import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
import numpy as np

# 1. Neurotoxicity Data
bin_centers = [-3.6, -3.4, -3.2, -3.0, -2.8, -2.6, -2.4, -2.2, -2.0, -1.8, -1.6, -1.4, -1.2, -1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2]

frequencies = [0.08, 0.20, 2.67, 9.75, 13.75, 17.11, 17.92, 19.17, 14.16, 3.40, 0.93, 0.61, 0.12, 0.04, 0.00, 0.00, 0.00, 0.00, 0.04, 0.04]

# 2. Gaussian Parameters (Provided)
amplitude = 19.98
mean = -2.428
sd = 0.4104

# Generate smooth curve
x_smooth = np.linspace(min(bin_centers), max(bin_centers), 300)
y_smooth = amplitude * np.exp(-0.5 * ((x_smooth - mean) / sd)**2)

# 3. Colors (Safety Traffic Light)
colors = []
for val in bin_centers:
    if val < -2.0:
        colors.append('mediumseagreen')
    elif val > -1.5:
        colors.append('firebrick')
    else:
        colors.append('gold')

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

# A. Bars
plt.bar(bin_centers, frequencies, width=0.18, color=colors, edgecolor='black', alpha=0.7, label='Data Frequency')

# B. Gaussian curve
plt.plot(x_smooth, y_smooth, color='orange', linewidth=2, label='Gaussian Fit')

# 5. Tags and Titles
plt.xlabel('Neurotoxicity Score (PLD50) [Lower is Safer]', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Neurotoxicity Risk Assessment', fontsize=14)

# Axle settings
plt.xticks(np.arange(-3.6, 0.4, 0.4))
plt.xlim(-3.8, 0.4)
plt.ylim(0, 34)

# 6. Legend
legend_elements = [
    Patch(facecolor='mediumseagreen', edgecolor='black', alpha=0.7, label='Low Risk / Safe (Score < -2.0)'),
    Patch(facecolor='gold', edgecolor='black', alpha=0.7, label='Moderate Risk / Neuroactive (-2.0 to -1.5)'),
    Patch(facecolor='firebrick', edgecolor='black', alpha=0.7, label='High Neurotoxicity Potential (Score > -1.5)'),
    Line2D([0], [0], color='orange', lw=2, label=f'Fit (Mean={mean}, SD={sd})'),
]

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

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

plt.show()