CF coordinates get forgotten after operation modifying coordinates
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调研方向
Start by reproducing the shown DataArray example and compare d.cf.coordinates with d.mean("time").cf.coordinates. Trace how CF coordinates are identified before and after the time reduction; done means the reduced result keeps TLON as longitude and TLAT as latitude without adding ULON or ULAT.
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描述
I have a DataArray that's similar, but not quite the same as the popds example dataset (the axes aren't labelled, and it has a time dimension):
<xarray.DataArray 'Tair_m' (time: 1, nj: 1080, ni: 1440)>
array([[[nan, nan, ..., nan, nan],
[nan, nan, ..., nan, nan],
...,
[nan, nan, ..., nan, nan],
[nan, nan, ..., nan, nan]]], dtype=float32)
Coordinates:
* time (time) datetime64[ns] 2255-01-01
TLON (nj, ni) float32 nan nan nan nan nan nan ... nan nan nan nan nan
TLAT (nj, ni) float32 nan nan nan nan nan nan ... nan nan nan nan nan
ULON (nj, ni) float32 nan nan nan nan nan nan ... nan nan nan nan nan
ULAT (nj, ni) float32 nan nan nan nan nan nan ... nan nan nan nan nan
Dimensions without coordinates: nj, ni
Attributes:
units: C
long_name: air temperature
cell_measures: area: tarea
cell_methods: time: mean
time_rep: averaged
And of course, the coordinates get correctly decoded:
>>> d.cf.coordinates
{'longitude': ['TLON'], 'latitude': ['TLAT']}
But if I take the time mean of this dataset, the correct coordinates get forgotten:
>>> d.mean("time").cf.coordinates
{'longitude': ['TLON', 'ULON'], 'latitude': ['TLAT', 'ULAT']}
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