Function test_min_count_specific

xarray/tests/test_duck_array_ops.py:871–907

(dask, func, dim)

@pytest.mark.parametrize("func", ["sum", "prod"])
@pytest.mark.parametrize("dim", [None, "a", "b"])
def test_min_count_specific(dask, func, dim):
    if dask and not has_dask:
        pytest.skip("requires dask")

    # Simple array with four non-NaN values.
    da = DataArray(np.ones((6, 6), dtype=np.float64) * np.nan, dims=("a", "b"))
    da[0][0] = 2
    da[0][3] = 2
    da[3][0] = 2
    da[3][3] = 2
    if dask:
        da = da.chunk({"a": 3, "b": 3})

    # Expected result if we set min_count to the number of non-NaNs in a
    # row/column/the entire array.
    if dim:
        min_count = 2
        expected = DataArray(
            [4.0, np.nan, np.nan] * 2, dims=("a" if dim == "b" else "b",)
        )
    else:
        min_count = 4
        expected = DataArray(8.0 if func == "sum" else 16.0)

    # Check for that min_count.
    with raise_if_dask_computes():
        actual = getattr(da, func)(dim, skipna=True, min_count=min_count)
    assert_dask_array(actual, dask)
    assert_allclose(actual, expected)

    # With min_count being one higher, should get all NaN.
    min_count += 1
    expected *= np.nan
    with raise_if_dask_computes():
        actual = getattr(da, func)(dim, skipna=True, min_count=min_count)
    assert_dask_array(actual, dask)
    assert_allclose(actual, expected)


@pytest.mark.parametrize("func", ["sum", "prod"])