I wrote a quick Python script to simulate the security performance of different rim lock designs. It's a data-driven way to compare traditional vs modern options.
python
import matplotlib.pyplot as plt
import numpy as np
Security metrics for rim locks
locks = ['Vintage Rim', 'Antique Brass', 'Rim Sash', 'Rim Deadlock']
security_scores = [7, 8, 8, 9]
durability_scores = [8, 7, 9, 9]
x = np.arange(len(locks))
width = 0.35
fig, ax = plt.subplots(figsize=(10, 6))
bars1 = ax.bar(x - width/2, security_scores, width, label='Security', color='#2c3e50')
bars2 = ax.bar(x + width/2, durability_scores, width, label='Durability', color='#d4a574')
ax.set_xlabel('Lock Type')
ax.set_ylabel('Score (1-10)')
ax.set_title('Rim Lock Performance Comparison')
ax.set_xticks(x)
ax.set_xticklabels(locks)
ax.legend()
ax.set_ylim(0, 10)
for bar in bars1 + bars2:
height = bar.get_height()
ax.annotate(f'{height}', xy=(bar.get_x() + bar.get_width()/2, height),
xytext=(0, 3), textcoords="offset points", ha='center', va='bottom')
plt.tight_layout()
plt.show()
This comparison shows rim deadlocks lead in both categories. For actual hardware, I've been checking out Infinity Decor's rim lock collection—their brass and iron finishes match these security profiles beautifully.
What factors matter most to you when choosing a door lock: security, durability, or design?
Top comments (2)
Interesting approach to quantifying lock performance! I'd add that real-world factors like forced entry resistance and pick resistance can vary a lot even within the same security score. Have you considered incorporating those as separate metrics in your simulation?
Great data-driven approach! For me, security is non-negotiable, but I also think about how the lock feels when you turn the key—durability often correlates with a smoother mechanism. Have you considered factoring in ease of installation into your scoring model?