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h5py package

h5py 2.10.0-2build1 source package in Ubuntu

Changelog

h5py (2.10.0-2build1) focal; urgency=medium

  * No-change rebuild to generate dependencies on python2.

 -- Matthias Klose <email address hidden>  Tue, 17 Dec 2019 12:34:03 +0000

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Uploaded by:
Matthias Klose
Uploaded to:
Focal
Original maintainer:
Debian Science Team
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

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File Size SHA-256 Checksum
h5py_2.10.0.orig.tar.gz 294.0 KiB 84412798925dc870ffd7107f045d7659e60f5d46d1c70c700375248bf6bf512d
h5py_2.10.0-2build1.debian.tar.xz 10.4 KiB 15580e0d5c495e4d7f3484a2e1c6ac20af6a5efa743a855e47c1212d7d61f5f5
h5py_2.10.0-2build1.dsc 2.5 KiB 8b838c7d0fc649a02aa71b42d29d07ffdb13a7b19e445d8db2ec7bccbbfc68c7

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Binary packages built by this source

python-h5py-doc: documentation for h5py

 HDF5 for Python (h5py) is a general-purpose Python interface to the
 Hierarchical Data Format library, version 5. HDF5 is a versatile, mature
 scientific software library designed for the fast, flexible storage of
 enormous amounts of data.
 .
 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.
 .
 This package provides the documentation.

python3-h5py: general-purpose Python interface to hdf5 (Python 3)

 HDF5 for Python (h5py) is a general-purpose Python interface to the
 Hierarchical Data Format library, version 5. HDF5 is a versatile, mature
 scientific software library designed for the fast, flexible storage of
 enormous amounts of data.
 .
 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.
 .
 This package provides the modules for Python 3.

python3-h5py-dbg: debug extensions for h5py (Python 3)

 HDF5 for Python (h5py) is a general-purpose Python interface to the
 Hierarchical Data Format library, version 5. HDF5 is a versatile, mature
 scientific software library designed for the fast, flexible storage of
 enormous amounts of data.
 .
 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.
 .
 This package provides the debug extensions for Python 3.