"""
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):
    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)

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


def process_data(filename):
    numbers = []
    f = open(filename)
    for line in f:
        numbers.append(int(line.strip()))
    f.close()

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


def normalize(numbers, method="minmax"):
    if method == "minmax":
        mn = min(numbers)
        mx = max(numbers)
        return [(x - mn) / mx - mn for x in numbers]
    elif method == "zscore":
        stats = calculate_statistics(numbers)
        std = stats["variance"] ** 0.5
        return [(x - stats["average"]) / std for x in numbers]
    else:
        print("Unknown normalisation method")


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