On Tue, Jun 23, 2009 at 12:46 PM, Pauli Virtanen<p...@iki.fi> wrote: > On 2009-06-23, Michael McNeil Forbes <mfor...@physics.ubc.ca> wrote: >> Is there a way of performing vectorized ?axpy (daxpy) operations >> without making copies or dropping into C? >> >> i.e: I want to do >> >> big = (10000,5000) >> A = np.ones(big,dtype=float) >> B = np.ones(big,dtype=float) >> a = 1.5 >> B += a*A > > I think the only available choice is to use the BLAS routines > from scipy.lib: > >>>> from scipy.lib.blas import get_blas_funcs >>>> axpy, = get_blas_funcs(['axpy'], [A, B]) >>>> res = axpy(A.ravel(), B.ravel(), A.size, a) >>>> res.base is B > True >>>> B[0,0] > 2.5 > > Works provided A and B are initially in C-order so that ravel() > doesn't create copies. If unsure, it's best to make use of the > return value of axpy and not assume B is modified in-place. > > [clip] >> There are exposed blas daxpy operations in scipy, but in the version I >> have (EPD), these also seem to make copies (though recent version seem >> to be fixed by looking at the source.) > > I don't see many relevant changes in scipy.lib recently, so I'm > not sure what change you mean by the above. >
Now and then I've thought about adding it as a ufunc. It wouldn't be hard. Chuck _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
