2. python codes for coupling coordination degree (CCD) analsis (5).txt

import pandas as pd
import numpy as np
from pathlib import Path

# ======================
# 1. Configuration
# ======================
INPUT_FILE = r"C:\python\spatial\subsystem_indices_all_cities.csv" 
FINAL_OUTPUT = "final_ccd_results.csv"

def calculate_ccd(df, eps=1e-12):
    """Calculates C (Coupling), T (Development), and D (Coordination)"""
    U1 = df["U_transport"].astype(float).clip(lower=0.0)
    U2 = df["U_population"].astype(float).clip(lower=0.0)
    U3 = df["U_industry"].astype(float).clip(lower=0.0)
    
    # 1. Coupling Degree (C)
    num = (U1 * U2 * U3).clip(lower=0.0)
    den = (((U1 + U2 + U3) / 3.0).clip(lower=eps) ** 3)
    C = np.power((num / den).clip(lower=0.0), 1.0 / 3.0).clip(0, 1)
    
    # 2. Comprehensive Development (T)
    T = (U1 + U2 + U3) / 3.0
    
    # 3. Coupling Coordination Degree (D)
    D = np.sqrt(C * T).clip(0, 1)
    return C, T, D

def classify(D):
    """Categorizes the degree of coordination into 6 stages"""
    bins = [-np.inf, 0.3, 0.4, 0.5, 0.6, 0.7, np.inf]
    # Ensure all labels are inside this list and separated by commas
    labels = [
        "Extremely Low [0,0.3)",
        "Low [0.3,0.4)",
        "Medium-Low [0.4,0.5)",
        "Medium [0.5,0.6)",
        "Medium-High [0.6,0.7)",
        "High [0.7,1]"
    ]
    # Uses right=False to match the second script's logic
    return pd.cut(D, bins=bins, labels=labels, right=False, include_lowest=True)

def main():
    try:
        if not Path(INPUT_FILE).exists():
            print(f"Error: {INPUT_FILE} not found.")
            return

        df = pd.read_csv(INPUT_FILE)
        
        # Calculate and assign all metrics
        # We assign C_coupling here specifically
        df['C_coupling'], df['T_Development'], df['D_Coordination'] = calculate_ccd(df)
        df['Coordination_Level'] = classify(df['D_Coordination'])
        
        # Save results to CSV
        out_path = Path(INPUT_FILE).parent / FINAL_OUTPUT
        df.to_csv(out_path, index=False, encoding="utf-8-sig")
        
        print(f"Success! Result saved to: {out_path}")
        # Preview includes the new C_coupling column
        print(df[['city_code', 'year', 'C_coupling', 'D_Coordination', 'Coordination_Level']].head(10))

    except Exception as e:
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    main()