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Elegant sciPy : the art of scientific Python

Author: Juan Nunez-Iglesias; Stéfan van der Walt; Harriet Dashnow
Publisher: Sebastopol, CA : O'Reilly Media, 2017.
Edition/Format:   Print book : English : First editionView all editions and formats
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,  Read more...

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Document Type: Book
All Authors / Contributors: Juan Nunez-Iglesias; Stéfan van der Walt; Harriet Dashnow
ISBN: 9781491922873 1491922877
OCLC Number: 1003531579
Description: 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. 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.
Other Titles: Art of scientific Python
Responsibility: Juan Nunez-Iglesias, Stéfan van der Walt, and Harriet Dashnow.

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