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

# 1. Original Data (ADT)
bin_centers = [0.1, 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 = [0.25, 0.47, 0.42, 0.55, 1.53, 1.78, 2.68, 3.65, 3.44, 5.27, 7.73, 9.13, 10.96, 13.16, 13.04, 12.06, 9.85, 3.99, 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. Standard colors
colors = []
for val in bin_centers:
    if val < 0.4:
        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 / Score of ADT', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Acute dermal toxicity Distribution', fontsize=14)

# Axle settings
plt.xticks(np.arange(0.0, 1.1, 0.1))
plt.xlim(0.0, 1.05)
plt.ylim(0, 16) 

# 6. Legend
legend_elements = [
    Patch(facecolor='green', edgecolor='black', label='Low Values'),
    Patch(facecolor='gold', edgecolor='black', label='Mid Values'),
    Patch(facecolor='firebrick', edgecolor='black', label='High Values'),
]

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

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

plt.show()