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

class TestTask(unittest.TestCase):
    def test_count_reads_above_threshold(self):
        import user_code
        importlib.reload(user_code)
        sample_fastq = [
            "@SEQ_ID1",
            "GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATTTTGGGGTTCAAAGCAGTATCGATCAAATAGTAA",
            "+",
            "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII",
            "@SEQ_ID2",
            "AGCTTAGCTAGCTACCTATATCTTGGTCTTGGCCG",
            "+",
            "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII",
            "@SEQ_ID3",
            "CGTAGCTAGCTAGCTGACTGATCGATCGTAGCTAGC",
            "+",
            "########IIIIIIIIIIIIIIIIIIIIIIIIIIIII"
        ]
        func = getattr(user_code, 'count_high_quality_reads', None)
        _dynamic_test(
            self,
            callable(func),
            "Function 'count_high_quality_reads' is defined.",
            "Function 'count_high_quality_reads' is not defined.",
        )
        result = func(sample_fastq, 30) if func else None
        _dynamic_test(
            self,
            result == 3,
            "Correctly counts 3 reads above threshold 30.",
            f"Expected 3, got {result}",
        )

    def test_ascii_to_phred_conversion(self):
        import user_code
        importlib.reload(user_code)
        # 'I' (ASCII 73) - 33 = 40; '#' (ASCII 35) - 33 = 2
        # Read 3: 8 '#'s (2) + 27 'I's (40) => avg = (8*2 + 27*40) / 35 = (16 + 1080)/35 = 31.2
        sample_fastq = [
            "@SEQ_ID3",
            "CGTAGCTAGCTAGCTGACTGATCGATCGTAGCTAGC",
            "+",
            "########IIIIIIIIIIIIIIIIIIIIIIIIIIIII"
        ]
        func = getattr(user_code, 'count_high_quality_reads', None)
        result = func(sample_fastq, 31) if func else None
        _dynamic_test(
            self,
            result == 1,
            "Correctly converts ASCII to Phred and counts 1 read above threshold 31.",
            f"Expected 1, got {result}",
        )
        result2 = func(sample_fastq, 32) if func else None
        _dynamic_test(
            self,
            result2 == 0,
            "Returns 0 when no read is above threshold.",
            f"Expected 0, got {result2}",
        )

    def test_empty_input(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'count_high_quality_reads', None)
        result = func([], 10) if func else None
        _dynamic_test(
            self,
            result == 0,
            "Returns 0 for empty input.",
            f"Expected 0, got {result}",
        )

    def test_handles_multiple_reads(self):
        import user_code
        importlib.reload(user_code)
        sample_fastq = [
            "@SEQ_ID1",
            "ATCG",
            "+",
            "IIII",
            "@SEQ_ID2",
            "ATCG",
            "+",
            "####",
            "@SEQ_ID3",
            "ATCG",
            "+",
            "IIII"
        ]
        func = getattr(user_code, 'count_high_quality_reads', None)
        result = func(sample_fastq, 20) if func else None
        _dynamic_test(
            self,
            result == 2,
            "Correctly counts 2 out of 3 reads above threshold 20.",
            f"Expected 2, got {result}",
        )

    def test_zero_reads_above_threshold(self):
        import user_code
        importlib.reload(user_code)
        sample_fastq = [
            "@SEQ_ID1",
            "ATCG",
            "+",
            "####",
            "@SEQ_ID2",
            "ATCG",
            "+",
            "####"
        ]
        func = getattr(user_code, 'count_high_quality_reads', None)
        result = func(sample_fastq, 5) if func else None
        _dynamic_test(
            self,
            result == 0,
            "Correctly returns 0 when no reads are above threshold.",
            f"Expected 0, 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

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: Analyze Read Quality

Ratkaisu

Challenge: Analyze Read Quality

Ratkaisu