issue_comments

What would the coordinates look like?

1. apply func also for coordinate
2. always apply mean to coordinate

If I think about my applications, I would probably always want to apply mean to dimension coordinates, but would like to be able to choose for non-dimension coordinates.

I would lean towards a coordinate based representation since it's a little more usable/certain to be correct.

I feel that this could become too complex in the case of irregularly spaced coordinates. I slightly favor the index-based approach (as in my function above), which one calls like python aggregate_da(da, {'lat': 2, 'lon': 2})

If we do that, we can just use scikit-image's block_reduce function, which is vectorized and works great with apply_ufunc.

FYI, I do this often in my work with this sort of function:

python import xarray as xr from skimage.measure import block_reduce def aggregate_da(da, agg_dims, suf='_agg'): input_core_dims = list(agg_dims) n_agg = len(input_core_dims) core_block_size = tuple([agg_dims[k] for k in input_core_dims]) block_size = (da.ndim - n_agg)*(1,) + core_block_size output_core_dims = [dim + suf for dim in input_core_dims] output_sizes = {(dim + suf): da.shape[da.get_axis_num(dim)]//agg_dims[dim] for dim in input_core_dims} output_dtypes = da.dtype da_out = xr.apply_ufunc(block_reduce, da, kwargs={'block_size': block_size}, input_core_dims=[input_core_dims], output_core_dims=[output_core_dims], output_sizes=output_sizes, output_dtypes=[output_dtypes], dask='parallelized') for dim in input_core_dims: new_coord = block_reduce(da[dim].data, (agg_dims[dim],), func=np.mean) da_out.coords[dim + suf] = (dim + suf, new_coord) return da_out

This is being discussed in #1192 under a different name.

Yes, we need this feature.