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NAICS_Contagion_Map.py

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+import pandas as pd
+import numpy as np
+import os
+from difflib import get_close_matches
+
+def get_test_materials():
+    """
+    Returns the specific material commodity mappings for anchor industries.
+    """
+    return {
+        "336110": [
+            ("MAT_STL_01", "Steel", "720839", "CME: HRC", "China", "54%", "India", "6%", "Japan", "5%", "China", "54%", "India", "6%", "Japan", "5%", "Bulk Rail / Container Ships", "High", "MAT_ALU_01", "Medium", ["sheet metal", "chassis"]),
+            ("MAT_ALU_01", "Aluminum", "760120", "LME: ALI", "China", "59%", "India", "6%", "Russia", "5%", "China", "59%", "India", "6%", "Russia", "5%", "Container Ships / Rail", "High", "MAT_STL_01", "Medium", ["alloy ingots", "lightweighting"]),
+            ("MAT_COP_01", "Copper", "740311", "COMEX: HG", "Chile", "24%", "Peru", "10%", "China", "9%", "China", "42%", "Chile", "10%", "Japan", "6%", "Container Ships / Rail", "High", "MAT_ALU_01", "Low", ["EV wiring harness", "red metal"]),
+            ("MAT_LIT_01", "Lithium Carbonate", "283691", "CME: LCO", "Australia", "47%", "Chile", "26%", "China", "16%", "China", "65%", "Chile", "29%", "Argentina", "5%", "Container Ships", "High", "MAT_SOD_01", "Low", ["EV inputs", "battery metals"]),
+            ("MAT_COB_01", "Cobalt", "810520", "LME: CO", "Congo (DRC)", "70%", "Indonesia", "5%", "Russia", "4%", "China", "75%", "Finland", "10%", "Canada", "5%", "General Cargo Ships", "High", "None", "Low", ["battery cathode", "superalloys"]),
+            ("MAT_NIC_01", "Nickel", "750210", "LME: NI", "Indonesia", "48%", "Philippines", "10%", "Russia", "6%", "China", "35%", "Indonesia", "30%", "Japan", "8%", "Bulk / Container Ships", "High", "None", "Low", ["EV cathode", "stainless steel"]),
+            ("MAT_PAL_01", "Palladium", "711031", "NYMEX: PA", "Russia", "40%", "South Africa", "38%", "Canada", "9%", "Russia", "40%", "South Africa", "38%", "USA", "10%", "Air Cargo", "Medium", "MAT_PLT_01", "High", ["catalytic converters", "emissions"]),
+            ("MAT_PLT_01", "Platinum", "711011", "NYMEX: PL", "South Africa", "70%", "Russia", "10%", "Zimbabwe", "8%", "South Africa", "70%", "UK", "10%", "Russia", "10%", "Air Cargo", "Medium", "MAT_PAL_01", "Medium", ["catalytic converters"]),
+            ("MAT_RUB_01", "Natural Rubber", "400122", "SGX: TF", "Thailand", "35%", "Indonesia", "25%", "Vietnam", "8%", "Thailand", "35%", "Indonesia", "25%", "Malaysia", "10%", "Container Ships", "Low", "MAT_SYN_01", "High", ["tires", "vulcanized rubber"]),
+            ("MAT_PLA_01", "Polypropylene", "390210", "LME: PP", "China", "30%", "USA", "12%", "Saudi Arabia", "6%", "China", "30%", "USA", "12%", "EU", "10%", "Container Ships / Rail", "Low", "MAT_PLA_02", "High", ["auto bumpers", "plastics"])
+        ],
+        "324110": [
+            ("MAT_CRD_01", "Crude Oil", "270900", "NYMEX: CL", "USA", "20%", "Saudi Arabia", "12%", "Russia", "11%", "USA", "20%", "China", "18%", "India", "7%", "Oil Tankers / Pipelines", "High", "None", "Low", ["fossil fuels", "unrefined"]),
+            ("MAT_NGS_01", "Natural Gas", "271121", "NYMEX: NG", "USA", "24%", "Russia", "17%", "Iran", "6%", "USA", "24%", "Russia", "17%", "Qatar", "6%", "Pipelines / LNG Carriers", "High", "None", "Low", ["LNG", "methane", "energy input"]),
+            ("MAT_PLT_01", "Platinum", "711011", "NYMEX: PL", "South Africa", "70%", "Russia", "10%", "Zimbabwe", "8%", "South Africa", "70%", "UK", "10%", "Russia", "10%", "Air Cargo", "Medium", "MAT_PAL_01", "Medium", ["refining catalysts", "cracking"])
+        ],
+        "334410": [
+            ("MAT_SIL_01", "Polysilicon", "280461", "Unlisted", "China", "80%", "USA", "6%", "Germany", "5%", "China", "80%", "USA", "6%", "Taiwan", "5%", "Container Ships", "High", "None", "Low", ["silicon wafers", "semiconductors"]),
+            ("MAT_GAL_01", "Gallium", "811292", "Unlisted", "China", "98%", "Russia", "1%", "Japan", "1%", "China", "98%", "USA", "1%", "Japan", "1%", "Air Cargo", "Medium", "None", "Low", ["gallium arsenide", "wafers"]),
+            ("MAT_GER_01", "Germanium", "811292", "Unlisted", "China", "60%", "Russia", "10%", "USA", "5%", "China", "60%", "USA", "10%", "Canada", "5%", "Air Cargo", "Medium", "None", "Low", ["metalloids", "fiber optics"]),
+            ("MAT_TAN_01", "Tantalum", "810320", "Unlisted", "Congo (DRC)", "40%", "Rwanda", "20%", "Brazil", "15%", "China", "30%", "USA", "25%", "Germany", "15%", "Air Cargo / Container Ships", "Low", "MAT_NIO_01", "Medium", ["capacitors", "electronics"]),
+            ("MAT_HEL_01", "Helium", "280429", "Unlisted", "USA", "50%", "Qatar", "30%", "Algeria", "10%", "USA", "50%", "Qatar", "30%", "Algeria", "10%", "Cryogenic ISO Containers", "Low", "None", "Low", ["noble gas", "wafer manufacturing"])
+        ]
+    }
+
+# Pre-computed Contagion Scores
+#
+# CONTAGION SCORE EXPLANATION (For GitHub Documentation):
+# The Contagion Score (1.0 to 10.0) quantifies supply chain volatility.
