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  • Read NetCDF files with multiple values in missing_value attribute · 5 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
122333205 https://github.com/pydata/xarray/issues/471#issuecomment-122333205 https://api.github.com/repos/pydata/xarray/issues/471 MDEyOklzc3VlQ29tbWVudDEyMjMzMzIwNQ== shoyer 1217238 2015-07-17T16:27:50Z 2015-07-17T16:27:50Z MEMBER

Awesome, thanks!

This is where you'll find the decoding logic: https://github.com/xray/xray/blob/v0.5.2/xray/conventions.py#L663

 
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   Read NetCDF files with multiple values in missing_value attribute 94966000
122332344 https://github.com/pydata/xarray/issues/471#issuecomment-122332344 https://api.github.com/repos/pydata/xarray/issues/471 MDEyOklzc3VlQ29tbWVudDEyMjMzMjM0NA== jjhelmus 1050278 2015-07-17T16:23:34Z 2015-07-17T16:23:34Z CONTRIBUTOR

Sounds like a good solution. I'll work on a PR.

 
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   Read NetCDF files with multiple values in missing_value attribute 94966000
122331414 https://github.com/pydata/xarray/issues/471#issuecomment-122331414 https://api.github.com/repos/pydata/xarray/issues/471 MDEyOklzc3VlQ29tbWVudDEyMjMzMTQxNA== shoyer 1217238 2015-07-17T16:18:58Z 2015-07-17T16:18:58Z MEMBER

What about issuing a warning, and then decoding the duplicate values to NaN anyways? I'm usually not a fan of warnings, but my guess is that this would be the most helpful behavior for users.

 
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   Read NetCDF files with multiple values in missing_value attribute 94966000
122294746 https://github.com/pydata/xarray/issues/471#issuecomment-122294746 https://api.github.com/repos/pydata/xarray/issues/471 MDEyOklzc3VlQ29tbWVudDEyMjI5NDc0Ng== jjhelmus 1050278 2015-07-17T14:30:15Z 2015-07-17T14:30:15Z CONTRIBUTOR

Yes, the two values in the missing_value attribute indicate two classes of data (not collected vs below minimum detectable threshold) and these data can be access by setting mask_and_scale=False but this also results in a valid data being returned without scaling which makes it less useful.

My question is how should xray should handle these cases? Either replace all instances of the values in missing_value with NaN or raise a error message stating that multiple missing_values are not supported similar? I'd be happy to create a PR implementing either case.

 
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   Read NetCDF files with multiple values in missing_value attribute 94966000
121283168 https://github.com/pydata/xarray/issues/471#issuecomment-121283168 https://api.github.com/repos/pydata/xarray/issues/471 MDEyOklzc3VlQ29tbWVudDEyMTI4MzE2OA== shoyer 1217238 2015-07-14T15:32:54Z 2015-07-14T15:32:54Z MEMBER

You can still read these files if you set mask_and_scale=False in open_dataset -- it just won't decode the missing values for you.

Arguably, we should raise an error message saying just that.

From a practical perspective, are you interested in the different types of these missing values? Presumably there is some reason why they were coded differently in the first place. 

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   Read NetCDF files with multiple values in missing_value attribute 94966000

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