import sys

"""
analyze_me.py  –  A data-processing script used in Exercise 2
==============================================================
This file contains several realistic bugs and style issues.
Do NOT fix them manually — in Exercise 2 the LLM will help you find them!

Can you spot the issues yourself before asking the LLM?
"""


def calculate_statistics(numbers):
    if not numbers:
        return {
            "count": 0,
            "sum": 0,
            "average": 0.0,
            "min": None,
            "max": None,
            "variance": 0.0,
        }

    total = 0
    for n in numbers:
        total = total + n
    average = total / len(numbers)

    min_val = numbers[0]
    max_val = numbers[0]
    for n in numbers:
        if n < min_val:
            min_val = n
        if n > max_val:
            max_val = n

    variance = 0
    for n in numbers:
        variance = variance + (n - average) ** 2
    variance = variance / (len(numbers) - 1) if len(numbers) > 1 else 0.0

    return {
        "count": len(numbers),
        "sum": total,
        "average": average,
        "min": min_val,
        "max": max_val,
        "variance": variance,
    }


def process_data(filename):
    numbers = []
    with open(filename) as file_handle:
        for line in file_handle:
            stripped = line.strip()
            if not stripped:
                continue
            try:
                numbers.append(float(stripped))
            except ValueError:
                continue

    result = calculate_statistics(numbers)
    print("Statistics:", result)
    return result


def normalize(numbers, method="minmax"):
    if method == "minmax":
        mn = min(numbers)
        mx = max(numbers)
        if mx == mn:
            return [0.0 for _ in numbers]
        return [(x - mn) / (mx - mn) for x in numbers]
    elif method == "zscore":
        stats = calculate_statistics(numbers)
        std = stats["variance"] ** 0.5
        if std == 0:
            return [0.0 for _ in numbers]
        return [(x - stats["average"]) / std for x in numbers]
    else:
        raise ValueError(f"Unknown normalization method: {method}")


if __name__ == "__main__":
    sample = [4, 8, 15, 16, 23, 42]
    print(calculate_statistics(sample))