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/783#issuecomment-193576856,https://api.github.com/repos/pydata/xarray/issues/783,193576856,MDEyOklzc3VlQ29tbWVudDE5MzU3Njg1Ng==,1217238,2016-03-08T02:56:28Z,2016-03-08T02:56:49Z,MEMBER,"As expected, the following all dask.array solution triggers this:

``` python
dates = pd.date_range('2001-01-01', freq='D', periods=1000)
sizes = pd.Series(dates, dates).resample('1M', how='count').values
chunks = (tuple(sizes), (100,))
x = da.ones((3630, 100), chunks=chunks)
assert x[240:270].shape == x[240:270].compute().shape
# AssertionError
```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,138332032
https://github.com/pydata/xarray/issues/783#issuecomment-193527245,https://api.github.com/repos/pydata/xarray/issues/783,193527245,MDEyOklzc3VlQ29tbWVudDE5MzUyNzI0NQ==,1217238,2016-03-08T00:36:14Z,2016-03-08T00:36:14Z,MEMBER,"Something like this might work to generate pathological chunks for dask.array:

```
dates = pandas.date_range('2000-01-01', freq='D', periods=1000)
sizes = pandas.Series(dates, dates).resample('1M', how='count').values
chunks = (tuple(sizes), (100,))
```

(I don't have xarray or dask installed on my work computer, but I could check this later)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,138332032
https://github.com/pydata/xarray/issues/783#issuecomment-193521326,https://api.github.com/repos/pydata/xarray/issues/783,193521326,MDEyOklzc3VlQ29tbWVudDE5MzUyMTMyNg==,1217238,2016-03-08T00:17:00Z,2016-03-08T00:17:00Z,MEMBER,"If you don't specify a chunksize, xarray should use each file as a full ""chunk"". So it would probably be useful to know the shapes of each array you are loading with `open_mfdataset`. My guess is that this issue only arises when indexing arrays consisting of differently sized chunks, which is exactly why using `.chunk` to set a fixed chunk size resolves this issue.

To be clear, all the logic implementing the chunking and indexing code for xarray objects containing dask arrays lives inside dask.array itself, not in our xarray wrapper (which is pretty thin). This doesn't make this any less of an issue for you, but I'm pretty sure (and I think @mrocklin agrees) that the bug here in probably in the dask.array layer.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,138332032
https://github.com/pydata/xarray/issues/783#issuecomment-192048274,https://api.github.com/repos/pydata/xarray/issues/783,192048274,MDEyOklzc3VlQ29tbWVudDE5MjA0ODI3NA==,1217238,2016-03-04T01:28:23Z,2016-03-04T01:28:23Z,MEMBER,"This does look very strange. I'm guessing it's a dask.array bug (cc @mrocklin).

Can you make a reproducible example? If so, we'll probably be able to figure this out. How do you make this data?

Tracking this sort of thing down is a good motivation for an eager-evaluation mode in dask.array... (https://github.com/dask/dask/issues/292)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,138332032