Patient Wait Time Cumulative KDE

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

np.random.seed(1111)
BG_COLOR = '#ffffff'
TEXT_COLOR = '#1f2937'
GRID_COLOR = '#e5e7eb'

# Wait time in minutes
urgent_care = np.random.exponential(15, 400) + 5
primary = np.random.exponential(25, 400) + 10
specialist = np.random.exponential(35, 400) + 15

fig, ax = plt.subplots(figsize=(10, 6), facecolor=BG_COLOR)
ax.set_facecolor(BG_COLOR)

x_range = np.linspace(0, 150, 500)

for data, color, label in [(urgent_care, '#F5276C', 'Urgent Care'), 
                            (primary, '#27D3F5', 'Primary Care'),
                            (specialist, '#F5B027', 'Specialist')]:
    kde = gaussian_kde(data)
    cdf = np.array([kde.integrate_box_1d(-np.inf, x) for x in x_range])
    ax.plot(x_range, cdf, color=color, linewidth=3, label=label)

ax.axvline(30, color='#22c55e', linestyle='--', alpha=0.7, linewidth=2)
ax.text(32, 0.3, '30min Target', color='#22c55e', fontsize=10)

ax.set_xlabel('Wait Time (minutes)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Cumulative Probability', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Patient Wait Time: Cumulative Distribution', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)

ax.tick_params(colors=TEXT_COLOR, labelsize=10)
for spine in ax.spines.values():
    spine.set_color(GRID_COLOR)
ax.yaxis.grid(True, color=GRID_COLOR, linewidth=0.5, alpha=0.7)
ax.legend(facecolor=BG_COLOR, edgecolor=GRID_COLOR, labelcolor=TEXT_COLOR, fontsize=10, loc='lower right')

plt.tight_layout()
plt.show()