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From a Python programmer's perspective, HDF5 provides a robust way to store data, organized by name in a tree-like fashion. You can create datasets (arrays on disk) hundreds of gigabytes in size, and perform random-access I/O on desired sections. Datasets are organized in a filesystem-like hierarchy using containers called "groups", and accessed using the tradional POSIX /path/to/resource syntax.
H5py provides a simple, robust read/write interface to HDF5 data from Python. Existing Python and Numpy concepts are used for the interface; for example, datasets on disk are represented by a proxy class that supports slicing, and has dtype and shape attributes. HDF5 groups are presented using a dictionary metaphor, indexed by name.
README file for h5py version 2.0.0 ================================== Websites -------- * Docs, general info: h5py.alfven.org * Downloads, FAQ, bug tracker: h5py.googlecode.com * Mailing list: h5py at googlegroups Prerequisites ------------- You need, at a minimum: * HDF5 1.8.3 or later * Python 2.6, 2.7 or 3.2 * Any modern version of NumPy Optionally: * Cython 0.13 or later, more»
h5py (2.0.1-2+b1) sid; urgency=low * Binary-only non-maintainer upload for powerpc; no source changes. * Rebuild against improved dh_numpy. -- powerpc Build Daemon (parry) <email@example.com> Sat, 25 Feb 2012 23:35:46 +0000 h5py (2.0.1-2) unstable; urgency=low * Build depend on libhdf5-dev instead of libhdf5-serial-dev to accomodate hdf5 transition. -- Soeren S more»
This package was debianized by Soeren Sonnenburg <firstname.lastname@example.org> on Wed, 26 Aug 2009 22:53:35 +0200. It was downloaded from http://code.google.com/p/h5py/ . If not otherwise mentioned Copyright: © 2008 Andrew Collette <email@example.com> License: Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditi more»