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Mastering Matplotlib

Mastering Matplotlib

By : Duncan M. McGreggor, Duncan M McGreggor
3.5 (8)
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Mastering Matplotlib

Mastering Matplotlib

3.5 (8)
By: Duncan M. McGreggor, Duncan M McGreggor

Overview of this book

If you are a scientist, programmer, software engineer, or student who has working knowledge of matplotlib and now want to extend your usage of matplotlib to plot complex graphs and charts and handle large datasets, then this book is for you.
Table of Contents (11 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Up to Speed
1. Getting Up to Speed
A brief historical overview of matplotlib
What's new in matplotlib 1.4
The intermediate matplotlib user
Prerequisites for this book
Python 3
Coding style
Installing matplotlib
Using IPython Notebooks with matplotlib
Advanced plots – a preview
Setting up the interactive backend
Summary
2
2. The matplotlib Architecture
2. The matplotlib Architecture
The original design goals
The current matplotlib architecture
The backend layer
The artist layer
The scripting layer
The supporting components of the matplotlib stack
matplotlib modules
The execution flow
The matplotlib architecture as it relates to this book
Summary
3
3. matplotlib APIs and Integrations
3. matplotlib APIs and Integrations
The procedural pylab API
The pyplot scripting API
The matplotlib object-oriented API
matplotlib in other frameworks
Summary
4
4. Event Handling and Interactive Plots
4. Event Handling and Interactive Plots
Event loops in matplotlib
Event handling
Summary
5
5. High-level Plotting and Data Analysis
5. High-level Plotting and Data Analysis
High-level plotting
Data analysis
Summary
6
6. Customization and Configuration
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6. Customization and Configuration
Customization
Configuration
Summary
7
7. Deploying matplotlib in Cloud Environments
7. Deploying matplotlib in Cloud Environments
Making a use case for matplotlib in the Cloud
An example – AWS and Docker
Summary
8
8. matplotlib and Big Data
8. matplotlib and Big Data
Big data
Working with large data sources
Visualizing large data
Other visualization tools
Summary
9
9. Clustering for matplotlib
9. Clustering for matplotlib
Clustering and parallel programming
The custom ZeroMQ cluster
Clustering with IPython
More clustering
Summary
10
Index
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...

End of Chapter 6
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