Ebook Download Sonic Universe 2: 30 Years LaterBy Sonic Scribes

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Sonic Universe 2: 30 Years LaterBy Sonic Scribes

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Book Details:

• Format: Paperback
• Publication Date: 3/6/2012
• Pages: 112

• Sales Rank: #362441 in Books
• Brand: Archie Comics
• Model: FBA-|280884
• Published on: 2012-03-06
• Released on: 2012-03-06
• Original language: English
• Number of items: 1
• Dimensions: 10.20" h x .29" w x 6.65" l, .60 pounds
• Binding: Paperback
• 112 pages

About the Author
THE SONIC SCRIBES are the many talented writers and artists who have helped create Archie Comics' popular and record-setting Sonic the Hedgehog comic books and graphic novels for two decades. They include early favorites such as Angelo DeCesare, James Fry, Art Mawhinney and Ken Penders, recent greats like Ian Flynn, Ben Bates, Tracy Yardley! and Jamal Peppers, and veterans like Patrick "Spaz" Spaziante and Steven Butler—and many more!

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Get Free Ebook Python and HDF5: Unlocking Scientific DataBy Andrew Collette

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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, 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

• Amazon Sales Rank: #740135 in eBooks
• Published on: 2013-10-21
• Released on: 2013-10-21
• Format: Kindle eBook

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.

Python and HDF5: Unlocking Scientific DataBy Andrew Collette PDF
Python and HDF5: Unlocking Scientific DataBy Andrew Collette EPub
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Python and HDF5: Unlocking Scientific DataBy Andrew Collette iBooks
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Python and HDF5: Unlocking Scientific DataBy Andrew Collette PDF

Python and HDF5: Unlocking Scientific DataBy Andrew Collette PDF

Python and HDF5: Unlocking Scientific DataBy Andrew Collette PDF

Python and HDF5: Unlocking Scientific DataBy Andrew Collette PDF

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