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A library for reading hdf5 files with GNU Octave
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jromazanov/hdf5oct
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hdf5oct - a HDF5 wrapper for GNU Octave ======================================= Copyright 2012 Tom Mullins Copyright 2015 Tom Mullins, Anton Starikov, Thorsten Liebig, Stefan Großhauser Copyright 2008-2013 Andrew Collette This is a library for GNU Octave for reading hdf5 files. At the moment it provides the following functions: h5read: modeled after Matlab's h5read, which can read subsets of a dataset. Octave's load function will attempt to read an entire dataset, which for very large datasets is undesired. h5util's h5read function does slightly better, reading only 2d slices of 3d datasets, but that's still fairly limiting. This exposes libhdf5's H5Sselect_hyperslab in a way which tries to be compatible with Matlab. h5readatt: Most of this function was written by thliebig. It allows to read scalar HDF5 attributes of some types. h5write: Write a matrix to a dataset. This will either overwrite an already existing dataset, or allow to append hyperslabs to existing datasets. h5writeatt: Attach an attribute to an object. h5create: Create a dataset and specify its extent dimensions, datatype and chunk size. h5delete: Delete a group, dataset, or attribute. Note that only few of the HDF5 datatypes are supported by each of the functions hdf5oct at the moment, typically one or several of double, integer and string. # INSTALLATION ######################### To install, just use make This will produce a package file named "hdf5oct-*.tar.gz" . Then you may either install the package with make install or you may start GNU Octave and install the package manually (using the correct file name) with the command pkg install hdf5oct-0.2.0.tar.gz This will put the *.oct files somewhere where Octave will find them. You can try running make test # DEINSTALLATION ######################### To uninstall the package you may want to use make uninstall # TODO ################################# - write h5info, h5disp - support compression flags for h5create - read string typed datasets - read string-array typed attributes - write more comprehensive tests instead of a few random choices. Also test for error conditions.
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