Mailman 3 November 2005 - SciPy-Dev

newcore atlas info on Gentoo
by Darren Dale 20 Jan '06

20 Jan '06

In order to get scipy-0.3.2 to build on Gentoo Linux, I use site.cfg in the distutils module. This is necessary because Gentoo has renamed the libf77blas.* libraries to libblas.* in order to allow users to easily switch between different libraries, ATLAS and Intel's MKL for example. When I compile newcore using the old site.cfg, my atlas info is properly reported. However, when I compile newscipy, the atlas info is reportedly unavailable. I looked into system_info.atlas_info and friends, and discovered that _lib_names is not informed by site.cfg, it is hardcoded with ['f77blas', 'cblas'], or ['ptf77blas','ptcblas'], etc. If I remove the 'f77', my atlas info is correctly reported. Darren

3 10

MoinMoin demo site online
by Andrew Straw 17 Dec '05

17 Dec '05

OK, I've set up a demo site: http://astraw.com/scipy I've only converted a couple of pages, and I've made one new one to detail my findings. The new one, which may be most informative is: http://astraw.com/scipy/moin.fcg/ScipyPublicFaceTest The converted pages are the home page and http://astraw.com/scipy/moin.fcg/documentation http://astraw.com/scipy/moin.fcg/documentation/FAQ I spent a decent amount of time configuring the access control lists to be realistic, so if you'd like to be added to the "EditorGroup", let me know. You won't be able to edit pages (or see the edit buttons) unless you login. Robert Kern wrote: >Andrew Straw wrote: > > > >>I hereby volunteer myself to set up Moin for the public face scipy.org, >>although whether it's possible to give me login permissions to do so is >>another question... I'm also happy to help someone (Joe Cooper?) do the >>same. >> >> > >Hooray for volunteers! It's greatly appreciated. > >In the meantime, it might be helpful if you were to set up a mock >version that we could gawk at. If you could work out configuring Moin >appropriately (modulo the differences between your machine and the >virtualmin setup) and installing/writing whatever plugins/tweaks/hacks >you deem appropriate, that would go a long way towards convincing us >that it's a good approach. We generally run things by _fait accompli_ >around here. > > >

5 9

problem with linalg.cholesky?
by Andrew Jaffe 04 Dec '05

04 Dec '05

hi all, (apologies that a similar question has appeared elsewhere...) In the newest incarnation of scipy_core, I am having trouble with the cholesky(a) routine. Here is some minimal code reproducing the bug (on OS X) ------------------------------------------------------------ from scipy import identity, __core_version__, Float64 import scipy.linalg as la print 'Scipy version: ', __core_version__ i = identity(4, Float64) print 'identity matrix:' print i print 'about to get cholesky decomposition' c = la.cholesky(i) print c ------------------------------------------------------------ which gives ------------------------------------------------------------ Scipy version: 0.6.1 identity matrix: [[ 1. 0. 0. 0.] [ 0. 1. 0. 0.] [ 0. 0. 1. 0.] [ 0. 0. 0. 1.]] about to get cholesky decomposition Traceback (most recent call last): File "/Users/jaffe/Desktop/bad_cholesky.py", line 13, in ? c = la.cholesky(i) File "/Library/Frameworks/Python.framework/Versions/2.4/lib/ python2.4/site-packages/scipy/linalg/basic_lite.py", line 117, in cholesky_decomposition results = lapack_routine('L', n, a, m, 0) lapack_lite.LapackError: Parameter a is not contiguous in lapack_lite.dpotrf ------------------------------------------------------------ (The cholesky decomposition in this case should just be the matrix itself; the same error occurs with a complex matrix.) Any ideas? Could this have anything to do with _CastCopyAndtranspose in Basic_lite.py? (Since there are few other candidates for anything that actually changes the matrix.) Thanks in advance, A ______________________________________________________________________ Andrew Jaffe a.jaffe(a)imperial.ac.uk Astrophysics Group +44 207 594-7526 Blackett Laboratory, Room 1013 FAX 7541 Imperial College, Prince Consort Road London SW7 2AZ ENGLAND http://astro.imperial.ac.uk/~jaffe

