html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2511#issuecomment-992699334,https://api.github.com/repos/pydata/xarray/issues/2511,992699334,IC_kwDOAMm_X847K2PG,2448579,2021-12-13T17:21:20Z,2021-12-13T17:21:20Z,MEMBER,"IIUC this cannot work lazily in most cases if you have dimension coordinate variables. When xarray constructs the output after indexing, it will try to index those coordinate variables so that it can associate the right timestamp (for e.g) with the output.

The example from @ulijh should work though (it has no dimension coordinate or indexed variables)

``` python
import xarray as xr
import dask.array as da
import numpy as np
da = xr.DataArray(np.random.rand(3*4*5).reshape((3,4,5))).chunk(dict(dim_0=1))
idcs = da.argmax('dim_2')
da[dict(dim_2=idcs)]
```

The example by @rafa-guedes (thanks for that one!) could be made to work I think.

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

darr = xr.DataArray(data=[0.2, 0.4, 0.6], coords={""z"": range(3)}, dims=(""z"",))
good_indexer = xr.DataArray(
    data=np.random.randint(0, 3, 8).reshape(4, 2).astype(int),
    coords={""y"": range(4), ""x"": range(2)},
    dims=(""y"", ""x"")
)
bad_indexer = xr.DataArray(
    data=da.random.randint(0, 3, 8).reshape(4, 2).astype(int),
    coords={""y"": range(4), ""x"": range(2)},
    dims=(""y"", ""x"")
)

In [5]: darr                                                                                                                                                            
Out[5]: 
<xarray.DataArray (z: 3)>
array([0.2, 0.4, 0.6])
Coordinates:
  * z        (z) int64 0 1 2

In [6]: good_indexer                                                     
Out[6]: 
<xarray.DataArray (y: 4, x: 2)>
array([[0, 1],
       [2, 2],
       [1, 2],
       [1, 0]])
Coordinates:
  * y        (y) int64 0 1 2 3
  * x        (x) int64 0 1

In [7]: bad_indexer                                                      
Out[7]: 
<xarray.DataArray 'reshape-417766b2035dcb1227ddde8505297039' (y: 4, x: 2)>
dask.array<reshape, shape=(4, 2), dtype=int64, chunksize=(4, 2), chunktype=numpy.ndarray>
Coordinates:
  * y        (y) int64 0 1 2 3
  * x        (x) int64 0 1

In [8]: darr[good_indexer]                                                                                                                                              
Out[8]: 
<xarray.DataArray (y: 4, x: 2)>
array([[0.2, 0.4],
       [0.6, 0.6],
       [0.4, 0.6],
       [0.4, 0.2]])
Coordinates:
    z        (y, x) int64 0 1 2 2 1 2 1 0
  * y        (y) int64 0 1 2 3
  * x        (x) int64 0 1
```

We can copy the dimension coordinates of the output (x,y) directly from the indexer. And the dimension coordinate on the input (z) should be a dask array in the output (since z is not a dimension coordinate in the output, this should be fine)

","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,374025325
https://github.com/pydata/xarray/issues/2511#issuecomment-568107398,https://api.github.com/repos/pydata/xarray/issues/2511,568107398,MDEyOklzc3VlQ29tbWVudDU2ODEwNzM5OA==,2448579,2019-12-20T22:14:34Z,2019-12-20T22:14:34Z,MEMBER,I don't think any one is working on it. We would appreciate it if you could try to fix it.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,374025325