pytables-announce — PyTables announcement list

Re: [Pytables-announce] [Pytables-users] ANN: PyTables 2.2b3 released

[Francesc A. <fa...@py...>]

Oops, I ran too fast and the tarfile was incomplete :-/  I've uploaded a new, 
corrected tarball.  Hope this time it works smoothly.  Sorry for the 
inconveniences.

Francesc

A Wednesday 24 February 2010 20:19:20 Francesc Alted escrigué:
> Hi List,
> 
> I'm glad to announce you the third beta version of PyTables 2.2 series.  As
> you should know by now, I've added support for the high-performance Blosc
> compressor, so, if you are using compression (and if you are concerned
>  about getting the most out of your data, you should) you will see PyTables
>  to be faster than ever before.
> 
> Although I've already tested Blosc quite a lot, it is still in beta, but
>  I'm confident that if enough people help me in testing the beast, we can
>  make it stable enough to be marked apt for production in a few months.
> 
> And now, the official announcement:
> 
> 
> ===========================
>  Announcing PyTables 2.2b3
> ===========================
> 
> 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.
> 
> This is the third, and most probably last, beta version of 2.2 release.
> The main addition in this beta version is the addition of Blosc
> (http://blosc.pytables.org), a high-speed compressor that is meant to
> work at similar speeds, or higher, than the memory-cache bandwidth in
> modern processors.  This will allow for very high performance in
> internal, in-memory PyTables computations while still using compression.
> Remember that Blosc is still in *beta* and it is not meant for
> production purposes yet.  You have been warned!
> 
> In case you want to know more in detail what has changed in this
> version, have a look at:
> http://www.pytables.org/moin/ReleaseNotes/Release_2.2b3
> 
> You can download a source package with generated PDF and HTML docs, as
> well as binaries for Windows, from:
> http://www.pytables.org/download/preliminary
> 
> For an on-line version of the manual, visit:
> http://www.pytables.org/docs/manual-2.2b3
> 
> 
> Resources
> =========
> 
> About PyTables:
> 
> http://www.pytables.org
> 
> About the HDF5 library:
> 
> http://hdfgroup.org/HDF5/
> 
> About NumPy:
> 
> http://numpy.scipy.org/
> 
> 
> Acknowledgments
> ===============
> 
> 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.  Most
> specially, a lot of kudos go to the HDF5 and NumPy (and numarray!)
> makers.  Without them, PyTables simply would not exist.
> 
> 
> Share your experience
> =====================
> 
> Let us know of any bugs, suggestions, gripes, kudos, etc. you may
> have.
> 
> 
> ----
> 
>   **Enjoy data!**
> 

-- 
Francesc Alted

[Pytables-announce] ANN: PyTables 2.2b3 released

[Francesc A. <fa...@py...>]

Hi List,

I'm glad to announce you the third beta version of PyTables 2.2 series.  As 
you should know by now, I've added support for the high-performance Blosc 
compressor, so, if you are using compression (and if you are concerned about 
getting the most out of your data, you should) you will see PyTables to be 
faster than ever before.

Although I've already tested Blosc quite a lot, it is still in beta, but I'm 
confident that if enough people help me in testing the beast, we can make it 
stable enough to be marked apt for production in a few months.

And now, the official announcement:


===========================                                                                                            
 Announcing PyTables 2.2b3                                                                                             
===========================                                                                                            

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.  

This is the third, and most probably last, beta version of 2.2 release.
The main addition in this beta version is the addition of Blosc        
(http://blosc.pytables.org), a high-speed compressor that is meant to  
work at similar speeds, or higher, than the memory-cache bandwidth in  
modern processors.  This will allow for very high performance in       
internal, in-memory PyTables computations while still using compression.
Remember that Blosc is still in *beta* and it is not meant for          
production purposes yet.  You have been warned!                         

In case you want to know more in detail what has changed in this
version, have a look at:                                        
http://www.pytables.org/moin/ReleaseNotes/Release_2.2b3         

You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:                                   
http://www.pytables.org/download/preliminary                          

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.2b3   


Resources
=========

About PyTables:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/


Acknowledgments
===============

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.  Most
specially, a lot of kudos go to the HDF5 and NumPy (and numarray!)
makers.  Without them, PyTables simply would not exist.


Share your experience
=====================

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


----

  **Enjoy data!**


-- 
Francesc Alted

[Pytables-announce] ANN: PyTables 2.2b2 released

[Francesc A. <fa...@py...>]

Hi List,

After completing the link support, I have taken the opportunity and, together 
with some other improvements (see below), I released PyTables 2.2b2.  I think 
this is a good way to expose the new functionality to people.  After that, I'm 
planning a third beta with Blosc support shortly followed by Python 2.2 final 
(in three months or so).

Please have a try at this beta and report your feedback.

Enjoy and happy holidays!


===========================
 Announcing PyTables 2.2b2
===========================

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.

This is the second beta version of 2.2 release.  The main addition is
the support for links.  All HDF5 kind of links are supported: hard, soft
and external.  Hard and soft links are similar to hard and symbolic
links in regular UNIX filesystems, while external links are more like
mounting external filesystems (in this case, HDF5 files) on top of
existing ones.  This allows for a considerable degree of flexibility
when defining your object tree.  See the new tutorial at:

http://www.pytables.org/docs/manual-2.2b2/ch03.html#LinksTutorial

Also, some other new features (like complete control of HDF5 chunk cache
parameters and native compound types in attributes), bug fixes and a
couple of (small) API changes happened.

In case you want to know more in detail what has changed in this
version, have a look at:
http://www.pytables.org/moin/ReleaseNotes/Release_2.2b2

You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:
http://www.pytables.org/download/preliminary

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.2b2


Resources
=========

About PyTables:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/


Acknowledgments
===============

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.  Most
specially, a lot of kudos go to the HDF5 and NumPy (and numarray!)
makers.  Without them, PyTables simply would not exist.


Share your experience
=====================

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


----

  **Enjoy data!**

  -- The PyTables Team



-- 
Francesc Alted

[Pytables-announce] ANN: PyTables 2.1.2 released

[Francesc A. <fa...@py...>]

Hi List,

I was planning to do a maintenance release for a while (mainly to cope with 
the API change in HDF5 1.8.3 affecting filters), and finally got some time to 
do it.  See below the official announcement.

