Monday, October 21, 2024
Sunday, September 8, 2024
Python Code for Linear Least Square Fit
import numpy as np import matplotlib.pyplot as plt # Example dataset data = np.loadtxt('LeastSquare.txt') x = data[:, 0] # First column: independent variable y = data[:, 1] # Second column: dependent variable # Perform linear least squares fit slope, intercept = np.polyfit(x, y, 1) # Generate line of best fit y_fit = slope * x + intercept # Print the results print(f"Slope: %.2f" % slope) print(f"Intercept: %.2f" % intercept) # Plot data points and line of best fit plt.scatter(x, y, label='Data Points') plt.plot(x, y_fit, color='red', label='Best Fit Line') plt.xlabel('x') plt.ylabel('y') plt.legend() plt.show() # Write result data to a text file with open('LeastSquareResult.txt', 'w') as file: # Write the header file.write('x\ty\ty_fit\n') # Write the data for c1, c2, c3 in zip(x, y, y_fit): file.write(f'{c1}\t{c2}\t{c3}\n') print("Data written to LeastSquareResult.txt")
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