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  • DocOtak · 2 ✖

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  • Read grid mapping and bounds as coords · 2 ✖

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  • CONTRIBUTOR · 2 ✖
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670991386 https://github.com/pydata/xarray/pull/2844#issuecomment-670991386 https://api.github.com/repos/pydata/xarray/issues/2844 MDEyOklzc3VlQ29tbWVudDY3MDk5MTM4Ng== DocOtak 868027 2020-08-09T01:11:09Z 2020-08-09T01:11:09Z CONTRIBUTOR

Yes, my view is that things in ancillary_variables should stay in the attrs of their variable (DataArray) and not moved to coords. Currently this PR will remove the ancillary_variables from the attrs of the variables in a file which have it. This appears to break CF defined connection between associated variables (like uncertainty and QC). While the information isn't lost, I would need to look in encoding to get it. It looks like the first reply in #4215 also didn't like putting ancillary_variables in the coords.

What would be really awesome is some sort of variable proxy I could replace the string names with actual references/pointers to the correct DataArray in the Dataset. 

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   Read grid mapping and bounds as coords 424265093
670808763 https://github.com/pydata/xarray/pull/2844#issuecomment-670808763 https://api.github.com/repos/pydata/xarray/issues/2844 MDEyOklzc3VlQ29tbWVudDY3MDgwODc2Mw== DocOtak 868027 2020-08-08T02:12:08Z 2020-08-08T02:12:08Z CONTRIBUTOR

I decided to try out this PR on some of the data files we are working with at my data office. In our datasets we have per variable quality flag information per variable uncertainty information. The CF way of tying all these together is via the ancillary_variables attribute. This PR pulls all these out into the Dataset coordinates. Since in the xarray data model (right now) the coordinates apply to an entire dataset, this feels inappropriate and maybe even breaking. The ancillary_variables attribute is not used in CF grid mapping or bounds as far as I can tell.

Here is an example using this PR (note that all the varN type names will be replaced with better variable names before we publish these):

```python In [1]: import xarray as xr

In [2]: ds = xr.open_dataset("examples/converted/06AQ19840719.nc")

In [3]: ds Out[3]: <xarray.Dataset> Dimensions: (N_LEVELS: 24, N_PROF: 38) Coordinates: var1_qc (N_PROF, N_LEVELS) float32 ... var4_qc (N_PROF, N_LEVELS) float32 ... var5_qc (N_PROF, N_LEVELS) float32 ... var6_qc (N_PROF, N_LEVELS) float32 ... var7_qc (N_PROF, N_LEVELS) float32 ... var8_qc (N_PROF, N_LEVELS) float32 ... var9_qc (N_PROF, N_LEVELS) float32 ... var10_qc (N_PROF, N_LEVELS) float32 ... var11_qc (N_PROF, N_LEVELS) float32 ... var12_qc (N_PROF, N_LEVELS) float32 ... var13_qc (N_PROF, N_LEVELS) float32 ... var14_qc (N_PROF, N_LEVELS) float32 ... var15_qc (N_PROF, N_LEVELS) float32 ... pressure (N_PROF, N_LEVELS) float64 ... latitude (N_PROF) float64 ... longitude (N_PROF) float64 ... time (N_PROF) datetime64[ns] ... expocode (N_PROF) object ... station (N_PROF) object ... cast (N_PROF) int8 ... sample (N_PROF, N_LEVELS) object ... Dimensions without coordinates: N_LEVELS, N_PROF Data variables: var0 (N_PROF) object ... var1 (N_PROF, N_LEVELS) object ... var2 (N_PROF) float32 ... var3 (N_PROF, N_LEVELS) float32 ... var4 (N_PROF, N_LEVELS) float32 ... var5 (N_PROF, N_LEVELS) float32 ... var6 (N_PROF, N_LEVELS) float32 ... var7 (N_PROF, N_LEVELS) float32 ... var8 (N_PROF, N_LEVELS) float32 ... var9 (N_PROF, N_LEVELS) float32 ... var10 (N_PROF, N_LEVELS) float32 ... var11 (N_PROF, N_LEVELS) float32 ... var12 (N_PROF, N_LEVELS) float32 ... var13 (N_PROF, N_LEVELS) float32 ... var14 (N_PROF, N_LEVELS) float32 ... var15 (N_PROF, N_LEVELS) float32 ... Attributes: Conventions: CF-1.8 CCHDO-0.1.dev157+g52933e0.d20200707 ```

This looks especially confusing when you ask for one specific variable:

python In [15]: ds.var6 Out[15]: <xarray.DataArray 'var6' (N_PROF: 38, N_LEVELS: 24)> array([[33.3965, 33.5742, 34.8769, ..., 34.9858, 34.9852, nan], [33.1129, 34.0742, 34.6595, ..., nan, nan, nan], [32.5328, 33.2687, 34.2262, ..., nan, nan, nan], ..., [35.0686, 35.09 , 35.1415, ..., nan, nan, nan], [35.0303, 35.0295, 35.0715, ..., nan, nan, nan], [35.0682, 35.0756, 35.0622, ..., nan, nan, nan]], dtype=float32) Coordinates: var1_qc (N_PROF, N_LEVELS) float32 ... var4_qc (N_PROF, N_LEVELS) float32 ... var5_qc (N_PROF, N_LEVELS) float32 ... var6_qc (N_PROF, N_LEVELS) float32 ... var7_qc (N_PROF, N_LEVELS) float32 ... var8_qc (N_PROF, N_LEVELS) float32 ... var9_qc (N_PROF, N_LEVELS) float32 ... var10_qc (N_PROF, N_LEVELS) float32 ... var11_qc (N_PROF, N_LEVELS) float32 ... var12_qc (N_PROF, N_LEVELS) float32 ... var13_qc (N_PROF, N_LEVELS) float32 ... var14_qc (N_PROF, N_LEVELS) float32 ... var15_qc (N_PROF, N_LEVELS) float32 ... pressure (N_PROF, N_LEVELS) float64 ... latitude (N_PROF) float64 ... longitude (N_PROF) float64 ... time (N_PROF) datetime64[ns] ... expocode (N_PROF) object ... station (N_PROF) object ... cast (N_PROF) int8 ... sample (N_PROF, N_LEVELS) object ... Dimensions without coordinates: N_PROF, N_LEVELS Attributes: whp_name: CTDSAL whp_unit: PSS-78 standard_name: sea_water_practical_salinity units: 1 reference_scale: PSS-78

 
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   Read grid mapping and bounds as coords 424265093

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