Robert Kern <robert.kern <at> gmail.com> writes: > > Shane Holloway wrote: > > To the vector-processing masters of numpy! > > > > I'm wanting to optimize calling a list (or array) of callable > > objects. Consider the following: > > > > vCallables = numpy.array([<python callables: fns, methods, lambdas, > > classes, builtin functions>]) > > vParam1 = numpy.array([<parameters for vCallables>]) > > vParam2 = numpy.array([<parameters for vCallables>]) > > vParam3 = numpy.array([<parameters for vCallables>]) > > vResults = numpy.array([None for e in vCallables]) > > > > Is it possible to have numpy call them in a way something like:: > > > > for i in xrange(vCallables.shape[0]): > > vResults[i] = vCallables[i] ( vParam1[i], vParam2[i], > > vParam3[i] ) > > > > > > With the core of that loop implemented in C? > > There's nothing sitting around, no. It's easy enough to write with Pyrex (and > easier without bothering with numpy); however, unless if all of your functions > are builtin, you won't see much, if any, speed gains. Python's function call > overhead will dominate the savings of a C for loop over a Python for loop. >
This is a feature I've been wanting for a long time, so I'm really glad that Shane brought this up. While I was hoping for a gain in speed, that isn't the only reason that I would like to see this added. In fact, the most compelling reason for me is that it's a very natural way to express certain operations. I really hope that support for this feature can be added to NumPy. - Andrew _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
