Introduction to SciPy

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

def linear_model(x, a, b):
    return a * x + b

def main():
    print("This program demonstrates how to use SciPy to fit a linear model to a set of data points.")
    
    # Generate synthetic data
    x_data = np.linspace(0, 10, 20)
    y_data = 2 * x_data + 1 + np.random.normal(0, 1, len(x_data))

    # Fit the linear model to the data
    params, _ = curve_fit(linear_model, x_data, y_data)

    # Generate the fitted curve
    x_fit = np.linspace(0, 10, 100)
    y_fit = linear_model(x_fit, *params)

    # Plot the data and the fitted curve
    plt.scatter(x_data, y_data, label='Data')
    plt.plot(x_fit, y_fit, color='red', label='Fitted Linear Model')

    plt.title('Linear Model Fitting using SciPy')
    plt.xlabel('x')
    plt.ylabel('y')
    plt.legend()
    plt.show()

if __name__ == "__main__":
    main()