanded to the run control, the .rc extension and -rc suffix trace their origins to 1965 and the Multics (short for Multiplexed Information and Computing Service) operating system, where rc stood for the run command. Like many software systems that were developed on UNIX- or BSD-based machines, matplotlib has an rc file where the control of matplotlib may be configured. This control is not limited to configuration files; one may also access an rc object via the matplotlib API. Each of these is covered in the following few...

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Authors (2)

Duncan M. McGreggor

Duncan M. McGreggor, having programmed with GOTOs in the 1980s, has made up for that through community service by making open source contributions for more than 20 years. He has spent a major part of the past 10 years dealing with distributed and scientific computing (in languages ranging from Python, Common Lisp, and Julia to Clojure and Lisp Flavored Erlang). In the 1990s, after serving as a linguist in the US Army, he spent considerable time working on projects related to MATLAB and Mathematica, which was a part of his physics and maths studies at the university. Since the mid 2000s, matplotlib and NumPy have figured prominently in many of the interesting problems that he has solved for his customers. With the most recent addition of the IPython Notebook, matplotlib and the suite of the Python scientific computing libraries remain some of his most important professional tools.

See other products by Duncan M. McGreggor

Duncan M McGreggor

Duncan M. McGreggor, having programmed with GOTOs in the 1980s, has made up for that through community service by making open source contributions for more than 20 years. He has spent a major part of the past 10 years dealing with distributed and scientific computing (in languages ranging from Python, Common Lisp, and Julia to Clojure and Lisp Flavored Erlang). In the 1990s, after serving as a linguist in the US Army, he spent considerable time working on projects related to MATLAB and Mathematica, which was a part of his physics and maths studies at the university. Since the mid 2000s, matplotlib and NumPy have figured prominently in many of the interesting problems that he has solved for his customers. With the most recent addition of the IPython Notebook, matplotlib and the suite of the Python scientific computing libraries remain some of his most important professional tools.

See other products by Duncan M McGreggor

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