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Soham-KT 2024-10-06 10:35:34 +05:30
parent 48ca80fb21
commit cc5911a3ad
1 changed files with 39 additions and 14 deletions
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51
maths/weddles_rule.py
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@ -1,4 +1,5 @@
from math import * import numpy as np
from sympy import lambdify, symbols, sympify
def get_inputs(): def get_inputs():
@ -6,7 +7,8 @@ def get_inputs():
Get user input for the function, lower limit, and upper limit. Get user input for the function, lower limit, and upper limit.
Returns: Returns:
tuple: A tuple containing the function as a string, the lower limit (a), and the upper limit (b) as floats. tuple: A tuple containing the function as a string, the lower limit (a),
and the upper limit (b) as floats.
Example: Example:
>>> from unittest.mock import patch >>> from unittest.mock import patch
@ -21,6 +23,24 @@ def get_inputs():
return func, a, b return func, a, b
def safe_function_eval(func_str):
"""
Safely evaluates the function by substituting x value using sympy.
Args:
func_str (str): Function expression as a string.
Returns:
float: The evaluated function result.
"""
x = symbols('x')
func_expr = sympify(func_str)
# Convert the function to a callable lambda function
lambda_func = lambdify(x, func_expr, modules=["numpy"])
return lambda_func
def compute_table(func, a, b, acc): def compute_table(func, a, b, acc):
""" """
Compute the table of function values based on the limits and accuracy. Compute the table of function values based on the limits and accuracy.
@ -35,14 +55,19 @@ def compute_table(func, a, b, acc):
tuple: A tuple containing the table of values and the step size (h). tuple: A tuple containing the table of values and the step size (h).
Example: Example:
>>> compute_table('1/(1+x**2)', 1, -1, 1) >>> compute_table(
([0.5, 0.4235294117647058, 0.36, 0.3076923076923077, 0.26470588235294124, 0.22929936305732482, 0.2], -0.3333333333333333) ... safe_function_eval('1/(1+x**2)'), 1, -1, 1
... )
(array([0.5 , 0.69230769, 0.9 , 1. , 0.9 ,
0.69230769, 0.5 ]), -0.3333333333333333)
""" """
h = (b - a) / (acc * 6) # Weddle's rule requires number of intervals as a multiple of 6 for accuracy
table = [0 for _ in range(acc * 6 + 1)] n_points = acc * 6 + 1
for j in range(acc * 6 + 1): h = (b - a) / (n_points - 1)
x = a + j / (acc * 6) x_vals = np.linspace(a, b, n_points)
table[j] = eval(func)
# Evaluate function values at all points
table = func(x_vals)
return table, h return table, h
@ -86,7 +111,8 @@ def compute_solution(add, table, h):
float: The final computed integral solution. float: The final computed integral solution.
Example: Example:
>>> compute_solution([4.33, 6.0, 0.0, -4.33], [0.0, 0.866, 1.0, 0.866, 0.0, -0.866, -1.0], 0.5235983333333333) >>> compute_solution([4.33, 6.0, 0.0, -4.33], [0.0, 0.866, 1.0, 0.866, 0.0,
... -0.866, -1.0], 0.5235983333333333)
0.7853975 0.7853975
""" """
return 0.3 * h * (sum(add) + table[0] + table[-1]) return 0.3 * h * (sum(add) + table[0] + table[-1])
@ -94,17 +120,16 @@ def compute_solution(add, table, h):
if __name__ == "__main__": if __name__ == "__main__":
from doctest import testmod from doctest import testmod
testmod() testmod()
func, a, b = get_inputs() func, a, b = get_inputs()
acc = 1 acc = 1
solution = None solution = None
while acc <= 100000: while acc <= 100_000:
table, h = compute_table(func, a, b, acc) table, h = compute_table(func, a, b, acc)
add = apply_weights(table) add = apply_weights(table)
solution = compute_solution(add, table, h) solution = compute_solution(add, table, h)
acc *= 10 acc *= 10
print(f"Solution: {solution}") print(f'Solution: {solution}')