🩹 Fix matrix provider

changelog.md

@@ -24,6 +24,9 @@
 - 🩹 Fix result data overwritten when using multiple dataset_groups (#1147)
 - 🩹 Fix for normalization issue described in #1157 (multi-gaussian irfs and multiple time ranges (streak))
 - 🩹 Fix for crash described in #1183 when doing an optimization using more than 30 datasets (#1184)
+<!-- Fix within the 0.7.0 release cycle, therefore hidden:
+- 🩹 Fix the matrix provider alignment/reduction ('grouping') issues introduced in #1175 (#1190)
+-->
 
 
 ### 📚 Documentation

glotaran/optimization/matrix_provider.py

@@ -644,35 +644,46 @@ def calculate(self):
 
     def calculate_aligned_matrices(self):
         """Calculate the aligned matrices of the dataset group."""
-        reduced_matrices = {
-            label: self.reduce_matrix(matrix_container, self._data_provider.get_global_axis(label))
-            for label, matrix_container in self._matrix_containers.items()
-        }
         full_clp_labels = self.align_full_clp_labels()
         for i, global_index_value in enumerate(self._data_provider.aligned_global_axis):
+            matrix_containers = []
             group_label = self._data_provider.get_aligned_group_label(i)
-            self._aligned_full_clp_labels[i] = full_clp_labels[group_label]
-            group_matrix = self.align_matrices(
-                [
-                    reduced_matrices[label][index]
-                    for label, index in zip(
-                        self._data_provider.group_definitions[group_label],
-                        self._data_provider.get_aligned_dataset_indices(i),
+            for label, index in zip(
+                self._data_provider.group_definitions[group_label],
+                self._data_provider.get_aligned_dataset_indices(i),
+            ):
+                matrix_container_temp = self._matrix_containers[label]
+                if matrix_container_temp.is_index_dependent:
+                    matrix_containers.append(
+                        MatrixContainer(
+                            clp_labels=matrix_container_temp.clp_labels,
+                            matrix=matrix_container_temp.matrix[index],
+                        )
                     )
-                ],
-                [
-                    self.group.dataset_models[label].scale  # type:ignore[misc]
-                    if self.group.dataset_models[label].scale is not None
-                    else 1
-                    for label in self._data_provider.group_definitions[group_label]
-                ],
+                else:
+                    matrix_containers.append(matrix_container_temp)
+
+            matrix_scales = [
+                self.group.dataset_models[label].scale
+                if self.group.dataset_models[label].scale is not None
+                else 1
+                for label in self._data_provider.group_definitions[group_label]
+            ]
+
+            group_matrix = self.align_matrices(
+                matrix_containers, matrix_scales  # type:ignore[arg-type]
             )
 
+            self._aligned_full_clp_labels[i] = full_clp_labels[group_label]
+            group_matrix_single = self.reduce_matrix(
+                group_matrix, np.array([self._data_provider.aligned_global_axis[i]])
+            )[0]
+
             weight = self._data_provider.get_aligned_weight(i)
             if weight is not None:
-                group_matrix = group_matrix.create_weighted_matrix(weight)
+                group_matrix_single = group_matrix_single.create_weighted_matrix(weight)
 
-            self._aligned_matrices[i] = group_matrix
+            self._aligned_matrices[i] = group_matrix_single
 
     def align_full_clp_labels(self) -> dict[str, list[str]]:
         """Align the unreduced clp labels.