Continue to use nanosecond-precision Timestamps in precision-sensitive areas

[spencerkclark]

This addresses the remaining cftime-related test failures in #7707 by introducing a function that always returns a nanosecond-precision Timestamp object. Despite no corresponding test failures, for safety I grepped and went ahead and replaced the pd.Timestamp constructor with this function in a few other areas. I also updated our documentation to replace any mentions of the "Timestamp-valid range" with "nanosecond-precision range" since Timestamps are now more flexible, and included a note that we have an issue open for relaxing this nanosecond-precision assumption in xarray eventually.

While in principle I think it would be fine if CFTimeIndex.to_datetimeindex returned a DatetimeIndex with non-nanosecond-precision values, since we don't use to_datetimeindex anywhere outside of tests, in its current state it was returning nonsense values:

>>> import pandas as pd
>>> import xarray as xr
>>> times = xr.cftime_range("0001", periods=5)
>>> times.to_datetimeindex()
DatetimeIndex(['1754-08-30 22:43:41.128654848',
               '1754-08-31 22:43:41.128654848',
               '1754-09-01 22:43:41.128654848',
               '1754-09-02 22:43:41.128654848',
               '1754-09-03 22:43:41.128654848'],
              dtype='datetime64[ns]', freq=None)

This is due to the assumption in cftime_to_nptime that the resulting array will have nanosecond-precision values. We can (and should) address this eventually, but for the sake of quickly supporting pandas version two I decided to be conservative and punt this off to be part of #7493. cftime_to_nptime is used in places other than to_datetimeindex, so modifying it has other impacts downstream.

[keewis]

to properly test this, I guess we'd need to merge #7724 first?

[jsignell]

to properly test this, I guess we'd need to merge #7724 first?

Otherwise the env in the upstream test will never solve right?

[keewis]

sorry, I forgot about being able to use the upstream-dev environment for testing. That should solve just fine since by design it ignores all pinned dependencies, but we currently get occasional segfaults and unrelated failing tests, and we only test on a single OS / python version.

[spencerkclark]

I'm fine waiting until #7724 is merged to let our main CI cover this. Indeed the upstream tests are flaky. Locally I just installed pandas 2 via pip to do testing during development.

[dcherian]

RTD failures are real:

WARNING: [autosummary] failed to import xarray.CFTimeIndex.is_all_dates.
Possible hints:
* ImportError: 
* AttributeError: type object 'CFTimeIndex' has no attribute 'is_all_dates'
* ModuleNotFoundError: No module named 'xarray.CFTimeIndex'
WARNING: [autosummary] failed to import xarray.CFTimeIndex.is_mixed.
Possible hints:
* ImportError: 
* AttributeError: type object 'CFTimeIndex' has no attribute 'is_mixed'
* ModuleNotFoundError: No module named 'xarray.CFTimeIndex'
WARNING: [autosummary] failed to import xarray.CFTimeIndex.is_monotonic.
Possible hints:
* ImportError: 
* AttributeError: type object 'CFTimeIndex' has no attribute 'is_monotonic'
* ModuleNotFoundError: No module named 'xarray.CFTimeIndex'
WARNING: [autosummary] failed to import xarray.CFTimeIndex.is_type_compatible.
Possible hints:
* AttributeError: type object 'CFTimeIndex' has no attribute 'is_type_compatible'
* ImportError: 
* ModuleNotFoundError: No module named 'xarray.CFTimeIndex'
WARNING: [autosummary] failed to import xarray.CFTimeIndex.set_value.
Possible hints:
* ImportError: 
* AttributeError: type object 'CFTimeIndex' has no attribute 'set_value'
* ModuleNotFoundError: No module named 'xarray.CFTimeIndex'
WARNING: [autosummary] failed to import xarray.CFTimeIndex.to_native_types.
Possible hints:
* ImportError: 
* AttributeError: type object 'CFTimeIndex' has no attribute 'to_native_types'
* ModuleNotFoundError: No module named 'xarray.CFTimeIndex'

[spencerkclark]

Thanks all for the help! Fingers crossed things should be all green now. Happy to address any more review comments.

[dcherian]

There are a bunch of warnings in the tests that could be silenced:

D:\a\xarray\xarray\xarray\tests\test_dataset.py:516: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.

But we can also just merge quickly to get a release out

[spencerkclark]

Thanks for noting that @dcherian -- I think I got to all of them now.

[dcherian]

Thanks for patiently working through this Spencer. I'll merge now, and then we can release tomorrow.