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/3762#issuecomment-583682751,https://api.github.com/repos/pydata/xarray/issues/3762,583682751,MDEyOklzc3VlQ29tbWVudDU4MzY4Mjc1MQ==,2448579,2020-02-08T01:22:12Z,2020-02-08T01:22:12Z,MEMBER,Actually it looks like this example is relevant: https://xarray.pydata.org/en/stable/examples/apply_ufunc_vectorize_1d.html,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,561921094
https://github.com/pydata/xarray/issues/3762#issuecomment-583659669,https://api.github.com/repos/pydata/xarray/issues/3762,583659669,MDEyOklzc3VlQ29tbWVudDU4MzY1OTY2OQ==,2448579,2020-02-07T23:28:41Z,2020-02-07T23:28:41Z,MEMBER,"`sonar_data` is a numpy array; it needs to be a dask array for things to be computed lazily.

Try adding `channel = channel.chunk({""time"": 1, ""depth_bin"":-1})`. It should then ""just work"" but I haven;t looked too closely. You'll need to call `normalized_depth_data.load()` to actually get concrete values.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,561921094