html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/6191#issuecomment-1209664972,https://api.github.com/repos/pydata/xarray/issues/6191,1209664972,IC_kwDOAMm_X85IGgXM,868027,2022-08-09T17:30:07Z,2022-08-09T17:30:07Z,CONTRIBUTOR,"Some additional info for when how to figure out the best way to address this.

For the decode using pandas approach, two things I tried worked: using a pandas.array with a nullable integer data type, or simulating what happens on x86_64 systems by checking for nans in the incoming array and setting those positions to `numpy.iinfo(np.int64).min`.

the pandas nullable integer array:
```python

    # note that is a capital i Int64 to use the nullable type.
    flat_num_dates_ns_int = pd.array(flat_num_dates * _NS_PER_TIME_DELTA[delta], dtype=""Int64"")
```
simulate x86:
```python

    flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
        np.int64
    )

    flat_num_dates_ns_int[np.isnan(flat_num_dates)] = np.iinfo(np.int64).min
```

The pandas solution is explicitly experimental in their docs, and the emulate version just feels ""hacky"" to me. These don't break any existing tests on my local machine.

cftime itself has no support for nan type missing values and will fail:

(on x86_64)
```python
>>> import numpy as np
>>> from xarray.coding.times import decode_cf_datetime
>>> decode_cf_datetime(np.array([0, np.nan]), ""days since 1950-01-01"", use_cftime=True)
Traceback (most recent call last):
  File ""<stdin>"", line 1, in <module>
  File ""/home/abarna/.pyenv/versions/3.8.5/lib/python3.8/site-packages/xarray/coding/times.py"", line 248, in decode_cf_datetime
    dates = _decode_datetime_with_cftime(flat_num_dates, units, calendar)
  File ""/home/abarna/.pyenv/versions/3.8.5/lib/python3.8/site-packages/xarray/coding/times.py"", line 164, in _decode_datetime_with_cftime
    cftime.num2date(num_dates, units, calendar, only_use_cftime_datetimes=True)
  File ""src/cftime/_cftime.pyx"", line 484, in cftime._cftime.num2date
TypeError: unsupported operand type(s) for +: 'cftime._cftime.DatetimeGregorian' and 'NoneType'
```

cftime is happy with masked arrays:
```python

>>> import cftime
>>> a1 = np.ma.masked_invalid(np.array([0, np.nan]))
>>> cftime.num2date(a1, ""days since 1950-01-01"")
masked_array(data=[cftime.DatetimeGregorian(1950, 1, 1, 0, 0, 0, 0), --],
             mask=[False,  True],
       fill_value='?',
            dtype=object)
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1114351614
https://github.com/pydata/xarray/issues/6191#issuecomment-1209567966,https://api.github.com/repos/pydata/xarray/issues/6191,1209567966,IC_kwDOAMm_X85IGIre,868027,2022-08-09T15:52:31Z,2022-08-09T15:52:31Z,CONTRIBUTOR,"I got caught by this one yesterday on an M1 machine. I did some digging and found what I think to be the underlying issue. The short explanation is that the time conversion functions do an `astype(np.int64)` or equivalent cast on arrays that contain nans. This is [undefined behavior](https://github.com/numpy/numpy/issues/13101#issuecomment-740058842) and very soon, doing this will[ start to emit RuntimeWarnings](https://github.com/numpy/numpy/pull/21437).

I knew from my own data files that it wasn't the first element of the array being substituted but whatever was in the units as the epoch. I started to poke at the xarray internals (and the CFtime internals) to try to get a minimal example working, eventually found the following:

On an M1:
```python

>>> from xarray.coding.times import _decode_datetime_with_pandas
>>> import numpy as np
>>> _decode_datetime_with_pandas(np.array([20000, float('nan')]),  ""days since 1950-01-01"", ""proleptic_gregorian"")
array(['2004-10-04T00:00:00.000000000', '1950-01-01T00:00:00.000000000'],
      dtype='datetime64[ns]')
>>> np.array(np.nan).astype(np.int64)
array(0)
```

On an x86_64:
```python

>>> from xarray.coding.times import _decode_datetime_with_pandas
>>> import numpy as np
>>> _decode_datetime_with_pandas(np.array([20000, float('nan')]),  ""days since 1950-01-01"", ""proleptic_gregorian"")
array(['2004-10-04T00:00:00.000000000',                           'NaT'],
      dtype='datetime64[ns]')
>>> np.array(np.nan).astype(np.int64)
array(-9223372036854775808)
```

This issue is not Apple/M1/clang specific, I tested on an aws graviton (arm) instance and got the same results with ubuntu/gcc:
```python
Python 3.10.4 (main, Jun 29 2022, 12:14:53) [GCC 11.2.0] on linux
Type ""help"", ""copyright"", ""credits"" or ""license"" for more information.
>>> from xarray.coding.times import _decode_datetime_with_pandas
>>> import numpy as np
>>> _decode_datetime_with_pandas(np.array([20000, float('nan')]),  ""days since 1950-01-01"", ""proleptic_gregorian"")
array(['2004-10-04T00:00:00.000000000', '1950-01-01T00:00:00.000000000'],
      dtype='datetime64[ns]')
>>> np.array(np.nan).astype(np.int64)
array(0)
```

Here is where the cast is happening on the internal xarray implementation, CFtime has similar casts in its implementation.
https://github.com/pydata/xarray/blob/8417f495e6b81a60833f86a978e5a8080a619aa0/xarray/coding/times.py#L237-L239","{""total_count"": 4, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 2, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1114351614