Sensor Calibration Data

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

np.random.seed(321)
expected = np.random.uniform(0, 100, 6000)
measured = expected + np.random.normal(0, 3, 6000) + 0.02 * expected

fig, ax = plt.subplots(figsize=(10, 8), facecolor='#fefce8')
ax.set_facecolor('#fef9c3')

colors = ['#fef9c3', '#fde047', '#facc15', '#eab308', '#ca8a04', '#a16207', '#854d0e', '#713f12']
cmap = LinearSegmentedColormap.from_list('amber', colors, N=256)

hb = ax.hexbin(expected, measured, gridsize=28, cmap=cmap, mincnt=1, edgecolors='white', linewidths=0.3)

cbar = plt.colorbar(hb, ax=ax, pad=0.02, shrink=0.8)
cbar.outline.set_edgecolor('#fde047')
cbar.ax.tick_params(colors='#854d0e', labelsize=9)
cbar.set_label('Samples', color='#854d0e', fontsize=10)

ax.plot([0, 100], [0, 100], '--', color='#ca8a04', linewidth=1.5, alpha=0.7, label='Ideal')

ax.set_xlabel('Expected Value', color='#854d0e', fontsize=11)
ax.set_ylabel('Measured Value', color='#854d0e', fontsize=11)
ax.tick_params(colors='#854d0e', labelsize=10)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color('#fde047')
ax.spines['bottom'].set_color('#fde047')
ax.legend(loc='upper left', facecolor='#fef9c3', edgecolor='#fde047')

plt.tight_layout()