import matplotlib.pyplot as plt
from matplotlib.patches import Patch
import numpy as np
from scipy.interpolate import make_interp_spline

# 1. CYP2D6 Inhibitor Data
bin_centers = [0.0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 
               0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95]

frequencies = [23.46, 26.09, 11.08, 5.74, 3.64, 2.91, 2.83, 2.35, 1.94, 
               2.02, 1.74, 1.29, 1.62, 1.58, 1.66, 1.54, 1.90, 2.22, 3.44, 0.93]

# 2. Smoothing
x_smooth = np.linspace(min(bin_centers), max(bin_centers), 300)
spl = make_interp_spline(bin_centers, frequencies, k=3)
y_smooth = spl(x_smooth)
y_smooth = [val if val > 0 else 0 for val in y_smooth] 

# 3. Colors (Traffic Light with Specific Alert)
colors = []
for val in bin_centers:
    if val < 0.15:
        colors.append('mediumseagreen')
    elif val < 0.8:
        colors.append('gold')
    else:
        colors.append('crimson')

# 4. Create the chart
plt.figure(figsize=(7, 6))

# Bars
plt.bar(bin_centers, frequencies, width=0.04, color=colors, edgecolor='black', alpha=0.8, label='Data Frequency')

# 5. Tags and Titles
plt.xlabel('Probability of CYP2D6 Inhibition', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('CYP2D6 Inhibition: Safety vs. The "Phenoconversion" Risk', fontsize=14)

# Axle settings
plt.xticks(np.arange(0.0, 1.05, 0.1))
plt.xlim(-0.05, 1.05)
plt.ylim(0, 30)

# 6. Legend
legend_elements = [
    Patch(facecolor='mediumseagreen', edgecolor='black', label='Safe Zone'),
    Patch(facecolor='gold', edgecolor='black', label='Moderate Risk'),
    Patch(facecolor='crimson', edgecolor='black', label='High Risk (Phenoconversion)'),
]

plt.legend(handles=legend_elements, loc='upper right', framealpha=0.95, ncol=1, fontsize=10)

plt.grid(axis='y', linestyle='--', alpha=0.5)
plt.tight_layout()

plt.show()