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
324350248,MDU6SXNzdWUzMjQzNTAyNDg=,2159,Concatenate across multiple dimensions with open_mfdataset,35968931,closed,0,,,27,2018-05-18T10:10:49Z,2019-09-16T18:54:39Z,2019-06-25T15:50:33Z,MEMBER,,,,"#### Code Sample

```python
# Create 4 datasets containing sections of contiguous (x,y) data
for i, x in enumerate([1, 3]):
    for j, y in enumerate([10, 40]):
        ds = xr.Dataset({'foo': (('x', 'y'), np.ones((2, 3)))},
                         coords={'x': [x, x+1],
                                 'y': [y, y+10, y+20]})

        ds.to_netcdf('ds.' + str(i) + str(j) + '.nc')

# Try to open them all in one go
ds_read = xr.open_mfdataset('ds.*.nc')
print(ds_read)
```
#### Problem description

Currently ``xr.open_mfdataset`` will detect a single common dimension and concatenate DataSets along that dimension. However a common use case is a set of NetCDF files which have two or more common dimensions that need to be concatenated along simultaneously (for example collecting the output of any large-scale simulation which parallelizes in more than one dimension simultaneously). For the behaviour of ``xr.open_mfdataset`` to be n-dimensional it should automatically recognise and concatenate along all common dimensions.

#### Expected Output
```
<xarray.Dataset>
Dimensions:  (x: 4, y: 6)
Coordinates:
  * x        (x) int64 1 2 3 4
  * y        (y) int64 10 20 30 40 50 60
Data variables:
    foo      (x, y) float64 dask.array<shape=(4, 6), chunksize=(2, 3)>
```

#### Current output of ``xr.open_mfdataset()``
```
<xarray.Dataset>
Dimensions:  (x: 4, y: 12)
Coordinates:
  * x        (x) int64 1 2 3 4
  * y        (y) int64 10 20 30 40 50 60 10 20 30 40 50 60
Data variables:
    foo      (x, y) float64 dask.array<shape=(4, 12), chunksize=(4, 3)>
```
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