Christopher Barker wrote: > > > A. M. Archibald wrote: > >> Why not write the algorithm in C? > > I did just that a while back, for Numeric. I've enclosed the code for > reference. > > Unfortunately, I never did figure out an efficient way to write this > sort of thing for all types, so it only does doubles. Also, it does a > bunch of special casing for discontiguous vs. contiguous arrays, and > clipping to an array vs a scaler for the min and max arguments. To do the actual clipping if the datatypes are 'native' is trivial in C: a single loop, a comparison, that's it. I have become an irrational C++ hater with the time, so I don't use template (and I don't think C++ is welcomed in core numpy). For those easy case of template use, autogen works well enough for me; my impression is that numpy uses a similar system, eg for ufunc, etc... You can look at scipy/Lib/sandbox/cdavid/src/levinson1d.tpl for an example of autogen to generate function for any datatype you want; if you need more fancy template facilities like partial specialization and other crazy things mere mortals like me will never understand in C++, then I am not sure autogen can be used.
I guess the method used in numpy is better to use for core functionalities, as it avoids the burden of installing one more tool for development. Now, I didn't know that clip was supposed to handle arrays as min/max values. At first, I didn't understand the need to care about contiguous/non contiguous; having non scalar for min/max makes it necessary to have special case for non contiguous. But again, it is important not to lose sight... The goal was to have faster clipping for matplotlib, and this cases are easy, because it is native type and scalar min/max, where contiguous or not does not matter as we traverse the input arrays element by element. If we pass non native endian, non contiguous arrays, there is actually a pretty good chance that the current implementation is already fast enough, and does not need to be changed anyway. Thanks for the suggestion and the precisions, David _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
