issues

What happened?

In recent versions of numpy, calling numpy.asarray(arr, dtype=numpy.int64) will raise a warning if the input array contains numpy.nan values. This line of code is used in xarray.coding.times.cast_to_int_if_safe(num):

python def cast_to_int_if_safe(num) -> np.ndarray: int_num = np.asarray(num, dtype=np.int64) if (num == int_num).all(): num = int_num return num

The function still returns the correct True/False values regardless of the warning.

What did you expect to happen?

No warning to be printed

Minimal Complete Verifiable Example

```Python import numpy import xarray

one_day = numpy.timedelta64(1, 'D') nat = numpy.timedelta64('nat')

timedelta_values = (numpy.arange(5) * one_day).astype('timedelta64[ns]') timedelta_values[2] = nat timedelta_values[4] = nat

dataset = xarray.Dataset(data_vars={ 'timedeltas': xarray.DataArray(data=timedelta_values, dims=['x']) }) dataset.to_netcdf('out.nc') ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

```Python $ python3 safe_cast.py /home/hea211/projects/emsarray/.conda/lib/python3.10/site-packages/xarray/coding/times.py:618: RuntimeWarning: invalid value encountered in cast int_num = np.asarray(num, dtype=np.int64)

$ ncdump out.nc netcdf out { dimensions: x = 5 ; variables: double timedeltas(x) ; timedeltas:_FillValue = NaN ; timedeltas:units = "days" ; data:

timedeltas = 0, 1, _, 3, _ ; } ```

Anything else we need to know?

I saw the numpy.can_cast function and tried to use that to solve the issue (see PR #7834), however this function did not do what I expected it to.

A search for other solutions to see whether an array of floating point values is representable as integers turned up Numpy: Check if float array contains whole numbers on Stack Overflow. There are a few solutions given in that question, although each has its drawbacks. The most complete solution appears to be is_integer_ufunc, which is a ufunc written in C. Unfortunately this is not installable via pip/conda, and is not included in numpy.

Environment

In numpy >= 1.24 unsafe casting raises a RuntimeWarning for an operation that xarray does often to check if casting is safe. numpy.can_cast looks like an alternative approach designed for this exact case.

• [ ] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

A netCDF4 dataset we use has a time variable defined as: double time(time) ; time:axis = "T" ; time:bounds = "time_bnds" ; time:calendar = "gregorian" ; time:long_name = "time" ; time:standard_name = "time" ; time:units = "days since 1970-01-01 00:00:00 00" ;

Note the units attribute, specifically a timezone offset of 00 without any +- sign.

xarray can successfully open this dataset and parse the time units, making a time variable with the expeced values. However, attempting to save this dataset (e.g. after slicing some geographic bounds or selecting a subset of variables), xarray would raise an error trying to reformat the time units.

This fix applies the same logic used in the decoding step to the encoding step - specifically, attempt to use pandas but if that fails then use cftime. The decoding step catches ValueError to do this, but ValueError was not caught in the encode workflow.

• [x] Tests added
• [x] Passes pre-commit run --all-files
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst