Ubuntu
pytables package

pytables 3.1.1-0ubuntu1 source package in Ubuntu

Changelog

pytables (3.1.1-0ubuntu1) trusty; urgency=medium

  [ Antonio Valentino ]
  * New upstream release
  * debian/control
    - debug package now depend on python(3)-dbg, python(3)-numpy-dbg,
      pyhon(3)-numexpr-dbg instead of recommending them
      (Closes: #742557)
    - use versioned dependency for lzo (>= 0.0~r114) since one of the
      symbols used in blosc is missing in earlier versions
  * debian/patches
    - new patch (0003-Better-control-of-verbosity-in-unittests.patch)
      for better control of verbosity in unittests
    - new patch 0005-Do-not-fetch-icons-for-external-web-sites.patch
      to avoid fetching data from external web sites in html pages of
      the doc
    - the 0003-Only-use-msse2-flag-on-x86_64.patch has been updated
      after the upstream changes and renamed into
      0004-Never-use-the-msse2-flag-explicitly.patch
    - all remaining patches has been refreshed

  [ Julian Taylor ]
  * upload to ubuntu trusty
 -- Julian Taylor <email address hidden>   Mon, 14 Apr 2014 18:47:27 +0200

Upload details

Uploaded by:
Julian Taylor
Uploaded to:
Trusty
Original maintainer:
Ubuntu Developers
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Trusty release universe python

Downloads

File Size SHA-256 Checksum
pytables_3.1.1.orig.tar.gz 3.6 MiB 75c7351204a8356513a7d99663250a3cfcf377b4e5e24c4bcaeab39e6ba01268
pytables_3.1.1-0ubuntu1.debian.tar.gz 18.0 KiB c46a76a01ae76fdcdb938b05db64ef7f4c56575c31774b48d9635ff1b59bf205
pytables_3.1.1-0ubuntu1.dsc 2.9 KiB a3a59c72d8df06586e0b28d7b2b222ff283b9d92b9c63c754f95887282cd06dc

Available diffs

View changes file

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: No summary available for python-tables-doc in ubuntu utopic.

No description available for python-tables-doc in ubuntu utopic.

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.