6 17

pickling problem
by Darren Dale 01 Dec '05

01 Dec '05

I'm having some trouble with unpickled scipy arrays. The issue shows up when I try to plot in matplotlib, but I think it is a scipy problem. import pickle import scipy import pylab a = scipy.arange(10) pickle.dump(a,file('temp.p','w')) b = pickle.load(file('temp.p')) print type(b), type(scipy.array(b)) pylab.plot(scipy.array(b)) # no error here pylab.plot(b) # error here Traceback (most recent call last): File "pickletest.py", line 8, in ? pylab.plot(b) File "/usr/lib64/python2.4/site-packages/matplotlib/pylab.py", line 2055, in plot ret = gca().plot(*args, **kwargs) File "/usr/lib64/python2.4/site-packages/matplotlib/axes.py", line 2636, in plot for line in self._get_lines(*args, **kwargs): File "/usr/lib64/python2.4/site-packages/matplotlib/axes.py", line 267, in _grab_next_args yield self._plot_1_arg(remaining[0], **kwargs) File "/usr/lib64/python2.4/site-packages/matplotlib/axes.py", line 189, in _plot_1_arg markerfacecolor=color, File "/usr/lib64/python2.4/site-packages/matplotlib/lines.py", line 209, in __init__ self.set_data(xdata, ydata) File "/usr/lib64/python2.4/site-packages/matplotlib/lines.py", line 265, in set_data y = ma.ravel(y) File "/usr/lib64/python2.4/site-packages/Numeric/MA/MA.py", line 1810, in ravel d = Numeric.ravel(filled(a)) File "/usr/lib64/python2.4/site-packages/Numeric/MA/MA.py", line 222, in filled return Numeric.array(a) ValueError: cannot handle misaligned or not writeable arrays. Darren

