reminder to me about how to use pyplot and np.polyfit

pyplot_example.py

import numpy as np
import matplotlib.pyplot as plt

# Original data points
x_vals = np.array([-0.5, -23.6, -58.3])
y_vals = np.array([1, 47, 110])

# Linear regression (best-fit line)
coeffs = np.polyfit(x_vals, y_vals, 1)
linear_fit = coeffs[0] * x_vals + coeffs[1]

# f(x) = -2x
fx_test = -2 * x_vals

# Plotting
plt.figure(figsize=(8, 5))
plt.scatter(x_vals, y_vals, color='red', label='Data Points')
plt.plot(x_vals, linear_fit, label=f'Best Fit: {coeffs[0]:.4f}x + {coeffs[1]:.4f}', color='blue')
#plt.plot(x_vals, fx_test, label='f(x) = -2x', color='green', linestyle='--')

plt.title('Comparison of Function Fits')
plt.xlabel('x')
plt.ylabel('f(x)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

output looks like: