Hi Aron,
There is an implementation of nanpercentile for ndarray in xclim[0]. See
`calc_perc`[1]
Bear in mind it's not exposed in the public API, so I would only use it as an
example implementation.
You may also find a performance script and report (quite poorly written, sorry)
on a gist[2]. I'm not sure how accurate it is against the latest numpy version.
Plus the interpolation configuration is limited to the 9 methods of R through
alpha and beta parameter (similar to scipy `mquantiles`).
Thus, you can't use "nearest", "lower", "higher", "midpoint" methods but:
- "linear" (np default) would be alpha=1 and beta=1.
- "median_unbiased" (recommended default [3]) would be alpha=1/3 and beta=1/3.
Cheers,
Abel Aoun
[0] https://github.com/Ouranosinc/xclim
[1] https://github.com/Ouranosinc/xclim/blob/master/xclim/core/utils.py#L240
[2] https://gist.github.com/bzah/2a84d050b8a1aed1b40a2ed1526e1f12
[3]
https://www.researchgate.net/publication/222105754_Sample_Quantiles_in_Statistical_Packages
- Mail original -
De: "Aron Gergely"
À: "Discussion of Numerical Python"
Envoyé: Vendredi 16 Septembre 2022 10:56:28
Objet: [Numpy-discussion] Ways to achieve faster np.nanpercentile() calculation?
Hi all,
On my system, np.nanpercentile() is orders of magnitude (>100x) slower
than np.percentile().
I use numpy 1.23.1
Wondering if there is a way to speed it up.
I came across this workaround for 3D arrays:
https://krstn.eu/np.nanpercentile()-there-has-to-be-a-faster-way/
But I would need a generalized solution that works on N dimensions.
So I started adopting the above - but wondering if I am reinventing the
wheel here?
Is there already a python package that implements a speedier
nanpercentile for numpy? (similar idea as the 'Bottleneck' package?)
Or other known workarounds to achieve the same result?
Best regards,
Aron
___
NumPy-Discussion mailing list -- numpy-discussion@python.org
To unsubscribe send an email to numpy-discussion-le...@python.org
https://mail.python.org/mailman3/lists/numpy-discussion.python.org/
Member address: a...@cerfacs.fr
___
NumPy-Discussion mailing list -- numpy-discussion@python.org
To unsubscribe send an email to numpy-discussion-le...@python.org
https://mail.python.org/mailman3/lists/numpy-discussion.python.org/
Member address: arch...@mail-archive.com
