
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import pandas as pd
import importlib

class TestTask(unittest.TestCase):
    def setUp(self):
        self.device_data = pd.DataFrame({
            'device_id': ['A01', 'A02', 'A03', 'A04', 'A05'],
            'reading': [22.5, 35.1, -10.2, 28.0, 48.7]
        })
        self.min_value = 0.0
        self.max_value = 40.0

    def test_rows_within_range_only(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.clean_and_transform_device_data(self.device_data, self.min_value, self.max_value)
        # Only rows with reading between 0 and 40 (inclusive) should remain
        expected_ids = ['A01', 'A02', 'A04']
        actual_ids = list(result['device_id']) if result is not None and 'device_id' in result else []
        _dynamic_test(
            self,
            actual_ids == expected_ids,
            f"Rows with readings in range [{self.min_value}, {self.max_value}] are kept only.",
            f"Expected device_ids {expected_ids}, got {actual_ids}.",
        )

    def test_reading_fahrenheit_column_exists(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.clean_and_transform_device_data(self.device_data, self.min_value, self.max_value)
        _dynamic_test(
            self,
            result is not None and 'reading_fahrenheit' in result.columns,
            "New column 'reading_fahrenheit' exists.",
            "Column 'reading_fahrenheit' does not exist in result.",
        )

    def test_reading_fahrenheit_values_correct(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.clean_and_transform_device_data(self.device_data, self.min_value, self.max_value)
        # Check conversion for each remaining row
        if result is not None and 'reading' in result.columns and 'reading_fahrenheit' in result.columns:
            for i, row in result.iterrows():
                c = row['reading']
                f = row['reading_fahrenheit']
                expected_f = c * 9/5 + 32
                _dynamic_test(
                    self,
                    abs(f - expected_f) < 1e-6,
                    f"reading_fahrenheit for reading {c} is correct.",
                    f"Expected {expected_f} for reading {c}, got {f}.",
                )
        else:
            _dynamic_test(
                self,
                False,
                "",
                "Result DataFrame missing required columns.",
            )

    def test_shape_and_content(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.clean_and_transform_device_data(self.device_data, self.min_value, self.max_value)
        # The result should have 3 rows and 3 columns
        _dynamic_test(
            self,
            result is not None and result.shape == (3, 3),
            "Result DataFrame has correct shape (3, 3).",
            f"Result shape expected (3, 3), got {None if result is None else result.shape}.",
        )
        # Check device_id order and readings
        expected = pd.DataFrame({
            'device_id': ['A01', 'A02', 'A04'],
            'reading': [22.5, 35.1, 28.0],
            'reading_fahrenheit': [22.5*9/5+32, 35.1*9/5+32, 28.0*9/5+32]
        })
        if result is not None:
            for idx, row in expected.iterrows():
                actual_row = result.iloc[idx] if idx < len(result) else None
                _dynamic_test(
                    self,
                    actual_row is not None and actual_row['device_id'] == row['device_id'] and abs(actual_row['reading'] - row['reading']) < 1e-6 and abs(actual_row['reading_fahrenheit'] - row['reading_fahrenheit']) < 1e-6,
                    f"Row {idx} matches expected values.",
                    f"Row {idx} does not match. Expected {row.to_dict()}, got {None if actual_row is None else actual_row.to_dict()}.",
                )

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

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Challenge: Clean and Transform Device Data

Ratkaisu

Challenge: Clean and Transform Device Data

Ratkaisu