add new minmax function

[mdtanker]

Adds an equivalanet function to maxabs but for calculating the min and max values of arrays, or optionally user-specified percentiles of the values.

This is useful for

1. if you want to plot a series of datasets with the same colorscale so you need to calculate the overall min/max for all the datasets
2. if you want to get robust colormap limits, excluding outliers by using percentiles, such as the 2nd and 98th percentiles.

Relevant issues/PRs:

Implements the function requested in #525
Follows the percentiles approach from #524 and #523

[santisoler]

Thanks @mdtanker for pushing this forward! I just left a few minor suggestions. Let me know what do you think!

It might be also nice to check if min_percentile <= max_percentile so we are ensure that min <= max regardless of the choices of percentile values.

[mdtanker]

Here are some timings for future reference:

import numpy as np
import verde as vd
a = np.random.uniform(size=(27, 100))

---

• Using np.nanmin or np.nanmax is ~x2 slower than the non-nan functions

  %timeit vd.minmax(a, nan=True)
  %timeit vd.minmax(a, nan=False)
  
  >>>48.8 μs ± 1.94 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
  >>>20.2 μs ± 1.84 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

• Using percentiles is ~4x slower than min/max

  • Only calculating 1 percentile, and using min/max for the other saves some time

  %timeit vd.minmax(a, min_percentile=0, max_percentile=100, nan=True)
  %timeit vd.minmax(a, min_percentile=1, max_percentile=99, nan=True)
  %timeit vd.minmax(a, min_percentile=0, max_percentile=99, nan=True)
  
  >>>50.5 μs ± 2.56 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
  >>>289 μs ± 26.5 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
  >>>179 μs ± 3.93 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

• np.nanpercentile is ~5-10x slower than np.percentile

  %timeit vd.minmax(a, min_percentile=0, max_percentile=100, nan=False)
  %timeit vd.minmax(a, min_percentile=1, max_percentile=99, nan=False)
  %timeit vd.minmax(a, min_percentile=0, max_percentile=99, nan=False)
  
  >>>20.7 μs ± 843 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
  >>>232 μs ± 17.7 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
  >>>111 μs ± 5.5 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

[santisoler]

Looking good, @mdtanker. Just left a few comments. Let me know what do you think!

[leouieda]

@mdtanker I want to get this released with v1.9 (#537) and was wondering what the status is. Is there anything blocking this that I could help with?

[leouieda]

By the way, I merged the recent changes from main here which should fix some of the unrelated failures.

[mdtanker]

Nothing stopping it just haven't had time, can get it done tomorrow though!

[leouieda]

Ah awesome! Thanks! Don't forget to add yourself to the AUTHORS file as well.

[mdtanker]

Ok this should be ready to go! I think I resolved all the review comments from @santisoler, but it seems the review is still blocking the merge? Is there something I should do on my end?

[leouieda]

Hi @mdtanker sorry, was having a look and had a couple more comments.

Reviewed changes and approved.

[leouieda]

Merged! Thanks for this @mdtanker!