""" Numerical integration or quadrature for a smooth function f with known values at x_i This method is the classical approach of summing 'Equally Spaced Abscissas' method 2: "Simpson Rule" """ def method_2(boundary: list[int], steps: int) -> float: # "Simpson Rule" # int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn) """ Calculate the definite integral of a function using Simpson's Rule. :param boundary: A list containing the lower and upper bounds of integration. :param steps: The number of steps or resolution for the integration. :return: The approximate integral value. >>> round(method_2([0, 2, 4], 10), 10) 2.6666666667 >>> round(method_2([2, 0], 10), 10) -0.2666666667 >>> round(method_2([-2, -1], 10), 10) 2.172 >>> round(method_2([0, 1], 10), 10) 0.3333333333 >>> round(method_2([0, 2], 10), 10) 2.6666666667 >>> round(method_2([0, 2], 100), 10) 2.5621226667 >>> round(method_2([0, 1], 1000), 10) 0.3320026653 >>> round(method_2([0, 2], 0), 10) Traceback (most recent call last): ... ZeroDivisionError: Number of steps must be greater than zero >>> round(method_2([0, 2], -10), 10) Traceback (most recent call last): ... ZeroDivisionError: Number of steps must be greater than zero """ if steps <= 0: raise ZeroDivisionError("Number of steps must be greater than zero") h = (boundary[1] - boundary[0]) / steps a = boundary[0] b = boundary[1] x_i = make_points(a, b, h) y = 0.0 y += (h / 3.0) * f(a) cnt = 2 for i in x_i: y += (h / 3) * (4 - 2 * (cnt % 2)) * f(i) cnt += 1 y += (h / 3.0) * f(b) return y def make_points(a, b, h): x = a + h while x < (b - h): yield x x = x + h def f(x): # enter your function here y = (x - 0) * (x - 0) return y def main(): a = 0.0 # Lower bound of integration b = 1.0 # Upper bound of integration steps = 10.0 # number of steps or resolution boundary = [a, b] # boundary of integration y = method_2(boundary, steps) print(f"y = {y}") if __name__ == "__main__": import doctest doctest.testmod() main()