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

class TestTask(unittest.TestCase):
    def test_values_within_range(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.clean_titration_volumes([3.2, 5.0, 12.5, 49.9, 51.0])
        expected = [5.0, 12.5, 49.9]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Function returns only values between 5 and 50 inclusive.",
            f"Expected {expected}, got {result}",
        )

    def test_exclude_below_5(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.clean_titration_volumes([1, 2, 4.9, 5, 6])
        expected = [5, 6]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Values less than 5 are excluded.",
            f"Expected {expected}, got {result}",
        )

    def test_exclude_above_50(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.clean_titration_volumes([49, 50, 50.1, 60])
        expected = [49, 50]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Values greater than 50 are excluded.",
            f"Expected {expected}, got {result}",
        )

    def test_all_valid(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.clean_titration_volumes([5, 10, 20, 30, 40, 50])
        expected = [5, 10, 20, 30, 40, 50]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Function works for list of all valid values.",
            f"Expected {expected}, got {result}",
        )

    def test_all_excluded(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.clean_titration_volumes([1, 2, 4.99, 51, 60])
        expected = []
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Function returns empty list if all values are excluded.",
            f"Expected {expected}, 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

An engaging, hands-on course introducing Python programming through real-world chemistry problems. Learn to clean experimental data, perform molar calculations, visualize spectra, and analyze reaction rates using Python and scientific libraries.

Learn Python basics through chemistry-themed examples, focusing on variables, data types, and simple calculations relevant to laboratory work.

Dive into real-world chemistry data: clean, process, and analyze raw experimental results using Python.

Explore how to visualize and analyze chemical spectra and reaction rates using Python's scientific libraries.

Challenge: Titration Data Cleaner

Solution