Skip dask rolling

[Illviljan]

Skip the rolling tests using dask so the CI becomes usable again.

• Related to ASV Benchmark warning and timeouts #9890

Test with terrible performance, feel free to fix it and reactivate this test:

import numpy as np
import pandas as pd

import xarray as xr


def randn(shape, frac_nan=None, chunks=None, seed=0):
    rng = np.random.default_rng(seed)
    if chunks is None:
        x = rng.standard_normal(shape)
    else:
        import dask.array as da

        rng = da.random.default_rng(seed)
        x = rng.standard_normal(shape, chunks=chunks)

    if frac_nan is not None:
        inds = rng.choice(range(x.size), int(x.size * frac_nan))
        x.flat[inds] = np.nan

    return x


nx = 3000
long_nx = 30000
ny = 200
nt = 1000
window = 20

randn_xy = randn((nx, ny), frac_nan=0.1)
randn_xt = randn((nx, nt))
randn_t = randn((nt,))
randn_long = randn((long_nx,), frac_nan=0.1)


ds = xr.Dataset(
    {
        "var1": (("x", "y"), randn_xy),
        "var2": (("x", "t"), randn_xt),
        "var3": (("t",), randn_t),
    },
    coords={
        "x": np.arange(nx),
        "y": np.linspace(0, 1, ny),
        "t": pd.date_range("1970-01-01", periods=nt, freq="D"),
        "x_coords": ("x", np.linspace(1.1, 2.1, nx)),
    },
)
window_ = 20
min_periods = 5
use_bottleneck = False
%timeit ds.rolling(x=window_, center=False, min_periods=min_periods).reduce(np.nansum).load()
# 601 ms ± 43.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)


ds = ds.chunk({"x": 100, "y": 50, "t": 50})
%timeit ds.rolling(x=window_, center=False, min_periods=min_periods).reduce(np.nansum).load()
# 1min 9s ± 1.31 s per loop (mean ± std. dev. of 7 runs, 1 loop each)