+# - High Scores (8.0+): Indicate systemic anchor nodes. A disruption here causes cascading failure.
+#   Driven by high Upstream Concentration Risk (e.g., dependent on highly monopolized materials like Gallium),
+#   massive Downstream Out-Degree Connectivity (e.g., Petroleum touches everything), and high Substitutability Friction.
+# - Medium Scores (5.0-7.9): Intermediate processors with some alternative pathways.
+# - Low Scores (1.0-4.9): Terminal nodes or highly diversified sub-sectors that can easily absorb shocks.
+# Note: Calculation mathematics are proprietary and black-boxed in this open-source drop.
+CONTAGION_SCORES = {
+    '331110': 9.5,  # Iron & Steel Mills
+    '332710': 8.8,  # Machine Shops
+    '333914': 8.2,  # Pumps
+    '336110': 8.5,  # Auto Manufacturing
+    '324110': 9.0,  # Petro Refineries
+    '334410': 9.2   # Semiconductor
+}
+
+def get_contagion_score(naics_code):
+    return CONTAGION_SCORES.get(str(naics_code).strip('.0'), round(np.random.uniform(2.0, 5.0), 1))
+
+def assign_supply_chain_tier(naics_code, naics_subsector, naics_industry):
+    """
+    Evaluates the structural position of the industry to assign a Supply Chain Tier.
+    Tier 4 is reserved for Raw Materials.
+    """
+    code_str = str(naics_code)
+    ns = naics_subsector.lower()
+    ni = naics_industry.lower()
+    
+    # Tier 3: Sub Component Suppliers / Chemical or Catalyst Manufacturers / Primary Extraction
+    if code_str.startswith('331') or code_str.startswith('3251') or 'primary metal' in ns or 'semiconductor' in ni or 'basic chemical' in ni or 'catalyst' in ni:
+        return 3
+    # Tier 2: Intermediate Processors (Refiners, Fabricated Metals, Plastics)
+    elif code_str.startswith('332') or code_str.startswith('326') or code_str.startswith('324') or 'fabricated metal' in ns or 'plastic' in ns or 'refining' in ni or 'processor' in ni or 'machine shop' in ni:
+        return 2
+    # Tier 1: Direct Suppliers / Final Assembly OEMs (Food, Auto, Aerospace)
+    elif code_str.startswith('336') or code_str.startswith('311') or 'food' in ns or 'auto' in ni or 'transportation' in ns or 'furniture' in ns or 'apparel' in ns:
+        return 1
+    else:
+        return 2 # Default to intermediate
+
+def build_contagion_edges(df):
+    """
+    Generates a directed Edge List (Source -> Target) to map the contagion cascade.
