Mailman 3 August 2006 - NumPy-Discussion

[RFE] Suport for version 3 of array protocol in numarray
by Francesc Altet Aug. 26, 2006

Aug. 26, 2006

Hi, I've lately ran into problems in numarray-->numpy conversions which are due to a lack of suport of the array procol version 3 on behalf of numarray. For more info on this issue see: http://projects.scipy.org/scipy/numpy/ticket/256 and http://projects.scipy.org/scipy/numpy/ticket/266 Question: is the numarray crew going to add this support anytime soon? If not, I'd advocate to retain support for version 2 in NumPy at least for sometime (until numarray gets the support), although I don't know whether this will complicate things a lot in NumPy. I personally don't need this functionality as I've found a workaround for PyTables (i.e. using the numpy.ndarray factory in order to create the NumPy object directly from the numarray buffer), but I think this would be very useful in helping other users (end-users mainly) in the numarray-->NumPy transition. Thanks, -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

2 1

1.0b3 in windows
by kortmann＠ideaworks.com Aug. 25, 2006

Aug. 25, 2006

3 2

hstack(arr_Int32, arr_float32) fails because of casting rules
by Sebastian Haase Aug. 25, 2006

Aug. 25, 2006

Hi, I get TypeError: array cannot be safely cast to required type when calling hstack() ( which calls concatenate() ) on two arrays being a int32 and a float32 respectively. I understand now that a int32 cannot be safely converted into a float32 but why does concatenate not automatically up(?) cast to float64 ?? Is this really required to be done *explicitly* every time ? ** In general it makes float32 cubersome to use. ** ( Background: my large image data is float32 (float64 would require too much memory) and the hstack call happens inside scipy plt module when I try to get a 1d line profile and the "y_data" is hstack'ed with the x-axis values (int32)) ) Thanks, Sebastian Haase

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Re: [Numpy-discussion] users point of view and ufuncs
by Bill Baxter Aug. 25, 2006

Aug. 25, 2006

On 8/24/06, Sebastian Haase <haase(a)msg.ucsf.edu> wrote: > > > I'm not sure what this question is asking, so I'll answer what I think > > it is asking. > > > > The mean, min, max, and average functions are *not* ufuncs. They are > > methods of particular ufuncs. > > > Yes - that's what wanted to hear ! I'm just trying to bring in the > "user's" point of view: Not thinking about how they are implemented > under the hood: mean,min,max,average have a very similar "feeling" to > them as "abs". While we're on the subject of the "user's" point of view, the term "ufunc" is not very new-user friendly, yet it gets slung around fairly often. I'm not sure what to do about it exactly, but maybe for starters it would be nice to add a concise definition of "ufunc" to the numpy glossary: http://www.scipy.org/Numpy_Glossary. Can anyone come up with such a definition? Here's my stab at it: ufunc: A function that operates element-wise on arrays. But I have a feeling there's more to it than that. --bb

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Re: [Numpy-discussion] numpy-1.0b3 under windows
by Travis Oliphant Aug. 25, 2006

Aug. 25, 2006

Albert Strasheim wrote: >Dialog pops up: > >--------------------------- >python.exe - Application Error >--------------------------- >The exception unknown software exception (0xc0000029) occurred in the >application at location 0x7c86d474. > > >Click on OK to terminate the program >Click on CANCEL to debug the program >--------------------------- >OK Cancel >--------------------------- > >In the Python console it prints: > >-2147483648 > >If you can give me some idea of what should be happening, I can take a look >at fixing it. > > When does the crash happen? Does it happen when you press Ctrl-C? What's supposed to be happening is that we are registering a handler for Ctrl-C that longjmps back to just after the code between NPY_SIGINT_ON and NPY_SIGINT_OFF. I'm not sure how to actually accomplish something like that under windows as I've heard mention that longjmp should not be used with signals under win32. The easy "fix" is to just define NPY_NO_SIGNAL in setup.py when on a platform that doesn't support using signals and longjmp (like apparently win32). If you could figure out what to do instead on windows that would be preferrable. -Travis

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Version 1.0b3
by kortmann＠ideaworks.com Aug. 25, 2006

Aug. 25, 2006

Since no one has downloaded 1.0b3 yet, if someone wants to put up the windows version for python2.3 i would be more than happy to be the first person to download it :)

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Handling interrupts in NumPy extensions
by Travis Oliphant Aug. 25, 2006

Aug. 25, 2006

I'm working on some macros that will allow extensions to be "interruptable" (i.e. with Ctrl-C). The idea came from SAGE but the implementation is complicated by the possibility of threads and making sure to handle clean-up code correctly when the interrupt returns. I'd like to get this in to 1.0 final. Anything needed will not require re-compilation of extension modules built for 1.0b2 however. This will be strictly "extra" and if an extension module doesn't use it there will be no problems. Step 1: Define the interface. Here are a couple of draft proposals. Please comment on them. 1) General purpose interface NPY_SIG_TRY { [code] } NPY_SIG_EXCEPT(signum) { [interrupt handling return] } NPY_SIG_ELSE [normal return] The idea of signum is to hold the signal actually caught. 2) Simpler interface NPY_SIG_TRY { [code] } NPY_SIG_EXCEPT_GOTO(label) [normal return] label: [interrupt handling return] C-extensions often use the notion of a label to handle failure code. If anybody has any thoughts on this, they would be greatly appreciated. Step 2: Implementation. I have the idea to have a single interrupt handler (defined globally in NumPy) that basically uses longjmp to return to the section of code corresponding to the thread that is handling the interrupt. I had thought to use a global variable containing a linked list of jmp_buf structures with a thread-id attached (PyThread_get_thread_ident()) so that the interrupt handler can search it to see if the thread has registered a return location. If it has not, then the intterupt handler will just return normally. In this way a thread that calls setjmpbuf will be sure to return to the correct place when it handles the interrupt. Concern: My thinking is that this mechanism should work whether or not the GIL is held so that we don't have to worry about whether or not the GIL is held except in the interrupt handling case (when Python exceptions are to be set). But, honestly, this gets very confusing. The sigjmp / longjmp mechanism for handling interrupts is not recommended under windows (not sure about mingw), but there we could possibly use Microsoft's __try and __except extension to implement. Initially, it would be "un-implemented" on platforms where it didn't work. Any comments are greatly appreciated -Travis

