Python and HDF5: Unlocking Scientific Data
Gain hands-on experience with HDF5 for storing scientific data in Python. This practical guide quickly gets you up to speed on the details, best practices, and pitfalls of using HDF5 to archive and share numerical datasets ranging in size from gigabytes to terabytes.

Through real-world examples and practical exercises, you’ll explore topics such as scientific datasets, hierarchically organized groups, user-defined metadata, and interoperable files. Examples are applicable for users of both Python 2 and Python 3. If you’re familiar with the basics of Python data analysis, this is an ideal introduction to HDF5.

  • Get set up with HDF5 tools and create your first HDF5 file
  • Work with datasets by learning the HDF5 Dataset object
  • Understand advanced features like dataset chunking and compression
  • Learn how to work with HDF5’s hierarchical structure, using groups
  • Create self-describing files by adding metadata with HDF5 attributes
  • Take advantage of HDF5’s type system to create interoperable files
  • Express relationships among data with references, named types, and dimension scales
  • Discover how Python mechanisms for writing parallel code interact with HDF5
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Python and HDF5: Unlocking Scientific Data
Gain hands-on experience with HDF5 for storing scientific data in Python. This practical guide quickly gets you up to speed on the details, best practices, and pitfalls of using HDF5 to archive and share numerical datasets ranging in size from gigabytes to terabytes.

Through real-world examples and practical exercises, you’ll explore topics such as scientific datasets, hierarchically organized groups, user-defined metadata, and interoperable files. Examples are applicable for users of both Python 2 and Python 3. If you’re familiar with the basics of Python data analysis, this is an ideal introduction to HDF5.

  • Get set up with HDF5 tools and create your first HDF5 file
  • Work with datasets by learning the HDF5 Dataset object
  • Understand advanced features like dataset chunking and compression
  • Learn how to work with HDF5’s hierarchical structure, using groups
  • Create self-describing files by adding metadata with HDF5 attributes
  • Take advantage of HDF5’s type system to create interoperable files
  • Express relationships among data with references, named types, and dimension scales
  • Discover how Python mechanisms for writing parallel code interact with HDF5
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Python and HDF5: Unlocking Scientific Data

Python and HDF5: Unlocking Scientific Data

by Andrew Collette
Python and HDF5: Unlocking Scientific Data
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Python and HDF5: Unlocking Scientific Data

Python and HDF5: Unlocking Scientific Data

by Andrew Collette

Overview

Gain hands-on experience with HDF5 for storing scientific data in Python. This practical guide quickly gets you up to speed on the details, best practices, and pitfalls of using HDF5 to archive and share numerical datasets ranging in size from gigabytes to terabytes.

Through real-world examples and practical exercises, you’ll explore topics such as scientific datasets, hierarchically organized groups, user-defined metadata, and interoperable files. Examples are applicable for users of both Python 2 and Python 3. If you’re familiar with the basics of Python data analysis, this is an ideal introduction to HDF5.

  • Get set up with HDF5 tools and create your first HDF5 file
  • Work with datasets by learning the HDF5 Dataset object
  • Understand advanced features like dataset chunking and compression
  • Learn how to work with HDF5’s hierarchical structure, using groups
  • Create self-describing files by adding metadata with HDF5 attributes
  • Take advantage of HDF5’s type system to create interoperable files
  • Express relationships among data with references, named types, and dimension scales
  • Discover how Python mechanisms for writing parallel code interact with HDF5

Product Details

ISBN-13: 9781449367831
Publisher: O'Reilly Media, Incorporated
Publication date: 11/11/2013
Pages: 148
Product dimensions: 6.90(w) x 9.10(h) x 0.50(d)

About the Author

Andrew Collette holds a Ph.D. in physics from UCLA, and works as a laboratory research scientist at the University of Colorado. He has worked with the Python-NumPy-HDF5 stack at two multimillion-dollar research facilities; the first being the Large Plasma Device at UCLA (entirely standardized on HDF5), and the second being the hypervelocity dust accelerator at the Colorado Center for Lunar Dust and Atmospheric Studies, University of Colorado at Boulder. Additionally, Dr. Collette is a leading developer of the HDF5 for Python (h5py) project.

Table of Contents

  • Preface
  • Chapter 1: Introduction
  • Chapter 2: Getting Started
  • Chapter 3: Working with Datasets
  • Chapter 4: How Chunking and Compression Can Help You
  • Chapter 5: Groups, Links, and Iteration: The "H" in HDF5
  • Chapter 6: Storing Metadata with Attributes
  • Chapter 7: More About Types
  • Chapter 8: Organizing Data with References, Types, and Dimension Scales
  • Chapter 9: Concurrency: Parallel HDF5, Threading, and Multiprocessing
  • Chapter 10: Next Steps
  • Index
  • Colophon
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