Drop row with all False to avoid grp.sum() > 0

src/ert/analysis/_es_update.py

@@ -444,33 +444,38 @@ def analysis_ES(
                     )
                     c_bool = c_AY > correlation_threshold
                     # Some parameters might be significantly correlated to the exact same
-                    # responses, making up what we cann a parameter group.
+                    # responses, making up what we call a `parameter group``.
                     # 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 == False, axis=1)
+                    param_groups = param_groups[~row_with_all_false]
+
                     for grp in param_groups:
-                        if grp.sum() > 0:
-                            param_idx = np.where((c_bool == grp).all(axis=1))[0]
-                            # A_chunk = A[param_batch_idx, :][param_idx, :]
-                            X_chunk = temp_storage[parameter.name][param_batch_idx, :][
-                                param_idx, :
-                            ]
-                            Y_chunk = Y[grp, :]
-                            observation_errors_loc = observation_errors[grp]
-                            observation_values_loc = observation_values[grp]
-                            smoother.fit(
-                                Y_chunk,
-                                observation_errors_loc,
-                                observation_values_loc,
-                                noise=noise[grp],
-                                truncation=truncation,
-                                inversion=ies.InversionType(module.inversion),
-                                param_ensemble=param_ensemble,
-                            )
-                            temp_storage[parameter.name][
-                                param_batch_idx[param_idx], :
-                            ] = smoother.update(X_chunk)
+                        # Find the rows matching the parameter group
+                        matching_rows = np.all(c_bool == grp, axis=1)
+                        # Get the indices of the matching rows
+                        row_indices = np.where(matching_rows)[0]
+                        X_chunk = temp_storage[parameter.name][param_batch_idx, :][
+                            row_indices, :
+                        ]
+                        Y_chunk = Y[grp, :]
+                        observation_errors_loc = observation_errors[grp]
+                        observation_values_loc = observation_values[grp]
+                        smoother.fit(
+                            Y_chunk,
+                            observation_errors_loc,
+                            observation_values_loc,
+                            noise=noise[grp],
+                            truncation=truncation,
+                            inversion=ies.InversionType(module.inversion),
+                            param_ensemble=param_ensemble,
+                        )
+                        temp_storage[parameter.name][
+                            param_batch_idx[row_indices], :
+                        ] = smoother.update(X_chunk)
         else:
             for parameter in update_step.parameters:
                 smoother.fit(