
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

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: Curve Approximation

Ratkaisu