Python and HDF5

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.

Chapter 1 Introduction
Python and HDF5
What Exactly Is HDF5?
Chapter 2 Getting Started
HDF5 Basics
Setting Up
The HDF5 Tools
Your First HDF5 File
Chapter 3 Working with Datasets
Dataset Basics
Reading and Writing Data
Resizing Datasets
Chapter 4 How Chunking and Compression Can Help You
Contiguous Storage
Chunked Storage
Setting the Chunk Shape
Performance Example: Resizable Datasets
Filters and Compression
Other Filters
Third-Party Filters
Chapter 5 Groups, Links, and Iteration: The "H" in HDF5
The Root Group and Subgroups
Group Basics
Working with Links
Iteration and Containership
Multilevel Iteration with the Visitor Pattern
Copying Objects
Object Comparison and Hashing
Chapter 6 Storing Metadata with Attributes
Attribute Basics
Real-World Example: Accelerator Particle Database
Chapter 7 More About Types
The HDF5 Type System
Integers and Floats
Fixed-Length Strings
Variable-Length Strings
Compound Types
Complex Numbers
Enumerated Types
Booleans
The array Type
Opaque Types
Dates and Times
Chapter 8 Organizing Data with References, Types, and Dimension Scales
Object References
Region References
Named Types
Dimension Scales
Chapter 9 Concurrency: Parallel HDF5, Threading, and Multiprocessing
Python Parallel Basics
Threading
Multiprocessing
MPI and Parallel HDF5
Chapter 10 Next Steps
Asking for Help
Contributing
Index
Colophon