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# Bibliography

[HDFG1]
  The HDF Group. What is HDF5?. Concise description about HDF5 capabilities and its differences from earlier versions (HDF4). http://hdfgroup.org/whatishdf5.html.

[HDFG2]
  The HDF Group. Introduction to HDF5. Introduction to the HDF5 data model and programming model. http://hdfgroup.org/HDF5/doc/H5.intro.html.

[HDFG3]
  The HDF Group. The HDF5 table programming model. Examples on using HDF5 tables with the C API. http://hdfgroup.org/HDF5/Tutor/h5table.html.

[MERTZ]
  David Mertz. Objectify. On the ‘Pythonic’ treatment of XML documents as objects(II). Article describing XML Objectify, a Python module that allows working with XML documents as Python objects. Some of the ideas presented here are used in PyTables. http://www-106.ibm.com/developerworks/xml/library/xml-matters2/index.html.

[CYTHON]
  Stefan Behnel, Robert Bradshaw, Dag Sverre Seljebotn, and Greg Ewing. Cython. A language that makes writing C extensions for the Python language as easy as Python itself. http://cython.org.

[NETCDF1]
  Glenn Davis, Russ Rew, Steve Emmerson, John Caron, and Harvey Davies. NetCDF. Network Common Data Form. An interface for array-oriented data access and a library that provides an implementation of the interface. http://www.unidata.ucar.edu/packages/netcdf/.

[NETCDF2]
  Russ Rew, Mike Folk, and et al. NetCDF-4. Network Common Data Form version 4. Merging the NetCDF and HDF5 Libraries. http://www.unidata.ucar.edu/software/netcdf/netcdf-4/.

[NUMPY]
  Travis Oliphant and et al. NumPy. Scientific Computing with Numerical Python. The latest and most powerful re-implementation of Numeric to date. It implements all the features that can be found in Numeric and numarray, plus a bunch of new others. In general, it is more efficient as well. http://numeric.scipy.org/.

[NUMERIC]
  David Ascher, Paul F Dubois, Konrad Hinsen, Jim Hugunin, and Travis Oliphant. Numerical Python. Package to speed-up arithmetic operations on arrays of numbers. http://sourceforge.net/projects/numpy/.

[NUMARRAY]
  Perry Greenfield, Todd Miller, Richard L White, J. C. Hsu, Paul Barrett, Jochen Küpper, and Peter J Verveer. Numarray. Reimplementation of Numeric which adds the ability to efficiently manipulate large numeric arrays in ways similar to Matlab and IDL. Among others, Numarray provides the record array extension. http://stsdas.stsci.edu/numarray/.

[NUMEXPR]
  David Cooke, Francesc Alted, and et al. Numexpr. Fast evaluation of array expressions by using a vector-based virtual machine. It is an enhaced computing kernel that is generally faster (between 1x and 10x, depending on the kind of operations) than NumPy at evaluating complex array expressions. http://code.google.com/p/numexpr.

[ZLIB]
  JeanLoup Gailly and Mark Adler. zlib. A Massively Spiffy Yet Delicately Unobtrusive Compression Library. A standard library for compression purposes. http://www.gzip.org/zlib/.

[LZO]
  Markus F Oberhumer. LZO. A data compression library which is suitable for data de-/compression in real-time. It offers pretty fast compression and decompression with reasonable compression ratio. http://www.oberhumer.com/opensource/.

[BZIP2]
  Julian Seward. bzip2. A high performance lossless compressor. It offers very high compression ratios within reasonable times. http://www.bzip.org/.

[BLOSC]
  Francesc Alted. Blosc. A blocking, shuffling and loss-less compression library. A compressor designed to transmit data from memory to CPU (and back) faster than a plain memcpy(). http://blosc.pytables.org/.

[GNUWIN32]
  Alexis Wilke, Jerry S., Kees Zeelenberg, and Mathias Michaelis. GnuWin32. GNU (and other) tools ported to Win32. GnuWin32 provides native Win32-versions of GNU tools, or tools with a similar open source licence. http://gnuwin32.sourceforge.net/.

[PYSCO]
  Armin Rigo. Psyco. A Python specializing compiler. Run existing Python software faster, with no change in your source. http://psyco.sourceforge.net.

[SCIPY1]
  Konrad Hinsen. Scientific Python. Collection of Python modules useful for scientific computing. http://dirac.cnrs-orleans.fr/ScientificPython.

[SCIPY2]
  Eric Jones, Travis Oliphant, Pearu Peterson, and et al. SciPy. Scientific tools for Python. SciPy supplements the popular Numeric module, gathering a variety of high level science and engineering modules together as a single package. http://www.scipy.org.

[OPTIM]
  Francesc Alted and Ivan Vilata. Optimization of file openings in PyTables. This document explores the savings of the opening process in terms of both CPU time and memory, due to the adoption of a LRU cache for the nodes in the object tree. http://www.pytables.org/docs/NewObjectTreeCache.pdf.

[OPSI]
  Francesc Alted and Ivan Vilata. OPSI: The indexing system of PyTables 2 Professional Edition. Exhaustive description and benchmarks about the indexing engine that comes with PyTables Pro. http://www.pytables.org/docs/OPSI-indexes.pdf.

[VITABLES]
  Vicent Mas. ViTables. A GUI for PyTables/HDF5 files. It is a graphical tool for browsing and editing files in both PyTables and HDF5 formats. http://www.vitables.org.

[GIT]
  Git is a free and open source, distributed version control system designed to handle everything from small to very large projects with speed and efficiency http://git-scm.com.

[SPHINX]
  Sphinx is a tool that makes it easy to create intelligent and beautiful documentation, written by Georg Brandl and licensed under the BSD license http://sphinx.pocoo.org/.

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