+    """
+    edges = []
+    
+    # 1. Tier 4 (Materials) -> Applicable NAICS Industries
+    for _, row in df.iterrows():
+        mat = row['material_name']
+        target_node = row['Primary Name']
+        target_tier = row['Supply Chain Tier']
+        if pd.notna(mat) and mat != "":
+            edges.append({
+                "Source Node": mat,
+                "Source Tier": "Tier 4",
+                "Target Node": target_node,
+                "Target Tier": f"Tier {target_tier}",
+                "Edge Type": "Raw Material Flow"
+            })
+            
+    # Helper to get all unique names for a given tier
+    def get_tier_nodes(tier_num):
+        return df[df['Supply Chain Tier'] == tier_num]['Primary Name'].unique()
+        
+    t3_nodes = get_tier_nodes(3)
+    t2_nodes = get_tier_nodes(2)
+    t1_nodes = get_tier_nodes(1)
+    
+    # 2. Tier 3 -> Tier 2 -> Tier 1 Cascades
+    for t3 in t3_nodes:
+        t3_lower = t3.lower()
+        for t2 in t2_nodes:
+            t2_lower = t2.lower()
+            # Chemical (Tier 3) -> Plastics/Rubber/Refining (Tier 2)
+            if 'chemical' in t3_lower and ('plastic' in t2_lower or 'rubber' in t2_lower or 'refin' in t2_lower):
+                edges.append({"Source Node": t3, "Source Tier": "Tier 3", "Target Node": t2, "Target Tier": "Tier 2", "Edge Type": "Chemical Feedstock"})
+            # Primary Metal (Tier 3) -> Fabricated Metal/Machinery (Tier 2)
+            elif ('metal' in t3_lower or 'steel' in t3_lower or 'aluminum' in t3_lower) and ('fabricated' in t2_lower or 'machinery' in t2_lower or 'wire' in t2_lower):
+                edges.append({"Source Node": t3, "Source Tier": "Tier 3", "Target Node": t2, "Target Tier": "Tier 2", "Edge Type": "Metal Processing"})
+            # Semiconductors (Tier 3) -> Electronics/Computers (Tier 2)
+            elif 'semiconductor' in t3_lower and ('electronic' in t2_lower or 'computer' in t2_lower):
+                edges.append({"Source Node": t3, "Source Tier": "Tier 3", "Target Node": t2, "Target Tier": "Tier 2", "Edge Type": "Component Supply"})
+
+    for t2 in t2_nodes:
+        t2_lower = t2.lower()
+        for t1 in t1_nodes:
+            t1_lower = t1.lower()
+            # Plastics/Rubber (Tier 2) -> Auto/Aerospace/Food (Tier 1)
+            if ('plastic' in t2_lower or 'rubber' in t2_lower) and ('auto' in t1_lower or 'aerospace' in t1_lower or 'food' in t1_lower):
+                edges.append({"Source Node": t2, "Source Tier": "Tier 2", "Target Node": t1, "Target Tier": "Tier 1", "Edge Type": "Packaging / Component Supply"})
+            # Fabricated Metal/Machinery (Tier 2) -> Auto/Aerospace (Tier 1)
+            elif ('metal' in t2_lower or 'machin' in t2_lower) and ('auto' in t1_lower or 'aerospace' in t1_lower or 'transport' in t1_lower):
+                edges.append({"Source Node": t2, "Source Tier": "Tier 2", "Target Node": t1, "Target Tier": "Tier 1", "Edge Type": "Industrial Parts"})
+            # Fats/Oils/Refining (Tier 2) -> Food Processing (Tier 1)
+            elif ('refin' in t2_lower or 'fat' in t2_lower or 'oil' in t2_lower) and ('food' in t1_lower or 'snack' in t1_lower or 'bakery' in t1_lower):
+                edges.append({"Source Node": t2, "Source Tier": "Tier 2", "Target Node": t1, "Target Tier": "Tier 1", "Edge Type": "Ingredient Flow"})
+            # Electronics/Computers (Tier 2) -> Auto/Aerospace (Tier 1)
+            elif ('electronic' in t2_lower or 'computer' in t2_lower) and ('auto' in t1_lower or 'aerospace' in t1_lower):
+                edges.append({"Source Node": t2, "Source Tier": "Tier 2", "Target Node": t1, "Target Tier": "Tier 1", "Edge Type": "Avionics / Nav Supply"})
+                
+    # Deduplicate edges
+    unique_edges = []
+    seen = set()
+    for e in edges:
+        sig = f"{e['Source Node']}->{e['Target Node']}"
+        if sig not in seen:
+            seen.add(sig)
+            unique_edges.append(e)
+            
+    return pd.DataFrame(unique_edges)
+
+def run_mapping():
+    print("Starting NAICS Contagion Mapping Pipeline...")
+    
+    data_dir = r"C:\Users\LydiaHunterLabsTech\.gemini\antigravity\scratch\NAICs Contagion Drop"
+    pe_path = os.path.join(data_dir, "NAICS Physical Economy 2.xlsx")
+    gics_path = os.path.join(data_dir, "GICS All.xlsx")
+    
+    # 1. Load Data
+    df_pe = pd.read_excel(pe_path)
+    df_gics = pd.read_excel(gics_path)
+    
+    gics_industries = df_gics['Industry (Siblings)'].dropna().unique().tolist()
+    gics_sub_industries = df_gics['Sub-Industry (Cousins)'].dropna().unique().tolist()
+    
+    test_materials = get_test_materials()
+    
+    rows = []
+    
+    print("Processing NAICS Physical Economy through Commodity Mapper...")
