Short-circuiting find would be nice. Right now, to 'find' something you first make a bool array, then iterate over it. If all you want is the first index where x[i] = e, not very efficient.
What I just described is a find with a '==' predicate. Not sure if it's worthwhile to consider other predicates. Maybe call it 'find_first' Mark Miller wrote: > I'd love to see something like a "count_unique" function included. The > numpy.unique function is handy, but it can be a little awkward to > efficiently go back and get counts of each unique value after the > fact. > > -Mark > > > > On Wed, Jun 1, 2011 at 8:17 AM, Keith Goodman <kwgood...@gmail.com> wrote: >> On Tue, May 31, 2011 at 8:41 PM, Charles R Harris >> <charlesr.har...@gmail.com> wrote: >>> On Tue, May 31, 2011 at 8:50 PM, Bruce Southey <bsout...@gmail.com> wrote: >> >>>> How about including all or some of Keith's Bottleneck package? >>>> He has tried to include some of the discussed functions and tried to >>>> make them very fast. >>> >>> I don't think they are sufficiently general as they are limited to 2 >>> dimensions. However, I think the moving filters should go into scipy, either >>> in ndimage or maybe signals. Some of the others we can still speed of >>> significantly, for instance nanmedian, by using the new functionality in >>> numpy, i.e., numpy sort has worked with nans for a while now. It looks like >>> call overhead dominates the nanmax times for small arrays and this might >>> improve if the ufunc machinery is cleaned up a bit more, I don't know how >>> far Mark got with that. >> >> Currently Bottleneck accelerates 1d, 2d, and 3d input. Anything else >> falls back to a slower, non-cython version of the function. The same >> goes for int32, int64, float32, float64. >> >> It should not be difficult to extend to higher nd and more dtypes >> since everything is generated from template. The problem is that there >> would be a LOT of cython auto-generated C code since there is a >> separate function for each ndim, dtype, axis combination. >> >> Each of the ndim, dtype, axis functions currently has its own copy of >> the algorithm (such as median). Pulling that out and reusing it should >> save a lot of trees by reducing the auto-generated C code size. >> >> I recently added a partsort and argpartsort. >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
