[SciPy-Dev] SciPy Goal

Zachary Pincus zachary.pincus@yale....
Wed Jan 4 21:16:01 CST 2012

Just one point here: one of the current shortcomings in scipy from my perspective is interpolation, which is spread between interpolate, signal, and ndimage, each package with strengths and inexplicable (to a new user) weaknesses.

One trouble spot is the fact that it's not clear that ndimage is where one ought to turn for general interpolation/resampling of gridded data (a topic which comes up at least once every couple months on the list).

>>> - ndimage : difficult one. hard to understand code, may not see much development either way.
>> This overlaps with scikits-image but has quite a bit of useful functionality on its own.   The package is fairly mature and just needs maintenance.  
> Again, pretty basic stuff in there, but I could be persuaded to go to scikits-image since it *is* image specific and might be better maintained. 

See above. The interpolation stuff is pretty useful for a lot of tasks that aren't really "imaging" per se, but which involve gridded data. (GIS, e.g.) Similarly, the code for convolutions and similar (median filtering, e.g.) seems pretty generally useful and in many ways better than what's in scipy.signal for certain tasks.

I'm less certain about the morphological operations and the connected-components labeling, which might be more task-specific and fit better with scikits-image? (Probably after a re-write in Cython?)


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