* invert distances after checking for outliers

* check for outliers in weights
mesoscope · Oct 9, 2023 · bff176e · bff176e
1 parent 469ebe6
commit bff176e
Showing 1 changed file with 12 additions and 4 deletions.
diff --git a/cellpack/autopack/Gradient.py b/cellpack/autopack/Gradient.py
@@ -207,14 +207,16 @@ def set_weights_by_mode(self):
 
         self.scaled_distances = self.get_normalized_values(self.distances)
 
-        if self.invert == "distance":
-            self.scaled_distances = 1.0 - self.scaled_distances
-
-        if (max(self.scaled_distances) > 1.0) or (min(self.scaled_distances) < 0.0):
+        if (numpy.nanmax(self.scaled_distances) > 1.0) or (
+            numpy.nanmin(self.scaled_distances) < 0.0
+        ):
             raise ValueError(
                 "CHECK CALCULATED DISTANCES",
                 f"Max: {max(self.scaled_distances)}, Min: {min(self.scaled_distances)}",
             )
+
+        if self.invert == "distance":
+            self.scaled_distances = 1.0 - self.scaled_distances
         if self.weight_mode == "linear":
             self.weight = 1.0 - self.scaled_distances
         elif self.weight_mode == "square":
@@ -232,6 +234,12 @@ def set_weights_by_mode(self):
         # normalize the weight
         self.weight = self.get_normalized_values(self.weight)
 
+        if (numpy.nanmax(self.weight) > 1.0) or (numpy.nanmin(self.weight) < 0.0):
+            raise ValueError(
+                "CHECK CALCULATED WEIGHTS",
+                f"Max: {max(self.weight)}, Min: {min(self.weight)}",
+            )
+
         self.weight[numpy.isnan(self.weight)] = 0
 
         if self.invert == "weight":