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
617
experiments/lab014_bpsk_ber_curve.py
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617
experiments/lab014_bpsk_ber_curve.py
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@@ -0,0 +1,617 @@
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"""
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Lab014. Измерение BER для BPSK в канале AWGN.
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Программа:
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1. Генерирует 1 000 000 случайных битов.
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2. Преобразует их в BPSK-символы.
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3. Добавляет комплексный гауссов шум.
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4. Демодулирует принятый сигнал.
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5. Измеряет экспериментальный BER.
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6. Рассчитывает теоретический BER.
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7. Оценивает вероятность повреждения пакетов разной длины.
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8. Строит BER-кривую.
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9. Сохраняет результаты в CSV.
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Упрощения модели:
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- один бит передаётся одним BPSK-символом;
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- один символ представлен одним IQ-сэмплом;
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- частотная и фазовая синхронизация идеальны;
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- межсимвольные искажения отсутствуют;
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- канал содержит только AWGN.
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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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expm1,
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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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import numpy as np
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# ============================================================
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# Настройки эксперимента
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# ============================================================
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BIT_COUNT = 1_000_000
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EB_N0_VALUES_DB = [
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-4.0,
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-2.0,
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0.0,
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2.0,
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4.0,
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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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RANDOM_SEED = 2026
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OUTPUT_DIRECTORY = Path(
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"data/processed/lab014"
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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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GRAPH_PATH = (
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OUTPUT_DIRECTORY
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/ "lab014_bpsk_ber_curve.png"
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)
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CSV_PATH = (
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OUTPUT_DIRECTORY
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/ "lab014_bpsk_ber_results.csv"
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)
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# ============================================================
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# Размеры пакетов для оценки PER
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# ============================================================
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# Пакет из Lab013:
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#
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# заголовок 8 байт
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# PAYLOAD 16 байт
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# CRC 4 байта
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#
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# Итого 28 байт = 224 бита
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SHORT_PACKET_BITS = 224
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# Типовой пакет изображения:
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#
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# заголовок протокола 8 байт
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# заголовок фрагмента 12 байт
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# данные JPEG 512 байт
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# CRC 4 байта
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#
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# Итого 536 байт
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IMAGE_PACKET_BITS = 536 * 8
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# ============================================================
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# Вспомогательные функции
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# ============================================================
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def bpsk_modulate(
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bits: np.ndarray,
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) -> np.ndarray:
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"""
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Преобразовать биты в BPSK-символы.
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Отображение:
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0 → -1
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1 → +1
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"""
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bits = np.asarray(
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bits,
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dtype=np.uint8,
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)
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if bits.ndim != 1:
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raise ValueError(
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"bits должен быть одномерным массивом"
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)
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if not np.all(
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(bits == 0) | (bits == 1)
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):
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raise ValueError(
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"bits должен содержать только 0 и 1"
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)
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symbols = (
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2.0 * bits.astype(np.float64)
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- 1.0
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)
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return symbols.astype(
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np.complex128
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)
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def add_awgn(
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iq_samples: np.ndarray,
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eb_n0_db: float,
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random_generator: np.random.Generator,
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) -> np.ndarray:
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"""
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Добавить комплексный AWGN-шум.
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Энергия BPSK-символа равна единице.
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Для одного бита на символ:
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Es/N0 = 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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# Для комплексного AWGN каждая компонента
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# I и Q получает половину полной мощности шума.
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noise_sigma = sqrt(
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1.0
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/ (
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2.0
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* eb_n0_linear
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)
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)
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noise = noise_sigma * (
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random_generator.standard_normal(
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len(iq_samples)
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)
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+ 1j
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* random_generator.standard_normal(
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len(iq_samples)
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)
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)
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return iq_samples + noise
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def bpsk_demodulate(
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received_iq: np.ndarray,
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) -> np.ndarray:
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"""
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Демодулировать BPSK по знаку компоненты I.
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I < 0 → бит 0
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I >= 0 → бит 1
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"""
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return (
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received_iq.real >= 0.0
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).astype(np.uint8)
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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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Рассчитать теоретический BER когерентной BPSK
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в канале 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 ber_to_per(
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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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Оценить Packet Error Rate из BER.
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Предполагается:
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- ошибки отдельных битов независимы;
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- пакет считается повреждённым,
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если ошибся хотя бы один бит.
