
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import numpy as np

class TestTask(unittest.TestCase):
    def test_basic_mole_calculation(self):
        import user_code
        importlib.reload(user_code)
        masses = np.array([18.0, 36.0, 54.0])
        molar_mass = 18.0
        result = user_code.calculate_moles(masses, molar_mass)
        expected = np.array([1.0, 2.0, 3.0])
        _dynamic_test(
            self,
            isinstance(result, np.ndarray) and np.allclose(result, expected),
            "Correct mole calculation for standard input.",
            f"Expected {expected}, got {result}",
        )

    def test_empty_array(self):
        import user_code
        importlib.reload(user_code)
        masses = np.array([])
        molar_mass = 18.0
        result = user_code.calculate_moles(masses, molar_mass)
        _dynamic_test(
            self,
            isinstance(result, np.ndarray) and result.size == 0,
            "Returns empty NumPy array for empty input.",
            f"Expected empty array, got {result}",
        )

    def test_floating_point_division(self):
        import user_code
        importlib.reload(user_code)
        masses = np.array([5.5, 11.0, 2.2])
        molar_mass = 2.0
        result = user_code.calculate_moles(masses, molar_mass)
        expected = np.array([2.75, 5.5, 1.1])
        _dynamic_test(
            self,
            isinstance(result, np.ndarray) and np.allclose(result, expected),
            "Supports floating-point division for accurate mole calculations.",
            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: Batch Molar Calculations

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Challenge: Batch Molar Calculations

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