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From: Antonio V. <ant...@ti...> - 2014-03-25 23:16:29
|
=========================== Announcing PyTables 3.1.1 =========================== We are happy to announce 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. As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.io/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/3.1.1 For an online version of the manual, visit: http://pytables.github.io/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 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 Developers |
|
From: Antonio V. <ant...@ti...> - 2014-02-04 22:59:25
|
=========================== 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". New, user visible, features include: - a new lossy filter for HDF5 datasets (EArray, CArray, VLArray and Table objects). The *quantization* filter truncates floating point data to a specified precision before writing to disk. This can significantly improve the performance of compressors (many thanks to Andreas Hilboll). - support for the H5FD_SPLIT HDF5 driver (thanks to simleo) - all new features introduced in the Blosc_ 1.3.x series, and in particular the ability to leverage different compressors within Blosc_ are now available in PyTables via the blosc filter (a big thank you to Francesc) - the ability to save/restore the default value of :class:`EnumAtom` types Also, installations of the HDF5 library that have a broken support for the *long double* data type (see the `Issues with H5T_NATIVE_LDOUBLE`_ thread on the HFG5 forum) are detected by PyTables 3.1.0 and the corresponding features are automatically disabled. Users that need support for the *long double* data type should ensure to build PyTables against an installation of the HDF5 library that is not affected by the bug. .. _`Issues with H5T_NATIVE_LDOUBLE`: http://hdf-forum.184993.n3.nabble.com/Issues-with-H5T-NATIVE-LDOUBLE-tt4026450.html As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.io/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/3.1.0 For an online version of the manual, visit: http://pytables.github.io/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 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 Developers |
|
From: Antonio V. <ant...@ti...> - 2014-01-22 10:43:18
|
Dear PyTables users, the PyTables development team is happy to announce the availability of PyTables v3.1.0rc2. Thanks to the user feedback we was able to address some issue that was still present in the RC1, so we decided to release a new RC version that should hopefully be the last one before the final release. 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. Again we encourage all users to test this RC2 version in their applications and report issues to help to make PyTables 3.1 final even better. The official announcement: ============================== Announcing PyTables 3.1.0rc2 ============================== We are happy to announce PyTables 3.1.0rc2. This is a feature release. 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". New, user visible, features include: - a new lossy filter for HDF5 datasets (EArray, CArray, VLArray and Table objects). The *quantization* filter truncates floating point data to a specified precision before writing to disk. This can significantly improve the performance of compressors (many thanks to Andreas Hilboll). - support for the H5FD_SPLIT HDF5 driver (thanks to simleo) - all new features introduced in the Blosc_ 1.3.x series, and in particular the ability to leverage different compressors within Blosc_ are now available in PyTables via the blosc filter (a big thank you to Francesc) - the ability to save/restore the default value of :class:`EnumAtom` types Also, installations of the HDF5 library that have a broken support fo the *long double* data type (see the `Issues with H5T_NATIVE_LDOUBLE`_ thread on the HFG5 forum) are detected by PyTables 3.1.0 and the corresponding features are automatically disabled. Users that need support for the *long double* data type should ensure to build PyTables against an installation of the HDF5 library that is not affected by the bug. .. _`Issues with H5T_NATIVE_LDOUBLE`: http://hdf-forum.184993.n3.nabble.com/Issues-with-H5T-NATIVE-LDOUBLE-tt4026450.html As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.io/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/3.1.0 For an online version of the manual, visit: http://pytables.github.io/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 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 Developers |
|
From: Antonio V. <ant...@ti...> - 2014-01-18 10:41:25
|
Dear PyTables users, On behalf of the PyTables development team I'm happy to announce the availability of PyTables v3.1.0rc1. Some of the core internal components of PyTables has been rewritten for this release. This should ensure a better code organization and a better compatibility future Python versions. We strongly encourage all users to test this RC1 version in their applications and report any kind of issue and performance regression. It is important to stress that the user feedback is fundamental to get a better quality of the final release. And now, the official announcement: ============================== Announcing PyTables 3.1.0rc1 ============================== We are happy to announce PyTables 3.1.0rc1. This is a feature release. 