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Mastering Matplotlib

By : Duncan M. McGreggor, Duncan M McGreggor

3.5 (8)

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.

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. 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. 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. 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. Event Handling and Interactive Plots

4. Event Handling and Interactive Plots

Event loops in matplotlib

Event handling

Summary

5. High-level Plotting and Data Analysis

5. High-level Plotting and Data Analysis

High-level plotting

Data analysis

Summary

6. Customization and Configuration

6. Customization and Configuration

Customization

Configuration

Summary

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. matplotlib and Big Data

8. matplotlib and Big Data

Big data

Working with large data sources

Visualizing large data

Other visualization tools

Summary

9. Clustering for matplotlib

9. Clustering for matplotlib

Clustering and parallel programming

The custom ZeroMQ cluster

Clustering with IPython

More clustering

Summary

Index

Index

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The artist layer

The artist layer constitutes the bulk of what matplotlib actually does—the generation of the plots for the purpose of display, manipulation, and publication. Most work in the artist layer is performed by a number of classes, most of which are derived from the Artist base class.

The artist layer is concerned with things such as the lines, shapes, axes, text, and so on. These are the subclasses of the Artist class that define things such as the following:

A canvas-artist coordinate transformation
Visibility
A clip box that defines the paintable area
Labels
A callback registry instance to handle user interaction events

The Artist subclasses can be classified into one of the following two groups:

Primitives
Containers

The following two sections provide more details about these groups.

Primitives

The matplotlib artist primitives are classes of graphical objects that are supposed to be painted on a figure's canvas. These include, but are not limited to, the following:

Line2D
Shape...

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