| Document Type
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BL
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| Record Number
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851397
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| Main Entry
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Nunez-Iglesias, Juan.
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| Title & Author
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Elegant sciPy : : the art of scientific Python /\ Juan Nunez-Iglesias, Stéfan van der Walt, and Harriet Dashnow.
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| Edition Statement
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First edition.
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| Publication Statement
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Sebastopol, CA :: O'Reilly Media,, 2017.
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| Page. NO
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1 online resource (xxii, 251 pages) :: color illustrations
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| ISBN
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149192294X
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: 1491922958
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: 9781491922941
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: 9781491922958
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1491922877
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9781491922873
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9781491922927
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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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.
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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.
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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.
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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.
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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.
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| Subject
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Numerical analysis.
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| Subject
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Python (Computer program language)
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| Subject
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COMPUTERS-- Programming Languages-- Python.
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| Subject
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Numerical analysis.
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| Subject
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Python (Computer program language)
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| Dewey Classification
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005.133
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| LC Classification
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QA76.73.P98
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| Added Entry
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Dashnow, Harriet.
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Van der Walt, Stéfan.
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| Parallel Title
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Art of scientific Python
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