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

# 1. F30 Data (Probability)
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 = [0.04, 1.74, 2.35, 2.71, 2.91, 2.75, 2.71, 3.48, 3.52, 3.64, 4.29, 
     3.64, 3.60, 4.98, 6.15, 6.96, 7.48, 8.29, 11.08, 12.99, 4.69]

# 2. Curve smoothing (Spline for asymmetric distribution)
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. Define colors (Traffic Light F30)
colors = []
for val in x:
    # High probability of exceeding 30%
    if val >= 0.7:
        colors.append('green')
    # Low probability (Probably F < 30%)
    elif val <= 0.3:
        colors.append('firebrick')
    # Doubt zone
    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('Probability of Bioavailability > 30% (F30 Score)', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('F30 Bioavailability Distribution', fontsize=14)

# Axle settings
plt.xticks(np.arange(0, 1.1, 0.1))
plt.xlim(-0.05, 1.05)
plt.ylim(0, 14)

# 6. Vertical Legend (ON THE LEFT, because the data is on the right)
legend_elements = [
    Patch(facecolor='green', edgecolor='black', alpha=0.7, label='Likely F > 30% (Score > 0.7)'),
    Patch(facecolor='gold', edgecolor='black', alpha=0.7, label='Uncertain (Score 0.3 - 0.7)'),
    Patch(facecolor='firebrick', edgecolor='black', alpha=0.7, label='Likely F < 30% (Score < 0.3)')
]

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()