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Mastering Matplotlib

You're reading from   Mastering Matplotlib A practical guide that takes you beyond the basics of matplotlib and gives solutions to plot complex data

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Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781783987542
Length 292 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Up to Speed 2. The matplotlib Architecture FREE CHAPTER 3. matplotlib APIs and Integrations 4. Event Handling and Interactive Plots 5. High-level Plotting and Data Analysis 6. Customization and Configuration 7. Deploying matplotlib in Cloud Environments 8. matplotlib and Big Data 9. Clustering for matplotlib Index

Clustering and parallel programming


The term clustering may have a number of operational definitions depending on the situation that one is facing or the organization that one is working with. In this chapter, we will use the term in a very general sense to indicate a system of computing nodes across which a task may be split and whose parts may be executed in parallel with all the system nodes. We won't specify what nodes are, as they may be anything from a collection of processes on the same machine or a computer network to virtual machines or physical computers on a network.

The word "cluster" alludes to a logical collection, but in our definition there is a more important word—parallel. For our purposes, clusters exist to make running code in parallel more efficient or convenient. The topic of parallel computing is a vast one and has an interesting history. However, it rose to greater prominence in 2003 due to the physical limitations that were encountered by the chip-making industry...

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