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Mastering Matplotlib

By : Duncan M. McGreggor

3.5 (8)

Mastering Matplotlib

3.5 (8)

By: 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

Customer Reviews

3.5 (8)

5 star

37.5%

4 star

12.5%

3 star

25%

2 star

12.5%

1 star

12.5%

More clustering

There are other interesting options if you wish to parallelize the Python code and run it on clusters on problems that require you to visualize computationally intensive problems. Of particular interest is MIT's StarCluster project, which runs on Amazon AWS (EC2) and supports the Open Grid Engine (formerly known as the Sun Grid Engine). Furthermore, StarCluster has an IPython cluster plugin, which lets you easily run the IPython.parallel code on EC2. For more information on this, be sure to refer to this chapter's IPython Notebook, the StarCluster documentation, and the IPython documentation for parallel computations.

Visually different images

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Tech Concepts

36

Programming languages

73

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