NumPy-Discussion April 2008

numpy-discussion@python.org

127 participants
190 discussions

OSX installer: please test
by Christopher Burns 26 Apr '08

26 Apr '08

I've built a Universal Mac binary for numpy 1.1.0. If Mac people would kindly test it, I'd appreciate any feedback. Download here: https://cirl.berkeley.edu/numpy/numpy-1.1.0rc1-py2.5-macosx10.5.dmg Technical details: - Built on OSX 10.5.2, Intel Core 2 Duo - Using XCode 3.0 with gcc 4.0.1 and the Accelerate.framework 1.4.2, gfortran 4.2.1 and Python 2.5.2 from python.org (MacPython) - Used bdist_mpkg 0.4.3 to make the distribution Since it uses the MacPython, it will install numpy under here: /Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages Thanks -- Christopher Burns Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/

10 18

Generating Bell Curves (was: Using normal() )
by Rich Shepard 26 Apr '08

26 Apr '08

Thanks to several of you I produced test code using the normal density function, and it does not do what we need. Neither does the Gaussian function using fwhm that I've tried. The latter comes closer, but the ends do not reach y=0 when the inflection point is y=0.5. So, let me ask the collective expertise here how to generate the curves that we need. We need to generate bell-shaped curves given a midpoint, width (where y=0) and inflection point (by default, y=0.5) where y is [0.0, 1.0], and x is usually [0, 100], but can vary. Using the NumPy arange() function to produce the x values (e.g, arange(0, 100, 0.1)), I need a function that will produce the associated y values for a bell-shaped curve. These curves represent the membership functions for fuzzy term sets, and generally adjacent curves overlap where y=0.5. It would be a bonus to be able to adjust the skew and kurtosis of the curves, but the controlling data would be the center/midpoint and width, with defaults for inflection point, and other parameters. I've been searching for quite some time without finding a solution that works as we need it to work. TIA, Rich -- Richard B. Shepard, Ph.D. | Integrity Credibility Applied Ecosystem Services, Inc. | Innovation <http://www.appl-ecosys.com> Voice: 503-667-4517 Fax: 503-667-8863

10 16

Does Unreasonable Matrix Behavior affect Scipy Sparse
by Dinesh B Vadhia 26 Apr '08

26 Apr '08

I had b = Ax working where A is sparse using scipy.sparse. I'm now using the latest svn and b = Ax is not working and returns garbage results. Nothing has changed except the latest svn. Any thoughts? Dinesh

3 4

"aligned" matrix / ctypes
by Zachary Pincus 25 Apr '08

25 Apr '08

Hello all, I need to allocate a numpy array that I will then pass to a camera driver (via ctypes) so that the driver can fill the array with pixels. The catch is that the driver requires that rows of pixels start at 4- byte boundaries. The sample C++ code given for allocating memory for this is (pixels are unsigned shorts): // Two bytes for each pixel, then round // up to the next multiple of four. long width_bytes = ( ( 2 * width_pixels ) + 3 ) & -4; long allocated_size = width_bytes * height; unsigned char* image_data = new unsigned char[allocated_size]; I could make an empty uint8 numpy array of the required shape (allocated_size,) and adjust its dtype, shape, and strides to get the desired result. I'd then feed the array's ctypes data attribute to the driver to get filled in. Alternately I could allocate the data buffer via ctypes and then construct an array around it. Is either option better? How does one construct a numpy array around a ctypes memory object? Can the array "take over" the memory management for the buffer? Thanks for any suggestions, Zach

6 16

Fixing #736 and possible memory leak
by Charles R Harris 25 Apr '08

25 Apr '08

Hi, I've been looking into ticket #736 and playing with some things. In arrayobject.c starting at line 8534 I added a check for strings. if (PyString_Check(op)) { r = Array_FromPyScalar(op, newtype); } if (PySequence_Check(op)) { PyObject *thiserr = NULL; /* necessary but not sufficient */ Py_INCREF(newtype); r = Array_FromSequence(op, newtype, flags & FORTRAN, min_depth, max_depth); if (r == NULL && (thiserr=PyErr_Occurred())) { if (PyErr_GivenExceptionMatches(thiserr, PyExc_MemoryError)) { return NULL; } I think there may be a failure to decrement the reference to newtype unless Array_FromSequence does that (nasty side effect); Anyway, the added check for a string fixes the conversion problem for such things as int32('123'). There remains a problem with array('123', dtype=int32) and with array(['123','123'], dtype=int32), but I think I can track those down. The question is, will changing the current behavior so that strings get converted to numbers cause problems with other programs out there. I suspect I also need to check that strings are converted this way only when the type is explicitly given, not detected. Chuck

3 6

problem with view() and strided arrays?
by Zachary Pincus 25 Apr '08

25 Apr '08

Hello all, I'm writing up a general function to allocate aligned numpy arrays (I'll post it shortly, as Anne suggested that such a function would be useful). However, I've run into trouble with using ndarray.view() in odd corner- cases: In : numpy.__version__ Out: '1.1.0.dev5077' In : a = numpy.ones((3,8),dtype=numpy.uint8) In : a.view(numpy.uint16) Out: array([[257, 257, 257, 257], [257, 257, 257, 257], [257, 257, 257, 257]], dtype=uint16) In : a = numpy.ones((3,9),dtype=numpy.uint8) In : a[:,1:].view(numpy.uint16) ValueError: new type not compatible with array. In : a[:,:-1].view(numpy.uint16) ValueError: new type not compatible with array. In : numpy.array(a[:,:-1]).view(numpy.uint16) Out: array([[257, 257, 257, 257], [257, 257, 257, 257], [257, 257, 257, 257]], dtype=uint16) It seems like 'view' won't create a view where the stride length is not a whole multiple of the dtype's itemsize. However, since strides are measured in bytes (right?), this shouldn't be a problem. Is this a minor bug? Or am I woefully misunderstanding the issue involved here? Thanks, Zach