Enjoy!

===========================
 Announcing PyTables 2.1.2
===========================

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.

This is maintenance release.  Some bugs has been fixed, and support
for latest HDF5 1.8.3 libraries is there.  Also, instructions on how
to find LZO binaries for Windows has been added to the User's Guide.

In case you want to know more in detail what has changed in this
version, have a look at:
http://www.pytables.org/moin/ReleaseNotes/Release_2.1.2

You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:
http://www.pytables.org/download/stable

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.1.2

You may want to fetch an evaluation version for PyTables Pro from:
http://www.pytables.org/download/evaluation


Resources
=========

About PyTables:

http://www.pytables.org

About the HDF5 library:

http://www.hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/


Acknowledgments
===============

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.  Most
specially, a lot of kudos go to the HDF5 and NumPy (and numarray!)
makers.  Without them, PyTables simply would not exist.


Share your experience
=====================

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


----

  **Enjoy data!**

  -- The PyTables Team

[Pytables-announce] ANN: PyTables 2.2b1

[Francesc A. <fa...@py...>]

Hi List,

This is for inform you about the first beta release for PyTables 2.2.
You will find there some interesting new features, but no question
that the most appealing one is the new `tables.Expr` class.  You can
think about it as powerful evaluator for generic mathematical
expressions of NumPy arrays as well as disk-based datasets.

`tables.Expr` works like a sort of replacement of the `numpy.memmap`
module, but it has the next advantages over the latter:

* It can evaluate whatever Numexpr expression without need to take
  care of temporaries.  For example, it can compute expressions like:
  "a*b-1" or "(a*arctan2(b,c)*sqrt(d))**2-1" where 'a','b','c' and 'd'
  can be any PyTables homogeneous dataset or NumPy array, in an
  optimal way (i.e. avoiding temporaries and making an effective use
  of the computational resources of your machine).

* Contrarily to `numpy.memmap`, `tables.Expr` works for *arbitrarily*
  large datasets, no matter your platform is 32-bit or 64-bit or your
  available virtual memory: if your disk can keep your input and
  output datasets, you will be able to do your computations.

* In the PyTables tradition, it can make use of compression
  transparently, so even in the case that your datasets does not fit
  on-disk, there is still a chance that the compressed ones do.

Finally, and although in most of scenarios compression does actually
improve the speed of I/O, it is true that CPU is still the main
bottleneck when compressing/decompressing.  This is being addressed.

So, for those of you that need to work with datasets that defies your
computer capabilities, please give the `tables.Expr` a try and report
your experience.  I'll be glad to try to hear you back!

Keep reading for instructions on finding the new code and documentation.


===========================
 Announcing PyTables 2.2b1
===========================

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.

This is the first beta of the PyTables 2.2 series.  Here, you will
find support for NumPy's extended slicing in all `Leaf` objects as
well as an updated Numexpr module (to 1.3.1), which can lead to up a
25% improvement of the time for both in-kernel and indexed queries for
unaligned columns in tables (which can be a quite common situation).

But perhaps the most interesting feature is the introduction of the
`Expr` class, which allows evaluating expressions containing general
array-like objects.  It can evaluate expressions (like '3*a+4*b') that
operate on *arbitrary large* arrays while optimizing the resources
(basically main memory and CPU cache memory) required to perform them.
It works similarly to the Numexpr package, but in addition to NumPy
objects, it also accepts disk-based homogeneous arrays, like the
`Array`, `CArray`, `EArray` and `Column` PyTables objects.

You can find the documentation about the new `Expr` class at:
http://www.pytables.org/docs/manual-2.2b1/ch04.html#ExprClass

In case you want to know more in detail what has changed in this
version, have a look at:
http://www.pytables.org/moin/ReleaseNotes/Release_2.2b1

You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:
http://www.pytables.org/download/preliminary

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.2b1


Resources
=========

About PyTables:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/


Acknowledgments
===============

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.  Most
specially, a lot of kudos go to the HDF5 and NumPy (and numarray!)
makers.  Without them, PyTables simply would not exist.


Share your experience
=====================

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


----

  **Enjoy data!**

  -- The PyTables Team

-- 
Francesc Alted

[Pytables-announce] [ANN] PyTables and PyTables Pro 2.1.1 released

[Francesc A. <fa...@py...>]

Hi List,

I'm glad to announce the immediate availability of PyTables 2.1.1.  
This is a maintenance release that fixes some important problems with 
the previous 2.1 version.  Also, binaries for Windows and Python 2.6 
are provided now.  Those include the latest release of HDF5 (1.8.2), 
for an improved performance and stability.  The new binaries have been 
exhaustively tested, but tell me in case you are experiencing any 
problem.

And now, the official announcement:

===========================
 Announcing PyTables 2.1.1
===========================

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.

This is a maintenance release, so you should not expect API changes.
Instead, a handful of bugs, like `File` not being subclassable,
incorrectly retrieved default values for data types, a memory leak,
and more, have been fixed.  Besides, some enhancements have been
implemented, like improved Unicode support for filenames, better
handling of Unicode attributes, and the possibility to create very
large data types exceeding 64 KB in size (with some limitations).
Last but not least, this is the first PyTables version fully tested
against Python 2.6.  It is worth noting that binaries for Windows and
Python 2.6 wears the newest HDF5 1.8.2 libraries (instead of the
traditional HDF5 1.6.x) now.

In case you want to know more in detail what has changed in this
version, have a look at:
http://www.pytables.org/moin/ReleaseNotes/Release_2.1.1

You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:
http://www.pytables.org/download/stable

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.1.1

You may want to fetch an evaluation version for PyTables Pro from:
http://www.pytables.org/download/evaluation


Resources
=========

About PyTables:

http://www.pytables.org

About the HDF5 library:

http://www.hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/


Acknowledgments
===============

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.  Most
specially, a lot of kudos go to the HDF5 and NumPy (and numarray!)
makers.  Without them, PyTables simply would not exist.


