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

# 1. CLp data
x = [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
     0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1]

y = [0.04, 3.20, 8.78, 6.51, 5.18, 6.23, 4.89, 4.77, 5.26, 4.21, 4.29, 
     5.02, 4.73, 4.73, 6.07, 6.23, 6.84, 6.76, 4.69, 1.42, 0.16]

# 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 <= 0.3:
        colors.append('green')     # Low CL
    elif val >= 0.7:
        colors.append('firebrick') # High CL
    else:
        colors.append('gold')      # Moderate

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

# Bars and Line
plt.bar(x, y, width=0.04, color=colors, edgecolor='black', alpha=0.7, label='Data Frequency')

# 5. Tags
plt.xlabel('Probability of High Clearance (> 5 ml/min/kg)', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Plasma Clearance (CLp) Prediction', fontsize=14)

# Axle settings
plt.xticks(np.arange(0, 1.1, 0.1))
plt.xlim(-0.05, 1.05)
plt.ylim(0, 10) 

# 6. VERTICAL LEGEND (INSIDE)
legend_elements = [
    Patch(facecolor='green', edgecolor='black', alpha=0.7, label='Low Clearance / Long Half-life (< 0.3)'),
    Patch(facecolor='gold', edgecolor='black', alpha=0.7, label='Moderate Clearance (0.3 - 0.7)'),
    Patch(facecolor='firebrick', edgecolor='black', alpha=0.7, label='High Clearance / Short Half-life (> 0.7)')
]

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

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

plt.show()