SciPy and NumPy

67 Pages·2012·2.68 MB·English

by  Eli Bressert

SciPy and NumPy
Eli Bressert
Beijing•Cambridge•Farnham•Ko¨ln•Sebastopol•Tokyo
9781449305468_text.pdf 1 10/31/12 2:35 PM
SciPyandNumPy
byEliBressert
Copyright©2013EliBressert.Allrightsreserved.
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Table of Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 WhySciPyandNumPy? 1
1.2 GettingNumPyandSciPy 2
1.3 WorkingwithSciPyandNumPy 3
2. NumPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 NumPyArrays 5
2.2 BooleanStatementsandNumPyArrays 10
2.3 ReadandWrite 12
2.4 Math 14
3. SciPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1 OptimizationandMinimization 17
3.2 Interpolation 22
3.3 Integration 26
3.4 Statistics 28
3.5 SpatialandClusteringAnalysis 32
3.6 SignalandImageProcessing 38
3.7 SparseMatrices 40
3.8 ReadingandWritingFilesBeyondNumPy 41
4. SciKit:TakingSciPyOneStepFurther. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1 Scikit-Image 43
4.2 Scikit-Learn 48
5. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.1 Summary 55
5.2 What’sNext? 55
iii
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Preface
Python,ahigh-levellanguagewitheasy-to-readsyntax,ishighlyflexible,whichmakes
itanideallanguagetolearnanduse.ForscienceandR&D,afewextrapackagesareused
tostreamlinethedevelopmentprocessandobtaingoalswiththefeweststepspossible.
Among the best of these are SciPy and NumPy. This book gives a brief overview of
differenttoolsinthesetwoscientificpackages,inordertojumpstarttheiruseinthe
reader’sownresearchprojects.
NumPy and SciPy are the bread-and-butter Python extensions for numerical arrays
andadvanceddataanalysis.Hence,knowingwhattoolstheycontainandhowtouse
themwillmakeanyprogrammer’slifemoreenjoyable.Thisbookwillcovertheiruses,
rangingfromsimplearraycreationtomachinelearning.
Audience
Anyonewithbasic(andupward)knowledgeofPythonisthetargetedaudienceforthis
book.AlthoughthetoolsinSciPyandNumPyarerelativelyadvanced,usingthemis
simpleandshouldkeepevenanovicePythonprogrammerhappy.
ContentsofthisBook
This book covers the basics of SciPy and NumPy with some additional material.
The first chapter describes what the SciPy and NumPy packages are, and how to
accessandinstallthemonyourcomputer.Chapter2goesoverthebasicsofNumPy,
startingwitharraycreation.Chapter3,whichcomprisesthebulkofthebook,covers
asmallsampleofthevoluminousSciPytoolbox.Thischapterincludesdiscussionand
examples on integration, optimization, interpolation, and more. Chapter 4 discusses
two well-known scikit packages: scikit-image and scikit-learn. These provide much
more advanced material that can be immediately applied to real-world problems. In
Chapter5,theconclusion,wediscusswhattodonextforevenmoreadvancedmaterial.
v
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Thisbookisheretohelpyougetyourjobdone.Ingeneral,youmayusethecodein
this book in your programs and documentation. You do not need to contact us for
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Copyright2013EliBressert,978-1-449-30546-8.”
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Acknowledgments
I would like to thank Meghan Blanchette and Julie Steele, my current and previous
editors, for their patience, help, and expertise. This book wouldn’t have materialized
withouttheirassistance.Thetips,warnings,andpackagetoolsdiscussedinthebook
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were much improved thanks to the two book reviewers: Tom Aldcroft and Sarah
Kendrew.Colleaguesandfriendsthathavehelpeddiscusscertainaspectsofthisbook
and bolstered my drive to get it done are Leonardo Testi, Nate Bastian, Diederik
Kruijssen, Joao Alves, Thomas Robitaille, and Farida Khatchadourian. A big thanks
goes to my wife and son, Judith van Raalten and Taj Bressert, for their help and
inspiration,andwillingnesstodealwithmebeinghuddledawaybehindthecomputer
forendlesshours.
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