What happened: The idea is to use a xarray dataset (stored as dummy zarr file), which is subsequently filled with the region argument, as explained in the documentation. Ideally, almost nothing is stored to disk upfront.

It seems the current implementation is only designed to either store coordinates for the whole dataset and write them to disk or to write without coordinates. I failed to understand this from the documentation and tried to create a dataset without coordinates and fill it with a dataset subset with coordinates. It gave some inconclusive errors depending on the actual code example (see below). ValueError: parameter 'value': expected array with shape (0,), got (10,) or ValueError: conflicting sizes for dimension 'x': length 10 on 'x' and length 30 on 'foo'

It might also be a bug and it should in fact be possible to add a dataset with coordinates to a dummy dataset without coordinates. Then there seems to be an issue regarding the handling of the variables during storing the region.

... or I might just have done it wrong... and I'm looking forward to suggestions.

What you expected to happen:

Either an error message telling me that that i should use coordinates during creation of the dummy dataset. Alternatively, if this is a bug and should be possible then it should just work.

Minimal Complete Verifiable Example:

```python import dask.array import xarray as xr import numpy as np

error = 1 # choose between 0 (no error), 1, 2, 3

dummies = dask.array.zeros(30, chunks=10)

chunks in coords are not taken into account while saving!?

coord_x = dask.array.zeros(30, chunks=10) # or coord_x = np.zeros((30,)) if error == 0: ds = xr.Dataset({"foo": ("x", dummies)}, coords={"x":coord_x}) else: ds = xr.Dataset({"foo": ("x", dummies)})

print(ds) path = "./tmp/test.zarr" ds.to_zarr(path, mode='w', compute=False, consolidated=True)

create a new dataset to be input into a region

ds = xr.Dataset({"foo": ('x', np.arange(10))},coords={"x":np.arange(10)})

if error == 1: ds.to_zarr(path, region={"x": slice(10, 20)}) # ValueError: parameter 'value': expected array with shape (0,), got (10,) elif error == 2: ds.to_zarr(path, region={"x": slice(0, 10)}) ds.to_zarr(path, region={"x": slice(10, 20)}) # ValueError: conflicting sizes for dimension 'x': length 10 on 'x' and length 30 on 'foo' elif error == 3: ds.to_zarr(path, region={"x": slice(0, 10)}) ds = xr.Dataset({"foo": ('x', np.arange(10))},coords={"x":np.arange(10)}) ds.to_zarr(path, region={"x": slice(10, 20)}) # ValueError: parameter 'value': expected array with shape (0,), got (10,) else: ds.to_zarr(path, region={"x": slice(10, 20)})

ds = xr.open_zarr(path) print('reopen',ds['x']) ```

Anything else we need to know?:

Environment:

What happened: When setting a DataArray with loc, xarray converts bool masks to the coord data type. Therefore loc does not use a boolean mask but tries to match the indexes of the coord.

Minimal Complete Verifiable Example:

```python import numpy as np import xarray as xr

x = np.arange(10).astype(np.float64) fx = np.arange(10).astype(np.float64) da = xr.DataArray(fx,dims=['x'],coords={'x':x}) mask = np.zeros((10,)) mask[1::2] = 1 mask = mask.astype(bool) da.loc[{'x':~mask}] = np.arange(5)+10 ```

Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/matthmey/miniconda3/envs/analytics/lib/python3.7/site-packages/xarray/core/dataarray.py", line 214, in __setitem__ self.data_array[pos_indexers] = value File "/home/matthmey/miniconda3/envs/analytics/lib/python3.7/site-packages/xarray/core/dataarray.py", line 767, in __setitem__ self.variable[key] = value File "/home/matthmey/miniconda3/envs/analytics/lib/python3.7/site-packages/xarray/core/variable.py", line 854, in __setitem__ indexable[index_tuple] = value File "/home/matthmey/miniconda3/envs/analytics/lib/python3.7/site-packages/xarray/core/indexing.py", line 1164, in __setitem__ array[key] = value ValueError: shape mismatch: value array of shape (5,) could not be broadcast to indexing result of shape (10,)

Anything else we need to know?: <del>Could be fixed by replacing the line, but maybe this is not the cleanest solution.</del> I tried fixing it with the following, which works for the above code but fails for other cases. if label.dtype != np.bool: label = maybe_cast_to_coords_dtype(label, coord.dtype)

Environment:

Xarray is facing the same issues in its current rolling implementation (DataArrayRolling and DatasetRolling) as described in this pandas issue. Namely, the construct methods stride parameter is applied after the rolling is computed. Technically, we are computing more than we would need to because we partially throwing it away due to striding.

In PR #3607 the issue is solved for the ...Rolling's __iter__ function but not for the construct, reduce and _bottleneck_reduce methods. Since the way Xarray's rolling is implemented relies on numpy, we could introduce a sliding window function as described here.

Any opinions?