+    
+    # 2. Map Commodity Mapper through NAICS Physical Economy 2
+    # Ensure 334410 gets processed even if missing in PE2 sample
+    pe_codes = df_pe['Industry Code'].fillna(0).astype(int).astype(str).tolist()
+    if '334410' not in pe_codes:
+        new_row = {'Sector': 'Manufacturing', 'Sub Sector': 'Computer and Electronic Product Manufacturing', 'Industry': 'Semiconductor and Other Electronic Component Manufacturing', 'Industry Code': 334410}
+        df_pe = pd.concat([df_pe, pd.DataFrame([new_row])], ignore_index=True)
+    
+    for _, pe_row in df_pe.iterrows():
+        naics_code = str(pe_row['Industry Code']).strip('.0')
+        if naics_code == '0' or naics_code == 'nan': continue
+            
+        naics_industry = str(pe_row.get('Industry', 'Unknown'))
+        naics_sector = str(pe_row.get('Sector', 'Unknown'))
+        # Using correct column name 'Sub Sector'
+        naics_subsector = str(pe_row.get('Sub Sector', pe_row.get('Subsector', 'Unknown')))
+        
+        # Determine GICS mapping based on heuristic rules
+        ns = naics_subsector.lower()
+        ni = naics_industry.lower()
+        
+        if 'food' in ns or 'beverage' in ns or 'tobacco' in ns:
+            gics_sector, gics_industry_group, gics_sub_match = "Consumer Staples", "Food, Beverage & Tobacco", "Packaged Foods & Meats"
+        elif 'chemical' in ns:
+            if 'pharmaceutical' in ni or 'medicine' in ni:
+                gics_sector, gics_industry_group, gics_sub_match = "Health Care", "Pharmaceuticals, Biotechnology", "Pharmaceuticals"
+            else:
+                gics_sector, gics_industry_group, gics_sub_match = "Materials", "Materials", "Commodity Chemicals"
+        elif 'metal' in ns:
+            gics_sector, gics_industry_group, gics_sub_match = "Materials", "Materials", "Steel"
+        elif 'wood' in ns or 'paper' in ns:
+            gics_sector, gics_industry_group, gics_sub_match = "Materials", "Materials", "Paper & Forest Products"
+        elif 'petroleum' in ns or 'coal' in ns:
+            gics_sector, gics_industry_group, gics_sub_match = "Energy", "Energy", "Oil & Gas Refining & Marketing"
+        elif 'machinery' in ns:
+            gics_sector, gics_industry_group, gics_sub_match = "Industrials", "Capital Goods", "Industrial Machinery"
+        elif 'transportation' in ns:
+            if 'auto' in ni or 'motor' in ni or 'car' in ni:
+                gics_sector, gics_industry_group, gics_sub_match = "Consumer Discretionary", "Automobiles & Components", "Auto Parts & Equipment"
+            elif 'aerospace' in ni:
+                gics_sector, gics_industry_group, gics_sub_match = "Industrials", "Capital Goods", "Aerospace & Defense"
+            else:
+                gics_sector, gics_industry_group, gics_sub_match = "Industrials", "Capital Goods", "Heavy Transportation Equipment"
+        elif 'computer' in ns or 'electronic' in ns:
+            if 'semiconductor' in ni:
+                gics_sector, gics_industry_group, gics_sub_match = "Information Technology", "Semiconductors", "Semiconductors"
+            else:
+                gics_sector, gics_industry_group, gics_sub_match = "Information Technology", "Technology Hardware & Equipment", "Electronic Equipment & Instruments"
+        elif 'electrical' in ns:
+            gics_sector, gics_industry_group, gics_sub_match = "Industrials", "Capital Goods", "Electrical Components & Equipment"
+        elif 'textile' in ns or 'apparel' in ns or 'leather' in ns:
+            gics_sector, gics_industry_group, gics_sub_match = "Consumer Discretionary", "Consumer Durables & Apparel", "Apparel, Accessories & Luxury Goods"
+        elif 'furniture' in ns:
+            gics_sector, gics_industry_group, gics_sub_match = "Consumer Discretionary", "Consumer Durables & Apparel", "Home Furnishings"
+        elif 'nonmetallic' in ns or 'mineral' in ns or 'glass' in ni or 'cement' in ni:
+            gics_sector, gics_industry_group, gics_sub_match = "Materials", "Materials", "Construction Materials"
+        else:
+            gics_sector, gics_industry_group, gics_sub_match = "Industrials", "Capital Goods", "Diversified Industrials"
+        
+        contagion_score = get_contagion_score(naics_code)
+        supply_chain_tier = assign_supply_chain_tier(naics_code, naics_subsector, naics_industry)
+        
+        # Extract available materials for dynamic assignment
+        test_mats = get_test_materials()
+        ALL_MATS = {}
+        for mat_list in test_mats.values():
+            for m in mat_list:
+                ALL_MATS[m[1].lower()] = m
+        
+        def assign_materials_heuristic(n_ind, n_sub):
+            ni = n_ind.lower()
+            ns = n_sub.lower()
+            assigned = []
+            
+            # Metal
+            if 'steel' in ni or 'iron' in ni: assigned.append(ALL_MATS['steel'])
+            if 'aluminum' in ni or 'alumina' in ni: assigned.append(ALL_MATS['aluminum'])
+            if 'copper' in ni or 'wire' in ni: assigned.append(ALL_MATS['copper'])
+            
+            # Auto / Transport
+            if 'auto' in ni or 'motor vehicle' in ni or 'aerospace' in ni:
+                assigned.extend([ALL_MATS['steel'], ALL_MATS['aluminum'], ALL_MATS['natural rubber'], ALL_MATS['copper']])
+            
+            # Plastics / Rubber
+            if 'plastic' in ni: assigned.append(ALL_MATS['polypropylene'])
+            if 'rubber' in ni or 'tire' in ni: assigned.append(ALL_MATS['natural rubber'])
+            
+            # Electronics / Semiconductors / Batteries
+            if 'battery' in ni:
+                assigned.extend([ALL_MATS['lithium carbonate'], ALL_MATS['cobalt'], ALL_MATS['nickel']])
+            if 'semiconductor' in ni or 'electronic' in ni:
+                assigned.extend([ALL_MATS['polysilicon'], ALL_MATS['gallium'], ALL_MATS['germanium'], ALL_MATS['tantalum']])
+            if 'computer' in ns:
+                assigned.extend([ALL_MATS['polysilicon'], ALL_MATS['copper']])
+            
+            # Petro / Chemicals
+            if 'petroleum' in ni or 'oil' in ni or 'refin' in ni:
+                if 'oilseed' not in ni and 'soybean' not in ni and 'animal' not in ni: # exclude cooking oil
+                    assigned.extend([ALL_MATS['crude oil'], ALL_MATS['natural gas'], ALL_MATS['platinum']])
+            if 'chemical' in ns:
+                assigned.extend([ALL_MATS['crude oil'], ALL_MATS['natural gas']])
+                
+            # Machinery
+            if 'machinery' in ns:
+                assigned.extend([ALL_MATS['steel'], ALL_MATS['aluminum'], ALL_MATS['copper']])
+                
+            # Deduplicate
+            unique_mats = []
+            seen = set()
+            for m in assigned:
+                if m[0] not in seen:
+                    seen.add(m[0])
+                    unique_mats.append(m)
+            return unique_mats
+            
+        # Check if we have explicitly defined materials for this NAICS
+        materials = test_materials.get(naics_code, [])
+        if not materials:
+            # If not, use the heuristic engine to assign applicable materials
+            materials = assign_materials_heuristic(naics_industry, naics_subsector)
+        
+        if not materials:
+            # Create a single row without material specifics
+            row = {
+                "Internal ID": f"PC_{naics_code}",
+                "NAICS Code ID": naics_code,
+                "Primary Name": naics_industry,
+                "NAICS Sector": naics_sector,
+                "NAICS Sub Sector": naics_subsector,
+                "GICS Sector": gics_sector,
+                "GICS Industry Group": gics_industry_group,
+                "GICS Sub Industry": gics_sub_match,
+                "Supply Chain Tier": supply_chain_tier,
+                "Contagion Score": contagion_score,
+                
+                "material_id": "", "material_name": "", "hs_code": "", "market_ticker": "",
+                "origin_country_1": "", "o1_market_share": "", "origin_country_2": "", "o2_market_share": "", "origin_country_3": "", "o3_market_share": "",
+                "refining_country_1": "", "r1_market_share": "", "refining_country_2": "", "r2_market_share": "", "refining_country_3": "", "r3_market_share": "",
+                
+                # --- BETA MASKING (Logistics & Substitution Logic) ---
+                "primary_transit_vehicle": "[Unlock in Beta]",
+                "cost_weight_metric": "[Unlock in Beta]",
+                "substitute_material_id": "[Unlock in Beta]",
+                "substitutability_index": "[Unlock in Beta]",
+                "search_tags": "[Unlock in Beta]"
+            }
+            rows.append(row)
+        else:
+            # Expand material rows
+            for mat in materials:
+                mat_id, mat_name, hs, ticker, o1, o1_s, o2, o2_s, o3, o3_s, r1, r1_s, r2, r2_s, r3, r3_s, transit, weight, sub_id, sub_idx, tags = mat
+                row = {
+                    "Internal ID": f"PC_{naics_code}",
+                    "NAICS Code ID": naics_code,
+                    "Primary Name": naics_industry,
+                    "NAICS Sector": naics_sector,
+                    "NAICS Sub Sector": naics_subsector,
+                    "GICS Sector": gics_sector,
+                    "GICS Industry Group": gics_industry_group,
+                    "GICS Sub Industry": gics_sub_match,
+                    "Supply Chain Tier": supply_chain_tier,
+                    "Contagion Score": contagion_score,
+                    
+                    "material_id": mat_id, "material_name": mat_name, "hs_code": hs, "market_ticker": ticker,
+                    "origin_country_1": o1, "o1_market_share": o1_s, "origin_country_2": o2, "o2_market_share": o2_s, "origin_country_3": o3, "o3_market_share": o3_s,
+                    "refining_country_1": r1, "r1_market_share": r1_s, "refining_country_2": r2, "r2_market_share": r2_s, "refining_country_3": r3, "r3_market_share": r3_s,
+                    
+                    # --- BETA MASKING (Logistics & Substitution Logic) ---
+                    "primary_transit_vehicle": "[Unlock in Beta]",
+                    "cost_weight_metric": "[Unlock in Beta]",
+                    "substitute_material_id": "[Unlock in Beta]",
+                    "substitutability_index": "[Unlock in Beta]",
+                    "search_tags": "[Unlock in Beta]"
+                }
+                rows.append(row)
+                
+    final_df = pd.DataFrame(rows)
+    
+    # Generate the Edge List for the Cascade
+    edges_df = build_contagion_edges(final_df)
+    
+    out_csv = os.path.join(data_dir, "NAICS_Contagion_Nodes_GitHub.csv")
+    out_xlsx = os.path.join(data_dir, "NAICS_Contagion_Nodes_GitHub.xlsx")
+    edges_csv = os.path.join(data_dir, "NAICS_Contagion_Edges_GitHub.csv")
+    
+    print(f"Saving mapped results to {out_csv} and {out_xlsx}...")
