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  • patrickcgray · 3 ✖

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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
625290593 https://github.com/pydata/xarray/issues/3996#issuecomment-625290593 https://api.github.com/repos/pydata/xarray/issues/3996 MDEyOklzc3VlQ29tbWVudDYyNTI5MDU5Mw== patrickcgray 2497349 2020-05-07T14:30:18Z 2020-05-07T14:30:18Z NONE

Hi @dcherian thanks for the help, though this method seems a bit clunky it worked well and was reasonably fast.

 
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   MODIS L2 Data Missing Data Variables and Geolocation Data 605608998
618478015 https://github.com/pydata/xarray/issues/3996#issuecomment-618478015 https://api.github.com/repos/pydata/xarray/issues/3996 MDEyOklzc3VlQ29tbWVudDYxODQ3ODAxNQ== patrickcgray 2497349 2020-04-23T15:49:46Z 2020-04-23T15:49:46Z NONE

Thanks for the help @dcherian, that does work to get at the variables, such as xds = xr.open_dataset(fn, group='geophysical_data'), but then it is missing all the coordinate data from thenavigation_data` group and other groups. Is there a preferred way (or just a good example) of opening multiple groups and merging them into a final dataset?

 
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   MODIS L2 Data Missing Data Variables and Geolocation Data 605608998
545986180 https://github.com/pydata/xarray/issues/1115#issuecomment-545986180 https://api.github.com/repos/pydata/xarray/issues/1115 MDEyOklzc3VlQ29tbWVudDU0NTk4NjE4MA== patrickcgray 2497349 2019-10-24T15:59:35Z 2019-10-24T15:59:35Z NONE

I see that this PR never made it through and there is a somewhat similar PR finished here: https://github.com/pydata/xarray/pull/2350 though it doesn't do exactly what was proposed in this PR. Is there a suggested approach for performing cross-correlation on multiple DataArray?

 
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   Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339

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