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

class TestTask(unittest.TestCase):
    def test_chained_odds_squared_basic(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.chained_odds_squared(range(10))
        expected = [1, 9, 25, 49, 81]
        _dynamic_test(
            self,
            hasattr(user_code, 'chained_odds_squared') and callable(user_code.chained_odds_squared),
            "Function 'chained_odds_squared' is defined and callable.",
            "Function 'chained_odds_squared' is not defined or not callable."
        )
        _dynamic_test(
            self,
            hasattr(user_code, 'filter_odds') and callable(user_code.filter_odds),
            "Function 'filter_odds' is defined and callable.",
            "Function 'filter_odds' is not defined or not callable."
        )
        _dynamic_test(
            self,
            hasattr(user_code, 'square_numbers') and callable(user_code.square_numbers),
            "Function 'square_numbers' is defined and callable.",
            "Function 'square_numbers' is not defined or not callable."
        )
        _dynamic_test(
            self,
            result is not None and list(result) == expected,
            f"Correct output list for range(10): {expected}",
            f"Expected {expected}, got {list(user_code.chained_odds_squared(range(10)))}"
        )
    def test_chained_odds_squared_empty(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.chained_odds_squared([])
        expected = []
        _dynamic_test(
            self,
            result is not None and list(result) == expected,
            "Returns empty list for empty input.",
            f"Expected empty list, got {list(user_code.chained_odds_squared([]))}"
        )
    def test_chained_odds_squared_all_even(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.chained_odds_squared([2, 4, 6, 8])
        expected = []
        _dynamic_test(
            self,
            result is not None and list(result) == expected,
            "Returns empty list for all even input.",
            f"Expected empty list, got {list(user_code.chained_odds_squared([2,4,6,8]))}"
        )
    def test_chained_odds_squared_negative_numbers(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.chained_odds_squared([-5, -4, -3, -2, -1, 0, 1])
        expected = [25, 9, 1, 1]
        _dynamic_test(
            self,
            result is not None and list(result) == expected,
            "Correctly handles negative and zero values.",
            f"Expected {expected}, got {list(user_code.chained_odds_squared([-5, -4, -3, -2, -1, 0, 1]))}"
        )
    def test_chained_odds_squared_non_list_iterable(self):
        import user_code
        importlib.reload(user_code)
        def gen():
            for i in range(3, 8):
                yield i
        result = user_code.chained_odds_squared(gen())
        expected = [9, 25, 49]
        _dynamic_test(
            self,
            result is not None and list(result) == expected,
            "Works with any iterable, not just lists.",
            f"Expected {expected}, got {list(user_code.chained_odds_squared(range(3,8)))}"
        )

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

Beherrschung von Packing/Unpacking, flexiblen Funktionsargumenten, Higher-Order-Funktionen, Lambdas, Variablenbereich, Closures, Iteratoren und Dekoratoren. Konzipiert für Lernende, die einen Einführungskurs in Python abgeschlossen haben und effizienteren, ausdrucksstärkeren und leistungsfähigeren Code schreiben möchten.

Erkunden Sie die Kunst des Packens und Entpackens von Listen, Tupeln und Dictionaries, um ausdrucksstärkeren und effizienteren Python-Code zu schreiben.

Tauchen Sie ein in die Welt der höherwertigen Funktionen, das Übergeben von Funktionen als Argumente und das Schreiben prägnanter anonymer Funktionen mit Lambda.

Entschlüsseln der Geheimnisse des Variablenbereichs und Erlernen, wie verschachtelte Funktionen und Closures eine leistungsstarke Kapselung ermöglichen.

Vertiefung in das Iterationsprotokoll von Python, benutzerdefinierte Iteratoren und Generatorfunktionen für effiziente Datenverarbeitung.

Challenge: Chaining and Composing Iterators

Lösung