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Python and HDF5: Unlocking Scientific Data by [Collette, Andrew]

Python and HDF5: Unlocking Scientific Data 1st Edition, Kindle Edition

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Product description

Book Description

Unlocking Scientific Data

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.

Product details

  • Format: Kindle Edition
  • File Size: 2089 KB
  • Print Length: 152 pages
  • Simultaneous Device Usage: Unlimited
  • Publisher: O'Reilly Media; 1 edition (21 Oct. 2013)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ISBN-10: 1491944994
  • ISBN-13: 978-1491944998
  • ASIN: B00G2QUU6U
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Enabled
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #141,935 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most helpful customer reviews on 4.7 out of 5 stars 5 reviews
Fortran guy
5.0 out of 5 starsI occasionally use Python (I am a crusty old F90 type of DOE lab physicist who generally accesses HDF5 from Fortran directly for data analysis and simulation code I/O) and this book made it very easy to write simple scripts to write/read/analyze data in HDF5
29 July 2017 - Published on
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A. Zubarev
5.0 out of 5 starsAn excellent technical read, concise, professional
28 November 2013 - Published on
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