Mailman 3 September 2005 - NumPy-Discussion

PyArray_ContiguousFromObject & PyDECREF
by Michiel Jan Laurens de Hoon 16 May '11

16 May '11

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

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Release of SciPy Core 0.4 (Beta)
by Travis Oliphant 03 Oct '05

03 Oct '05

This is to announce the release of SciPy Core 0.4.X (beta) It is available at sourceforge which you can access by going to http://numeric.scipy.org Thanks to all who helped me with this release, especially Robert Kern Pearu Peterson Now, let's start getting this stable.... -Travis Oliphant

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numarray: Possible hash collision problem
by Edward C. Jones 28 Sep '05

28 Sep '05

hash(numarray.arange(1000)) == hash(numarray.arange(10000)) The hash value changes each time I enter the Python interpreter. I have always assumed that hashing was deterministic. Is it?

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Determining the condition number of matrix
by Neilen Marais 27 Sep '05

27 Sep '05

Hi Does numeric have a facility to estimate the condition number of a matrix? Thanks Neilen -- you know its kind of tragic we live in the new world but we've lost the magic -- Battery 9 (www.battery9.co.za)

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Piecewise functions.
by Andrea Riciputi 26 Sep '05

26 Sep '05

Hi all, this is probably an already discussed problem, but I've not been able to find a solution even after googling a lot. I've a piecewise defined function: / | f1(x) if x <= a f(x) = | | f2(x) if x > a \ where f1 and f2 are not defined outside the above range. How can I define such a function in Python in order to apply (map) it to an array ranging from values smaller to values bigger than a? Thanks, Andrea.

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Release of scipy core beta will happen next week.
by Travis Oliphant 26 Sep '05

26 Sep '05

At the SciPy 2005 conference I announced that I was hoping to get a beta of the new scipy (core) (aka Numeric3 aka Numeric Next Generation) released by the end of the conference. This did not happen. Some last minute features were suggested by Fernando Perez that I think will be relatively easy to add and make the release that much stronger. Look for the beta announcement next week. For the impatient, the svn server is always available: http://svn.scipy.org/svn/scipy_core/branches/newcore -Travis O.

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python-numeric in FC4: good idea?
by Stephen Walton 26 Sep '05

26 Sep '05

In my ongoing struggles with FC4, I now find that the Fedora developers have put Numeric 23.7 into the core as python-numeric and built pygtk2 against it, so the latter has a dependency on the former. I think this is a bad idea unless the core developers are going to include ATLAS and build Numeric against it. After all, this community and the Scipy one have gone to great lengths to optimize ATLAS and build Numeric and numarray (and presumably the new scipy core) against it. But I'm probably not going to convince the Redhat folks on my own. So, questions are: can someone more familiar with pygtk2 than me tell me what parts of it depend on Numeric and why? Can we start a campaign to put ATLAS into Fedora Core if Numeric is going to be there too?

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Important PayPal! Account Information.
by service＠paypal.com 23 Sep '05

23 Sep '05

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array indexing in numarray
by Joost van Evert 22 Sep '05

22 Sep '05

Hi list, I have two questions on numarray indexing: 1) does anyone know an elegant way to assign values to a non-contiguous slice. Like: In [1]:from numarray import * In [2]:a = arange(25).resize((5,5)) In [3]:a Out[3]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) In [4]:i = [1,3] In [5]:transpose(a[i])[i]=1 ---------------------------------------------------------------- exceptions.ValueError Traceback (most recent call last) ... ValueError: Invalid destination array: partial indices require contiguous non-byteswapped destination 2) And second: why didn't you choose to return all combinations of indices: In [8]:a[i,i] Out[8]:array([ 6, 18]) Where I, in my humble opinion, would prefer to see all possible combinations. e.g. a[[1,3],[1,3]] = a[[1,1,3,3],[1,3,1,3]] Anyone has an idea how to easyly obtain this behaviour? Greets+thanks, Joost

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numarray bug in gaussian_filter1d?
by Alexandre Guimond 22 Sep '05

22 Sep '05

Hi. I think I found a bug in gaussian_filter1d. roughly the function creates a 1d kernel and then calls correlate1d. The problem I see is that the kernel should be mirrored prior to calling correlate1d since we want to convolve, not correlate. as a result: >>> import numarray.nd_image >>> numarray.nd_image.gaussian_filter1d( ( 0.0, 1.0, 0.0 ), 1, order = 1, axis = 0, mode = 'constant' ) array([-0.24197145, 0. , 0.24197145]) >>> when it should be [ 0.24197145, 0. , -0.24197145]) (notice the change in the sign of coefficients) Or did I get that wrong? alex.

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