Open Census MCP Server

#!/usr/bin/env python3 import json from pathlib import Path def extract_geography_weights(): """Extract geography weights from complete enriched universe.""" input_file = "2023_ACS_Enriched_Universe.json" output_file = "../concepts/geo_similarity_scalars.json" print(f"📊 Reading complete weighted file: {input_file}") try: with open(input_file, 'r') as f: data = json.load(f) except FileNotFoundError: print(f"❌ File not found: {input_file}") return except json.JSONDecodeError: print(f"❌ Invalid JSON in: {input_file}") return print(f"✅ Loaded data structure") # Extract geography weights geo_weights = {} processed = 0 # Check data structure if 'variables' in data: variables = data['variables'] print(f"📋 Found {len(variables)} variables in 'variables' key") elif isinstance(data, dict): variables = data print(f"📋 Found {len(variables)} variables at root level") else: print(f"❌ Unexpected data structure: {type(data)}") return # Extract geography weights from each variable for var_id, var_data in variables.items(): if isinstance(var_data, dict): # Look for linear geography weight first linear_weights = var_data.get('category_weights_linear', {}) if 'geography' in linear_weights: geo_weights[var_id] = linear_weights['geography'] processed += 1 else: # Fallback to log weights if linear not available log_weights = var_data.get('category_weights_log', {}) if 'geography' in log_weights: geo_weights[var_id] = log_weights['geography'] processed += 1 print(f"✅ Extracted geography weights for {processed:,} variables") if processed == 0: print("❌ No geography weights found - check data structure") # Debug: show sample variable structure sample_vars = list(variables.items())[:3] for var_id, var_data in sample_vars: print(f"Sample variable {var_id}:") if isinstance(var_data, dict): print(f" Keys: {list(var_data.keys())}") else: print(f" Type: {type(var_data)}") return # Create output directory Path("data").mkdir(exist_ok=True) # Save geography weights with open(output_file, 'w') as f: json.dump(geo_weights, f, indent=2, sort_keys=True) print(f"💾 Saved geography weights to {output_file}") # Show statistics if geo_weights: weights = list(geo_weights.values()) min_weight = min(weights) max_weight = max(weights) avg_weight = sum(weights) / len(weights) print(f"\n📊 Geography Weight Statistics:") print(f" Variables: {len(geo_weights):,}") print(f" Min weight: {min_weight:.3f}") print(f" Max weight: {max_weight:.3f}") print(f" Avg weight: {avg_weight:.3f}") # Weight distribution low_spatial = sum(1 for w in weights if w < 0.30) med_spatial = sum(1 for w in weights if 0.30 <= w < 0.38) high_spatial = sum(1 for w in weights if w >= 0.38) print(f"\n🗺️ Spatial Variance Categories:") print(f" Low spatial variation (<0.30): {low_spatial:,} variables ({low_spatial/len(weights)*100:.1f}%)") print(f" Medium spatial variation (0.30-0.38): {med_spatial:,} variables ({med_spatial/len(weights)*100:.1f}%)") print(f" High spatial variation (>=0.38): {high_spatial:,} variables ({high_spatial/len(weights)*100:.1f}%)") print(f"\n✅ Ready to update GeoAdvisor with complete geography weights!") if __name__ == "__main__": extract_geography_weights()