Share your experience
=====================

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


----

  **Enjoy data!**

  -- The PyTables Team

-- 
Francesc Alted

[Pytables-announce] ANN: PyTables 2.1 (final) released

[Francesc A. <fa...@py...>]

Hi List,

I'm very proud to announce the availability of the newest and hottest 
release of PyTables: PyTables 2.1.  While a lot of stuff is new, many 
efforts have gone in checking that everything works as it should.  In 
fact, it is probably the most thorough tested version to date, as more 
than 50000 tests (for the Pro version) that sucessfully pass in the 
main platforms (Win, Linux and MacOSX) can vouch for.  So, 2.1 is the 
recommended version now for use in production environments.

Also, a new pricing scheme for PyTables Pro has been made official.  The 
idea is that nobody needing the features in Pro could be left outside. 
See:

http://www.pytables.org/moin/PyTablesProPricing

for details.

And now, the official annoucement:

===========================
 Announcing PyTables 2.1
===========================

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.

PyTables 2.1 introduces important improvements, like much faster node
opening, creation or navigation, a file-based way to fine-tune the
different PyTables parameters (fully documented now in a new appendix of
the manual) and support for multidimensional atoms in EArray/CArray
objects.

Regarding the Pro edition, four different kinds of indexes are supported
so that the user can choose the best for her needs.  Also, and due to
the introduction of the concept of chunkmaps in OPSI, the responsiveness
of complex queries with low selectivity has improved quite a lot.  And
last but not least, it is possible now to sort tables by a specific
field with no practical limit in size (tables up to 2**48 rows).

Also, a lot of work has gone in the reworking of the "Optimization tips"
chapter of the manual where many benchmarks have been redone using newer
software and machines and a few new sections have been added.  In
particular, see the new "Fine-tuning the chunksize" section where you
will find an in-deep introduction to the subject of chunking and the
"Indexing and Solid State Disks (SSD)" where the advantages of using
low-latency SSD disks have been analysed in the context of indexation.

In case you want to know more in detail what has changed in this
version, have a look at ``RELEASE_NOTES.txt`` in the tarball.  Find the
HTML version for this document at:
http://www.pytables.org/moin/ReleaseNotes/Release_2.1

You can download a source package of the version 2.1 with
generated PDF and HTML docs and binaries for Windows from
http://www.pytables.org/download/stable

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.1

Finally, you can get an evaluation version for PyTables Pro in:
http://www.pytables.org/download/evaluation


Resources
=========

Go to the PyTables web site for more details:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/


Acknowledgments
===============

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 not exist.

Share your experience
=====================

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

----

  **Enjoy data!**

  -- The PyTables Team

[Pytables-announce] ANN: PyTables 2.1rc2 is ready for testing

[Francesc A. <fa...@py...>]

Hi List,

Although my plans were to release PyTables 2.1 final this week, the 
relatively high number of fixes done since 2.1rc1 made me to decide 
releasing a new candidate before 2.1.  Please test it out and report 
any problems that you may encounter.

  Thanks!

Francesc

============================
 Announcing PyTables 2.1rc2
============================

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.

This is the second release candidate for 2.1, and I have decided to
release it because many bugs have been fixed and some enhancements have
been added since 2.1rc1.  For details, see the ``RELEASE_NOTES.txt`` at:
http://www.pytables.org/moin/ReleaseNotes/Release_2.1rc2

PyTables 2.1 introduces important improvements, like much faster node
opening, creation or navigation, a file-based way to fine-tune the
different PyTables parameters (fully documented now in a new appendix of
the UG) and support for multidimensional atoms in EArray/CArray objects.

Regarding the Pro edition, four different kind of indexes are supported
so that the user can choose the best for her needs.  Also, and due to
the introduction of the concept of chunkmaps in OPSI, the responsiveness
of complex queries with low selectivity has improved quite a lot.  And
last but not least, it is possible now to sort tables by a specific
field with no practical limit in size (tables up to 2**48 rows).

You can download a source package of the version 2.1rc2 with
generated PDF and HTML docs and binaries for Windows from
http://www.pytables.org/download/preliminary

Finally, and for the first time, an evaluation version for PyTables Pro
has been made available in:
http://www.pytables.org/download/evaluation
Please read the evaluation license for terms of use of this version:
http://www.pytables.org/moin/PyTablesProEvaluationLicense

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.1rc2


Resources
=========

Go to the PyTables web site for more details:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/

Acknowledgments
===============

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 not exist.

Share your experience
=====================

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

----

  **Enjoy data!**

  -- The PyTables Team



-- 
Francesc Alted

[Pytables-announce] ANN: PyTables 2.1rc1 available

[Francesc A. <fa...@py...>]

Hi List,

As promised, the first release candidate for PyTables 2.1 is available.  
I think it is mature enough for people put its hands on it and give it 
an in-depth testing.  For 2.1 final I just plan to complete the 
documentation (I want to add a too much long promised "How to choose a 
proper chunkshape" section to the "Optimization tips" in User's Guide) 
and address possible important bugs that you may report.

Also, I'm delivering an evaluation version for Pro for first time.  Hope 
you like it.

PS:  This will be the last version were Ivan Vilata has participated.  
He has found a new job that hopefully will allow him to have a life ;-)
I'd like to publicly give him a big THANK YOU for all his outstanding 
contributions to the PyTables library.  I'll miss you very much, Ivan.

And now, for the official announcement:

============================
 Announcing PyTables 2.1rc1
============================

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.

In PyTables 2.1rc1 many new features and a handful of bugs have been
addressed.  This is a release candidate, so, in addition to the tarball,
binaries for Windows are provided too.  Also, the API has been frozen
and you should only expect bug fixes and documentation improvements for
2.1 final (due to release in a couple of weeks now).

This version introduces important improvements, like much faster node
opening, creation or navigation, a file-based way to fine-tune the
different PyTables parameters (fully documented now in a new appendix of
the UG) and support for multidimensional atoms in EArray/CArray objects.

