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:

importnumpyasnpimportpandasaspdimportxarrayasxrdefrandn(shape, frac_nan=None, chunks=None, seed=0):
rng=np.random.default_rng(seed)
ifchunksisNone:
x=rng.standard_normal(shape)
else:
importdask.arrayasdarng=da.random.default_rng(seed)
x=rng.standard_normal(shape, chunks=chunks)
iffrac_nanisnotNone:
inds=rng.choice(range(x.size), int(x.size*frac_nan))
x.flat[inds] =np.nanreturnxnx=3000long_nx=30000ny=200nt=1000window=20randn_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_=20min_periods=5use_bottleneck=False%timeitds.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})
%timeitds.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)