Andrew Collette - Python and HDF5
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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, youll 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 youre familiar with the basics of Python data analysis, this is an ideal introduction to HDF5.
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Over the past several years, Python has emerged as a credible alternative toscientific analysis environments like IDL or MATLAB. Stable core packagesnow exist for handling numerical arrays (NumPy), analysis (SciPy), andplotting (matplotlib). A huge selection of more specialized software is alsoavailable, reducing the amount of work necessary to write scientific codewhile also increasing the quality of results.
As Python is increasingly used to handle large numerical datasets, moreemphasis has been placed on the use of standard formats for data storageand communication. HDF5, the most recent version of the HierarchicalData Format originally developed at the National Center for Supercomputing Applications (NCSA), has rapidly emerged as themechanism of choice for storing scientific data in Python. At the same time,many researchers who use (or are interested in using) HDF5 have been drawn toPython for its ease of use and rapid development capabilities.
This book provides an introduction to using HDF5 from Python, and isdesigned to be useful to anyone with a basic background in Python dataanalysis. Only familiarity with Python and NumPy is assumed. Special emphasisis placed on the native HDF5 feature set, rather than higher-level abstractionson the Python side, to make the book as useful as possible for creatingportable files.
Finally, this book is intended to support both users of Python 2 and Python 3.While the examples are written for Python 2, any differences that may tripyou up are noted in the text.
The following typographical conventions are used in this book:
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I would like to thank Quincey Koziol, Elena Pourmal, Gerd Heber, and the others atthe HDF Group for supporting the use of HDF5 by the Python community.This book benefited greatly from reviewer comments, including those byEli Bressert and Anthony Scopatz, as well as the dedication and guidanceof OReilly editor Meghan Blanchette.
Darren Dale and many others deserve thanks for contributing tothe h5py project, along with Francesc Alted, Antonio Valentino, and fellowauthors of PyTables who first brought the HDF5 and Python worlds together.I would also like to thank Steve Vincena and Walter Gekelman of theUCLA Basic Plasma Science Facility, where I first began working withlarge-scale scientific datasets.
When I was a graduate student, I had a serious problem: a brand-new dataset,made up of millions of data points collected painstakingly over a full week on anationally recognized plasma research device, that contained values that weremuch too small.
About 40 orders of magnitude too small.
My advisor and I huddled in his office, in front of the shiny new G5 Power Macthat ran our visualization suite, and tried to figure out what was wrong.The data had been acquired correctly from the machine. It looked like the original raw file from the experimentsdigitizer was fine. I had written a (very large) script in the IDLprogramming language on my Thinkpad laptop to turn the raw data into files thevisualization tool could use. This in-house format was simplicity itself:just a short fixed-width header and then a binary dump of thefloating-point data. Even so, I spent another hour or so writing a program toverify and plot the files on my laptop. They were fine. And yet, whenloaded into the visualizer, all the data that looked so beautiful in IDLturned into a featureless, unstructured mush of values all around 10-41.
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