issue_comments: 286171415

@shoyer No chunking as the dataset was quite small (360x720x30). Also, the calculation is along the time dimension so this effectively disappears for each lat/lon. Hence initial surprise why it was coming up with this chunk/reshape issue since I thought all it has to do is unstack 'allpoints'

If I print one of the dask arrays from within the function print sT dask.array<from-va..., shape=(11L,), dtype=float64, chunksize=(11L,)> This is 11L because the calculation returns 11 values per point to an xr.Dataset.

Others have no chunks because they are single values (for each point) print p_value dask.array<from-va..., shape=(), dtype=float64, chunksize=()> Only returns one value per point The object returned (xr.Dataset) from the .apply function comes out with chunks: mle.chunks Frozen(SortedKeysDict({'allpoints': (1, 1, 1, 1, 1......(allpoints)....., 1, 1), 'T': (11L,)}))

and looks like: <xarray.Dataset> Dimensions: (T: 11, allpoints: 259200) Coordinates: * T (T) int32 1 5 10 15 20 25 30 40 50 75 100 * allpoints (allpoints) MultiIndex - allpoints_level_0 (allpoints) float64 40.25 40.25 40.25 40.25 40.25 ... - allpoints_level_1 (allpoints) float64 22.75 23.25 23.75 24.25 24.75 ... Data variables: xi (allpoints) float64 -0.6906 -0.6906 -0.6906 -0.6906 ... mu (allpoints) float64 9.969e+36 9.969e+36 9.969e+36 ... sT (allpoints, T) float64 9.969e+36 9.969e+36 9.969e+36 ... KS_p_value (allpoints) float64 3.8e-12 3.8e-12 3.8e-12 3.8e-12 ... sigma (allpoints) float64 5.297e-24 5.297e-24 5.297e-24 ... KS_statistic (allpoints) float64 0.6321 0.6321 0.6321 0.6321 ...