import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.patches import Patch
import numpy as np
from scipy.interpolate import PchipInterpolator

# 1. Original Data (Aromatase Endocrine Disruption)
bins_aro = np.array([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, 1.0])
freq_aro = np.array([26.71, 19.02, 10.23, 7.09, 4.46, 4.25, 4.16, 3.10, 2.46, 2.00, 2.00, 1.95, 2.04, 2.00, 2.29, 2.21, 2.76, 1.27, 0.0, 0.0, 0.0])

# 2. Statistical Calculations
mean_val = np.average(bins_aro, weights=freq_aro)

# PCHIP interpolation for empirical trend
interpolator = PchipInterpolator(bins_aro, freq_aro)
x_fit = np.linspace(0, 1.0, 500)
y_fit = interpolator(x_fit)
y_fit = np.clip(y_fit, 0, None)

# 3. Color Function (Aromatase Probability Traffic Light)
def get_colors(bins):
    return ['#008000' if b < 0.4 else '#FFD700' if b <= 0.7 else '#B22222' for b in bins]
colors_hex = get_colors(bins_aro)
face_colors = [mcolors.to_rgba(c, alpha=0.60) for c in colors_hex]
edge_colors = [mcolors.to_rgba(c, alpha=0.90) for c in colors_hex]

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

# Draw bars and empirical curve
plt.bar(bins_aro, freq_aro, width=0.04, color=face_colors, edgecolor=edge_colors, linewidth=1.5, zorder=2)

# 5. Tags and Titles
plt.xlabel('Aromatase Interaction Probability (EDC Potential)', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Endocrine Disruption: Aromatase Interaction', fontsize=14)

# 6. Structured Legend
legend_elements = [
    Patch(facecolor=mcolors.to_rgba('#008000', 0.6), edgecolor='#008000', label='Non-Interactor (< 0.4)'),
    Patch(facecolor=mcolors.to_rgba('#FFD700', 0.6), edgecolor='#FFD700', label='Weak/Moderate Interaction (0.4 - 0.7)'),
    Patch(facecolor=mcolors.to_rgba('#B22222', 0.6), edgecolor='#B22222', label='Strong Aromatase Disruptor (> 0.7)')
]
plt.legend(handles=legend_elements, loc='upper right', framealpha=0.95, fontsize=10)

plt.grid(axis='y', linestyle=':', alpha=0.7, zorder=0)
plt.xlim(-0.05, 1.05)
plt.ylim(0, 30)
plt.tight_layout()

plt.show()