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PER = 1 - (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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"packet_bit_count должен быть положительным"
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)
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if ber == 0.0:
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return 0.0
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if ber == 1.0:
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return 1.0
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# Такая запись численно устойчивее,
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# чем прямое возведение в степень
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# для очень маленьких BER.
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return -expm1(
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packet_bit_count
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* log1p(-ber)
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)
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# ============================================================
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# Генерация исходной последовательности
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# ============================================================
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bit_generator = np.random.default_rng(
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RANDOM_SEED
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)
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transmitted_bits = bit_generator.integers(
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low=0,
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high=2,
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size=BIT_COUNT,
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dtype=np.uint8,
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)
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transmitted_iq = bpsk_modulate(
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transmitted_bits
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)
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# ============================================================
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# Эксперимент при разных Eb/N0
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# ============================================================
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results = []
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for experiment_index, eb_n0_db in enumerate(
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EB_N0_VALUES_DB
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):
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noise_generator = np.random.default_rng(
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RANDOM_SEED
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+ 1000
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+ experiment_index
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)
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received_iq = add_awgn(
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iq_samples=transmitted_iq,
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eb_n0_db=eb_n0_db,
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random_generator=noise_generator,
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)
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received_bits = bpsk_demodulate(
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received_iq
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)
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bit_error_count = int(
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np.count_nonzero(
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transmitted_bits
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!= received_bits
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)
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)
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experimental_ber = (
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bit_error_count
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/ BIT_COUNT
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)
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theoretical_ber = theoretical_bpsk_ber(
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eb_n0_db
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)
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short_packet_per = ber_to_per(
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theoretical_ber,
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SHORT_PACKET_BITS,
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)
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image_packet_per = ber_to_per(
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theoretical_ber,
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IMAGE_PACKET_BITS,
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)
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results.append(
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{
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"eb_n0_db": eb_n0_db,
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"bit_errors": bit_error_count,
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"experimental_ber": experimental_ber,
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"theoretical_ber": theoretical_ber,
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"short_packet_per": short_packet_per,
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"image_packet_per": image_packet_per,
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}
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)
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# ============================================================
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# Вывод основных результатов
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# ============================================================
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print(
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"=== Lab014. BER-кривая BPSK ==="
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)
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print("\nКоличество переданных битов:")
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print(
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f"{BIT_COUNT:,}".replace(",", " ")
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)
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print("\nРезультаты:")
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print(
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f"{'Eb/N0':>9}"
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f"{'Ошибки':>12}"
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f"{'BER эксперимент':>19}"
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f"{'BER теория':>16}"
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f"{'PER 224 бит':>16}"
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f"{'PER 4288 бит':>17}"
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)
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print("-" * 89)
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for result in results:
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print(
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f"{result['eb_n0_db']:>6.1f} дБ"
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f"{result['bit_errors']:>12}"
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f"{result['experimental_ber']:>19.6e}"
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f"{result['theoretical_ber']:>16.6e}"
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f"{result['short_packet_per']:>16.6f}"
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f"{result['image_packet_per']:>17.6f}"
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)
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# ============================================================
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# Сохранение CSV
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# ============================================================
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with CSV_PATH.open(
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"w",
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newline="",
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encoding="utf-8-sig",
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) as csv_file:
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fieldnames = [
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"eb_n0_db",
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"bit_errors",
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"experimental_ber",
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"theoretical_ber",
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"short_packet_per_224_bits",
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"image_packet_per_4288_bits",
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]
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writer = DictWriter(
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csv_file,
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fieldnames=fieldnames,
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)
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writer.writeheader()
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for result in results:
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writer.writerow(
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{
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"eb_n0_db": result["eb_n0_db"],
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"bit_errors": result["bit_errors"],
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"experimental_ber": (
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result["experimental_ber"]
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),
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"theoretical_ber": (
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result["theoretical_ber"]
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),
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"short_packet_per_224_bits": (
|
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result["short_packet_per"]
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),
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"image_packet_per_4288_bits": (
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result["image_packet_per"]
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),
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}
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)
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# ============================================================
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# Подготовка данных для графика
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# ============================================================
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eb_n0_plot_values = np.array(
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[
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||||
result["eb_n0_db"]
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for result in results
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||||
],
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||||
dtype=np.float64,
|
||||
)
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||||
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experimental_ber_values = np.array(
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[
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result["experimental_ber"]
|
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for result in results
|
||||
],
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dtype=np.float64,
|
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)
|
||||
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theoretical_ber_values = np.array(
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[
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result["theoretical_ber"]
|
||||
for result in results
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],
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||||
dtype=np.float64,
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)
|
||||
|
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|
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# Нулевой измеренный BER невозможно показать
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# на логарифмической шкале.
