(
n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
)
| 1607 | @pytest.mark.parametrize("n", [0, 1, 2]) |
| 1608 | @pytest.mark.parametrize("dim", [None, "time"]) |
| 1609 | def test_cov( |
| 1610 | n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray] |
| 1611 | ) -> None: |
| 1612 | da_a, da_b = array_tuples[n] |
| 1613 | |
| 1614 | if dim is not None: |
| 1615 | |
| 1616 | def np_cov_ind(ts1, ts2, a, x): |
| 1617 | # Ensure the ts are aligned and missing values ignored |
| 1618 | ts1, ts2 = broadcast(ts1, ts2) |
| 1619 | valid_values = ts1.notnull() & ts2.notnull() |
| 1620 | |
| 1621 | # While dropping isn't ideal here, numpy will return nan |
| 1622 | # if any segment contains a NaN. |
| 1623 | ts1 = ts1.where(valid_values) |
| 1624 | ts2 = ts2.where(valid_values) |
| 1625 | |
| 1626 | return np.ma.cov( |
| 1627 | np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()), |
| 1628 | np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()), |
| 1629 | ddof=ddof, |
| 1630 | )[0, 1] |
| 1631 | |
| 1632 | expected = np.zeros((3, 4)) |
| 1633 | for a in [0, 1, 2]: |
| 1634 | for x in [0, 1, 2, 3]: |
| 1635 | expected[a, x] = np_cov_ind(da_a, da_b, a=a, x=x) |
| 1636 | actual = xr.cov(da_a, da_b, dim=dim, ddof=ddof) |
| 1637 | assert_allclose(actual, expected) |
| 1638 | |
| 1639 | else: |
| 1640 | |
| 1641 | def np_cov(ts1, ts2): |
| 1642 | # Ensure the ts are aligned and missing values ignored |
| 1643 | ts1, ts2 = broadcast(ts1, ts2) |
| 1644 | valid_values = ts1.notnull() & ts2.notnull() |
| 1645 | |
| 1646 | ts1 = ts1.where(valid_values) |
| 1647 | ts2 = ts2.where(valid_values) |
| 1648 | |
| 1649 | return np.ma.cov( |
| 1650 | np.ma.masked_invalid(ts1.data.flatten()), |
| 1651 | np.ma.masked_invalid(ts2.data.flatten()), |
| 1652 | ddof=ddof, |
| 1653 | )[0, 1] |
| 1654 | |
| 1655 | expected = np_cov(da_a, da_b) |
| 1656 | actual = xr.cov(da_a, da_b, dim=dim, ddof=ddof) |
| 1657 | assert_allclose(actual, expected) |
| 1658 | |
| 1659 | |
| 1660 | @pytest.mark.parametrize("n", [0, 1, 2]) |
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