Function test_covcorr_consistency

xarray/tests/test_computation.py:1711–1727

(
    n: int, dim: str | None, array_tuples: tuple[xr.DataArray, xr.DataArray]
)

@pytest.mark.parametrize("n", range(9))
@pytest.mark.parametrize("dim", [None, "time", "x"])
def test_covcorr_consistency(
    n: int, dim: str | None, array_tuples: tuple[xr.DataArray, xr.DataArray]
) -> None:
    da_a, da_b = array_tuples[n]
    # Testing that xr.corr and xr.cov are consistent with each other
    # 1. Broadcast the two arrays
    da_a, da_b = broadcast(da_a, da_b)
    # 2. Ignore the nans
    valid_values = da_a.notnull() & da_b.notnull()
    da_a = da_a.where(valid_values)
    da_b = da_b.where(valid_values)

    expected = xr.cov(da_a, da_b, dim=dim, ddof=0) / (
        da_a.std(dim=dim) * da_b.std(dim=dim)
    )
    actual = xr.corr(da_a, da_b, dim=dim)
    assert_allclose(actual, expected)


@requires_dask