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". New, user visible, features include: - a new lossy filter for HDF5 datasets (EArray, CArray, VLArray and Table objects). The *quantization* filter truncates floating point data to a specified precision before writing to disk. This can significantly improve the performance of compressors (many thanks to Andreas Hilboll). - support for the H5FD_SPLIT HDF5 driver (thanks to simleo) - all new features introduced in the Blosc_ 1.3.x series, and in particular the ability to leverage different compressors within Blosc_ are now available in PyTables via the blosc filter (a big thank you to Francesc) - the ability to save/restore the default value of :class:`EnumAtom` types Also, installations of the HDF5 library that have a broken support fo the *long double* data type (see the `Issues with H5T_NATIVE_LDOUBLE`_ thread on the HFG5 forum) are detected by PyTables 3.1.0rc1 and the corresponding features are automatically disabled. Users that need support for the *long double* data type should ensure to build PyTables against an installation of the HDF5 library that is not affected by the bug. .. _`Issues with H5T_NATIVE_LDOUBLE`: http://hdf-forum.184993.n3.nabble.com/Issues-with-H5T-NATIVE-LDOUBLE-tt4026450.html As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.io/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/3.1.0rc1 For an online version of the manual, visit: http://pytables.github.io/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 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 Developers |
|
From: Antonio V. <ant...@ti...> - 2013-06-01 11:33:51
|
=========================== 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. What's new ========== A short summary of main new features: - Since this release, PyTables now provides full support to Python 3 - The entire code base is now more compliant with coding style guidelines described in PEP8. - Basic support for HDF5 drivers. It now is possible to open/create an HDF5 file using one of the SEC2, DIRECT, LOG, WINDOWS, STDIO or CORE drivers. - Basic support for in-memory image files. An HDF5 file can be set from or copied into a memory buffer. - Implemented methods to get/set the user block size in a HDF5 file. - All read methods now have an optional *out* argument that allows to pass a pre-allocated array to store data. - Added support for the floating point data types with extended precision (Float96, Float128, Complex192 and Complex256). - Consistent ``create_xxx()`` signatures. Now it is possible to create all data sets Array, CArray, EArray, VLArray, and Table from existing Python objects. - Complete rewrite of the `nodes.filenode` module. Now it is fully compliant with the interfaces defined in the standard `io` module. Only non-buffered binary I/O is supported currently. Please refer to the RELEASE_NOTES document for a more detailed list of changes in this release. As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.io/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/3.0.0 For an online version of the manual, visit: http://pytables.github.io/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 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 Developers |
|
From: Antonio V. <ant...@ti...> - 2013-05-29 17:17:30
|
Announcing PyTables 3.0.0rc3 ============================ We are happy to announce PyTables 3.0.0rc3. Changes from 3.0rc2 to 3.0rc3 ----------------------------- * Fixed a crash on 32bit platforms. * Fixed a couple of issue related to stepped read/iteration on tables (see :issue:`260` and :issue:`262`). **Enjoy data!** -- The PyTables Developers |
|
From: Antonio V. <ant...@ti...> - 2013-05-17 18:23:53
|
============================= 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. 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. Changes from 3.0rc1 to 3.0rc2 ============================= - The internal Blosc_ library has been upgraded to version 1.2.3. - All methods of the :class:`Table` class that take *start*, *stop* and *step* parameters (including :meth:`Table.read`, :meth:`Table.where`, :meth:`Table.iterrows`, etc) have been redesigned to have a consistent behaviour. The meaning of the *start*, *stop* and *step* and their default values now always work exactly like in the standard :class:`slice` objects. Closes :issue:`44` and :issue:`255`. - The :meth:`iterrows` method of :class:`*Array` and :class:`Table` as well as the :meth:`Table.itersorted` now behave like functions in the standard :mod:`itertools` module. If the *start* parameter is provided and *stop* is None then the array/table is iterated from *start* to the last line. In PyTables < 3.0 only one element was returned. - Fixed :issue:`119`, :issue:`230` and :issue:`232`, where an index on :class:`Time64Col` (only, :class:`Time32Col` was ok) hides the data on selection from a Tables. Thanks to Jeff Reback. - Fixed an issue of the :meth:`Table.itersorted` with reverse iteration (closes :issue:`252` and :issue:`253`). -- Antonio Valentino |
|
From: Antonio V. <ant...@ti...> - 2013-05-10 19:45:03
|
============================= 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. 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. What's new ========== A short summary of main new features: - Since this release, PyTables now provides full support to Python 3 - The entire code base is now more compliant with coding style guidelines described in PEP8. - Basic support for HDF5 drivers. It now is possible to open/create an HDF5 file using one of the SEC2, DIRECT, LOG, WINDOWS, STDIO or CORE drivers. - Basic support for in-memory image files. An HDF5 file can be set from or copied into a memory buffer. - Implemented methods to get/set the user block size in a HDF5 file. - All read methods now have an optional *out* argument that allows to pass a pre-allocated array to store data. - Added support for the floating point data types with extended precision (Float96, Float128, Complex192 and Complex256). - Consistent ``create_xxx()`` signatures. Now it is possible to create all data sets Array, CArray, EArray, VLArray, and Table from existing Python objects. - Complete rewrite of the `nodes.filenode` module. Now it is fully compliant with the interfaces defined in the standard `io` module. Only non-buffered binary I/O is supported currently. Please refer to the RELEASE_NOTES document for a more detailed list of changes in this release. As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.io/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/3.0.0rc1 For an online version of the manual, visit: http://pytables.github.io/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 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 Developers |
|
From: Antonio V. <ant...@ti...> - 2013-04-27 11:51:24
|
============================= 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. Almost all the main numeric/scientific packages for python already support Python 3 so we are very happy that now also PyTables can provide this important feature. What's new ========== A short summary of main new features: - Since this release PyTables provides full support to Python 3 - The entire code base is now more compliant with coding style guidelines describe in the PEP8. - Basic support for HDF5 drivers. Now it is possible to open/create an HDF5 file using one of the SEC2, DIRECT, LOG, WINDOWS, STDIO or CORE drivers. - Basic support for in-memory image files. An HDF5 file can be set from or copied into a memory buffer. - Implemented methods to get/set the user block size in a HDF5 file. - All read methods now have an optional *out* argument that allows to pass a pre-allocated array to store data. - Added support for the floating point data types with extended precision (Float96, Float128, Complex192 and Complex256). Please refer to the RELEASE_NOTES document for a more detailed list of changes in this release. As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.io/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/3.0.0b1 For an online version of the manual, visit: http://pytables.github.io/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 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 |
|
From: Antonio V. <ant...@ti...> - 2012-07-20 20:36:30
|
========================== 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. Now PyTables only supports HDF5 v1.8.4+. All the code has been updated to the new HDF5 API. Supporting only HDF5 1.8 series is beneficial for future development. Documentation has been improved. As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.com/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/2.4.0 For an online version of the manual, visit: http://pytables.github.com/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 |
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From: Antonio V. <ant...@ti...> - 2012-07-07 18:47:41
|
-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 =========================== 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. Now PyTables only supports HDF5 v1.8.4+. All the code has been updated to the new HDF5 API. Supporting only HDF5 1.8 series is beneficial for future development. As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.com/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/2.4.0b1 For an online version of the manual, visit: http://pytables.github.com/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux) Comment: Using GnuPG with Mozilla - http://enigmail.mozdev.org/ iEYEARECAAYFAk/4hDwACgkQ1JUs2CS3bP7TUwCfcobS3KI7L/6k3Bbbt2VBOz5B TqAAn0DhrSdtd7XTPOj0RR/mpr2FtseE =T5iQ -----END PGP SIGNATURE----- |
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From: Antonio V. <ant...@ti...> - 2011-11-24 08:04:43
|
Hi PyTables users, I'm happy to announce that debian/ubuntu packages for PyTables v.2.3.1 are now available for download on sf.net http://sourceforge.net/projects/pytables/files/pytables/2.3.1/ and also, for ubuntu users, from the following PPA: https://launchpad.net/%7Ea.valentino/+archive/eotools Ubuntu 11.10 only at he moment. The official debian/ununtu package is for PyTables 2.1.2. Please note that the packaging code has bee rewritten from scratch so feedback is very appreciated. enjoy -- Antonio Valentino |
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From: Antonio V. <ant...@ti...> - 2011-10-28 21:18:25
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-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 =========================== Announcing 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. In case you want to know more in detail what has changed in this version, have a look at: http://pytables.github.com/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/@VERSION@ For an on-line version of the manual, visit: http://pytables.github.com/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than 1 tenth of a second. 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 -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux) Comment: Using GnuPG with Mozilla - http://enigmail.mozdev.org/ iEYEARECAAYFAk6rHAkACgkQ1JUs2CS3bP70gACgpdlVdblRzwgKtNZyWWFjUtf3 GgAAoL0/ji/6NMTVJeRxYCm4FXZJ8vpd =3kQm -----END PGP SIGNATURE----- |
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From: Antonio V. <ant...@ti...> - 2011-09-21 19:52:42
|
=========================== Announcing PyTables 2.3 =========================== We are happy to announce PyTables 2.3. This release comes after about 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: - the new and powerful indexing engine: OPSI - a fine-tuned LRU cache for both metadata (nodes) and regular data * The entire documentation set has been converted to ReStructuredTest and Sphinx As always, a large amount of bugs have been addressed and squashed too. In case you want to know more in detail what has changed in this version, have a look at: http://pytables.github.com/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/2.3 For an on-line version of the manual, visit: http://pytables.github.com/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than 1 tenth of a second. 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 |
