issues

Is your feature request related to a problem? Please describe. Currently if a zarr store has a missing chunk, it is treaded as all missing. This is an upstream functionality but one for which there may soon be a kwarg allowing you to instead raise an error in these instances (https://github.com/zarr-developers/zarr-python/pull/489). This is valuable in situations where you would like to distinguish intentional NaN data from I/O errors that caused you to not write some chunks. Here's an example of a problematic case in this situation (courtesy of @delgadom ):

python import xarray as xr import numpy as np xr.Dataset({'myarr': (('x', 'y'), [[0., np.nan], [2., 3.]]), 'x': [0, 1], 'y': [0, 1]}).chunk({'x': 1, 'y': 1}).to_zarr('myzarr.zarr'); print('\n\ndata read into xarray\n' + '-'*30) print(xr.open_zarr('myzarr.zarr').compute().myarr) print('\n\nstructure of zarr store\n' + '-'*30) ! ls -R myzarr.zarr print('\n\nremove a chunk\n' + '-'*30 + '\nrm myzarr.zarr/myarr/1.0') ! rm myzarr.zarr/myarr/1.0 print('\n\ndata read into xarray\n' + '-'*30) print(xr.open_zarr('myzarr.zarr').compute().myarr)

This prints:

``` data read into xarray

<xarray.DataArray 'myarr' (x: 2, y: 2)> array([[ 0., nan], [ 2., 3.]]) Coordinates: * x (x) int64 0 1 * y (y) int64 0 1 structure of zarr store

myzarr.zarr: myarr x y myzarr.zarr/myarr: 0.0 0.1 1.0 1.1 myzarr.zarr/x: 0 myzarr.zarr/y: 0 remove a chunk

rm myzarr.zarr/myarr/1.0 data read into xarray

<xarray.DataArray 'myarr' (x: 2, y: 2)> array([[ 0., nan], [nan, 3.]]) Coordinates: * x (x) int64 0 1 * y (y) int64 0 1 ```

Describe the solution you'd like I'm not sure where a kwarg to the __init__ method of a zarr Array object would come into play within open_zarr or open_dataset (once https://github.com/zarr-developers/zarr-python/pull/489 is merged), but I figured I'd ask this question to see if anyone could point me in the right direction and to get ready for when that zarr feature exists. Happy to file a PR once I know where I'm looking. Couldn't figure it out with some initial browsing

MCVE Code Sample

python import numpy as np import xarray as xr x1 = xr.Dataset( { "temperature": (("y", "x"), 20 * np.random.rand(6).reshape(2, 3)) }, coords={"y": [0, 1], "x": [10, 20, 30]}, ) x2 = xr.Dataset( { "temperature": (("y", "x"), 20 * np.random.rand(6).reshape(2, 3)) }, coords={"y": [2, 3], "x": [10, 20, 30]}, ) x3 = xr.Dataset( { "temperature": (("y", "x"), 20 * np.random.rand(6).reshape(2, 3)) }, coords={"y": [2, 3], "x": [40, 50, 60]}, ) xr.combine_by_coords([x1,x2,x3])

Expected Output

python <xarray.Dataset> Dimensions: (x: 6, y: 4) Coordinates: * x (x) int64 10 20 30 40 50 60 * y (y) int64 0 1 2 3 Data variables: temperature (y, x) float64 14.11 19.19 10.77 nan ... 4.86 10.57 4.38 15.09

Problem Description

Currently, it throws the following error: python ValueError: The supplied objects do not form a hypercube because sub-lists do not have consistent lengths along dimension0 This is b/c combine_by_coords calls xr.core.combine._check_shape_tile_ids, which mandates that the passed datasets form a complete hypercube. This check functiono also serves the purpose of mandating that the dimension depths are the same. Could we pull that part out as a separate function and, for combine_by_coords, only call this first part but NOT mandate that the hypercube is complete? The expected behavior, in my mind, should be to simply replace the missing tiles of the hypercube with fill_value. I'll file a PR to this effect and welcome comments.

Output of xr.show_versions()