
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

Un curso integral diseñado para estudiantes con sólidos conocimientos matemáticos y habilidades intermedias o avanzadas en Python, enfocado en aprovechar SciPy para la computación científica, optimización, integración y operaciones matemáticas avanzadas.

Explora la estructura, los módulos principales y las características esenciales de SciPy para la computación científica.

Sumérgete en las potentes capacidades de álgebra lineal de SciPy para resolver problemas matemáticos del mundo real.

Domina técnicas de optimización y algoritmos de búsqueda de raíces utilizando SciPy para problemas científicos e ingenieriles.

Explora operaciones matemáticas avanzadas, incluyendo integración, interpolación y procesamiento básico de señales con SciPy.

Desafío: Aplicaciones de Valores Propios

Solución