Elegant SciPy : the art of Scientific Python / Juan Nunez-Iglesias, Stéfan van der Walt and Harriet Dashnow.

Format:

Book

Author/Creator:

Nunez-Iglesias, Juan, author.

Van der Walt, Stéfan, author.

Dashnow, Harriet, author.

Language:

English

Subjects (All):

Python (Computer program language).

Numerical analysis.

Science--Mathematics--Data processing.

Science.

Statistics--Data processing.

Statistics.

Physical Description:

1 online resource (277 pages) : color illustrations

Edition:

First edition.

Other Title:

Art of scientific Python

Place of Publication:

Beijing : O'Reilly, 2017.

System Details:

text file

Summary:

Welcome to Scientific Python and its community. If you’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. You’ll learn how to write elegant code that’s clear, concise, and efficient at executing the task at hand. Throughout the book, you’ll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you’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 SciPy’s sparse module Perform linear algebra by using SciPy packages Explore image alignment (registration) with SciPy’s optimize module Process large datasets with Python data streaming primitives and the Toolz library

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.

Introduction to the Data: What Is Gene Expression?NumPy N-Dimensional Arrays; Why Use ndarrays Instead of Python Lists?; Vectorization; Broadcasting; Exploring a Gene Expression Dataset; Reading in the Data with pandas; Normalization; Between Samples; Between Genes; Normalizing Over Samples and Genes: RPKM; Taking Stock; Chapter 2. Quantile Normalization with NumPy and SciPy; Getting the Data; Gene Expression Distribution Differences Between Individuals; Biclustering the Counts Data; Visualizing Clusters; Predicting Survival; Further Work: Using the TCGA's Patient Clusters.

Further Work: Reproducing the TCGA's clustersChapter 3. Networks of Image Regions with ndimage; Images Are Just NumPy Arrays; Exercise: Adding a Grid Overlay; Filters in Signal Processing; Filtering Images (2D Filters); Generic Filters: Arbitrary Functions of Neighborhood Values; Exercise: Conway's Game of Life; Exercise: Sobel Gradient Magnitude; Graphs and the NetworkX library; Exercise: Curve Fitting with SciPy; Region Adjacency Graphs; Elegant ndimage: How to Build Graphs from Image Regions; Putting It All Together: Mean Color Segmentation.

Chapter 4. Frequency and the Fast Fourier TransformIntroducing Frequency; Illustration: A Birdsong Spectrogram; History; Implementation; Choosing the Length of the DFT; More DFT Concepts; Frequencies and Their Ordering; Windowing; Real-World Application: Analyzing Radar Data; Signal Properties in the Frequency Domain; Windowing, Applied; Radar Images; Further Applications of the FFT; Further Reading; Exercise: Image Convolution; Chapter 5. Contingency Tables Using Sparse Coordinate Matrices; Contingency Tables; Exercise: Computational Complexity of Confusion Matrices.

Exercise: Alternative Algorithm to Compute the Confusion MatrixExercise: Multiclass Confusion Matrix; scipy.sparse Data Formats; COO Format; Exercise: COO Representation; Compressed Sparse Row Format; Applications of Sparse Matrices: Image Transformations; Exercise: Image Rotation; Back to Contingency Tables; Exercise: Reducing the Memory Footprint; Contingency Tables in Segmentation; Information Theory in Brief; Exercise: Computing Conditional Entropy; Information Theory in Segmentation: Variation of Information; Converting NumPy Array Code to Use Sparse Matrices.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed August 23, 2017).

ISBN:

9781491922941

149192294X

9781491922927

1491922923

9781491922873

1491922877

9781491922958

1491922958

OCLC:

1001514385