Regarding the Pro edition, 3 different kind of indexes have been added
so that the user can choose the best for her needs.  Also, and due to
the introduction of the concept of chunkmaps in OPSI, the responsiveness
of complex queries with low selectivity has improved quite a lot.  And
last but not least, it is possible now to sort completely tables that
are ordered by a specific field, with no practical limit in size (up to
2**48 rows, that is, around 281 trillion of rows).  More info in:
http://www.pytables.org/moin/PyTablesPro#WhatisnewinforthcomingPyTablesPro2.1

In case you want to know more in detail what has changed in this
version, have a look at ``RELEASE_NOTES.txt`` in the tarball.  Find the
HTML version for this document at:
http://www.pytables.org/moin/ReleaseNotes/Release_2.1rc1

You can download a source package of the version 2.1rc1 with
generated PDF and HTML docs and binaries for Windows from
http://www.pytables.org/download/preliminary

Finally, and for the first time, an evaluation version for PyTables Pro
has been made available in:
http://www.pytables.org/download/evaluation
Please read the evaluation license for terms of use of this version:
http://www.pytables.org/moin/PyTablesProEvaluationLicense

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.1rc1


Resources
=========

Go to the PyTables web site for more details:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/

Acknowledgments
===============

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 not exist.

Share your experience
=====================

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

----

  **Enjoy data!**

  -- The PyTables Team

[Pytables-announce] PyTables 2.1b3 ready for beta-testing

[Francesc A. <fa...@py...>]

Hi List,

After several months of intense hacking, I'm happy to announce the 
availability of PyTables 2.1 beta3 as well as PyTables Pro 2.1 beta3.  
This will probably be the last beta before announcing a release 
candidate later this month.  My plan is to not further change the API 
until the final release, unless I have a compelling reason to do so.

You can get the software from:

http://www.pytables.org/download/preliminary/tables-2.1b3.tar.gz

For those with a Pro license, I've dropped the:

tables-2.1b3.devpro.tar.gz

tarball in their regular download areas.

Please notice that, as this is a beta release, you will have to compile 
the beast yourself.  Also, be in mind that this is a beta quality 
release and not apt for production purposes (I've tested it only with 
Linux 32-bit and 64-bit, but not on Win nor MacOSX).

I'd be very grateful if people can get its hands onto this release and 
act as beta-testers.  Finally, I'm taking some holidays for the rest of 
the week, so expect a response time adequate to this fact ;-)

And now, what's new in 2.1b3:

=======================================
 Release notes for PyTables 2.1 series
=======================================

:Author: Francesc Alted i Abad
:Contact: fa...@py...
:Author: Ivan Vilata i Balaguer
:Contact: iv...@se...


Changes from 2.0.4 to 2.1b3
===========================

Main improvements
-----------------

- Now, when opening a node, that will be done directly (i.e. without
  populating first all the parent directories).  So, for opening
  pre-known group and leaf locations, the new code is substantially
  faster (in fact, the cost of these operations is O(1) now).

- The `EArray.truncate()` method has been generalized and implemented as
  `Leaf.truncate()`.  Now, it is possible to truncate all *enlargeable*
  datasets (i.e. all except `Array` and `CArray` objects).  Fixes #174.

- Disabling the LRU node cache is now supported by setting the
  NODE_MAX_SLOTS (in parameters.py) to 0 (this can also be achieved
  through the `nodeCacheSize` parameter of openFile() function).
  Disabling this cache may be useful in situations where you suspect
  that maintaining a LRU node cache is actually reducing performance.
  Besides, this figure can also be negative, meaning that all the
  touched nodes will be kept in an internal dictionary.  See more info
  about this features in the updated "Getting the most from the node LRU
  cache" section of chapter 5 of User's Guide.


Main improvements (Pro edition)
-------------------------------

- New light indexes that can take up to 4x less space than 2.0 indexes,
  and more than 15x less space than indexes in traditional databases.
  Four levels of index "lightness", namely ``ultralight``, ``light``,
  ``medium`` and ``full`` (the latter being the one that implemented the
  2.0 version), are available so that the user will be able to choose
  the most appropriate for her needs.

- The index query code has been completely revamped and it is based now
  on the concept of chunkmaps.  This allows for a much more effective
  way to retrieve table data in queries that have low selectivity, while
  retaining good performance for high selectivity ones.

- A new query optimizer being able to use several indexes simultaneously
  in a broad range of complex queries. For example, in the query::

    (((c_int32 == 3) | (c_bool == True)) & (c_int32 == 5)) & (c_extra > 
0)

  if ``c_int32`` and ``c_bool`` columns are indexed but ``c_extra`` is
  not, both ``c_int32`` and ``c_bool`` indexes will be used. That will
  greatly enhance the response times of potentially complicated queries.

- An additional optimization in the index creation process permits to
  achieve completely sorted indexes (CSI), allowing not only to get
  better performance in queries, but also to create completely sorted
  tables ordered by a specific field.


API additions from 2.0.4 to 2.1b3
---------------------------------

- The `AttributeSet` class has received the next dictionary like
  methods: `__getitem__()`, `__setitem__()` and `__delitem__()`, so that
  you can do things like::

    for name in node._v_attrs._f_list():
        print "name: %s, value: %s" % (name, node._v_attrs[name])

- New `File.fileno()` added.  This returns the underlying OS file
  descriptor for the file.  This is meant to allow `File` objects to
  better interact with the `fcntl` module.

- A new `chunkshape` argument has been added to `Leaf.copy()` allowing
  to specify a chunkshape.  It can also take the special values 'auto'
  (compute a sensible value) and 'keep' (keep the original value, which
  is the default).

- Added a new '--chunkshape' flag to the `ptrepack` console command that
  corresponds to the new `chunkshape` added to `Leaf.copy()`.

- `File.copyNode()` can copy now complete hierarchies directly from the
  root.  This can be useful when one wants to create a new file by
  merging the contents of others.


API additions from 2.0.4 to 2.1b3 (Pro edition)
-----------------------------------------------

- A new `Table.itersorted()` iterator allows to iterate through a table
  following the order of a certain index.  It supports iteration on
  ranges, including negative steps (i.e. reverse sorted order).

- New `Table.readSorted()` method that can read a table following the
  order of a certain index.  It supports the reads on ranges, including
  negative steps (i.e. reverse sorted order).

- New `Table.colindexes` property that returns a dictionary with the
  indexes of the indexed columns in table.

- A new `sortby` argument has been added to Table.copy() allowing to a
  Table to be sorted during the copy operation.

- Added a new `propindexes` argument in `Table.copy()`.  If true, the
  indexes in the source table are propagated (created) to the new table.
  If false (the default), the indexes are not propagated.

