In many scientific and engineering applications, you often need to calculate the area under a curve when an exact formula for the integral is not available. This is common in real-world scenarios, such as determining the total distance traveled by an object when you know its velocity at different times but do not have a simple equation for the path. You can use numerical integration to approximate this area efficiently with SciPy's `scipy.integrate.quad` function.


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

class TestTask(unittest.TestCase):
    def test_total_distance_0_to_5(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.total_distance_traveled(0, 5)
        # Analytical integral: int(3t^2+2t+1) dt from 0 to 5 = [t^3 + t^2 + t] from 0 to 5 = (125+25+5)-(0) = 155
        expected = 155.0
        _dynamic_test(
            self,
            math.isclose(result, expected, rel_tol=1e-6),
            "Correct total distance for interval [0, 5]",
            f"Expected {expected}, got {result} for [0, 5]",
        )

    def test_total_distance_2_to_8(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.total_distance_traveled(2, 8)
        # int(3t^2+2t+1) dt = [t^3 + t^2 + t] from 2 to 8
        # At 8: 512 + 64 + 8 = 584
        # At 2: 8 + 4 + 2 = 14
        expected = 584 - 14
        _dynamic_test(
            self,
            math.isclose(result, expected, rel_tol=1e-6),
            "Correct total distance for interval [2, 8]",
            f"Expected {expected}, got {result} for [2, 8]",
        )

    def test_total_distance_same_limits(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.total_distance_traveled(3, 3)
        expected = 0.0
        _dynamic_test(
            self,
            math.isclose(result, expected, rel_tol=1e-12),
            "Returns zero when start_time == end_time",
            f"Expected 0.0, got {result} for [3, 3]",
        )

    def test_total_distance_reversed_limits(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.total_distance_traveled(5, 0)
        # Should be negative of [0,5] case
        expected = -155.0
        _dynamic_test(
            self,
            math.isclose(result, expected, rel_tol=1e-6),
            "Returns negative distance for reversed interval",
            f"Expected {expected}, got {result} for [5, 0]",
        )

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.

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

Challenge: Area Under a Curve

Soluzione

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Challenge: Area Under a Curve

Soluzione