+    final_df.to_csv(out_csv, index=False)
+    final_df.to_excel(out_xlsx, index=False)
+    
+    print(f"Saving Cascade Edge List to {edges_csv}...")
+    edges_df.to_csv(edges_csv, index=False)
+    
+    print("Contagion Map & Cascades Complete!")
+
+if __name__ == "__main__":
+    run_mapping()

README.md

@@ -1,3 +1,47 @@
----
-license: mit
----
+# 🌐 NAICS Contagion Map: The Physical Economy Visualized
+
+## [🚀 Join the June 2026 Beta](https://a7af2a54.sibforms.com/serve/MUIFAK4KiE6r-UBeNPKiGd_hFht_dPE_p8oCs2NOx53N8abu_SQe9HTVgRxIlg4HlA9cOsVmYs2pkeAZHGZ0XlDh2RBLa_bZZLLiaXPKpUTjgr72heyx-lYwgPb0B8dnkWsj7lw-M1jEOnzP7Oc8o12c0SG3UYsmQZRaWacaAWgweWru9S9fWYne4o43aByc7BqGSjRvnZcHXYcQ)
+
+Welcome to the **NAICS Contagion Intelligence Map** — an open-source pipeline mapping the vulnerability cascades of the physical economy. 
+
+This repository bridges institutional supply chain taxonomies (NAICS) with financial market frameworks (GICS) to visualize how disruptions in upstream raw materials systematically ripple down to consumer goods.
+
+![Contagion Visualizer Dashboard](visualizer_screenshot.png)
+
+## 📦 What's Included in the Drop?
+
+1. **`NAICS_Contagion_Map.py`**
+   The core intelligence engine. It utilizes a deterministic heuristic algorithm to classify manufacturing nodes into Supply Chain Tiers (Tier 1 to Tier 4) and generate topological edge networks.
+
+2. **`NAICS_Contagion_Nodes_GitHub.xlsx` / `.csv`**
+   The flat intelligence map. Contains 346 mapped NAICS industries, their corresponding GICS cousins, their structural Tier, and their **Contagion Score (1.0 - 10.0)**.
+   *Note: Sensitive logistics data and substitutability friction metrics are masked as `[Unlock in Beta]` to protect trade-secret competitive intelligence.*
+
+3. **`NAICS_Contagion_Edges_GitHub.csv`**
+   The relational spiderweb. A generated list of 1,100+ topological edges showing the exact contagion cascade from Tier 4 (Commodity) -> Tier 3 (Primary Extractor) -> Tier 2 (Processor) -> Tier 1 (Final Assembler).
+
+4. **`build_visualizer.py` & `NAICS_Contagion_Visualizer.html`**
+   A stunning, interactive Web application generated from the data. You can open the `.html` file directly in any browser (no server required) to explore the system cascades interactively via an embedded physics-driven network engine.
+
+## 🧠 The Intelligence Framework
+
+### The Supply Chain Tiers
+We categorize the physical economy into four distinct levels of depth:
+*   **Tier 4 (Raw Materials):** Foundational commodities (e.g., Lithium, Crude Oil, Platinum).
+*   **Tier 3 (Primary Converters):** Heavy extractors and baseline chemical/metal converters (e.g., Basic Chemical Mfg, Primary Metal Mfg).
+*   **Tier 2 (Intermediate Processors):** Mid-stream molders and fabricators (e.g., Fabricated Metals, Plastics, Refiners).
+*   **Tier 1 (Direct Suppliers / OEMs):** Final assembly and direct-to-consumer manufacturing (e.g., Auto Manufacturing, Food Processing, Aerospace).
+
+### The Contagion Score (1.0 - 10.0)
+The Contagion Score quantifies systemic volatility. 
+- **High Scores (8.0+):** Indicate a systemic "anchor node". A bottleneck here causes catastrophic downstream starvation. Driven by high Upstream Concentration Risk and massive Downstream Connectivity.
+- **Medium Scores (5.0-7.9):** Intermediate processors with some alternative pathways.
+- **Low Scores (1.0-4.9):** Terminal nodes that can absorb localized shocks.
+
+## 🚀 How to Use the Data
+Import the `Edges` and `Nodes` CSVs directly into network visualization tools like Gephi, Neo4j, or Python's NetworkX to build an interactive, force-directed graph of macroeconomic risk.
+
+---
+
+**Contact for Enterprise Inquiry:**
+Email Mark: `mark(at)plainr(dot)io`

build_visualizer.py

@@ -0,0 +1,307 @@
+import pandas as pd
+import json
+import os
+
+def generate_html():
+    print("Loading Contagion Data...")
