Add persona_weights support to PopulationNetwork

Fixes:
- Added persona_weights parameter to PopulationNetwork.__init__
- Updated _generate_population_variants to use custom weights when provided
- This allows Opinion Equilibria page to use custom persona distributions

Files modified:
- src/influence/population_network.py: Added persona_weights support

Note: Sidebar 'Web App' label issue persists - pages.toml may need Streamlit >1.40
to be fully functional. This is a cosmetic issue only.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

src/influence/population_network.py

@@ -24,6 +24,7 @@ class PopulationNetwork:
         homophily: float = 0.5,
         variation_level: VariationLevel = VariationLevel.MODERATE,
         random_seed: int = None,
+        persona_weights: Dict[str, float] = None,
     ):
         """
         Initialize population network.
@@ -35,12 +36,14 @@ class PopulationNetwork:
             homophily: Homophily parameter (0-1, higher = more clustering)
             variation_level: How much to vary persona characteristics
             random_seed: Random seed for reproducibility
+            persona_weights: Optional custom distribution weights for personas
         """
         self.base_personas = base_personas
         self.population_size = population_size
         self.network_type = network_type
         self.homophily = homophily
         self.variation_level = variation_level
+        self.persona_weights = persona_weights
 
         if random_seed is not None:
             random.seed(random_seed)
@@ -65,20 +68,33 @@ class PopulationNetwork:
         """Generate population variants from base personas"""
         variants = []
 
-        # Distribute population across base personas
-        variants_per_base = self.population_size // len(self.base_personas)
-        remainder = self.population_size % len(self.base_personas)
-
-        for i, base_persona in enumerate(self.base_personas):
-            # Generate variants for this base persona
-            count = variants_per_base + (1 if i < remainder else 0)
-
-            generator = VariantGenerator(base_persona, self.variation_level)
-            persona_variants = [
-                generator.generate_variant(f"_v{len(variants) + j}")
-                for j in range(count)
-            ]
-            variants.extend(persona_variants)
+        if self.persona_weights:
+            # Use custom weights to distribute population
+            for base_persona in self.base_personas:
+                weight = self.persona_weights.get(base_persona.persona_id, 0)
+                count = int(round(weight * self.population_size))
+
+                generator = VariantGenerator(base_persona, self.variation_level)
+                persona_variants = [
+                    generator.generate_variant(f"_v{len(variants) + j}")
+                    for j in range(count)
+                ]
+                variants.extend(persona_variants)
+        else:
+            # Distribute population evenly across base personas
+            variants_per_base = self.population_size // len(self.base_personas)
+            remainder = self.population_size % len(self.base_personas)
+
+            for i, base_persona in enumerate(self.base_personas):
+                # Generate variants for this base persona
+                count = variants_per_base + (1 if i < remainder else 0)
+
+                generator = VariantGenerator(base_persona, self.variation_level)
+                persona_variants = [
+                    generator.generate_variant(f"_v{len(variants) + j}")
+                    for j in range(count)
+                ]
+                variants.extend(persona_variants)
 
         return variants