
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import io
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

class TestTask(unittest.TestCase):
    def setUp(self):
        # Create a small test FASTQ file
        self.fastq_file = "sample_reads.fastq"
        self.fastq_content = (
            "@SEQ_ID1\\n"
            "GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATTTTTTTTTTTAATTTT\\n"
            "+\\n"
            "IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII\\n"
            "@SEQ_ID2\\n"
            "CAGTCTGATCGTAGCTAGCTGACTGATCGTAGCTAGCTAGCTAGCTGACTGATCG\\n"
            "+\\n"
            "BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB\\n"
            "@SEQ_ID3\\n"
            "AGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC\\n"
            "+\\n"
            "JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ\\n"
        )
        with open(self.fastq_file, 'w') as f:
            f.write(self.fastq_content)

    def tearDown(self):
        if os.path.exists(self.fastq_file):
            os.remove(self.fastq_file)

    def test_average_read_quality_typical(self):
        import user_code
        importlib.reload(user_code)
        # 'I' = 40, 'B' = 33, 'J' = 41
        q1 = user_code.average_read_quality('IIIIIIIIII')
        q2 = user_code.average_read_quality('BBBBBBBBBB')
        q3 = user_code.average_read_quality('JJJJJJJJJJ')
        _dynamic_test(
            self,
            abs(q1 - 40.0) < 1e-6,
            "Correct mean Phred score for string of 'I's",
            f"Expected 40.0, got {q1}"
        )
        _dynamic_test(
            self,
            abs(q2 - 33.0) < 1e-6,
            "Correct mean Phred score for string of 'B's",
            f"Expected 33.0, got {q2}"
        )
        _dynamic_test(
            self,
            abs(q3 - 41.0) < 1e-6,
            "Correct mean Phred score for string of 'J's",
            f"Expected 41.0, got {q3}"
        )

    def test_average_read_quality_empty(self):
        import user_code
        importlib.reload(user_code)
        q = user_code.average_read_quality("")
        _dynamic_test(
            self,
            q == 0,
            "Returns 0 for empty quality string",
            f"Expected 0, got {q}"
        )

    def test_read_fastq_qualities_values(self):
        import user_code
        importlib.reload(user_code)
        qualities = user_code.read_fastq_qualities(self.fastq_file)
        # Each read: 52 chars, 'I'=40, 'B'=33, 'J'=41
        expected = [40.0, 33.0, 41.0]
        _dynamic_test(
            self,
            isinstance(qualities, list) and len(qualities) == 3,
            "Reads correct number of reads from FASTQ",
            f"Expected 3 reads, got {len(qualities)}"
        )
        for i, q in enumerate(qualities):
            _dynamic_test(
                self,
                abs(q - expected[i]) < 1e-6,
                f"Read {i+1} has correct average quality",
                f"Read {i+1} expected {expected[i]}, got {q}"
            )

    def test_plot_quality_histogram_runs(self):
        import user_code
        importlib.reload(user_code)
        qualities = [40.0, 33.0, 41.0]
        try:
            user_code.plot_quality_histogram(qualities)
            _dynamic_test(
                self,
                True,
                "plot_quality_histogram runs without error",
                "plot_quality_histogram raised an error"
            )
        except Exception as e:
            _dynamic_test(
                self,
                False,
                "plot_quality_histogram runs without error",
                f"plot_quality_histogram raised: {e}"
            )

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: Visualize Read Quality

Oplossing

Challenge: Visualize Read Quality

Oplossing