NumPy-Discussion March 2004

numpy-discussion@python.org

34 participants
38 discussions

by Michiel Jan Laurens de Hoon

Dear pythoneers, In Chapter 12 ("Writing a C extension") in the Numerical Python manual, in the first example PyArray_ContiguousFromObject is used together with PyDECREF, while in the last example PyArray_ContiguousFromObject is used but the arrays are not PyDECREF'ed before the function returns. From Chapter 13 ("C API Reference") it seems that a PyDECREF is needed in both cases. Or am I missing something here? Is PyDECREF not needed for some reason in the last example? Thanks in advance, Michiel de Hoon U Tokyo First example: ============== static PyObject * trace(PyObject *self, PyObject *args) { PyObject *input; PyArrayObject *array; double sum; int i, n; if (!PyArg_ParseTuple(args, "O", &input)) return NULL; array = (PyArrayObject *) PyArray_ContiguousFromObject(input, PyArray_DOUBLE, 2, 2); if (array == NULL) return NULL; n = array->dimensions[0]; if (n > array->dimensions[1]) n = array->dimensions[1]; sum = 0.; for (i = 0; i < n; i++) sum += *(double *)(array->data + i*array->strides[0] + i*array->strides[1]); Py_DECREF(array); return PyFloat_FromDouble(sum); } Last example: ============= static PyObject * matrix_vector(PyObject *self, PyObject *args) { PyObject *input1, *input2; PyArrayObject *matrix, *vector, *result; int dimensions[1]; double factor[1]; double real_zero[1] = {0.}; long int_one[1] = {1}; long dim0[1], dim1[1]; extern dgemv_(char *trans, long *m, long *n, double *alpha, double *a, long *lda, double *x, long *incx, double *beta, double *Y, long *incy); if (!PyArg_ParseTuple(args, "dOO", factor, &input1, &input2)) return NULL; matrix = (PyArrayObject *) PyArray_ContiguousFromObject(input1, PyArray_DOUBLE, 2, 2); if (matrix == NULL) return NULL; vector = (PyArrayObject *) PyArray_ContiguousFromObject(input2, PyArray_DOUBLE, 1, 1); if (vector == NULL) return NULL; if (matrix->dimensions[1] != vector->dimensions[0]) { PyErr_SetString(PyExc_ValueError, "array dimensions are not compatible"); return NULL; } dimensions[0] = matrix->dimensions[0]; result = (PyArrayObject *)PyArray_FromDims(1, dimensions, PyArray_DOUBLE); if (result == NULL) return NULL; dim0[0] = (long)matrix->dimensions[0]; dim1[0] = (long)matrix->dimensions[1]; dgemv_("T", dim1, dim0, factor, (double *)matrix->data, dim1, (double *)vector->data, int_one, real_zero, (double *)result->data, int_one); return PyArray_Return(result); } -- Michiel de Hoon, Assistant Professor University of Tokyo, Institute of Medical Science Human Genome Center 4-6-1 Shirokane-dai, Minato-ku Tokyo 108-8639 Japan http://bonsai.ims.u-tokyo.ac.jp/~mdehoon

10 years, 6 months

blitz++ and numarray

by Faheem Mitha

Hi, I'm considering the use of the Blitz++ C++ library (http://www.oonumerics.org/blitz/) along with numarray/Numeric. I want to pass an array down to C++ and then manipulate it using Blitz++. My current thinking is to use the Numeric interface to pass down the array, and then copy it to a blitz++ array object. This seems straightforward except I am a little concerned about those reference counting issues, which I don't properly understand (I'm a Python beginner). I do get the impression that it is mainly an issue if I am working with Python objects in C/C++ code, though, and I am mostly going to try to avoid doing so by declaring C++ objects within the C++ code and working with those as much as possible. Other possibilities include somehow using the Boost.Python package, though I am not sure how. One advantage of this is that it apparently takes care of the reference counting issue. However, I don't want to learn yet another API. Also, weave.blitz() from SciPy looks relevant/useful, but this automatically generates its own C++ code, and I'd prefer to write my own. Can anyone suggested a good basic tutorial which discusses the issues of reference counting in a C/C++ API context? I have tried reading the official documentation and will continue to do so, but something gentler would be useful. Comments, suggestions gratefully appreciated. Please cc me on any reply to faheem(a)email.unc.edu. Thanks in advance. Faheem.

