Jaime, One thing that might be a good selling point would be if you built-in threaded operations on image stacks, which I think are pretty common in all forms of microscopy now. I'm thinking particularly of `ni.convolve()`, where one might want to repeatedly call the function on every image frame in the stack. Generally multi-threading kernel operations like convolve on single images is tricky, but if you have a stack it's much more straight-forward to parallelize. Right now I would do such a thing with a thread pool in Python, but it would scale a lot better if there was C-level support. E.g.: import multiprocessing as mp import numpy as np import scipy.ndimage as ni # funcHandle must release the GIL def stackFunc( funcHandle, images, funcArgs ): tPool = mp.ThreadPool( self.n_threads ) slices = self.images.shape[0] # Build parameters list for the threaded processes, consisting of index tArgs = [None] * slices for J in np.arange(slices): tArgs[J] = (J, images, funcArgs) # All operations are done 'in-place' tPool.map( funcHandle, tArgs ) tPool.close() tPool.join() Sincerely, Robert On Mon, Jun 26, 2017 at 2:30 AM, Jaime Fernández del Río < jaime.frio@gmail.com> wrote:

Hi all,

I have started working on an ambitious refactor of scipy.ndimage. Some more context on what I would like to achieve can be found here <https://mail.python.org/pipermail/scipy-dev/2017-June/021986.html>.

scikit-image is probably the biggest "customer" of ndimage, so even though the API and performance are not expected to change, you may want to have a say.

Assuming there is no major concern with the overall plan, I would also like to poke this community for help with:

- figuring out a sensible test strategy: Ralf Gommers points out that we should probably use scikit-image's test suite regularly to complement the relatively few tests currently in ndimage: is the development version of scikit-image stable enough for this purpose or do you suggest using a specific stable release? Any other ideas? - PR reviewing. The idea is to make this refactor as incremental as possible. That means many smaller interdependent PRs. Even if you don't have commit rights to the scipy repository you are probably better versed than most of us in image processing algorithms, so if anyone can keep an eye on ndimage PRs <https://github.com/scipy/scipy/pulls?q=is%3Apr+is%3Aopen+label%3Ascipy.ndima...> and provide occasional reviews it would be greatly appreciated. - expert consulting: some of the code in ndimage is pretty hard to understand, e.g. I struggle with spline interpolation. If anyone with the proper academic knowledge is willing to answer stupid questions, please let me know. - the refactoring itself: if anyone would like to be a part of this, it would be my pleasure! A basic command of the C language is needed, but I'm more than willing to mentor beginners.

Thanks!

Jaime

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