Mailman 3 June 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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"import numpy" is slow
by Andrew Dalke Aug. 5, 2008

Aug. 5, 2008

(Trying again now that I'm subscribed. BTW, there's no link to the subscription page from numpy.scipy.org .) The initial 'import numpy' loads a huge number of modules, even when I don't need them. Python 2.5 (r25:51918, Sep 19 2006, 08:49:13) [GCC 4.0.1 (Apple Computer, Inc. build 5341)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import sys >>> len(sys.modules) 28 >>> import numpy >>> len(sys.modules) 256 >>> len([s for s in sorted(sys.modules) if 'numpy' in s]) 127 >>> numpy.__version__ '1.1.0' >>> As a result, I assume that's the reason my program's startup cost is quite high. [josiah:~/src/fp] dalke% time python -c 'a=4' 0.014u 0.038s 0:00.05 80.0% 0+0k 0+1io 0pf+0w [josiah:~/src/fp] dalke% time python -c 'import numpy' 0.161u 0.279s 0:00.44 97.7% 0+0k 0+9io 0pf+0w My total runtime is something like 1.4 seconds, and the only thing I'm using NumPy for is to make an array of doubles that I can pass to a C extension. (I could use the array module or ctypes, but figured numpy is more useful for downstream code.) Why does numpy/__init__.py need to import all of these other modules and submodules? Any chance of cutting down on the number, in order to improve startup costs? Andrew dalke(a)dalkescientific.com

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arccosh for complex numbers, goofy choice of branch
by Charles R Harris July 20, 2008

July 20, 2008

OK, Which branch do we want to use. As it currently is in numpy and scipy.special arccosh(1.5) = 0.96242365011920694 arccosh(1.5+0j) = -0.96242365011920705 + 0.0j This is consistent with gsl, but inconsistent with Mathematica, NAG, Maple, and probably all sensible implementations which use the generally accepted principal value. I've left this inconsistency raising an error in the ufunc tests until we make a decision. It might be nice to know what FORTRAN and MatLab do with this. Chuck

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