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Apr 11, 2023
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Clarify vectorized indexing documentation #7747

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Clarify vectorized indexing documentation
mccloskey Apr 11, 2023
7243b7d
Update doc/user-guide/indexing.rst
dcherian Apr 11, 2023
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7 changes: 6 additions & 1 deletion doc/user-guide/indexing.rst
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Expand Up @@ -352,7 +352,6 @@ dimensions:
ind_x = xr.DataArray([0, 1], dims=["x"])
ind_y = xr.DataArray([0, 1], dims=["y"])
da[ind_x, ind_y] # orthogonal indexing
da[ind_x, ind_x] # vectorized indexing

Slices or sequences/arrays without named-dimensions are treated as if they have
the same dimension which is indexed along:
Expand Down Expand Up @@ -399,6 +398,12 @@ These methods may also be applied to ``Dataset`` objects
Vectorized indexing may be used to extract information from the nearest
grid cells of interest, for example, the nearest climate model grid cells
to a collection specified weather station latitudes and longitudes.
To trigger vectorized indexing behavior
you will need to provide the selection dimensions with a new
shared output dimension name. In the example below, the selections
of the closest latitude and longitude are renamed to an output
dimension named "points":


.. ipython:: python

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