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

class TestTask(unittest.TestCase):
    def test_returns_correct_length(self):
        import user_code
        importlib.reload(user_code)
        points = user_code.approximate_ellipse(7, 2, 50)
        _dynamic_test(
            self,
            isinstance(points, list) and len(points) == 50,
            "Returned list has correct length for num_points=50",
            f"Returned list length is {len(points) if isinstance(points, list) else 'not a list'} instead of 50",
        )

    def test_points_are_tuples_of_floats(self):
        import user_code
        importlib.reload(user_code)
        points = user_code.approximate_ellipse(4, 1, 10)
        all_tuples = all(isinstance(pt, tuple) and len(pt) == 2 for pt in points)
        all_floats = all(isinstance(pt[0], float) and isinstance(pt[1], float) for pt in points)
        _dynamic_test(
            self,
            all_tuples and all_floats,
            "All points are tuples of two floats",
            "Not all points are tuples of two floats",
        )

    def test_first_and_last_points_not_identical(self):
        import user_code
        importlib.reload(user_code)
        points = user_code.approximate_ellipse(3, 2, 30)
        not_identical = points[0] != points[-1]
        _dynamic_test(
            self,
            not_identical,
            "First and last points are not identical",
            "First and last points should not be identical",
        )

    def test_points_on_ellipse(self):
        import user_code
        importlib.reload(user_code)
        a = 6
        b = 4
        num_points = 36
        points = user_code.approximate_ellipse(a, b, num_points)
        # Check a few random points for ellipse equation
        for i in [0, num_points // 4, num_points // 2]:
            x, y = points[i]
            lhs = (x / a) ** 2 + (y / b) ** 2
            _dynamic_test(
                self,
                abs(lhs - 1.0) < 1e-10,
                f"Point ({x:.4f}, {y:.4f}) satisfies the ellipse equation",
                f"Point ({x:.4f}, {y:.4f}) does not satisfy the ellipse equation (got {lhs})",
            )

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

Learn the fundamentals of geometric modelling using Python. Explore how to represent, manipulate, and approximate geometric figures programmatically. This course covers basic shapes, transformations, and practical approximation techniques, making geometric concepts accessible and interactive.

Get started with the core concepts of geometric modelling and how Python can be used to represent and manipulate basic shapes.

Delve into geometric transformations such as translation, rotation, and scaling, and see how they affect shapes in Python.

Learn how to approximate curves and complex shapes using polygons and other techniques in Python.

Challenge: Ellipse Approximation

Ratkaisu