@max-sixty The reason is that my method is basically a special case of point-wise indexing: http://xarray.pydata.org/en/stable/indexing.html#more-advanced-indexing You can get the same result by calling: ```python core_dim_locs = {key: value for key, value in core_dim_locs_from_cond(mask, new_dim_name="newdim")}

pointwise selection

data.sel( dim_0=outliers_subset["dim_0"], dim_1=outliers_subset["dim_1"], dim_2=outliers_subset["dim_2"] ) ``` (Note that you loose chunk information by this method, that's why it is less efficient)

When you want to select random items from a N-dimensional array, you can either model the result as some sparse array or by stacking the dimensions. (OK, stacking the dimensions means also a sparse COO encoding...)