Split experiments from tests
The tests/ directory held 50 laboratory programs and no tests. They model channels, run hundreds of repetitions and write CSV, PNG and reports; calling that a test suite blocked introducing a real one, because any pytest run would have collected the labs and re-executed every experiment. - move all 50 lab programs to experiments/ with git mv, preserving history - rewrite the 38 cross-imports between labs from tests.labNNN to experiments.labNNN - leave tests/ empty for actual fast checks of protocol/ - point quick_gate and the hook at the new layout and add experiments/ to the syntax sweep - update the paths quoted in the Lab042 specification and the verifier agent definition This also defuses the import-time work finding without touching 41 files: the labs still create directories and write files on import, but nothing imports them now except the gate, which does so deliberately. Gate passes: syntax clean, protocol imports, 15 lab modules import, 2 functional suites run. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
LittleSam129
810
experiments/lab015_fragment_size_optimization.py
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810
experiments/lab015_fragment_size_optimization.py
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@@ -0,0 +1,810 @@
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"""
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Lab015. Подбор оптимального размера фрагмента изображения.
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Программа сравнивает фрагменты размером:
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- 64 байта;
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- 128 байт;
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- 256 байт;
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- 512 байт;
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- 1024 байта.
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Для каждого размера рассчитываются:
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1. Количество фрагментов изображения.
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2. Служебные расходы протокола.
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3. Теоретический BER BPSK.
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4. Вероятность успешной доставки DATA-пакета.
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5. Вероятность успешной доставки ACK.
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6. Среднее число передач одного фрагмента.
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7. Ожидаемый полный радиообмен с ARQ.
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8. Время передачи фотографии при 20 кбит/с.
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9. Эффективная полезная скорость.
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Модель канала:
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- когерентная BPSK;
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- AWGN;
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- ошибки битов независимы;
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- повреждённый пакет отбрасывается по CRC;
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- применяется Stop-and-Wait ARQ;
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- DATA и ACK работают при одинаковом Eb/N0.
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Не учитываются:
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- ожидание тайм-аута;
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- паузы между DATA и ACK;
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- преамбула;
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- синхронизация;
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- FEC;
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- многолучёвость;
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- частотные и фазовые ошибки.
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"""
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from csv import DictWriter
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from math import (
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erfc,
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exp,
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log1p,
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sqrt,
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)
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from pathlib import Path
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import matplotlib.pyplot as plt
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from protocol.image_fragments import (
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encode_image_fragment,
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split_image_bytes,
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)
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from protocol.packet import (
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MESSAGE_TYPE_ACK,
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MESSAGE_TYPE_IMAGE_FRAGMENT,
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build_packet,
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)
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# ============================================================
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# Настройки
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# ============================================================
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SOURCE_PATH = Path(
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"data/raw/lab009_source.jpg"
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)
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OUTPUT_DIRECTORY = Path(
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"data/processed/lab015"
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)
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OUTPUT_DIRECTORY.mkdir(
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parents=True,
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exist_ok=True,
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)
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FRAGMENT_SIZES = [
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64,
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128,
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256,
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512,
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1024,
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]
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EB_N0_VALUES_DB = [
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6.0,
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8.0,
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10.0,
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12.0,
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]
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CHANNEL_BITRATE_BPS = 20_000
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IMAGE_ID_BASE = 2026071600
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CSV_PATH = (
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OUTPUT_DIRECTORY
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/ "lab015_fragment_results.csv"
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)
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TIME_GRAPH_PATH = (
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OUTPUT_DIRECTORY
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/ "lab015_transfer_time.png"
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)
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GOODPUT_GRAPH_PATH = (
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OUTPUT_DIRECTORY
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/ "lab015_effective_goodput.png"
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)
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# ============================================================
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# Вспомогательные функции
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# ============================================================
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def theoretical_bpsk_ber(
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eb_n0_db: float,
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) -> float:
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"""
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Теоретический BER когерентной BPSK в AWGN.
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BER = 0.5 * erfc(sqrt(Eb/N0))
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"""
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eb_n0_linear = 10.0 ** (
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eb_n0_db / 10.0
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)
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return 0.5 * erfc(
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sqrt(eb_n0_linear)
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)
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def packet_success_probability(
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ber: float,
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packet_bit_count: int,
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) -> float:
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"""
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Вероятность того, что весь пакет будет принят
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без единой битовой ошибки.
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P_success = (1 - BER) ** N
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"""
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if not 0.0 <= ber <= 1.0:
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raise ValueError(
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"BER должен находиться в диапазоне 0...1"
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)
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if packet_bit_count <= 0:
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raise ValueError(
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"Размер пакета должен быть положительным"
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)
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if ber == 0.0:
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return 1.0
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if ber == 1.0:
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return 0.0
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# Численно устойчивый вариант выражения:
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#
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# (1 - BER) ** N
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return exp(
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packet_bit_count
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* log1p(-ber)
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)
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def format_bytes(
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byte_count: float,
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) -> str:
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"""
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Представить объём в удобном виде.
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"""
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if byte_count < 1024:
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return f"{byte_count:.0f} байт"
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kibibytes = byte_count / 1024
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if kibibytes < 1024:
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return f"{kibibytes:.2f} КиБ"
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mebibytes = kibibytes / 1024
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return f"{mebibytes:.2f} МиБ"
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def format_duration(
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seconds: float,
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) -> str:
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"""
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Представить длительность в удобном виде.
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"""
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if seconds < 1:
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return f"{seconds * 1000:.0f} мс"
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if seconds < 60:
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return f"{seconds:.2f} с"
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if seconds < 3600:
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minutes = int(seconds // 60)
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remaining_seconds = seconds % 60
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return (
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f"{minutes} мин "
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f"{remaining_seconds:.1f} с"
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)
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hours = int(seconds // 3600)
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remaining_minutes = (
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seconds % 3600
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) / 60
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return (
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f"{hours} ч "
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f"{remaining_minutes:.1f} мин"
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)
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# ============================================================
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# Проверка исходного JPEG
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# ============================================================
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if not SOURCE_PATH.exists():
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raise FileNotFoundError(
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f"Не найден файл: {SOURCE_PATH}. "
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"Сначала необходимо выполнить Lab009."
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)
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source_bytes = SOURCE_PATH.read_bytes()
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if not source_bytes:
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raise ValueError(
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"Исходный JPEG пуст"
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)
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source_size = len(source_bytes)
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# ============================================================
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# Размер ACK
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# ============================================================
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ack_packet = build_packet(
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payload=b"",
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message_type=MESSAGE_TYPE_ACK,
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sequence_number=0,
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)
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ack_packet_size_bytes = len(
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ack_packet
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)
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ack_packet_size_bits = (
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ack_packet_size_bytes * 8
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)
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# ============================================================
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# Основной расчёт
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# ============================================================
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results = []
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for fragment_size_index, fragment_size in enumerate(
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FRAGMENT_SIZES
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):
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fragments = split_image_bytes(
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image_bytes=source_bytes,
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image_id=(
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IMAGE_ID_BASE
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+ fragment_size_index
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),
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fragment_data_size=fragment_size,
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)
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data_packet_sizes_bytes = []
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for fragment in fragments:
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fragment_payload = encode_image_fragment(
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fragment
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)
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data_packet = build_packet(
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payload=fragment_payload,
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message_type=(
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MESSAGE_TYPE_IMAGE_FRAGMENT
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),
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sequence_number=(
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fragment.fragment_index
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),
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)
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data_packet_sizes_bytes.append(
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len(data_packet)
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)
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ideal_data_bytes = sum(
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data_packet_sizes_bytes
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)
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ideal_ack_bytes = (
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len(fragments)
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* ack_packet_size_bytes
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)
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ideal_total_bytes = (
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ideal_data_bytes
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+ ideal_ack_bytes
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)
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ideal_efficiency_percent = (
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source_size
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/ ideal_total_bytes
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* 100
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)
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for eb_n0_db in EB_N0_VALUES_DB:
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ber = theoretical_bpsk_ber(
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eb_n0_db
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)
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ack_success_probability = (
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packet_success_probability(
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ber=ber,
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packet_bit_count=(
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ack_packet_size_bits
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),
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)
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)
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expected_data_bytes = 0.0
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expected_ack_bytes = 0.0
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expected_data_transmissions = 0.0
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data_success_probabilities = []
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for data_packet_size_bytes in (
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data_packet_sizes_bytes
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):
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data_packet_size_bits = (
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data_packet_size_bytes * 8
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)
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data_success_probability = (
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packet_success_probability(
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ber=ber,
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packet_bit_count=(
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data_packet_size_bits
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),
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)
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)
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data_success_probabilities.append(
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data_success_probability
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)
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# Для успешного завершения попытки должны
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# одновременно правильно пройти DATA и ACK.
