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

# 1. Bioavailability F 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]

# Frequencies in %
y = [0.28, 2.87, 4.73, 4.85, 4.49, 4.69, 4.41, 5.22, 4.33, 4.37, 5.46, 
     5.38, 4.73, 5.46, 5.38, 6.47, 6.67, 6.35, 7.77, 5.38, 0.69]

# 2. Curve 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] # Limpieza

# 3. Define colors (Traffic Light F50)
colors = []
for val in x:
    # High (> 0.5)
    if val > 0.5:
        colors.append('green')
    # Low (< 0.3)
    elif val < 0.3:
        colors.append('firebrick')
    # Average (0.3 - 0.5)
    else:
        colors.append('gold')

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

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

# 5. Tags and Titles
plt.xlabel('Oral Bioavailability Fraction (F) [0-1]', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Oral Bioavailability Distribution', fontsize=14)

# Axle settings
plt.xticks(np.arange(0, 1.1, 0.1))
plt.xlim(-0.05, 1.05)
plt.ylim(0, 9) 

# 6. Vertical Legend (Left so as not to cover the high bars)
legend_elements = [
    Patch(facecolor='green', edgecolor='black', alpha=0.7, label='High Bioavailability (> 50%)'),
    Patch(facecolor='gold', edgecolor='black', alpha=0.7, label='Medium (30% - 50%)'),
    Patch(facecolor='firebrick', edgecolor='black', alpha=0.7, label='Low Bioavailability (< 30%)')
]

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

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

plt.show()