h5py 3.7.0-8 source package in Ubuntu

Changelog

h5py (3.7.0-8) unstable; urgency=medium

  * Team upload.
  * sparc64: skip test_reference_field

 -- Drew Parsons <email address hidden>  Mon, 20 Feb 2023 17:33:32 +0100

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

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Mantic release universe python
Lunar release universe python

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File Size SHA-256 Checksum
h5py_3.7.0-8.dsc 2.8 KiB 9a0d07031642769026e53f9c5e18c75e2f6576f5c40021bd7c347d1c995323d9
h5py_3.7.0.orig.tar.gz 383.2 KiB 3fcf37884383c5da64846ab510190720027dca0768def34dd8dcb659dbe5cbf3
h5py_3.7.0-8.debian.tar.xz 19.4 KiB f5feb1bc831fdf4061910a4d1aee1f8d1216c5be1eefe9ddf43627b8d1682e74

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Binary packages built by this source

hdf5-plugin-lzf: hdf5 plugin to lzf compression library

 HDF5 (Hierarchical Data Format library, version 5) is a versatile,
 mature scientific software library designed for the fast, flexible
 storage of enormous amounts of data.
 .
 This package provides a plugin to the HDF5 LZF filter for the LZF
 compression library. Plugins are built for both serial (single
 processor) jobs (libhdf5-dev) and for multiprocessor (threaded) jobs
 (libhdf5-mpi-dev).

hdf5-plugin-lzf-dbgsym: debug symbols for hdf5-plugin-lzf
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

 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 is a simple dependency package which depends on the serial or
 MPI build of h5py and provides test data files.

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

 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, built with support
 for MPI (multiprocessor) jobs.

python3-h5py-mpi-dbgsym: debug symbols for python3-h5py-mpi
python3-h5py-serial: general-purpose Python interface to hdf5 (Python 3 serial)

 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, built for serial
 (single processor) jobs.

python3-h5py-serial-dbgsym: debug symbols for python3-h5py-serial