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  • ktyle · 4 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
450692965 https://github.com/pydata/xarray/issues/2624#issuecomment-450692965 https://api.github.com/repos/pydata/xarray/issues/2624 MDEyOklzc3VlQ29tbWVudDQ1MDY5Mjk2NQ== ktyle 1961038 2018-12-31T21:44:39Z 2018-12-31T21:44:39Z NONE

Ok, thanks all for the advice. Clearly further subdivisions of the multi-level variables are in order.

However, working with a single level (sea-level pressure) from our CFSR datasets, I find that if I specify the chunksize on the Time dimension when using xr.open_mfdataset, the to_zarr function fails on the resulting dataset with a "non-uniform chunksize" error.

If, however, I take the resulting dataset and "re-chunk" with the .chunk method, although the two datasets "look identical", the to_zarr write succeeds.

Link to notebook:

https://nbviewer.jupyter.org/url/www.atmos.albany.edu/facstaff/ktyle/temp/Xarray_to_zarr_ex1.ipynb 

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   Xarray to Zarr error (in compress / numcodecs functions)  393214032
449146417 https://github.com/pydata/xarray/issues/2624#issuecomment-449146417 https://api.github.com/repos/pydata/xarray/issues/2624 MDEyOklzc3VlQ29tbWVudDQ0OTE0NjQxNw== ktyle 1961038 2018-12-20T21:49:15Z 2018-12-20T21:59:33Z NONE

@rabernat Yeah I think the chunksize in the time dimension is too large:

```` <xarray.Dataset>

Dimensions: (lat: 361, lev: 32, lon: 720, time: 2920) Coordinates: * lat (lat) float32 -90.0 -89.5 -89.0 -88.5 -88.0 ... 88.5 89.0 89.5 90.0 * lon (lon) float32 -180.0 -179.5 -179.0 -178.5 ... 178.5 179.0 179.5 * lev (lev) float32 1000.0 975.0 950.0 925.0 ... 50.0 30.0 20.0 10.0 * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00 Data variables: g (time, lev, lat, lon) float32 dask.array<shape=(2920, 32, 361, 720), chunksize=(1460, 32, 361, 720)> ```

 
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   Xarray to Zarr error (in compress / numcodecs functions)  393214032
363587681 https://github.com/pydata/xarray/issues/1882#issuecomment-363587681 https://api.github.com/repos/pydata/xarray/issues/1882 MDEyOklzc3VlQ29tbWVudDM2MzU4NzY4MQ== ktyle 1961038 2018-02-06T22:31:31Z 2018-02-06T22:31:31Z NONE

Although not active on the Xarray github, I am an early adopter and active user of the software and am looking for a good excuse to go to scipy for the first time ...I would be glad to assist!

 
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   xarray tutorial at SciPy 2018? 293913247
250777441 https://github.com/pydata/xarray/issues/1020#issuecomment-250777441 https://api.github.com/repos/pydata/xarray/issues/1020 MDEyOklzc3VlQ29tbWVudDI1MDc3NzQ0MQ== ktyle 1961038 2016-09-30T15:38:01Z 2016-09-30T15:38:01Z NONE

Good to know, and since the system I'm running on has 96 GB of RAM, I think your statement about pandas is correct too, as I also get the memory error when running on a smaller (18GB) dataset.

 
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   Memory error when converting dataset to dataframe 180080354

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