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From: Antonio V. <ant...@ti...> - 2011-09-21 19:49:30
|
=========================== Announcing PyTables 2.3 =========================== We are happy to announce PyTables 2.3. This release comes after about 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: - the new and powerful indexing engine: OPSI - a fine-tuned LRU cache for both metadata (nodes) and regular data * The entire documentation set has been converted to ReStructuredTest and Sphinx As always, a large amount of bugs have been addressed and squashed too. In case you want to know more in detail what has changed in this version, have a look at: http://pytables.github.com/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/2.3 For an on-line version of the manual, visit: http://pytables.github.com/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than 1 tenth of a second. 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 |
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From: Francesc A. <fa...@py...> - 2011-09-11 09:19:58
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Hey Antonio, that sounds terrific :) Thanks for all the new Governance Team. You all Josh, Anthony and Antonio rocks! Keep the good work! Francesc 2011/9/11 Antonio Valentino <ant...@ti...> > =========================== > Announcing PyTables 2.3rc1 > =========================== > > 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: > > - the new and powerful indexing engine: OPSI > - a fine-tuned LRU cache for both metadata (nodes) and regular data > > * The entire documentation set has been converted to ReStructuredTest and > Sphinx > > As always, a large amount of bugs have been addressed and squashed too. > > In case you want to know more in detail what has changed in this > version, have a look at: > http://pytables.github.com/release_notes.html > > You can download a source package with generated PDF and HTML docs, as > well as binaries for Windows, from: > http://sourceforge.net/projects/pytables/files/pytables/2.3rc1 > > For an on-line version of the manual, visit: > http://pytables.github.com/usersguide/index.html > > > What it is? > =========== > > 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 includes OPSI, a new indexing technology, > allowing to perform data lookups in tables exceeding 10 gigarows > (10**10 rows) in less than 1 tenth of a second. > > > 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 > > > ------------------------------------------------------------------------------ > Using storage to extend the benefits of virtualization and iSCSI > Virtualization increases hardware utilization and delivers a new level of > agility. Learn what those decisions are and how to modernize your storage > and backup environments for virtualization. > http://www.accelacomm.com/jaw/sfnl/114/51434361/ > _______________________________________________ > Pytables-announce mailing list > Pyt...@li... > https://lists.sourceforge.net/lists/listinfo/pytables-announce > -- Francesc Alted |
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From: Antonio V. <ant...@ti...> - 2011-09-11 08:57:26
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=========================== Announcing PyTables 2.3rc1 =========================== 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: - the new and powerful indexing engine: OPSI - a fine-tuned LRU cache for both metadata (nodes) and regular data * The entire documentation set has been converted to ReStructuredTest and Sphinx As always, a large amount of bugs have been addressed and squashed too. In case you want to know more in detail what has changed in this version, have a look at: http://pytables.github.com/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/2.3rc1 For an on-line version of the manual, visit: http://pytables.github.com/usersguide/index.html What it is? =========== 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 includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than 1 tenth of a second. 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 |
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From: Francesc A. <fa...@py...> - 2011-06-03 18:02:08
|
Dear PyTables users, Fortunately, now that it seems like there will be some opportunities for PyTables to be maintained, I'm happy to announce that, hereby, PyTables Pro drops its original, commercial license, and acquires a BSD license. The new ``LICENSE.txt`` in the root directory states this. You can find the SVN sources in: http://www.pytables.org/svn/pytables/PyTablesPro and browse the sources via Trac too in: http://pytables.org/trac/browser/PyTablesPro @ people interested in the future maintenance, please feel free to use Pro as a possible base for the future PyTables (with no Pro suffix anymore). Some caveats about doing this though: - The version format for the Pro version is something like 'X.Y.Zpro', and such a 'pro' suffix is necessary in certain parts of the code in order to enable the Pro-specific features (indexing and caching, mainly). It should be easy to get rid of this, but needs some modification of the code. - The manual is the same in PyTables Pro than PyTables standard, but if the new maintainers choose to put Pro as the default, the notes of the style "... is only available in PyTables Pro." should be obviously removed. - The copyright for the years 2002-2010 should be respected, and I have added a new entry for 2011 which is attributed to "PyTables maintainers", in honor to those braves that are undertaking the maintenance task of PyTables. Please change this 'author' by something more meaningful if you feel like. So, enjoy data with PyTables Pro (BSD-flavored :) ! -- Francesc Alted |
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From: Francesc A. <fa...@py...> - 2011-06-01 16:42:17
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Dear PyTables users, After more than 8 years since the first public version of PyTables (October 2002) and almost 4 years since the first release of PyTables Pro (July 2007), it is time for me to start new challenges in my career. The reason for this change is that I have found a new job and, unfortunately, I'll not have time for doing any further development, except for perhaps delivering a couple of additional maintenance releases during the year to come, and participating from time to time in the pytables-user list. In case anyone is willing to adopt PyTables and push it with more features, he would be absolutely welcome and I'll do my best to help him in this endeavour. Regarding PyTables Pro, I am still undecided, but I'm open to release it as open source (BSD-like) too. At any rate, I don't plan to sell PyTables Pro licenses anymore. Also, I'm committing myself to maintain the pytables.org web site for at least one additional year (up to May 2012), and I'd glad to help transferring it (or part of it) to anyone who wants to become a future maintainer. Finally, I would like to thank to all of you who have contributed with bug reports, patches, encouragements and, last but not least, monetary support. Without you the PyTables project would have not lasted too much (after all, 8 years of life is a quite important figure for a modest project like this :) Thank you! -- Francesc Alted |
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From: Francesc A. <fa...@py...> - 2010-11-05 15:41:52
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Hi List, I'm really proud to announce the availability of PyTables and PyTables Pro 2.2.1. This release has been tested pretty toughly, and it is the recommended one to use in production. Also, and due to popular demand, the new evaluation version for PyTables Pro is based now on previous Pro 2.2. I think this way more people will be able to check the performance boost that the multi-core additions (Numexpr, Blosc) introduced in 2.2 is bringing to Pro. Thanks for all who have contributed reports, patches and suggestions. Without you, PyTables would be much less useful. And now, the official announcement, =========================== Announcing PyTables 2.2.1 =========================== This is maintenance release. The updgrading is recommended for all that are running PyTables in production environments. What's new ========== Many fixes have been included, as well as a fair bunch of performance improvements. Also, the Blosc compression library has been updated to 1.1.2, in order to prevent locks in some scenarios. Finally, the new evaluation version of PyTables Pro is based on the previous Pro 2.2. 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_2.2.1 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-2.2.1 What it is? =========== 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. 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 |
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From: Francesc A. <fa...@py...> - 2010-11-03 13:20:39
|
Hi List, I've just released PyTables 2.2.1 RC1. You can see a list of improvements for this release in: http://www.pytables.org/moin/ReleaseNotes/Release_2.2.1rc1 Please download the beast from: http://www.pytables.org/download/pytables-2.2.1rc1/ and tell me how it goes. If everything develops as expected, I'll release 2.2.1 (final) by the end of the week. Cheers! -- Francesc Alted |
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From: Francesc A. <fa...@py...> - 2010-08-01 11:32:43
|
Hi, After the Theano talk in last EuroSciPy I suddenly realized that it would not be too difficult to implement a multi-threaded version of Numexpr. Well, as usual I was terribly wrong and it took me a *long* week to do the job :-/ Anyway the thing is done now, so... enjoy! Note for PyTables users: Numexpr does not include changes in the API/ABI, so the upgrade is recommended. ======================== Announcing Numexpr 1.4 ======================== Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. What's new ========== The main improvement in this version is the support for multi-threading in pure C. Threading in C provides the best performance in nowadays multi-core machines. In addition, this avoids the GIL that hampers performance in many Python apps. Just to wet your appetite, look into this page where the implementation is briefly described and where some benchmarks are shown: http://code.google.com/p/numexpr/wiki/MultiThreadVM In case you want to know more in detail what has changed in this version, see: http://code.google.com/p/numexpr/wiki/ReleaseNotes or have a look at RELEASE_NOTES.txt in the tarball. Where I can find Numexpr? ========================= The project is hosted at Google code in: http://code.google.com/p/numexpr/ And you can get the packages from PyPI as well: http://pypi.python.org/pypi Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy! -- Francesc Alted |
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From: Francesc A. <fa...@py...> - 2010-07-01 19:27:42
|
=================================
Announcing PyTables 2.2 (final)
=================================
I'm happy to announce PyTables 2.2 (final). After 18 months of
continuous development and testing, this is, by far, the most powerful
and well-tested release ever. I hope you like it too.
