+1 for a conflict of interest policy. A member of another group reviewing
and pulling a change is a reasonable expectation. Explicit is better than
implicit.
___
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/li
Won't any solution not using hdf5 or some other chunked on disk storage
method load the whole cube into memory?
___
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/listinfo/numpy-discussion
Nope. Numpy only works on in memory arrays. You can determine your own
chunking strategy using hdf5, or something like dask can figure that
strategy out for you. With numpy you might worry about not accidentally
making duplicates or intermediate arrays, but that's the extent of memory
optimization
I wouldn't be surprised at all if calling max in addition to argmax wasn't
as fast or faster than indexing the array using argmax. Regardless, just
use that then profile when you're done with the whole thing and see if
there's any gains to be made. Very likely not here.
-elliot
On Wed, Oct 30, 20
ven 1% of
your run time, but it depends on what your doing. Part of python with
numpy is slightly not caring about big O because trying to be clever is
rarely worth it in my experience.
On Thu, Oct 31, 2019 at 12:35 AM Daniele Nicolodi
wrote:
> On 30/10/2019 22:42, Elliot Hallmark wrote:
> &
