Introduce param_correlation_sets

Fix typing

src/ert/analysis/_es_update.py

@@ -478,32 +478,38 @@ def analysis_ES(
                     )
                     c_bool = c_AY > correlation_threshold
                     # Some parameters might be significantly correlated
-                    # to the exact same responses,
-                    # making up what we call a `parameter group``.
+                    # to the exact same responses.
                     # We want to call the update only once per such parameter group
                     # to speed up computation.
-                    param_groups = np.unique(c_bool, axis=0)
-
-                    # Drop the parameter group that does not correlate to any responses.
-                    row_with_all_false = np.all(~param_groups, axis=1)
-                    param_groups = param_groups[~row_with_all_false]
+                    # Here we create a collection of unique sets of parameter-to-observation
+                    # correlations.
+                    param_correlation_sets: npt.NDArray[np.bool_] = np.unique(
+                        c_bool, axis=0
+                    )
+                    # Drop the correlation set that does not correlate to any responses.
+                    row_with_all_false = np.all(~param_correlation_sets, axis=1)
+                    param_correlation_sets = param_correlation_sets[~row_with_all_false]
 
-                    for grp in param_groups:
+                    for param_correlation_set in param_correlation_sets:
                         # Find the rows matching the parameter group
-                        matching_rows = np.all(c_bool == grp, axis=1)
+                        matching_rows = np.all(c_bool == param_correlation_set, axis=1)
                         # Get the indices of the matching rows
                         row_indices = np.where(matching_rows)[0]
                         X_chunk = temp_storage[param_group.name][param_batch_idx, :][
                             row_indices, :
                         ]
-                        S_chunk = S[grp, :]
-                        observation_errors_loc = observation_errors[grp]
-                        observation_values_loc = observation_values[grp]
+                        S_chunk = S[param_correlation_set, :]
+                        observation_errors_loc = observation_errors[
+                            param_correlation_set
+                        ]
+                        observation_values_loc = observation_values[
+                            param_correlation_set
+                        ]
                         smoother.fit(
                             S_chunk,
                             observation_errors_loc,
                             observation_values_loc,
-                            noise=noise[grp],
+                            noise=noise[param_correlation_set],
                             truncation=truncation,
                             inversion=ies.InversionType(module.inversion),
                             param_ensemble=param_ensemble,