2 7

12 errors using 0.7.3.1538 0.4.2_1461
by Nils Wagner 01 Dec '05

01 Dec '05

====================================================================== ERROR: Check whether adding a dense matrix to a sparse matrix works ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 148, in check_add_dense sum1 = self.dat + self.datsp File "/usr/local/lib/python2.4/site-packages/scipy/sparse/sparse.py", line 502, in __r add__ raise NotImplementedError, 'adding a scalar to a CSC matrix is ' \ NotImplementedError: adding a scalar to a CSC matrix is not yet supported ====================================================================== ERROR: check_matmat (scipy.sparse.sparse.test_sparse.test_csc) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 84, in check_matmat assert_array_almost_equal((a*bsp).todense(), dot(a,b)) AttributeError: 'scipy.ndarray' object has no attribute 'todense' ====================================================================== ERROR: Check whether adding a dense matrix to a sparse matrix works ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 148, in check_add_dense sum1 = self.dat + self.datsp File "/usr/local/lib/python2.4/site-packages/scipy/sparse/sparse.py", line 173, in __r add__ return csc.__radd__(other) File "/usr/local/lib/python2.4/site-packages/scipy/sparse/sparse.py", line 502, in __r add__ raise NotImplementedError, 'adding a scalar to a CSC matrix is ' \ NotImplementedError: adding a scalar to a CSC matrix is not yet supported ====================================================================== ERROR: check_matmat (scipy.sparse.sparse.test_sparse.test_csr) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 84, in check_matmat assert_array_almost_equal((a*bsp).todense(), dot(a,b)) AttributeError: 'scipy.ndarray' object has no attribute 'todense' ====================================================================== ERROR: Check whether adding a dense matrix to a sparse matrix works ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 149, in check_add_dense assert_array_equal(sum1, 2*self.dat) File "/usr/local/lib/python2.4/site-packages/scipy/test/testing.py", line 724, in asse rt_array_equal reduced = ravel(equal(x,y)) File "/usr/local/lib/python2.4/site-packages/scipy/sparse/sparse.py", line 146, in __c mp__ raise TypeError, "comparison of sparse matrices not implemented" TypeError: comparison of sparse matrices not implemented ====================================================================== ERROR: check_matmat (scipy.sparse.sparse.test_sparse.test_dok) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 84, in check_matmat assert_array_almost_equal((a*bsp).todense(), dot(a,b)) AttributeError: 'scipy.ndarray' object has no attribute 'todense' ====================================================================== ERROR: Check whether adding a dense matrix to a sparse matrix works ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 148, in check_add_dense sum1 = self.dat + self.datsp File "/usr/local/lib/python2.4/site-packages/scipy/sparse/sparse.py", line 502, in __r add__ raise NotImplementedError, 'adding a scalar to a CSC matrix is ' \ NotImplementedError: adding a scalar to a CSC matrix is not yet supported ====================================================================== ERROR: check_matmat (scipy.sparse.test_sparse.test_csc) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 84, in check_matmat assert_array_almost_equal((a*bsp).todense(), dot(a,b)) AttributeError: 'scipy.ndarray' object has no attribute 'todense' ====================================================================== ERROR: Check whether adding a dense matrix to a sparse matrix works ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 148, in check_add_dense sum1 = self.dat + self.datsp File "/usr/local/lib/python2.4/site-packages/scipy/sparse/sparse.py", line 173, in __r add__ return csc.__radd__(other) File "/usr/local/lib/python2.4/site-packages/scipy/sparse/sparse.py", line 502, in __r add__ raise NotImplementedError, 'adding a scalar to a CSC matrix is ' \ NotImplementedError: adding a scalar to a CSC matrix is not yet supported ====================================================================== ERROR: check_matmat (scipy.sparse.test_sparse.test_csr) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 84, in check_matmat assert_array_almost_equal((a*bsp).todense(), dot(a,b)) AttributeError: 'scipy.ndarray' object has no attribute 'todense' ====================================================================== ERROR: Check whether adding a dense matrix to a sparse matrix works ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 149, in check_add_dense assert_array_equal(sum1, 2*self.dat) File "/usr/local/lib/python2.4/site-packages/scipy/test/testing.py", line 724, in asse rt_array_equal reduced = ravel(equal(x,y)) File "/usr/local/lib/python2.4/site-packages/scipy/sparse/sparse.py", line 146, in __c mp__ raise TypeError, "comparison of sparse matrices not implemented" TypeError: comparison of sparse matrices not implemented ====================================================================== ERROR: check_matmat (scipy.sparse.test_sparse.test_dok) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.4/site-packages/scipy/sparse/tests/test_sparse.py", line 84, in check_matmat assert_array_almost_equal((a*bsp).todense(), dot(a,b)) AttributeError: 'scipy.ndarray' object has no attribute 'todense' ---------------------------------------------------------------------- Ran 1355 tests in 6.372s FAILED (errors=12)

5 11

A scalar is no 1 \times 1 matrix ?
by Nils Wagner 01 Dec '05

01 Dec '05

>>> linalg.inv(2) Traceback (most recent call last): File "<stdin>", line 1, in ? File "/usr/lib64/python2.4/site-packages/scipy/linalg/basic.py", line 183, in inv raise ValueError, 'expected square matrix' ValueError: expected square matrix >>> linalg.inv(mat(2)) array([[ 0.5]]) Is this behaviour wanted ? Nils

2 1

Three cheers for Travis!
by Ed Schofield 01 Dec '05

01 Dec '05

I'd like to thank Travis for his tireless work answering feature requests, fixing bugs, and generally doing a superb job. Hip hip hooray! SciPy is becoming very good, very fast. I have another couple of 0-dim array bugs to report (as a reward :D) >>> b = array(2) >>> b.argmax() 0 >>> b.argmin() Segmentation fault

3 6

Re: [SciPy-dev] newcore and gcc4/gfortran (only 3 tests fail now)
by Ravikiran Rajagopal 30 Nov '05