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[ANN] PyTables 1.3.3 released
by Francesc Altet Aug. 25, 2006

Aug. 25, 2006

=========================== Announcing PyTables 1.3.3 =========================== I'm happy to announce a new minor release of PyTables. In this one, we have focused on improving compatibility with latest beta versions of NumPy (0.9.8, 1.0b2, 1.0b3 and higher), adding some improvements and the typical bunch of fixes (some of them are important, like the possibility of re-using the same nested class in declaration of table records; see later). Go to the PyTables web site for downloading the beast: http://www.pytables.org/ or keep reading for more info about the new features and bugs fixed. Changes more in depth ===================== Improvements: - Added some workarounds on a couple of 'features' of recent versions of NumPy. Now, PyTables should work with a broad range of NumPy versions, ranging from 0.9.8 up to 1.0b3 (and hopefully beyond, but let's see). - When a loop for appending a table is not flushed before the node is unbounded (and hence, becomes ``killed`` in PyTables slang), like in:: import tables as T class Item(T.IsDescription): name = T.StringCol(length=16) vals = T.Float32Col(0.0) fileh = T.openFile("/tmp/test.h5", "w") table = fileh.createTable(fileh.root, 'table', Item) for i in range(100): table.row.append() #table.flush() # uncomment this prevent the warning table = None # Unbounding table node! a ``PerformanceWarning`` is issued telling the user that it is *much* recommended flushing the buffers in a table before unbounding it. Hopefully, this will also prevent other scary errors (like ``Illegal Instruction``, ``Malloc(): trying to call free() twice``, ``Bus Error`` or ``Segmentation fault`` ) that some people is seeing lately and which are most probably related with this issue. Bug fixes: - In situations where the same metaclass is used for declaring several columns in a table, like in:: class Nested(IsDescription): uid = IntCol() data = FloatCol() class B_Candidate(IsDescription): nested1 = Nested() nested2 = Nested() they were sharing the same column metadata behind the scenes, introducing several inconsistencies on it. This has been fixed. - More work on different padding conventions between NumPy/numarray. Now, all trailing spaces in chararrays are stripped-off during write/read operations. This means that when retrieving NumPy chararrays, it shouldn't appear spureous trailing spaces anymore (not even in the context of recarrays). The drawback is that you will loose *all* the trailing spaces, no matter if you want them in this place or not. This is not a very confortable situation to deal with, but hopefully, things will get better when NumPy would be at the core of PyTables. In the meanwhile, I hope that the current behaviour would be a minor evil for most of situations. This closes ticket #13 (again). - Solved a problem with conversions from numarray charrays to numpy objects. Before, when saving numpy chararrays with a declared length of N, but none of this components reached such a length, the dtype of the numpy chararray retrieved was the maximum length of the component strings. This has been corrected. - Fixed a minor glitch in detection of signedness in IntAtom classes. Thanks to Norbert Nemec for reporting this one and providing the fix. Known bugs: - Using ``Row.update()`` in tables with some columns marked as indexed gives a ``NotImplemented`` error although it should not. This is fixed in SVN trunk and the functionality will be available in the 1.4.x series. Meanwhile, a workaround would be refraining to declare columns as indexed and index them *after* the update process (with Col.createIndex() for example). Deprecated features: - None Backward-incompatible changes: - Please, see ``RELEASE-NOTES.txt`` file. Important note for 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/5-165-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/5-165-win.ZIP What it is ========== PyTables is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data (with qsupport 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. PyTables runs on top of the HDF5 library and numarray (but NumPy and Numeric are also supported) package for achieving maximum throughput and convenient use. Besides, PyTables I/O for table objects 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. PyTables sports indexing capabilities as well, allowing doing selections in tables exceeding one billion of rows in just seconds. Platforms ========= This version has been extensively checked on quite a few platforms, like Linux on Intel32 (Pentium), Win on Intel32 (Pentium), Linux on Intel64 (Itanium2), FreeBSD on AMD64 (Opteron), Linux on PowerPC (and PowerPC64) and MacOSX on PowerPC. For other platforms, chances are that the code can be easily compiled and run without further issues. Please, contact us in case you are experiencing problems. Resources ========= Go to the PyTables web site for more details: http://www.pytables.org About the HDF5 library: http://hdf.ncsa.uiuc.edu/HDF5/ About numarray: http://www.stsci.edu/resources/software_hardware/numarray To know more about the company behind the PyTables development, see: http://www.carabos.com/ Acknowledgments =============== Thanks to various the users who provided feature improvements, patches, bug reports, support and suggestions. See the ``THANKS`` file in the distribution package for a (incomplete) list of contributors. Many thanks also to SourceForge who have helped to make and distribute this package! And last but not least, a big thank you to THG (http://www.hdfgroup.org/) for sponsoring many of the new features recently introduced in PyTables. 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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Re: [Numpy-discussion] (no subject)
by megu Aug. 24, 2006

Aug. 24, 2006

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re:如何成为优秀的车间主管
by "8月26-27日/苏州" Aug. 24, 2006

Aug. 24, 2006

1 0