issues: 512205079

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
512205079 MDU6SXNzdWU1MTIyMDUwNzk= 3445 Merge fails when sparse Dataset has overlapping dimension values 4605410 open 0     3 2019-10-24T22:08:12Z 2021-07-08T17:43:57Z   NONE      

Sparse numpy arrays used in a merge operation seem to fail under certain coordinate settings. for example, this works perfectly:

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

data_array1 = xr.DataArray(data,name='default', dims=['source','receiver','time'], coords={'source':['X.1'], 'receiver':['X.2'], 'time':time}).to_dataset() data_array2 = xr.DataArray(data,name='default', dims=['source','receiver','time'], coords={'source':['X.2'], 'receiver':['X.1'], 'time':time}).to_dataset()

dataset1 = xr.merge([data_array1,data_array2])

```

But this raises an IndexError: Only indices with at most one iterable index are supported. from the sparse package:

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

data = sparse.COO.from_numpy(np.random.uniform(-1,1,(1,1,100))) time = np.linspace(0,1,num=100)

data_array1 = xr.DataArray(data,name='default', dims=['source','receiver','time'], coords={'source':['X.1'], 'receiver':['X.2'], 'time':time}).to_dataset() data_array2 = xr.DataArray(data,name='default', dims=['source','receiver','time'], coords={'source':['X.2'], 'receiver':['X.1'], 'time':time}).to_dataset()

dataset1 = xr.merge([data_array1,data_array2]) ```

I have noticed this occurs when the merger would seem to add dimensions filled with nan values.

 
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