30 Nov '05

Hi, A little more progress. I can now compile and run tests on gcc4/gfortran without any segfaults. This is on Fedora Core 4, with gcc 4.0.2 branch SVN checkout from Nov 9 along with a few patches from Redhat. I can make the source/binary RPMs available to anyone - I don't have a website to host it though. I think Arnd's build is messed up because of his installed g77. He needs the following patch before he can actually get a true compiled version: Index: scipy/distutils/fcompiler/gnu.py =================================================================== --- scipy/distutils/fcompiler/gnu.py (revision 1513) +++ scipy/distutils/fcompiler/gnu.py (working copy) @@ -230,7 +230,26 @@ module_dir_switch = '-M' module_include_switch = '-I' + def get_libraries(self): + opt = [] + d = self.get_libgcc_dir() + if d is not None: + g2c = 'gfortran-pic' + f = self.static_lib_format % (g2c, self.static_lib_extension) + if not os.path.isfile(os.path.join(d,f)): + g2c = 'gfortran' + else: + g2c = 'gfortran' + if sys.platform=='win32': + opt.append('gcc') + if g2c is not None: + opt.append(g2c) + if sys.platform == 'darwin': + opt.append('cc_dynamic') + return opt + + if __name__ == '__main__': from distutils import log log.set_verbosity(2) Note that this patch handles only gfortran. I don't have g95 available to test. Could someone commit this and makesure the proper g95 library is used for that case? > Which is perfectly understandable! > Just for the record - I also tried the gcc4/g77 variant, which gets beyond > the above point, but segfaults at another place: There are now only 3 errors with core 0.7.1.1513 and scipy 0.4.2_1446: ====================================================================== ERROR: check_dot (scipy.lib.blas.test_blas.test_fblas1_simple) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/scipy/lib/blas/tests/test_blas.py", line 75, in check_dot assert_almost_equal(f([3j,-4,3-4j],[2,3,1]),-9+2j) SystemError: NULL result without error in PyObject_Call ====================================================================== ERROR: check_dot (scipy.linalg.blas.test_blas.test_fblas1_simple) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/scipy/linalg/tests/test_blas.py", line 75, in check_dot assert_almost_equal(f([3j,-4,3-4j],[2,3,1]),-9+2j) SystemError: NULL result without error in PyObject_Call ====================================================================== ERROR: line-search Newton conjugate gradient optimization routine ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/scipy/optimize/tests/test_optimize.py", line 103, in check_ncg full_output=False, disp=False, retall=False) File "/usr/lib/python2.4/site-packages/scipy/optimize/optimize.py", line 969, in fmin_ncg alphak, fc, gc, old_fval = line_search_BFGS(f,xk,pk,gfk,old_fval) File "/usr/lib/python2.4/site-packages/scipy/optimize/optimize.py", line 556, in line_search_BFGS phi_a2 = apply(f,(xk+alpha2*pk,)+args) File "/usr/lib/python2.4/site-packages/scipy/optimize/optimize.py", line 133, in function_wrapper return function(x, *args) File "/usr/lib/python2.4/site-packages/scipy/optimize/tests/test_optimize.py", line 30, in func raise RuntimeError, "too many iterations in optimization routine" RuntimeError: too many iterations in optimization routine ---------------------------------------------------------------------- Ran 1370 tests in 114.800s FAILED (errors=3) I do not think any of these failures are related to gfortran, but I could be horribly wrong. How can I help debug these failures? I vaguely recall looking at check_dot a few months ago while debugging a segfault, but not much else. Regards, Ravi

4 11

segfault
by Lisandro Dalcin 30 Nov '05

30 Nov '05

I am using scipy_core-0.6.1 downloaded from SouceForge. I got the following segfault: [dalcinl@trantor dalcinl]$ python Python 2.4.2 (#1, Nov 7 2005, 16:51:05) [GCC 3.3.3 20040412 (Red Hat Linux 3.3.3-7)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> from scipy.base import * >>> a = array(1) >>> a.dtype = float Segmentation fault [dalcinl@trantor dalcinl]$ -- Lisandro Dalcín --------------- Centro Internacional de Métodos Computacionales en Ingeniería (CIMEC) Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) PTLC - Güemes 3450, (3000) Santa Fe, Argentina Tel/Fax: +54-(0)342-451.1594

