Update phase/compute.py

modified descriptions of percentage-based components

phase/compute.py

@@ -63,16 +63,16 @@ class ParticipationAdoptionIndex:
         effective_groups_rounded = int(round(effective_groups))
         
         self.components_1 = {
-            "PCR (Participation Coverage Ratio)": {"Value": pcr, "Description": "≈1 → Participation closely matches the target population. ≈0 → Participation reached very few of the intended population."},
+            "PCR (Participation Coverage Ratio)": {"Value": pcr, "Description": "≈100 → Participation closely matches the target population. ≈0 → Participation reached very few of the intended population."},
             "SPI-A (Silent Participation Inference - Silent Non-Adoption)": {"Value": spi_a, "Description": "High → Large segments of the target population were not reached or excluded. Low → Minimal non-participation across the target population."},
             "SPI-B (Silent Participation Inference - Silent Adoption)": {"Value": spi_b, "Description": "High → Silent participation dominates. Low → Expressive participation dominates."},
-            "PEG (Participation-to-Expression Gap)": {"Value": peg, "Description": "≈1 → High expression gap among participants. ≈0 → Minimal expression gap."}
+            "PEG (Participation-to-Expression Gap)": {"Value": peg, "Description": "≈100 → High expression gap among participants. ≈0 → Minimal expression gap."}
         }
 
         self.components_2 = {
             "PCI (Participation Concentration Index)": 
                 {"Value": pci, 
-                 "Description": "≈1 → Participation is concentrated to certain groups. ≈0 → Participation is balanced across groups."
+                 "Description": "≈100 → Participation is concentrated to certain groups. ≈0 → Participation is balanced across groups."
                 },
             "Group Summary": 
                 {"Minimum Participating Groups": f"At least {effective_groups_int} groups meaningfully participated.",