On Sun, May 11, 2008 at 9:50 PM, Anne Archibald <[EMAIL PROTECTED]> wrote: > 2008/5/11 Robert Kern <[EMAIL PROTECTED]>: > >> Basically, you need 3 arrays: functions implementing the type-specific >> inner loops, void* extra data to pass to these functions, and an array >> of arrays containing the type signatures of the ufunc. In numpy, we >> already have generic implementations of the loop functions for common >> combinations of types. In your case, for a binary function taking two >> doubles and returning a double, we have PyUFunc_dd_d(). As its extra >> void* data, it takes a function pointer that actually implements the >> element-wise operation. So lets start making the arrays: > > Great! Thanks! > > Is it possible to provide a specialized implementation of reduce()? > (Since reduce() can be implemented more efficiently than doing it > pairwise.)
I don't think so, no. >> PyUFunc_None, // The identity element for reduction. >> // No good one to use for this function, >> // unfortunately. > > Is it safe to use minus infinity, or is this going to give people all > kinds of warnings if they have seterr() set? Perhaps, but ufuncs only allow 0 or 1 for this value, currently. Also, I was wrong about using PyUFunc_ff_f. Instead, use PyUFunc_ff_f_As_dd_d. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
