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

# 1. OCT2 Inhibitor Facts
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]

frequencies = [0.57, 18.57, 19.17, 12.14, 9.75, 5.87, 4.85, 4.29, 
               3.96, 3.16, 2.99, 3.64, 4.33, 3.56, 2.87, 0.28]

# 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 (Kidney Traffic Light)
colors = []
for val in bin_centers:
    if val < 0.2:
        colors.append('mediumseagreen')
    elif val < 0.5:
        colors.append('gold')
    else:
        colors.append('firebrick')

# 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 OCT2 Inhibition', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('OCT2 Inhibition Potential (Renal Clearance Safety)', fontsize=14)

# Axle settings
plt.xticks(np.arange(-0.1, 1.0, 0.1))
plt.xlim(-0.02, 1.0)
plt.ylim(0, 22)

# 6. Legend
legend_elements = [
    Patch(facecolor='mediumseagreen', edgecolor='black', label='Low Inhibition (Kidney Safe)'),
    Patch(facecolor='gold', edgecolor='black', label='Moderate Inhibition'),
    Patch(facecolor='firebrick', edgecolor='black', label='High Inhibition (DDI Risk)'),
]

plt.legend(handles=legend_elements, loc='upper right', framealpha=0.95)

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

plt.show()