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

class TestTask(unittest.TestCase):
    def test_returns_list_of_correct_length(self):
        import user_code
        importlib.reload(user_code)
        res = user_code.calculate_coverage([0, 1, 2], 5)
        _dynamic_test(
            self,
            isinstance(res, list) and len(res) == 5,
            "Returned list has correct length equal to genome_length",
            f"Expected list of length 5, got {res} with length {len(res) if isinstance(res, list) else 'not a list'}",
        )

    def test_correct_counts(self):
        import user_code
        importlib.reload(user_code)
        res = user_code.calculate_coverage([0, 1, 2, 2, 3, 5], 6)
        expected = [1, 1, 2, 1, 0, 1]
        _dynamic_test(
            self,
            isinstance(res, list) and res == expected,
            "Correct coverage calculated for sample input",
            f"Expected {expected}, got {res}",
        )

    def test_empty_read_starts(self):
        import user_code
        importlib.reload(user_code)
        res = user_code.calculate_coverage([], 4)
        expected = [0, 0, 0, 0]
        _dynamic_test(
            self,
            isinstance(res, list) and res == expected,
            "Returns zeros for empty read_starts",
            f"Expected {expected}, got {res}",
        )

    def test_ignores_out_of_range(self):
        import user_code
        importlib.reload(user_code)
        res = user_code.calculate_coverage([-1, 0, 5, 2], 4)
        expected = [1, 0, 1, 0]
        _dynamic_test(
            self,
            isinstance(res, list) and res == expected,
            "Ignores out-of-range start positions",
            f"Expected {expected}, got {res}",
        )

    def test_all_out_of_range(self):
        import user_code
        importlib.reload(user_code)
        res = user_code.calculate_coverage([-2, 4, 8], 4)
        expected = [0, 0, 0, 0]
        _dynamic_test(
            self,
            isinstance(res, list) and res == expected,
            "Returns all zeros if all start positions are out of range",
            f"Expected {expected}, got {res}",
        )

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

A beginner-friendly course introducing Python programming through real-world bioinformatics problems. Learn to handle biological data formats, analyze DNA sequences, process next-generation sequencing data, and build reproducible analysis pipelines—all with engaging, hands-on tasks and clear explanations.

Dive into sequence analysis techniques, including alignment basics and motif searching, to uncover biological insights from DNA and protein sequences.

Learn how to process next-generation sequencing (NGS) data, including read quality analysis and coverage calculations, using Python.

Explore gene expression analysis using RNA-seq data and learn how to build reproducible bioinformatics workflows with Python.

Challenge: Calculate Genome Coverage

Ratkaisu

Challenge: Calculate Genome Coverage

Ratkaisu