Allow interpolating along singleton dimensions #10747
Description
Is your feature request related to a problem?
Currently, interpolating along a singleton dimension will always yield nan values.
E.g.
import xarray as xr
da = xr.DataArray([[0, 1]], coords={'a': [1.], 'b': [2., 3.]}).interp(a=1., b=2.5)raises a warning
***\site-packages\scipy\interpolate\_interpolate.py:479: RuntimeWarning: invalid value encountered in divide
slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]
and returns array with correct shape, but all values nan:
<xarray.DataArray ()> Size: 8B
array(nan)
Coordinates:
a float64 8B 1.0
b float64 8B 2.5
Having only a single coordinate, interpolation is not well defined, of course.
But giving the exact coordinate value in interp(), should simply do a sel() along this dimension before interpolation.
That seems unambiguous and would be what users may reasonably expect.
This is particularly used, when interpolating with coordinates determined by computed dicts, like:
kwargs = dict(a=1., b=2.5) # from external input
da.interp(**kwargs)Describe the solution you'd like
import xarray as xr
da = xr.DataArray([[0, 1]], coords={'a': [1.], 'b': [2., 3.]}).interp(a=1., b=2.5)should yield
<xarray.DataArray ()> Size: 8B
array(0.5)
Coordinates:
a float64 8B 1.0
b float64 8B 2.5
Describe alternatives you've considered
Of course, singleton dimensions can be detected and special-cases manually:
import xarray as xr
kwargs = dict(a=1., b=2.5) # from external input
da = xr.DataArray([[0, 1]], coords={'a': [1.], 'b': [2., 3.]})
sel_kwargs = {k: v for k, v in kwargs.items() if da.sizes[k] == 1}
interp_kwargs = {k: v for k, v in kwargs.items() if k not in sel_kwargs.keys()}
da.sel(**sel_kwargs).interp(**interp_kwargs)This yields the desired behaviour, but feels very clumsy.
Additional context
No response