Thanks @maboualidev; I saw that @andersy005 posted about this too. I haven't had a chance to look deeply into your new package, but I am intrigued by the concept. I think patterns for working with data defined over intervals, be they in time or some other dimension, are something useful and should be explored.

1475 is a good thread in particular if you are interested in ideas for how cell boundaries (and operations that depend on them) might be represented most cleanly within xarray. Discussion there seems somewhat dormant at the moment, but I'd jump in there if you have comments, ideas, or questions.

I wanted to bring attention to a package that we are working on that originally started with remapping time axis. The package is called AxisUtilities and is available at https://github.com/coderepocenter/AxisUtilities.

It doesn’t yet have any support for CFTime yet (well it does support it now; but you need to manually convert cftime to a number for now) But we are working on it (so that the cftime to number conversion is more automatic). We have the basis there. We are now working on it to make it easier to use and remove certain steps.

It follows the ESMF or SCRIP interpolation pattern, i.e. once you make the remapper object, you could use it for multiple data set as long as the source and destination axis has not changed.

Note that at the moment, if we pass np.datetime64 objects that exceed the allowed time span, the function yields garbage without failing. Is this something we want to fix as well ?

One option is to convert array and offset to microseconds first, then compute the delta, but this may break people's code.

That would indeed be a very clean approach (I don't know why that did not occur to me earlier!). In the past that kind of conversion used to have a bug, but it has been fixed as of NumPy 1.15 (see https://github.com/numpy/numpy/issues/11096).

How about replacing array = np.asarray(pd.Series(array.ravel())).reshape(array.shape) by array = array.astype("timedelta64") ? with numpy 1.17 your example works and the test suite only fails on unrelated netcdf string errors.

Got it, thanks !

Yes, there's a simple workaround for that at least, https://github.com/pydata/xarray/pull/3631#discussion_r359325745, but I agree it would be nice if we didn't need to worry about that.

Another issue with datetime_to_numeric happens with: import xarray as xr import cftime i = xr.CFTimeIndex(xr.cftime_range('2000-01-01', periods=2)) xr.core.duck_array_ops.datetime_to_numeric(i, cftime.DatetimeGregorian(2, 1, 1), datetime_unit='D')

```python

TypeError Traceback (most recent call last) pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.array_to_timedelta64()

pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.parse_timedelta_string()

TypeError: object of type 'datetime.timedelta' has no len()

During handling of the above exception, another exception occurred:

OverflowError Traceback (most recent call last) <ipython-input-50-b03d9c4f220d> in <module> ----> 1 xr.core.duck_array_ops.datetime_to_numeric(i, cftime.DatetimeGregorian(2, 1, 1), datetime_unit='D')

~/src/xarray/xarray/core/duck_array_ops.py in datetime_to_numeric(array, offset, datetime_unit, dtype) 395 else: 396 offset = min(array) --> 397 array = array - offset 398 399 if not hasattr(array, "dtype"): # scalar is converted to 0d-array

~/src/xarray/xarray/coding/cftimeindex.py in sub(self, other) 431 432 if isinstance(other, (CFTimeIndex, cftime.datetime)): --> 433 return pd.TimedeltaIndex(np.array(self) - np.array(other)) 434 elif isinstance(other, pd.TimedeltaIndex): 435 return CFTimeIndex(np.array(self) - other.to_pytimedelta())

~/.conda/envs/xclim3/lib/python3.6/site-packages/pandas/core/indexes/timedeltas.py in new(cls, data, unit, freq, start, end, periods, closed, dtype, copy, name, verify_integrity) 256 257 tdarr = TimedeltaArray._from_sequence( --> 258 data, freq=freq, unit=unit, dtype=dtype, copy=copy 259 ) 260 return cls._simple_new(tdarr._data, freq=tdarr.freq, name=name)

~/.conda/envs/xclim3/lib/python3.6/site-packages/pandas/core/arrays/timedeltas.py in _from_sequence(cls, data, dtype, copy, freq, unit) 270 freq, freq_infer = dtl.maybe_infer_freq(freq) 271 --> 272 data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit) 273 freq, freq_infer = dtl.validate_inferred_freq(freq, inferred_freq, freq_infer) 274

~/.conda/envs/xclim3/lib/python3.6/site-packages/pandas/core/arrays/timedeltas.py in sequence_to_td64ns(data, copy, unit, errors) 971 if is_object_dtype(data.dtype) or is_string_dtype(data.dtype): 972 # no need to make a copy, need to convert if string-dtyped --> 973 data = objects_to_td64ns(data, unit=unit, errors=errors) 974 copy = False 975

~/.conda/envs/xclim3/lib/python3.6/site-packages/pandas/core/arrays/timedeltas.py in objects_to_td64ns(data, unit, errors) 1096 values = np.array(data, dtype=np.object_, copy=False) 1097 -> 1098 result = array_to_timedelta64(values, unit=unit, errors=errors) 1099 return result.view("timedelta64[ns]") 1100

pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.array_to_timedelta64()

pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.convert_to_timedelta64()

pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds()

OverflowError: Python int too large to convert to C long ```