Systematic identification of variables from an xarray Dataset #886

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1. jthielen

   added

   Type: FeatureNew functionalityNew functionality

   Area: XarrayPertains to xarray integrationPertains to xarray integration

   on Jul 10, 2018
2. dopplershift commented on Jul 11, 2018

   

   dopplershift

   on Jul 11, 2018

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   Is it worth allowing overwriting rather than telling people to do:

   my_data.attrs()['standard_name'] = 'air_temperature'
3. jthielen commented on Jul 11, 2018

   

   jthielen

   on Jul 11, 2018

   Last edited by jthielen

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   I'm not sure...that definitely seems like a good approach for DataArrays, but since (I'd presume) this is most useful on Datasets, that approach would end up like

   my_data['temperature_isobaric'].attrs()['standard_name'] = 'air_temperature'
   my_data['relative_humidity_isobaric'].attrs()['standard_name'] = 'relative_humidity'
   my_data['geopotential_height_isobaric'].attrs()['standard_name'] = 'geopotential_height'

   versus something less verbose such as

   data.metpy.parse_cf(variables={'temperature_isobaric': 'air_temperature',
                                  'relative_humidity_isobaric': 'relative_humidity',
                                  'geopotential_height_isobaric': 'geopotential_height'})

   I'd prefer the second, but what do you think?
4. dopplershift commented on Jul 11, 2018

   

   dopplershift

   on Jul 11, 2018

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   Do you have a current set of data where this feature is necessary?
5. jthielen commented on Jul 11, 2018

   

   jthielen

   on Jul 11, 2018

   Last edited by jthielen

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   In regards to the feature of systematic identification itself, it's mostly just the motivating example mentioned above at this point, but I could also see it opening up possibilities for calculations in the future if the user just passed a dataset, and the function could pull out what it needed.

   In regards to filling in the standard_name, most sets of data I've been working with would need this, especially since most of the GRIB-converted data coming from THREDDS servers I've used are missing the standard_name attribute (this includes the NARR and Irma GFS examples in staticdata). Also, no surprise, but non-post-processed WRF output seems to lack it as well.

   But, based on actually looking into this now and finding how common it is for datasets to be missing the standard_name attribute, would it be necessary to have programmatic ways of identifying the type of variable for this to be practical? Or is a different approach not based on standard_name needed? (If so either way, it seems like something that would take too much effort to be worked on right now.)