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

# 1. OCT1 Inhibitor Data
bin_centers = [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]

frequencies = [20.51, 11.33, 10.40, 5.70, 6.35, 4.69, 4.89, 3.96, 
               4.25, 4.57, 4.05, 3.96, 3.20, 2.63, 2.55, 2.63, 3.07, 1.25]

# 2. Smoothing (Trend line)
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 (Predominant green on the left)
colors = []
for val in bin_centers:
    if val < 0.3:
        colors.append('mediumseagreen')
    elif val < 0.6:
        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 OCT1 Inhibition', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('OCT1 Inhibition Potential (Hepatic Cation Transport)', fontsize=14)

# Axle settings
plt.xticks(np.arange(0.05, 0.95, 0.1))
plt.xlim(0.0, 0.95)
plt.ylim(0, 25)

# 6. Legend
legend_elements = [
    Patch(facecolor='mediumseagreen', edgecolor='black', label='Low Inhibition (Standard)'),
    Patch(facecolor='gold', edgecolor='black', label='Moderate Inhibition'),
    Patch(facecolor='firebrick', edgecolor='black', label='High Inhibition (Specific Cations)'),
]

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

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

plt.show()