Mailman 3 December 2009 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker 11 Jul '23

11 Jul '23

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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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell 22 Jul '21

22 Jul '21

David Froger <david.froger.info <at> gmail.com> writes: > Hy,My question is about reading Fortran binary file (oh no this question > again...) I've posted this before, but I finally got it cleaned up for the Cookbook. For this purpose I use a subclass of file that has methods for reading unformatted Fortran data. See http://www.scipy.org/Cookbook/FortranIO/FortranFile. I'd gladly see this in numpy or scipy somewhere, but I'm not sure where it belongs. > program makeArray > implicit none > integer,parameter:: nx=10,ny=20 > real(4),dimension(nx,ny):: ux,uy,p > integer :: i,j > open(11,file='uxuyp.bin',form='unformatted') > do i = 1,nx > do j = 1,ny > ux(i,j) = real(i*j) > uy(i,j) = real(i)/real(j) > p (i,j) = real(i) + real(j) > enddo > enddo > write(11) ux,uy > write(11) p > close(11) > end program makeArray When I run the above program compiled with gfortran on my Intel Mac, I can read it back with:: >>> import numpy as np >>> from fortranfile import FortranFile >>> f=FortranFile('uxuyp.bin', endian='<') >>> uxuy = f.readReals(prec='f') # 'f' for default reals >>> len(uxuy) 400 >>> ux = np.array(uxuy[:200]).reshape((20,10)).T >>> uy = np.array(uxuy[200:]).reshape((20,10)).T >>> p = f.readReals('f').reshape((20,10)).T >>> ux array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.], [ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28., 30., 32., 34., 36., 38., 40.], [ 3., 6., 9., 12., 15., 18., 21., 24., 27., 30., 33., 36., 39., 42., 45., 48., 51., 54., 57., 60.], [ 4., 8., 12., 16., 20., 24., 28., 32., 36., 40., 44., 48., 52., 56., 60., 64., 68., 72., 76., 80.], [ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.], [ 6., 12., 18., 24., 30., 36., 42., 48., 54., 60., 66., 72., 78., 84., 90., 96., 102., 108., 114., 120.], [ 7.,Proxy-Connection: keep-alive Cache-Control: max-age=0 14., 21., 28., 35., 42., 49., 56., 63., 70., 77., 84., 91., 98., 105., 112., 119., 126., 133., 140.], [ 8., 16., 24., 32., 40., 48., 56., 64., 72., 80., 88., 96., 104., 112., 120., 128., 136., 144., 152., 160.], [ 9., 18., 27., 36., 45., 54., 63., 72., 81., 90., 99., 108., 117., 126., 135., 144., 153., 162., 171., 180.], [ 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110., 120., 130., 140., 150., 160., 170., 180., 190., 200.]]) >>> uy array([[ 1. , 0.5 , 0.33333334, 0.25 , 0.2 , 0.16666667, 0.14285715, 0.125 , 0.11111111, 0.1 , 0.09090909, 0.08333334, 0.07692308, 0.07142857, 0.06666667, 0.0625 , 0.05882353, 0.05555556, 0.05263158, 0.05 ], [ 2. , 1. , 0.66666669, 0.5 , 0.40000001, 0.33333334, 0.2857143 , 0.25 , 0.22222222, 0.2 , 0.18181819, 0.16666667, 0.15384616, 0.14285715, 0.13333334, 0.125 , 0.11764706, 0.11111111, 0.10526316, 0.1 ], [ 3. , 1.5 , 1. , 0.75 , 0.60000002, 0.5 , 0.42857143, 0.375 , 0.33333334, 0.30000001, 0.27272728, 0.25 , 0.23076923, 0.21428572, 0.2 , 0.1875 , 0.17647059, 0.16666667, 0.15789473, 0.15000001], [ 4. , 2. , 1.33333337, 1. , 0.80000001, 0.66666669, 0.5714286 , 0.5 , 0.44444445, 0.40000001, 0.36363637, 0.33333334, 0.30769232, 0.2857143 , 0.26666668, 0.25 , 0.23529412, 0.22222222, 0.21052632, 0.2 ], [ 5. , 2.5 , 1.66666663, 1.25 , 1. , 0.83333331, 0.71428573, 0.625 , 0.55555558, 0.5 , 0.45454547, 0.41666666, 0.38461539, 0.35714287, 0.33333334, 0.3125 , 0.29411766, 0.27777779, 0.2631579 , 0.25 ], [ 6. , 3. , 2. , 1.5 , 1.20000005, 1. , 0.85714287, 0.75 , 0.66666669, 0.60000002, 0.54545456, 0.5 , 0.46153846, 0.42857143, 0.40000001, 0.375 , 0.35294119, 0.33333334, 0.31578946, 0.30000001], [ 7. , 3.5 , 2.33333325, 1.75 , 1.39999998, 1.16666663, 1. , 0.875 , 0.77777779, 0.69999999, 0.63636363, 0.58333331, 0.53846157, 0.5 , 0.46666667, 0.4375 , 0.41176471, 0.3888889 , 0.36842105, 0.34999999], [ 8. , 4. , 2.66666675, 2. , 1.60000002, 1.33333337, 1.14285719, 1. , 0.8888889 , 0.80000001, 0.72727275, 0.66666669, 0.61538464, 0.5714286 , 0.53333336, 0.5 , 0.47058824, 0.44444445, 0.42105263, 0.40000001], [ 9. , 4.5 , 3. , 2.25 , 1.79999995, 1.5 , 1.28571427, 1.125 , 1. , 0.89999998, 0.81818181, 0.75 , 0.69230771, 0.64285713, 0.60000002, 0.5625 , 0.52941179, 0.5 , 0.47368422, 0.44999999], [ 10. , 5. , 3.33333325, 2.5 , 2. , 1.66666663, 1.42857146, 1.25 , 1.11111116, 1. , 0.90909094, 0.83333331, 0.76923078, 0.71428573, 0.66666669, 0.625 , 0.58823532, 0.55555558, 0.52631581, 0.5 ]]) >>> p array([[ 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.], [ 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.], [ 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.], [ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.], [ 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.], [ 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.], [ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.], [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.], [ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.], [ 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]]) Note that you have to provide the shape information for ux and uy because fortran writes them together as a stream of 400 numbers. -Neil

