Announcing PyTables 3.2.0

We are happy to announce PyTables 3.2.0.

IMPORTANT NOTICE:

If you are a user of PyTables, it needs your help to keep going. Please
read the next thread as it contains important information about the
future (or lack of it) of the project:

https://groups.google.com/forum/#!topic/pytables-users/yY2aUa4H7W4

Thanks!
*******... read more

PyTables 3.1.1

This is a bug-fix release that addresses a critical bug that make PyTables
unusable on some platforms.

What's new

• Fixed a critical bug that caused an exception at import time.
  The error was triggered when a bug in long-double detection is detected
  in the HDF5 library (see :issue:275) and numpy_ does not expose
  float96 or float128. Closes :issue:344.
• The internal Blosc_ library has been updated to version 1.3.5.
  This fixes a false buffer overrun condition that made c-blosc to fail,
  even if the problem was not real.... read more

Announcing PyTables 3.1.0

We are happy to announce PyTables 3.1.0.

This is a feature release. The upgrading is recommended for users that
are running PyTables in production environments.

What's new

Probably the most relevant changes in this release are internal improvements
like the new node cache that is now compatible with the upcoming Python 3.4
and the registry for open files has been deeply reworked. The caching feature
of file handlers has been completely dropped so now PyTables is a little bit
more "thread friendly".... read more

Announcing PyTables 3.1.0rc2

We are happy to announce PyTables 3.1.0rc2.

Changes from 3.0rc1 to 3.1.0rc2

• HDF5 versions lower than 1.8.7 are not fully compatible with PyTables
  3.1. A partial support to HDF5 < 1.8.7 is still provided but in that
  case multiple file opens are not allowed at all (even in read-only
  mode).
• Fixed selection on float columns when NaNs are present (closes
  :issue:327 and :issue:330)
• The :meth:_FileRegistry.remove method now correctly removes keys
  that don't have associated handles
• Minor style and formatting improvements
• Close the file handle before trying to delete the corresponding file.
  Fixes a test failure on Windows.
• Use integer division for computing indices (fixes some warning on
  Windows)
• Fixed some warning related to non-unicode file names (the Windows
  bytes API has been deprecated in Python 3.4)
• Better documentation for the new file handles management system and
  its backward incompatible behaviours.
  Also some clarification has been added to the description of the
  :attr:File.open_count property.... read more

Announcing PyTables 3.1.0rc1

We are happy to announce PyTables 3.1.0rc1.

This is a feature release. The upgrading is recommended for users that
are running PyTables in production environments.

What's new

Probably the most relevant changes in this release are internal improvements
like the new node cache that is now compatible with the upcoming Python 3.4
and the registry for open files has been deeply reworked. The caching feature
of file handlers has been completely dropped so now PyTables is a little bit
more "thread friendly".... read more

Announcing PyTables 3.0.0

We are happy to announce PyTables 3.0.0.

PyTables 3.0.0 comes after about 5 years from the last major release
(2.0) and 7 months since the last stable release (2.4.0).

This is new major release and an important milestone for the PyTables project
since it provides the long waited support for Python 3.x, which has been around
for 4 years.

Almost all of the core numeric/scientific packages for Python already support
Python 3 so we are very happy that now also PyTables can provide this
important feature.... read more

=============================
Announcing PyTables 3.0.0rc2
=============================

We are happy to announce PyTables 3.0.0rc2.

PyTables 3.0.0rc2 comes after about 5 years from the last major release
(2.0) and 7 months since the last stable release (2.4.0).

This is new major release and an important milestone for the PyTables project
since it provides the long waited support for Python 3.x, which has been around
for 4 years.... read more

=============================
Announcing PyTables 3.0.0rc1
=============================

We are happy to announce PyTables 3.0.0rc1.

PyTables 3.0.0rc1 comes after about 5 years from the last major release
(2.0) and 7 months since the last stable release (2.4.0).

This is new major release and an important milestone for the PyTables project
since it provides the long waited support for Python 3.x, which has been around
for 4 years.... read more

=============================
Announcing PyTables 3.0.0b1
=============================

We are happy to announce PyTables 3.0.0b1.

PyTables 3.0.0b1 comes after about 5 years from the last major release
(2.0) and 7 months since the last stable release (2.4.0).

This is new major release and an important milestone for the PyTables project
since it provides the long waited support for Python 3.x that is being around
for already 4 years now.... read more

==========================
Announcing PyTables 2.4.0
==========================

We are happy to announce PyTables 2.4.0.

This is an incremental release which includes many changes to prepare for
future Python 3 support.

What's new

This release includes support for the float16 data type and read-only support
for variable length string attributes.

The handling of HDF5 errors has been improved. The user will no longer see
HDF5 error stacks dumped to the console. All HDF5 error messages are trapped
and attached to a proper Python exception.... read more

===========================
Announcing PyTables 2.4.0rc1
===========================

We are happy to announce PyTables 2.4.0rc1.

This is an incremental release which includes many changes to prepare for
future Python 3 support.

What's new

This release includes support for the float16 data type and read-only support
for variable length string attributes.

The handling of HDF5 errors has been improved. The user will no longer see
HDF5 error stacks dumped to the console. All HDF5 error messages are trapped
and attached to a proper Python exception.... read more

===========================
Announcing PyTables 2.4.0b1
===========================

We are happy to announce PyTables 2.4.0b1.

This is an incremental release which includes many changes to prepare for
future Python 3 support.

What's new

This release includes support for the float16 data type and read-only support
for variable length string attributes.

The handling of HDF5 errors has been improved. The user will no longer see
HDF5 error stacks dumped to the console. All HDF5 error messages are trapped
and attached to a proper Python exception.... read more

We are happy to announce PyTables 2.3.1.

This is a bugfix release. Upgrading is recommended for users that are
running PyTables in production environments.

What's new

This release includes a small number of changes. It only fixes a couple of
bugs that are considered serious even if they should not impact a large
number of users:

- :issue:`113` caused installation of PyTables 2.3 to fail on hosts with
multiple python versions installed.
- :issue:`111` prevented to read scalar datasets of UnImplemented types.... read more

We are happy to announce PyTables 2.3.
This release comes after abour 10 months of development and after that
Francesc Altet, the creator of PyTables, ceased activities with the project.

Thank you Francesc.

Also the project has been moved to GitHub: http://github.com/PyTables/PyTables.

What's new

The main new features in 2.3 series are:

* PyTables now includes the codebase of PyTables Pro (now release under open
source license) gaining a lot of performance improvements and some new
features like:... read more

This is a new major release of PyTables, and probably the last major one of the 1.x series (i.e. with numarray at the core). On it, we have implemented better code to deal with table buffers, enhanced the capability for reading native HDF5 files, enhanced support for 64-bit platforms (but not with Python 2.5: see Special Warning section below), better support for AIX, optional automatic parent creation and the traditional amount of bug fixes.... read more

This is a new minor release of PyTables. There you will find, among
other things, improved support for NumPy strings and the ability to
create indexes of NumPy-flavored tables (this capability was broken in
earlier versions).

*Important note*: one of the fixes addresses an important bug that shows
when browsing files with lots of nodes, making PyTables to
crash. Because of this, an upgrade is encouraged.... read more

This is a new major release of PyTables. The most remarkable feature
added in this version is a complete support (well, almost, because
unicode arrays are not there yet) for NumPy objects. Better support for
native HDF5 is there as well. As an aside, I'm happy to inform you that
the PyTables web page has been converted into a wiki so now you can
contribute to the project with recipes or any other document.
Try it out!

===========================
Announcing PyTables 1.2.1
===========================

This is a maintenance version. Only bugs has been fixed in it.

Go to the PyTables web site for downloading the beast:
http://pytables.sourceforge.net/

or keep reading for more info about the new features and bugs fixed in
this version.

Changes more in depth

Improvements:

- None

Bug fixes:

- Table.flush() is called automatically before disposing a table object
from the user space. This avoids a problem that appears when the user
does not explicitely do this and the table is unbounded and rebounded
alter on (using getNode() for example).... read more

This version sports a completely new in-memory tree implementation
based around a *node cache system*. This system loads nodes only when
needed and unloads them when they are rarely used. The new feature
allows the opening and creation of HDF5 files with large hierarchies
very quickly and with a low memory consumption (the object tree is no
longer completely loaded in-memory), while retaining all the powerful
browsing capabilities of the previous implementation of the object
tree.... read more

Announcing PyTables 1.1

On this version you will find support for a nice set of new features,
like nested datatypes, enumerated datatypes, nested iterators, support
for native multidimensional attributes, introduced CArray, a new
object for dealing with compressed arrays, bzip2 compression support
and more. Many bugs has been addressed as well.

Enjoy Data!

=========================
Announcing PyTables 1.0
=========================

The Carabos crew is very proud to announce the immediate availability
of **PyTables release 1.0**. On this release you will find a series
of exciting new features, being the most important the Undo/Redo
capabilities, support for objects (and indexes!) with more than 2**31
rows, better I/O performance for Numeric objects, new time datatypes
(useful for time-stamping fields), support for Octave HDF5 files and
improved support for HDF5 native files.... read more

PyTables 0.9.1 released today. This release is
mainly a maintenance version. In it, some bugs has been fixed and
a few improvements has been made. One important thing is that
chunk sizes in EArrays has been re-tuned to get much better
performance and compression rations. Besides, it has been tested
against the latest Python 2.4 and all test units seems to pass
fine. More info in the release notes: ... read more

On this release you will find a series of quite
exciting new features, being the most important the indexing
capabilities, in-kernel selections, support for complex datatypes and
the possibility to modify values in both tables *and* arrays (yeah,
finally :).

Check the release notes for more info:

http://sourceforge.net/project/shownotes.php?release_id=280479

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 numarray 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
saving and retrieving different kinds of
datasets. It also provides flexible indexed
access on disk to anywhere in the data.... read more

In this release you will find:
- Variable Length Arrays (VLA's) for saving a collection
of variable length of elements in each row of a dataset.
- Enlargeable Arrays (EA's) for enlarge homogeneous
datasets on disk.
- Powerful replication capabilities, ranging from single leaves
up to complete hierarchies.
- With the introduction of the UnImplemented class, greatly
improved HDF5 native file import capabilities.
- Two new useful utilities: ptdump & ptrepack.
- Improved documentation (with the help of Scott Prater).
- Enhanced platform support. New platforms: MacOSX, FreeBSD,
Linux64, IRIX64 (yes, a clean 64-bit port is there) and
probably more.
- More test units (now exceeding 800).
- Many other minor improvements.... read more