| 1667 | if dim is not None: |
| 1668 | |
| 1669 | def np_corr_ind(ts1, ts2, a, x): |
| 1670 | # Ensure the ts are aligned and missing values ignored |
| 1671 | ts1, ts2 = broadcast(ts1, ts2) |
| 1672 | valid_values = ts1.notnull() & ts2.notnull() |
| 1673 | |
| 1674 | ts1 = ts1.where(valid_values) |
| 1675 | ts2 = ts2.where(valid_values) |
| 1676 | |
| 1677 | return np.ma.corrcoef( |
| 1678 | np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()), |
| 1679 | np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()), |
| 1680 | )[0, 1] |
| 1681 | |
| 1682 | expected = np.zeros((3, 4)) |
| 1683 | for a in [0, 1, 2]: |