I've done some benchmarking and it seems that the packed storage comes with
a runtime penalty which agrees with a few links I've found online
https://blog.debroglie.net/2013/09/01/lapack-and-packed-storage/
http://stackoverflow.com/questions/8941678/lapack-are-operations-on-packed-storage-matrices
I agree that this seems more like a scipy feature than a numpy feature.
Users with structured matrices often use a sparse matrix format, though the
API for using them in solvers could use some work. (I have a
work-in-progress PR along those lines here:
https://github.com/scipy/scipy/pull/6331)
Pe
Hi all,
I have implemented a proposed enhancement for the np.gradient function that
allows to compute the gradient on non uniform grids. (PR:
https://github.com/numpy/numpy/pull/8446)
The proposed implementation has a behaviour/signature is similar to that of
Matlab/Octave. As argument it can take
> It seems a generalized ufunc "all_equal" with signature (i),(i)->() and short
> circuit logic once the first non equal element is encountered would be an
> important performance improvement.
How does array_equal() perform?
-CHB
___
NumPy-Discussion
