[SciPy-Dev] SciPy Goal
Wed Jan 4 23:32:58 CST 2012
On Wed, Jan 4, 2012 at 10:36 PM, Travis Oliphant <email@example.com> wrote:
> Great points.
> I agree that interpolation still needs love. I've had the exact same concern multiple times before. It comes up quite a bit in classes.
> It looks like interpolate and signal are still areas that I can spend some free time. I know Warren has spent time in signal. Is anyone else working on interpolate --- I can check this of course myself, but just in case someone is following this conversation who is interested in coordinating.
There have been several starts on a control system toolbox that has
some overlap with scipy.signal, but I haven't heard of any discussion
in a while.
The scipy wavelets look like a complete mystery, the docs are sparse,
and with a google search I found only a single example of it's usage.
> We may need to continue the conversation about ndimage.
> I appreciate the patience with me after my being silent for a while. I'm technically between jobs as I recently left Enthought. I just re-did my mail account setup so now I see all scipy-dev and numpy-discussion mails instead of having to remember to go look at the conversations.
> On Jan 4, 2012, at 9:16 PM, Zachary Pincus wrote:
>> 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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