
import unittest
import user_code
import ast
import re   
import importlib
import csv
import io
import sys
import importlib
import asyncio
import time
from contextlib import redirect_stdout
import unittest

class TestTask(unittest.TestCase):
    def test_boil_water_output_and_timing(self):
        import user_code
        
        # Patch asyncio.sleep to actually wait and measure time
        # We'll reload the module to re-run the coroutine
        # We'll capture stdout to check the print output
        code = ""
        with open("user_code.py", "r") as f:
            code = f.read()
        ns = {}
        buffer = io.StringIO()
        start = time.time()
        with redirect_stdout(buffer):
            exec(code, ns)
        end = time.time()
        output = normalize_text(buffer.getvalue())
        expected_first = normalize_text("Kettle is on...")
        expected_second = normalize_text("Ding! Water is boiled.")
        _dynamic_test(
            self,
            expected_first in output,
            'Output includes "Kettle is on..."',
            f'Expected "Kettle is on..." in output, got: {output}'
        )
        _dynamic_test(
            self,
            expected_second in output,
            'Output includes "Ding! Water is boiled."',
            f'Expected "Ding! Water is boiled." in output, got: {output}'
        )
        # Check time between prints is at least 1.8 seconds (allowing for minor timing errors)
        _dynamic_test(
            self,
            (end - start) >= 2.8,
            'Coroutine waits at least 3 seconds before printing second message',
            f'Expected at least 3 seconds delay, got: {end - start} seconds'
        )

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

Acquisizione di competenze pratiche nella gestione dei moduli e delle importazioni in Python per una gestione efficiente del codice. Padronanza delle tecniche avanzate di gestione degli errori per applicazioni più affidabili, automazione delle operazioni sui file con la gestione dei file e apprendimento di strategie di testing robuste con Pytest e Unittest per garantire l'integrità del codice.

Master Python's exception system for robust error handling and debugging.

Explore advanced techniques for file operations and resource management using context managers.

Compare and apply Python's multithreading and multiprocessing modules for concurrent programming.

Master asynchronous programming in Python using the asyncio library for scalable I/O-bound applications.

Explore how to write, organize, and run tests using Python's unittest and pytest frameworks.

Challenge: The Digital Tea Kettle

Soluzione