Re: [Pytables-users] ANN: PyTables 3.0 final
Many thanks for keeping such a great piece of work up and running. I've just seen some features in the release notes, features which I was going to need in the very near future! Great job! Best regards Seref Arikan On Sat, Jun 1, 2013 at 12:33 PM, Antonio Valentino antonio.valent...@tiscali.it wrote: === 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 -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2 ___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
Re: [Pytables-users] ANN: PyTables 3.0 final
Congratulations All! This is a huge and important milestone for PyTables and I am glad to have been a part of it! Be Well Anthony On Sat, Jun 1, 2013 at 6:33 AM, Antonio Valentino antonio.valent...@tiscali.it wrote: === 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 -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2 ___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
Re: [Pytables-users] ANN: PyTables 3.0 final
My congrats for the hard effort too. I am very pleased to see the PyTables project so healty and well managed. Thanks to all the developers, most specially Antonio and Anthony. You guys rock! Francesc El 02/06/2013 17:54, Anthony Scopatz scop...@gmail.com va escriure: Congratulations All! This is a huge and important milestone for PyTables and I am glad to have been a part of it! Be Well Anthony On Sat, Jun 1, 2013 at 6:33 AM, Antonio Valentino antonio.valent...@tiscali.it wrote: === 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 -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2 ___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2
Re: [Pytables-users] ANN: PyTables 3.0 final
Thank you from a happy user :))) On Sat, Jun 1, 2013 at 8:33 AM, Antonio Valentino antonio.valent...@tiscali.it wrote: === 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 -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2 ___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
