Data units is incorrectly infered
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 48/100
Research direction
Reproduce the issue using the seasonal prediction dataset and inspect the Julia dataset expressions shown for ds[Variable="t2m"] and ds[Variable="v10"]. Trace how variable properties are assigned from the source metadata. Done means each variable retains its own units, long_name, standard_name, and related properties instead of inheriting another variable's values.
Written by the indexing model from the issue text.
Description
I have a seasonal prediction dataset with 16 variables. It seems that thay all inherit the same definition, which is one of the 16 variables.
What we should get:
for ivar in list(ds.keys()):
print(ivar, ds[ivar].attrs)
rsn {'units': 'kg m**-3', 'long_name': 'Snow density'}
tclw {'units': 'kg m**-2', 'long_name': 'Total column cloud liquid water'}
tciw {'units': 'kg m**-2', 'long_name': 'Total column cloud ice water'}
sd {'units': 'm of water equivalent', 'long_name': 'Snow depth', 'standard_name': 'lwe_thickness_of_surface_snow_amount'}
lsp {'units': 'm', 'long_name': 'Large-scale precipitation', 'standard_name': 'lwe_thickness_of_stratiform_precipitation_amount'}
cp {'units': 'm', 'long_name': 'Convective precipitation', 'standard_name': 'lwe_thickness_of_convective_precipitation_amount'}
sf {'units': 'm of water equivalent', 'long_name': 'Snowfall', 'standard_name': 'lwe_thickness_of_snowfall_amount'}
u10 {'units': 'm s**-1', 'long_name': '10 metre U wind component'}
v10 {'units': 'm s**-1', 'long_name': '10 metre V wind component'}
t2m {'units': 'K', 'long_name': '2 metre temperature'}
d2m {'units': 'K', 'long_name': '2 metre dewpoint temperature'}
tp {'units': 'm', 'long_name': 'Total precipitation'}
fg10 {'units': 'm s**-1', 'long_name': '10 metre wind gust since previous post-processing'}
mx2t24 {'units': 'K', 'long_name': 'Maximum temperature at 2 metres in the last 24 hours'}
mn2t24 {'units': 'K', 'long_name': 'Minimum temperature at 2 metres in the last 24 hours'}
mean2t24 {'units': 'K', 'long_name': 'Mean temperature at 2 metres in the last 24 hours'}
What we get instead:
ds[Variable="t2m"].properties
Dict{String, Any} with 7 entries:
"missing_value" => -32767
"units" => "K"
"add_offset" => 279.534
"long_name" => "Maximum temperature at 2 metres in the last 24 hours"
"scale_factor" => 0.00103738
"standard_name" => "lwe_thickness_of_snowfall_amount"
"_FillValue" => -32767
ds[Variable="v10"].properties
Dict{String, Any} with 7 entries:
"missing_value" => -32767
"units" => "K"
"add_offset" => 279.534
"long_name" => "Maximum temperature at 2 metres in the last 24 hours"
"scale_factor" => 0.00103738
"standard_name" => "lwe_thickness_of_snowfall_amount"
"_FillValue" => -32767
- Dominant language
- Julia
- Stars
- 132
- Forks
- 25
- PR merge metrics
- No merged PRs in 30d
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