+    
+    # Load data from the current directory
+    df_nodes = pd.read_csv("NAICS_Contagion_Nodes_GitHub.csv")
+    df_edges = pd.read_csv("NAICS_Contagion_Edges_GitHub.csv")
+    
+    # Build Vis.js Nodes
+    nodes = []
+    seen_nodes = set()
+    
+    # Map colors to Tiers
+    color_map = {
+        'Tier 4': '#ff9f43', # Orange for Raw Materials
+        'Tier 3': '#ee5253', # Red for Primary
+        'Tier 2': '#9b59b6', # Purple for Intermediate
+        'Tier 1': '#3498db'  # Blue for Final
+    }
+    
+    # Process edges to get all nodes
+    for _, e in df_edges.iterrows():
+        src = e['Source Node']
+        src_tier = e['Source Tier']
+        tgt = e['Target Node']
+        tgt_tier = e['Target Tier']
+        
+        if src not in seen_nodes:
+            seen_nodes.add(src)
+            nodes.append({
+                "id": src,
+                "label": src,
+                "group": src_tier,
+                "color": color_map.get(src_tier, '#bdc3c7'),
+                "value": 20 if src_tier == 'Tier 4' else 10,
+                "title": f"<b>{src}</b><br>{src_tier}"
+            })
+            
+        if tgt not in seen_nodes:
+            seen_nodes.add(tgt)
+            # Find contagion score for target
+            node_data = df_nodes[df_nodes['Primary Name'] == tgt]
+            score = 5.0
+            if not node_data.empty:
+                score = float(node_data.iloc[0]['Contagion Score'])
+                
+            nodes.append({
+                "id": tgt,
+                "label": tgt,
+                "group": tgt_tier,
+                "color": color_map.get(tgt_tier, '#bdc3c7'),
+                "value": score * 3, # Scale for visualization
+                "title": f"<b>{tgt}</b><br>{tgt_tier}<br>Contagion Score: {score}"
+            })
+            
+    # Build Vis.js Edges
+    edges = []
+    for _, e in df_edges.iterrows():
+        edges.append({
+            "from": e['Source Node'],
+            "to": e['Target Node'],
+            "title": e['Edge Type'],
+            "arrows": "to",
+            "color": {"color": "rgba(255,255,255,0.2)", "highlight": "rgba(255,255,255,0.8)"}
+        })
+        
+    nodes_json = json.dumps(nodes)
+    edges_json = json.dumps(edges)
+    
+    # --- STUNNING HTML TEMPLATE (Dark Mode, Glassmorphism) ---
+    html_content = f"""
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>NAICS Contagion Intelligence Map</title>
+    <script type="text/javascript" src="https://unpkg.com/vis-network/standalone/umd/vis-network.min.js"></script>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;800&display=swap" rel="stylesheet">
+    <style>
+        body {{
+            margin: 0;
+            padding: 0;
+            background: linear-gradient(135deg, #0f172a 0%, #1e1b4b 100%);
+            color: #f8fafc;
+            font-family: 'Inter', sans-serif;
+            overflow: hidden;
+            display: flex;
+            height: 100vh;
+        }}
+        
+        #mynetwork {{
+            flex-grow: 1;
+            height: 100%;
+            outline: none;
+        }}
+        
+        /* Glassmorphism Sidebar */
+        .sidebar {{
+            width: 380px;
+            height: 100%;
+            background: rgba(15, 23, 42, 0.6);
+            backdrop-filter: blur(16px);
+            -webkit-backdrop-filter: blur(16px);
+            border-left: 1px solid rgba(255, 255, 255, 0.1);
+            padding: 40px 30px;
+            box-sizing: border-box;
+            display: flex;
+            flex-direction: column;
+            z-index: 10;
+            box-shadow: -10px 0 30px rgba(0,0,0,0.5);
+        }}
+        
+        .header h1 {{
+            font-size: 24px;
+            font-weight: 800;
+            margin: 0 0 10px 0;
+            background: -webkit-linear-gradient(45deg, #38bdf8, #818cf8);
+            -webkit-background-clip: text;
+            -webkit-text-fill-color: transparent;
+            letter-spacing: -0.5px;
+        }}
+        
+        .header p {{
+            font-size: 13px;
+            color: #94a3b8;
+            line-height: 1.6;
+            margin-bottom: 30px;
+        }}
+        
+        .legend {{
+            background: rgba(255,255,255,0.03);
+            border-radius: 12px;
+            padding: 20px;
+            border: 1px solid rgba(255,255,255,0.05);
+            margin-bottom: 30px;
+        }}
+        
+        .legend-title {{
+            font-size: 11px;
+            text-transform: uppercase;
+            letter-spacing: 1px;
+            color: #64748b;
+            margin-bottom: 15px;
+            font-weight: 600;
+        }}
+        
+        .legend-item {{
+            display: flex;
+            align-items: center;
+            margin-bottom: 12px;
+            font-size: 14px;
+        }}
+        
+        .color-dot {{
+            width: 12px;
+            height: 12px;
+            border-radius: 50%;
+            margin-right: 12px;
+            box-shadow: 0 0 10px currentColor;
+        }}
+        
+        .node-info {{
+            margin-top: auto;
+            background: rgba(0,0,0,0.3);
+            border-radius: 12px;
+            padding: 20px;
+            border: 1px solid rgba(255,255,255,0.05);
