Refactors excluded points handling in diffraction minimization

Moves the logic for updating excluded points from minimization to ExcludedRegions, ensuring better modularity.

Author: AndrewSazonov

src/easydiffraction/analysis/minimization.py

@@ -121,21 +121,6 @@ def _residual_function(self,
         # Sync parameters back to objects
         self.minimizer._sync_result_to_parameters(parameters, engine_params)
 
-        # Update the excluded points in experiments
-        # TODO: This should not be handled here every time
-        #  This implementation is just very quick and dirty
-        for experiment in experiments._items.values():
-            experiment.datastore.pattern.excluded = np.full(experiment.datastore.pattern.x.shape,
-                                                            fill_value=False,
-                                                            dtype=bool)  # Reset excluded points
-            excluded_regions = experiment.excluded_regions._items  # List of excluded regions
-            if excluded_regions:  # If there are any excluded regions
-                for idx, point in enumerate(experiment.datastore.pattern.x):  # Set excluded points
-                    for region in excluded_regions.values():
-                        if region.minimum.value <= point <= region.maximum.value:
-                            experiment.datastore.pattern.excluded[idx] = True
-                            break
-
         # Prepare weights for joint fitting
         num_expts: int = len(experiments.ids)
         if weights is None:
@@ -161,13 +146,7 @@ def _residual_function(self,
             y_meas: np.ndarray = experiment.datastore.pattern.meas
             y_meas_su: np.ndarray = experiment.datastore.pattern.meas_su
             excluded: np.ndarray = experiment.datastore.pattern.excluded
-            # TODO: Excluded points must be handled differently.
-            #  They should not contribute to the residuals.
-            diff = np.where(
-                excluded,
-                0.0,  # Excluded points contribute zero to the residuals
-                (y_meas - y_calc) / y_meas_su  # Normalized residuals
-            )
+            diff = ((y_meas - y_calc) / y_meas_su)[~excluded]  # Exclude points that are marked as excluded
             diff *= np.sqrt(weight)  # Residuals are squared before going into reduced chi-squared
             residuals.extend(diff)
 

src/easydiffraction/experiments/collections/excluded_regions.py

@@ -1,3 +1,4 @@
+import numpy as np
 from typing import Type
 
 from easydiffraction.core.objects import (
@@ -44,7 +45,7 @@ def __init__(self,
 
 class ExcludedRegions(Collection):
     """
-    Collection of LinkedPhase instances.
+    Collection of ExcludedRegion instances.
     """
     @property
     def _type(self) -> str:
@@ -53,3 +54,31 @@ def _type(self) -> str:
     @property
     def _child_class(self) -> Type[ExcludedRegion]:
         return ExcludedRegion
+
+    def on_item_added(self, item: ExcludedRegion) -> None:
+        """
+        Called when a new item is added to the collection.
+        """
+        # Update the excluded points in experiments
+        # TODO: This implementation is very quick and dirty
+        #  It should be improved to only update the points that are affected
+        #  by the new excluded region, not all of them
+
+        #expt_name = self.datablock_id
+        #minimum = item.minimum.value
+        #maximum = item.maximum.value
+
+        experiment = self._parent
+        excluded_regions = experiment.excluded_regions._items  # List of excluded regions
+
+        if excluded_regions:  # If there are any excluded regions
+            pattern = experiment.datastore.pattern
+            pattern.excluded = np.full(pattern.x.shape,
+                                       fill_value=False,
+                                       dtype=bool)  # Reset excluded points
+
+            for idx, point in enumerate(pattern.x):  # Set excluded points
+                for region in excluded_regions.values():
+                    if region.minimum.value <= point <= region.maximum.value:
+                        experiment.datastore.pattern.excluded[idx] = True
+                        break