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Coordinates passed to interp have nan values #3924

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Coordinates passed to interp have nan values #3924

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4 changes: 2 additions & 2 deletions xarray/core/missing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -549,8 +549,8 @@ def _localize(var, indexes_coords):
indexes = {}
for dim, [x, new_x] in indexes_coords.items():
index = x.to_index()
imin = index.get_loc(np.min(new_x.values), method="nearest")
imax = index.get_loc(np.max(new_x.values), method="nearest")
imin = index.get_loc(np.nanmin(new_x.values), method="nearest")
imax = index.get_loc(np.nanmax(new_x.values), method="nearest")
Comment on lines +552 to +553
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At first I was a little uneasy about your suggestion to require a newer version of NumPy, but after reading about the history of this issue a little more, I think something along those lines may actually be the simplest approach (perhaps you were ahead of me on this).

It turns out that np.min and np.max behave like np.nanmin and np.nanmax for time-like types in versions of NumPy prior to 1.18.1 (see numpy/numpy#14841 (comment)), so we can continue to use those for time-like types and earlier versions of NumPy and still obtain the desired behavior. I think something like the following would work:

Suggested change
imin = index.get_loc(np.nanmin(new_x.values), method="nearest")
imax = index.get_loc(np.nanmax(new_x.values), method="nearest")
if LooseVersion(np.__version__) < LooseVersion("1.18.1") and new_x.dtype.kind in "mM":
# In older versions of NumPy min and max (incorrectly) ignore NaT values
# for time-like types -- that is they behave like nanmin and nanmax
# should -- but nanmin and nanmax raise an error.
imin = index.get_loc(np.min(new_x.values), method="nearest")
imax = index.get_loc(np.max(new_x.values), method="nearest")
else:
imin = index.get_loc(np.nanmin(new_x.values), method="nearest")
imax = index.get_loc(np.nanmax(new_x.values), method="nearest")


indexes[dim] = slice(max(imin - 2, 0), imax + 2)
indexes_coords[dim] = (x[indexes[dim]], new_x)
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