
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import user_code

class TestTask(unittest.TestCase):
    def setUp(self):
        # Save the current figure state
        plt.close('all')

    def test_plot_original_and_transformed(self):
        # Test with a simple quadrilateral and a translation
        quad = [(0,0), (1,0), (1,1), (0,1)]
        T = np.array([
            [1, 0, 2],
            [0, 1, 3],
            [0, 0, 1]
        ])
        fig = plt.figure()
        user_code.apply_transformations_and_plot(quad, [T])
        # There should be two lines (original and transformed)
        ax = plt.gca()
        lines = ax.get_lines()
        _dynamic_test(
            self,
            len(lines) == 2,
            "Both original and transformed quadrilaterals are plotted.",
            f"Expected 2 lines for original and transformed shapes, got {len(lines)}."
        )

    def test_vertices_connected_and_closed(self):
        quad = [(0,0), (3,0), (3,2), (0,2)]
        S = np.array([
            [2,0,0],
            [0,0.5,0],
            [0,0,1]
        ])
        plt.figure()
        user_code.apply_transformations_and_plot(quad, [S])
        ax = plt.gca()
        lines = ax.get_lines()
        # Check that the first and last vertices are the same (closed shape)
        for line in lines:
            xdata = line.get_xdata()
            ydata = line.get_ydata()
            _dynamic_test(
                self,
                np.isclose(xdata[0], xdata[-1]) and np.isclose(ydata[0], ydata[-1]),
                "Quadrilateral is closed (first and last vertex are the same).",
                f"Quadrilateral is not closed: x0={xdata[0]}, xN={xdata[-1]}, y0={ydata[0]}, yN={ydata[-1]}"
            )

    def test_transformation_order(self):
        # Test that transformations are applied in the correct order
        quad = [(0,0), (1,0), (1,1), (0,1)]
        # Scaling by 2, then translation by (1,1)
        S = np.array([
            [2,0,0],
            [0,2,0],
            [0,0,1]
        ])
        T = np.array([
            [1,0,1],
            [0,1,1],
            [0,0,1]
        ])
        # If order is S then T, (0,0) -> (0,0) -> (1,1), (1,0) -> (2,0) -> (3,1)
        plt.figure()
        user_code.apply_transformations_and_plot(quad, [T, S])
        ax = plt.gca()
        lines = ax.get_lines()
        # The second line is the transformed quadrilateral
        xdata = lines[1].get_xdata()
        ydata = lines[1].get_ydata()
        # The transformed first vertex should be (2,2) (scale then translate)
        _dynamic_test(
            self,
            np.isclose(xdata[0], 2) and np.isclose(ydata[0], 2),
            "Transformations are applied in the correct order.",
            f"Expected first transformed vertex at (2,2), got ({xdata[0]},{ydata[0]})"
        )

    def test_any_valid_quadrilateral(self):
        # Try with a non-rectangular quadrilateral and a rotation
        quad = [(1,2), (4,2), (3,5), (0,4)]
        theta = np.pi / 4
        R = np.array([
            [np.cos(theta), -np.sin(theta), 0],
            [np.sin(theta),  np.cos(theta), 0],
            [0, 0, 1]
        ])
        plt.figure()
        try:
            user_code.apply_transformations_and_plot(quad, [R])
            _dynamic_test(
                self,
                True,
                "Function handles any valid quadrilateral and transformation.",
                "Function failed on a valid quadrilateral and transformation."
            )
        except Exception as e:
            _dynamic_test(
                self,
                False,
                "Function handles any valid quadrilateral and transformation.",
                f"Function raised an exception: {e}"
            )

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: Combined Transformations

Ratkaisu