
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
from rdkit import Chem
from rdkit.Chem import Descriptors
import importlib

class TestTask(unittest.TestCase):
    def test_output_length(self):
        import user_code
        importlib.reload(user_code)
        test_smiles = ["CCO", "c1ccccc1", "O=C=O", "invalid"]
        result = user_code.get_molecular_weights(test_smiles)
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == len(test_smiles),
            "Output list has the same length as input list",
            f"Expected length {len(test_smiles)}, got {len(result)}",
        )

    def test_valid_smiles_weights(self):
        import user_code
        importlib.reload(user_code)
        test_smiles = ["CCO", "c1ccccc1", "O=C=O"]
        expected = [Descriptors.MolWt(Chem.MolFromSmiles(smi)) for smi in test_smiles]
        result = user_code.get_molecular_weights(test_smiles)
        _dynamic_test(
            self,
            isinstance(result, list) and all(abs(a - b) < 0.01 for a, b in zip(result, expected)),
            "Correct molecular weights for valid SMILES",
            f"Expected {expected}, got {result}",
        )

    def test_invalid_smiles(self):
        import user_code
        importlib.reload(user_code)
        test_smiles = ["invalid", "XYZ", "123"]
        result = user_code.get_molecular_weights(test_smiles)
        _dynamic_test(
            self,
            isinstance(result, list) and all(x is None for x in result),
            "Returns None for all invalid SMILES",
            f"Expected all None, got {result}",
        )

    def test_mixed_valid_invalid(self):
        import user_code
        importlib.reload(user_code)
        test_smiles = ["CCO", "invalid", "O=C=O", "bad"]
        expected = [Descriptors.MolWt(Chem.MolFromSmiles("CCO")), None, Descriptors.MolWt(Chem.MolFromSmiles("O=C=O")), None]
        result = user_code.get_molecular_weights(test_smiles)
        _dynamic_test(
            self,
            isinstance(result, list) and all(
                (x is None and y is None) or (x is not None and y is not None and abs(x - y) < 0.01)
                for x, y in zip(result, expected)
            ),
            "Handles mix of valid and invalid SMILES",
            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

Dive into the world of chemoinformatics using Python! This course introduces you to essential concepts such as molecular representations, descriptors, similarity, clustering, and QSAR modeling. Through engaging theory and hands-on challenges, you'll learn to analyze chemical data, compute molecular properties, and build predictive models—all with beginner-friendly explanations and real-world examples.

Explore how molecules are represented digitally and learn to parse and interpret these representations using Python.

Learn how to compare molecules, find similar compounds, and group them for drug discovery applications.

Apply chemoinformatics techniques to screen and rank compound libraries for drug discovery.

Challenge: RDKit Molecule Parsing

Ratkaisu

Challenge: RDKit Molecule Parsing

Ratkaisu