- New public `Index.readSorted()` and `Index.readIndices()` methods that
  allow direct access to the index data.

- Added new '--sortby' (sort a table by a column key), '--forceCSI'
  (force the creation of a CSI index) and '--propindexes' (propagate the
  indexes in original tables) flags to the `ptrepack` utility.


Bug fixes
---------

- In order to avoid a long-standing bug, all the possible 64-bit class
  attributes of leaf objects (like `nrows`, `shape` or `nrow`) have been
  converted into a new `SizeType` type (actually an alias for
  `numpy.int64`).  This change should be backward compatible with
  existing programs, so you should not need any action to adapt to this.
  Fixes #118.

- When in `ptrepack` a range is not specified, all the elements of
  leaves are copied now.  Before, only the first row was copied, which
  was clearly wrong.

- The `Atom` default value (`Atom.dflt`) is honored now when creating
  `CArrays`.  Fixes #176.


Backward incompatible API changes from 2.0.4 to 2.1b3
=====================================================

- The semantics of `Leaf.copy()` has changed: before the chunkshape of
  destination was computed 'auto'matically while now the default is that
  the value is 'keep't.  This behaviour is thought to satisfy better the
  least surprise principle.

- The `trMap` argument has been removed from the `tables.openFile()`
  function.  Also, the `Node._v_hdf5name` attribute has been removed as
  well.  Fixes #117.

- The `sort` parameter of `Table.itersequence()` has been removed as it
  will not allow to sort sequences larger than memory.  Moreover, it is
  not clear that the sorting operation would be a clear advantage in
  every situation.


Backward incompatible API changes from 2.0.4 to 2.1b3 (Pro edition)
===================================================================

- The `Column.createIndex()` has received a new parameter named `kind`
  which is the first now in the argument list.  This is intentional and
  *incompatible* with previous arglist, so that people should update
  their existing `Column.createIndex()` calls.

- Added a new `Column.createCSIndex()` as a handy way to create a
  completely sorted index (CSI).

- The `Table.indexFilters` property has been removed (after a period of
  ``DeprecationWarnings``).  If you want to change filters in indexes,
  please use the `filters` parameter of the `Column.createIndex()`
  method (and the like).

- `Table.willQueryUseIndexing()` has changed its return value from a
  list to a frozen set of usable indexed columns.

- Now, the copy of the 'AUTO_INDEX' system attribute of the `Index`
  class is done only if the `copyuserattrs` in `Table.copy()` is true
  (the default).


----

  **Enjoy data!**

  -- The PyTables Team


-- 
Francesc Alted
Freelance developer
Tel +34-964-282-249

[Pytables-announce] New features for PyTables Pro 2.1 and new pricing schema are available

[Francesc A. <fa...@py...>]

Hi,

Maybe some of you will be interested in knowing what I'm cooking for the 
next release of PyTables Pro 2.1 (to released sometime during 2008).  
An overview of this can be seen in the new PyTables Pro page:

http://www.pytables.org/moin/PyTablesPro

Also, for those of you that are interested in acquiring a license of 
PyTables Pro I've managed to come with a new pricing schema.  It indeed 
took more than expected, but here it is:

http://www.pytables.org/moin/PyTablesProPricing

As you can see, the prices have dropped very significantly since our 
cooperative closed in April.  Besides, now that the Euro currency seems 
to be in free fall [1], perhaps it's a good time for people to buy ;-)

[1]http://finance.yahoo.com/currency/convert?amt=1&from=EUR&to=USD&submit=Convert

Cheers!

-- 
Francesc Alted
Freelance developer
Tel +34-964-282-249

[Pytables-announce] ANN: release 1.2.4 is available for testing

[Vicent M. (V+) <vm...@ca...>]

Hi,

recently some posts have arrived to the ViTables users group regarding the
lack of a double-clickable application bundle for Mac OS X users of ViTables.
This new release includes a shell script for generating this bundle.
The script has been kindly updated by Ivan Vilata, its original author.
Apart from that, no new functionalities are present in this release.

Warning: I've not tested the shell script (because I've no Mac OS X platforms 
available :-(.

Comments from Mac OS X users will be really appreciated.

Enjoy it!

-- 
::
 \ / Vicent Mas http://www.carabos.com
 0;0 
/ \ Cárabos Coop. Enjoy Data
V V
 " "

[Pytables-announce] pytables.org migrated

[Francesc A. <fa...@py...>]

Hi List,

This is only to inform you that I've finally found the time to migrate 
the pytables.org site to another LVM provider (Linode).  Based on what 
I've heard about Linode (and my own experience too), I hope that the 
new site will provide far better service than the old one.

I've changed the DNS to point to the new server one hour ago, so I hope 
that by Monday 14th everyone will be using the new infraestructure.

If you experiment some glitch with the new pytables.org, please tell me.

Cheers,

-- 
Francesc Alted
Freelance developer
Tel +34-964-282-249

[Pytables-announce] PyTables & PyTables Pro 2.0.4 released

[Francesc A. <fa...@py...>]

Hi List,

I'm glad to announce you the availability of PyTables & PyTables Pro 
2.0.4.  As you know, I'm now the main (should I say the only one? :-/) 
responsible now for maintaining and improving PyTables, so now it is 
more important than ever that you contribute by testing, reporting 
flaws that you may detect or even better, provide patches or new code!

However, I hope to see Ivan around (he certainly follows this list) the 
project: his contribution to PyTables has been *huge* in the last years 
and without him PyTables would not have been the high quality software 
that it is now.  I positively know that he doesn't like at all that 
people praise him, specially in public, so this is a good reason for 
doing that :)  Many, many thanks, Ivan!

Anyway, you can find the new things (mainly fixes) in 2.0.4 here:

http://www.pytables.org/moin/ReleaseNotes/Release_2.0.4

See below the official announcement.

Enjoy your data!
  Francesc

===========================
 Announcing PyTables 2.0.4
===========================

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.

