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Beginning

Python

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Vaingast

Shelve in

Programming Languages/General

User level:

Beginning–Intermediate

www.apress.com

SOURCE CODE ONLINE

BOOKS FOR PROFESSIONALS BY PROFESSIONALS

Beginning Python Visualization:

Crafting Visual Transformation Scripts

We are visual animals. But before we can see the world in its true splendor, our brains,

just like our computers, have to sort and organize raw data, and then transform that

data to produce new images of the world. Beginning Python Visualization: Crafting Visual

Transformation Scripts, Second Edition discusses turning many types of data sources,

big and small, into useful visual data. And, you will learn Python as part of the bargain.

In this second edition you’ll learn about Spyder, which is a Python IDE with MATLAB

-like

features. Here and throughout the book, you’ll get detailed exposure to the growing

IPython project for interactive visualization. In addition, you’ll learn about the changes

in NumPy and SciPy that have occurred since the first edition. Along the way, you’ll get

many pointers and a few visual examples.

As part of this update, you’ll learn about matplotlib in detail; this includes creating

3D graphs and using the basemap package that allows you to render geographical

maps. Finally, you’ll learn about image processing, annotating, and filtering, as well as

how to make movies using Python. This includes learning how to edit/open video files

and how to create your own movie, all with Python scripts

Beginning Python Visualization teaches you:

• How to present visual information instead of data soup

• How to set up an open source environment ready for data visualization

• How to do numerical and textual processing

• How to draw graphs and plots based on textual and numerical data

using NumPy, Spyder, and more

• How to explore and use new visual libraries including matplotlib’s

3D graphs and basemap package

• How to build and use interactive visualization using IPython

SECOND

EDITION

9781484 200537

54999

ISBN 978-1-4842-0053-7

Contents at a Glance

About the Author �� xix

About the Technical Reviewer �� xxi

Acknowledgments �� xxiii

Introduction �� xxv

Chapter 1: Navigating the World of Data Visualization ■ ��1

Chapter 2: The Environment ■ ��31

Chapter 3: Python for Programmers ■ ��55

Chapter 4: Data Organization ■ ��109

Chapter 5: Processing Text Files ■ ��141

Chapter 6: Graphs and Plots ■ ��189

Chapter 7: Math Games ■ ��233

Chapter 8: Science and Visualization ■ ��269

Chapter 9: Image Processing ■ ��307

Chapter 10: Advanced File Processing ■ ��343

Appendix: Additional Source Listing ■ ��371

Index ��379

xxv

Introduction

I have always been drawn to math and computers, ever since I was a kid playing computer games on my Sinclair ZX81.

When I attended university, I had a special interest in numerical analysis, a eld that I feel combines math and

computers ideally. During my career, I learned of MATLAB, widely popular for digital signal processing, numerical

analysis, and feedback and control. MATLAB’s strong suits include a high-level programming language, excellent

graphing capabilities, and numerous packages from almost every imaginable engineering eld. But I found that

MATLAB wasn’t enough. I worked with very large les and needed the ability to manipulate both text and data.

So I combined Perl, AWK, and Bash scripts to write programs that automate data analysis and visualization. And along

the way, I’ve developed practices and ideas involving the organization of data, such as ways to ensure le names are

unique and self-explanatory.

With the increasing popularity of the Internet, I learned about GNU/Linux and the open source movement.

I’ve made an eort to use open source software whenever possible, and so I’ve learned of GNU-Octave and gnuplot,

which together provide excellent scientic computing functionality. at t well on my Linux machine: Bash scripts,

Perl and AWK, GNU-Octave, and gnuplot.

Knowing I was interested in programming languages and open source software, a friend suggested I give Python

a try. My rst impression was that it was just another programming language: I could do almost anything I needed

with Perl and Bash, resorting to C/C++ if things got hairy. And I’d still need GNU-Octave and gnuplot, so what was

the advantage? Eventually, I did learn Python and discovered that it is far better than my previous collection of tools.

Python provides something that is extremely appealing: it’s a one-stop shop—you can do it all in Python.

