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  • philippemiron · 3 ✖

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  • [Bug]: reading NaT/NaN on M1 ARM chip · 3 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1134796696 https://github.com/pydata/xarray/issues/6191#issuecomment-1134796696 https://api.github.com/repos/pydata/xarray/issues/6191 IC_kwDOAMm_X85Do5-Y philippemiron 387624 2022-05-23T15:08:27Z 2022-05-23T15:09:48Z NONE

It is replaced by the first value of the array. If you change to:

time = pd.date_range(start="2022-01-02",end="2022-01-11").to_pydatetime()

the NaT is replaced by '2022-01-02T00:00:00.000000000'. Maybe it is stored internally as a time_origin and some time_delta, and the NaT are replaced by 0?

 
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   [Bug]: reading NaT/NaN on M1 ARM chip 1114351614
1022224801 https://github.com/pydata/xarray/issues/6191#issuecomment-1022224801 https://api.github.com/repos/pydata/xarray/issues/6191 IC_kwDOAMm_X8487emh philippemiron 387624 2022-01-26T14:01:43Z 2022-01-26T14:02:43Z NONE

I'm actually using miniforge which natively supports ARM64. Uninstalling netCDF4 does not fix the issue. And actually, opening the same file as follow:

from netCDF4 import Dataset f = Dataset('test.nc') f['time'][:] gives the expected results (dates are not recognized but the nan is there): masked_array(data=[0.0, 1.0, 2.0, 3.0, --, 5.0, 6.0, 7.0, 8.0, 9.0], mask=[False, False, False, False, True, False, False, False, False, False], fill_value=nan)

 
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   [Bug]: reading NaT/NaN on M1 ARM chip 1114351614
1021611725 https://github.com/pydata/xarray/issues/6191#issuecomment-1021611725 https://api.github.com/repos/pydata/xarray/issues/6191 IC_kwDOAMm_X8485I7N philippemiron 387624 2022-01-25T21:10:28Z 2022-01-25T21:10:28Z NONE

So far I haven't spotted any other issues with libnetcdf.

 
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   [Bug]: reading NaT/NaN on M1 ARM chip 1114351614

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