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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

Customization

On the journey through the lands of matplotlib, one of the signposts for intermediate territories is an increased need for fine-grained control over the libraries in the ecosystem. In our case, this means being able to tweak matplotlib for particular use cases such as specialty scales or projections, complex layouts, or a custom look and feel.

Creating a custom style

The first customization topic that we will cover is that of the new style support introduced in matplotlib 1.4. In the previous notebook, we saw how to get a list of the available styles:

In [2]: print(plt.style.available)
        ['bmh', 'ggplot', 'fivethirtyeight', 'dark_background',
        'grayscale']

Now, we're going to see how we can create and use one of our own custom styles.

You can create custom styles and use them by calling style.use with the path or URL to the style sheet. Alternatively, if you save the <style-name>.mplstyle file to the ~...

End of Section 2
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