Thanks Stephan,
A few other things small things I'd like to see: - Support for slicing, even if it's expensive.
Slicing is on the TODO list. Only needs a bit of plumbing work.
- A strict way to set the shape without automatic expansion, if desired (e.g., if shape is provided in the constructor).
You can set the initial shape in the constructor. Or you mean a flag to freeze the shape once it's set and have `__setitem__(out-of-bounds)` raise an error?
- Default to the dtype of the fill_value. NumPy does this for np.full.
Thanks for the suggestion --- done and implemented!
* Data types and casting rules. For now, I basically piggy-back on numpy's rules. There are several slightly different ones (numba has one?), and there might be an opportunity to simplify the rules. OTOH, inventing one more subtly different set of rules might be a bad idea.
Yes, please follow NumPy.
One thing I'm wondering is the numpy rule that scalars never upcast arrays. Is it something people actually rely on? [In my experience, I only had to work around it, but my experience might be singular.]
You could actually use a mix of __array_prepare__ and __array_wrap__ to make (non-generalized) ufuncs work, e.g., for functions like np.sin:
- In __array_prepare__, return the non-fill values of the array concatenated with the fill value. - In __array_wrap__, reshape all but the last element to build a new sparse array, using the last element for the new fill value.
This would be a neat trick and get you most of what you could hope for from __numpy_ufunc__.
This is really neat indeed! I've flagged it in https://github.com/ev-br/sparr/issues/35 At the moment, I'm dealing with something much less cool, which I suspect I'm not the first one: given m a MapArray and csr a scipy.sparse matrix, - m * csr produces a MapArray holding the result of the elementwise multiplication, but - csr * m fails with the dimension mismatch error when dimensions are OK for elementwise multiply but not matrix multiply. The failure is somewhere in the scipy.sparse code. I tried playing with __array_priority__, but so far I did not manage to convince scipy.sparse matrices to defer cleanly to the right-hand multiplier (left-hand multiplier is OK). Cheers, Evgeni