stratify gives strange results if heights run in opposite directions

[nhsavage]

z_targ = np.array([0.0, 1.0, 2.0, 3.0, 4.0])

z_source = np.array([0., 0.5, 1.5, 2.5, 3.5, 4.5])
data_source = z_source.copy()
out  = stratify.interpolate(z_targ, z_source, data_source)
print(out)

#  now flip data to be interpolated
z_source = np.flip(z_source)
data_source = np.flip(data_source)
out  = stratify.interpolate(z_targ, z_source, data_source)
print(out)

Gives:

[0. 1. 2. 3. 4.]
[ 0. nan nan nan nan]

Either the reversed heights should raise a ValuError or (better) the input heights should be flipped internally before doing the interpolation

[trexfeathers]

Thanks @nhsavage, this is a known area to the devs, so it's good to have it flagged here 👍

[bjlittle]

@nhsavage If you also flip the z_targ, then stratify works as expected

>>> z_targ = np.flip(z_targ)
>>> out  = stratify.interpolate(z_targ, z_source, data_source)
>>> print(out)
[4. 3. 2. 1. 0.]

Is this a reasonable answer?

[nhsavage]

@nhsavage If you also flip the z_targ, then stratify works as expected
Is this a reasonable answer?

yes I know - I have used this a workd around in my code but shouldn't stratify at least raise an exception to avoid this sort of thing happening silently?

[bjlittle]

What would you prefer happened here?

I'm guessing to at least raise an exception, but ideally perform the interpolation i.e., give the result [0. 1. 2. 3. 4.] with your flipped z_source and data_source, right?

[nhsavage]

What would you prefer happened here?

I'm guessing to at least raise an exception, but ideally perform the interpolation i.e., give the result [0. 1. 2. 3. 4.] with your flipped z_source and data_source, right?

yes that's exactly right

[bjlittle]

Thanks @nhsavage 😄

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