Mailman 3 May 2005 - NumPy-Discussion

numarray.ma docs confuse Numeric/numarray
by Simon Burton 10 May '05

10 May '05

It seems the masked array docs mention Numeric in a few places when numarray was intended. Patch attached. Simon. -- Simon Burton, B.Sc. Licensed PO Box 8066 ANU Canberra 2601 Australia Ph. 61 02 6249 6940 http://arrowtheory.com

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numarray memmap and gc
by Simon Burton 10 May '05

10 May '05

Hi all, an odd problem that has bitten me a couple of times: I build an array from a memmap, the memmap goes out of scope and gets GC'ed (closed), then inexplicable data errors occur: numarray.libnumarray.error: NA_updateDataPtr: error getting read buffer data ptr The solution I came up with was to assign the memmap to a unique attribute of the array ('source_mmap_dont_gc_me'). Then it only gets GC'ed when all it's arrays are GC'ed. So, is it reasonable to make this standard in numarray: slices remember their memmaps and NumArrays remember their buffers (I assume the latter already happens). bye, Simon. -- Simon Burton, B.Sc. Licensed PO Box 8066 ANU Canberra 2601 Australia Ph. 61 02 6249 6940 http://arrowtheory.com

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ANN: numarray-1.2.3
by Todd Miller 10 May '05

10 May '05

numarray-1.2.3 is a bugfix release for numarray-1.2.2 which fixes a problem with universal function setup caching which noticeably impaired 1.2.2 small array performance. Get it if you are new to numarray, haven't upgraded to 1.2.2 yet, or use a lot of small arrays. numarray-1.2.3 is here: http://sourceforge.net/project/showfiles.php?group_id=1369&package_id=32367 Thanks to Ralf Juengling for quietly reporting this and working with me to identify and fix the problem.

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Expansion of Matrix
by Reidar Strand Hagen 10 May '05

10 May '05

Hi Sorry if this is an obvious question, but I'm getting close to loosing sleep over it now :) If I have a matrix: 1 2 3 4 5 6 7 8 9 is there a nice general way to expand the matrix to: 1 0 2 3 4 0 5 6 0 0 0 0 7 0 8 9 The actual dataset in question has dimensions 128 * 128 * 128; so big enough for me to worry quite abit about duplication and general speed. vennlig hilsen / regards Reidar Strand Hagen

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[Fwd: Re: [Numpy-discussion] Re: ANN: numarray-1.2.3 -- segfault in in my C program]
by Todd Miller 10 May '05

10 May '05

Forgot to reply-all.

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PyTables 1.0 released
by Francesc Altet 09 May '05

