issue_comments

I think this would also work (due to the intrinsic broadcasting behavior of numpy functions): python def get_chebyshev_distances_xarray_ufunc(array, dye_array): return abs(array - dye_array).max(axis=-2)

OK, I think you're doing this right. You want an output with shape ['all_sites', 'dye_sites'] right?

My suggestion would be to add an explicit call to np.broadcast_arrays() at the start of your applied function. This will make the dimensions a little easier to understand. ```python def get_chebyshev_distances_xarray_ufunc(array, dye_array): array, dye_array = np.broadcast_arrays(array, dye_array) # array is a 3D numpy array with logical dimensions ['all_sites', 'dye_sites', 'dim_1'] # dye_array is a 3D numpy array with logical dimensions ['all_sites', 'dye_sites', 'dim_1']

# compute the distance matrix
# return a numpy array with logical dimensions ['all_sites', 'dye_sites']

``` (edit note: fixed dimensions)

actually, scratch that

The notion of "core dimensions" in apply_ufunc() is definitely quite tricky to understand.

That said, I think this is (mostly) doing the right thing: - Your inputs have dimensions ['row_a', 'dim_1'] and ['row_b', 'dim_1'] - Xarray broadcasts over dimensions that aren't included in "core dimensions" , so inputs are broadcast to have dimensions like ['row_a', 'row_b', 'dim_1'] and ['row_a', 'row_b', 'dim_1'].

This is probably especially confusing because the unlabeled versions of da and db are given "broadcastable" shapes (1000, 1, 100) and (1000, 100) rather than the fully "broadcast" shapes of (1000, 1000, 100) and (1000, 1000, 100), which would make it more obvious what is going on.

For your specific use case: maybe you meant to specify input_core_dims=[['row_a'], ['row_b']] instead? That version would give inputs with dimensions like ['dim_1', 'row_a'] and ['dim_1', 'row_b'].

More generally: I think we really need a version of apply() that doesn't do this confusing broadcasting and dimension reordering. See https://github.com/pydata/xarray/issues/1130 for discussion about that.

unlabeled versions of da and db are given "broadcastable" shapes (1, 1000, 100) and (1000, 100)

Is it (1000, 1, 100) as my code seems to return, or, as you said (1, 1000, 100)? Is it deterministic?

That was a typo (I'll fix it). It should be (1000, 1, 100).

The behavior is definitely deterministic, if hard to understand!

Maybe it would help to describe what you were trying to do here. What should "one unit" of your calculation look like?