issue_comments: 457798514

Maybe it would help to describe what you were trying to do here.

Sure - thanks!

I have a dataset that's long, the sample code shown below is 200k rows, but the full dataset will be much larger. I'm interested in pairwise distances except not for all rows, just the distances for few thousand rows, wrt to the full 200k.

Here's how I hack this together:

My starting array

```python

df_array = xr.DataArray(df) df_array = df_array.rename({PIVOT: 'all_sites'}) df_array

<xarray.DataArray (all_sites: 185084, dim_1: 245)> array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]) Coordinates: * all_sites (all_sites) object '0.gravatar.com||gprofiles.js||Gravatar.init' ... 'кÑ\x83Ñ\x80Ñ\x81Ñ\x8b.1Ñ\x81енÑ\x82Ñ\x8fбÑ\x80Ñ\x8f.Ñ\x80Ñ\x84||store.js||store.set' * dim_1 (dim_1) object 'AnalyserNode.connect' ... 'HTMLCanvasElement.previousSibling' ```

My slice of the array

python sites_of_interest = [sub list of all sites] df_dye_array = xr.DataArray(df.loc[sites_of_interest]) df_dye_array = df_dye_array.rename({PIVOT: 'dye_sites'})

Chunk

python df_array_c = df_array.chunk({'all_sites': 10_000}) df_dye_array_c = df_dye_array.chunk({'dye_sites': 100})

Get distances

```python def get_chebyshev_distances_xarray_ufunc(df_array, df_dye_array): chebyshev = lambda x: np.abs(df_array[:,0,:] - x).max(axis=1) result = np.apply_along_axis(chebyshev, 1, df_dye_array).T return result

distance_array = xr.apply_ufunc( get_chebyshev_distances_xarray_ufunc, df_array_c, df_dye_array_c, dask='parallelized', output_dtypes=[float], input_core_dims=[['dim_1'], ['dim_1']], ) ```

What I get out is an array with the length of my original array and the width of my sites of interest where each number is the chebyshev distance between their respective rows of the original dataset (which are 245 long).