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. HSE data
datos_crudos = """Bin Center % Frequency
0 48.1953290870488
0.05 23.3970276008493
0.1 10.1910828025478
0.15 5.1380042462845
0.2 3.26963906581741
0.25 2.63269639065817
0.3 1.52866242038217
0.35 1.23142250530786
0.4 1.10403397027601
0.45 1.06157112526539
0.5 0.467091295116773
0.55 0.59447983014862
0.6 0.467091295116773
0.65 0.339702760084926
0.7 0.254777070063694
0.75 0
0.8 0.0849256900212314
0.85 0.0424628450106157
0.9 0
0.95 0
1 0"""
# 2. PROCESSING
lineas = datos_crudos.strip().split('\n')[1:]
bins_array = []
freq_array = []
for linea in lineas:
b, f = linea.strip().split('\t')
bins_array.append(float(b))
freq_array.append(float(f))
bins = np.array(bins_array)
freq = np.array(freq_array)
mean_val = np.average(bins, weights=freq)
interpolator = PchipInterpolator(bins, freq)
x_fit = np.linspace(min(bins), max(bins), 500)
y_fit = interpolator(x_fit)
y_fit = np.clip(y_fit, 0, None)
def get_colors(b_array):
return ['#008000' if b < 0.3 else '#FFD700' if b <= 0.7 else '#B22222' for b in b_array]
colors_hex = get_colors(bins)
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]
# 3. CREATION OF THE GRAPH
plt.figure(figsize=(7, 6))
plt.bar(bins, freq, width=0.04, color=face_colors, edgecolor=edge_colors, linewidth=1.5, zorder=2)
# 4. LABELS AND TITLES
plt.xlabel('HSE Expression Potential', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Cellular Stress Response: Heat Shock Element (HSE)', fontsize=14)
legend_elements = [
Patch(facecolor=mcolors.to_rgba('#008000', 0.6), edgecolor='#008000', label='Non-Toxic / Safe (< 0.3)'),
Patch(facecolor=mcolors.to_rgba('#FFD700', 0.6), edgecolor='#FFD700', label='Moderate Stress (0.3 - 0.7)'),
Patch(facecolor=mcolors.to_rgba('#B22222', 0.6), edgecolor='#B22222', label='High Cell Stress (> 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, max(freq) * 1.15)
plt.tight_layout()
plt.show()