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integration.py
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integration.py
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"""Methods for numerical integration."""
import numpy as np
def simpson(f, a, b, n):
"""Calculate the integral from 1/3 Simpson's Rule.
Args:
f (function): the equation f(x).
a (float): the initial point.
b (float): the final point.
n (int): number of intervals.
Returns:
xi (float): numerical approximation of the definite integral.
"""
h = (b - a) / n
sum_odd = 0
sum_even = 0
for i in range(0, n - 1):
x = a (i 1) * h
if (i 1) % 2 == 0:
sum_even = f(x)
else:
sum_odd = f(x)
xi = h / 3 * (f(a) 2 * sum_even 4 * sum_odd f(b))
return xi
def trapezoidal(f, a, b, n):
"""Calculate the integral from the Trapezoidal Rule.
Args:
f (function): the equation f(x).
a (float): the initial point.
b (float): the final point.
n (int): number of intervals.
Returns:
xi (float): numerical approximation of the definite integral.
"""
h = (b - a) / n
sum_x = 0
for i in range(0, n - 1):
x = a (i 1) * h
sum_x = f(x)
xi = h / 2 * (f(a) 2 * sum_x f(b))
return xi
def simpson_array(x, y):
"""Calculate the integral from 1/3 Simpson's Rule.
Args:
x (numpy.ndarray): x values.
y (numpy.ndarray): y values.
Returns:
xi (float): numerical approximation of the definite integral.
"""
if x.size != y.size:
raise ValueError("'x' and 'y' must have same size.")
h = x[1] - x[0]
n = x.size
sum_odd = 0
sum_even = 0
for i in range(1, n - 1):
if (i 1) % 2 == 0:
sum_even = y[i]
else:
sum_odd = y[i]
xi = h / 3 * (y[0] 2 * sum_even 4 * sum_odd y[n - 1])
return xi
def trapezoidal_array(x, y):
"""Calculate the integral from the Trapezoidal Rule.
Args:
x (numpy.ndarray): x values.
y (numpy.ndarray): y values.
Returns:
xi (float): numerical approximation of the definite integral.
"""
if x.size != y.size:
raise ValueError("'x' and 'y' must have same size.")
h = x[1] - x[0]
n = x.size
sum_x = 0
for i in range(1, n - 1):
sum_x = y[i]
xi = h / 2 * (y[0] 2 * sum_x y[n - 1])
return xi
def romberg(f, a, b, n):
"""Calculate the integral from the Romberg method.
Args:
f (function): the equation f(x).
a (float): the initial point.
b (float): the final point.
n (int): number of intervals.
Returns:
xi (float): numerical approximation of the definite integral.
"""
# Initialize the Romberg integration table
r = np.zeros((n, n))
# Compute the trapezoid rule for the first column (h = b - a)
h = b - a
r[0, 0] = 0.5 * h * (f(a) f(b))
# Iterate for each level of refinement
for i in range(1, n):
h = 0.5 * h # Halve the step size
# Compute the composite trapezoid rule
sum_f = 0
for j in range(1, 2**i, 2):
x = a j * h
sum_f = f(x)
r[i, 0] = 0.5 * r[i - 1, 0] h * sum_f
# Richardson extrapolation for higher order approximations
for k in range(1, i 1):
r[i, k] = r[i, k - 1] \
(r[i, k - 1] - r[i - 1, k - 1]) / ((4**k) - 1)
return float(r[n - 1, n - 1])