Mailman 3 May 2005 - NumPy-Discussion

numarray 1.3.1 setup patch for mac OS X veclib
by Andrew Jaffe 22 May '05

22 May '05

Hi all, I've had some trouble getting the numarray-1.3.1 setup scripts to recognize the optimized BLAS/LAPACK, veclib, on OS X. But I think I've tracked down the problems. First, the section of addons.py that checks for the existence of the framework (around line 47) needs to set lapack_dirs = [], I think, since it needs to exist later on. Second, the section after that that sets lapack_compile_args, lapack_link_args and lapack_include_dirs for *all* cases (lines 57-60) overwrites lapack_link_args from the if...elif...else above. Finally, cblas.h isn't included correctly, since the framework option is only used for linking, not compiling. I've attached a patchfile for addons.py below; I switched the two sections and added to the framework if... clause. Use it with > patch addons.py addons.patch I hope this is a reasonable way to propagate this to the developers. Please let me know if it doesn't work for any reason, or if it breaks any other install, which I haven't checked. I assume it works on my machine, since: > otool -L build/lib.darwin-7.9.0-Power_Macintosh-2.3/numarray/linear_algebra/ lapack_lite2.so /System/Library/Frameworks/Python.framework/Versions/2.3/Python (compatibility version 2.3.0, current version 2.3.0) /System/Library/Frameworks/vecLib.framework/Versions/A/vecLib (compatibility version 1.0.0, current version 153.2.0) /usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 71.1.3) Andrew p.s. only tested under OS X 10.3.9 (latest version of Panther). ______________________________________________________________________ Andrew Jaffe a.jaffe(a)imperial.ac.uk Astrophysics Group +44 207 594-7526 Blackett Laboratory, Room 1013 FAX 7541 Imperial College, Prince Consort Road London SW7 2AZ ENGLAND http://astro.imperial.ac.uk/~jaffe

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applying function under certain conditions
by Christian Meesters 20 May '05

20 May '05

Hi I have a large 1D numarray array with some huge values and lots of zeros. Now, for better representation I wanted to plot the logarithm. Well, calculating the logarithm is not such a good idea if you have zeros in your array ... However, how do you implement such cases like "only apply a function if a certain condition is true", I'd like to ask whether there are nice short snippets which could show such cases. My own code is (unnecessarily?) long and ugly. I tried to combine 'where' and 'greater', but didn't succeed. I guess I'm just don't see the wood for the trees. How would you write this in my case? TIA Regards, Christian

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Strange behavior of the RandomArray module
by Tom Denniston 19 May '05

19 May '05

I am running under python 1.4 and windows XP. I get the following results in my python interpretter: -------------------------------------------- n=0 import RandomArray while 1: arr = RandomArray.randint(0, 2509, 252) for x in arr: if x>2508: raise "Found number greater than 2508: " + str(x) + " on iteration " + str(n) n=n+1 Traceback (most recent call last): File "<pyshell#43>", line 5, in -toplevel- raise "Found number greater than 2508: " + str(x) + " on iteration " + str(n) Found number greater than 2508: 2509 on iteration 91719 ------------------------------------------- Even though the Numeric reference (see below) on RandomArray class claims that the number returned is always strictly less than the max. Does anyone know what might cause this problem? randint(minimum, maximum, shape=ReturnFloat) The randint() function returns an array of the specified shape and containing random (standard) integers greater than or equal to minimum and strictly less than maximum . If no shape is specified, a single number is returned. (exerpt from http://www.pfdubois.com/numpy/html2/numpy-19.html#pgfId-303108)

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numarray.nd_image: max, min not preserved by rotate
by Edward C. Jones 19 May '05

19 May '05

I have up-to-date (5/18/05) Debian unstable on a PC. I compiled Python and numarray from source and I keep them in "/usr/local". Python is version 2.4.1 and numarray is version 1.3.1. The output of the program below is: 0.0 1.0 -0.107813523267 1.10781352327 I would expect a rotated image to have min == 0.0 and max == 1.0. The max and min of arr2 occur near the edge of the rotated half plane. This looks like a bug in nd_image. ------------------------- #! /usr/bin/env python import numarray, numarray.nd_image arr = numarray.zeros((256, 256), 'Float64') arr[128:, :] = 1.0 print arr.min(), arr.max() arr2 = numarray.nd_image.rotate(arr, 30, reshape=False) print arr2.min(), arr2.max()

