Update phase/compute.py

placed noncomparable components into a separate dictionary (components_2)

phase/compute.py

@@ -6,7 +6,8 @@ class ParticipationAdoptionIndex:
         self.feedback_volume = feedback_volume
         self.w_pos = w_pos
         self.w_neg = w_neg
-        self.components = {}
+        self.components_1 = {}
+        self.components_2 = {}
 
     # ---------- helper functions ---------- 
     def _compute_pci(self, participants_by_group):
@@ -22,20 +23,20 @@ class ParticipationAdoptionIndex:
         return sum(s ** 2 for s in shares)
 
     def _normalize_components(self):
-        total = sum(abs(v["value"]) for v in self.components.values())
+        total = sum(abs(v["value"]) for v in self.components_1.values())
 
         if total == 0:
-            for k in self.components:
-                if self.components[k]["value"] is not None:
-                    self.components[k]["value"] = 0.0
+            for k in self.components_1:
+                if self.components_1[k]["value"] is not None:
+                    self.components_1[k]["value"] = 0.0
         else:
-            for k in self.components:
-                self.components[k]["value"] /= total
+            for k in self.components_1:
+                self.components_1[k]["value"] /= total
     
     def _sort_components(self):
         return dict(
             sorted(
-                self.components.items(),
+                self.components_1.items(),
                 key=lambda item: abs(item[1]["value"]),
                 reverse=True
             )
@@ -65,15 +66,18 @@ class ParticipationAdoptionIndex:
         # SPA: Sentiment Participation Assymetry
         # PEG: Participation-to-Expression Gap
         
-        self.components = {
+        self.components_1 = {
             "PCR": {"value": pcr, "description": "Participation Ratio"},
-            "PCI": {"value": pci, "description": "Participation Concentration"},
             "SPI-A": {"value": spi_a, "description": "Silent Non-Adoption"},
             "SPI-B": {"value": spi_b, "description": "Silent Adoption"},
-            "SPA": {"value": spa, "description": "Sentiment Asymmetry"},
             "PEG": {"value": peg, "description": "Participation-to-Expression Gap"}
         }
 
+        self.components_2 = {
+            "PCI": {"value": pci, "description": "Participation Concentration"},
+            "SPA": {"value": spa, "description": "Sentiment Asymmetry"},
+        }
+
         self._normalize_components()
 
-        return self._sort_components()
+        return self._sort_components(), self.components_2