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Mastering Matplotlib

Mastering Matplotlib

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

Mastering Matplotlib

3.5 (8)
By: Duncan M. McGreggor
Buy this Book

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
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Up to Speed
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1. Getting Up to Speed
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A brief historical overview of matplotlib
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What's new in matplotlib 1.4
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The intermediate matplotlib user
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Prerequisites for this book
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Python 3
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Coding style
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Installing matplotlib
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Using IPython Notebooks with matplotlib
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Advanced plots – a preview
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Setting up the interactive backend
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Summary
2
2. The matplotlib Architecture
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2. The matplotlib Architecture
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The original design goals
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The current matplotlib architecture
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The backend layer
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The artist layer
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The scripting layer
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The supporting components of the matplotlib stack
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matplotlib modules
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The execution flow
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The matplotlib architecture as it relates to this book
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Summary
3
3. matplotlib APIs and Integrations
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3. matplotlib APIs and Integrations
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The procedural pylab API
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The pyplot scripting API
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The matplotlib object-oriented API
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matplotlib in other frameworks
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Summary
4
4. Event Handling and Interactive Plots
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4. Event Handling and Interactive Plots
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Event loops in matplotlib
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Event handling
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Summary
5
5. High-level Plotting and Data Analysis
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5. High-level Plotting and Data Analysis
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High-level plotting
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Data analysis
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Summary
6
6. Customization and Configuration
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6. Customization and Configuration
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Customization
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Configuration
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Summary
7
7. Deploying matplotlib in Cloud Environments
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7. Deploying matplotlib in Cloud Environments
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Making a use case for matplotlib in the Cloud
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An example – AWS and Docker
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Summary
8
8. matplotlib and Big Data
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8. matplotlib and Big Data
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Big data
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Working with large data sources
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Visualizing large data
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Other visualization tools
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Summary
9
9. Clustering for matplotlib
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9. Clustering for matplotlib
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Clustering and parallel programming
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The custom ZeroMQ cluster
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Clustering with IPython
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More clustering
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Summary
10
Index
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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 ~/.matplotlib/stylelib directory (you may need to create it...

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Mastering Matplotlib
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