Mailman 3 May 2004 - 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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numarray equivalent to sign() function
by Mike Zingale 26 Jun '04

26 Jun '04

Hi, I am in the process of converting some code from Numeric to numarray, and it seems that numarray no longer has the sign() function -- is that so? ex: sign(-30.0) = -1 sign(0) = 0 sign(1000) = 1 Is there a replacement? Thanks, Mike

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Little Pen-i-sMake U Shy To Screw?? Hehe where
by Cheryle Rosalyn 31 May '04

31 May '04

mountain season laughed perfect done justice now tie maybe excite my loose experience listen conscience across likely slipped step ache thought

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numarray.where confusion
by Alok Singhal 27 May '04

27 May '04

Hi, I am having trouble understanding how exactly "where" works in numarray. What I am trying to do: I am preparing a two-level mask in an array and then assign values to the array where both masks are true: >>> from numarray import * >>> a = arange(10) >>> # First mask >>> m1 = where(a > 5) >>> a[m1] array([6, 7, 8, 9]) >>> # Second mask >>> m2 = where(a[m1] < 8) >>> a[m1][m2] array([6, 7]) >>> # So far so good >>> # Now change some values >>> a[m1][m2] = array([10, 20]) >>> a[m1][m2] array([6, 7]) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> # Didn't work >>> # Let's try a temporary variable >>> t = a[m1] >>> t[m2] array([6, 7]) >>> t[m2] = array([10, 20]) >>> t[m2], t (array([10, 20]), array([10, 20, 8, 9])) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) So, my assignment to a[m1][m2] seems to work (no messages), but it doesn't produce the effect I want it to. I have read the documentation but I couldn't find something that would explain this behavior. So my questions: - did I miss something important in the documentation, - I am expecting something I shouldn't, or - there is a bug in numarray? Thanks, Alok -- Alok Singhal (as8ca(a)virginia.edu) __ Graduate Student, dept. of Astronomy / _ University of Virginia \_O \ http://www.astro.virginia.edu/~as8ca/ __/

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image correlations
by John Hunter 25 May '04

25 May '04

I have a series of luminance images that I want to do some correlation analyses on. Each image is an MxN frame of a movie. I want to compute the correlation between a given pixel i,j, and every other pixel in the image over each frame. That is, if I assume xij is a numFrames length time series, and xkl is another numFrames length time series (the pixel intensities at two points in the image over time), I want to compute the corrcoeff(xij, xkl) for every kl with ij fixed. I know I could do this by looping over the pixels of the image, but I'm hoping for something a bit faster. Any suggestions? John Hunter

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Read only array?
by Shin, Daehyok 21 May '04

21 May '04

I am wondering if there is any way to set an array as read-only, so that any trial to modify values of elements in the array raises some warning. Is it a cool feature to prevent unexpected side effect? Daehyok Shin (Peter)

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Performance issues with numarray.searchsorted()
by Francesc Alted 20 May '04

20 May '04

Hi, I'm willing to use a lot the searchsorted function in numarray, but I'm a bit surprised about the poor performance of it. Look at that: >>> from time import time >>> import numarray >>> import Numeric >>> na=numarray.arange(1000*1000) >>> nu=Numeric.arange(1000*1000) >>> t1=time();numarray.searchsorted(na,200*1000);time()-t1 200000 0.00055098533630371094 >>> t1=time();Numeric.searchsorted(nu,200*1000);time()-t1 200000 7.7962875366210938e-05 It may seem that Numeric is better optimised, but the standard python module bisect is even faster than numarray.searchsorted: >>> import bisect >>> t1=time();bisect.bisect_left(na,200*1000);time()-t1 200000 8.8930130004882812e-05 >>> t1=time();bisect.bisect_left(nu,200*1000);time()-t1 200000 8.6069107055664062e-05 So, bisect performance is similar to that of Numeric searchsorted and both are almost an order of magnitude better than numarray searchsorted. This a little bit surprising as the bisect_left module is written in plain python. From the python 2.3.3 sources: def bisect_left(a, x, lo=0, hi=None): if hi is None: hi = len(a) while lo < hi: mid = (lo+hi)//2 if a[mid] < x: lo = mid+1 else: hi = mid return lo I'm using python2.3.3, numarray 0.9.1 (latest CVS) and Debian Linux (sid). Cheers, -- Francesc Alted

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random_array.poisson(0) gives -1 and -2
by Sebastian Haase 19 May '04

19 May '04

Hi, the random_array poisson functions returns negative values if mean=0: >>>from numarray import random_array as ra >>> ra.seed(x=1, y=1) >>> ra.poisson(0) 5 >>> ra.poisson(0) -2 My "math book" tells me that it should be always zero. This seems to be a constructed case, but I'm using this to put "quantum statistic" into a simulated image: obj = na.array( something ) imageFromDetector = ra.poisson( obj ) + gaussianNoiseArray The object array might have lots of zeros surrounding the "actual object". Thinking of a fluorescent object sending out photons it makes sense to not get any photons at all from 'empty' regions. I'm using numarray 0.8; Thanks for numarray, Sebastian Haase

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RE: [Numpy-discussion] Linear solvers
by Nadav Horesh 19 May '04

19 May '04

The simplest solution is to use "solve_linear_equations" functin from numarray.linear_algebra. For more sophisticate solution you may try scipy (www.scipy.org) Nadav -----Original Message----- From: Craig Rasmussen [mailto:crasmussen@lanl.gov] Sent: Wed 19-May-04 17:59 To: numarray Cc: Subject: [Numpy-discussion] Linear solvers I need to solve a matrix equation of the form A u = q for u, where u and q are N-vectors and A is a symmetric-positive-definite matrix. For now, at least, A is a 2-D matrix. I would like to use Python and numarray. Are there Python modules that I could use to obtain a solution for u? Thanks, Craig ------------------------------------------------------- This SF.Net email is sponsored by: SourceForge.net Broadband Sign-up now for SourceForge Broadband and get the fastest 6.0/768 connection for only $19.95/mo for the first 3 months! http://ads.osdn.com/?ad_id=2562&alloc_id=6184&op=click _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

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Error in choose (bug?)
by Álvaro Tejero Cantero 19 May '04

19 May '04

I have a matrix of particle collision times: times[i,j] gives the time for particle "i" to collide with particle "j". If I do, in order to get the first expected collision time for each particle, the following (random array for testing purposes) : >>> N=30 >>> times = rnd.random([N,N]) >>> choose(argmin(times,transpose(times)) Segmentation fault (of the python interactive shell!!!) With N=100 I get a more informative traceback, and rest within the shell: Traceback (most recent call last): File "<stdin>", line 1, in ? File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 1670, in choose return _choose(selector, population, outarr, clipmode) File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 1579, in __call__ result = self._doit(computation_mode, woutarr, cfunc, ufargs, 0) File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 1564, in _doit blockingparameters) ValueError: _operator_compute: too many inputs + outputs For N=10,N=15 I get the expected output, but for N=20 I get again the brutal segfault... regards, á. -- Álvaro Tejero Cantero http://alqua.org -- documentos libres free documents

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