ffill's tolerance argument can be strings

[Illviljan]

What happened?

ffill, bfill reindex etc. have tolerance arguments that also supports strings. And we test for it here:

xarray/xarray/tests/test_groupby.py

Lines 2016 to 2025 in 2120808

	def test_upsample_tolerance(self):
	# Test tolerance keyword for upsample methods bfill, pad, nearest
	times = pd.date_range("2000-01-01", freq="1D", periods=2)
	times_upsampled = pd.date_range("2000-01-01", freq="6h", periods=5)
	array = DataArray(np.arange(2), [("time", times)])
	# Forward fill
	actual = array.resample(time="6h").ffill(tolerance="12h")
	expected = DataArray([0.0, 0.0, 0.0, np.nan, 1.0], [("time", times_upsampled)])
	assert_identical(expected, actual)

But our typing assumes it's floats only:

xarray/xarray/core/resample.py

Lines 69 to 94 in 2120808

	def pad(self, tolerance: float | Iterable[float] | None = None) -> T_Xarray:
	"""Forward fill new values at up-sampled frequency.
	Parameters
	----------
	tolerance : float | Iterable[float] | None, default: None
	Maximum distance between original and new labels to limit
	the up-sampling method.
	Up-sampled data with indices that satisfy the equation
	``abs(index[indexer] - target) <= tolerance`` are filled by
	new values. Data with indices that are outside the given
	tolerance are filled with ``NaN`` s.
	Returns
	-------
	padded : DataArray or Dataset
	"""
	obj = self._drop_coords()
	(grouper,) = self.groupers
	return obj.reindex(
	{self._dim: grouper.full_index}, method="pad", tolerance=tolerance
	)
	ffill = pad
	def backfill(self, tolerance: float | Iterable[float] | None = None) -> T_Xarray:

What did you expect to happen?

Since our pytests pass, mypy should pass as well.

Minimal Complete Verifiable Example

import numpy as np
import pandas as pd

import xarray as xr

# https://github.com/pydata/xarray/blob/2120808bbe45f3d4f0b6a01cd43bac4df4039092/xarray/tests/test_groupby.py#L2016
# Test tolerance keyword for upsample methods bfill, pad, nearest
times = pd.date_range("2000-01-01", freq="1D", periods=2)
times_upsampled = pd.date_range("2000-01-01", freq="6h", periods=5)
array = xr.DataArray(np.arange(2), [("time", times)])

# Forward fill
actual = array.resample(time="6h").ffill(tolerance="12h")
expected = xr.DataArray([0.0, 0.0, 0.0, np.nan, 1.0], [("time", times_upsampled)])
xr.testing.assert_identical(expected, actual)

Environment

master

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