[Numpy-discussion] [ANN] PyTables 2.0b2 relased

Francesc Altet faltet@carabos....
Wed Apr 4 14:11:18 CDT 2007

 Announcing PyTables 2.0b2

PyTables is a library for managing hierarchical datasets and designed to
efficiently cope with extremely large amounts of data with support for
full 64-bit file addressing.  PyTables runs on top of the HDF5 library
and NumPy package for achieving maximum throughput and convenient use.

The PyTables development team is happy to announce the public
availability of the second *beta* version of PyTables 2.0. This will
hopefully be the last beta version of 2.0 series, so we need your
feedback if you want your issues to be solved before 2.0 final would be

You can download a source package of the version 2.0b2 with
generated PDF and HTML docs and binaries for Windows from

For an on-line version of the manual, visit:
Please have in mind that some sections in the manual can be obsolete
(specially the "Optimization tips" chapter).  Other chapters should be
fairly up-to-date though (although still a bit in state of flux).

In case you want to know more in detail what has changed in this
version, have a look at ``RELEASE_NOTES.txt``.  Find the HTML version
for this document at:

If you are a user of PyTables 1.x, probably it is worth for you to look
at ``MIGRATING_TO_2.x.txt`` file where you will find directions on how
to migrate your existing PyTables 1.x apps to the 2.0 version.  You can
find an HTML version of this document at

Keep reading for an overview of the most prominent improvements in
PyTables 2.0 series.

New features of PyTables 2.0

- NumPy is finally at the core!  That means that PyTables no longer
  needs numarray in order to operate, although it continues to be
  supported (as well as Numeric).  This also means that you should be
  able to run PyTables in scenarios combining Python 2.5 and 64-bit
  platforms (these are a source of problems with numarray/Numeric
  because they don't support this combination as of this writing).

- Most of the operations in PyTables have experimented noticeable
  speed-ups (sometimes up to 2x, like in regular Python table
  selections).  This is a consequence of both using NumPy internally and
  a considerable effort in terms of refactorization and optimization of
  the new code.

- Combined conditions are finally supported for in-kernel selections.
  So, now it is possible to perform complex selections like::

      result = [ row['var3'] for row in
                 table.where('(var2 < 20) | (var1 == "sas")') ]


      complex_cond = '((%s <= col5) & (col2 <= %s)) ' \
                     '| (sqrt(col1 + 3.1*col2 + col3*col4) > 3)'
      result = [ row['var3'] for row in
                 table.where(complex_cond % (inf, sup)) ]

  and run them at full C-speed (or perhaps more, due to the cache-tuned
  computing kernel of Numexpr, which has been integrated into PyTables).

- Now, it is possible to get fields of the ``Row`` iterator by
  specifying their position, or even ranges of positions (extended
  slicing is supported).  For example, you can do::

      result = [ row[4] for row in table    # fetch field #4
                 if row[1] < 20 ]
      result = [ row[:] for row in table    # fetch all fields
                 if row['var2'] < 20 ]
      result = [ row[1::2] for row in       # fetch odd fields
                 table.iterrows(2, 3000, 3) ]

  in addition to the classical::

      result = [row['var3'] for row in table.where('var2 < 20')]

- ``Row`` has received a new method called ``fetch_all_fields()`` in
  order to easily retrieve all the fields of a row in situations like::

      [row.fetch_all_fields() for row in table.where('column1 < 0.3')]

  The difference between ``row[:]`` and ``row.fetch_all_fields()`` is
  that the former will return all the fields as a tuple, while the
  latter will return the fields in a NumPy void type and should be
  faster.  Choose whatever fits better to your needs.

- Now, all data that is read from disk is converted, if necessary, to
  the native byteorder of the hosting machine (before, this only
  happened with ``Table`` objects).  This should help to accelerate
  applications that have to do computations with data generated in
  platforms with a byteorder different than the user machine.

- The modification of values in ``*Array`` objects (through __setitem__)
  now doesn't make a copy of the value in the case that the shape of the
  value passed is the same as the slice to be overwritten. This results
  in considerable memory savings when you are modifying disk objects
  with big array values.

- All the leaf constructors (except Array) have received a new
  ``chunkshape`` argument that lets the user to explicitly select the
  chunksizes for the underlying HDF5 datasets (only for advanced users).

- All the leaf constructors have received a new parameter called
  ``byteorder`` that lets the user specify the byteorder of their data
  *on disk*.  This effectively allows to create datasets in other
  byteorders than the native platform.

- Native HDF5 datasets with ``H5T_ARRAY`` datatypes are fully supported
  for reading now.

- The test suites for the different packages are installed now, so you
  don't need a copy of the PyTables sources to run the tests.  Besides,
  you can run the test suite from the Python console by using::

  >>> tables.tests()


Go to the PyTables web site for more details:


About the HDF5 library:


About NumPy:


To know more about the company behind the development of PyTables, see:



Thanks to many users who provided feature improvements, patches, bug
reports, support and suggestions.  See the ``THANKS`` file in the
distribution package for a (incomplete) list of contributors.  Many
thanks also to SourceForge who have helped to make and distribute this
package!  And last, but not least thanks a lot to the HDF5 and NumPy
(and numarray!) makers. Without them PyTables simply would exists.

Share your experience

Let us know of any bugs, suggestions, gripes, kudos, etc. you may


  **Enjoy data!**

  -- The PyTables Team

>0,0<   Francesc Altet     http://www.carabos.com/
V   V   Cárabos Coop. V.   Enjoy Data

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