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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
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
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9. Clustering for matplotlib
Clustering and parallel programming
The custom ZeroMQ cluster
Clustering with IPython
More clustering
Summary
10
Index
Index

The custom ZeroMQ cluster

In this section, we will create a task pipeline, which is a messaging pattern that we mentioned briefly when discussing the Disco project's answer to MapReduce. Task pipelines can be viewed as a generalization of MapReduce in that they support data flows through a sequence of steps, where each step provides the results from its processing to the next step in the flow. We will accomplish this by using ZeroMQ to create a messaging topology that is suitable for the execution of embarrassingly parallel code in a number of worker processes.

Note

The descriptive term embarrassingly parallel was adopted in online parallelization discussions after its use in an article named Matrix Computation on Distributed Memory Multiprocessors that was written by Cleve Moler. The parallelizing of serially biased code is notoriously difficult, and problems that were obviously or easily parallelizable were described by using this phrase.

ZeroMQ is an asynchronous messaging framework, which...

End of Section 2
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