
import unittest
import user_code
import ast
import re   
import unittest
import importlib
import numpy as np

class TestTask(unittest.TestCase):
    def test_returns_true_for_all_negative_real_parts(self):
        import user_code
        importlib.reload(user_code)
        # Stable system: eigenvalues -1, -2
        A = np.array([[-1, 0], [0, -2]])
        result = user_code.is_system_stable(A)
        _dynamic_test(
            self,
            result == True,
            "Returns True for matrix with eigenvalues -1, -2 (all negative real parts)",
            f"Expected True for stable system, got {result}",
        )

    def test_returns_false_for_non_negative_real_part(self):
        import user_code
        importlib.reload(user_code)
        # Unstable system: eigenvalues 1, -2
        B = np.array([[1, 0], [0, -2]])
        result = user_code.is_system_stable(B)
        _dynamic_test(
            self,
            result == False,
            "Returns False for matrix with eigenvalues 1, -2 (one non-negative real part)",
            f"Expected False for unstable system, got {result}",
        )

    def test_handles_complex_eigenvalues(self):
        import user_code
        importlib.reload(user_code)
        # Eigenvalues: -1+2j, -1-2j (real part -1)
        C = np.array([[-1, -2], [2, -1]])
        result = user_code.is_system_stable(C)
        _dynamic_test(
            self,
            result == True,
            "Returns True for matrix with complex eigenvalues with negative real parts",
            f"Expected True for stable system with complex eigenvalues, got {result}",
        )

    def test_handles_larger_matrix(self):
        import user_code
        importlib.reload(user_code)
        # 3x3 matrix, eigenvalues -1, -2, -3
        D = np.diag([-1, -2, -3])
        result = user_code.is_system_stable(D)
        _dynamic_test(
            self,
            result == True,
            "Returns True for 3x3 matrix with all negative real eigenvalues",
            f"Expected True for stable 3x3 system, got {result}",
        )

    def test_returns_false_for_zero_real_part(self):
        import user_code
        importlib.reload(user_code)
        # Eigenvalues: 0, -1
        E = np.array([[0, 0], [0, -1]])
        result = user_code.is_system_stable(E)
        _dynamic_test(
            self,
            result == False,
            "Returns False for matrix with eigenvalue 0 (real part not negative)",
            f"Expected False for system with eigenvalue 0, 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

Et omfattende kurs utviklet for studenter med solid matematisk bakgrunn og middels til avanserte Python-ferdigheter, med fokus på å utnytte SciPy for vitenskapelig databehandling, optimalisering, integrasjon og avanserte matematiske operasjoner.

Utforsk strukturen, kjernemodulene og essensielle funksjoner i SciPy for vitenskapelig databehandling.

Fordyp deg i SciPys kraftige lineær algebra-funksjoner for å løse matematiske problemer fra virkeligheten.

Bli ekspert på optimaliseringsteknikker og rotfinningsalgoritmer ved bruk av SciPy for vitenskapelige og tekniske problemer.

Utforsk avanserte matematiske operasjoner inkludert integrasjon, interpolasjon og grunnleggende signalbehandling med SciPy.

Utfordring: Egenverdiapplikasjoner

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