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SVD errors
by mtrumpis＠berkeley.edu 04 Jun '13

04 Jun '13

Hello list.. I've run into two SVD errors over the last few days. Both errors are identical in numpy/scipy. I've submitted a ticket for the 1st problem (numpy ticket #990). Summary is: some builds of the lapack_lite module linking against system LAPACK (not the bundled dlapack_lite.o, etc) give a "LinAlgError: SVD did not converge" exception on my matrix. This error does occur using Mac's Accelerate framework LAPACK, and a coworker's Ubuntu LAPACK version. It does not seem to happen using ATLAS LAPACK (nor using Octave/Matlab on said Ubuntu) Just today I've come across a negative singular value cropping up in an SVD of a different matrix. This error does occur on my ATLAS LAPACK based numpy, as well as on the Ubuntu setup. And once again, it does not happen in Octave/Matlab. I'm using numpy 1.3.0.dev6336 -- don't know what the Ubuntu box is running. Here are some npy files for the two different cases: https://cirl.berkeley.edu/twiki/pub/User/MikeTrumpis/noconverge_operator.npy https://cirl.berkeley.edu/twiki/pub/User/MikeTrumpis/negsval_operator.npy Mike

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example reading binary Fortran file
by David Froger 13 Feb '13

13 Feb '13

Hy, My question is about reading Fortran binary file (oh no this question again...) Until now, I was using the unpack module like that : def lread(f,fourBeginning,fourEnd,*tuple): from struct import unpack """Reading a Fortran binary file in litte-endian""" if fourBeginning: f.seek(4,1) for array in tuple: for elt in xrange(array.size): transpose(array).flat[elt] = unpack(array.dtype.char,f.read(array.itemsize))[0] if fourEnd: f.seek(4,1) After googling, I read that fopen and npfille was deprecated, and we should use numpy.fromfile and ndarray.tofile, but despite of the documentaion, the cookbook, the mailling list and google, I don't succed in making a simple example. Considering the simple Fortran code below what is the Python script to read the four arrrays? What about if my pc is little endian and the file big endian? I think it will be a good idea to put the Fortran writting-arrays code and the Python reading-array script in the cookbook and maybe a page to help people comming from Fortran to start with Python ? Best, David Froger program makeArray implicit none integer,parameter:: nx=10,ny=20 real(4),dimension(nx,ny):: ux,uy,p integer :: i,j open(11,file='uxuyp.bin',form='unformatted') do i = 1,nx do j = 1,ny ux(i,j) = real(i*j) uy(i,j) = real(i)/real(j) p (i,j) = real(i) + real(j) enddo enddo write(11) ux,uy write(11) p close(11) end program makeArray

