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==============
Other Material
==============
Videos
======
These are the videos of a series dedicated to introduce the main features of
PyTables in a visual and easy to grasp manner.
More videos will be made available with the time:
* `HDF5 is for Lovers, SciPy 2012 Tutorial <http://www.youtube.com/watch?v=Nzx0HAd3FiI>`_:
a beginer's introduction to PyTables and HDF5.
* `PyTables, part I: Introduction
<http://showmedo.com/videos/video?name=1780000&fromSeriesID=178>`_:
HDF5 file creation, the object tree, homogeneous array storage, natural
naming, working with attributes.
* `PyTables, part II: Working with tables
<http://showmedo.com/videos/video?name=1780010&fromSeriesID=178>`_:
Creation of tables with multidimensional and nested columns, and how to
efficiently query them.
Presentations
=============
Here are the slides of some presentations about PyTables that you may find
useful:
* HDF5 is for Lovers, SciPy 2012 Tutorial, July 2012, Austin, TX, USA,
`slides (pdf) <https://raw.github.com/scopatz/scipy2012/master/hdf5/scopatz_scipy2012_hdf5.pdf>`_,
`video <http://www.youtube.com/watch?v=Nzx0HAd3FiI>`_,
`exercises <https://github.com/scopatz/scipy2012/tree/master/hdf5/exer>`_,
`solutions <https://github.com/scopatz/scipy2012/tree/master/hdf5/sol>`_, and
`repository <https://github.com/scopatz/scipy2012>`_.
* `An on-disk binary data container <http://www.pytables.org/docs/PUG-Austin-2012-v3.pdf>`_.
Talk given at the `Austin Python Meetup <http://www.meetup.com/austinpython>`_,
Austin, TX, USA (May 2012).
* `Large Data Analysis with Python <http://www.pytables.org/docs/LargeDataAnalysis.pdf>`_.
Seminar given at the `German Neuroinformatics Node <http://www.g-node.org>`_,
Munich, Germany (November 2010).
* `Highly Efficient Computations In Python: Well Beyond NumPy
<http://pytables.org/EuroSciPy2010/HighlyEfficientComputations.pdf>`_.
Tutorial given at `EuroSciPy 2010 <https://www.euroscipy.org/conference/euroscipy2010>`_
conference in Paris, France (July 2010).
* `Starving CPUs (and coping with that in PyTables)
<http://www.pytables.org/docs/StarvingCPUs-PyTablesUsages.pdf>`_.
Seminar given at `FOM Institute for Plasma Physics Rijnhuizen <http://www.rijnhuizen.nl/>`_,
The Netherlands (September 2009).
* `On The Data Access Issue (or Why Modern CPUs Are Starving)
<http://www.pytables.org/docs/StarvingCPUs.pdf>`_.
Keynote presented at `EuroSciPy 2009 <https://www.euroscipy.org/>`_ conference
in Leipzig, Germany (July 2009).
* `An Overview of Future Improvements to OPSI
<http://www.pytables.org/docs/THG-2007-PlansForNewOPSI.pdf>`_.
Informal talk given at the `THG headquarters <http://www.hdfgroup.org>`_ in
Urbana-Champaign, Illinois, USA (October 2007).
* `Finding Needles in a Huge DataStack
<http://www.pytables.org/docs/FindingNeedles.pdf>`_.
Talk given at the **EuroPython 2006 Conference**, held at CERN, Genève,
Switzerland (July 2006).
* `Presentation given at the "HDF Workshop 2005"
<http://www.pytables.org/docs/HDF_IX_Workshop.pdf>`_, held at San Francisco,
USA (December 2005).
* `I <http://www.pytables.org/docs/taller-sf1-color.pdf>`_ and
`II <http://www.pytables.org/docs/taller-sf2-color.pdf>`_ **Workshop in Free
Software and Scientific Computing** given at the Universitat Jaume I,
Castelló, Spain (October 2004). In Catalan.
* `Presentation given at the "SciPy Workshop 2004"
<http://www.pytables.org/docs/SciPy04.pdf>`_, held at Caltech, Pasadena,
USA (September 2004).
* `Slides <http://www.pytables.org/docs/EuroPython2003.pdf>`_ of presentation
given at **EuroPython Conference** in Charleroi, Belgium (June 2003).
* `Presentation for the "iParty5" <http://www.pytables.org/docs/iparty2003.pdf>`_
held at Castelló, Spain (May 2003). In Spanish.
* `Talk <http://www.pytables.org/docs/pycon2003.pdf>`_ on PyTables given at
the **PyCon 2003 Convention** held at Washington, USA (March 203).
Reports
=======
* White Paper on `OPSI indexes <http://www.pytables.org/docs/OPSI-indexes.pdf>`_,
explaining the powerful new indexing engine in PyTables Pro.
* `Performance study <http://www.pytables.org/docs/NewObjectTreeCache.pdf>`_
on how the new object tree cache introduced in PyTables 1.2 can accelerate
the opening of files with a large number of objects, while being quite less
memory hungry.
* `Paper version <http://www.pytables.org/docs/pycon2003-paper.pdf>`_ of the
presentation at PyCon2003.
Other sources for examples
==========================
The examples presented above show just a little amount of the full capabilities
of PyTables.
Please check out the documentation and the :file:`examples/` directory in the
source package for more examples.
|