3 2

Standard deviations
by Ed Schofield 30 Nov '05

30 Nov '05

Hi all, I have three questions related to standard deviations and variances in scipy. First, can someone explain the behaviour of array.std() without any arguments? >>> a = arange(30).reshape(3,10) >>> a array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]]) >>> a.std() array([ 2.99856287, 2.85723522, 2.74647109, 2.67007684, 2.63104804, 2.63104804, 2.67007684, 2.74647109, 2.85723522, 2.99856287]) I don't understand what these numbers represent. The correct standard deviations of the column vectors are given by: >>> a.std(0) array([ 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.]) and the standard deviations of the row vectors are: >>> a.std(1) array([ 3.02765035, 3.02765035, 3.02765035]) I would have expected a.std() to give the same output as >>> a.ravel().std() 8.8034084308295046 which is what a.mean() does. Second, I'd like to point out that some of the functions in Lib/stats/ have a different convention to scipy core about whether operations are performed row-wise or column-wise, and whether anyone would object to my changing the stats functions to operate column-wise. At the moment we get this: >>> average(a) array([ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.]) which is column-wise, but >>> std(a) array([ 3.02765035, 3.02765035, 3.02765035]) which is row-wise. I presume the default behaviour of std() and friends is just a historical relic. If so we'd be wise to get this straight well before a 1.0 release. Third, I'd like to request that we add an array.var() method to scipy core to compute an array's sample variance. At the moment it seems that there is no way to compute the sample variance of an array of numbers without installing the full scipy. Users needing to do this will either have to roll their own function in Python, like this: def var(A): m = len(A) return average((a-means)**2) * (m/(m-1.)) or square the output of std(). Both are less efficient than a native array.var() would be, requiring extra memory copying and, in the second case, squaring the result of a square root operation, which also introduces numerical imprecision. The extra code required is minimal. There's an example patch below, which works fine except that it inherits the weirdness of std(). Comments? :) -- Ed --------------------- Index: base/src/multiarraymodule.c =================================================================== --- base/src/multiarraymodule.c (revision 1534) +++ base/src/multiarraymodule.c (working copy) @@ -460,7 +460,61 @@ return obj1; } +static PyObject * +PyArray_Var(PyArrayObject *self, int axis, int rtype) +{ + PyObject *obj1=NULL, *obj2=NULL, *new=NULL; + PyObject *ret=NULL, *newshape=NULL; + int i, n; + intp val; + if ((new = _check_axis(self, &axis, 0))==NULL) return NULL; + + /* Compute and reshape mean */ + obj1 = PyArray_EnsureArray(PyArray_Mean((PyAO *)new, axis, rtype)); + if (obj1 == NULL) {Py_DECREF(new); return NULL;} + n = PyArray_NDIM(new); + newshape = PyTuple_New(n); + if (newshape == NULL) {Py_DECREF(obj1); Py_DECREF(new); return NULL;} + for (i=0; i<n; i++) { + if (i==axis) val = 1; + else val = PyArray_DIM(new,i); + PyTuple_SET_ITEM(newshape, i, PyInt_FromLong((long)val)); + } + obj2 = PyArray_Reshape((PyAO *)obj1, newshape); + Py_DECREF(obj1); + Py_DECREF(newshape); + if (obj2 == NULL) {Py_DECREF(new); return NULL;} + + /* Compute x = x - mx */ + obj1 = PyNumber_Subtract((PyObject *)self, obj2); + Py_DECREF(obj2); + if (obj1 == NULL) {Py_DECREF(new); return NULL;} + + /* Compute x * x */ + obj2 = PyArray_EnsureArray(PyNumber_Multiply(obj1, obj1)); + Py_DECREF(obj1); + if (obj2 == NULL) {Py_DECREF(new); return NULL;} + + /* Compute add.reduce(x*x,axis) */ + obj1 = PyArray_GenericReduceFunction((PyAO *)obj2, n_ops.add, + axis, rtype); + Py_DECREF(obj2); + if (obj1 == NULL) {Py_DECREF(new); return NULL;} + + n = PyArray_DIM(new,axis)-1; + Py_DECREF(new); + if (n<=0) n=1; + obj2 = PyFloat_FromDouble(1.0/((double )n)); + if (obj2 == NULL) {Py_DECREF(obj1); return NULL;} + ret = PyArray_EnsureArray(PyNumber_Multiply(obj1, obj2)); + Py_DECREF(obj1); + Py_DECREF(obj2); + + return ret; +} + + static PyObject * PyArray_Sum(PyArrayObject *self, int axis, int rtype) { Index: base/src/arraymethods.c =================================================================== --- base/src/arraymethods.c (revision 1534) +++ base/src/arraymethods.c (working copy) @@ -1225,6 +1225,23 @@ return PyArray_Std(self, axis, rtype.type_num); } +static char doc_var[] = "a.var(axis=None, rtype=None)"; + +static PyObject * +array_var(PyArrayObject *self, PyObject *args, PyObject *kwds) +{ + int axis=MAX_DIMS; + PyArray_Typecode rtype = {PyArray_NOTYPE, 0, 0}; + static char *kwlist[] = {"axis", "rtype", NULL}; + + if (!PyArg_ParseTupleAndKeywords(args, kwds, "|O&O&", kwlist, + PyArray_AxisConverter, + &axis, PyArray_TypecodeConverter, + &rtype)) return NULL; + + return PyArray_Var(self, axis, rtype.type_num); +} + static char doc_compress[] = "a.compress(condition=, axis=None)"; static PyObject * @@ -1501,6 +1518,8 @@ METH_VARARGS, doc_nonzero}, {"std", (PyCFunction)array_stddev, METH_VARARGS|METH_KEYWORDS, doc_stddev}, + {"var", (PyCFunction)array_var, + METH_VARARGS|METH_KEYWORDS, doc_var}, {"sum", (PyCFunction)array_sum, METH_VARARGS|METH_KEYWORDS, doc_sum}, {"cumsum", (PyCFunction)array_cumsum, Index: base/src/scalartypes.inc.src =================================================================== --- base/src/scalartypes.inc.src (revision 1534) +++ base/src/scalartypes.inc.src (working copy) @@ -972,7 +972,7 @@ /**begin repeat -#name=getfield, take, put, putmask, repeat, tofile, mean, trace, diagonal, clip, std, sum, cumsum, prod, cumprod, compress# +#name=getfield, take, put, putmask, repeat, tofile, mean, trace, diagonal, clip, std, var, sum, cumsum, prod, cumprod, compress# */ static PyObject * @@ -1144,6 +1144,8 @@ METH_VARARGS, NULL}, {"std", (PyCFunction)gentype_std, METH_VARARGS|METH_KEYWORDS, NULL}, + {"var", (PyCFunction)gentype_var, + METH_VARARGS|METH_KEYWORDS, NULL}, {"sum", (PyCFunction)gentype_sum, METH_VARARGS|METH_KEYWORDS, NULL}, {"cumsum", (PyCFunction)gentype_cumsum, Index: base/code_generators/generate_array_api.py =================================================================== --- base/code_generators/generate_array_api.py (revision 1534) +++ base/code_generators/generate_array_api.py (working copy) @@ -367,6 +367,10 @@ """, 'Std','PyArrayObject *, int, int','PyObject *'), + (r"""Var + """, + 'Var','PyArrayObject *, int, int','PyObject *'), + (r"""Sum """, 'Sum','PyArrayObject *, int, int','PyObject *'),

4 4