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confirmed_attempt_probability = (
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data_success_probability
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* ack_success_probability
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)
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if confirmed_attempt_probability == 0.0:
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raise RuntimeError(
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"Вероятность подтверждения "
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"оказалась равной нулю"
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)
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# Число DATA-передач до успешного ACK
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# подчиняется геометрическому распределению.
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expected_attempt_count = (
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1.0
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/ confirmed_attempt_probability
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)
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expected_data_transmissions += (
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expected_attempt_count
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)
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expected_data_bytes += (
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data_packet_size_bytes
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* expected_attempt_count
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)
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# ACK передаётся только после правильно
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# принятого DATA.
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#
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# Среднее количество передач ACK до
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# успешного ACK равно 1 / P_ACK.
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expected_ack_bytes += (
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ack_packet_size_bytes
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/ ack_success_probability
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)
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expected_total_bytes = (
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expected_data_bytes
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+ expected_ack_bytes
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)
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expected_transfer_seconds = (
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expected_total_bytes
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* 8
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/ CHANNEL_BITRATE_BPS
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)
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|
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expected_efficiency_percent = (
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source_size
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/ expected_total_bytes
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* 100
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)
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|
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effective_goodput_bps = (
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source_size
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* 8
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/ expected_transfer_seconds
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)
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average_data_transmissions = (
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expected_data_transmissions
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/ len(fragments)
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)
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|
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minimum_data_success_probability = min(
|
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data_success_probabilities
|
||||
)
|
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|
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maximum_data_success_probability = max(
|
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data_success_probabilities
|
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)
|
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|
||||
results.append(
|
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{
|
||||
"fragment_size": fragment_size,
|
||||
"fragment_count": len(fragments),
|
||||
"eb_n0_db": eb_n0_db,
|
||||
"ber": ber,
|
||||
"ideal_total_bytes": (
|
||||
ideal_total_bytes
|
||||
),
|
||||
"ideal_efficiency_percent": (
|
||||
ideal_efficiency_percent
|
||||
),
|
||||
"ack_success_probability": (
|
||||
ack_success_probability
|
||||
),
|
||||
"minimum_data_success_probability": (
|
||||
minimum_data_success_probability
|
||||
),
|
||||
"maximum_data_success_probability": (
|
||||
maximum_data_success_probability
|
||||
),
|
||||
"average_data_transmissions": (
|
||||
average_data_transmissions
|
||||
),
|
||||
"expected_total_bytes": (
|
||||
expected_total_bytes
|
||||
),
|
||||
"expected_transfer_seconds": (
|
||||
expected_transfer_seconds
|
||||
),
|
||||
"expected_efficiency_percent": (
|
||||
expected_efficiency_percent
|
||||
),
|
||||
"effective_goodput_bps": (
|
||||
effective_goodput_bps
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Вывод общих данных
|
||||
# ============================================================
|
||||
|
||||
print(
|
||||
"=== Lab015. Оптимальный размер фрагмента ==="
|
||||
)
|
||||
|
||||
print("\nИсходный JPEG:")
|
||||
|
||||
print(SOURCE_PATH)
|
||||
|
||||
print("\nРазмер JPEG:")
|
||||
|
||||
print(
|
||||
format_bytes(source_size)
|
||||
)
|
||||
|
||||
print("\nСкорость физического канала:")
|
||||
|
||||
print(
|
||||
CHANNEL_BITRATE_BPS / 1000,
|
||||
"кбит/с",
|
||||
)
|
||||
|
||||
print("\nРазмер ACK-пакета:")
|
||||
|
||||
print(
|
||||
ack_packet_size_bytes,
|
||||
"байт",
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Вывод результатов отдельно для каждого Eb/N0
|
||||
# ============================================================
|
||||
|
||||
for eb_n0_db in EB_N0_VALUES_DB:
|
||||
|
||||
selected_results = [
|
||||
result
|
||||
for result in results
|
||||
if result["eb_n0_db"] == eb_n0_db
|
||||
]
|
||||
|
||||
print(
|
||||
f"\n--- Eb/N0 = {eb_n0_db:.1f} дБ ---"
|
||||
)
|
||||
|
||||
print(
|
||||
f"{'Фрагмент':>10}"
|
||||
f"{'Кол-во':>9}"
|
||||
f"{'P DATA':>12}"
|
||||
f"{'Попыток':>11}"
|
||||
f"{'Объём с ARQ':>16}"
|
||||
f"{'Время':>16}"
|
||||
f"{'Goodput':>13}"
|
||||
)
|
||||
|
||||
print("-" * 87)
|
||||
|
||||
for result in selected_results:
|
||||
|
||||
print(
|
||||
f"{result['fragment_size']:>8} Б"
|
||||
f"{result['fragment_count']:>9}"
|
||||
f"{result['minimum_data_success_probability']:>12.6f}"
|
||||
f"{result['average_data_transmissions']:>11.2f}"
|
||||
f"{format_bytes(result['expected_total_bytes']):>16}"
|
||||
f"{format_duration(result['expected_transfer_seconds']):>16}"
|
||||
f"{result['effective_goodput_bps'] / 1000:>10.2f} кбит/с"
|
||||
)
|
||||
|
||||
optimum_result = min(
|
||||
selected_results,
|
||||
key=lambda item: (
|
||||
item["expected_transfer_seconds"]
|
||||
),
|
||||
)
|
||||
|
||||
print(
|
||||
"\nОптимальный размер фрагмента:"
|
||||
)
|
||||
|
||||
print(
|
||||
optimum_result["fragment_size"],
|
||||
"байт",
|
||||
)
|
||||
|
||||
print(
|
||||
"Ожидаемое время:",
|
||||
format_duration(
|
||||
optimum_result[
|
||||
"expected_transfer_seconds"
|
||||
]
|
||||
),
|
||||
)
|
||||
|
||||
print(
|
||||
"Полезная скорость:",
|
||||
f"{optimum_result['effective_goodput_bps'] / 1000:.2f}",
|
||||
"кбит/с",
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Сохранение CSV
|
||||
# ============================================================
|
||||
|
||||
with CSV_PATH.open(
|
||||
"w",
|
||||
newline="",
|
||||
encoding="utf-8-sig",
|
||||
) as csv_file:
|
||||
|
||||
fieldnames = [
|
||||
"fragment_size",
|
||||
"fragment_count",
|
||||
"eb_n0_db",
|
||||
"ber",
|
||||
"ideal_total_bytes",
|
||||
"ideal_efficiency_percent",
|
||||
"ack_success_probability",
|
||||
"minimum_data_success_probability",
|
||||
"maximum_data_success_probability",
|
||||
"average_data_transmissions",
|
||||
"expected_total_bytes",
|
||||
"expected_transfer_seconds",
|
||||
"expected_efficiency_percent",
|
||||
"effective_goodput_bps",
|
||||
]
|
||||
|
||||
writer = DictWriter(
|
||||
csv_file,
|
||||
fieldnames=fieldnames,
|
||||
)
|
||||
|
||||
writer.writeheader()
|
||||
|
||||
writer.writerows(results)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# График ожидаемого времени передачи
|
||||
# ============================================================
|
||||
|
||||
plt.figure(
|
||||
figsize=(10, 7)
|
||||
)
|
||||
|
||||
for eb_n0_db in EB_N0_VALUES_DB:
|
||||
|
||||
selected_results = [
|
||||
result
|
||||
for result in results
|
||||
if result["eb_n0_db"] == eb_n0_db
|
||||
]
|
||||
|
||||
fragment_sizes = [
|
||||
result["fragment_size"]
|
||||
for result in selected_results
|
||||
]
|
||||
|
||||
transfer_times = [
|
||||
result["expected_transfer_seconds"]
|
||||
for result in selected_results
|
||||
]
|
||||
|
||||
plt.plot(
|
||||
fragment_sizes,
|
||||
transfer_times,
|
||||
marker="o",
|
||||
label=f"Eb/N0 = {eb_n0_db:.0f} дБ",
|
||||
)
|
||||
|
||||
plt.xscale(
|
||||
"log",
|
||||
base=2,
|
||||
)
|
||||
|
||||
plt.yscale(
|
||||
"log",
|
||||
)
|
||||
|
||||
plt.xlabel(
|
||||
"Размер данных фрагмента, байт"
|
||||
)
|
||||
|
||||
plt.ylabel(
|
||||
"Ожидаемое время передачи, с"
|
||||
)
|
||||
|
||||
plt.title(
|
||||
"Влияние размера фрагмента "
|
||||
"на время передачи JPEG"
|
||||
)
|
||||
|
||||
plt.grid(
|
||||
True,
|
||||
which="both",
|
||||
)
|
||||
|
||||
plt.legend()
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
plt.savefig(
|
||||
TIME_GRAPH_PATH,
|
||||
dpi=160,
|
||||
)
|
||||
|
||||
plt.close()
|
||||
|
||||
|
||||
# ============================================================
|
||||
# График эффективной полезной скорости
|
||||
# ============================================================
|
||||
|
||||
plt.figure(
|
||||
figsize=(10, 7)
|
||||
)
|
||||
|
||||
for eb_n0_db in EB_N0_VALUES_DB:
|
||||
|
||||
selected_results = [
|
||||
result
|
||||
for result in results
|
||||
if result["eb_n0_db"] == eb_n0_db
|
||||
]
|
||||
|
||||
fragment_sizes = [
|
||||
result["fragment_size"]
|
||||
for result in selected_results
|
||||
]
|
||||
|
||||
goodput_values = [
|
||||
result["effective_goodput_bps"] / 1000
|
||||
for result in selected_results
|
||||
]
|
||||
|
||||
plt.plot(
|
||||
fragment_sizes,
|
||||
goodput_values,
|
||||
marker="o",
|
||||
label=f"Eb/N0 = {eb_n0_db:.0f} дБ",
|
||||
)
|
||||
|
||||
plt.xscale(
|
||||
"log",
|
||||
base=2,
|
||||
)
|
||||
|
||||
plt.xlabel(
|
||||
"Размер данных фрагмента, байт"
|
||||
)
|
||||
|
||||
plt.ylabel(
|
||||
"Полезная скорость, кбит/с"
|
||||
)
|
||||
|
||||
plt.title(
|
||||
"Полезная скорость JPEG "
|
||||
"с учётом CRC и ARQ"
|
||||
)
|
||||
|
||||
plt.grid(
|
||||
True,
|
||||
which="both",
|
||||
)
|
||||
|
||||
plt.legend()
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
plt.savefig(
|
||||
GOODPUT_GRAPH_PATH,
|
||||
dpi=160,
|
||||
)
|
||||
|
||||
plt.close()
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Автоматические проверки
|
||||
# ============================================================
|
||||
|
||||
assert results
|
||||
|
||||
assert CSV_PATH.exists()
|
||||
|
||||
assert TIME_GRAPH_PATH.exists()
|
||||
|
||||
assert GOODPUT_GRAPH_PATH.exists()
|
||||
|
||||
assert all(
|
||||
result["expected_transfer_seconds"] > 0
|
||||
for result in results
|
||||
)
|
||||
|
||||
assert all(
|
||||
0.0
|
||||
< result["ack_success_probability"]
|
||||
<= 1.0
|
||||
for result in results
|
||||
)
|
||||
|
||||
|
||||
print("\nCSV:")
|
||||
|
||||
print(CSV_PATH)
|
||||
|
||||
print("\nГрафик времени:")
|
||||
|
||||
print(TIME_GRAPH_PATH)
|
||||
|
||||
print("\nГрафик полезной скорости:")
|
||||
|
||||
print(GOODPUT_GRAPH_PATH)
|
||||
|
||||
print(
|
||||
"\nПроверка пройдена: "
|
||||
"оптимальный размер фрагмента рассчитан."
|
||||
)
|
||||
Reference in New Issue
Block a user