h5py 3.7.0-3 source package in Ubuntu

 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 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.

 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.

 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.

 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.