09 May '05

========================= Announcing PyTables 1.0 ========================= The Carabos crew is very proud to announce the immediate availability of **PyTables release 1.0**. On this release you will find a series of exciting new features, being the most important the Undo/Redo capabilities, support for objects (and indexes!) with more than 2**31 rows, better I/O performance for Numeric objects, new time datatypes (useful for time-stamping fields), support for Octave HDF5 files and improved support for HDF5 native files. What it is ========== **PyTables** is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data (with support for full 64-bit file addressing). It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code, makes it a very easy-to-use tool for high performance data storage and retrieval. It is built on top of the HDF5 library and the numarray package, and provides containers for both heterogeneous data (``Table``) and homogeneous data (``Array``, ``EArray``) as well as containers for keeping lists of objects of variable length (like Unicode strings or general Python objects) in a very efficient way (``VLArray``). It also sports a series of filters allowing you to compress your data on-the-fly by using different compressors and compression enablers. But perhaps the more interesting features are its powerful browsing and searching capabilities that allow you to perform data selections over heterogeneous datasets exceeding gigabytes of data in just tenths of second. Besides, the PyTables I/O is buffered, implemented in C and carefully tuned so that you can reach much better performance with PyTables than with your own home-grown wrappings to the HDF5 library. Changes more in depth ===================== Improvements: - New Undo/Redo feature (i.e. integrated support for undoing and/or redoing actions). This functionality lets you to put marks in specific places of your data operations, so that you can make your HDF5 file pop back (undo) to a specific mark (for example for inspecting how your data looked at that point). You can also go forward to a more recent marker (redo). You can even do jumps to the marker you want using just one instruction. - Reading Numeric objects from ``*Array`` and ``Table`` (Numeric columns) objects have a 50-100x speedup. With that, Louis Wicker reported that a speed of 350 MB/s can be achieved with Numeric objects (on a SGI Altix with a Raid 5 disk array) while with numarrays, this speed approaches 900 MB/s. This improvement has been possible mainly due to a nice recipe from Todd Miller. Thanks Todd! - With PyTables 1.0 you can finally create Tables, EArrays and VLArrays with more than 2**31 (~ 2 thousand millions) rows, as well as retrieve them. Before PyTables 1.0, retrieving data on these beasts was not well supported, in part due to limitations in some slicing functions in Python (that rely on 32-bit adressing). So, we have made the necessary modifications in these functions to support 64-bit indexes and integrated them into PyTables. As a result, our tests shows that this feature works just fine. - As a consequence of the above, you can now index columns of tables with more than 2**31 rows. For instance, indexes have been created for integer columns with 10**10 (yeah, 10 thousand million) rows in less than 1 hour using an Opteron @ 1.6 GHz system (~ 1 hour and half with a Xeon Intel32 @ 2.5 GHz platform). Enjoy! - Now PyTables supports the native HDF5 time types, both 32-bit signed integer and 64-bit fixed point timestamps. They are mapped to ``Int32`` and ``Float64`` values for easy manipulation. See the documentation for the ``Time32Col`` and ``Time64Col`` classes. - Massive internal reorganization of the methods that deal with the hierarchy. Hopefully, that will enable a better understanding of the code for anybody wanting to add/modify features. - The opening and copying of files with large number of objects has been made faster by correcting a typo in ``Table._open()``. Thanks to Ashley Walsh for sending a patch for this. - Now, one can modify rank-0 (scalar) ``EArray`` datasets. Thanks to Norbert Nemec for providing a patch for this. - You are allowed from this version on to add non-valid natural naming names as node or attribute names. A warning is issued to warn (but not forbid) you in such a case. Of course, you have to use the ``getattr()`` function so as to access such invalid natural names. - The indexes of ``Table`` and ``*Array`` datasets can be of long type besides of integer type. However, indexes in slices are still restricted to regular integer type. - The concept of ``READ_ONLY`` system attributes has disappeared. You can change them now at your own risk! However, you still cannot remove or rename system attributes. - Now, one can do reads in-between write loops using ``table.row`` instances. This is thanks to a decoupling in I/O buffering: now there is a buffer for reading and other for writing, so that no collisions take place anymore. Fixes #1186892. - Support for Octave HDF5 output format. Even complex arrays are supported. Thanks to Edward C. Jones for reporting this format. Backward-incompatible changes: - The format of indexes has been changed and indexes in files created with PyTables pre-1.0 versions are ignored now. However, ``ptrepack`` can still save your life because it is able to convert your old files into the new indexing format. Also, if you copy the affected tables to other locations (by using ``Leaf.copy()``), it will regenerate your indexes with the new format for you. - The API has changed a little bit (nothing serious) for some methods. See ``RELEASE-NOTES.txt`` for more details. Bug fixes: - Added partial support for native HDF5 chunked datasets. They can be read now, and even extended, but only along the first extensible dimension. This limitation may be removed when multiple extensible dimensions are supported in PyTables. - Formerly, when the name of a column in a table was subsumed in another column name, PyTables crashed while retrieving information of the former column. That has been fixed. - A bug prevented the use of indexed columns of tables that were in other hierarchical level than root. This is solved now. - When a ``Group`` was renamed you were not able to modify its attributes. This has been fixed. - When whether ``Table.modifyColumns()`` or ``Table.modifyRows()`` were called, a subsequent call to ``Table.flush()`` didn't really flush the modified data to disk. This works as intended now. - Fixed some issues when iterating over ``*Array`` objects using the ``List`` or ``Tuple`` flavor. Important note for Python 2.4 and Windows users =============================================== If you are willing to use PyTables with Python 2.4 in Windows platforms, you will need to get the HDF5 library compiled for MSVC 7.1, aka .NET 2003. It can be found at: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-164-win-net.ZIP Users of Python 2.3 on Windows will have to download the version of HDF5 compiled with MSVC 6.0 available in: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-164-win.ZIP Where can PyTables be applied? ============================== PyTables is not designed to work as a relational database competitor, but rather as a teammate. If you want to work with large datasets of multidimensional data (for example, for multidimensional analysis), or just provide a categorized structure for some portions of your cluttered RDBS, then give PyTables a try. It works well for storing data from data acquisition systems (DAS), simulation software, network data monitoring systems (for example, traffic measurements of IP packets on routers), very large XML files, or for creating a centralized repository for system logs, to name only a few possible uses. What is a table? ================ A table is defined as a collection of records whose values are stored in fixed-length fields. All records have the same structure and all values in each field have the same data type. The terms "fixed-length" and "strict data types" seem to be quite a strange requirement for a language like Python that supports dynamic data types, but they serve a useful function if the goal is to save very large quantities of data (such as is generated by many scientific applications, for example) in an efficient manner that reduces demand on CPU time and I/O resources. What is HDF5? ============= For those people who know nothing about HDF5, it is a general purpose library and file format for storing scientific data made at NCSA. HDF5 can store two primary objects: datasets and groups. A dataset is essentially a multidimensional array of data elements, and a group is a structure for organizing objects in an HDF5 file. Using these two basic constructs, one can create and store almost any kind of scientific data structure, such as images, arrays of vectors, and structured and unstructured grids. You can also mix and match them in HDF5 files according to your needs. Platforms ========= We are using Linux on top of Intel32 as the main development platform, but PyTables should be easy to compile/install on other UNIX machines. This package has also been successfully compiled and tested on a FreeBSD 5.4 with Opteron64 processors, a UltraSparc platform with Solaris 7 and Solaris 8, a SGI Origin3000 with Itanium processors running IRIX 6.5 (using the gcc compiler), Microsoft Windows and MacOSX (10.2 although 10.3 should work fine as well). In particular, it has been thoroughly tested on 64-bit platforms, like Linux-64 on top of an Intel Itanium, AMD Opteron (in 64-bit mode) or PowerPC G5 (in 64-bit mode) where all the tests pass successfully. Regarding Windows platforms, PyTables has been tested with Windows 2000 and Windows XP (using the Microsoft Visual C compiler), but it should also work with other flavors as well. Web site ======== Go to the PyTables web site for more details: http://pytables.sourceforge.net/ To know more about the company behind the PyTables development, see: http://www.carabos.com/ 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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Thanks!
by numpy-discussion＠lists.sourceforge.net 09 May '05

