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

class TestTask(unittest.TestCase):
    def test_returns_correct_length(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.approximate_curve_with_polygon(np.sin, (0, np.pi), 5)
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == 5,
            "Returned list has correct number of vertices (5)",
            f"Expected 5 vertices, got {len(result) if isinstance(result, list) else 'not a list'}",
        )

    def test_returns_tuples_of_xy(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.approximate_curve_with_polygon(np.sin, (0, np.pi), 4)
        all_tuples = all(isinstance(pt, tuple) and len(pt) == 2 for pt in result)
        _dynamic_test(
            self,
            all_tuples,
            "All elements are tuples of length 2 (x, y)",
            f"Not all elements are tuples of length 2: {result}",
        )
        all_nums = all(isinstance(pt[0], (int, float, np.floating)) and isinstance(pt[1], (int, float, np.floating)) for pt in result)
        _dynamic_test(
            self,
            all_nums,
            "All tuple elements are numbers (x, y)",
            f"Not all tuple elements are numbers: {result}",
        )

    def test_x_values_evenly_spaced(self):
        import user_code
        importlib.reload(user_code)
        interval = (0, 10)
        num = 6
        result = user_code.approximate_curve_with_polygon(np.cos, interval, num)
        x_vals = [pt[0] for pt in result]
        expected_x = np.linspace(interval[0], interval[1], num)
        close = np.allclose(x_vals, expected_x)
        _dynamic_test(
            self,
            close,
            "x-values are evenly spaced in the interval",
            f"x-values are not evenly spaced: {x_vals} vs expected {expected_x}",
        )

    def test_y_values_correct(self):
        import user_code
        importlib.reload(user_code)
        interval = (0, np.pi)
        num = 4
        result = user_code.approximate_curve_with_polygon(np.sin, interval, num)
        x_vals = np.linspace(interval[0], interval[1], num)
        y_expected = np.sin(x_vals)
        y_vals = [pt[1] for pt in result]
        close = np.allclose(y_vals, y_expected)
        _dynamic_test(
            self,
            close,
            "y-values are correct for the given curve_func",
            f"y-values do not match function output: {y_vals} vs expected {list(y_expected)}",
        )

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()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        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] = []
        changed = True

    return '\\n'.join(lines) if changed else code

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


test_main.py

Aprende los fundamentos de la modelización geométrica utilizando Python. Explora cómo representar, manipular y aproximar figuras geométricas de forma programática. Este curso abarca formas básicas, transformaciones y técnicas prácticas de aproximación, haciendo que los conceptos geométricos sean accesibles e interactivos.

Comience con los conceptos fundamentales del modelado geométrico y cómo Python puede utilizarse para representar y manipular formas básicas.

Profundiza en las transformaciones geométricas como la traslación, la rotación y la escala, y observa cómo afectan a las figuras en Python.

Aprenda a aproximar curvas y formas complejas utilizando polígonos y otras técnicas en Python.

Desafío: Aproximación de Curvas

Solución