pytables 3.1.0-1 source package in Ubuntu
Changelog
pytables (3.1.0-1) unstable; urgency=low * New upstream release (Closes: #734298) * Standard version bumped to 3.9.5 (no changes) * New python(3)-tables-lib packages. The old python(3)-tables packages have been split into python(3)-tables (containing common code for all platforms) and python(3)-tables-lib (containing only platform specific extensions). * New python-tables-data package including all data files used for unit testing * Added autopkgtests running testsuite * debian/patches - removed 0001-Fix-detection-of-platforms-supporting-blosc.patch (applied upstream) - removed 0003-disable-extended-float-support.patch no more necessary - remaining patches have been refreshed - new patch (0002-Use-system-compression-libs.patch) to use system compression libraries instead of internal copies * debian/control - added build dependencies form liblz4-dev and snappy-dev * temporary disabled tests for python3.x-dbg -- Antonio Valentino <email address hidden> Sun, 02 Mar 2014 15:47:11 +0000
Upload details
- Uploaded by:
- Debian Science Team
- Uploaded to:
- Sid
- Original maintainer:
- Debian Science Team
- Architectures:
- any all
- Section:
- python
- Urgency:
- Low Urgency
See full publishing history Publishing
Series | Published | Component | Section |
---|
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
pytables_3.1.0-1.dsc | 2.2 KiB | 406e3d83cea1f1667775efd5294954265e22cc9ce5fed11b1f4304701bf166bd |
pytables_3.1.0.orig.tar.gz | 3.5 MiB | f5cfa0cf1d3c49880a84a5643dce1380c846b2642097267eedc7852a5ffdc20e |
pytables_3.1.0-1.debian.tar.xz | 14.2 KiB | a21c1e012e9a51d9ab70b6241e23770802ad8c988ceec40848ff5c4b92635214 |
Available diffs
- diff from 3.0.0-2build1 (in Ubuntu) to 3.1.0-1 (532.4 KiB)
No changes file available.
Binary packages built by this source
- python-tables: hierarchical database for Python based on HDF5
PyTables is a hierarchical database package designed to efficiently
manage very large amounts of data. PyTables is built on top of the
HDF5 library and the NumPy package. It features an object-oriented
interface that, combined with natural naming and C-code generated
from Pyrex sources, makes it a fast, yet extremely easy to use tool
for interactively save and retrieve large amounts of data.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy, Numeric and numarray objects.
.
This is the Python 2 version of the package.
- python-tables-data: hierarchical database for Python based on HDF5 - test data
PyTables is a hierarchical database package designed to efficiently
manage very large amounts of data. PyTables is built on top of the
HDF5 library and the NumPy package. It features an object-oriented
interface that, combined with natural naming and C-code generated
from Pyrex sources, makes it a fast, yet extremely easy to use tool
for interactively save and retrieve large amounts of data.
.
This package includes daya fils used for unit testing.
- python-tables-dbg: hierarchical database for Python based on HDF5 (debug extension)
PyTables is a hierarchical database package designed to efficiently
manage very large amounts of data. PyTables is built on top of the
HDF5 library and the NumPy package. It features an object-oriented
interface that, combined with natural naming and C-code generated
from Pyrex sources, makes it a fast, yet extremely easy to use tool
for interactively save and retrieve large amounts of data.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy, Numeric and numarray objects.
.
This package contains the extension built for the Python 2 debug interpreter.
- python-tables-doc: hierarchical database for Python based on HDF5 - documentation
PyTables is a hierarchical database package designed to efficiently
manage very large amounts of data. PyTables is built on top of the
HDF5 library and the NumPy package. It features an object-oriented
interface that, combined with natural naming and C-code generated
from Pyrex sources, makes it a fast, yet extremely easy to use tool
for interactively save and retrieve large amounts of data.
.
This package includes the manual in PDF and HTML formats.
- python-tables-lib: hierarchical database for Python based on HDF5 (extension)
PyTables is a hierarchical database package designed to efficiently
manage very large amounts of data. PyTables is built on top of the
HDF5 library and the NumPy package. It features an object-oriented
interface that, combined with natural naming and C-code generated
from Pyrex sources, makes it a fast, yet extremely easy to use tool
for interactively save and retrieve large amounts of data.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy, Numeric and numarray objects.
.
This package contains the extension built for the Python 2 interpreter.
- python3-tables: hierarchical database for Python3 based on HDF5
PyTables is a hierarchical database package designed to efficiently
manage very large amounts of data. PyTables is built on top of the
HDF5 library and the NumPy package. It features an object-oriented
interface that, combined with natural naming and C-code generated
from Pyrex sources, makes it a fast, yet extremely easy to use tool
for interactively save and retrieve large amounts of data.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy, Numeric and numarray objects.
.
This is the Python 3 version of the package.
- python3-tables-dbg: hierarchical database for Python 3 based on HDF5 (debug extension)
PyTables is a hierarchical database package designed to efficiently
manage very large amounts of data. PyTables is built on top of the
HDF5 library and the NumPy package. It features an object-oriented
interface that, combined with natural naming and C-code generated
from Pyrex sources, makes it a fast, yet extremely easy to use tool
for interactively save and retrieve large amounts of data.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy, Numeric and numarray objects.
.
This package contains the extension built for the Python 3 debug interpreter.
- python3-tables-lib: hierarchical database for Python3 based on HDF5 (extension)
PyTables is a hierarchical database package designed to efficiently
manage very large amounts of data. PyTables is built on top of the
HDF5 library and the NumPy package. It features an object-oriented
interface that, combined with natural naming and C-code generated
from Pyrex sources, makes it a fast, yet extremely easy to use tool
for interactively save and retrieve large amounts of data.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy, Numeric and numarray objects.
.
This package contains the extension built for the Python 3 interpreter.