updated function signatures, type hints, and docstrings; modified function implementations and variable names.

Author: Soham-KT

maths/weddles_rule.py

@@ -1,14 +1,16 @@
+from collections.abc import Callable
+
 import numpy as np
 from sympy import lambdify, symbols, sympify
 
 
-def get_inputs() -> tuple:
+def get_inputs() -> tuple[str, float, float]:
     """
     Get user input for the function, lower limit, and upper limit.
 
     Returns:
-        tuple: A tuple containing the function as a string, the lower limit (a),
-               and the upper limit (b) as floats.
+        Tuple[str, float, float]: A tuple containing the function as a string,
+        the lower limit (a), and the upper limit (b) as floats.
 
     Example:
         >>> from unittest.mock import patch
@@ -23,50 +25,47 @@ def get_inputs() -> tuple:
     return func, lower_limit, upper_limit
 
 
-def safe_function_eval(func_str: str) -> float:
+def safe_function_eval(func_str: str) -> Callable:
     """
     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.
+        Callable: A callable lambda function for numerical evaluation.
+
     Examples:
         >>> f = safe_function_eval('x**2')
         >>> f(3)
         9
-
         >>> f = safe_function_eval('sin(x)')
         >>> round(f(3.14), 2)
         0.0
-
         >>> f = safe_function_eval('x + x**2')
         >>> f(2)
         6
     """
     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: float, lower_limit: float, upper_limit: float, acc: int
-) -> tuple:
+def compute_table(func: Callable, lower_limit: float,
+                  upper_limit: float, acc: int) -> tuple[np.ndarray, float]:
     """
     Compute the table of function values based on the limits and accuracy.
 
     Args:
-        func (str): The mathematical function with the variable 'x' as a string.
+        func (Callable): The mathematical function as a callable.
         lower_limit (float): The lower limit of the integral.
         upper_limit (float): The upper limit of the integral.
         acc (int): The number of subdivisions for accuracy.
 
     Returns:
-        tuple: A tuple containing the table of values and the step size (h).
+        Tuple[np.ndarray, float]: A tuple containing the table
+        of values and the step size (h).
 
     Example:
         >>> compute_table(
@@ -79,21 +78,19 @@ def compute_table(
     n_points = acc * 6 + 1
     h = (upper_limit - lower_limit) / (n_points - 1)
     x_vals = np.linspace(lower_limit, upper_limit, n_points)
-
-    # Evaluate function values at all points
-    table = func(x_vals)
+    table = func(x_vals)  # Evaluate function values at all points
     return table, h
 
 
-def apply_weights(table: list) -> list:
+def apply_weights(table: list[float]) -> list[float]:
     """
-    Apply Simpson's rule weights to the values in the table.
+    Apply Weddle's rule weights to the values in the table.
 
     Args:
-        table (list): A list of computed function values.
+        table (List[float]): A list of computed function values.
 
     Returns:
-        list: A list of weighted values.
+        List[float]: A list of weighted values.
 
     Example:
         >>> apply_weights([0.0, 0.866, 1.0, 0.866, 0.0, -0.866, -1.0])
@@ -103,7 +100,7 @@ def apply_weights(table: list) -> list:
     for i in range(1, len(table) - 1):
         if i % 2 == 0 and i % 3 != 0:
             add.append(table[i])
-        if i % 2 != 0 and i % 3 != 0:
+        elif i % 2 != 0 and i % 3 != 0:
             add.append(5 * table[i])
         elif i % 6 == 0:
             add.append(2 * table[i])
@@ -112,13 +109,13 @@ def apply_weights(table: list) -> list:
     return add
 
 
-def compute_solution(add: list, table: list, step_size: float) -> float:
+def compute_solution(add: list[float], table: list[float], step_size: float) -> float:
     """
     Compute the final solution using the weighted values and table.
 
     Args:
-        add (list): A list of weighted values from apply_weights.
-        table (list): A list of function values.
+        add (List[float]): A list of weighted values from apply_weights.
+        table (List[float]): A list of function values.
         step_size (float): The step size calculated from the limits and accuracy.
 
     Returns:
@@ -137,15 +134,15 @@ def compute_solution(add: list, table: list, step_size: float) -> float:
 
     testmod()
 
-    func_str, a, b = get_inputs()
-    acc = 1
-    solution = None
+    # func_str, a, b = get_inputs()
+    # acc = 1
+    # solution = None
 
-    func = safe_function_eval(func_str)
-    while acc <= 100_000:
-        table, h = compute_table(func, a, b, acc)
-        add = apply_weights(table)
-        solution = compute_solution(add, table, h)
-        acc *= 10
+    # func = safe_function_eval(func_str)
+    # while acc <= 100_000:
+    #     table, h = compute_table(func, a, b, acc)
+    #     add = apply_weights(table)
+    #     solution = compute_solution(add, table, h)
+    #     acc *= 10
 
-    print(f"Solution: {solution}")
+    # print(f"Solution: {solution}")