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||||
#
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||||
# Поэтому для графика ставим такую точку
|
||||
# на уровень половины одного наблюдаемого события.
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||||
measurement_floor = (
|
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0.5 / BIT_COUNT
|
||||
)
|
||||
|
||||
experimental_ber_for_plot = np.maximum(
|
||||
experimental_ber_values,
|
||||
measurement_floor,
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
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||||
# Построение BER-графика
|
||||
# ============================================================
|
||||
|
||||
figure = plt.figure(
|
||||
figsize=(10, 7)
|
||||
)
|
||||
|
||||
plt.semilogy(
|
||||
eb_n0_plot_values,
|
||||
theoretical_ber_values,
|
||||
marker="o",
|
||||
label="Теоретический BER BPSK",
|
||||
)
|
||||
|
||||
plt.semilogy(
|
||||
eb_n0_plot_values,
|
||||
experimental_ber_for_plot,
|
||||
marker="s",
|
||||
linestyle="--",
|
||||
label="Экспериментальный BER",
|
||||
)
|
||||
|
||||
plt.axhline(
|
||||
measurement_floor,
|
||||
linestyle=":",
|
||||
label=(
|
||||
"Предел измерения "
|
||||
f"{measurement_floor:.1e}"
|
||||
),
|
||||
)
|
||||
|
||||
plt.xlabel(
|
||||
"Eb/N0, дБ"
|
||||
)
|
||||
|
||||
plt.ylabel(
|
||||
"BER"
|
||||
)
|
||||
|
||||
plt.title(
|
||||
"BPSK в канале AWGN: "
|
||||
"эксперимент и теория"
|
||||
)
|
||||
|
||||
plt.grid(
|
||||
True,
|
||||
which="both",
|
||||
)
|
||||
|
||||
plt.legend()
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
figure.savefig(
|
||||
GRAPH_PATH,
|
||||
dpi=160,
|
||||
)
|
||||
|
||||
plt.close(
|
||||
figure
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Инженерные контрольные точки
|
||||
# ============================================================
|
||||
|
||||
print("\nИнженерные контрольные точки:")
|
||||
|
||||
for target_db in [
|
||||
6.0,
|
||||
8.0,
|
||||
10.0,
|
||||
]:
|
||||
|
||||
result = next(
|
||||
item
|
||||
for item in results
|
||||
if item["eb_n0_db"] == target_db
|
||||
)
|
||||
|
||||
print(
|
||||
f"\nEb/N0 = {target_db:.1f} дБ"
|
||||
)
|
||||
|
||||
print(
|
||||
"Теоретический BER:",
|
||||
f"{result['theoretical_ber']:.6e}",
|
||||
)
|
||||
|
||||
print(
|
||||
"PER короткого пакета 224 бита:",
|
||||
f"{result['short_packet_per'] * 100:.2f} %",
|
||||
)
|
||||
|
||||
print(
|
||||
"PER JPEG-пакета 4288 бит:",
|
||||
f"{result['image_packet_per'] * 100:.2f} %",
|
||||
)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Автоматические проверки
|
||||
# ============================================================
|
||||
|
||||
assert len(transmitted_bits) == BIT_COUNT
|
||||
|
||||
assert len(received_bits) == BIT_COUNT
|
||||
|
||||
assert all(
|
||||
0.0
|
||||
<= result["experimental_ber"]
|
||||
<= 1.0
|
||||
for result in results
|
||||
)
|
||||
|
||||
assert all(
|
||||
0.0
|
||||
<= result["theoretical_ber"]
|
||||
<= 1.0
|
||||
for result in results
|
||||
)
|
||||
|
||||
assert GRAPH_PATH.exists()
|
||||
|
||||
assert CSV_PATH.exists()
|
||||
|
||||
|
||||
print("\nГрафик BER:")
|
||||
|
||||
print(GRAPH_PATH)
|
||||
|
||||
print("\nТаблица CSV:")
|
||||
|
||||
print(CSV_PATH)
|
||||
|
||||
print(
|
||||
"\nПроверка пройдена: "
|
||||
"экспериментальная BER-кривая построена."
|
||||
)
|
||||
Reference in New Issue
Block a user