Mailman 3 September 2008 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker July 11, 2023

July 11, 2023

I've been browsing the numpy source. I'm wondering about mixed-mode arithmetic on arrays. I believe the way numpy handles this is that it never does mixed arithmetic, but instead converts arrays to a common type. Arguably, that might be efficient for a mix of say, double and float. Maybe not. But for a mix of complex and a scalar type (say, CDouble * Double), it's clearly suboptimal in efficiency. So, do I understand this correctly? If so, is that something we should improve?

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PyArray_ContiguousFromObject & PyDECREF
by Michiel Jan Laurens de Hoon May 17, 2011

May 17, 2011

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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Unpleasant behavior with poly1d and numpy scalar multiplication
by Fernando Perez Feb. 15, 2010

Feb. 15, 2010

Hi all, consider this little script: from numpy import poly1d, float, float32 p=poly1d([1.,2.]) three=float(3) three32=float32(3) print 'three*p:',three*p print 'three32*p:',three32*p print 'p*three32:',p*three32 which produces when run: In [3]: run pol1d.py three*p: 3 x + 6 three32*p: [ 3. 6.] p*three32: 3 x + 6 The fact that multiplication between poly1d objects and numbers is: - non-commutative when the numbers are numpy scalars - different for the same number if it is a python float vs a numpy scalar is rather unpleasant, and I can see this causing hard to find bugs, depending on whether your code gets a parameter that came as a python float or a numpy one. This was found today by a colleague on numpy 1.0.4.dev3937. It feels like a bug to me, do others agree? Or is it consistent with a part of the zen of numpy I've missed thus far? Thanks, f

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Compact way of performing array math with specified result type?
by Russell E. Owen Feb. 8, 2010

Feb. 8, 2010

I often find myself doing simple math on sequences of numbers (which might or might not be numpy arrays) where I want the result (and thus the inputs) coerced to a particular data type. I'd like to be able to say: numpy.divide(seq1, seq2, dtype=float) but ufuncs don't allow on to specify a result type. So I do this instead: numpy.array(seq1, dtype=float) / numpy.array(seq2, dtype=float) Is there a more compact solution (without having to create the result array first and supply it as an argument)? -- Russell

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Is anyone knowledgeable about dll deployment on windows ?
by David Cournapeau Dec. 1, 2009

Dec. 1, 2009

Hi, My head hurt trying to understand dll management with windows. I wanted to find a sane way to use dll for numscons, but I can't see how to do this, so I was wondering if anyone on this ML had any deep knowledge on how to install dll, and reuse them with python extensions ? The problem is the following: - you have some dll (say MKL) in a path (say C:\program files\MKL). - how do you tell another dll (more exactly a python extension, a .pyd) to look for mkl dll into the MKL path ? It seems that there is no rpath-like capability on windows, and the only way to load dll is to put them into some windows directory, or to put the dll path into PATH. Is it really the only way ? cheers, David

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Slicing/selection in multiple dimensions simultaneously
by Mike Ressler March 17, 2009

March 17, 2009

The following seems to be a wart: is it expected? Set up a 10x10 array and some indexing arrays: a=arange(100) a.shape=(10,10) q=array([0,2,4,6,8]) r=array([0,5]) Suppose I want to extract only the "even" numbered rows from a - then print a[q,:] <works - output deleted> Every fifth column: print a[:,r] <works - output deleted> Only the even rows of every fifth column: print a[q,r] --------------------------------------------------------------------------- <type 'exceptions.ValueError'> Traceback (most recent call last) /.../.../.../<ipython console> in <module>() <type 'exceptions.ValueError'>: shape mismatch: objects cannot be broadcast to a single shape But, this works: print a[q,:][:,r] [[ 0 5] [20 25] [40 45] [60 65] [80 85]] So why does the a[q,r] form have problems? Thanks for your insights. Mike -- mike.ressler(a)alum.mit.edu

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access ndarray in C++
by Thomas Hrabe Dec. 10, 2008

Dec. 10, 2008

Hi all! I am currently developing a python module in C/C++ which is supposed to access nd arrays as defined by the following command in python a = numpy.array([1,1,1]) I want to access the array the following way and use the nd array data for further processing in C. mymod.doSthg(a) The example code on http://numpy.sourceforge.net/numdoc/HTML/numdoc.htm#pgfId-36721 (!PyArg_ParseTuple(args, "O!",&PyArray_Type, &array)) does not work for nd arrays. I always get TypeError: argument 1 must be array, not numpy.ndarray I assume the error is the constant provided as the third parameter, saying that the imput is of PyArray_Type and no nd array. So here are my questions: 1. is there any good tutorial / example code for acessing nd arrays in C? 2. what is the difference between both (arrays and nd arrays? - I am new to python and heard there were different approaches to arrays and that nd arrays work better for multi dimensional applications. Is this right?) 3. which one of both will be used in the future? Thank you in advance for your help, Thomas

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error importing a f2py compiled module.
by Fabrice Silva Nov. 30, 2008

Nov. 30, 2008

Dear all I've tried to run f2py on a fortran file which used to be usable from python some months ago. Following command lines are applied with success (no errors raised) : f2py -m modulename -h tmpo.pyf --overwrite-signature tmpo.f f2py -m modulename -c --f90exec=/usr/bin/f95 tmpo.f The output of these commands is available here: http://paste.debian.net/7307 When importing in Python with "import modulename", I have an ImportError: Traceback (most recent call last): File "Solveur.py", line 44, in <module> import modulename as Modele ImportError: modulename.so: failed to map segment from shared object: Operation not permitted How can that be fixed ? Any suggestion ? Thanks -- Fabrice Silva LMA UPR CNRS 7051 - équipe S2M

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Bilateral filter
by Nadav Horesh Nov. 25, 2008

Nov. 25, 2008

Attached here my cython implementation of the bilateral filter, which is my first cython program. I would ask for the following: 1. Is there any way to speed up the code just by "cosmetic" modifications? 2. In particular I use the unportable gcc function __builtin_abs: Is there any way to access this in a portable way? 3. I would like to donate this code to scipy or any other suitable project. What can/should I do to realise it? Remarks: The code contains 3 end-user routines: 1. A pure python implementation: Easy to read and modify --- it can be cut out into a python source code. 2. A straight forward cython implementation: About 4 times as fast as the python implementation. 3. An optimised cython implementation earning another factor of 2 in speed (depends on the parameters used). I see this code as a "research grade" that would evolve in the near future, as I am working on a related project, and hopefully following your comments. Nadav

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Masked arrays and pickle/unpickle
by Anthony Floyd Nov. 7, 2008

Nov. 7, 2008

We have an application that has previously used masked arrays from Numpy 1.0.3. Part of saving files from that application involved pickling data types that contained these masked arrays. In the latest round of library updates, we've decided to move to the most recent version of matplotlib, which requires Numpy 1.1. Unfortunately, when we try to unpickle the data saved with Numpy 1.0.3 in the new code using Numpy 1.1.0, it chokes because it can't import numpy.core.ma for the masked arrays. A check of Numpy 1.1.0 shows that this is now numpy.ma.core. Does anyone have any advice on how we can unpickle the old data files and update the references to the new classes? Thanks, Anthony. -- Anthony Floyd, PhD Convergent Manufacturing Technologies Inc. 6190 Agronomy Rd, Suite 403 Vancouver BC V6T 1Z3 CANADA Email: Anthony.Floyd(a)convergent.ca | Tel: 604-822-9682 x102 WWW: http://www.convergent.ca | Fax: 604-822-9659 CMT is hiring: See http://www.convergent.ca for details

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