"""Экспериментальное сравнение структур данных."""

import argparse
import csv
import random
from pathlib import Path
from statistics import mean
from time import perf_counter

from phonebook import (
    bst_delete, bst_find, bst_insert,
    ht_delete, ht_find, ht_insert,
    ll_delete, ll_find, ll_insert,
)


def generate_records(size):
    return [(f"User_{index:05d}", f"+7{index:010d}") for index in range(size)]


def measure_once(structure, records, existing_names, missing_names, deleted_names, bucket_count):
    started = perf_counter()
    if structure == "LinkedList":
        data = None
        for name, phone in records:
            data = ll_insert(data, name, phone)
        insert_time = perf_counter() - started

        started = perf_counter()
        for name in existing_names + missing_names:
            ll_find(data, name)
        find_time = perf_counter() - started

        started = perf_counter()
        for name in deleted_names:
            data = ll_delete(data, name)
        delete_time = perf_counter() - started
    elif structure == "HashTable":
        data = [None] * bucket_count
        for name, phone in records:
            ht_insert(data, name, phone)
        insert_time = perf_counter() - started

        started = perf_counter()
        for name in existing_names + missing_names:
            ht_find(data, name)
        find_time = perf_counter() - started

        started = perf_counter()
        for name in deleted_names:
            ht_delete(data, name)
        delete_time = perf_counter() - started
    else:
        data = None
        for name, phone in records:
            data = bst_insert(data, name, phone)
        insert_time = perf_counter() - started

        started = perf_counter()
        for name in existing_names + missing_names:
            bst_find(data, name)
        find_time = perf_counter() - started

        started = perf_counter()
        for name in deleted_names:
            data = bst_delete(data, name)
        delete_time = perf_counter() - started

    return {"insert": insert_time, "find_110": find_time, "delete_50": delete_time}


def run_experiment(size=3000, repeats=5, seed=2026, output_dir="docs/data"):
    rng = random.Random(seed)
    sorted_records = generate_records(size)
    shuffled_records = sorted_records.copy()
    rng.shuffle(shuffled_records)
    modes = {"shuffled": shuffled_records, "sorted": sorted_records}
    rows = []

    for mode, records in modes.items():
        names = [record[0] for record in records]
        test_cases = []
        for run in range(1, repeats + 1):
            test_cases.append((
                run,
                rng.sample(names, min(100, size)),
                [f"None_{index}" for index in range(10)],
                rng.sample(names, min(50, size)),
            ))
        for structure in ("LinkedList", "HashTable", "BST"):
            for run, existing, missing, deleted in test_cases:
                timings = measure_once(structure, records, existing, missing, deleted, max(17, size * 2 + 1))
                for operation, elapsed in timings.items():
                    rows.append({
                        "structure": structure,
                        "mode": mode,
                        "operation": operation,
                        "run": run,
                        "time_seconds": elapsed,
                    })

    output = Path(output_dir)
    output.mkdir(parents=True, exist_ok=True)
    raw_path = output / "results_raw.csv"
    with raw_path.open("w", newline="", encoding="utf-8-sig") as file:
        writer = csv.DictWriter(file, fieldnames=rows[0].keys())
        writer.writeheader()
        writer.writerows(rows)

    groups = {}
    for row in rows:
        key = (row["structure"], row["mode"], row["operation"])
        groups.setdefault(key, []).append(row["time_seconds"])
    summary = [
        {"structure": key[0], "mode": key[1], "operation": key[2], "mean_seconds": mean(values)}
        for key, values in groups.items()
    ]
    summary_path = output / "results_summary.csv"
    with summary_path.open("w", newline="", encoding="utf-8-sig") as file:
        writer = csv.DictWriter(file, fieldnames=summary[0].keys())
        writer.writeheader()
        writer.writerows(summary)
    return raw_path, summary_path


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Сравнение структур телефонного справочника")
    parser.add_argument("--size", type=int, default=3000)
    parser.add_argument("--repeats", type=int, default=5)
    args = parser.parse_args()
    raw, summary = run_experiment(args.size, args.repeats)
    print(f"Полные замеры: {raw}\nСредние значения: {summary}")