Mailman 3 July 2007 - NumPy-Discussion

simple problem with arange / roundoff
by Eric Emsellem 31 Jul '07

31 Jul '07

Hi, I discovered a bug in one of my program probably due to a round-off problem in a "arange" statement. I use something like: step = (end - start) / (npix - 1.) gridX = num.arange(start-step/2., end+step/2., step) where I wish to get a simple 1D array with npix+1 numbers going from (start-step/2.) to (end+step/2.). But then, "arange" often gets me an array only going from "start-step/2." to "end - step/2." instead, due very probably to round-off problems (I guess it does not reach the last value because <<(start-step/2.) + npix * step >> is found to be larger than (end+step/2.). Here is an example: start = -30. end = 30. npix = 31 step = (end - start) / (npix - 1.) gridX = num.arange(start-step/2., end+step/2., step) array([-31., -29., -27., -25., -23., -21., -19., -17., -15., -13., -11., -9., -7., -5., -3., -1., 1., 3., 5., 7., 9., 11., 13., 15., 17., 19., 21., 23., 25., 27., 29.]) As you can see, it does not go up to 31., but only to 29, although step is = 2.0 Is there is a way out of this ? (except by doing the silly: gridX = num.arange(start-step/2., end+1.001*step/2., step) ) Thanks for any input there (and sorry for the silly question) Eric

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New buffer interface for py3k
by Christopher Barker 31 Jul '07

31 Jul '07

Hi all, I was just reading about py3k in Guido's Blog: http://www.artima.com/weblogs/viewpost.jsp?thread=208549 And I saw this: * At the C level, there will be a new, much improved buffer API, which will provide better integration with numpy. (PEP 3118) You can read PEP 3118 here: http://www.python.org/dev/peps/pep-3118/ Nice job, Travis and Carl (and everyone else who contributed this)! I'm looking forward to having this (particularly once it gets integrated into a wide variety of other packages) -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

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column_stack with mixed data types
by Ryan Krauss 31 Jul '07

31 Jul '07

I have data in a spreadsheet where the first column is an integer. the second is a float, columns 3-5 are strings, and columns 6 and 7 are floats. I have each column as a list, but when I use column_stack, I get back a 2D array of strings. What is the easiest way to get a recarray out of this list of lists? Is recarray my best/only choice? Thanks, Ryan

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Installing Numpy on Python 2.3 Windows
by Amir Hirsch 31 Jul '07

31 Jul '07

Hi Everyone, I'm trying to install the Numpy package on Python 2.3 running under Windows. I downloaded numpy-1.0.3.win32-py2.3.exe and ran it, and it complains that "Python version 2.3 required, which was not found in the registry" The Python 2.3 installation I am using came with OpenOffice.org 2.2 and it must not have registered python with Windows. I require PyUNO and Numpy (and PyOpenGL and Ctypes) to work together for the application I am developing and PyUno seems only to work with the OOo distribution of Python 2.3. I have gotten this all to work in multiple Linux environments (after much pain) with a script that installs all the necessary libraries from source, which will not work in the Windows environment. I'm not familiar with Windows, and any help would be greatly appreciated! Amir

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Memory leak for in-place Numeric+numpy addition
by Thomas J. Duck 30 Jul '07

30 Jul '07

Hi, There seems to be a memory leak when arrays are added in-place for mixed Numeric/numpy applications. For example, memory usage quickly ramps up when the following program is executed: import Numeric,numpy x = Numeric.zeros((2000,2000),typecode=Numeric.Float64) for j in range(200): print j y = numpy.zeros((2000,2000),dtype=numpy.float64) x += y If I use exclusively Numeric arrays, or exclusively numpy arrays, or add a Numeric array in-place to a numpy array, there is not a problem. It is only in the case that a numpy array is added in place to a Numeric array that the leak exists. Deleting the variable y in each iteration has no effect. I am using numpy 1.0.1-8 and Numeric 24.2-7 under Debian linux. I'm not sure if this is a numpy or Numeric problem, but thought I would send it along in case there is interest and the problem can be resolved. Unfortunately, I can't move to an exclusively numpy or Numeric approach because of the other packages that I depend on. Thanks, Tom

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Downsampling array, retaining min and max values in window
by Ludwig M Brinckmann 30 Jul '07

30 Jul '07

Hi there, I have a large array, lets say 40000 * 512, which I need to downsample by a factor of 4 in the y direction, by factor 3 in the x direction, so that my resulting arrays are 10000 * 170 (or 171 this does not matter very much) - but of all the values I will need to retain in the downsampled arrays the minimum and maximum of the original data, rather than computing an average or just picking every third/fourth value in the array. So essentially I have a 4*3 window, for which I need the min and max in this window, and store the result of applying this window to the original array as my results. What is the best way to do this? Regards Ludwig