After some months without new versions (I have been busy for a while
doing things not related with PyTables, unfortunately), I'm happy to
announce the availability of PyTables 2.0.4.  It fixes some important
issues, and now it is possible to use table selections in threaded
environments or ``EArray.truncate(0)`` can be used so that you can
completely void existing EArrays (only enabled if you have a recent
version, i.e. >= 1.8.0, of the HDF5 library installed).  Finally, the
usage of recent versions of NumPy (1.1) and HDF5 (1.8.1) has been tested
and, fortunately, they work just fine.

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:
http://www.pytables.org/moin/ReleaseNotes/Release_2.0.4

You can download a source package of the version 2.0.4 with
generated PDF and HTML docs and binaries for Windows from
http://www.pytables.org/download/stable/

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.0.4

*Important note for PyTables Pro users*: due to lack of resources, I'll 
not be delivering a MacOSX binary version of Pro for the time being 
(this is pretty easy to compile, though).  However, I'll continue 
offering the all-in-one binary for Windows (32-bit).


Migration Notes for PyTables 1.x users
======================================

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.x versions.  You can
find an HTML version of this document at
http://www.pytables.org/moin/ReleaseNotes/Migrating_To_2.x

Resources
=========

Go to the PyTables web site for more details:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/

Acknowledgments
===============

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 not exist.

Share your experience
=====================

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

----

  **Enjoy data!**

  -- The PyTables Team

-- 
Francesc Alted
Freelance developer
Tel +34-964-282-249

[Pytables-announce] Carabos Coop. V. is ceasing operations

[Francesc A. <fa...@ca...>]

Hello, list members,

Unfortunately, I have sad news for you: Carabos Coop. V. is ceasing
operations by May 1st, 2008.

During the past three years, we have tried very hard to produce
first-class software that is designed to operate in the most demanding
of environments.  While we have succeeded in meeting that goal
(although, as you are all well aware, it's a constantly moving
target), we haven't been so fortunate in creating a sustainable
business model to fund development.

Although we were very aware of the risks of creating a company around
a FLOSS model, we decided to go forward and started the cooperative
back in April 2005.  During our first few months of operation, Carabos
landed several contracts that made us optimistic for the future.
Unfortunately, after this initial promising start, not enough
contracts came in.  We were able to generate a bit more revenue by
selling proprietary front-ends and added features to the core
software, but not in sufficient quantity to cover our costs.

Having said this, we would like to reassure you that the Carabos crew
will continue to maintain and promote the software that we have
created on a best-effort basis.  In particular, Francesc Alted will
continue to support PyTables/PyTables Pro, with some help from Ivan
Vilata, while Vicent Mas will continue to support ViTables.

Our existing proprietary software will follow different paths.
PyTables Pro, the commercial counterpart of PyTables, will continue to
be run by Francesc Alted as an individual.  However, its current
liberation process will undergo significant changes (including a
revision of the prices); more info about this will be properly
announced on this list in the next few weeks.  Regarding ViTables, it
will become free software, as Vicent Mas will announce soon.

Finally, we want to thank everybody who has worked with Carabos all
these years, and let us say that it has been a great pleasure for us
to help you out.

Thank you!

The Carabos crew:
    Ivan Vilata (Member)
    Vicent Mas (Secretary)
    Francesc Alted (President)

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

[Pytables-announce] ANN: PyTables 2.0.3

[Francesc A. <fa...@ca...>]

Hi List,

After a couple of months without an update, I'm glad to introduce 
PyTables 2.0.3.  It fixes some important problems, and it is probably 
the most tested and stable PyTables up to date.  Following is the 
official annoucement.

Enjoy it!

===========================
 Announcing PyTables 2.0.3
===========================

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.

This is a maintenance release that mainly fixes a couple of important
bugs (bad update of multidimensional columns in table objects, and
problems using large indexes in 32-bit platforms), some small
enhancements, and most importantly, support for the HDF5 1.8.0 library.
Also, binaries have been compiled against the latest stable version of
HDF5, 1.6.7, released during the past February.  Thanks to the
broadening PyTables community for all the valuable feedback.

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:
http://www.pytables.org/moin/ReleaseNotes/Release_2.0.3

You can download a source package of the version 2.0.3 with
generated PDF and HTML docs and binaries for Windows from
http://www.pytables.org/download/stable/

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.0.3

Migration Notes for PyTables 1.x users
======================================

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.x versions.  You can
find an HTML version of this document at
http://www.pytables.org/moin/ReleaseNotes/Migrating_To_2.x

Resources
=========

Go to the PyTables web site for more details:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/

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

http://www.carabos.com/

Acknowledgments
===============

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 not exist.

Share your experience
=====================

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

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

[Pytables-announce] [ANN] Release of the second PyTables video

[Ivan V. i B. <iv...@ca...>]

======================================
 Release of the second PyTables video
======================================

Carabos [1]_ is happy to announce the second of a series of videos
dedicated to introducing the main features of PyTables to the public
in a visual and easy to grasp manner:

  http://www.carabos.com/videos/pytables-2-tables

PyTables [2]_ is a Free/Open Source package designed to handle massive
amounts of data in a simple, but highly efficient way, using the HDF5
file format and NumPy data containers.

.. [1] http://www.carabos.com/
.. [2] http://www.pytables.org/

Our second video explains how to work with tables, PyTables' main data
container.  It shows how to:

  * describe the structure of a table
  * create a table
  * iterate over a table
  * access tables by blocks
  * handle big tables
  * query a table

The video is only 15 minutes long, so you can watch it while you enjoy
a nice cup of coffee.

If you are used to SQL databases, you may also be interested in the
introduction to tables at http://www.pytables.org/moin/HintsForSQLUsers
You can also see more on table queries in the latest video about ViTables
(our PyTables GUI) at http://www.carabos.com/videos/vitables-2-queries

More videos about PyTables will be published in the near future, so
stay tuned on www.pytables.org for further announcements.

We would like to hear your opinion on the video so we can do it better
the next time.  We are also open to suggestions for the topics of
future videos.  You can contact us at pyt...@ca....

