Comments (3)
not sure you should expect the quantization to leave nans alone, unless the _FillValue or missing_value is set to nan
from netcdf4-python.
it turns out that even if the _FillValue is set to nan, the same problem occurs.
however, you can use
data = np.ma.masked_invalid(np.array([5.3, 6.2, 7.3, np.nan]))
and you end up with
netcdf test {
dimensions:
x = 4 ;
variables:
float tas(x) ;
tas:_QuantizeGranularBitRoundNumberOfSignificantDigits = 4 ;
data:
tas = 5.299805, 6.200195, 7.299805, _ ;
}
from netcdf4-python.
did you report this upstream?
from netcdf4-python.
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