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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

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...

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