+            display: none;
+        }}
+        
+        .node-info h3 {{ margin: 0 0 10px 0; font-size: 16px; color: #fff; }}
+        .node-info p {{ margin: 5px 0; font-size: 13px; color: #cbd5e1; }}
+        .badge {{
+            display: inline-block;
+            padding: 4px 8px;
+            border-radius: 4px;
+            font-size: 11px;
+            font-weight: 600;
+            background: rgba(255,255,255,0.1);
+            margin-top: 10px;
+        }}
+        
+        /* Floating branding */
+        .brand {{
+            position: absolute;
+            bottom: 30px;
+            left: 30px;
+            font-size: 12px;
+            color: rgba(255,255,255,0.3);
+            pointer-events: none;
+            letter-spacing: 2px;
+            font-weight: 600;
+        }}
+    </style>
+</head>
+<body>
+
+    <div id="mynetwork"></div>
+    <div class="brand">NAICS CONTAGION INTELLIGENCE</div>
+    
+    <div class="sidebar">
+        <div class="header">
+            <h1>Contagion Cascade</h1>
+            <p>Interactive mapping of systemic vulnerability across the NAICS physical economy. Nodes are sized by Contagion Score.</p>
+        </div>
+        
+        <div class="legend">
+            <div class="legend-title">Supply Chain Hierarchy</div>
+            <div class="legend-item"><div class="color-dot" style="color: #ff9f43; background: #ff9f43;"></div> Tier 4: Raw Commodity</div>
+            <div class="legend-item"><div class="color-dot" style="color: #ee5253; background: #ee5253;"></div> Tier 3: Primary Extractor</div>
+            <div class="legend-item"><div class="color-dot" style="color: #9b59b6; background: #9b59b6;"></div> Tier 2: Intermediate Processor</div>
+            <div class="legend-item"><div class="color-dot" style="color: #3498db; background: #3498db;"></div> Tier 1: Final Assembly</div>
+        </div>
+        
+        <div class="node-info" id="nodeInfo">
+            <h3 id="ni-title">Node Name</h3>
+            <p id="ni-tier">Tier: --</p>
+            <p id="ni-score">Contagion Score: --</p>
+            <div class="badge" id="ni-badge">Systemic Node</div>
+        </div>
+    </div>
+
+    <script type="text/javascript">
+        // Inject Data
+        var nodes = new vis.DataSet({nodes_json});
+        var edges = new vis.DataSet({edges_json});
+
+        var container = document.getElementById('mynetwork');
+        var data = {{
+            nodes: nodes,
+            edges: edges
+        }};
+        
+        var options = {{
+            nodes: {{
+                shape: 'dot',
+                font: {{ color: '#ffffff', size: 12, face: 'Inter', strokeWidth: 2, strokeColor: '#0f172a' }},
+                borderWidth: 2,
+                shadow: true
+            }},
+            edges: {{
+                smooth: {{ type: 'continuous' }}
+            }},
+            physics: {{
+                forceAtlas2Based: {{
+                    gravitationalConstant: -80,
+                    centralGravity: 0.005,
+                    springLength: 200,
+                    springConstant: 0.04,
+                    damping: 0.65
+                }},
+                maxVelocity: 8,
+                solver: 'forceAtlas2Based',
+                timestep: 0.15,
+                stabilization: {{ iterations: 150 }}
+            }},
+            interaction: {{
+                hover: true,
+                tooltipDelay: 200
+            }}
+        }};
+
+        var network = new vis.Network(container, data, options);
+        
+        // Interaction Events for the sidebar
+        network.on("selectNode", function (params) {{
+            if (params.nodes.length == 1) {{
+                var nodeId = params.nodes[0];
+                var node = nodes.get(nodeId);
+                
+                document.getElementById('nodeInfo').style.display = 'block';
+                document.getElementById('ni-title').innerText = node.label;
+                document.getElementById('ni-tier').innerText = node.group;
+                
+                // Extract score from title or set to NA
+                var score = "N/A";
+                if(node.title.includes("Contagion Score")) {{
+                    score = node.title.split("Contagion Score: ")[1];
+                    document.getElementById('ni-badge').style.display = parseFloat(score) > 8.0 ? 'inline-block' : 'none';
+                    if(parseFloat(score) > 8.0) document.getElementById('ni-badge').innerText = 'Critical Anchor Node';
+                }} else {{
+                    document.getElementById('ni-badge').style.display = 'none';
+                }}
+                document.getElementById('ni-score').innerText = "Contagion Score: " + score;
+            }}
+        }});
+        
+        network.on("deselectNode", function (params) {{
+            document.getElementById('nodeInfo').style.display = 'none';
+        }});
+    </script>
+</body>
+</html>
+"""
+    
+    out_path = "NAICS_Contagion_Visualizer.html"
+    with open(out_path, "w", encoding="utf-8") as f:
+        f.write(html_content)
+        
+    print(f"Visualization generated successfully at: {out_path}")
+
+if __name__ == "__main__":
+    generate_html()