Best regards,

::

	Ivan Vilata i Balaguer   >qo<   http://www.carabos.com/
	       Cárabos Coop. V.  V  V   Enjoy Data
	                          ""

[Pytables-announce] ANN: PyTables Pro 2.0.2.1 released

[Francesc A. <fa...@ca...>]

Hi List,

This is only to inform you that we have just released the 2.0.2.1=20
release of PyTables Pro.  It contains mainly an enhancement that=20
prevents the update of indexes of columns that are not affected in=20
situations like:

for row in table:
    row['mycol1'] =3D XXX
    row['mycol2'] =3D YYY
    row.update()
table.flush()

where, if table only has 'mycolumn1' and 'mycolumn3' as indexed columns,=20
just the 'mycolumn1' will be re-indexed.  Before, both 'mycolumn1'=20
and 'mycolumn3' were re-indexed.  More info about PyTables Pro in:
http://www.carabos.com/products/pytables-pro

Happy Hollidays for everybody!

=2D-=20
>0,0<   Francesc Altet =A0 =A0 http://www.carabos.com/
V   V   C=E1rabos Coop. V. =A0=A0Enjoy Data
 "-"

[Pytables-announce] PyTables hints for SQL users

[Ivan V. i B. <iv...@ca...>]

Hi all,

We've published a new tutorial/cookbook page in pytables.org which is
intended as a gentle guide to PyTables for users of SQL and relational
databases.  It describes the equivalent way in PyTables to perform most
basic operations under SQL.  You can find it at:

http://www.pytables.org/moin/HintsForSQLUsers

The current contents of the document are:

   1. Creating a new database
         1. A note on concurrency under PyTables
   2. Creating a table
         1. Table descriptions
         2. Column type declarations
         3. Using a description
   3. Creating an index
   4. Altering a table
   5. Dropping a table
   6. Inserting data
         1. A note on transactions
   7. Updating data
   8. Deleting data
   9. Reading data
         1. Iterating over rows
         2. Reading rows at once
   10. Selecting data
         1. Iterating over selected rows
         2. Reading seleted rows at once
         3. Getting the coordinates of selected rows
         4. A note on table joins
   11. Summary of row selection methods
   12. Sorting the results of a selection
   13. Grouping the results of a selection

Since it's a wiki page, you can add your own recipes and hints.  If you
think the page is missing some content you may want to see added, just
request it in the users mailing list or create an empty section in the
page.  We hope that this page proves itself useful to new users coming
=66rom the relational world.

We thank you for your contributions!  Best regards,

::

	Ivan Vilata i Balaguer   >qo<   http://www.carabos.com/
	       C=E1rabos Coop. V.  V  V   Enjoy Data
	                          ""

[Pytables-announce] ANN: ViTables 1.2.2 released

[Vicent M. (V+) <vm...@ca...>]

Hi,

We're happy to announce the release 1.2.2 of ViTables, the GUI for the PyTa=
bles
and PyTables Pro packages.

The current release focuses on fixing usability issues and bugs. It also gi=
ves
support for larger datasets (up to 2**64 rows). You can see the change log =
for
details:

http://www.carabos.com/products/vitables_changelog

If you are curious about how it looks like just watch the first introductory
video:

http://www.carabos.com/videos

Once more, you are encouraged to buy ViTables, test it, use it, and spread =
the
word.

And now, the official announcement:

=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D
Announcing ViTables 1.2.2
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

We are proud to present ViTables 1.2.2, the latest release of our viewer for
PyTables/HDF5 files.

As it happens with the entire PyTables family, the main strength of ViTable=
s is
its ability to manage really large datasets in a fast and comfortable manne=
r.
=46or example, with ViTables you can open a table with several thousand mil=
lion
rows almost instantaneously, and throw queries on it that run in typically =
less
than a few tenths of second (if PyTables Pro is behind it) with very low me=
mory
requirements. The fact that it runs on top of PyTables ensures its speed and
memory efficiency. Being a multiplatform application, ViTables runs flawles=
sly
in Unix (and hence, GNU/Linux), Mac OS X and Windows. Finally, for a better
user experience, we have created binary installers for Windows and Mac OS X.
The Unix version is installable using Python distutils.

In this release you will find some bug fixes and usability enhancements,
among them:

=2D capability for navigating datasets whith even 2**64 rows
=2D enhanced displaying of datasets
=2D Spanish translation added

Platforms
=2D--------

At the moment, ViTables has been fully tested only on GNU/Linux, Windows
and MacOS X platforms, but as it is made on top of Python, PyQt and
PyTables, its portability should be really good and it should work just
fine in other Unices.

How to get it
=2D------------

Go to:

http://www.carabos.com/buy

to find directions on how to buy it. You can get it under a personal licens=
e or
a site one.

Remember that, when you buy a license, you are contributing to the liberati=
on
process of PyTables Pro and ViTables. See:

http://www.carabos.com/liberation

for details.

Share your experience
=2D--------------------

We are very interested in your feedback about ViTables. Please send your
opinions, suggestions, bugs, etc. to vit...@ca....
Thank you!

Enjoy Data with ViTables, the troll of the PyTables family!

=2D-=20
::

 \ /	Vicent Mas	http://www.carabos.com
 0;0=09
/   \	C=C3=A1rabos Coop.	Enjoy Data
V   V
 " "

[Pytables-announce] ANN: PyTables and PyTables Pro 2.0 are out!

[Francesc A. <fa...@ca...>]

Hi List,

Here you have the official announcement for PyTables (& Pro) 2.0.2.  
Please have a try at the packages and report back any problem you may 
notice.  If not issues are detected with the packages, we will proceed 
tomorrow with the announcement in more general lists.

Enjoy!

============================================
 Announcing PyTables and PyTables Pro 2.0.2
============================================

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.
PyTables Pro adds OPSI, a powerful indexing engine for executing
very fast queries in large tables.

In this version, some bugs have been fixed, being the most important a
problem when moving or renaming a group.  Some small improvements have
been added as well.  Besides, a *critical* bug has been fixed in the Pro
version (the problem arose when doing repeated queries using the same
index). Because of this, an upgrade is strongly recommended.

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:
http://www.pytables.org/moin/ReleaseNotes/Release_2.0.2

You can download a source package of the version 2.0.2 with
generated PDF and HTML docs and binaries for Windows from
http://www.pytables.org/download/stable/

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.0.2


Migration Notes for PyTables 1.x users
======================================

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.x versions.  You can
find an HTML version of this document at
http://www.pytables.org/moin/ReleaseNotes/Migrating_To_2.x


Resources
=========

Go to the PyTables web site for more details:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/

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

http://www.carabos.com/

Acknowledgments
===============

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 not exist.