What's new
==========
The main new features in 2.2 series are:
* A new compressor called Blosc, designed to read/write data to/from
memory at speeds that can be faster than a system `memcpy()` call.
With it, many internal PyTables operations that are currently
bounded by CPU or I/O bandwith are speed-up. Some benchmarks:
http://blosc.pytables.org/trac/wiki/SyntheticBenchmarks
And a demonstration on how Blosc can improve PyTables performance:
http://www.pytables.org/docs/manual/ch05.html#chunksizeFineTune
* Support for HDF5 hard links, soft links and external links (kind of
mounting external filesystems). A new tutorial about its usage has
been added to the 'Tutorials' chapter of User's Manual. See:
http://www.pytables.org/docs/manual/ch03.html#LinksTutorial
* A new `tables.Expr` module (based on Numexpr) that allows to do
persistent, on-disk computations on many algebraic operations.
For a brief look on its performance, see:
http://pytables.org/moin/ComputingKernel
* Suport for 'fancy' indexing (i.e., à la NumPy) in all the data
containers in PyTables. Backported from the implementation in the
h5py project. Thanks to Andrew Collette for his fine work on this!
* Binaries for both Windows 32-bit and 64-bit are provided now.
As always, a large amount of bugs have been addressed and squashed too.
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_2.2
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-2.2
What it is?
===========
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.
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
|
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From: Francesc A. <fa...@py...> - 2010-06-17 12:50:23
|
Hi List,
I'm happy to announce the availability of PyTables 2.2rc2. It will be
probably the last release candidate version before 2.2 final, so please try to
test it as much as you can and report the problems that may appear.
In this release, I'm officially providing support for Windows 64-bit binaries
(tell me if you have problems here), and due to the exponential growth in
installers, I've decided to provide binaries just for Python 2.6 and 2.7.
Also, PyTables Pro users can test the 2.2rc2 version in the new 2.2rc2/
directory in their repository.
And now, the official announcement:
===========================
Announcing PyTables 2.2rc2
===========================
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 (and probably last) release candidate for PyTables
2.2, so please test it as much as you can before I declare the beast
stable. The main new features in 2.2 series are:
* A new compressor called Blosc, designed to read/write data to/from
memory at speeds that can be faster than a system `memcpy()` call.
With it, many internal PyTables operations that are currently
bounded by CPU or I/O bandwith are speed-up. Some benchmarks:
http://blosc.pytables.org/trac/wiki/SyntheticBenchmarks
* A new `tables.Expr` module (based on Numexpr) that allows to do
persistent, on-disk computations on many algebraic operations.
For a brief look on its performance, see:
http://pytables.org/moin/ComputingKernel
* Support for HDF5 hard links, soft links and automatic external links
(kind of mounting external filesystems). A new tutorial about its
usage has been added to the 'Tutorials' chapter of User's Manual.
* Suport for 'fancy' indexing (i.e., à la NumPy) in all the data
containers in PyTables. Backported from the implementation in the
h5py project. Thanks to Andrew Collette for his fine work on this!
As always, a large amount of bugs have been addressed and squashed too.
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_2.2rc2
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-2.2rc2
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
.. Local Variables:
.. mode: rst
.. coding: utf-8
.. fill-column: 72
.. End:
--
Francesc Alted
|
|
From: Francesc A. <fa...@py...> - 2010-05-20 18:29:09
|
Hi List, I'm happy to announce the first candidate release for PyTables 2.2 series. Among the most exciting improvements of this release is the support of threads in several parts of PyTables, namely Blosc and, optionally, Numexpr (which is out of the main distribution now and becomes a requisite). In particular, I'm quite happy of how performs the recent multi-threaded implementation that undergone Blosc in 0.9. It uses a pool of threads technique in order to reduce thread management to a bare minimum. When all the tests would be finished, I expect to release Blosc 1.0 very soon now (hopefully before PyTables 2.2 final). These additions will allow you to make full use of the raw speed of nowadays multi-core processors in the parts of the code that can use parallelism, and are only the beginning of a series of future multi-core improvements inside PyTables. Here it is the official announcement: =========================== Announcing PyTables 2.2rc1 =========================== 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 release candidate for PyTables 2.2. On it, Numexpr is not included anymore and is now a requisite and the Blosc compressor has been updated to 0.9, which comes with integrated support for threads. Also, Cython is used per default now to build Pyrex extensions. Finally, a handful of bugs have been addressed and squashed. 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_2.2rc1 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-2.2rc1 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 |
