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

This adds a line plotter based on LineCollections, called .lines at the moment.

I wanted to replace darray.plot() with using LineCollection instead. But unfortunately due to how many cases are supported (and tested in xarray) darray.plot() will continue using plt.plot.

xref:

4820

5622

What happened?

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

https://github.com/pydata/xarray/blob/2120808bbe45f3d4f0b6a01cd43bac4df4039092/xarray/tests/test_groupby.py#L2016-L2025

But our typing assumes it's floats only: https://github.com/pydata/xarray/blob/2120808bbe45f3d4f0b6a01cd43bac4df4039092/xarray/core/resample.py#L69-L94

What did you expect to happen?

Since our pytests pass, mypy should pass as well.

Minimal Complete Verifiable Example

```python 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

It's been unclear to me what methods are necessary to implement or not. I think decorating with @abstractmethod will help with that. It's a breaking change though and it could be disruptive.

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

What happened: When interpolating datasets integer arrays are forced to floats.

What you expected to happen: To retain the same dtype after interpolation.

Minimal Complete Verifiable Example:

```python import numpy as np import dask.array as da a = np.arange(0, 2) b = np.core.defchararray.add("long_variable_name", a.astype(str)) coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=da.array([0, 1], dtype=int), dims=["time"], coords=coords, ) ds1 = xr.Dataset(data_vars)

print(ds1) Out[35]: <xarray.Dataset> Dimensions: (time: 4) Coordinates: * time (time) float64 0.0 0.5 1.0 2.0 Data variables: long_variable_name0 (time) int32 dask.array<chunksize=(4,), meta=np.ndarray> long_variable_name1 (time) int32 dask.array<chunksize=(4,), meta=np.ndarray>

Interpolate:

ds1 = ds1.interp( time=da.array([0, 0.5, 1, 2]), assume_sorted=True, method="linear", kwargs=dict(fill_value="extrapolate"), )

dask array thinks it's an integer array:

print(ds1.long_variable_name0) Out[55]: <xarray.DataArray 'long_variable_name0' (time: 4)> dask.array<dask_aware_interpnd, shape=(4,), dtype=int32, chunksize=(4,), chunktype=numpy.ndarray> Coordinates: * time (time) float64 0.0 0.5 1.0 2.0

But once computed it turns out is a float:

print(ds1.long_variable_name0.compute()) Out[38]: <xarray.DataArray 'long_variable_name0' (time: 4)> array([0. , 0.5, 1. , 2. ]) Coordinates: * time (time) float64 0.0 0.5 1.0 2.0

```

Anything else we need to know?: An easy first step is to also force np.float_ in da.blockwise in missing.interp_func.

The more difficult way is to somehow be able to change back the dataarrays into the old dtype without affecting performance. I did a test simply adding .astype()to the returned value in missing.interp and it doubled the calculation time.

I was thinking the conversion to floats in scipy could be avoided altogether by adding a (non-)public option to ignore any dtype checks and just let the user handle the "unsafe" interpolations.

Related: https://github.com/scipy/scipy/issues/11093

Environment: