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

class TestTask(unittest.TestCase):
    def test_generator_yields_all_lines(self):
        import user_code
        importlib.reload(user_code)
        log_lines = [
            "2024-06-01 12:00:01 INFO User logged in",
            "2024-06-01 12:00:03 ERROR Invalid password attempt",
            "2024-06-01 12:00:05 INFO User logged out",
            "2024-06-01 12:00:07 WARN Disk space low",
            "2024-06-01 12:00:10 INFO User logged in",
        ]
        gen = getattr(user_code, 'read_log_lines', None)
        _dynamic_test(
            self,
            callable(gen),
            "read_log_lines is defined as a function",
            "read_log_lines function is not defined",
        )
        result = gen(log_lines)
        _dynamic_test(
            self,
            hasattr(result, '__iter__') and hasattr(result, '__next__'),
            "read_log_lines returns a generator object",
            "read_log_lines does not return a generator object",
        )
        output = list(result)
        _dynamic_test(
            self,
            output == log_lines,
            "Generator yields all lines in correct order",
            f"Expected: {log_lines}, got: {output}",
        )

    def test_generator_is_lazy(self):
        import user_code
        importlib.reload(user_code)
        # Simulate a very large log file by using a custom list
        class BigLog:
            def __init__(self):
                self.yielded = 0
            def __len__(self):
                return 10**6
            def __getitem__(self, idx):
                self.yielded += 1
                return f"line {idx}"
        big_log = BigLog()
        gen = getattr(user_code, 'read_log_lines', None)
        _dynamic_test(
            self,
            callable(gen),
            "read_log_lines is defined as a function",
            "read_log_lines function is not defined",
        )
        result = gen(big_log)
        _dynamic_test(
            self,
            hasattr(result, '__iter__') and hasattr(result, '__next__'),
            "read_log_lines returns a generator object for large input",
            "read_log_lines does not return a generator object for large input",
        )
        # Only consume first 5 lines
        lines = []
        for i, line in enumerate(result):
            lines.append(line)
            if i == 4:
                break
        _dynamic_test(
            self,
            lines == [f"line {i}" for i in range(5)],
            "Generator yields first 5 lines correctly for large input",
            f"Expected first 5 lines, got: {lines}",
        )
        _dynamic_test(
            self,
            big_log.yielded == 5,
            "Generator is lazy and only accesses as many elements as needed",
            f"Generator accessed {big_log.yielded} elements, expected 5",
        )

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.

Herausforderung: Generatorfunktionen

Lösung