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

# 1. Original Data (Ames Mutagenicity)
bin_centers = [0.0, 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, 0.95, 1.0]

frequencies = [0.04, 8.54, 12.99, 11.85, 9.81, 9.34, 8.20, 5.69, 5.77, 4.88, 3.91, 4.16, 2.76, 2.59, 2.93, 1.49, 1.66, 1.70, 0.93, 0.72, 0.04]

# 2. Generate the smoothed curve (Spline)
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 (Green < 0.3, Gold up to 0.7, Red >= 0.7)
colors = []
for val in bin_centers:
    if val < 0.3:
        colors.append('green')
    elif val < 0.7:
        colors.append('gold')
    else:
        colors.append('firebrick')

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

# Bars
plt.bar(bin_centers, frequencies, width=0.04, color=colors, edgecolor='black', alpha=0.6, label='Observed Data')

# 5. Tags and Titles
plt.xlabel('Probability of Ames Mutagenicity', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Ames Mutagenicity Distribution', fontsize=14)

# Axle settings
plt.xticks(np.arange(0.0, 1.05, 0.1))
plt.xlim(-0.05, 1.05)
plt.ylim(0, 15)

# 6. Legend
legend_elements = [
    Patch(facecolor='green', edgecolor='black', label='Ames Negative (Safe)'),
    Patch(facecolor='gold', edgecolor='black', label='Structural Warning'),
    Patch(facecolor='firebrick', edgecolor='black', label='Ames Positive (DNA Hazard)'),
]

plt.legend(handles=legend_elements, loc='upper right', framealpha=0.95, fontsize=10)

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

plt.show()