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.
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.nThrough 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
Über den Autor
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.
Inhaltsverzeichnis
Preface;
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Acknowledgments;
Chapter 1: Introduction;
1.1 Python and HDF5;
1.2 What Exactly Is HDF5?;
Chapter 2: Getting Started;
2.1 HDF5 Basics;
2.2 Setting Up;
2.3 The HDF5 Tools;
2.4 Your First HDF5 File;
Chapter 3: Working with Datasets;
3.1 Dataset Basics;
3.2 Reading and Writing Data;
3.3 Resizing Datasets;
Chapter 4: How Chunking and Compression Can Help You;
4.1 Contiguous Storage;
4.2 Chunked Storage;
4.3 Setting the Chunk Shape;
4.4 Performance Example: Resizable Datasets;
4.5 Filters and Compression;
4.6 Other Filters;
4.7 Third-Party Filters;
Chapter 5: Groups, Links, and Iteration: The "H" in HDF5;
5.1 The Root Group and Subgroups;
5.2 Group Basics;
5.3 Working with Links;
5.4 Iteration and Containership;
5.5 Multilevel Iteration with the Visitor Pattern;
5.6 Copying Objects;
5.7 Object Comparison and Hashing;
Chapter 6: Storing Metadata with Attributes;
6.1 Attribute Basics;
6.2 Real-World Example: Accelerator Particle Database;
Chapter 7: More About Types;
7.1 The HDF5 Type System;
7.2 Integers and Floats;
7.3 Fixed-Length Strings;
7.4 Variable-Length Strings;
7.5 Compound Types;
7.6 Complex Numbers;
7.7 Enumerated Types;
7.8 Booleans;
7.9 The array Type;
7.10 Opaque Types;
7.11 Dates and Times;
Chapter 8: Organizing Data with References, Types, and Dimension Scales;
8.1 Object References;
8.2 Region References;
8.3 Named Types;
8.4 Dimension Scales;
Chapter 9: Concurrency: Parallel HDF5, Threading, and Multiprocessing;
9.1 Python Parallel Basics;
9.2 Threading;
9.3 Multiprocessing;
9.4 MPI and Parallel HDF5;
Chapter 10: Next Steps;
10.1 Asking for Help;
10.2 Contributing;
Index;
Colophon;
Klappentext
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
