Mailman 3 December 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 … [View More]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 [View Less]

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Test failures on 2.6
by T J Nov. 18, 2010

Nov. 18, 2010

Hi, I'm getting a couple of test failures with Python 2.6, Numpy 1.2.0, Nose 0.10.4: nose version 0.10.4 .......................................................................................................................................................................................................................................................................................................................................................................................................… [View More]...................................F................K........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................./share/home/me/usr/lib/python2.6/site-packages/numpy/lib/tests/test_io.py:68: SyntaxWarning: assertion is always true, perhaps remove parentheses? assert(c.readlines(), ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................./share/home/me/usr/lib/python2.6/site-packages/numpy/ma/tests/test_core.py:1315: SyntaxWarning: assertion is always true, perhaps remove parentheses? assert(store._mask, True) /home/me/usr/lib/python2.6/site-packages/numpy/ma/tests/test_core.py:1322: SyntaxWarning: assertion is always true, perhaps remove parentheses? assert(store._mask, True) /home/me/usr/lib/python2.6/site-packages/numpy/ma/tests/test_core.py:1989: SyntaxWarning: assertion is always true, perhaps remove parentheses? assert(test.mask, [0,1,0,0,0,0,0,0,0,0]) ...............................................E................................................................................................................................................................................ ====================================================================== ERROR: Tests the min/max functions with explicit outputs ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/me/usr/lib/python2.6/site-packages/numpy/ma/tests/test_core.py", line 653, in test_minmax_funcs_with_output result = npfunc(xm,axis=0,out=nout) File "/home/me/usr/lib/python2.6/site-packages/numpy/core/fromnumeric.py", line 1525, in amin return amin(axis, out) File "/home/me/usr/lib/python2.6/site-packages/numpy/ma/core.py", line 2978, in min np.putmask(out, newmask, np.nan) ValueError: cannot convert float NaN to integer ====================================================================== FAIL: test_umath.TestComplexFunctions.test_against_cmath ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/me/usr/lib/python2.6/site-packages/nose-0.10.4-py2.6.egg/nose/case.py", line 182, in runTest self.test(*self.arg) File "/home/me/usr/lib/python2.6/site-packages/numpy/core/tests/test_umath.py", line 268, in test_against_cmath assert abs(a - b) < atol, "%s %s: %s; cmath: %s"%(fname,p,a,b) AssertionError: arcsin 2: (1.57079632679-1.31695789692j); cmath: (1.57079632679+1.31695789692j) ---------------------------------------------------------------------- Ran 1726 tests in 8.856s FAILED (KNOWNFAIL=1, errors=1, failures=1) <nose.result.TextTestResult run=1726 errors=1 failures=1> [View Less]

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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 … [View More]

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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 … [View More]

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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 … [View More]

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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 '… [View More]

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Automatic differentiation (was Re: second-order gradient)
by Rob Clewley March 11, 2009

March 11, 2009

> Maybe we should focus on writing a decent 'deriv' function then. I > know Konrad Hinsen's Scientific had a derivatives package > (Scientific.Functions.Derivatives) that implemented automatic > differentiation: > > http://en.wikipedia.org/wiki/Automatic_differentiation That would be great, but wouldn't that be best suited as a utility requiring Sympy? You'll want to take advantage of all sorts of symbolic classes, especially for any source code transformation approach. IMO … [View More]

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linalg.norm missing an 'axis' kwarg?!
by Hans Meine Feb. 10, 2009

Feb. 10, 2009

Hi, I have a 2D matrix comprising a sequence of vectors, and I want to compute the norm of each vector. np.linalg.norm seems to be the best bet, but it does not support axis. Wouldn't this be a nice feature? Greetings, Hans

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new incremental statistics project
by Bradford Cross Jan. 22, 2009

Jan. 22, 2009

This is a new project I just released. I know it is C#, but some of the design and idioms would be nice in numpy/scipy for working with discrete event simulators, time series, and event stream processing. http://code.google.com/p/incremental-statistics/

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