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 (p53 Activation / DNA Damage)
bins_p53 = 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_p53 = np.array([41.10, 21.49, 8.83, 5.77, 4.42, 3.01, 2.93, 1.95, 2.12, 1.87, 1.15, 1.10, 1.32, 1.27, 0.64, 0.55, 0.30, 0.13, 0.04, 0.0, 0.0])

# 2. Statistical Calculations
mean_val = np.average(bins_p53, weights=freq_p53)

# PCHIP interpolation to capture the asymmetric exponential peak
interpolator = PchipInterpolator(bins_p53, freq_p53)
x_fit = np.linspace(0, 1.0, 500)
y_fit = interpolator(x_fit)
y_fit = np.clip(y_fit, 0, None)

# 3. Color Function (Stress 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_p53)
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_p53, freq_p53, width=0.04, color=face_colors, edgecolor=edge_colors, linewidth=1.5, zorder=2)

# 5. Tags and Titles
plt.xlabel('p53 Activation Probability (Cellular Stress / DNA Damage)', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Genotoxicity & Apoptotic Trigger: p53 Pathway', fontsize=14)

# 6. Structured Legend
legend_elements = [
    Patch(facecolor=mcolors.to_rgba('#008000', 0.6), edgecolor='#008000', label='Non-Genotoxic / Safe (< 0.4)'),
    Patch(facecolor=mcolors.to_rgba('#FFD700', 0.6), edgecolor='#FFD700', label='Moderate Stress Inducer (0.4 - 0.7)'),
    Patch(facecolor=mcolors.to_rgba('#B22222', 0.6), edgecolor='#B22222', label='High DNA Damage/Apoptosis (> 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, 45)
plt.tight_layout()

plt.show()