Mailman 3 September 2001 - NumPy-Discussion

coercion for array-like objects for ufuncs
by Chris Myers 05 Sep '01

05 Sep '01

I have a Python object that contains a NumPy array (among other things), and I'd like it to behave like an array in certain circumstances. (For example, I can define __getitem__ on the class so that the underlying NumPy array is indexed.) I'd like to be able to act on such an object with a ufunc, but am stymied. For example, if y is an instance of this array-like class of mine, then I get a type error if I try a reduction on it: >>> Numeric.minimum.reduce(y) Traceback (most recent call last): File "<stdin>", line 1, in ? TypeError: function not supported for these types, and can't coerce to supported types Is there a way I can effect this coercion? I guess I'd prefer not to have to inherit from UserArray. Thanks, Chris ========================================================================== Chris Myers Cornell Theory Center -------------------------------------------------------------------------- 636 Rhodes Hall email: myers(a)tc.cornell.edu Cornell University phone: (607) 255-5894 / fax: (607) 254-8888 Ithaca, NY 14853 http://www.tc.cornell.edu/~myers ==========================================================================

3 2

Python and Numpy compiled on Athlon optimized gcc3.01
by Rob 04 Sep '01

04 Sep '01

Just for kicks last night I installed gcc3.01 which claims to have Athlon optimization, ie. -march=athlon. I recompiled Python and Numpy, and then ran a big simulation. The new compiler ran 200 seconds slower than the old gcc2.95 with plain -march=pentium. I need to go to the gcc website and see just what optimization they are claiming. Maybe I should have also used -02. Rob. -- The Numeric Python EM Project www.members.home.net/europax

1 0

another seemingly unNumpyizeable routine
by Rob 01 Sep '01

01 Sep '01

I hate to bother you guys, but this is how I learn. I have had some success using the take() and add.reduce() routines all by myself. However this one seems intractable: for IEdge in range(0,a.HybrdBoundEdgeNum): for JEdge in range(0,a.HybrdBoundEdgeNum): AVec[IEdge+a.TotInnerEdgeNum] += a.Cdd[IEdge,JEdge]*XVec[JEdge+a.TotInnerEdgeNum] ## below is my attempt at this, but it doesn't work ## h=a.TotInnerEdgeNum ## for IEdge in range(0,a.HybrdBoundEdgeNum): ## AVec[IEdge+h] = add.reduce(a.Cdd[IEdge,0:a.HybrdBoundEdgeNum] * XVec[h:(h+a.HybrdBoundEdgeNum)]) I think the problem lies in that add.reduce is using the slice 0:a.HybridBoundEdgeNum for Cdd, but then has to contend with XVec slice which is offset by h. Is there any elegant way to deal with this. This routine is part of a sparse matrix multiply routine. If I could find SparsePy, maybe I could eliminate it altogether. Thanks, Rob. -- The Numeric Python EM Project www.members.home.net/europax

1 1