I’ve shared my enthusiasm with friends and colleagues. Many who expressed interest with the ideas of data

processing and visualization would ask, “Can you recommend a book that teaches the ideas you’re preaching?”

And I would tell them, “Of course, numerous books cover this subject! But they didn’t want numerous books, just one,

with information distilled to focus on data analysis and visualization. I realized there wasn’t such a title, and this was

how the idea for this book originated.

What’s New in the Second Edition

Aside from using the most up-to-date version of Python that supports all the visualization packages (version 3.3 at the

time of the writing the second edition), I’ve also introduced the following additional content:

3-D plots and graphs•

Non-rectangular contour plots•

Matplotlib’s basemap• toolkit

Reading and writing MATLAB binary les•

Reading and writing data to • NumPy arrays

Reading and writing images to • NumPy arrays

Making movies•

IPython, IPython Notebook, and Spyder development environments•

■ IntroduCtIon

xxvi

Who This Book Is For

Although this book is about software, the target audience is not necessarily programmers or computer scientists.

I’ve assumed the reader’s main line of work is research or R&D, in his or her eld of interest, be it astrophysics, signal

and image processing, or biology. e audience includes the following:

Graduate and PhD students in exact and natural sciences (physics, biology, and chemistry) •

working on their thesis, dealing with large experimental data sets. e book also appeals to

students working on purely theoretical projects, as they require simulations and means to

analyze the results.

R&D engineers in the elds of electrical engineering (EE), mechanical engineering, and •

chemical engineering: engineers working with large sets of data from multiple sources.

In EE more specically, signal processing engineers, communication engineers, and systems

engineers will nd the book appealing.

Programmers and computer enthusiasts, unfamiliar with Python and the GNU/Linux world, •

but who are willing to dive into a new world of tools.

Hobbyist astronomers and other hobbyists who deal with data and are interested in using •

Python to support their hobby.

e book can be appealing to these groups for dierent reasons. For scientists and engineers, the book provides

the means to be more productive in their work, without investing a considerable amount of time learning new

tools and programs that constantly change. For programmers and computer enthusiasts, the book can serve as an

appetizer, opening up their world to Python. And because of the unique approach presented here, they might share

the enthusiasm the author has for this wonderful software world. Perhaps it will even entice them to be part of the

large and growing open source community, sharing their own code.

It is assumed that the reader does have minimal prociency with a computer, namely that he or she must

know how to manipulate les, install applications, view and edit les, and use applications to generate reports and

presentations. A background in numerical analysis, signal processing, and image processing, as well as programming,

is also helpful, but not required.

is book is not intended to serve as an encyclopedia of programming in Python and the covered packages.

Rather, it is meant to serve as an introduction to data analysis and visualization in Python, and it covers most of the

topics associated with that eld.

How This Book Is Structured

e book is designed so that you can easily skip back and forth as you engage various topics.

Chapter 1 is a case study that introduces the topics discussed throughout the book: data analysis, data

management, and, of course, data visualization. e case study involves reading GPS data, analyzing it, and plotting it

along with relevant annotations (direction of travel, speed, etc.). A fully functional Python script will be built from the

ground up, complemented with lots of explanations. e fruit of our work will be an eye-catching GPS route.

If you’re new to data analysis and visualization, consider reading Chapter 2 rst. e chapter describes how to

set up a development environment to perform the tasks associated with data analysis and visualization in Python,

including the selection of an OS, installing Python, and installing third-party packages.

If you’re new to Python, your next stop should be Chapter 3. In this chapter, I swiftly discuss the Python

programming language. I won’t be overly rehashing basic programming paradigms; instead I’ll provide a quick

overview of the building blocks for the Python programming.

Regardless of your Python programming experience, I highly encourage you to read Chapter 4 before

proceeding to the next chapters. Organization is the key to successful data analysis and visualization. is chapter

covers organizing data les, pros and cons of dierent le formats, le naming conventions, nding data les, and

automating le creation. e ideas in Chapter 4 are used throughout the book.

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