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/2256#issuecomment-449081085,https://api.github.com/repos/pydata/xarray/issues/2256,449081085,MDEyOklzc3VlQ29tbWVudDQ0OTA4MTA4NQ==,1197350,2018-12-20T17:49:13Z,2018-12-20T17:49:13Z,MEMBER,I'm going to close this. Please feel free to reopen if more discussion is needed.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,336458472
https://github.com/pydata/xarray/issues/2256#issuecomment-401087741,https://api.github.com/repos/pydata/xarray/issues/2256,401087741,MDEyOklzc3VlQ29tbWVudDQwMTA4Nzc0MQ==,1197350,2018-06-28T16:07:02Z,2018-06-28T16:07:02Z,MEMBER,"Zarr is most useful for very large, homogeneous arrays. The argo data are not that large, and are inhomogeneous. So I'm not sure zarr will really help you out that much here.

In your original post, you said you were doing ""cloud processing"", but later you referred to a cluster filesystem. Do you plan to put this data in object storage?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,336458472
https://github.com/pydata/xarray/issues/2256#issuecomment-400906996,https://api.github.com/repos/pydata/xarray/issues/2256,400906996,MDEyOklzc3VlQ29tbWVudDQwMDkwNjk5Ng==,1197350,2018-06-28T04:27:38Z,2018-06-28T04:27:38Z,MEMBER,"Thanks for the extra info!

I am still confused about what you are trying to achieve. What do you mean by ""cache""? Is your goal to compress the data so that it uses less space on disk? Or is it to provide a more ""analysis ready"" format?

In other words, why do you feel you need to transform this data to zarr? Why not just work directly with the netcdf files?

Sorry to keep asking questions rather than providing any answers! Just trying to understand your goals...","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,336458472
https://github.com/pydata/xarray/issues/2256#issuecomment-400905950,https://api.github.com/repos/pydata/xarray/issues/2256,400905950,MDEyOklzc3VlQ29tbWVudDQwMDkwNTk1MA==,1197350,2018-06-28T04:18:56Z,2018-06-28T04:18:56Z,MEMBER,"FYI, I edited your comment to place the output in block quotes (triple ``` before and after) so it is more readable.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,336458472
https://github.com/pydata/xarray/issues/2256#issuecomment-400902555,https://api.github.com/repos/pydata/xarray/issues/2256,400902555,MDEyOklzc3VlQ29tbWVudDQwMDkwMjU1NQ==,1197350,2018-06-28T03:51:34Z,2018-06-28T03:51:34Z,MEMBER,"Can you clarify what you are trying to achieve with the transformations?

Why not do something like this?
```python
for file in filenames:
    ds = xr.open_dataset(file)
    ds.to_zarr(file + '.zarr')
```

I'm particularly confused by this line:
```
cycles[int(ds.CYCLE_NUMBER.values[0])-1]=ds
```
Could it be that you are describing the ""straight pickle to zarr array"" workflow you referred to in your earlier post? This is definitely an unconventional and not recommended way to interface xarray with zarr. It would be better to use the built-in `.to_zarr()` function. We can help you debug why that isn't working well, but we need more information.

Specifically:

Could you please post the repr of a single netcdf dataset from this collection, i.e.
```python
ds = xr.open_dataset('file.nc')
print(ds)
```

Then could you call `ds.to_zarr()` and describe the contents of the resulting zarr store in more detail? (For example, could you list the directories within the store?)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,336458472
https://github.com/pydata/xarray/issues/2256#issuecomment-400899756,https://api.github.com/repos/pydata/xarray/issues/2256,400899756,MDEyOklzc3VlQ29tbWVudDQwMDg5OTc1Ng==,1197350,2018-06-28T03:31:34Z,2018-06-28T03:31:34Z,MEMBER,"I think this effort should be of great interest to a lot of computational oceanographers. I have worked a lot with both Argo data and zarr, but never yet tried to combine them.

I would recommend reading this guide if you have not done so already:
http://pangeo-data.org/data.html#guide-to-preparing-cloud-optimized-data

Then could you post the xarray repr of one of the netcdf files you are working with here? i.e.
```
ds = xr.open_dataset('file.nc')
print(ds)
```

And then finally post the full code you are using to read, transform, and output the zarr data.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,336458472