3 4

insert 1D to a 2D array and change it to 3D
by tournesol 25 Apr '08

25 Apr '08

Hi All. I just want to conver Fortran 77 source to Python. Here is my F77 source. DIMENSION A(25,60,13),B(25,60,13) open(15,file='data.dat') DO 60 K=1,2 READ(15,1602) ((B(I,J),J=1,60),I=1,25) 60 CONTINUE 1602 FORMAT(15I4) DO 63 K=1,10 DO 62 I=1,25 DO 62 J=1,60 A(I,J,K)=B(I,J) 62 CONTINUE 63 CONTINUE END Q1: Fortran-contiguous is ARRAY(row,colum,depth). How about the Python-contiguous ? array(depth,row,colum) ? Q2: How can I insert 1D to a 2D array and make it to 3D array. ex:) B:25x60 ==> A: 10X25X60 Any advice please.

3 2

ndarray.__mul__ is too gready?
by Pearu Peterson 25 Apr '08

25 Apr '08

Hi, Say, one defines a class A that does not like to have numpy arrays in the left-hand-side of an operation, say *, but in the rhs it is expected. For an example, consider A as a huge customized matrix that defines only matrix * vector where vector can be numpy array and scalar * matrix where scalar is python float or int. Users may accidentally hit vector * matrix and this should raise an exception. But when vector is numpy array, then ndarray.__mul__ calls element * A for each array element and here A.__rmul__ gets only the types of array elements (that are floats, for instance) as rhs and hence is not able to decide that it should return NotImplemented. Instead, it computes scalar * matrix and the final result will be array([element1*matrix, element2*matrix, ..]) which is not desired already because of the memory usage and should have raised an exception instead. Here is a small example illustrating the problem: class A: def __rmul__(self, other): if isinstance(other, ndarray): return NotImplemented if isinstance(other, (int, float)): return self # pretend other==1 for an example return NotImplemented def __repr__(self): return 'A()' >>> array([1,2,3]) * A() array([A(), A(), A()], dtype=object) # expected TypeError Is there any workaround to this problem? In other situations/applications the above behavior of ndarray.__mul__ is a feature but is there any way to avoid this feature? Thanks, Pearu

2 1

EPD PIL _imaging C module
by Lin Shao 24 Apr '08

24 Apr '08

Running scipy.test() in the latest EPD windows distribution, I got the following exception: ====================================================================== ERROR: test_imresize (scipy.misc.tests.test_pilutil.test_pilutil) ---------------------------------------------------------------------- Traceback (most recent call last): File "E:\Python25\lib\site-packages\scipy-0.6.0.0003-py2.5-win32.egg\scipy\misc\tests\test_pilutil.py", line 17, in test_imresize im1 = pilutil.imresize(im,T(1.1)) File "E:\Python25\lib\site-packages\scipy-0.6.0.0003-py2.5-win32.egg\scipy\misc\pilutil.py", line 225, in imresize im = toimage(arr) File "E:\Python25\lib\site-packages\scipy-0.6.0.0003-py2.5-win32.egg\scipy\misc\pilutil.py", line 104, in toimage image = Image.fromstring('L',shape,bytedata.tostring()) File "E:\Python25\lib\site-packages\PIL-1.1.6.0003_s-py2.5-win32.egg\PIL\Image.py", line 1743, in fromstring im = new(mode, size) File "E:\Python25\lib\site-packages\PIL-1.1.6.0003_s-py2.5-win32.egg\PIL\Image.py", line 1710, in new return Image()._new(core.fill(mode, size, color)) File "E:\Python25\lib\site-packages\PIL-1.1.6.0003_s-py2.5-win32.egg\PIL\Image.py", line 36, in __getattr__ raise ImportError("The _imaging C module is not installed") ImportError: The _imaging C module is not installed It's a bit strange because I do see _imging.pyd in the PIL folder. I'm using XP 32-bit. -lin

1 0

Using normal()
by Rich Shepard 24 Apr '08

24 Apr '08

Rereading "Guide to NumPy" once again, I saw what I had missed all the previous times: the normal() distribution function (Chapter 10, page 173). I have several questions on using it in my application. The syntax is normal(loc=0.0, scale=1.0, size=None), but I've not seen what those represent, nor how to properly invoke this function. A clue will be much appreciated. I want to call normal() passing at least the width of the curve(at the end points where y=0.0), and the center (where y=1.0). Being able to specify the y value of the inflection point (by default 0.5) would also be helpful. In the application's function I now have: from numpy import * x = nx.arange(0, 100, 0.1) y = nx.normal(center,width) # passed to the function when called and I then pass x,y to PyX for plotting. But python responds with: y = nx.normal(center,width) AttributeError: 'module' object has no attribute 'normal' Pointers to what I should be doing are needed. TIA, Rich -- Richard B. Shepard, Ph.D. | Integrity Credibility Applied Ecosystem Services, Inc. | Innovation <http://www.appl-ecosys.com> Voice: 503-667-4517 Fax: 503-667-8863

6 15

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NumPy-Discussion April 2008