1Bibliography 2============ 3 4.. _HDFG1: 5 6:ref:`[HDFG1] <HDFG1>` 7 The HDF Group. What is HDF5?. Concise description about HDF5 capabilities 8 and its differences from earlier versions (HDF4). 9 `<http://www.hdfgroup.org/HDF5/whatishdf5.html>`_. 10 11.. _HDFG2: 12 13:ref:`[HDFG2] <HDFG2>` 14 The HDF Group. Introduction to HDF5. Introduction to the HDF5 data model 15 and programming model. `<http://www.hdfgroup.org/HDF5/doc/H5.intro.html>`_. 16 17.. _HDFG3: 18 19:ref:`[HDFG3] <HDFG3>` 20 The HDF Group. The HDF5 table programming model. Examples on using HDF5 21 tables with the C API. `<http://www.hdfgroup.org/HDF5/Tutor/h5table.html>`_. 22 23.. _MERTZ: 24 25:ref:`[MERTZ] <MERTZ>` 26 David Mertz. Objectify. On the 'Pythonic' treatment of XML documents as 27 objects(II). Article describing XML Objectify, a Python module that 28 allows working with XML documents as Python objects. 29 Some of the ideas presented here are used in PyTables. 30 `<http://gnosis.cx/publish/programming/xml_matters_2.html>`_. 31 32.. _CYTHON: 33 34:ref:`[CYTHON] <CYTHON>` 35 Stefan Behnel, Robert Bradshaw, Dag Sverre Seljebotn, and Greg Ewing. 36 Cython. A language that makes writing C extensions for the Python 37 language as easy as Python itself. `<http://www.cython.org>`_. 38 39.. _NUMPY: 40 41:ref:`[NUMPY] <NUMPY>` 42 Travis Oliphant and et al. NumPy. Scientific Computing with Numerical 43 Python. The latest and most powerful re-implementation of Numeric to 44 date. 45 It implements all the features that can be found in Numeric and numarray, 46 plus a bunch of new others. In general, it is more efficient as well. 47 `<http://www.numpy.org>`_. 48 49.. _NUMEXPR: 50 51:ref:`[NUMEXPR] <NUMEXPR>` 52 David Cooke, Francesc Alted, and et al. Numexpr. Fast evaluation of array 53 expressions by using a vector-based virtual machine. 54 It is an enhaced computing kernel that is generally faster (between 1x 55 and 10x, depending on the kind of operations) than NumPy at evaluating 56 complex array expressions. `<http://code.google.com/p/numexpr>`_. 57 58.. _ZLIB: 59 60:ref:`[ZLIB] <ZLIB>` 61 JeanLoup Gailly and Mark Adler. zlib. A Massively Spiffy Yet Delicately 62 Unobtrusive Compression Library. A standard library for compression 63 purposes. `<http://www.gzip.org/zlib/>`_. 64 65.. _LZO: 66 67:ref:`[LZO] <LZO>` 68 Markus F Oberhumer. LZO. A data compression library which is suitable for 69 data de-/compression in real-time. It offers pretty fast compression and 70 decompression with reasonable compression ratio. 71 `<http://www.oberhumer.com/opensource/>`_. 72 73.. _BZIP2: 74 75:ref:`[BZIP2] <BZIP2>` 76 Julian Seward. bzip2. A high performance lossless compressor. 77 It offers very high compression ratios within reasonable times. 78 `<http://www.bzip.org/>`_. 79 80.. _BLOSC: 81 82:ref:`[BLOSC] <BLOSC>` 83 Francesc Alted. Blosc. A blocking, shuffling and loss-less compression 84 library. A compressor designed to transmit data from memory to CPU 85 (and back) faster than a plain memcpy(). 86 `<http://www.blosc.org/>`_. 87 88.. _GNUWIN32: 89 90:ref:`[GNUWIN32] <GNUWIN32>` 91 Alexis Wilke, Jerry S., Kees Zeelenberg, and Mathias Michaelis. 92 GnuWin32. GNU (and other) tools ported to Win32. 93 GnuWin32 provides native Win32-versions of GNU tools, or tools with a 94 similar open source licence. 95 `<http://gnuwin32.sourceforge.net/>`_. 96 97.. _PSYCO: 98 99:ref:`[PSYCO] <PSYCO>` 100 Armin Rigo. Psyco. A Python specializing compiler. 101 Run existing Python software faster, with no change in your source. 102 `<http://psyco.sourceforge.net>`_. 103 104.. _SCIPY1: 105 106:ref:`[SCIPY1] <SCIPY1>` 107 Konrad Hinsen. Scientific Python. Collection of Python modules useful for 108 scientific computing. 109 `<http://dirac.cnrs-orleans.fr/ScientificPython>`_. 110 111.. _SCIPY2: 112 113:ref:`[SCIPY2] <SCIPY2>` 114 Eric Jones, Travis Oliphant, Pearu Peterson, and et al. SciPy. 115 Scientific tools for Python. SciPy supplements the popular Numeric module, 116 gathering a variety of high level science and engineering modules 117 together as a single package. 118 `<http://www.scipy.org>`_. 119 120.. _OPTIM: 121 122:ref:`[OPTIM] <OPTIM>` 123 Francesc Alted and Ivan Vilata. Optimization of file openings in PyTables. 124 This document explores the savings of the opening process in terms of 125 both CPU time and memory, due to the adoption of a LRU cache for the 126 nodes in the object tree. 127 `<http://www.pytables.org/docs/NewObjectTreeCache.pdf>`_. 128 129.. _OPSI: 130 131:ref:`[OPSI] <OPSI>` 132 Francesc Alted and Ivan Vilata. OPSI: The indexing system of PyTables 2 133 Professional Edition. Exhaustive description and benchmarks about the 134 indexing engine that comes with PyTables Pro. 135 `<http://www.pytables.org/docs/OPSI-indexes.pdf>`_. 136 137.. _VITABLES: 138 139:ref:`[VITABLES] <VITABLES>` 140 Vicent Mas. ViTables. A GUI for PyTables/HDF5 files. 141 It is a graphical tool for browsing and editing files in both PyTables 142 and HDF5 formats. 143 `<http://vitables.org>`_. 144 145.. _GIT: 146 147:ref:`[GIT] <GIT>` 148 Git is a free and open source, distributed version control system designed 149 to handle everything from small to very large projects with speed and 150 efficiency `<http://git-scm.com>`_. 151 152.. _SPHINX: 153 154:ref:`[SPHINX] <SPHINX>` 155 Sphinx is a tool that makes it easy to create intelligent and beautiful 156 documentation, written by Georg Brandl and licensed under the BSD license 157 `<http://sphinx-doc.org>`_. 158 159.. |Kuepper| unicode:: K U+00FC pper .. Kuepper 160 161.. todo:: remove the above substitution. It is no more needed with sphinx 162 1.0.8 163