Elegant sciPy : the art of scientific Python

Başlık:

Yazar:

Nunez-Iglesias, Juan.

ISBN:

9781491922873

Ek Yazar:

Nunez-Iglesias, Juan.

Edition:

First edition.

Fiziksel Tanım:

xxii, 251 : color illustrations ; 24 cm

Contents:

Copyright; Table of Contents; Preface; Who Is This Book For?; Why SciPy?; What Is the SciPy Ecosystem?; The Great Cataclysm: Python 2 Versus Python 3; SciPy Ecosystem and Community; Free and Open Source Software (FOSS); GitHub: Taking Coding Social; Make Your Mark on the SciPy Ecosystem; A Touch of Whimsy with Your Py; Getting Help; Installing Python; Accessing the Book Materials; Diving In; Conventions Used in This Book; Use of Color; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Elegant NumPy: The Foundation of Scientific Python.

Konu Terimleri:

Python (Bilgisayar program dili)

Sayısal analiz.

Python (Computer program language)

Numerical analysis.

Added Author:

Walt, Stéfan van der.

Dashnow, Harriet.

Mevcut:*

Library	Materyal Türü	Barkod	Yer Numarası	Durum
Searching... Pamukkale Merkez Kütüphanesi	Kitap	0107489	QA76.73 N86 2017	Searching... Unknown

Bound With These Titles

On Order

Özet

Welcome to Scientific Python and its community. If youâ??re a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. Youâ??ll learn how to write elegant code thatâ??s clear, concise, and efficient at executing the task at hand.

Throughout the book, youâ??ll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, youâ??ll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries.

Explore the NumPy array, the data structure that underlies numerical scientific computation Use quantile normalization to ensure that measurements fit a specific distribution Represent separate regions in an image with a Region Adjacency Graph Convert temporal or spatial data into frequency domain data with the Fast Fourier Transform Solve sparse matrix problems, including image segmentations, with SciPyâ??s sparse module Perform linear algebra by using SciPy packages Explore image alignment (registration) with SciPyâ??s optimize module Process large datasets with Python data streaming primitives and the Toolz library

Author Notes

Juan Nunez-Iglesias is a freelance consultant and a Research Scientist at the University of Melbourne, Australia.
Stfan van der Walt, creator of scikit-image, is an assistant researcher at the Berkeley Institute for Data Science at UC Berkeley.
Harriet Dashnow is a bioinformatician at the Murdoch Childrens Research Institute.

Preface	p. vii
1 Elegant NumPy: The Foundation of Scientific Python	p. 1
Introduction to the Data: What Is Gene Expression?	p. 2
NumPy N-Dimenstional Arrays	p. 6
Why Use ndarrays Instead of Python Lists?	p. 8
Vectorization	p. 10
Broadcasting	p. 10
Exploring a Gene Expression Dataset	p. 12
Reading in the Data with pandas	p. 12
Normalization	p. 14
Between Samples	p. 14
Between Genes	p. 21
Normalizing Over Samples and Genes: RPKM	p. 24
Taking Stock	p. 30
2 Quantile Normalization with NumPy and SciPy	p. 31
Getting the Data	p. 33
Gene Expression Distribution Differences Between Individuals	p. 34
Biclustering the Counts Data	p. 37
Visualizing Clusters	p. 39
Predicting Survival	p. 42
Further Work: Using the TCGA's Patient Clusters	p. 46
Further Work: Reproducing the TCGA's clusters	p. 46
3 Networks of Image Regions with ndimage	p. 49
Images Are Just NumPy Arrays	p. 50
Exercise: Adding a Grid Overlay	p. 55
Filters in Signal Processing	p. 56
Filtering Images (2D Filters)	p. 63
Generic Filters: Arbitrary Functions of Neighborhood Values	p. 66
Exercise: Conway's Game of Life	p. 67
Exercise: Sobel Gradient Magnitude	p. 68
Graphs and the NetworkX library	p. 68
Exercise: Curve Fitting with SciPy	p. 72
Region Adjacency Graphs	p. 73
Elegant ndimage: How to Build Graphs from Image Regions	p. 76
Putting It All Together: Mean Color Segmentation	p. 78
4 Frequency and the Fast Fourier Transform	p. 81
Introducing Frequency	p. 81
Illustration: A Birdsong Spectrogram	p. 84
History	p. 90
Implementation	p. 91
Choosing the Length of the DFT	p. 92
More DFT Concepts	p. 94
Frequencies and Their Ordering	p. 94
Windowing	p. 100
Real-World Application; Analyzing Radar Data	p. 105
Signal Properties in the Frequency Domain	p. 111
Windowing, Applied	p. 115
Radar Images	p. 117
Further Applications of the EFT	p. 122
Further Reading	p. 122
Exercise: Image Convolution	p. 123
5 Contingency Tables Using Sparse Coordinate Matrices	p. 125
Contingency Tables	p. 127
Exercise: Computational Complexity of Confusion Matrices	p. 128
Exercise: Alternative Algorithm to Compute the Confusion Matrix	p. 128
Exercise: Multiclass Confusion Matrix	p. 128
Scipy.sparse Data Formats	p. 129
COO Format	p. 129
Exercise: COO Representation	p. 130
Compressed Sparse Row Format	p. 130
Applications of Sparse Matrices: Image Transformations	p. 133
Exercise: Image Rotation	p. 138
Back to Contingency Tables	p. 139
Exercise: Reducing the Memory Footprint	p. 140
Contingency Tables in Segmentation	p. 140
Information Theory in Brief	p. 142
Exercise: Computing Conditional Entropy	p. 144
Information Theory in Segmentation: Variation of Information	p. 145
Converting NumPy Array Code to Use Sparse Matrices	p. 147
Using Variation of Information	p. 149
Further Work: Segmentation in Practice	p. 156
6 Linear Algebra in SciPy	p. 157
Linear Algebra Basics	p. 157
Laplacian Matrix of a Graph	p. 158
Exercise: Rotation Matrix	p. 159
Laplacians with Brain Data	p. 165
Exercise: Showing the Affinity View	p. 170
Exercise Challenge: Linear Algebra with Sparse Matrices	p. 170
PageRank: Linear Algebra for Reputation and Importance	p. 171
Exercise: Dealing with Dangling Nodes	p. 176
Exercise: Equivalence of Different Eigenvector Methods	p. 176
Concluding Remarks	p. 176
7 Function Optimization in SciPy	p. 177
Optimization in SciPy: scipy.optimize	p. 179
An Example: Computing Optimal Image Shift	p. 180
Image Registration with Optimize	p. 186
Avoiding Local Minima with Basin flopping	p. 190
Exercise: Modify the align Function	p. 190
"What Is Best?": Choosing the Right Objective Function	p. 191
8 Big Data in Little Laptop with Toolz	p. 199
Streaming with yield	p. 200
Introducing the Toolz Streaming Library	p. 203
k-mer Counting and Error Correction	p. 206
Currying: The Spice of Streaming	p. 210
Back to Counting k-mers	p. 212
Exercise: PCA of Streaming Data	p. 214
Markov Model from a Full Genome	p. 214
Exercise: Online Unzip	p. 217
Epilogue	p. 221
Appendix: Exercise Solutions	p. 225
Index	p. 247