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

# 1. Original Data (Eye Corrosion)
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 = [46.16, 15.21, 6.19, 3.40, 2.79, 2.47, 1.86, 1.66, 1.42, 1.09, 1.01, 1.13, 0.93, 1.09, 1.09, 1.50, 0.97, 1.50, 1.86, 2.43, 4.25]

# 2. Generate the smoothed curve (Spline) since it is NOT Gaussian
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 (Logic adapted to Eye Corrosion: Most are safe)
colors = []
for val in bin_centers:
    if val < 0.2:
        colors.append('green')
    elif val < 0.8:
        colors.append('gold')
    else:
        colors.append('firebrick')

# 4. Create the chart (Your 7x6 style)
plt.figure(figsize=(7, 6))

# Bars (Actual data)
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 Eye Corrosion', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Eye Corrosion Distribution', fontsize=14)

# Axle adjustments (Eye Corrosion reaches 46%, we raise the Y limit)
plt.xticks(np.arange(0.0, 1.05, 0.1))
plt.xlim(-0.05, 1.05)
plt.ylim(0, 50) 

# 6. Legend
legend_elements = [
    Patch(facecolor='green', edgecolor='black', label='Safe (Non-Corrosive)'),
    Patch(facecolor='gold', edgecolor='black', label='Potential Irritant'),
    Patch(facecolor='firebrick', edgecolor='black', label='Corrosive 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()