17 years, 8 months

Re: [Numpy-discussion] A couple more Numeric incompatibilitiesand a possible bug

by Gary Ruben

> > >>> cos(1) > > 0.54030227661132813 > > > > gives a different result to cos(1.) <snip> > > Do others think precision is being lost unnecessarily? > > No. Do you have any suggestions? Yes. My problem is that numarray is not replicating the behaviour of the Python math module, which presumably coerces immediately to the float precision of the underlying platform. This is probably a float64 in Windows. If you don't explicitly specify that float32 is to be used, shouldn't the default be for a rank-0 value to immediately coerce ints to the same precision as the native Python float type on the underlying platform, since you know it will be coerced to that later and in the meantime you've lost precision because you've applied a function to a value of lower precision? > > Finally, has anyone got any comment about whether the __repr__ versus __str__ display of object array members is the expected behaviour? > > > > Yes. The basic look is exactly what I wanted: > > >>> a = numarray.objects.fromlist(range(10)) > >>> str(a) > '[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]' > >>> repr(a) > 'ObjectArray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])' > > But I'm open to suggestions and recognize that the handling of > spaces/item sizing is weak (although it's not pronounced in this simple > example). Do people want something else? > > Regards, > Todd Your example doesn't demonstrate the problem I'm talking about because for integers the __str__ and __repr__ methods are identical. However, it does show a difference between Numeric and numarray, which would display your str(a) as '[0 1 2 3 4 5 6 7 8 9 ]' If you make an ObjectArray containing objects whose __repr__ and __str__ methods are not the same, I think you need a way of displaying the contained objects via their type-specific __str__ method. Numeric did this by default, whereas numarray has changed that behaviour. I'll go back to my example again. In my example str() does not display the array elements in the same way that Numeric does. In my case, numarray does this: >>> print ArrayOfErr([909., 802., 677., 585.], 1.0) [Err(909.0,1.0,1.0), Err(802.0,1.0,1.0), Err(677.0,1.0,1.0), Err(585.0,1.0,1.0)] >>> print str(ArrayOfErr([909., 802., 677., 585.], 1.0)) [Err(909.0,1.0,1.0), Err(802.0,1.0,1.0), Err(677.0,1.0,1.0), Err(585.0,1.0,1.0)] >>> print repr(ArrayOfErr([909., 802., 677., 585.], 1.0)) ObjectArray([Err(909.0,1.0,1.0), Err(802.0,1.0,1.0), Err(677.0,1.0,1.0), Err(585.0,1.0,1.0)]) ie. str() is not using the __str__ method I have defined for my Err class. Numeric correctly uses my class's Err.__str__ method and does this instead: >>> print ArrayOfErr([909., 802., 677., 585.], 1.0) [909.0 +1.0/-1.0 802.0 +1.0/-1.0 677.0 +1.0/-1.0 585.0 +1.0/-1.0 ] In summary, I think te behaviour should be >>> print str(ArrayOfErr([909., 802., 677., 585.], 1.0)) [909.0 +1.0/-1.0 802.0 +1.0/-1.0 677.0 +1.0/-1.0 585.0 +1.0/-1.0 ] >>> print repr(ArrayOfErr([909., 802., 677., 585.], 1.0)) ObjectArray([Err(909.0,1.0,1.0), Err(802.0,1.0,1.0), Err(677.0,1.0,1.0), Err(585.0,1.0,1.0)]) I hope I've clarified what I'm getting at, although I probably haven't :-) Gary -- ___________________________________________________________ Sign-up for Ads Free at Mail.com http://promo.mail.com/adsfreejump.htm

17 years, 8 months

Re: Re: Message

by numpy-discussion＠lists.sourceforge.net

Your document is attached.

