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

class TestTask(unittest.TestCase):
    def test_returns_only_calibrated_above_threshold(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'calibrate_and_filter_readings', None)
        _dynamic_test(
            self,
            callable(func),
            "Function 'calibrate_and_filter_readings' is defined.",
            "Function 'calibrate_and_filter_readings' is not defined."
        )
        # Sensor readings, calibration factor, threshold
        readings = [1.0, 5.0, 10.0, 15.0]
        calibration_factor = 2.0
        threshold = 15.0
        # Calibrated: [1*2+2=4, 5*2+2=12, 10*2+2=22, 15*2+2=32]
        # Only 22, 32 >= 15
        result = func(readings, calibration_factor, threshold) if func else None
        expected = [22.0, 32.0]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            f"Correctly returns only calibrated readings >= threshold: {expected}",
            f"Expected {expected}, got {result}"
        )

    def test_applies_calibration_formula(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'calibrate_and_filter_readings', None)
        readings = [3.0]
        calibration_factor = 4.0
        threshold = 0.0
        # Calibrated: 3*4+2 = 14
        result = func(readings, calibration_factor, threshold) if func else None
        _dynamic_test(
            self,
            isinstance(result, list) and result == [14.0],
            "Calibration formula applied correctly.",
            f"Expected [14.0], got {result}"
        )

    def test_all_filtered_out(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'calibrate_and_filter_readings', None)
        readings = [1.0, 2.0]
        calibration_factor = 1.0
        threshold = 10.0
        # Calibrated: [1*1+2=3, 2*1+2=4] -> both < 10
        result = func(readings, calibration_factor, threshold) if func else None
        _dynamic_test(
            self,
            isinstance(result, list) and result == [],
            "Returns empty list when all readings are filtered out.",
            f"Expected [], got {result}"
        )

    def test_all_included(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'calibrate_and_filter_readings', None)
        readings = [5.0, 6.0]
        calibration_factor = 2.0
        threshold = 0.0
        # Calibrated: [12, 14] (all >= 0)
        result = func(readings, calibration_factor, threshold) if func else None
        _dynamic_test(
            self,
            isinstance(result, list) and result == [12.0, 14.0],
            "All values included when threshold is low enough.",
            f"Expected [12.0, 14.0], got {result}"
        )

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

Master Python as a powerful tool for automation engineering. This course guides you through practical and advanced Python techniques to automate repetitive tasks, analyze data, and interact with systems, all tailored for automation engineers. Each section blends engaging theory with hands-on, real-world challenges.

Learn how Python can automate repetitive and time-consuming tasks commonly faced by automation engineers, from log parsing to report generation.

Dive into data manipulation and analysis using Python and pandas, tailored for automation engineers handling structured data.

Master Python techniques for interacting with systems, automating tests, and analyzing results—core skills for automation engineers.

Challenge: Batch Process Sensor Data

Ratkaisu

Challenge: Batch Process Sensor Data

Ratkaisu