Ubuntu
h5py package

h5py 2.7.1-1ubuntu1 source package in Ubuntu

Changelog

h5py (2.7.1-1ubuntu1) artful; urgency=medium

  * Move python-sphinx to BD.

 -- Gianfranco Costamagna <email address hidden>  Thu, 07 Sep 2017 09:30:01 +0200

Upload details

Uploaded by:
LocutusOfBorg on 2017-09-07
Uploaded to:
Artful
Original maintainer:
Debian Science Team
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section

Downloads

File Size SHA-256 Checksum
h5py_2.7.1.orig.tar.gz 258.0 KiB 180a688311e826ff6ae6d3bda9b5c292b90b28787525ddfcb10a29d5ddcae2cc
h5py_2.7.1-1ubuntu1.debian.tar.xz 7.0 KiB ebd1a48e82cffa939ff77f7281b0734cac31431dc225e50ec3597dc8535d4a61
h5py_2.7.1-1ubuntu1.dsc 2.6 KiB 4c5e87030702bbfaea7fc68b6c2dfbe4d8641c9b7a63eded060fb678aa92edbf

View changes file

Binary packages built by this source

python-h5py: general-purpose Python interface to hdf5 (Python 2)

 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 2.

python-h5py-dbg: debug extensions for h5py (Python 2)

 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 2.

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.