17 years, 8 months

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17 years, 8 months

Re: [Numpy-discussion] A couple more Numeric incompatibilitiesand apossible bug

by Gary Ruben

Thanks for pointing this out Alexander, I admit that I hadn't tried any native Python container types to see how they behave. I was basing my comments on Numeric's behaviour. When I have time, maybe I should raise a question on the Python developer's list. It seems like something that may have been overlooked. regards, Gary ----- Original Message ----- From: Alexander Schmolck <a.schmolck(a)gmx.net> Date: Mon, 29 Mar 2004 13:52:20 +0100 To: "numpy-discussion" <numpy-discussion(a)lists.sourceforge.net> Subject: Re: [Numpy-discussion] A couple more Numeric incompatibilitiesand apossible bug > "Gary Ruben" <gazzar(a)email.com> writes: > > In summary, I think te behaviour should be > >>>> print str(ArrayOfErr([909., 802., 677., 585.], 1.0)) > > [909.0 +1.0/-1.0 802.0 +1.0/-1.0 677.0 +1.0/-1.0 585.0 +1.0/-1.0 ] > > > >>>> print repr(ArrayOfErr([909., 802., 677., 585.], 1.0)) > > ObjectArray([Err(909.0,1.0,1.0), Err(802.0,1.0,1.0), Err(677.0,1.0,1.0), Err(585.0,1.0,1.0)]) > > Such behavior would seem sensible, but I don't think it's what the builtin > collections do (not sure why): > > >>> n = 1.1 > >>> n > 1.1000000000000001 > >>> [n] > [1.1000000000000001] > >>> str(n) > '1.1' > >>> str([n]) > '[1.1000000000000001]' > > > 'as -- ___________________________________________________________ Sign-up for Ads Free at Mail.com http://promo.mail.com/adsfreejump.htm

17 years, 8 months

numarray C api examples

by Faheem Mitha

The README in numarray-0.9/Examples refers to the non-existent file setup.py (at least in that directory). There are other files called setup.py elsewhere in the tarball, but they don't look like they are supposed to compile the examples. What am I missing? Thanks, Faheem. ******************************************************************** This directories gives examples and benchmarks ot he usage of numarray. To install do ,---- | python setup.py install `---- in this directory. This will install all examples. You can test all of them by running ,---- | python testall.py `---- or go to the individual subdirectories and look at the examples more closely. ********************************************************************

17 years, 8 months

Re: [Numpy-discussion] A couple more Numeric incompatibilitiesand a possible bug

by Gary Ruben

> I'm guessing, but I think you would like to see: > > >>> abs(-1.) > 1.0 > >>> abs(1) > 1 Hi Todd, Sorry if I wasn't clear. Yes, that's what I was expecting to see. Thanks for logging it and thanks for the explanation of why it's happening. I'd also like to restate the other issues in the email to which that one referred, which haven't been commented on. The cos function result >>> cos(1) 0.54030227661132813 gives a different result to cos(1.) Is this just because of numarray's stated departures from the way type coercion is done in Numeric? My guess is that numarray is casting the integer to a rank-0 Float32 array which is then coerced back to the native Python float type, which presumably is a float64 on the Windows platform. Do others think precision is being lost unnecessarily? Finally, has anyone got any comment about whether the __repr__ versus __str__ display of object array members is the expected behaviour? thanks, Gary -- ___________________________________________________________ Sign-up for Ads Free at Mail.com http://promo.mail.com/adsfreejump.htm

17 years, 8 months

indexing question

by Faheem Mitha

Dear People, Suppose I create a numarray arry (say). A numeric array would be fine too; it probably does not matter. >>>import numarray >>>foo = numarray.reshape(numarray.arange(9),(3,3)) array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) Is there some way to select the indexes corresponding only to (say) two rows or columns? ie suppose I just want the first and the fourth row or the first and third columns? Is there some clean way to do this which does not involve extracting individual rows or columns? Ie. I want something like >>> foo[?,:] array([[ 0, 1, 2, 3], [12, 13, 14, 15]]) etc. Suggestions appreciated. Please cc me; I'm not subscribed. Thanks in advance. Faheem.

17 years, 8 months

Re: [Numpy-discussion] A couple more Numeric incompatibilitiesand a possible bug

by Gary Ruben

Actually, this reproduces the problem: >>> from numarray import * >>> from numarray.objects import * >>> abs(-1.) ObjectArray(1.0) >>> abs(-1) ObjectArray(1) > Please ignore this bit of my previous message: > > > >>> abs(-1) > > ObjectArray(1) > > Something in my environment (probably me) got a bit confused. > > Gary -- ___________________________________________________________ Sign-up for Ads Free at Mail.com http://promo.mail.com/adsfreejump.htm

17 years, 8 months

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NumPy-Discussion March 2004