In many scientific and engineering applications, you often encounter nonlinear equations that cannot be solved analytically and require numerical methods. The `scipy.optimize` module provides powerful algorithms to find the roots of such equations, enabling you to model and analyze real-world systems. In this challenge, you will apply your understanding of root-finding by solving a nonlinear equation that represents a physical process using `scipy.optimize.root`.


import unittest
import user_code
import ast
import re   
import unittest
import importlib
import math
import re

class TestTask(unittest.TestCase):
    def test_solution_is_float(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'solve_nonlinear_equation', None)
        _dynamic_test(
            self,
            callable(func),
            "solve_nonlinear_equation is defined and callable",
            "solve_nonlinear_equation function is missing or not callable"
        )
        result = func()
        _dynamic_test(
            self,
            isinstance(result, float),
            "Returned value is a float",
            f"Returned value is not a float: {result}"
        )

    def test_equation_is_solved(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'solve_nonlinear_equation', None)
        eq_func = getattr(user_code, 'physical_process_equation', None)
        _dynamic_test(
            self,
            callable(func) and callable(eq_func),
            "Functions are callable",
            "solve_nonlinear_equation or physical_process_equation is missing or not callable"
        )
        root_val = func()
        val = eq_func(root_val)
        _dynamic_test(
            self,
            abs(val) < 1e-6,
            f"Root value {root_val} satisfies the equation (f(x) ≈ 0)",
            f"Root value {root_val} does not satisfy the equation: f(x) = {val}"
        )

    def test_initial_guess_used(self):
        code = ""
        with open("user_code.py", "r") as f:
            code = f.read()
        # Remove all whitespace for robust matching
        code_no_ws = re.sub(r"\s+", "", code)
        found = ("root(physical_process_equation,2.0" in code_no_ws)
        _dynamic_test(
            self,
            found,
            "Initial guess 2.0 is used in root-finding",
            "Expected initial guess of 2.0 in root-finding call"
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\s{2,}", " ", text)
    text = re.sub(r"\s*([,:?])\s*", r"\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    for i, node in enumerate(tree.body):
        if isinstance(node, ast.Assign):
            for target in node.targets:
                if isinstance(target, ast.Name) and target.id == var_name:
                    start_line = node.lineno - 1
                    line = lines[start_line]
                    indent = ' ' * (len(line) - len(line.lstrip()))
                    lines[start_line] = f"{indent}{var_name} = {value}"
                    next_line = len(lines)
                    for next_node in tree.body[i+1:]:
                        if hasattr(next_node, 'lineno'):
                            next_line = next_node.lineno - 1
                            break
                    if next_line > start_line + 1:
                        lines[start_line+1:next_line] = []
                    
                    return '\\n'.join(lines)
    return code

if __name__ == "__main__":
    unittest.main()


test_main.py

A comprehensive course designed for students with strong math backgrounds and intermediate to advanced Python skills, focusing on leveraging SciPy for scientific computing, optimization, integration, and advanced mathematical operations.

Explore the structure, core modules, and essential features of SciPy for scientific computing.

Dive into SciPy's powerful linear algebra capabilities for solving real-world mathematical problems.

Master optimization techniques and root-finding algorithms using SciPy for scientific and engineering problems.

Explore advanced mathematical operations including integration, interpolation, and basic signal processing with SciPy.

Challenge: Solving Nonlinear Equations

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Challenge: Solving Nonlinear Equations

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