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dtype '|S0' not understood
by David Warde-Farley 07 Oct '12

07 Oct '12

Howdy, It seems it's possible using e.g. In [25]: dtype([('foo', str)])Out[25]: dtype([('foo', '|S0')]) to get yourself a zero-length string. However dtype('|S0') results in a TypeError: data type not understood. I understand the stupidity of creating a 0-length string field but it's conceivable that it's accidental. For example, it could lead to a situation where you've created that field, are missing all the data you had meant to put in it, serialize with np.save, and upon np.load aren't able to get _any_ of your data back because the dtype descriptor is considered bogus (can you guess why I thought of this scenario?). It seems that either dtype(str) should do something more sensible than zero-length string, or it should be possible to create it with dtype('| S0'). Which should it be? David

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PyArray_ContiguousFromObject & PyDECREF
by Michiel Jan Laurens de Hoon 17 May '11

17 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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A faster median (Wirth's method)
by Sturla Molden 01 Dec '10

01 Dec '10

We recently has a discussion regarding an optimization of NumPy's median to average O(n) complexity. After some searching, I found out there is a selection algorithm competitive in speed with Hoare's quick select. It has the advantage of being a lot simpler to implement. In plain Python: import numpy as np def wirthselect(array, k): """ Niklaus Wirth's selection algortithm """ a = np.ascontiguousarray(array) if (a is array): a = a.copy() l = 0 m = a.shape[0] - 1 while l < m: x = a[k] i = l j = m while 1: while a[i] < x: i += 1 while x < a[j]: j -= 1 if i <= j: tmp = a[i] a[i] = a[j] a[j] = tmp i += 1 j -= 1 if i > j: break if j < k: l = i if k < i: m = j return a Now, the median can be obtained in average O(n) time as: def median(x): """ median in average O(n) time """ n = x.shape[0] k = n >> 1 s = wirthselect(x, k) if n & 1: return s[k] else: return 0.5*(s[k]+s[:k].max()) The beauty of this is that Wirth select is extremely easy to migrate to Cython: import numpy ctypedef numpy.double_t T # or whatever def wirthselect(numpy.ndarray[T, ndim=1] array, int k): cdef int i, j, l, m cdef T x, tmp cdef T *a _array = np.ascontiguousarray(array) if (_array is array): _array = _array.copy() a = <T *> _array.data l = 0 m = <int> a.shape[0] - 1 with nogil: while l < m: x = a[k] i = l j = m while 1: while a[i] < x: i += 1 while x < a[j]: j -= 1 if i <= j: tmp = a[i] a[i] = a[j] a[j] = tmp i += 1 j -= 1 if i > j: break if j < k: l = i if k < i: m = j return _array For example, we could have a small script that generates withselect for all NumPy dtypes (T as template), and use a dict as jump table. Chad, you can continue to write quick select using NumPy's C quick sort in numpy/core/src/_sortmodule.c.src. When you are done, it might be about 10% faster than this. :-) Reference: http://ndevilla.free.fr/median/median.pdf Best regards, Sturla Molden

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Test failures on 2.6
by T J 18 Nov '10

18 Nov '10

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 ..........................................................................................................................................................................................................................................................................................................................................................................................................................................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>

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Poll: Semantics for % in Cython
by Dag Sverre Seljebotn 04 May '10

04 May '10

(First off, is it OK to continue polling the NumPy list now and then on Cython language decisions? Or should I expect that any interested Cython users follow the Cython list?) In Python, if I write "-1 % 5", I get 4. However, in C if I write "-1 % 5" I get -1. The question is, what should I get in Cython if I write (a % b) where a and b are cdef ints? Should I [ ] Get 4, because it should behave just like in Python, avoiding surprises when adding types to existing algorithms (this will require extra logic and be a bit slower) [ ] Get -1, because they're C ints, and besides one isn't using Cython if one doesn't care about performance Whatever we do, this also affects the division operator, so that one in any case will have a==(a//b)*b+a%b. (Orthogonal to this, we can introduce compiler directives to change the meaning of the operator from the default in a code blocks, and/or make special functions for the semantics that are not chosen as default.) -- Dag Sverre

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Unpleasant behavior with poly1d and numpy scalar multiplication
by Fernando Perez 15 Feb '10

15 Feb '10

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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