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

# 1. PPB data
x = [-0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 
     0.6, 0.7, 0.8, 0.9, 1, 1.1]

y = [0.12, 0.77, 2.06, 2.79, 3.32, 5.50, 4.98, 5.02, 6.92, 5.91, 
     6.88, 8.17, 13.59, 20.99, 11.85, 1.13]

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

# 3. Colors
colors = []
for val in x:
    if val >= 0.7:
        colors.append('green')
    elif val <= 0.2:
        colors.append('firebrick')
    else:
        colors.append('gold')

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

# Bars and Line
plt.bar(x, y, width=0.08, color=colors, edgecolor='black', alpha=0.7, label='Data Frequency')

# 5. Tags
plt.xlabel('Plasma Protein Binding (PPB) Ratio', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('PPB Distribution', fontsize=14)

# Axle settings
plt.xticks(np.arange(-0.4, 1.2, 0.1))
plt.xlim(-0.5, 1.2)
plt.ylim(0, 23)

# 6. LEGEND (Top left)
legend_elements = [
    Patch(facecolor='green', edgecolor='black', alpha=0.7, label='High Binding (> 70%) - Depot Effect'),
    Patch(facecolor='gold', edgecolor='black', alpha=0.7, label='Moderate Binding (20% - 70%)'),
    Patch(facecolor='firebrick', edgecolor='black', alpha=0.7, label='Low Binding (< 20%) - Rapid Clearance')
]

# The legend stays up
plt.legend(handles=legend_elements, loc='upper left', ncol=1, framealpha=0.9)

# 7. EXPLANATORY TEXT
plt.text(-0.45, 17.5, '* Values < 0 and > 1 are regression model artifacts\nrepresenting 0% and 100% extremes.', 
         fontsize=9, style='italic', bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))

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

plt.show()