order of fits correspond order in graph, fit result window has correct order, see #109

src/gui_qt/main/management.py

@@ -58,11 +58,18 @@ class GraphDict(OrderedDict):
     def list(self):
         return [(k, v.title) for k, v in self.items()]
 
-    def active(self, key: str):
-        if key:
-            return [(self._data[i].id, self._data[i].name) for i in self[key]]
-        else:
+    def active(self, key: str, return_val: str = 'both'):
+        if not key:
             return []
+        else:
+            if return_val == 'both':
+                return [(self._data[i].id, self._data[i].name) for i in self[key]]
+            elif return_val == 'id':
+                return [self._data[i].id for i in self[key]]
+            elif return_val == 'name':
+                return [self._data[i].name for i in self[key]]
+            else:
+                raise ValueError(f'return_val got wrong value {return_val!r}')
 
     def current_sets(self, key: str):
         if key:
@@ -148,6 +155,10 @@ class UpperManagement(QtCore.QObject):
     def active_sets(self):
         return self.graphs.active(self.current_graph)
 
+    @property
+    def active_id(self):
+        return self.graphs.active(self.current_graph, return_val='id')
+
     def get_attributes(self, graph_id: str, attr: str) -> dict[str, Any]:
         return {self.data[i].id: getattr(self.data[i], attr) for i in self.graphs[graph_id].sets}
 
@@ -431,8 +442,17 @@ class UpperManagement(QtCore.QObject):
 
             m_complex = model_p['complex']
 
-            for set_id, set_params in model_p['parameter'].items():
+            # sets are not in active order but in order they first appeared in fit dialog
+            # iterate over order of set id in active order and access parameter inside loop
+            # instead of directly looping
+            list_ids = list(model_p['parameter'].keys())
+            set_order = [self.active_id.index(i) for i in list_ids]
+            for pos in set_order:
+                set_id = list_ids[pos]
+
                 data_i = self.data[set_id]
+                set_params = model_p['parameter'][set_id]
+
                 if we.lower() == 'deltay':
                     we = data_i.y_err**2
 
@@ -635,7 +655,7 @@ class UpperManagement(QtCore.QObject):
 
     def save_fit_parameter(self, fname: str | pathlib.Path, fit_sets: list[str] = None):
         if fit_sets is None:
-            fit_sets = [s for (s, _) in self.active_sets]
+            fit_sets = [s for s in self.active_id]
 
         for set_id in fit_sets:
             data = self.data[set_id]
@@ -1004,7 +1024,7 @@ class UpperManagement(QtCore.QObject):
     def show_statistics(self, mode):
         x, y, = [], []
 
-        for i, _ in self.active_sets:
+        for i in self.active_id:
             _temp = self.data[i]
             try:
                 x.append(float(_temp.name))
@@ -1015,7 +1035,7 @@ class UpperManagement(QtCore.QObject):
     @QtCore.pyqtSlot()
     def calc_magn(self):
         new_id = []
-        for k, _ in self.active_sets:
+        for k in self.active_id:
             dataset = self.data[k]
             if isinstance(dataset, SignalContainer):
                 new_value = dataset.copy(full=True)
@@ -1027,7 +1047,7 @@ class UpperManagement(QtCore.QObject):
     @QtCore.pyqtSlot()
     def center(self):
         new_id = []
-        for k, _ in self.active_sets:
+        for k in self.active_id:
             new_value = self.data[k].copy(full=True)
             new_value.x -= new_value.x[np.argmax(new_value.y.real)]
             new_id.append(self.add(new_value))
@@ -1066,7 +1086,7 @@ class UpperManagement(QtCore.QObject):
     def bds_deriv(self):
         new_sets = []
 
-        for (set_id, _) in self.active_sets:
+        for set_id in self.active_id:
             data_i = self.data[set_id]
             diff = data_i.data.diff(log=True)
             new_data = Points(x=diff.x, y=-np.pi/2*diff.y.real)
@@ -1093,7 +1113,7 @@ class UpperManagement(QtCore.QObject):
         self.newData.emit(new_sets, kwargs['graph'])
 
     def skip_points(self, offset: int, step: int, invert: bool = False, copy: bool = False):
-        for k, _ in self.active_sets:
+        for k in self.active_id:
             src = self.data[k]
             if invert:
                 mask = np.mod(np.arange(offset, src.x.size+offset), step) != 0