API Latency vs Payload Size

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

np.random.seed(42)

# API performance data
payload_size = np.random.exponential(500, 5000)  # KB
latency = 10 + payload_size * 0.05 + np.random.exponential(20, 5000)

fig, ax = plt.subplots(figsize=(10, 8), facecolor='#020B14')
ax.set_facecolor('#020B14')

# Custom colormap: deep_purple to pink
colors = ['#020B14', '#1a0d2e', '#5314E6', '#F527B0']
cmap = LinearSegmentedColormap.from_list('purple_pink', colors, N=256)

h = ax.hist2d(payload_size, latency, bins=50, cmap=cmap, cmin=1)
cbar = plt.colorbar(h[3], ax=ax, pad=0.02)
cbar.set_label('Request Count', color='white', fontsize=11)
cbar.ax.yaxis.set_tick_params(color='white')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='white')

ax.set_xlabel('Payload Size (KB)', fontsize=11, color='white', fontweight='500')
ax.set_ylabel('Latency (ms)', fontsize=11, color='white', fontweight='500')
ax.set_title('API Latency vs Payload Size', fontsize=14, color='white', fontweight='bold', pad=15)

ax.tick_params(colors='white', labelsize=9)
for spine in ax.spines.values():
    spine.set_color('#333333')

plt.tight_layout()
plt.show()