h5py 3.9.0-4 source package in Ubuntu
Changelog
h5py (3.9.0-4) unstable; urgency=medium * Team upload. * update h5py/__init__.py to enable fallback mechanism both ways. If h5py-serial is not installed then load h5py-mpi in serial jobs. If h5py-mpi is not installed than load h5py-serial in MPI jobs. Obviously at least one of them needs to be installed. This enables h5py to work transparently in serial jobs if installed via "apt install python3-h5py python3-h5py-mpi" (which would skip installation of python3-h5py-serial) * the fallback mechanism makes it possible for h5py to operate in both serial and MPI environments with only python3-h5py-serial or with only python3-h5py-mpi installed (obviously the MPI-IO features are only available with python3-h5py-mpi) * add handling of H5PY_NEVER_USE_MPI environment to force use of h5py-serial in MPI jobs. If both H5PY_NEVER_USE_MPI and H5PY_ALWAYS_USE_MPI are set then H5PY_ALWAYS_USE_MPI takes priority (h5py-mpi is loaded). * document fallback and H5PY_NEVER_USE_MPI behaviour in README.Debian -- Drew Parsons <email address hidden> Fri, 13 Oct 2023 13:16:16 +0200
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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
See full publishing history Publishing
Series | Published | Component | Section |
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Downloads
File | Size | SHA-256 Checksum |
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h5py_3.9.0-4.dsc | 2.8 KiB | 37117fc4e31bee328f6b8e9f7e3c4e3ecf0f576674b8a4cf5191de9aca2e9454 |
h5py_3.9.0.orig.tar.gz | 393.4 KiB | e604db6521c1e367c6bd7fad239c847f53cc46646f2d2651372d05ae5e95f817 |
h5py_3.9.0-4.debian.tar.xz | 21.0 KiB | db52867f627347fe2649d6fc8a3b51fe19f7d7fc8aec52f9a27fe32e25c62ce4 |
No changes file available.
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