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epydoc documentation of numpy package
by Sebastian Haase 30 Jul '07

30 Jul '07

Hi, For general interest - I just found this web page: http://stsdas.stsci.edu/pyraf/stscidocs/imagestats_pkg/imagestats_api/numpy… I seems to me to be a nice overview of all functions in numpy. Question: is there an "official" version of this on the scipy.org site ? Or a link? Comments? -Sebastian Haase

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Build external C functions that take numpy arrays as parameters and return numpy arrays
by computer_guy 30 Jul '07

30 Jul '07

Hi Everyone, I am going to write some external C functions that takes in numpy arrays as parameters and return numpy arrays. I have the following questions: 1. What should I do in my C code? 2. Can I use any C compiler to build my library that takes numpy arrays? I am using Windows XP and Visual Studio 2005. 3. How can I generate the python binding? Thanks, cg

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a.flat[3:7] is a copy?
by Tom Goddard 29 Jul '07

29 Jul '07

Does taking a slice of a flatiter always make a copy? That appears to be the behaviour in numpy 1.0.3. For example a.flat[1:3][0] = 5 does not modify the original array a, even when a is contiguous. Is this a bug? >>> import numpy as n >>> n.version.version '1.0.3' >>> a = n.zeros((2,3), n.int32) >>> a array([[0, 0, 0], [0, 0, 0]]) >>> b = a.flat[1:3] >>> b[0] = 5 >>> b array([5, 0]) >>> a array([[0, 0, 0], [0, 0, 0]]) >>> b = None >>> a array([[0, 0, 0], [0, 0, 0]]) This behavior does not seem to match what is described in the Numpy book (Dec 7, 2006 version), section 3.1.3 "Other attributes", page 51: "flat Returns an iterator object (numpy.flatiter) that acts like a 1-d version of the array. 1-d indexing works on this array and it can be passed in to most routines as an array wherein a 1-d array will be constructed from it. The new 1-d array will reference this array's data if this array is C-style contiguous, otherwise, new memory will be allocated for the 1-d array, the UPDATEIFCOPY flag will be set for the new array, and this array will have its WRITEABLE flag set FALSE until the the last reference to the new array disappears. When the last reference to the new 1-d array disappears, the data will be copied over to this non-contiguous array. This is done so that a.flat effectively references the current array regardless of whether or not it is contiguous or non-contiguous." Tom Goddard UC San Francisco

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Intel MKL 9.1 on Windows (was: Re: VMWare Virtual Appliance...)
by Albert Strasheim 28 Jul '07

28 Jul '07

Cancel that. It seems the problems with these two tests are being caused by Intel MKL 9.1 on Windows. However, 9.0 works fine. You basically you have 2 options when linking against MKL on Windows as far as the mkl_libs go. [mkl] include_dirs = C:\Program Files\Intel\MKL\9.1\include library_dirs = C:\Program Files\Intel\MKL\9.1\ia32\lib mkl_libs = mkl_c, libguide40 lapack_libs = mkl_lapack or mkl_libs = mkl_c, libguide I think libguide is the library that contains various thread and OpenMP related bits and pieces. If you link against libguide, you get the following error when running the NumPy tests: OMP abort: Initializing libguide.lib, but found libguide.lib already initialized. This may cause performance degradation and correctness issues. Set environment variable KMP_DUPLICATE_LIB_OK=TRUE to ignore this problem and force the program to continue anyway. Please note that the use of KMP_DUPLICATE_LIB_OK is unsupported and using it may cause undefined behavior. For more information, please contact Intel(R) Premier Support. I think this happens because multiple submodules inside NumPy are linked against this libguide library, but this caused some initialization code to be executed multiple times inside the same process, which shouldn't happen. If one sets KMP_DUPLICATE_LIB_OK=TRUE, the tests actually work with Intel MKL 9.0, but no matter what you do with Intel MKL 9.1, i.e., - link against libguide40 or - link against libguide and don't set KMP_... or - link against libguide and set KMP_... the following tests always segfault: numpy.core.tests.test_defmatrix.test_casting.check_basic numpy.core.tests.test_numeric.test_dot.check_matmat Cheers, Albert On Tue, 12 Jun 2007, Albert Strasheim wrote: > I've set up a 32-bit Windows XP guest inside VMWare Server 1.0.3 on a 64-bit > Linux machine and two of the NumPy tests are segfaulting for some strange > reason. They are: > > numpy.core.tests.test_defmatrix.test_casting.check_basic > numpy.core.tests.test_numeric.test_dot.check_matmat > > Do these pass for you? I'm inclined to blame VMWare at this point...

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