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Re: [Numpy-discussion] Wrapping C array
by Todd Miller 18 May '05

18 May '05

On Wed, 2005-05-18 at 12:58, Carlos Pita wrote: > > > The documentation for PyArray_FromDimsAndData > > > that I quoted in my previous post seems to suggest > > > that a copy is done. But browsing the web > > > > I'd appreciate it if you could send me a reference to where that is > > because it is stale. > > I take the excerpt from the numarray user manual from > the python2.3-numarray 1.1.1 debian (sarge) package. > But it's updated in the cvs: > > \begin{cfuncdesc}{PyObject*}{PyArray_FromDimsAndData}{int nd, int *dims, int type, char *data} > This function will allocate a numarray of the specified shape and type > which will refer to the data buffer specified by \var{data}. The contents > of \var{data} will not be copied nor will \var{data} be deallocated upon > the deletion of the array. > \end{cfuncdesc} > > It seems to be ok. When it says "nor will \var{data} be deallocated upon > the deletion of the array", does it mean that the python buffer object > ref count won't be decremented ? Or, more generally, the created > numarray will refer directly to the C array segments exposed by the > python buffer object or will keep a reference to this object? It means the internal buffer object refers to but does not "own" the array data passed into PyArray_FromDimsAndData(). At deallocation time, the internal buffer object is DECREF'd and disappears but the storage referred to by the data parameter does not. So, I/O buffers or other statically allocated arrays should work. Regards, Todd

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Wrapping C array
by Carlos Pita 18 May '05

18 May '05

Hi! I need to wrap a pre-existent C array into a numarray. It shouldn't be a copy of the original array because this is an audio buffer with a fixed address and also input/output copying from/to the buffer would be overkilling, so the numarray *must* be lightly wrapping the buffer and not a memcpy of it. But NA_NewArray duplicates the passed buffer so I'm not sure how to achieve the goal stated above or if it's possible at all. Can you help me? Regards, Carlos

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About Reshape()
by andrea_gavana＠tin.it 13 May '05

13 May '05

Hello NG, I am not really a guru with Numeric, and I am trying to port some Matlab code to Python, using Numeric. The code is working, but it is slown down because of the different implementation (ordering) of reshape() in Matlab and Numeric. For example, in Matlab I can do: a = 1:10; reshape(a,5,2) ans = 1 6 2 7 3 8 4 9 5 10 In Numeric: a = range(1, 11) Numeric.reshape(a, (5,2)) array([[ 1, 2], [ 3, 4], [ 5, 6], [ 7, 8], [ 9, 10]]) Hmm... let's try again... Numeric.reshape(a, (2,5)) array([[ 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10]]) Hmm... the only solution I found: Numeric.transpose(Numeric.reshape(a, (2,5))) array([[ 1, 6], [ 2, 7], [ 3, 8], [ 4, 9], [ 5, 10]]) This continuous use of transpose() is killing the performances of my scripts... is there a workaround? Am I missing something so obvious? Thank you for every suggestion. Andrea.

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Square All Elements Of A Matrix
by andrea_gavana＠tin.it 13 May '05

13 May '05

Hello NG, in one script, I generated a matrix "dx" which has a (200000, 10) shape. Well, everything is working fine (matrix multiplication, sum, exponentiation etc...), but I'm having problems in squaring all elements of this matrix. Suppose I have: import Numeric import random dx = Numeric.ones((200000,10), Numeric.Float0, savespace=1) dx[:,:] = random.random() td1 = dx*2.0 ---> This takes about 0.2 seconds td2 = dx**2.0 ---> This takes about 6 seconds Why this difference? And why it is so slow the exponentiation? I have tried the same operation in Matlab and it was almost instantaneous. I don't want to stick with Matlab, noting the great job the Numeric developers have done. Is there something that I am missing? Sorry if it is a basic question. Andrea.

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tuple vs. list as index
by Sebastian Haase 13 May '05

13 May '05

Hi, Is this really obvious to everybody: >>> type(a) <class 'numarray.numarraycore.NumArray'> >>> a.shape (12, 13, 2, 5) >>> a[(0,0)].shape (2, 5) >>> a[[0,0]].shape (2, 13, 2, 5) >>> na.take(a,[0,0]).shape (2, 13, 2, 5) Is there some explanation in the documentation ? (Is this the same in Numeric / Numeric3 ?) Thanks, Sebastian Haase

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SECU - Urgent account verification!
by State Employees Credit Union 10 May '05

10 May '05

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