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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
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

Chapter 4. Event Handling and Interactive Plots

This chapter marks the beginning of our transition into specialized topics. While we will continue making references to the preceding overview, the subsequent chapters will focus on taking you deeper into the domain of advanced matplotlib usage.

In this chapter, we will delve into matplotlib events and interactive plots and cover the following topics:

  • A general overview of event-based systems
  • The anatomy of an event loop
  • The GUI toolkit event loop basics
  • Event loops in IPython and matplotlib
  • Event handling in matplotlib
  • Events for mouse, keyboard, axes, figures, and picking
  • Compound events

The material presented here will be pulled from matplotlib's artist and backend layers, and it should provide a nice starting point for the vast world of user interaction in data visualization.

To follow along with this chapter's code, clone the notebook's repository and start up IPython by using the following code:

$ git clone https://github...

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