Share your experience
=====================

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

----

  **Enjoy data!**

  -- The PyTables Team

[Pytables-announce] [ANN] Release of the first PyTables video

[Ivan V. i B. <iv...@ca...>]

=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D
 Release of the first PyTables video
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

`Carabos <http://www.carabos.com/>`_ is very proud to announce the
first of a series of videos dedicated to introducing the main features
of PyTables to the public in a visual and easy to grasp manner.

  http://www.carabos.com/videos/pytables-1-intro

`PyTables <http://www.pytables.org/>`_ is a Free/Open Source package
designed to handle massive amounts of data in a simple, but highly
efficient way, using the HDF5 file format and NumPy data containers.

This first video is an introductory overview of PyTables, covering the
following topics:

  * HDF5 file creation
  * the object tree
  * homogeneous array storage
  * natural naming
  * working with attributes

With a running length of little more than 10 minutes, you may sit back
and watch it during any short break.

More videos about PyTables will be published in the near future.  Stay
tuned on www.pytables.org for the announcement of the new videos.

We would like to hear your opinion on the video so we can do it better
the next time.  We are also open to suggestions for the topics of
future videos.

Best regards,

::

	Ivan Vilata i Balaguer   >qo<   http://www.carabos.com/
	       C=E1rabos Coop. V.  V  V   Enjoy Data
	                          ""

[Pytables-announce] ANN: ViTables 1.2 released

[Vicent M. (V+) <vm...@ca...>]

Hi people, we are very proud to finally release ViTables 1.2!  After a long
period of hard work, we at Carabos expect this new release to be a big step
forward in terms of efficiency, stability and usability.

Of course, you are encouraged to buy ViTables, test it, use it, and spread =
the
word.

And now, the official announcement:

=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D
Announcing ViTables 1.2
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

We are proud to present ViTables 1.2, the latest release of our viewer for
PyTables/HDF5 files.

As it happens with the entire PyTables family, the main strength of ViTables
is its ability to manage really large datasets in a fast and comfortable
manner. For example, with ViTables you can open a table with one thousand
million rows in a few tenths of second, with very low memory requirements.
Also important is the fact that it is designed to be a multiplatform
application, i.e. it runs flawlessly in Unix (and hence, GNU/Linux), Mac OS=
 X
and Windows. The fact that it runs on top of PyTables ensures its speed and
memory efficiency. Finally, for a better user experience, we have created
binary installers for Windows and Mac OS X. The Unix version is installable
using Python distutils.

In this release you will find some bug fixes and the following new features:

 - support for PyTables 2.0.
 - dependency on numarray package completely removed. Now the only numerical
   Python package required is NumPy.
 - table filtering supporting complex conditions (with any number of column=
s).

Usability enhancements have been added too:

 - a new help system for table query and attribute edition dialogs.
 - single unified selection between workspace and tree viewer.
 - improved dialogs, new keyboard shortcuts and toolbar buttons

Platforms
=2D--------

At the moment, ViTables has been fully tested only on GNU/Linux, Windows
and MacOS X platforms, but as it is made on top of Python, PyQt and
PyTables, its portability should be really good and it should work just
fine in other Unices.

How to get it
=2D------------

Go to:

http://www.carabos.com/buy

to find directions on how to buy it. You can get it under a personal license
or a site one.

Remember that, when you buy a license, you are contributing to the liberati=
on
process of PyTables Pro and ViTables 1.2. See:

http://www.carabos.com/liberation

for details.

Share your experience
=2D--------------------

We are very interested in your feedback about ViTables. Please send your
opinions, suggestions, bugs, etc. to vit...@ca....
Thank you!

Enjoy Data with ViTables, the troll of the PyTables family!

=2D-=20
::

 \ /	Vicent Mas	http://www.carabos.com
 0;0=09
/   \	C=C3=A1rabos Coop.	Enjoy Data
V   V
 " "

[Pytables-announce] MD5 checksum for PyTables?

[David E. S. <Dav...@no...>]

My security officer and sysadmins require MD5 checksums (or some other digital signature) for every piece of software that we 
request for installation, and I can't find such in any of the PyTables web pages or repositories.  Is that available in a place I 
haven't looked or can the PyTables author(s) provide one?

Many thanks.

-- 
David E. Sallis, Software Architect
General Dynamics Information Technology
NOAA Coastal Data Development Center
Stennis Space Center, Mississippi
228.688.3805
dav...@gd...
dav...@no...
--------------------------------------------
"Better Living Through Software Engineering"
--------------------------------------------

[Pytables-announce] ANN: PyTables and PyTables Pro 2.0.1 available

[Francesc A. <fa...@ca...>]

=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D
 Announcing PyTables and PyTables Pro 2.0.1
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

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.

This is a maintenance release that mainly fixes (quite a few of) bugs,
as well as some small enhancements (support for accessing table rows
beyond 2**31 rows in 32-bit platforms and reduced memory footprint in
table I/O).  Also, binaries have been compiled against the latest stable
version of HDF5, 1.6.6, released during the past August.  Thanks to the
broadening PyTables community for all the valuable feedback.

Moreover, the Pro version has received an optimization in the node cache
that allows for a 2x improvement in time retrieval of nodes in cache.
With this, PyTables Pro can be now up to 20x faster than regular
PyTables when handling a large amount of nodes simultaneously.

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:
http://www.pytables.org/moin/ReleaseNotes/Release_2.0.1

You can download a source package of the version 2.0.1 with
generated PDF and HTML docs and binaries for Windows from
http://www.pytables.org/download/stable/

=46or an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.0.1

Migration Notes for PyTables 1.x users
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

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.x versions.  You can
find an HTML version of this document at
http://www.pytables.org/moin/ReleaseNotes/Migrating_To_2.x

Resources
=3D=3D=3D=3D=3D=3D=3D=3D=3D

Go to the PyTables web site for more details:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/

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

http://www.carabos.com/

Acknowledgments
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

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 not exist.

Share your experience
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

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

=2D-=20
>0,0<   Francesc Altet =A0 =A0 http://www.carabos.com/
V   V   C=E1rabos Coop. V. =A0=A0Enjoy Data
 "-"