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  • xarray · 1 ✖
id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
1114351614 I_kwDOAMm_X85Ca6f- 6191 [Bug]: reading NaT/NaN on M1 ARM chip philippemiron 387624 closed 0     8 2022-01-25T20:52:39Z 2023-09-17T08:15:28Z 2023-09-17T08:15:28Z NONE      

What happened?

I have nan values in a date vector stored in a netCDF. When I read on my ARM Apple computer with xr.open_dataset(), it is not properly recognized.

For example, the following data is stored in a NetCDF: date = pd.date_range(...) date[4] = nan

Then when I read the file: date[4] is set to date[0], which is the first date of the range instead of a 'NaT'.

I understand that this issue is quite weird and it doesn't seem to happen on other OS. Actually, I try on MacOS (with an intel processor) and on two different Linux computers, and in those configurations, date[4] is properly set to 'NaT' after opening the netCDF with xr.open_dataset(). Note that I tried with the same version of xarray as well as with different versions, and I just can't seem to reproduce this issue on any machine except on the M1 ARM chip.

What did you expect to happen?

I expect the following result after running the minimal example:

array(['2022-01-01T00:00:00.000000000', '2022-01-02T00:00:00.000000000', '2022-01-03T00:00:00.000000000', '2022-01-04T00:00:00.000000000', 'NaT', '2022-01-06T00:00:00.000000000', '2022-01-07T00:00:00.000000000', '2022-01-08T00:00:00.000000000', '2022-01-09T00:00:00.000000000', '2022-01-10T00:00:00.000000000'], dtype='datetime64[ns]')

Minimal Complete Verifiable Example

```python import xarray as xr import pandas as pd import numpy as np

time = pd.date_range(start="2022-01-01",end="2022-01-10").to_pydatetime() time[4] = np.datetime64("NaT")

ds = xr.Dataset( data_vars=dict( time=(["nt"], time), ), ) ds.to_netcdf('test.nc')

ds_r = xr.open_dataset('test.nc') ds_r.time ```

Relevant log output

python array(['2022-01-01T00:00:00.000000000', '2022-01-02T00:00:00.000000000', '2022-01-03T00:00:00.000000000', '2022-01-04T00:00:00.000000000', '2022-01-01T00:00:00.000000000', '2022-01-06T00:00:00.000000000', '2022-01-07T00:00:00.000000000', '2022-01-08T00:00:00.000000000', '2022-01-09T00:00:00.000000000', '2022-01-10T00:00:00.000000000'], dtype='datetime64[ns]')

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS

commit: None python: 3.10.1 | packaged by conda-forge | (main, Dec 22 2021, 01:38:36) [Clang 11.1.0 ] python-bits: 64 OS: Darwin OS-release: 21.2.0 machine: arm64 processor: arm byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.1 libnetcdf: 4.8.1

xarray: 0.20.2 pandas: 1.3.5 numpy: 1.21.5 scipy: 1.7.3 netCDF4: 1.5.8 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.5.1.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2021.12.0 distributed: 2021.12.0 matplotlib: 3.5.1 cartopy: 0.20.1 seaborn: None numbagg: None fsspec: 2021.11.1 cupy: None pint: None sparse: None setuptools: 60.0.4 pip: 21.3.1 conda: None pytest: None IPython: 8.0.0 sphinx: None

 
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   completed xarray 13221727 issue

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