Using fundamental constants is a key part of scientific computing. SciPy provides the `scipy.constants` module, which gives you access to many of the most important physical constants, such as **Planck's constant** (`h`) and the **speed of light** (`c`). These constants are essential when you need to solve physics or engineering problems that require precise calculations. One common scenario is calculating the **energy of a photon** when you know its frequency, which is a practical task in physics, chemistry, and engineering.


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

class TestTask(unittest.TestCase):
    def test_photon_energy_5e14(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.photon_energy(5e14)
        expected = getattr(user_code, 'h', None) * 5e14
        _dynamic_test(
            self,
            math.isclose(result, expected, rel_tol=1e-12),
            "Function returns correct energy for frequency 5e14 Hz",
            f"Expected {expected}, got {result}",
        )

    def test_photon_energy_zero(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.photon_energy(0)
        _dynamic_test(
            self,
            result == 0.0,
            "Function returns 0.0 for frequency 0",
            f"Expected 0.0, got {result}",
        )

    def test_photon_energy_1e15(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.photon_energy(1e15)
        expected = getattr(user_code, 'h', None) * 1e15
        _dynamic_test(
            self,
            math.isclose(result, expected, rel_tol=1e-12),
            "Function returns correct energy for frequency 1e15 Hz",
            f"Expected {expected}, got {result}",
        )

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

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

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Challenge: Constants in Scientific Calculations

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Challenge: Constants in Scientific Calculations

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