09 May '05

------------------ Virus Warning Message (on the network) Found virus WORM_MYTOB.Y in file document.pif The file document.pif is moved to /var/quarantine/virus/virSCXFk3g6P. This is a machine-generated message, please do not reply via e-mail. If you have questions, please contact the Lucent Help Desk at +1 888 300 0770. --------------------------------------------------------- Mail transaction failed. Partial message is available. ------------------ Virus Warning Message (on the network) document.pif is removed from here because it contains a virus. ---------------------------------------------------------

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Returned mail: User unknown
by MAILER-DAEMON＠dnrc.bell-labs.com 09 May '05

09 May '05

The original message was received at Mon, 9 May 2005 21:24:14 -0400 (EDT) from guard.research.bell-labs.com [135.104.2.10] ----- The following addresses had permanent fatal errors ----- <joe(a)dnrc.bell-labs.com> ----- Transcript of session follows ----- ... while talking to chair.dnrc.bell-labs.com.: >>> RCPT To:<joe(a)dnrc.bell-labs.com> <<< 550 <joe(a)dnrc.bell-labs.com>... User unknown 550 <joe(a)dnrc.bell-labs.com>... User unknown

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Please confirm your request to join IErussian
by Yahoo! Groups 05 May '05

05 May '05

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InterScan NT Alert
by postmaster＠interface.ru 04 May '05

04 May '05

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