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.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3445/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
458236359,MDU6SXNzdWU0NTgyMzYzNTk=,3035,"Feature Request: Additional parallel IO format support: ASDF, ph5, or similar",4605410,open,0,,,1,2019-06-19T21:32:04Z,2020-04-07T06:42:15Z,,NONE,,,,"#### Problem description

Currently, Xarray supports reading/writing a variety of dataformats. Geoscience data in particular is a stated use-case domain. However, in the documentation, it seems as though geoscience mostly refers to hydrologic and atmospheric data. 

It would be very useful to more domains of geoscience if xarray also supported read/writes to formats encountered regularly in geophysics, either something like [ph5](https://github.com/PIC-IRIS/PH5/), [ASDF](http://seismicdata.github.io/pyasdf/index.html), or the like. Already projects like [obsplus](https://github.com/niosh-mining/obsplus) deliver some xarray->seismic formats -> xarray functionality, but have yet to venture into the parallel read/write operations that make xarray so attractive. 

I am not sure what the overhead would be in adapting xarray to use these existing packages, or in creating common interfaces for use with these packages.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3035/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue