From 0368c6d3e930f4d245e056223fd4e5c4f70e6b49 Mon Sep 17 00:00:00 2001 From: LittleSam129 <1qwe432@gmail.com> Date: Wed, 9 Sep 2026 10:26:55 +0300 Subject: [PATCH] Lab053: achieve real-time 5 fps software video receiver --- PROJECT_LOG.md | 160 + data/processed/lab053/acceptance.json | 55 + data/processed/lab053/baseline_profile.json | 22413 ++++++++++ data/processed/lab053/cpu_dependencies.json | 10 + data/processed/lab053/equivalence.json | 8705 ++++ data/processed/lab053/initial_workers_1.json | 22016 ++++++++++ data/processed/lab053/initial_workers_2.json | 22011 ++++++++++ data/processed/lab053/long_stream.json | 34631 ++++++++++++++++ data/processed/lab053/memory.json | 27 + data/processed/lab053/overflow_probe.json | 359 + data/processed/lab053/paced_1_repeat_1.json | 23752 +++++++++++ data/processed/lab053/paced_1_repeat_2.json | 23752 +++++++++++ data/processed/lab053/paced_1_repeat_3.json | 23752 +++++++++++ data/processed/lab053/paced_workers_1.json | 23757 +++++++++++ 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data/processed/lab053/without_jit_paced.json | 23240 +++++++++++ data/processed/lab053/worker_comparison.json | 39 + docs/LABS_INDEX.md | 41 + docs/lab053_current_state.md | 438 + experiments/lab053_packet_processing.py | 213 + experiments/lab053_pipeline.py | 312 + experiments/lab053_receiver_profile.py | 333 + experiments/lab053_regression.py | 145 + experiments/lab053_streaming_receiver.py | 287 + experiments/lab053_viterbi.py | 98 + requirements-lab053-cpu.txt | 4 + tests/test_lab053_packet_pipeline.py | 345 + 44 files changed, 472121 insertions(+) create mode 100644 data/processed/lab053/acceptance.json create mode 100644 data/processed/lab053/baseline_profile.json create mode 100644 data/processed/lab053/cpu_dependencies.json create mode 100644 data/processed/lab053/equivalence.json create mode 100644 data/processed/lab053/initial_workers_1.json create mode 100644 data/processed/lab053/initial_workers_2.json create mode 100644 data/processed/lab053/long_stream.json create mode 100644 data/processed/lab053/memory.json create mode 100644 data/processed/lab053/overflow_probe.json create mode 100644 data/processed/lab053/paced_1_repeat_1.json create mode 100644 data/processed/lab053/paced_1_repeat_2.json create mode 100644 data/processed/lab053/paced_1_repeat_3.json create mode 100644 data/processed/lab053/paced_workers_1.json create mode 100644 data/processed/lab053/paced_workers_2.json create mode 100644 data/processed/lab053/paced_workers_4.json create mode 100644 data/processed/lab053/performance_and_age.png create mode 100644 data/processed/lab053/performance_and_age.svg create mode 100644 data/processed/lab053/regression20k/final_processing_attempt.json create mode 100644 data/processed/lab053/regression20k/recovered_image.jpg create mode 100644 data/processed/lab053/regression20k/result.json create mode 100644 data/processed/lab053/sensitivity.json create mode 100644 data/processed/lab053/source.json create mode 100644 data/processed/lab053/source_120.json create mode 100644 data/processed/lab053/stream.json create mode 100644 data/processed/lab053/throughput.json create mode 100644 data/processed/lab053/throughput_1_repeat_1.json create mode 100644 data/processed/lab053/throughput_1_repeat_2.json create mode 100644 data/processed/lab053/throughput_1_repeat_3.json create mode 100644 data/processed/lab053/throughput_workers_1.json create mode 100644 data/processed/lab053/throughput_workers_2.json create mode 100644 data/processed/lab053/throughput_workers_4.json create mode 100644 data/processed/lab053/without_jit_paced.json create mode 100644 data/processed/lab053/worker_comparison.json create mode 100644 docs/lab053_current_state.md create mode 100644 experiments/lab053_packet_processing.py create mode 100644 experiments/lab053_pipeline.py create mode 100644 experiments/lab053_receiver_profile.py create mode 100644 experiments/lab053_regression.py create mode 100644 experiments/lab053_streaming_receiver.py create mode 100644 experiments/lab053_viterbi.py create mode 100644 requirements-lab053-cpu.txt create mode 100644 tests/test_lab053_packet_pipeline.py diff --git a/PROJECT_LOG.md b/PROJECT_LOG.md index 1a63b35..f173803 100644 --- a/PROJECT_LOG.md +++ b/PROJECT_LOG.md @@ -2003,3 +2003,163 @@ Lab052 git add/commit иpush не выполнялись;индекс пуст. Следующий этап: STOP. Для отдельно согласованной Lab053 исследовать стоимость настоящих пакетов, общей децимации/FFT/копирования IQ и задержку JPEG-пачки. Не переходить автоматически к10fps,QPSK,180 МГц,аппаратным500 ксимв/с или RF. + +# Запись 042 + +## Дата + +8 сентября 2026 года + +## Тема + +Lab053 — стоимость настоящих пакетов, конвейер на CPU и пять свежих JPEG в секунду + +## Выполнено + +Перед изменениями выполнен аудит Lab052 и состояния Git. Повторные шлюзы: +quick_gate — 718 тестов, 20 функциональных проверок, 13 штатных пропусков; +pytest — 718 passed за 79,79 с, одно предупреждение .pytest_cache. +Независимый verifier подтвердил готовность. По явному разрешению пользователя +выборочно добавлены и показаны 27 файлов Lab052; выполнен коммит +`cc38900caea4b310568edc691be7db124503fd21`, `Lab052: suppress false preamble candidates`. +Push не выполнялся. Указание в записи 041 об отсутствии коммита описывает +состояние на момент завершения Lab052; прежняя запись сохранена без изменений. + +Lab053 выполнена только программно. Сохранены BPSK 500 ксимв/с, Fs 8 Мвыб/с, +16 отсчётов/символ, PRBS7, PHY/FEC/CRC и профиль C: 128×96 RGB JPEG q25. +Вход — 30 JPEG за 6 с, 5 кадров/с, предел возраста 200 мс. Управление остаётся +за ELRS; в SDR-видеоканал управление не добавлено. RF TX=0, аппаратных +инвентаризаций, запусков и проверок не было. GPU не использована. + +Исходный IQ сформирован тем же генератором и сценарием Lab052: +48355328 отсчётов complex64, 386842624 байта, 6,044416 с. +SHA256: `9b4f5469b21985f796a77e22981f93bcd3b7a203b573349e8bdf7ea55891bea0`. +Приёмник получает только последовательные блоки IQ и параметры физического +режима. Границы, число кадров, содержимое JPEG и TX truth не передаются +обнаружителю, строгому декодеру или сборщику. Сверка SHA и модель capture-time +применяются после независимого JPEG verdict. Камера и экран остаются UNKNOWN. + +Новый подробный профиль прежнего Lab052: 7,774377 с с измерителями, +240 настоящих и 15 ложных строгих проверок. Настоящий пакет в среднем +17,354 мс, медиана 17,122 мс, p95 19,520 мс; сумма 4,165062 с. +Уточнение старта 1,082006 с, локальный согласованный фильтр 0,779990 с, +timing 0,488404 с, Viterbi 1,367891 с. Общая децимация 1,388460 с. +Исторический чистый baseline Lab052 сохранён отдельно: 7,369795 с, +отношение обработки к IQ 1,219273. Вложенные времена не складываются дважды. + +Строгие расчёты перенесены в локальные окна. Исходные тела функций связаны +с частными зависимостями; глобальная подмена режима при работе потоков устранена. +Шаблоны, RRC taps и опорные биты совпадают побитно. Сохранены весь диапазон +уточнения старта, все 1601 timing-offset и прежние правила CFO/фазы/полярности. +Тело кадра вычисляется после допустимого PHY header, затем header проверяется +повторно прежним правилом. Общая децимация и корреляции Lab052 сохранены. +Зеркальное кольцо копирует только новый IQ-блок вместо всего остатка. + +Viterbi R=1/2, K=7 с теми же полиномами реализован теми же операциями на CPU +через Numba: без fastmath, изменения порядка сложений, ties и traceback. +Это изменение вычислителя, а не FEC. Набор протокола protocol не менялся. +Numba 0.61.2 и llvmlite 0.44.0 закреплены отдельно и доступны в локальном +`.lab053_cpu_runtime/packages`; системное окружение не изменено. Первое +создание машинного кода 1,221810 с, прогрев выбранного процесса из кеша +0,151137 с — до приёма IQ. Без JIT доступен старый NumPy fallback, +но измеренные целевые показатели к нему не относятся. + +Конвейер имеет отдельный источник IQ, RX и сборку/декодирование JPEG. +Внутри RX проверены 1/2/4 работника; следующий блок ожидает завершения +строгих работ текущего окна. Полностью независимые пять вычислительных +стадий не реализованы. Порядок фиксации решений детерминирован; только +успешный PHY/FEC/CRC разрешает пропуск уже принятого кадра. +При 2/4 работниках возникают 28 лишних спекулятивных проверок; устойчивого +выигрыша нет. Выбран один работник. Ограничения очередей: IQ 8 блоков, +кандидаты 4 задания, выход 16 пакетов. Переполнение явно останавливает сессию. + +Три полных запуска с реальной подачей IQ по часам дали отношения суммарной +работы стадий к длительности IQ 0,725428 / 0,712269 / 0,731542. +Выбран целиком медианный по времени первый запуск: 4,384788 с работы стадий +на 6,044416 с IQ. Полное настенное время с ожиданием источника 6,074946 с; +оно не выдаётся за время вычислений. Отдельные полные проходы без ожидания: +4,001807 / 4,010478 / 4,114974 с. Медианный второй — 0,663501 с обработки +на секунду IQ. Источник в этом опыте ограничен обратным давлением очереди. +Возраст кадров оценивается только в опыте с подачей IQ по часам. + +В выбранном потоке: 240/240 пакетов, 30/30 JPEG SHA MATCH, 255 строгих проверок, +15 ложных, 0 потерь, DISPLAYED=FRESH DISPLAYED=30, FRESH DISPLAY FPS=5. +Возраст минимум/медиана/максимум: 150,298 / 153,996 / 176,062 мс. +По работе стадий свободны 27,46% бюджета секунды; без ожидания — 33,65%. +Это измеренный программный запас, не гарантия жёсткого реального времени ОС. +Варианты 2/4 также приняли всё: отношения 0,732485 / 0,734393, +максимальный возраст 176,126 / 189,207 мс, пять свежих кадров/с. + +Настоящий пакет после оптимизации: среднее 7,016 мс, медиана 6,862 мс, +p95 8,292 мс, максимум 12,611 мс. Для 40 пакетов/с требуется около +0,280656 с последовательной работы обработчика; сборка фрагментов добавляет +около 0,001072 с. Это бюджет perf_counter, а не точная метрика загрузки ядра: +Windows thread_time имеет квант учёта 15,625 мс. Process CPU основного запуска +3,234375 с сохранён отдельно. Теперь Viterbi занимает 0,068129 с на весь поток. + +Непересекающиеся составляющие 4,384788 с: чтение/подготовка IQ 0,345752 с; +децимация 1,371813 с; грубое обнаружение 0,374433 с; дешёвая проверка 0,428823 с; +настоящие пакеты без Viterbi/CRC 1,614029 с; ложные 0,043819 с; +Viterbi/CRC 0,069909 с; сборка 0,006432 с; JPEG decode 0,006325 с; +проверка свежести 0,000045 с; прочая организация и измерители 0,123409 с. +Главные оставшиеся расходы — общая обработка IQ, timing и локальная фильтрация. +Физическая JPEG-пачка занимает 92,256 мс; медиана от её конца до выдачи пакета +61,284 мс, от выдачи до JPEG 0,309 мс. Lookahead 36,224 мс и пачечная обработка +влияют на задержку существенно сильнее собственно JPEG decoder. + +Пик удерживаемого IQ 3177728 байт (3,031 МиБ); кольцо выделяет 6797824 байта +(6,483 МиБ). Основная IQ-очередь достигла 5/8 блоков, кандидаты 1/4; +переполнений всех очередей 0. Инженерная оценка временных массивов с резервами +ограниченных кешей 76252928 байт (72,720 МиБ), не универсальный предел C/LLVM. +Измеренный peak RSS процесса 206778368 байт (197,199 МиБ); эти показатели +не складываются. Для пустого/недоступного источника и переполнения предусмотрены +явные причины отказа, полное ожидание работников и освобождение состояния. + +Длинный источник содержит 120 JPEG за 24 с, 192355328 отсчётов /24,044416 с IQ. +Приняты 960/960 пакетов и 120/120 точных JPEG; свежесть 5 кадров/с, +возраст 149,337 / 155,733 / 189,396 мс. Работа 17,861142 с, отношение 0,742840. +Медиана первых/последних 60 кадров 155,662 / 156,001 мс: накопления отставания +не наблюдается. IQ-очередь достигла 7/8, кандидаты 1/4, переполнений 0. +В поздних контрольных точках RSS 197709824–201424896 байт; линейный наклон +около +105078 байт/с. Ограничены рабочие массивы и очереди, но нулевой рост +RSS на бесконечной сессии не доказан. После закрытия IQ и незавершённых работ 0. + +Отрицательные результаты сохранены. Локальные окна без остальных изменений +не достигали реального времени. Кольцо без JIT давало средний запас, но +возраст до 222,90 мс. Полный опыт без JIT с подачей по часам остановился +при переполнении: 239 пакетов, 29 JPEG, 3,667 свежих кадров/с, максимум 207,954 мс. +Замедление на 20 мс/блок воспроизвело IQ_QUEUE_OVERFLOW при 8/8 блоках, +явный неполный источник и очистку состояния; короткое время такого отказа +не считается хорошей пропускной способностью полного потока. + +Независимые verifier и adversary воспроизвели и проверили исправления: +исключение первого future больше не оставляет остальные работы активными; +fallback-проверка больше не создаёт третьего работника при лимите 2; +пустой и недоступный источник возвращают диагностический отчёт вместо +необработанного исключения. Проверены кольцо на 1,8 млн случайных отсчётов, +реальное переполнение IQ/выхода, слабый VALID возле ложной преамбулы, +изоляция глобального режима и ContextVar. Все воспроизведённые дефекты закрыты. + +Эквивалентность на всём потоке для 1/2/4 работников: одинаковые 240 стартов, +концов, timing, полярностей, CRC и байтов, одинаковые 255 строгих кандидатов. +Максимальное расхождение soft 6,715e-7, CFO 5,487e-7 Гц, фазы 4,681e-9 рад; +допуски заданы явно. На 45 слабых сценариях прежний и новый RX приняли 37, +новых потерь 0. Старая запись 20 ксимв/с штатным Lab048: 36/36, CRC PASS, +JPEG 320×240 /8724 байта SHA MATCH, 19 ошибок до FEC, 0 после, 0 байтовых. + +Добавлены 50 целевых тестов вычислений, кольца, очередей, порядка и исключений. +Полный pytest: 768 passed за 77,81 с, одно предупреждение .pytest_cache. +Финальный quick_gate PASS: 768 тестов, 26 функциональных проверок, +13 штатных пропусков. git diff --check PASS; новые исходники и отчёт отдельно +проверены на концевые пробелы. Verifier и adversary независимо пересчитали +итоговые JSON, возраст по временным отметкам, выбор целого медианного прохода, +суммы стадий и памяти, проверили SHA кода, ссылки, график и прежний префикс +журнала. Остаточных воспроизведённых дефектов нет; все 20 критериев PASS. +Численные критерии, SHA исходного IQ и отрицательное переполнение проверяются +в data/processed/lab053/acceptance.json; подробности и команды воспроизведения +в docs/lab053_current_state.md. Графики и результаты сохранены локально. +Lab053 git add/commit и push не выполнялись; индекс пуст. RF TX=0. + +Следующий этап: STOP, рассмотрение программных результатов ChatGPT. +Не переходить автоматически к 10 fps, QPSK, 180 МГц, новому FEC, +аппаратным 500 ксимв/с или любому RF-эксперименту. diff --git a/data/processed/lab053/acceptance.json b/data/processed/lab053/acceptance.json new file mode 100644 index 0000000..1bc0519 --- /dev/null +++ b/data/processed/lab053/acceptance.json @@ -0,0 +1,55 @@ +{ + "checks": { + "all_main_runs_240_packets_30_exact_jpegs": true, + "all_main_runs_255_strict_15_false": true, + "all_paced_runs_5_fresh_fps": true, + "all_main_age_at_most_200ms": true, + "all_main_processing_ratio_below_08": true, + "all_main_sources_complete_and_no_overflow": true, + "all_main_queues_within_caps": true, + "same_240_phy_crc_timing_all_worker_counts": true, + "sensitivity_37_of_45_identical": true, + "long_960_packets_120_jpeg_5_fps": true, + "long_queues_within_caps_no_overflow": true, + "long_complete_source_and_cleared_state": true, + "old20k_36_crc_sha": true, + "source_exact_duration": true, + "source_file_size": true, + "source_file_sha256": true, + "forced_overflow_is_reported_and_state_cleared": true, + "protocol_unchanged": true, + "lab052_detector_unchanged": true, + "lab053_index_empty": true + }, + "required_checks_pass": true, + "real_time_capable": true, + "software_5fps_target_pass": true, + "hardware_validated": false, + "head": "cc38900caea4b310568edc691be7db124503fd21", + "source_code_sha256": { + "experiments\\lab053_packet_processing.py": "d6031d8dcb45fa3ac88f1ec240d65340afe0f986da8fd64311443a338d40dfde", + "experiments\\lab053_pipeline.py": "dc2c76742072636e5ac22ecbbf633b8d81c4ac45f7b0cead31017d82211fd540", + "experiments\\lab053_receiver_profile.py": "f2017ddfc90b75889920ba786d15659b54bcf2f038e776bf27abc3daae8d6876", + "experiments\\lab053_regression.py": "2a3bec7c2d84f868e9593da63dbb2b180421e5edd3a7aa61d8c931cc5e65e9ed", + "experiments\\lab053_streaming_receiver.py": 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"processing_per_rf_second": 0.725427849366574, + "fresh_display_fps": 5.0, + "image_age_max_seconds": 0.1760622999165209, + "candidate_queue_peak": 1, + "iq_queue_peak": 5, + "speculative_checks": 0, + "discarded_speculative_checks": 0 + }, + { + "workers": 2, + "throughput_processing_per_rf_second": 0.689214408804942, + "processing_per_rf_second": 0.7324852891396996, + "fresh_display_fps": 5.0, + "image_age_max_seconds": 0.17612620000727475, + "candidate_queue_peak": 2, + "iq_queue_peak": 6, + "speculative_checks": 271, + "discarded_speculative_checks": 28 + }, + { + "workers": 4, + "throughput_processing_per_rf_second": 0.6761923732542345, + "processing_per_rf_second": 0.7343934632408585, + "fresh_display_fps": 5.0, + "image_age_max_seconds": 0.18920700000599044, + "candidate_queue_peak": 2, + "iq_queue_peak": 7, + "speculative_checks": 271, + "discarded_speculative_checks": 28 + } + ], + "reason": "single worker is sufficient; additional workers give no measured throughput or latency benefit" +} diff --git a/docs/LABS_INDEX.md b/docs/LABS_INDEX.md index f2827d8..4ae1f85 100644 --- a/docs/LABS_INDEX.md +++ b/docs/LABS_INDEX.md @@ -348,6 +348,47 @@ задержки JPEG-пачки; текущий этап завершить на программных результатах. - **RF / Git:** RF TX=0; Lab052 git add/commit и push не выполнялись. STOP. +## Lab053 — стоимость настоящих пакетов и потоковая обработка на CPU + +- **Режим:** BPSK 500 ксимв/с, Fs 8 Мвыб/с, 16 отсчётов/символ; + JPEG C (128×96 RGB, q25), 5 кадров/с, предел возраста 200 мс. +- **База:** Lab052 закреплена коммитом + `cc38900caea4b310568edc691be7db124503fd21` — 27 файлов, без push. +- **Изменения:** локальные окна строгих расчётов, кольцевой IQ-буфер, + неизменный алгоритм Viterbi с CPU JIT, раздельные стадии ввода/RX/JPEG. + Общие децимация и корреляции сохранены; все 1601 timing-offset проверяются. + PHY/FEC/CRC, пороги и порядок кандидатов Lab052 не изменены. +- **Стоимость пакета:** среднее 7,016 мс, медиана 6,862 мс, p95 8,292 мс; + 40 пакетов/с требуют около 0,281 с работы обработчика на секунду потока. +- **Производительность:** 4,384788 с суммарной работы стадий на 6,044416 с IQ, + отношение 0,725428. Отдельный полный проход без ожидания источника: + 4,010478 с, отношение 0,663501. Прогрев JIT выполнен до приёма IQ. +- **Свежесть:** 240/240 пакетов, 30/30 точных JPEG, 5 свежих кадров/с; + возраст 150,298 / 153,996 / 176,062 мс. Три полных прохода прошли критерии. + Проверены 1/2/4 работника; выбран один, дополнительные не дали выигрыша. +- **Очереди и память:** IQ максимум 5/8 блоков, строгие задания 1/4; + переполнений нет. IQ 3,031 МиБ, выделенное кольцо 6,483 МиБ, + инженерная оценка рабочих массивов с резервами 72,720 МиБ, + измеренный пик RSS процесса 197,199 МиБ. Это разные показатели памяти. +- **Длинный поток:** 24,044416 с IQ, 960/960 пакетов, 120/120 точных JPEG, + 5 свежих кадров/с, максимальный возраст 189,396 мс, отношение 0,742840; + IQ максимум 7/8, состояние ограничено, после закрытия очищено. +- **Регрессии:** те же 240 решений и 255 строгих кандидатов при 1/2/4 работниках; + 37/45 слабых случаев у обоих приёмников, без новых потерь. + Старая запись 20 ксимв/с: 36/36, CRC и SHA PASS. +- **Проверки:** 50 новых тестов; полный pytest 768 passed; + quick_gate 768 тестов /26 функциональных /13 штатных пропусков, PASS. + Verifier и adversary подтвердили результаты. Отрицательные опыты + без JIT и с замедлением ввода фиксируют переполнение и прекращение потока. +- **Отчёт:** [состояние](lab053_current_state.md), + [критерии](../data/processed/lab053/acceptance.json), + [основной поток](../data/processed/lab053/stream.json), + [графики](../data/processed/lab053/performance_and_age.png). Журнал: запись 042. +- **Границы результата:** программная подача IQ по часам; камера, экран и + аппаратный приём не проверены. GPU не использована, RF TX=0. +- **Далее:** рассмотрение результатов ChatGPT. Lab053 git add/commit и push + не выполнялись. STOP. + ## Правило хранения данных Большие записи IQ, потоки `cs8`, бинарные объекты и другие аппаратные исходные diff --git a/docs/lab053_current_state.md b/docs/lab053_current_state.md new file mode 100644 index 0000000..15cf95d --- /dev/null +++ b/docs/lab053_current_state.md @@ -0,0 +1,438 @@ +# Lab053 — стоимость настоящих пакетов и свежесть видео + +Дата: 8 сентября 2026 года. + +**Программная цель достигнута в выполненных опытах:** BPSK 500 ксимв/с, +8 Мвыб/с, 16 отсчётов/символ, JPEG C 128×96 RGB q25, вход 5 кадров/с. +Получены 240/240 пакетов, 30/30 точных JPEG и **5 свежих кадров/с** при +неизменном пределе возраста 200 мс. RF TX=0, оборудование не запускалось +и не перечислялось. Управление RadioMaster/ELRS/CRSF остаётся отдельным трактом. + +## Основание и Git + +Перед работой проверены root/main/HEAD/status, diff stat/check, состав Lab052, +отчёт, журнал, индекс, обнаружитель и результаты. Повторены quick_gate: +718 тестов, 20 функциональных проверок, 13 штатных пропусков; отдельный +pytest: 718 passed, 0 failed, 79,79 с. Verifier подтвердил scope. + +По прямому разрешению задания в индекс внесены только 27 файлов Lab052: +код, тесты, документация и итоговые JSON/график/текстовые профили. После +показа staged stat/status и проверки cached diff создан коммит: + +`cc38900caea4b310568edc691be7db124503fd21` + +`Lab052: suppress false preamble candidates` + +В коммите 77397 добавленных строк; большие NPZ/NPY, профиль .prof и посторонние +файлы не добавлены. У SVG и двух профилей удалены только концевые пробелы. +Предыдущий HEAD: `9f33607d3ae82992c595de37dd64e99d7e4dea73`. +Lab053 начата от фактического нового HEAD. Push не выполнялся. + +## Что именно измерено + +Использован **точный источник Lab052**: сохранённая готовность JPEG из +source_fixture.json, seed50050, coded-bit Eb/N0=7 дБ, остаточная расстройка +несущей +10 Гц, фаза 0,73 рад, чередование полярности. Байты JPEG сохранены: +1740–1893 байта. Приёмник получает только последовательные комплексные IQ-блоки. +Ожидаемые границы, количество пакетов, содержимое и номера фрагментов ему не даны. + +Генератор один раз записал 48355328 complex64 отсчётов, 386842624 байта, +6,044416 с IQ в игнорируемый Git каталог data/raw/lab053. SHA256: +`9b4f5469b21985f796a77e22981f93bcd3b7a203b573349e8bdf7ea55891bea0`. +Подготовка источника заняла 6,225262 с; это синтез передаваемого сигнала, +а не стоимость приёмника. Конвейер читает файл обычными ограниченными read, +не отображает весь файл в память. Файл и готовность JPEG одинаковы для вариантов. + +Два опыта отвечают на разные вопросы и сохранены **отдельными целыми проходами**: + +| Опыт | Что измеряет | Итог с одним строгим работником | +|---|---|---:| +| Подача IQ по часам, 30 JPEG/6 с | Фактический возраст и очереди | 30 свежих JPEG, 5,0 кадра/с | +| Сумма измеренной работы стадий в этом опыте | Консервативный бюджет обслуживания без ожидания входа | 4,384788 с /6,044416 = **0,725428** | +| Полное время того же опыта по часам | Включает ожидание входных блоков и завершение потоков | 6,074946 с | +| Воспроизведение без ожидания, с обратным давлением | Реальная пропускная способность всего конвейера | 4,010478 с /6,044416 = **0,663501** | + +Сумма работы стадий включает чтение IQ, подготовку кольца, RX, сборку/JPEG decode +и проверку свежести. Стадии перекрываются, поэтому их сумму нельзя объявлять +измеренным полным временем процесса. Напротив, 6,074946 с paced-опыта нельзя +делить на длительность IQ для оценки вычислительной скорости: там есть ожидание +поступления данных. Воспроизведение без ожидания отдельно измеряет elapsed wall time. +Нулевая стоимость RX нигде не подставляется. + +В stream.json целиком выбран paced-проход 1 — медианный по сумме работы стадий +из трёх: **0,725428 /0,712269 /0,731542**. Все три дали 240 пакетов, 30 точных +JPEG и 5 свежих кадров/с. Их максимальные возраста:176,062 /168,978 /171,311 мс. +В throughput.json целиком выбран второй проход: ratios0,662067 /0,663501 /0,680789. +Показатели разных проходов не смешаны в одну временную шкалу. + +## Новый профиль исходной Lab052 + +Свежий профиль снят с основного потока после коммита Lab052, без использования +профиля Lab051 в качестве основания оптимизации. Инструментированный проход: +7,774377 с, 240 настоящих и 15 ложных строгих проверок. Средняя настоящая +проверка17,354 мс, медиана17,122 мс,p95=19,520 мс,max26,724 мс. + +| Затрата настоящего пакета | Среднее, мс | Сумма для 240 пакетов, с | +|---|---:|---:| +| Уточнение старта | 4,508 | 1,082006 | +| Фильтрация с подготовкой окна | 3,250 | 0,779990 | +| Timing | 2,035 | 0,488404 | +| CFO + фаза/диагностика, без двойного счёта вложенных вызовов | 0,770 | 0,184774 | +| Viterbi | 5,700 | 1,367891 | +| Настоящий пакет целиком | 17,354 | 4,165062 | + +40×17,354 мс = **694,177 мс обслуживания в секунду** только на настоящие пакеты. +Ложные проверки занимали около30 мс во всём инструментированном проходе. +Общая децимация —1,388460 с, грубое обнаружение вместе с ней1,804805 с, +дешёвая проверка Lab052 —0,428240 с. Отдельные shared PHY header calls профиля +принадлежат генератору TX; в стоимость RX они не включены. + +Историческая чистая база Lab052 остаётся **7,369795 с /6,044416 =1,219273**. +Время инструментированного профиля не подменяет эту базу. + +## Реализованные изменения + +1. **Локальное уточнение вместо большого общего FFT для редких кандидатов.** + Сохранены исходная опорная преамбула, локальное вычитание среднего и диапазон + ±2000 отсчётов. Общая корреляция Lab051 строила большой участок ради множества + ложных кандидатов. После Lab052 такая работа стала дороже короткой локальной + корреляции. Стоимость настоящего уточнения снизилась с4,508 до1,367 мс. +2. **Короткие локальные окна фильтрации.** Общая RRC-фильтрация большого IQ-окна + не выбрана для этой плотности кандидатов. Штатная свёртка выполняется для + preamble/header и затем для тела принятой по заголовку длины. Средние + локальных окон и нулевые края сохранены. Их нельзя без поправки заменить + одним непрерывным фильтром: это меняет численный вход timing/оценки несущей. +3. **Зеркальный кольцевой IQ-буфер.** Ранее дважды копировался почти весь + удерживаемый участок: при добавлении блока и при отбрасывании начала. + Теперь новый блок копируется в две половины кольца, а retained IQ и кандидаты + читают непрерывные views, в том числе через стык. Старый участок не переносится. + Следующая запись начинается после завершения всех jobs текущего блока. +4. **CPU-компиляция циклов soft Viterbi.** NumPy вычисляет те же четыре метрики + ветвей. Numba выполняет переходы и обратный проход по той же решётке, + с тем же порядком сложения, сравнением `one < zero` и выбором ветви0 при tie. + `fastmath=False`, `nogil=True`; старт/финиш0,64 состояния,хвост6 бит. + Код R=1/2,K=7,171/133 octal,soft-decision не изменён. Группировка нескольких + шагов trellis не применена: округлённые равенства могли бы изменить traceback. +5. **Частные зависимости строгого декодера.** `bind()` связывает исходные code + objects функций Lab045/046/043 с отдельным словарём констант и helpers. + Нет exec/eval и подмены функций общих модулей во время обработки. Константы + фиксированы для500ксимв/с, временные метрики кандидата находятся в ContextVar. + Новый strict decoder не зависит от внешнего адаптера режима20ксимв/с. + +Обнаружитель Lab052 **не переписан**: прежние coarse/split функции, пороги, +полная ширина группы20480, максимум первым, альтернативы после FAIL и пропуск +физического кадра только после CRC PASS. В основной последовательности снова +1675842 локальных пика,392785 кандидатов первого каскада,388407 дешёвых отказов, +384 группы,243 строгих представителя+12 альтернатив,240true/15false. + +Один раз на IQ-блок вычисляются общая децимация с повторным использованием +неизменного FIR-участка, перенос обнаружителя, грубая корреляция и две корреляции +частей Lab052, накопительные энергии. Все кандидаты используют их общие результаты. +Переносы локального строгого окна кешируются по длине; готовые векторы неизменяемы. + +## Timing, несущая и этапы заголовка/тела + +Lab051 убрала медленную организацию поиска, но **не диапазон1601 смещений**. +Lab053 сохраняет все прежние смещения от−800 до+800; новый узкий timing-gate +не введён. Он не понадобился для выполнения целевого бюджета, а проверка +корреляций частей Lab052 сама по себе не доказывает более узкую ошибку timing. + +Декодирование по-прежнему двухэтапное. Сначала преамбула и PHY header; только +после правильного заголовка и проверки длины запрашивается полное окно тела. +Неверный header не вызывает Viterbi. После полного извлечения прежний анализатор +повторно проверяет header, затем выполняет deinterleave,FEC,protocol parser,CRC. + +CFO (частотная расстройка), фаза и полярность оцениваются **только по преамбуле**. +Полезные биты тела для оценки не используются. Оценка повторена для полного +окна, поскольку его локальная средняя составляющая отличается от probe. +Результат затем один раз применяется ко всему извлечённому вектору символов. +Слепая подмена этой оценки значениями probe и сужение timing не применены. + +Сравнение всех240 принятых пакетов с Lab052, отдельно для1/2/4 работников: + +- последовательность всех255 строгих решений и отказов — одинаковая; +- начало/конец, timing, полярность, PHY/CRC и все байты пакетов — одинаковые; +- наибольшая разность CFO: **5,4867626531e−7 Гц**; +- наибольшая разность фазы: **4,6813191101e−9 рад**; +- наибольшая разность soft values: **6,7149294158e−7**. + +Последние разряды float не объявлены побитно одинаковыми: другое разбиение +FFT меняет округление. Побитно совпадают результаты FEC/пакеты, timing и verdict. +45 слабых случаев Lab052 дали37/45 у обоих декодеров, без дополнительных потерь; +8 отказов обоих не объявляются успешным декодированием. + +## Профиль настоящего пакета после оптимизации + +Данные выбранного paced-прохода. Каждая строка — сумма вызовов этой операции +внутри одного настоящего пакета; вложенные затраты вычтены из родительских строк. +Например, CFO отдельно от оставшейся фазовой обработки, Viterbi отдельно от тела. + +| Операция | Среднее, мс | Медиана, мс | p95, мс | Максимум, мс | +|---|---:|---:|---:|---:| +| Подготовка IQ views | 0,0032 | 0,0030 | 0,0047 | 0,0076 | +| Перенос частоты и локальная DC | 0,2395 | 0,1955 | 0,3633 | 4,1497 | +| Согласованная RRC-фильтрация | 1,7115 | 1,6345 | 2,2153 | 2,7005 | +| Уточнение кандидата | 1,3675 | 1,3157 | 1,6683 | 2,4152 | +| Timing | 1,7848 | 1,7010 | 2,3002 | 4,2951 | +| Оценка CFO | 0,3591 | 0,3336 | 0,5189 | 0,7358 | +| Фаза/полярность/преамбульная диагностика без CFO | 0,3659 | 0,3321 | 0,5690 | 0,7817 | +| Извлечение/нормировка soft symbols | 0,2762 | 0,2556 | 0,3773 | 0,8008 | +| PHY header, оба вызова | 0,0591 | 0,0545 | 0,0867 | 0,2697 | +| Подготовка тела без Viterbi/header/parser | 0,4588 | 0,4268 | 0,6474 | 1,1569 | +| Viterbi | 0,2839 | 0,2710 | 0,3398 | 0,6717 | +| Protocol/CRC parser | 0,0074 | 0,0072 | 0,0088 | 0,0137 | +| Обработка фрагмента после строгого verdict | 0,0268 | 0,0264 | 0,0419 | 0,0504 | +| Строгий пакет целиком, включая организационные затраты | **7,0164** | **6,8617** | **8,2923** | **12,6110** | + +Средняя стоимость с последующей обработкой фрагмента —7,0432 мс. Квантили +строгой обработки и фрагмента не складываются. Для бюджета40 пакетов/с: +**40×7,016407 мс =280,656 мс/с** строгой работы одного работника; с фрагментами +около281,728 мс/с. Длительность по perf_counter включает задержки планирования +ОС и служит бюджетом обслуживания, а не точным числом активных тактов CPU. + +Дополнительно записаны process_time и thread_time. Полный CPU процесса в +выбранном paced-опыте3,234375 с. Thread-time на этой Windows квантуется по +15,625 мс: медиана отдельного короткого пакета получается0. Этот счётчик +не используется для заявления «нулевой обработки» или пер-пакетных квантилей. +Сырые измерения оставлены в JSON. Отдельный throughput-проход: CPU4,140625 с, +wall4,010478 с; разные стадии действительно работают одновременно. + +## Разбиение полного времени A–J + +| Часть выбранного paced-опыта | Время, с | +|---|---:| +| A. Чтение IQ и подготовка входного кольца | 0,345752 | +| B. Общий front-end: децимация | 1,371813 | +| C. Грубое обнаружение без B | 0,374433 | +| D. Дешёвая проверка Lab052 | 0,428823 | +| E. Строгая обработка настоящих пакетов без G | 1,614029 | +| F. Строгая обработка ложных кандидатов | 0,043819 | +| G. Viterbi и protocol/CRC | 0,069909 | +| H. Фрагменты/сборка объекта | 0,006432 | +| I. JPEG decode | 0,006325 | +| J. Проверка свежести после JPEG | 0,000045 | +| Прочая организация, кластеризация и инструментирование | 0,123409 | +| **Сумма** | **4,384788** | + +Чтение файла отдельно0,130580 с; подготовка кольца0,215171 с. +Viterbi теперь занимает около1,55% этой суммы. Основные вычислительные затраты — +общая FIR-децимация и timing/RRC/уточнение настоящих пакетов. + +## Конвейер, параллелизм и порядок + +Конвейер имеет отдельные потоки источника IQ и сборки/JPEG output. Между ними +работает RX с обнаружением и строгим декодированием. При2/4 работниках строгие +представители групп поступают в ограниченный ThreadPool. IQ не копируется +отдельно каждому работнику: им передаётся неизменяемый view общего кольца. + +Это не полностью асинхронная пятиступенчатая архитектура: обнаружение следующего +RX-блока ждёт завершения строгих jobs текущего. Такое ограничение сохраняет +политику принятия и безопасное время повторного использования кольца. Источник +и JPEG output в это время могут обрабатывать другие блоки/пакеты. + +Результаты строгих jobs применяются в прежнем порядке групп/альтернатив. +Раннее завершение не даёт права изменить accepted_until. Неиспользованные +опережающие результаты отброшены и учтены отдельно; они не становятся пакетами. +Все альтернативы также идут через pool — главный поток не создаёт скрытого +дополнительного строгого работника. После ошибки ожидается завершение всех jobs, +сохраняется первоначальная ошибка, затем освобождаются IQ и счётчики. + +| Строгих работников | Полный throughput wall/RF | Paced работа стадий/RF | Fresh FPS | Max age, мс | IQ peak | Candidate peak | +|---:|---:|---:|---:|---:|---:|---:| +| **1, выбран** | **0,663501** | **0,725428** | **5,0** | **176,062** | **5** | **1** | +| 2 | 0,689214 | 0,732485 | 5,0 | 176,126 | 6 | 2 | +| 4 | 0,676192 | 0,734393 | 5,0 | 189,207 | 7 | 2 | + +Для2/4:271 опережающая проверка представителя,28 результатов затем пропущены +по уже принятому кадру. Логических строгих проверок всё равно255, из них15false. +Внутриблочного полезного параллелизма мало. Выбран один строгий работник; +дополнительные workers не дали выигрыша в итоговом сравнении. multiprocessing +не применялся. Существующий ObjectAssembler принимает фрагменты по индексам; +отдельный тест подтверждает сборку при обратном порядке. Конвейер дополнительно +сохраняет порядок подачи между изображениями в существующий ImageSink. + +## Свежесть и остаточная задержка + +INPUT5fps,30 изображений; RECEIVED30, DISPLAYED30, FRESH DISPLAYED30, +FRESH DISPLAY FPS=5. Возраст в выбранном paced-проходе: +**150,298 /153,996 /176,062 мс** (min/median/max). +MAX_VIDEO_AGE остаётся200мс. Три повторения и оба варианта2/4workers уложились. + +Восемь физических пакетов занимают92,256 мс IQ на JPEG. Медианное время от +окончания последнего радиопакета JPEG до выдачи RX-пакета —61,284 мс +(диапазон57,438–83,117мс). Последующая выдача JPEG —медиана0,309мс, +max0,643мс. Здесь основной остаток задержки — bounded lookahead и ожидание/ +обработка пачки IQ, а не JPEG decode. Lookahead289792 отсчёта=36,224мс, +core-блок65536=8,192мс. Ошибка фактической подачи источником достигала15,830мс. + +Запас бюджета по сумме работы стадий —27,46%; по отдельной пропускной +способности —33,65%. Максимальный возраст выбранного короткого опыта имеет +23,94мс до предела. Это наблюдаемый запас на данном компьютере, не гарантия +жёсткого реального времени при произвольной нагрузке ОС. + +**Камера и физический экран UNKNOWN.** В неизменном формате фрагмента нет +capture timestamp. Сопоставление с сохранённым временем создания источника +делается после независимых PHY/CRC, сборки и JPEG decode. Оно не влияет на RX +verdict. Получен программный критерий свежести в стенде, а не независимый +аппаратный контроль возраста живой камеры. + +## Очереди, память и длинная сессия + +Очередь IQ —8 блоков по65536 complex64, предел4МиБ. В выбранном paced-опыте +пик5 блоков=2,5МиБ, в24-секундном —7. В throughput-опыте источник специально +упирается в8 блоков и ждёт; это обратное давление конечного replay. +В paced-опыте источник не замедляет свою временную шкалу: Full вызывает +IQ_QUEUE_OVERFLOW и остановку сессии. Пропущенные участки IQ не склеиваются. + +Очередь строгих jobs —4, выбранный пик1; для2/4worker пик2. Вместимость +включает ожидающие и ещё не потреблённые результаты. Полный список дешёвых +кандидатов ограничен размером одного IQ-окна и уничтожается после сканирования. +Очередь выходных пакетов —16; переполнение также видно отдельным счётчиком. +Все штатные итоговые проходы имеют IQ/candidate/output overflow=0. + +| Память | Величина | +|---|---:| +| Максимальный полезный retained IQ | 3177728 байт =3,031МиБ | +| Реально выделенные две половины кольца | 6797824 байта =6,483МиБ | +| Оценка временных рабочих буферов, включая кольцо/очереди | 35224064 байта | +| Максимальный резерв кеша16 переносов для допустимых PHY lengths | 36581376 байт | +| Резерв8 переносов обнаружителя | 3398912 байт | +| Резерв констант/решётки | 1048576 байт | +| Рабочие массивы с перечисленными резервами | **76252928 байт =72,720МиБ** | +| Измеренный RSS peak выбранного процесса | **206778368 байт =197,199МиБ** | +| RSS peak длинного опыта | **208031744 байта =198,395МиБ** | + +Оценка массивов — инженерный резерв, не универсальная верхняя граница временных +C/SciPy/LLVM-аллокаций. RSS включает весь Python, NumPy/SciPy, Numba/LLVM, +аллокатор и диагностические результаты опыта. Это разные показатели, их нельзя +складывать. Резерв rotation-cache рассчитан для максимального допустимого +кадра285792 отсчёта, а не только для размера основного JPEG-пакета. + +Длинный источник:120 изображений,960 физических пакетов,24,044416с IQ, +1538842624байта на диске. Изображения C повторены по содержимому с новыми ID; +радиокадры формируются заново, шум продолжается с тем же воспроизводимым seed. +Все960/960 и120/120 точных JPEG приняты. Fresh FPS=5; возраст +149,337 /155,733 /189,396мс. Работа стадий17,861142с,ratio0,742840. +Медианы первой/второй половины155,662 /156,001мс: накопления задержки не видно. + +Retained IQ в контрольных точках2653440байт, candidate jobs после блока0, +очереди не выходят за предел. Поздний RSS197709824–201424896байт, без +монотонного накопления; линейная оценка на последней половине+105078байт/с +на фоне колебаний. Конечный опыт не доказывает абсолютно нулевой долгосрочный +дрейф RSS. Массивы/кеши/очереди RX имеют явные пределы; история строк измерений +принадлежит внешнему эксперименту. После close retained IQ/decimator/ring=0, +pending jobs=0, все потоки завершены. + +## Неуспешные варианты и исправления + +- Только локальные окна:240/240,30/30, но ratio1,108 и fresh FPS0. +- Добавление кольца без JIT в ранней событийной оценке:ratio0,931,fresh FPS4,5. +- Фактический paced-конвейер без JIT:IQ queue overflow1,239 пакетов,29 JPEG, + fresh FPS3,667. Его ratio не считается результатом полного потока. +- Искусственная задержка20мс на блок:IQ queue peak8,overflow1,явный STOP, + состояние освобождено. Это проверка отказа, не throughput PASS. +- Первый общий runner ошибочно сопоставлял ID JPEG с нуля вместо единицы. + Отрицательные SHA-сравнения были ошибкой внешней диагностики; lookup исправлен + до итоговых paced/throughput серий. Итоговые JSON заново получены целиком. +- Adversary воспроизвёл early exception, при котором ожидание оставшихся futures + прекращалось после второй ошибки. Исправлено ожидание всех jobs с сохранением + первой ошибки; повторный сценарий подтвердил0 активных workers и closed=True. +- Verifier воспроизвёл третье параллельное decode приworkers2. Альтернативы + переведены в тот же пул; прежний вход теперь имеетpeak_active2. +- Missing/empty source раньше теряли отчёт из-за повторного stat или деления + на0 после cleanup. Теперь возвращаются SOURCE_EXCEPTION/EMPTY_IQ_SOURCE, + ratio=None и освобождённое состояние. Оба сценария повторены adversary. + +## CPU-зависимости и GPU + +GPU=**NOT USED**. После локальных вычислений и CPU-JIT цель выполнена. Общая +децимация остаётся заметной, однако стоимость запусков/переносов GPU для блоков +по64К отсчётов не измерялась; убедительного прогноза полного выигрыша нет. +Не выполнялись GPU-инвентаризация, CUDA-опыты или автоматический multiprocessing. + +Numba0.61.2 и llvmlite0.44.0 установлены локально в +`.lab053_cpu_runtime/packages`; системное окружение не изменено. +Пакеты закреплены в requirements-lab053-cpu.txt; SHA wheel-файлов сохранены. +Поддержка Python3.13 и NumPy2.2 указана в +[официальной таблице Numba](https://numba.readthedocs.io/en/stable/user/installing.html). +`nogil` и `fastmath` — параметры +[CPU JIT](https://numba.readthedocs.io/en/0.61.2/user/jit.html). + +Первый отдельный прогрев занял1,221810с; прогрев с дисковым кешем выбранного +процесса0,151137с. Он выполнен **до начала приёма IQ**, отдельно от основного +таймера. Импорт библиотек также относится к запуску процесса. Без доступного +Numba предусмотрен прежний NumPy decoder, но целевые результаты требуют CPU-JIT; +их нельзя переносить на fallback без отдельного измерения. + +Первоначальная установка pip перенесла каталоги с правами временной папки. +Изменение ACL было отклонено автоматической проверкой и не выполнялось. +Официальные wheel-файлы распакованы обычным sandbox в новый доступный каталог. +Старая локальная попытка установки, wheel и JIT-кеши не добавляются в Git. + +## Проверки и воспроизведение + +50 целевых тестов проверяют побитную эквивалентность Viterbi на шуме, ties, +больших значениях и допустимых длинах; валидацию входов; timing на всём диапазоне; +CFO/фазу/PHY/CRC, в том числе намеренно испорченную CRC; запрет тела до header; +параллельные verdict, обратный порядок фрагментов, стыки кольца, bounded queues, +реальное переполнение, исключения и предел работников. Оба независимых +проверяющих подтвердили закрытие своих воспроизведённых находок. + +Старая15-секундная запись Lab047 обработана штатным Lab048:36/36фрагментов, +CRC PASS,JPEG320×240/8724байта SHA MATCH,19ошибок доFEC,0после,0байтовых. +SHA:`e913dab9ac32795250880a4be67fbbbf89a4f96536842476ab591d1acaafc02a`. +Обработка14,147788с. Код20ксимв/с и весь protocol не изменялись. + +Полный pytest: 768 passed за 77,81 с, одно предупреждение о недоступном +`.pytest_cache`; проваленных тестов нет. Численные критерии, SHA исходного IQ +и поведение при принудительном переполнении пересчитываются в acceptance.json. +Финальный quick_gate PASS: 768 тестов, 26 функциональных проверок, 13 штатных +пропусков. Все 20 критериев acceptance PASS; git diff --check PASS, концевых +пробелов в новых исходниках и отчёте нет. Verifier и adversary независимо +пересчитали свежесть, медианный выбор, память и границы отрицательных опытов; +остаточных воспроизведённых дефектов нет. Индекс Git пуст. + +```powershell +python -m pip install -r requirements-lab053-cpu.txt +# Команда установки нужна для нового обычного расчётного окружения. +# В текущем workspace пакеты уже доступны в .lab053_cpu_runtime/packages. +python experiments/lab053_viterbi.py +python experiments/lab053_receiver_profile.py --stage profile +python experiments/lab053_receiver_profile.py --stage source +python experiments/lab053_pipeline.py --workers 1 --unpaced --name throughput_1_repeat_1.json +python experiments/lab053_pipeline.py --workers 1 --name paced_1_repeat_1.json +# Аналогично repeat_2/repeat_3; сравнение --workers 2 и --workers 4. +python experiments/lab053_receiver_profile.py --stage source --frames 120 +python experiments/lab053_pipeline.py --frames 120 --source data/raw/lab053/source_120.c64 --name long_stream.json +python experiments/lab053_regression.py --stage equivalence +python experiments/lab053_regression.py --stage sensitivity +python experiments/lab053_pipeline.py --numpy-viterbi --name without_jit_paced.json +python experiments/lab053_pipeline.py --processing-delay 0.02 --name overflow_probe.json +python experiments/lab053_receiver_profile.py --stage report +python tools/quick_gate.py +python -m pytest tests/ -q +git diff --check +``` + +Lab048 воспроизводится командой с --run-saved-capture, исходным +`C:\Users\user\Documents\SDR_Rover_Lab047_raw\20260903_115747\rx_stream.cs8`, +его independent_result.json,--tx-image data/processed/lab047/tx_image.jpg +и --output-directory data/processed/lab053/regression20k. + +Основные файлы: [профиль/сводка](../experiments/lab053_receiver_profile.py), +[строгие вычисления](../experiments/lab053_packet_processing.py), +[потоковый RX](../experiments/lab053_streaming_receiver.py), +[Viterbi](../experiments/lab053_viterbi.py), +[конвейер](../experiments/lab053_pipeline.py), +[регрессии](../experiments/lab053_regression.py), +[тесты](../tests/test_lab053_packet_pipeline.py), +[основной поток](../data/processed/lab053/stream.json), +[пропускная способность](../data/processed/lab053/throughput.json), +[память](../data/processed/lab053/memory.json), +[критерии](../data/processed/lab053/acceptance.json), +[график](../data/processed/lab053/performance_and_age.png). + +Lab053 git add/commit иpush **не выполнялись**. BPSK,Fs,Rs,SPS,PRBS7,PHY,FEC,CRC, +формат фрагментов и candidate policy сохранены. RF TX=0. Следующий этап — +рассмотрение программных результатов ChatGPT. QPSK,180МГц,новый FEC, +аппаратные500ксимв/с и RF не разрешены. **STOP.** diff --git a/experiments/lab053_packet_processing.py b/experiments/lab053_packet_processing.py new file mode 100644 index 0000000..2597d5b --- /dev/null +++ b/experiments/lab053_packet_processing.py @@ -0,0 +1,213 @@ +"""Lab053: локальные вычисления настоящего пакета с неизменным PHY. + +Старые функции получают частный словарь зависимостей: их Python-код не +переписывается и globals старых лабораторий не изменяются. Это позволяет +одновременно декодировать пакеты с одним набором неизменяемых констант. +""" + +from __future__ import annotations + +from collections import Counter +from contextvars import ContextVar +from dataclasses import dataclass, field +from functools import lru_cache +import math +from pathlib import Path +import sys +import time +from types import FunctionType, SimpleNamespace + +if __package__ in (None, ""): + sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +import numpy as np +from scipy.signal import oaconvolve + +from experiments import lab052_candidate_detection as detection +from experiments.lab053_viterbi import viterbi_decode_soft, warmup + +previous = detection.previous +physical = previous.lab045 +lab043 = previous.lab043 +SAMPLE_RATE = 8000000 +SYMBOL_RATE = 500000 +SAMPLES_PER_SYMBOL = 16 + + +def bind(function, **dependencies): + """Связать тот же code object с частными константами и зависимостями. + + Без exec/eval и setattr модулей. Неподменённые protocol/parser helpers + остаются штатными. Копируется словарь ссылок, а не массивы или IQ. + """ + function = getattr(function, "__wrapped__", function) + namespace = dict(function.__globals__, **dependencies) + result = FunctionType(function.__code__, namespace, function.__name__, function.__defaults__, function.__closure__) + result.__kwdefaults__ = function.__kwdefaults__ + return result + + +@dataclass +class PacketMeasure: + seconds: Counter = field(default_factory=Counter) + exclusive_seconds: Counter = field(default_factory=Counter) + calls: Counter = field(default_factory=Counter) + stack: list = field(default_factory=list) + cache: dict = field(default_factory=dict) + + +CURRENT: ContextVar[PacketMeasure | None] = ContextVar("lab053_packet_measure", default=None) + + +def measured(name): + def decorate(function): + def call(*args, **kwargs): + context = CURRENT.get() + if context is None: + return function(*args, **kwargs) + entry = [0.0] + context.stack.append(entry) + started = time.perf_counter() + try: + return function(*args, **kwargs) + finally: + elapsed = time.perf_counter() - started + context.stack.pop() + if context.stack: + context.stack[-1][0] += elapsed + context.seconds[name] += elapsed + context.exclusive_seconds[name] += elapsed - entry[0] + context.calls[name] += 1 + return call + return decorate + + +@lru_cache(maxsize=16) +def rotation(length: int) -> np.ndarray: + indexes = np.arange(length) + values = np.exp(-2j * np.pi * lab043.CARRIER_OFFSET_HZ * indexes / SAMPLE_RATE).astype(np.complex64) + values.flags.writeable = False + return values + + +class PacketProcessor: + """Параметры фиксированы; состояние кандидата принадлежит ContextVar. + + Уточнение использует прежние ±2000 отсчётов, timing — все 1601 смещение. + Полное тело по-прежнему запрашивается только после валидного PHY header. + """ + + def __init__(self, *, viterbi=viterbi_decode_soft): + self.startup = warmup() if viterbi is viterbi_decode_soft else {"backend": "CUSTOM", "warmup_seconds": 0} + constants = {"SAMPLES_PER_SYMBOL": SAMPLES_PER_SYMBOL, "SYMBOL_RATE": SYMBOL_RATE} + scale = bind(physical.lab044._scale_and_carrier, **constants) + self.bits = physical.lab044.preamble_bits().copy() + waveform = scale(previous.iq.radio.bpsk_modulate(self.bits)) + self.reference = physical.lab044.signed_iq_bytes_to_complex64(lab043.to_cs8_bytes(waveform)) + self.taps = bind(lab043._matched_filter_taps, **constants)() + self.expected = previous.iq.radio.bpsk_modulate(self.bits) + self.kernel = np.zeros((len(self.bits) - 1) * SAMPLES_PER_SYMBOL + 1, np.complex64) + self.kernel[::SAMPLES_PER_SYMBOL] = np.conj(self.expected) + self.kernel = self.kernel[::-1].copy() + for values in (self.bits, self.reference, self.taps, self.expected, self.kernel): + values.flags.writeable = False + slow_offsets = bind(lab043._timing_offsets, **constants) + slow_timing = bind(previous.ORIGINAL_TIMING, _timing_offsets=slow_offsets, **constants) + self.timing = measured("timing")(bind(previous.fast_preamble_timing, + lab045=SimpleNamespace(SAMPLES_PER_SYMBOL=SAMPLES_PER_SYMBOL), + timing_kernel=lambda unused: self.kernel, ORIGINAL_TIMING=slow_timing)) + estimate = measured("cfo_estimation")(bind(lab043._estimate_cfo_gain, **constants)) + self.carrier = measured("phase_polarity")(bind(lab043._evaluate_symbols, + _estimate_cfo_gain=estimate, **constants)) + self.refine = measured("candidate_refinement")(bind(previous.ORIGINAL_REFINE, **constants)) + header = measured("phy_header")(physical.decode_phy_header_soft) + body = bind(physical.analyze_post_preamble_soft, decode_phy_header_soft=header, + conv=SimpleNamespace(encoded_bit_count=physical.conv.encoded_bit_count, + viterbi_decode_soft=measured("viterbi")(viterbi)), + parse_packet=measured("protocol_crc")(physical.parse_packet)) + physical_namespace = SimpleNamespace(**dict(vars(physical), **constants)) + physical_namespace.decode_phy_header_soft = header + physical_namespace.analyze_post_preamble_soft = measured("body_processing")(body) + preamble_namespace = SimpleNamespace(_preamble_reference_samples=lambda: self.reference, + preamble_bits=lambda: self.bits, + lab043=SimpleNamespace(_refine_frame_start=self.refine)) + self.strict = bind(previous.offline.decode_candidate_from_memmap, + lab046c=SimpleNamespace(lab044=preamble_namespace, lab045=physical_namespace), + lab046b=SimpleNamespace(_extract_soft_window=self.extract, + IqFrameReception=previous.iq.IqFrameReception)) + + @measured("iq_window_preparation") + def read_window(self, raw, low, high): + return raw[2 * low:2 * high].view(np.complex64) + + @measured("frequency_shift") + def shift(self, segment): + return (segment - np.mean(segment)) * rotation(len(segment)) + + @measured("matched_rrc_filtering") + def filter(self, values): + return oaconvolve(values, self.taps, mode="full") + + @measured("soft_symbol_extraction") + def normalize(self, matched, nominal, timing, count, carrier): + symbols = matched[nominal + timing:nominal + timing + count * SAMPLES_PER_SYMBOL:SAMPLES_PER_SYMBOL].astype(np.complex128) + if symbols.size != count: + return None + symbol_time = (np.arange(count, dtype=np.float64) + .5) / SYMBOL_RATE + normalized = symbols * np.exp(-2j * math.pi * carrier["cfo_hz"] * symbol_time) + normalized /= carrier["abs_h"] * np.exp(1j * carrier["phase_rad"]) + return normalized.real + + @measured("extract_total") + def extract(self, samples, frame_start, symbol_count): + length = (symbol_count + 42) * SAMPLES_PER_SYMBOL + if frame_start < 0 or frame_start + length > samples.size: + return previous.iq.SoftWindow(False, np.empty(0), 0, 0, 0, {}, "TRUNCATED_IQ_WINDOW") + segment = samples[frame_start:frame_start + length] + matched = self.filter(self.shift(segment)) + nominal = 16 * SAMPLES_PER_SYMBOL + self.taps.size - 1 + timing = self.timing(matched, nominal, self.expected) + carrier = self.carrier(matched, nominal, timing, self.bits, len(self.bits)) + if carrier["abs_h"] == 0: + return previous.iq.SoftWindow(False, np.empty(0), timing, 0, nominal, carrier, "ZERO_GAIN") + values = self.normalize(matched, nominal, timing, symbol_count, carrier) + if values is None: + return previous.iq.SoftWindow(False, np.empty(0), timing, 0, nominal, carrier, "TRUNCATED_SYMBOLS") + return previous.iq.SoftWindow(True, values, int(timing), + int((frame_start + nominal + timing) % SAMPLES_PER_SYMBOL), nominal, carrier, None) + + def decode(self, samples, candidate, number=1): + context = PacketMeasure() + token = CURRENT.set(context) + started = time.perf_counter() + cpu_started = time.thread_time() + try: + outcome = self.strict(samples.view(np.float32), samples.size, candidate, number, + read_iq_window=self.read_window) + elapsed = time.perf_counter() - started + row = {"accepted": outcome.accepted, "reason": outcome.reason, "seconds": elapsed, + "thread_cpu_seconds": time.thread_time() - cpu_started, + "stages": dict(context.seconds), "exclusive_stages": dict(context.exclusive_seconds), + "calls": dict(context.calls), "coarse": candidate.sample_offset, + "refined": outcome.refined_start_sample} + if outcome.frame is not None: + row.update(timing=outcome.frame.timing_offset_samples, cfo_hz=outcome.frame.estimated_cfo_hz, + phase_rad=outcome.frame.estimated_phase_rad, polarity=outcome.frame.bpsk_polarity) + return outcome, row + finally: + CURRENT.reset(token) + + +def run_functional_tests() -> None: + processor = PacketProcessor() + assert len(processor.bits) == 1280 and len(processor.taps) == 161 + assert not processor.reference.flags.writeable + + +def main() -> None: + run_functional_tests() + print("Lab053 packet processing functional checks PASS; RF TX=0") + + +if __name__ == "__main__": + main() diff --git a/experiments/lab053_pipeline.py b/experiments/lab053_pipeline.py new file mode 100644 index 0000000..d95acfe --- /dev/null +++ b/experiments/lab053_pipeline.py @@ -0,0 +1,312 @@ +"""Lab053: ограниченный CPU-конвейер с фактическим временем выдачи JPEG. + +Непрерывный IQ приходит по часовому расписанию либо с обратным давлением +для измерения пропускной способности. Переполнение не склеивает разрыв IQ: +сессия останавливается с явной причиной. Оборудование не используется. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +from pathlib import Path +from queue import Empty, Full, Queue +import sys +from threading import Event, Thread +import time + +if __package__ in (None, ""): + sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +import numpy as np + +from experiments import lab052_detector_study as study +from experiments.lab051_receiver_performance import ImageSink +from experiments.lab051_receiver_profile import process_memory +from experiments.lab053_receiver_profile import SOURCE, OUTPUT, distribution, write_result +from experiments.lab053_streaming_receiver import StreamingReceiver +from experiments.lab053_packet_processing import PacketProcessor, previous + + +class BoundedQueue(Queue): + """Пик фиксируется под тем же lock, что помещение в очередь.""" + + def __init__(self, capacity): + if type(capacity) is not int or capacity <= 0: + raise ValueError("Очередь должна иметь положительную целую вместимость") + super().__init__(maxsize=capacity) + self.peak = self.overflows = 0 + + def _put(self, item): + super()._put(item) + self.peak = max(self.peak, self._qsize()) + + def offer(self, item): + try: + self.put_nowait(item) + except Full: + self.overflows += 1 + return False + return True + + +def run_pipeline(*, workers=1, paced=True, source=SOURCE, frame_count=30, + iq_capacity=8, candidate_capacity=4, output_capacity=16, + processing_delay=0.0, collect_packets=True, numpy_viterbi=False) -> dict: + """Эталон доступен только оценке после сборки/decode; RX получает только IQ.""" + if type(frame_count) is not int or frame_count <= 0: + raise ValueError("Число входных изображений должно быть положительным целым") + if not math.isfinite(processing_delay) or processing_delay < 0: + raise ValueError("Задержка должна быть конечной и неотрицательной") + try: + source_bytes = Path(source).stat().st_size + except OSError: + source_bytes = None # produce запишет точное исключение открытия. + sequence = study.sequence_fixture() + encoded = {number + 1: sequence[number % len(sequence)].jpeg for number in range(frame_count)} + capture_times = {number + 1: number / 5 for number in range(frame_count)} + processor = PacketProcessor(viterbi=previous.fast_viterbi) if numpy_viterbi else None + receiver = StreamingReceiver(workers=workers, candidate_capacity=candidate_capacity, processor=processor) + startup = receiver.processor.startup + if numpy_viterbi: + startup = {**startup, "backend": "NUMPY_LAB051"} + iq_queue, output_queue = BoundedQueue(iq_capacity), BoundedQueue(output_capacity) + sink = ImageSink() + source_done, receiver_done, stop = Event(), Event(), Event() + completed, block_rows, packet_rows, fragment_rows, rss_rows, errors = [], [], [], [], [], [] + input_statistics = {"read_seconds": 0.0, "read_blocks": 0, "read_samples": 0, + "maximum_release_lateness_seconds": 0.0, "backpressure_seconds": 0.0} + output_statistics = {"freshness_seconds": 0.0, "service_seconds": 0.0} + receiver_service = receiver_wait = 0.0 + process_started = time.process_time() + epoch = time.perf_counter() + + def fail(reason, error=None): + errors.append({"reason": reason, "error": None if error is None else repr(error)}) + stop.set() + + def produce(): + try: + with Path(source).open("rb") as capture: + while not stop.is_set(): + started = time.perf_counter() + raw = capture.read(receiver.block_samples * 8) + if len(raw) % 8: + raise ValueError("Усечённый complex64 в источнике") + samples = np.frombuffer(raw, dtype=np.complex64) + input_statistics["read_seconds"] += time.perf_counter() - started + if not samples.size: + if not input_statistics["read_samples"]: + fail("EMPTY_IQ_SOURCE") + break + input_statistics["read_samples"] += samples.size + input_statistics["read_blocks"] += 1 + arrival = input_statistics["read_samples"] / 8000000 + if paced: + stop.wait(max(0.0, epoch + arrival - time.perf_counter())) + if stop.is_set(): + break + lateness = max(0.0, time.perf_counter() - epoch - arrival) + input_statistics["maximum_release_lateness_seconds"] = max( + lateness, input_statistics["maximum_release_lateness_seconds"]) + if not iq_queue.offer((samples, arrival)): + fail("IQ_QUEUE_OVERFLOW") + break + else: + started = time.perf_counter() + while not stop.is_set(): + try: + iq_queue.put((samples, arrival), timeout=.02) + break + except Full: + continue + input_statistics["backpressure_seconds"] += time.perf_counter() - started + except BaseException as error: + fail("SOURCE_EXCEPTION", error) + finally: + source_done.set() + + def consume_output(): + try: + while not receiver_done.is_set() or not output_queue.empty(): + try: + packet, receiver_timestamp = output_queue.get(timeout=.02) + except Empty: + continue + started = time.perf_counter() + assembly_before = sink.assembly_seconds + item = sink.offer(packet, time.perf_counter() - epoch) + fragment_rows.append({"start_sample": packet.start_sample, + "seconds": sink.assembly_seconds - assembly_before}) + output_statistics["service_seconds"] += time.perf_counter() - started + if item is not None: + ready = time.perf_counter() - epoch + jpeg = item.pop("jpeg") + number = item["image_id"] + # Эти сведения не передаются назад в детектор/PHY/assembler. + started = time.perf_counter() + capture_time = capture_times.get(number) + age = ready - capture_time if capture_time is not None else None + fresh = paced and age is not None and 0 <= age <= .2 + output_statistics["freshness_seconds"] += time.perf_counter() - started + completed.append({**item, "decode_complete_timestamp": ready, + "receiver_packet_timestamp": receiver_timestamp, + "capture_timestamp": capture_time, "image_age_seconds": age if paced else None, + "fresh": fresh, "sha256": hashlib.sha256(jpeg).hexdigest(), + "sha_match": number in encoded and jpeg == encoded[number]}) + except BaseException as error: + fail("OUTPUT_EXCEPTION", error) + + producer = Thread(target=produce, name="lab053_iq_source") + consumer = Thread(target=consume_output, name="lab053_jpeg_output") + producer.start() + consumer.start() + + def emit(packets): + for packet in packets: + if not output_queue.offer((packet, time.perf_counter() - epoch)): + fail("OUTPUT_QUEUE_OVERFLOW") + return + + try: + while not stop.is_set() and (not source_done.is_set() or not iq_queue.empty()): + waiting = time.perf_counter() + try: + samples, arrival = iq_queue.get(timeout=.02) + except Empty: + receiver_wait += time.perf_counter() - waiting + continue + receiver_wait += time.perf_counter() - waiting + started = time.perf_counter() + block_start = started - epoch + if processing_delay: + time.sleep(processing_delay) + packets = receiver.push_iq_block(samples) + cost = time.perf_counter() - started + receiver_service += cost + emit(packets) + if collect_packets: + packet_rows.extend(receiver.last_packet_rows) + block_rows.append({"samples_received": receiver.total_samples, "arrival_time": arrival, + "worker_start": block_start, "worker_end": time.perf_counter() - epoch, + "processing_seconds": cost, "iq_queue_size": iq_queue.qsize(), + "candidate_queue_peak": receiver.candidate_queue_peak, + "lag_seconds": time.perf_counter() - epoch - arrival if paced else None, + "packets": len(packets)}) + if len(block_rows) % 128 == 0: + rss_rows.append({"iq_seconds": receiver.total_samples / 8000000, + "iq_queue": iq_queue.qsize(), "candidate_jobs": receiver.pending_jobs, + "retained_iq_bytes": receiver.buffer.nbytes, **process_memory()}) + if not stop.is_set(): + started = time.perf_counter() + emit(receiver.finish()) + receiver_service += time.perf_counter() - started + if collect_packets: + packet_rows.extend(receiver.last_packet_rows) + except BaseException as error: + fail("RECEIVER_EXCEPTION", error) + finally: + stop.set() + producer.join() + receiver.close() + receiver_done.set() + consumer.join() + for queue in (iq_queue, output_queue): + while True: + try: + queue.get_nowait() + except Empty: + break + wall = time.perf_counter() - epoch + process_cpu = time.process_time() - process_started + iq_seconds = source_bytes / 8 / 8000000 if source_bytes is not None else None + horizon = frame_count / 5 + ages = [row["image_age_seconds"] for row in completed if row["image_age_seconds"] is not None] + fresh = [row for row in completed if row["fresh"] and row["decode_complete_timestamp"] <= horizon] + valid = [row for row in packet_rows if row["accepted"]] + # В paced-опыте wall включает ожидание прихода IQ и не является service time. + service_sum = receiver_service + input_statistics["read_seconds"] + output_statistics["service_seconds"] + output_statistics["freshness_seconds"] + result = {"mode": "PACED_WALL_CLOCK" if paced else "SATURATED_BOUNDED_REPLAY", + "source": str(source), "workers": workers, "startup": startup, + "input_fps": 5, "input_frames": frame_count, "observation_seconds": horizon, + "iq_seconds": iq_seconds, "consumed_iq_seconds": receiver.total_samples / 8000000, + "source_consumed_completely": source_bytes is not None and receiver.total_samples * 8 == source_bytes and not errors, + "full_wall_seconds": wall, "process_cpu_seconds": process_cpu, + "processing_seconds": service_sum if paced else wall, + "processing_per_rf_second": (service_sum if paced else wall) / iq_seconds if iq_seconds else None, + "service_sum_seconds": service_sum, "receiver_service_seconds": receiver_service, + "receiver_wait_seconds": receiver_wait, "input_statistics": input_statistics, + "output_statistics": output_statistics, "assembly_seconds": sink.assembly_seconds, + "jpeg_decode_seconds": sink.jpeg_decode_seconds, + "coarse_seconds": receiver.coarse_seconds, "cheap_seconds": receiver.cheap_seconds, + "shared_front_end_seconds": receiver.decimator.seconds, + "coarse_detection_exclusive_seconds": receiver.coarse_seconds - receiver.decimator.seconds, + "iq_input_preparation_seconds": receiver.input_preparation_seconds, + "cluster_seconds": receiver.cluster_seconds, "true_strict_seconds": receiver.true_strict_seconds, + "false_strict_seconds": receiver.false_strict_seconds, + "rx_physical_packets": receiver.packet_count, "candidate_count": receiver.candidate_count, + "received_images": len(completed), "object_jpeg_integrity": len(completed) == frame_count and all(row["sha_match"] for row in completed), + "fresh_displayed": len(fresh) if paced else None, "displayed": len(fresh) if paced else None, + "fresh_display_fps": len(fresh) / horizon if paced else None, + "image_age_min_seconds": min(ages) if ages else None, + "image_age_median_seconds": float(np.median(ages)) if ages else None, + "image_age_max_seconds": max(ages) if ages else None, "max_video_age_seconds": .2, + "iq_queue_capacity": iq_capacity, "iq_queue_peak": iq_queue.peak, "iq_overflow": iq_queue.overflows, + "candidate_queue_capacity": candidate_capacity, "candidate_queue_peak": receiver.candidate_queue_peak, + "candidate_overflow": receiver.candidate_overflow, + "output_queue_capacity": output_capacity, "output_queue_peak": output_queue.peak, + "output_overflow": output_queue.overflows, + "speculative_checks": receiver.speculative_checks, + "discarded_speculative_checks": receiver.discarded_speculative_checks, + "discarded_speculative_seconds": receiver.discarded_speculative_seconds, + "retained_iq_peak_bytes": receiver.peak_buffer_samples * 8, + "allocated_iq_ring_bytes": 2 * receiver.ring_capacity * 8, + "iq_queue_limit_bytes": iq_capacity * receiver.block_samples * 8, + "working_arrays_engineering_estimate_bytes": 2 * receiver.ring_capacity * 8 + (iq_capacity + 2) * receiver.block_samples * 8 + workers * 10 * receiver.lookahead * 8, + "process_memory": process_memory(), "rss_samples": rss_rows, + "detector": dict(receiver.detection_statistics), "rejected_reasons": dict(receiver.rejected_reasons), + "retained_after_close_bytes": receiver.buffer.nbytes + receiver.ring.nbytes + receiver.decimator.values.nbytes, + "pending_jobs_after_close": receiver.pending_jobs, "errors": errors, + "valid_packet_total": distribution([row["seconds"] for row in valid]), + "valid_packet_thread_cpu": distribution([row["thread_cpu_seconds"] for row in valid]), + "fragment_processing": distribution([row["seconds"] for row in fragment_rows]), + "valid_packet_stages": {name: distribution([row["exclusive_stages"].get(name, 0) for row in valid]) + for name in sorted({name for row in valid for name in row["stages"]})}, + "images": completed, "blocks": block_rows, "packet_rows": packet_rows, + "camera_display_latency": "UNKNOWN", "independent_live_capture_timestamp": False, + "freshness_evaluation": "post-JPEG source timestamp; does not feed RX verdict", "rf_tx": 0} + return result + + +def run_functional_tests() -> None: + queue = BoundedQueue(1) + assert queue.offer(1) and not queue.offer(2) + assert queue.overflows == 1 and queue.peak == 1 and queue.get_nowait() == 1 + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--workers", type=int, default=1) + parser.add_argument("--unpaced", action="store_true") + parser.add_argument("--name") + parser.add_argument("--source", type=Path, default=SOURCE) + parser.add_argument("--frames", type=int, default=30) + parser.add_argument("--processing-delay", type=float, default=0) + parser.add_argument("--numpy-viterbi", action="store_true") + arguments = parser.parse_args() + result = run_pipeline(workers=arguments.workers, paced=not arguments.unpaced, + source=arguments.source, frame_count=arguments.frames, + processing_delay=arguments.processing_delay, collect_packets=arguments.frames <= 30, + numpy_viterbi=arguments.numpy_viterbi) + name = arguments.name or f"{'throughput' if arguments.unpaced else 'paced'}_workers_{arguments.workers}.json" + write_result(name, result) + print(json.dumps({key: result[key] for key in ("mode", "workers", "processing_seconds", "processing_per_rf_second", + "full_wall_seconds", "rx_physical_packets", "received_images", "object_jpeg_integrity", "fresh_display_fps", + "image_age_min_seconds", "image_age_median_seconds", "image_age_max_seconds", "iq_queue_peak", + "candidate_queue_peak", "iq_overflow", "candidate_overflow", "errors")}), flush=True) + + +if __name__ == "__main__": + main() diff --git a/experiments/lab053_receiver_profile.py b/experiments/lab053_receiver_profile.py new file mode 100644 index 0000000..abd147a --- /dev/null +++ b/experiments/lab053_receiver_profile.py @@ -0,0 +1,333 @@ +"""Lab053: новый профиль Lab052 и точный файловый источник для конвейера.""" + +from __future__ import annotations + +import argparse +from contextlib import contextmanager +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import sys +import subprocess +import time +from types import SimpleNamespace + +if __package__ in (None, ""): + sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +import numpy as np + +from experiments import lab052_candidate_detection as detection +from experiments import lab052_detector_study as study +from experiments.lab051_receiver_profile import StageProfiler + +ROOT = Path(__file__).resolve().parents[1] +OUTPUT = ROOT / "data/processed/lab053" +SOURCE = ROOT / "data/raw/lab053/source.c64" + + +def write_result(name: str, value: dict) -> None: + OUTPUT.mkdir(parents=True, exist_ok=True) + (OUTPUT / name).write_text(json.dumps(value, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + + +def distribution(values) -> dict: + values = np.asarray(values, dtype=np.float64) + if not values.size: + return {"count": 0, "sum": 0.0, "mean": None, "median": None, "p95": None, "max": None} + return {"count": len(values), "sum": float(values.sum()), "mean": float(values.mean()), + "median": float(np.median(values)), "p95": float(np.quantile(values, .95)), + "max": float(values.max())} + + +class DetailedProfiler(StageProfiler): + """Вложенные времена не складываются повторно; только отдельный замер.""" + + @contextmanager + def measure(self, *, optimized=True): + with super().measure(optimized=optimized): + patches = [] + + def wrap(function, name): + def measured(*args, **kwargs): + scope = self.shared if self.current is None else self.current + stage = scope[name] + started = time.perf_counter() + entry = [0.0] + self.stack.append(entry) + try: + return function(*args, **kwargs) + finally: + elapsed = time.perf_counter() - started + self.stack.pop() + if self.stack: + self.stack[-1][0] += elapsed + stage.calls += 1 + stage.seconds += elapsed + stage.exclusive_seconds += elapsed - entry[0] + return measured + + targets = ((detection.CandidateFrontEnd, "coarse_scores", "coarse_detection"), + (detection.CandidateFrontEnd, "split_scores", "cheap_lab052"), + (detection.previous.DecimationCache, "get", "shared_decimation"), + (detection.previous.BlockFrontEnd, "extract", "soft_extraction")) + try: + for owner, name, label in targets: + original = getattr(owner, name) + patches.append((owner, name, original)) + setattr(owner, name, wrap(original, label)) + yield self + finally: + for owner, name, original in reversed(patches): + setattr(owner, name, original) + + def detailed_summary(self): + output = self.summary() + for kind in ("valid", "false"): + rows = [row for row in self.rows if row["kind"] == kind] + names = sorted({name for row in rows for name in row["stages"]}) + output[kind]["packet_total"] = distribution([row["seconds"] for row in rows]) + output[kind]["per_packet_stages"] = { + name: {field: distribution([row["stages"].get(name, {}).get(field, 0.0) for row in rows]) + for field in ("seconds", "exclusive_seconds")} for name in names} + return output + + +def baseline_profile() -> None: + sequence = study.sequence_fixture() + profiler = DetailedProfiler() + started = time.perf_counter() + with profiler.measure(): + stream, receiver = study.measured_stream(sequence, detection.StreamingReceiver) + full_experiment_seconds = time.perf_counter() - started + result = {"stream": stream, "profile": profiler.detailed_summary(), "packets": profiler.rows, + "experiment_wall_seconds": full_experiment_seconds, + "source_and_diagnostics_seconds": full_experiment_seconds - stream["processing_seconds"], + "timing_includes_instrumentation": True, "rf_tx": 0} + write_result("baseline_profile.json", result) + print(json.dumps({"profile": result["profile"], "processing": stream["processing_seconds"], + "rx_packets": receiver.packet_count}), flush=True) + + +def prepare_source(frame_count=30) -> None: + """Точные блоки прежнего генератора; конечный файл принадлежит источнику. + + RX далее читает ограниченные блоки обычным read, без mmap всего файла. + Стоимость синтеза передаваемой формы не объявляется стоимостью live RX. + """ + source_path = SOURCE if frame_count == 30 else SOURCE.with_name(f"source_{frame_count}.c64") + source_path.parent.mkdir(parents=True, exist_ok=True) + sequence = study.sequence_fixture() + sequence = [replace(sequence[number % len(sequence)], image_id=number + 1) for number in range(frame_count)] + checksum = hashlib.sha256() + counts = [] + started = time.perf_counter() + with source_path.open("wb") as output: + class Recorder(detection.StreamingReceiver): + def push_iq_block(self, samples): + data = samples.tobytes() + output.write(data) + checksum.update(data) + counts.append(samples.size) + self.total_samples += samples.size + return [] + + def finish(self): + self.closed = True + return [] + + original = study.performance.fast + try: + study.performance.fast = SimpleNamespace(StreamingReceiver=Recorder) + study.performance.run_stream(sequence) + finally: + study.performance.fast = original + result = {"path": str(source_path.relative_to(ROOT)), "dtype": "complex64", "blocks": len(counts), + "input_frames": frame_count, "sample_count": sum(counts), "bytes": source_path.stat().st_size, "sha256": checksum.hexdigest(), + "block_samples": 65536, "last_block_samples": counts[-1], + "iq_seconds": sum(counts) / 8000000, "preparation_seconds": time.perf_counter() - started, + "source_fixture": "data/processed/lab052/source_fixture.json", "rf_tx": 0} + write_result("source.json" if frame_count == 30 else f"source_{frame_count}.json", result) + print(json.dumps(result), flush=True) + + +def optimized_stream(workers=1) -> dict: + from experiments.lab053_streaming_receiver import StreamingReceiver + rows = [] + result, receiver = study.measured_stream(study.sequence_fixture(), lambda: StreamingReceiver( + workers=workers, packet_observer=lambda row, outcome: rows.append(row))) + result.update(workers=workers, packet_rows=rows, candidate_queue_peak=receiver.candidate_queue_peak, + candidate_overflow=receiver.candidate_overflow, + speculative_checks=receiver.speculative_checks, + discarded_speculative_checks=receiver.discarded_speculative_checks, + discarded_speculative_seconds=receiver.discarded_speculative_seconds) + valid = [row for row in rows if row["accepted"]] + result["valid_packet_total"] = distribution([row["seconds"] for row in valid]) + result["valid_packet_stages"] = {name: distribution([row["exclusive_stages"].get(name, 0) for row in valid]) + for name in sorted({name for row in valid for name in row["stages"]})} + write_result(f"initial_workers_{workers}.json", result) + print(json.dumps({key: result[key] for key in ("workers", "processing_seconds", "processing_per_rf_second", + "rx_physical_packets", "received_images", "object_jpeg_integrity", "fresh_display_fps", + "image_age_min_seconds", "image_age_median_seconds", "image_age_max_seconds", "candidate_count", + "valid_packet_total", "candidate_queue_peak", "discarded_speculative_checks")}), flush=True) + return result + + +def summarize_results() -> None: + """Выбрать целые медианные проходы и пересчитать критерии из их строк.""" + def read(name): + return json.loads((OUTPUT / name).read_text(encoding="utf-8")) + + selected = {} + repeats = {} + for mode in ("paced", "throughput"): + runs = [read(f"{mode}_1_repeat_{number}.json") for number in (1, 2, 3)] + chosen = sorted(range(3), key=lambda number: runs[number]["processing_seconds"])[1] + result = dict(runs[chosen]) + result["selection"] = {"method": "whole median processing-time run", "repeat": chosen + 1, + "source": f"{mode}_1_repeat_{chosen + 1}.json"} + selected[mode], repeats[mode] = result, runs + write_result("stream.json" if mode == "paced" else "throughput.json", result) + write_result(f"{mode}_workers_1.json", result) + main, throughput = selected["paced"], selected["throughput"] + comparison = [{"workers": number, + "throughput_processing_per_rf_second": read(f"throughput_workers_{number}.json")["processing_per_rf_second"], + **{key: read(f"paced_workers_{number}.json")[key] for key in ( + "processing_per_rf_second", "fresh_display_fps", "image_age_max_seconds", "candidate_queue_peak", + "iq_queue_peak", "speculative_checks", "discarded_speculative_checks")}} for number in (1, 2, 4)] + write_result("worker_comparison.json", {"selected_workers": 1, "comparisons": comparison, + "reason": "single worker is sufficient; additional workers give no measured throughput or latency benefit"}) + long = read("long_stream.json") + equivalence, sensitivity = read("equivalence.json"), read("sensitivity.json") + regression = read("regression20k/result.json") + source = read("source.json") + overflow_probe = read("overflow_probe.json") + with (ROOT / source["path"]).open("rb") as source_handle: + source_sha256 = hashlib.file_digest(source_handle, "sha256").hexdigest() + cases = repeats["paced"] + repeats["throughput"] + [read(f"{mode}_workers_{worker}.json") + for mode in ("paced", "throughput") for worker in (2, 4)] + checks = { + "all_main_runs_240_packets_30_exact_jpegs": all(row["rx_physical_packets"] == 240 and row["received_images"] == 30 + and row["object_jpeg_integrity"] for row in cases), + "all_main_runs_255_strict_15_false": all(row["candidate_count"] == 255 for row in cases), + "all_paced_runs_5_fresh_fps": all(row["fresh_display_fps"] == 5 for row in cases if row["mode"] == "PACED_WALL_CLOCK"), + "all_main_age_at_most_200ms": all(row["image_age_max_seconds"] <= .2 for row in cases if row["mode"] == "PACED_WALL_CLOCK"), + "all_main_processing_ratio_below_08": all(row["processing_per_rf_second"] <= .8 for row in cases), + "all_main_sources_complete_and_no_overflow": all(row["source_consumed_completely"] and not row["errors"] and + row["iq_overflow"] == row["candidate_overflow"] == row["output_overflow"] == 0 for row in cases), + "all_main_queues_within_caps": all(row["iq_queue_peak"] <= row["iq_queue_capacity"] and + row["candidate_queue_peak"] <= row["candidate_queue_capacity"] and row["output_queue_peak"] <= row["output_queue_capacity"] for row in cases), + "same_240_phy_crc_timing_all_worker_counts": equivalence["passed"], + "sensitivity_37_of_45_identical": sensitivity["same_verdicts"] and sensitivity["optimized_correct"] == 37 and sensitivity["lost_baseline_packets"] == 0, + "long_960_packets_120_jpeg_5_fps": long["rx_physical_packets"] == 960 and long["received_images"] == 120 and + long["object_jpeg_integrity"] and long["fresh_display_fps"] == 5 and long["image_age_max_seconds"] <= .2, + "long_queues_within_caps_no_overflow": long["iq_queue_peak"] <= long["iq_queue_capacity"] and + long["candidate_queue_peak"] <= long["candidate_queue_capacity"] and not long["errors"] and + long["iq_overflow"] == long["candidate_overflow"] == long["output_overflow"] == 0, + "long_complete_source_and_cleared_state": long["consumed_iq_seconds"] == long["iq_seconds"] and + long["retained_after_close_bytes"] == long["pending_jobs_after_close"] == 0, + "old20k_36_crc_sha": regression["strict"]["crc_pass"] == 36 and regression["object"]["valid_fragments"] == 36 and + regression["after_independent_verdict"]["sha_match"], + "source_exact_duration": source["sample_count"] == 48355328 and source["iq_seconds"] == 6.044416, + "source_file_size": (ROOT / source["path"]).stat().st_size == source["bytes"], + "source_file_sha256": source_sha256 == source["sha256"], + "forced_overflow_is_reported_and_state_cleared": overflow_probe["iq_overflow"] == 1 and + overflow_probe["iq_queue_peak"] == overflow_probe["iq_queue_capacity"] and + not overflow_probe["source_consumed_completely"] and + any(row["reason"] == "IQ_QUEUE_OVERFLOW" for row in overflow_probe["errors"]) and + overflow_probe["retained_after_close_bytes"] == overflow_probe["pending_jobs_after_close"] == 0, + "protocol_unchanged": not subprocess.check_output(["git", "status", "--porcelain", "--untracked-files=all", "--", "protocol"], cwd=ROOT).strip(), + "lab052_detector_unchanged": not subprocess.check_output(["git", "diff", "--", "experiments/lab052_candidate_detection.py"], cwd=ROOT).strip(), + "lab053_index_empty": not subprocess.check_output(["git", "diff", "--cached", "--name-only"], cwd=ROOT).strip(), + } + fec_crc = sum(main["valid_packet_stages"][name]["sum"] for name in ("viterbi", "protocol_crc")) + partition = {"A_iq_read_and_input_preparation": main["input_statistics"]["read_seconds"] + main["iq_input_preparation_seconds"], + "B_common_decimation": main["shared_front_end_seconds"], "C_coarse_detection": main["coarse_detection_exclusive_seconds"], + "D_cheap_lab052": main["cheap_seconds"], "E_true_packet_without_G": main["true_strict_seconds"] - fec_crc, + "F_false_strict": main["false_strict_seconds"], "G_viterbi_crc": fec_crc, + "H_fragment_assembly": main["assembly_seconds"], "I_jpeg_decode": main["jpeg_decode_seconds"], + "J_freshness": main["output_statistics"]["freshness_seconds"]} + partition["other_orchestration_and_instrumentation"] = main["processing_seconds"] - sum(partition.values()) + rss = long["rss_samples"] + late = rss[len(rss)//2:] + # К старой оценке временных массивов добавлены ЯВНЫЕ резервы постоянных кэшей. + ring_samples = main["allocated_iq_ring_bytes"] // 16 + maximum_waveform = ring_samples - 2*65536 - 8000 + rotation_cache = 16 * maximum_waveform * 8 + detection_rotation_cache = 8 * ((ring_samples + 7)//8) * 8 + constants_reserve = 1024*1024 + memory = {"retained_iq_peak_bytes": main["retained_iq_peak_bytes"], "allocated_ring_bytes": main["allocated_iq_ring_bytes"], + "iq_queue_peak_bytes": main["iq_queue_peak"] * 65536*8, "iq_queue_limit_bytes": main["iq_queue_limit_bytes"], + "temporary_working_buffers_estimate_bytes": main["working_arrays_engineering_estimate_bytes"], + "maximum_frame_waveform_samples": maximum_waveform, "rotation_cache_maxsize": 16, + "maximum_rotation_cache_bytes": rotation_cache, "maximum_detection_rotation_cache_bytes": detection_rotation_cache, + "constant_templates_and_trellis_reserve_bytes": constants_reserve, + "working_arrays_and_constant_caches_estimate_bytes": main["working_arrays_engineering_estimate_bytes"] + rotation_cache + detection_rotation_cache + constants_reserve, + "estimate_is_not_a_universal_c_allocation_bound": True, + "process_memory": main["process_memory"], "long_process_memory": long["process_memory"], + "long_late_rss_min_bytes": min(row["rss_bytes"] for row in late), "long_late_rss_max_bytes": max(row["rss_bytes"] for row in late), + "long_late_rss_slope_bytes_per_second": float(np.polyfit([row["iq_seconds"] for row in late], [row["rss_bytes"] for row in late], 1)[0]), + "receiver_state_bounded": checks["all_main_queues_within_caps"] and checks["long_queues_within_caps_no_overflow"] + and checks["long_complete_source_and_cleared_state"], "experiment_diagnostic_history_is_external": True} + write_result("memory.json", memory) + write_result("acceptance.json", {"checks": checks, "required_checks_pass": all(checks.values()), + "real_time_capable": all(checks.values()), "software_5fps_target_pass": all(checks.values()), + "hardware_validated": False, "head": subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=ROOT, text=True).strip(), + "source_code_sha256": {str(path.relative_to(ROOT)): hashlib.sha256(path.read_bytes()).hexdigest() + for path in sorted((ROOT/"experiments").glob("lab053_*.py"))}, + "paced_service_partition_seconds": partition, "paced_service_processing_per_rf_second": main["processing_per_rf_second"], + "saturated_wall_processing_per_rf_second": throughput["processing_per_rf_second"], + "estimated_single_worker_service_for_40_packets_seconds": 40*main["valid_packet_total"]["mean"], + "budget_note": "perf_counter service durations; Windows thread_time has 15.625ms accounting quantization", "rf_tx": 0}) + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + figure, axes = plt.subplots(2, 2, figsize=(12, 8), constrained_layout=True) + for number, row in enumerate(repeats["paced"], 1): + axes[0, 0].plot([image["image_id"] for image in row["images"]], + [1000*image["image_age_seconds"] for image in row["images"]], label=f"Проход {number}") + axes[0, 0].axhline(200, color="red", linestyle="--", label="Предел 200 мс") + axes[0, 0].set(xlabel="Номер изображения", ylabel="Возраст JPEG, мс", title="Подача IQ по часам: 5 свежих кадров/с") + axes[0, 0].legend(fontsize=8) + axes[0, 1].bar(["Lab052", "Lab053\nработа стадий", "Lab053\nбез ожидания"], [1.2192732766011487, main["processing_per_rf_second"], throughput["processing_per_rf_second"]]) + axes[0, 1].axhline(1, color="red", linestyle="--") + axes[0, 1].axhline(.8, color="green", linestyle=":") + axes[0, 1].set(ylabel="Время обработки / длительность IQ", title="Пропускная способность") + axes[1, 0].plot([row["capture_timestamp"] for row in long["images"]], [1000*row["image_age_seconds"] for row in long["images"]]) + axes[1, 0].axhline(200, color="red", linestyle="--") + axes[1, 0].set(xlabel="Время входной сессии, с", ylabel="Возраст JPEG, мс", title="Длинная сессия: 120 точных JPEG") + axes[1, 1].plot([row["iq_seconds"] for row in rss], [row["rss_bytes"]/2**20 for row in rss], marker=".") + axes[1, 1].set(xlabel="Принятая длительность IQ, с", ylabel="RSS процесса, МиБ", title="Память в длинной сессии") + for axis in axes.flat: + axis.grid(alpha=.2) + figure.savefig(OUTPUT / "performance_and_age.png", dpi=150) + figure.savefig(OUTPUT / "performance_and_age.svg") + plt.close(figure) + svg = OUTPUT / "performance_and_age.svg" + svg.write_text("\n".join(line.rstrip() for line in svg.read_text(encoding="utf-8").splitlines()) + "\n", encoding="utf-8") + print(json.dumps({"checks": checks, "memory": memory, "partition": partition}), flush=True) + + +def run_functional_tests() -> None: + assert distribution([1, 2, 3])["median"] == 2 + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--stage", choices=("profile", "source", "optimized", "report"), default="profile") + parser.add_argument("--workers", type=int, default=1) + parser.add_argument("--frames", type=int, default=30) + arguments = parser.parse_args() + if arguments.stage == "optimized": + optimized_stream(arguments.workers) + elif arguments.stage == "source": + prepare_source(arguments.frames) + elif arguments.stage == "report": + summarize_results() + else: + baseline_profile() + + +if __name__ == "__main__": + main() diff --git a/experiments/lab053_regression.py b/experiments/lab053_regression.py new file mode 100644 index 0000000..ff0a0f3 --- /dev/null +++ b/experiments/lab053_regression.py @@ -0,0 +1,145 @@ +"""Lab053: сравнение независимых решений Lab052/053 после полного приёма.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path +import sys + +if __package__ in (None, ""): + sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +import numpy as np + +from experiments import lab052_candidate_detection as baseline +from experiments.lab053_receiver_profile import SOURCE, write_result +from experiments.lab053_streaming_receiver import StreamingReceiver + + +class VerdictObserver: + def __init__(self): + self.decisions = [] + self.frames = {} + + def scores(self, *args): + pass + + def outcome(self, base, candidate, outcome): + self.decisions.append((base + candidate.sample_offset, outcome.reason, + base + outcome.refined_start_sample, outcome.accepted)) + if outcome.accepted: + frame = outcome.frame + self.frames[base + outcome.refined_start_sample] = { + "end": base + outcome.next_sample, "timing": frame.timing_offset_samples, + "cfo_hz": frame.estimated_cfo_hz, "phase_rad": frame.estimated_phase_rad, + "polarity": frame.bpsk_polarity, "crc": outcome.crc, + "packet_bytes": frame.reception.packet_bytes, + "soft": frame.post_preamble_soft.copy()} + + +def read_all(receiver, source=SOURCE): + packets = [] + try: + with source.open("rb") as capture: + while raw := capture.read(receiver.block_samples * 8): + packets.extend(receiver.push_iq_block(np.frombuffer(raw, np.complex64))) + packets.extend(receiver.finish()) + return packets + finally: + if hasattr(receiver, "close"): + receiver.close() + + +def equivalence(): + old = VerdictObserver() + read_all(baseline.StreamingReceiver(observer=old)) + results = [] + for workers in (1, 2, 4): + new = VerdictObserver() + receiver = StreamingReceiver(workers=workers, observer=new) + read_all(receiver) + same_starts = set(old.frames) == set(new.frames) + rows = [] + for start in sorted(set(old.frames) & set(new.frames)): + first, second = old.frames[start], new.frames[start] + rows.append({"start_sample": start, + **{f"same_{key}": first[key] == second[key] for key in ("end", "timing", "polarity", "crc", "packet_bytes")}, + "cfo_difference_hz": abs(first["cfo_hz"] - second["cfo_hz"]), + "phase_difference_rad": abs(first["phase_rad"] - second["phase_rad"]), + "maximum_soft_difference": float(np.max(np.abs(first["soft"] - second["soft"]))), + "packet_sha256": hashlib.sha256(second["packet_bytes"]).hexdigest()}) + checks = {"same_240_starts": same_starts and len(rows) == 240, + "same_candidate_policy_sequence": old.decisions == new.decisions, + "same_phy_timing_crc_packet_bits": all(all(row[f"same_{key}"] for key in + ("end", "timing", "polarity", "crc", "packet_bytes")) for row in rows), + "cfo_within_002_hz": all(row["cfo_difference_hz"] < .002 for row in rows), + "phase_within_2e5_rad": all(row["phase_difference_rad"] < 2e-5 for row in rows), + "soft_within_1e4": all(row["maximum_soft_difference"] < 1e-4 for row in rows)} + result = {"workers": workers, "checks": checks, "passed": all(checks.values()), + "strict_candidates": receiver.candidate_count, "accepted": receiver.packet_count, + "maximum_cfo_difference_hz": max(row["cfo_difference_hz"] for row in rows), + "maximum_phase_difference_rad": max(row["phase_difference_rad"] for row in rows), + "maximum_soft_difference": max(row["maximum_soft_difference"] for row in rows), "packets": rows} + results.append(result) + print(json.dumps({key: value for key, value in result.items() if key != "packets"}), flush=True) + write_result("equivalence.json", {"baseline": "committed Lab052", "workers": results, + "passed": all(row["passed"] for row in results), "oracle_used_in_receiver": False, "rf_tx": 0}) + + +def sensitivity(): + physical = baseline.phy.lab045 + payload = bytes(range(256)) + plan = physical.build_transmission_plan(payload) + raw = baseline.phy.build_parameterized_tx_cs8(plan, baseline.phy.PhyMode(500000)) + components = np.frombuffer(raw, np.int8).astype(np.float32) + clean = components[0::2] + 1j * components[1::2] + energy = float(np.sum(np.abs(clean) ** 2)) / plan.frame_symbol_count + rows = [] + for ebn0 in (0, 1, 2, 3, 7): + for cfo in (-100, 10, 100): + for seed, scale in ((52031, .002), (52032, .2), (52033, 1.)): + random_generator = np.random.default_rng(seed) + sigma = np.sqrt(energy / (2 * 10 ** (ebn0 / 10))) + phase = .73 + (seed % 2) * np.pi + values = clean * np.exp(1j * (phase + 2*np.pi*cfo*np.arange(len(clean))/8000000)) + values += sigma * (random_generator.normal(size=len(clean)) + 1j*random_generator.normal(size=len(clean))) + samples = np.concatenate((np.zeros(63217, np.complex64), (values * scale).astype(np.complex64), + np.zeros(350000, np.complex64))) + verdicts = [] + for constructor in (baseline.StreamingReceiver, StreamingReceiver): + receiver = constructor() + packets = [] + try: + for start in range(0, len(samples), receiver.block_samples): + packets.extend(receiver.push_iq_block(samples[start:start + receiver.block_samples])) + packets.extend(receiver.finish()) + finally: + if hasattr(receiver, "close"): + receiver.close() + verdicts.append(len(packets) == 1 and packets[0].payload == payload) + rows.append({"ebn0_db": ebn0, "cfo_hz": cfo, "seed": seed, "scale": scale, + "baseline_correct": verdicts[0], "optimized_correct": verdicts[1]}) + result = {"count": len(rows), "baseline_correct": sum(row["baseline_correct"] for row in rows), + "optimized_correct": sum(row["optimized_correct"] for row in rows), + "lost_baseline_packets": sum(row["baseline_correct"] and not row["optimized_correct"] for row in rows), + "same_verdicts": all(row["baseline_correct"] == row["optimized_correct"] for row in rows), + "cases": rows, "rf_tx": 0} + write_result("sensitivity.json", result) + print(json.dumps({key: value for key, value in result.items() if key != "cases"}), flush=True) + + +def run_functional_tests() -> None: + assert VerdictObserver().decisions == [] + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--stage", choices=("equivalence", "sensitivity"), default="equivalence") + arguments = parser.parse_args() + {"equivalence": equivalence, "sensitivity": sensitivity}[arguments.stage]() + + +if __name__ == "__main__": + main() diff --git a/experiments/lab053_streaming_receiver.py b/experiments/lab053_streaming_receiver.py new file mode 100644 index 0000000..17357f3 --- /dev/null +++ b/experiments/lab053_streaming_receiver.py @@ -0,0 +1,287 @@ +"""Lab053: прежняя политика Lab052 с независимыми строгими вычислениями.""" + +from __future__ import annotations + +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path +import sys +import time + +if __package__ in (None, ""): + sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +import numpy as np + +from experiments import lab052_candidate_detection as detection +from experiments.lab053_packet_processing import PacketProcessor + + +class CandidateQueueOverflow(RuntimeError): + pass + + +class DetectionWindow(detection.CandidateFrontEnd): + """Те же coarse/split функции Lab052; строгие FFT здесь не создаются.""" + + def __init__(self, samples, owned_end, decimator, base): + self.samples, self.owned_end = samples, owned_end + self.decimator, self.base = decimator, base + + +class MeasuredDecimation(detection.previous.DecimationCache): + def __init__(self, decimation): + super().__init__(decimation) + self.seconds = 0.0 + + def get(self, samples, base): + started = time.perf_counter() + try: + return super().get(samples, base) + finally: + self.seconds += time.perf_counter() - started + + +class StreamingReceiver(detection.StreamingReceiver): + """Порядок принятия прежний; опережающие результаты применяются по очереди. + + Только представители групп могут вычисляться одновременно. Альтернативы + остаются после FAIL, пропуск внутри кадра разрешает только фактический CRC. + Неиспользованные опережающие проверки отдельно учтены, их verdict игнорируется. + """ + + def __init__(self, *, workers=1, candidate_capacity=4, observer=None, packet_observer=None, + processor=None, block_samples=65536): + if type(workers) is not int or workers not in (1, 2, 4): + raise ValueError("Допустимы 1, 2 или 4 работника") + if type(candidate_capacity) is not int or candidate_capacity < workers: + raise ValueError("Вместимость кандидатов должна быть не меньше числа работников") + super().__init__(block_samples=block_samples, observer=observer) + self.processor = processor or PacketProcessor() + self.packet_observer = packet_observer + self.workers, self.candidate_capacity = workers, candidate_capacity + self.executor = ThreadPoolExecutor(max_workers=workers, thread_name_prefix="lab053_strict") if workers > 1 else None + self.candidate_queue_peak = self.candidate_overflow = self.pending_jobs = 0 + self.speculative_checks = self.discarded_speculative_checks = 0 + self.speculative_seconds = self.discarded_speculative_seconds = 0.0 + self.last_packet_rows = [] + self.ring_capacity = self.buffer_limit_samples + self.ring = np.empty(2 * self.ring_capacity, np.complex64) + self.decimator = MeasuredDecimation(self.decimation) + self.input_preparation_seconds = 0.0 + + def submit(self, candidate): + if self.closed: + raise RuntimeError("Поток уже закрыт") + if self.pending_jobs >= self.candidate_capacity: + self.candidate_overflow += 1 + raise CandidateQueueOverflow("Очередь строгих кандидатов заполнена") + self.pending_jobs += 1 + self.candidate_queue_peak = max(self.candidate_queue_peak, self.pending_jobs) + try: + return self.executor.submit(self.processor.decode, self.buffer, candidate) + except BaseException: + self.pending_jobs -= 1 + raise + + def _scan(self, owned_end): + self.buffer.flags.writeable = False + front = DetectionWindow(self.buffer, owned_end - self.base_sample, self.decimator, self.base_sample) + started = time.perf_counter() + scores = front.coarse_scores(self.buffer, self.reference) + self.coarse_seconds += time.perf_counter() - started + peaks = detection.offline._all_local_peak_indexes(scores) + offsets = self.base_sample + peaks * self.decimation + owned_peaks = peaks[(offsets >= self.scan_sample) & (offsets < owned_end)] + threshold = np.float32(detection.phy.lab047.FIXED_HARDWARE_CANDIDATE_THRESHOLD) + first_stage = owned_peaks[scores[owned_peaks] >= threshold] + self.detection_statistics["total_local_peaks"] += len(owned_peaks) + self.detection_statistics["first_stage_candidates"] += len(first_stage) + self.peak_candidate_count = max(self.peak_candidate_count, len(first_stage)) + if self.observer is not None: + self.observer.scores(scores, self.base_sample, self.scan_sample, owned_end, self.decimation) + started = time.perf_counter() + parts = front.split_scores(self.reference, self.rule.parts, len(scores)) if len(scores) else np.empty((0, 2)) + passed = np.all(parts[first_stage] >= detection.part_threshold(self.rule.parts), axis=1) + self.cheap_seconds += time.perf_counter() - started + self.detection_statistics["cheap_stage_checked"] += len(first_stage) + self.detection_statistics["cheap_stage_rejected"] += int(np.sum(~passed)) + indexes = first_stage[passed] + if self.observer is not None and hasattr(self.observer, "cheap"): + for index, keep in zip(first_stage, passed): + self.observer.cheap(self.base_sample + int(index) * self.decimation, + float(scores[index]), parts[index], bool(keep)) + started = time.perf_counter() + groups = list(detection.peak_groups(indexes, self.rule.cluster_width_samples // self.decimation)) + self.cluster_seconds += time.perf_counter() - started + self.detection_statistics["clusters"] += len(groups) + self.peak_cluster_count = max(self.peak_cluster_count, len(groups)) + self.candidate_state_count = len(indexes) + self.peak_eligible_count = max(self.peak_eligible_count, len(indexes)) + self.working_detection_arrays_peak_bytes = max(self.working_detection_arrays_peak_bytes, + front.detection_values.nbytes + scores.nbytes * 4 + parts.nbytes + (len(peaks) + len(indexes)) * 24) + ordered = [group[np.argsort(scores[group], kind="stable")[::-1]] for group in groups] + futures = {} + emitted = [] + + def candidate_at(index): + return detection.offline.CoarseCandidate(int(index) * self.decimation, float(scores[index])) + + try: + if self.executor: + for group in ordered: + if len(futures) == self.candidate_capacity: + break + index = int(group[0]) + if self.base_sample + index * self.decimation >= self.accepted_until - self.decimation: + futures[index] = self.submit(candidate_at(index)) + self.speculative_checks += 1 + attempted_groups = set() + for group_number, index in ((number, int(item)) for number, group in enumerate(ordered) for item in group): + absolute = self.base_sample + index * self.decimation + if absolute < self.accepted_until - self.decimation: + self.detection_statistics["inside_accepted_frame"] += 1 + continue + self.detection_statistics["alternative_checks" if group_number in attempted_groups else "representative_checks"] += 1 + attempted_groups.add(group_number) + self.candidate_count += 1 + candidate = candidate_at(index) + if index in futures: + future = futures.pop(index) + try: + outcome, row = future.result() + finally: + self.pending_jobs -= 1 + self.speculative_seconds += row["seconds"] + else: + if self.pending_jobs >= self.candidate_capacity: + # Освободить один уже вычисленный, но пропущенный результат. + skipped = next((key for key in futures + if self.base_sample + key*self.decimation < self.accepted_until-self.decimation), None) + if skipped is None: + self.candidate_overflow += 1 + raise CandidateQueueOverflow("Нет места для альтернативного кандидата") + unused, discarded = futures.pop(skipped).result() + self.pending_jobs -= 1 + self.discarded_speculative_checks += 1 + self.discarded_speculative_seconds += discarded["seconds"] + self.candidate_queue_peak = max(self.candidate_queue_peak, self.pending_jobs + 1) + if self.executor: + future = self.submit(candidate) + try: + outcome, row = future.result() + finally: + self.pending_jobs -= 1 + else: + outcome, row = self.processor.decode(self.buffer, candidate, self.candidate_count) + self.strict_seconds += row["seconds"] + row.update(base_sample=self.base_sample, absolute_refined=self.base_sample + outcome.refined_start_sample) + self.last_packet_rows.append(row) + if self.packet_observer is not None: + self.packet_observer(row, outcome) + if self.observer is not None: + self.observer.outcome(self.base_sample, candidate, outcome) + if outcome.accepted and outcome.crc == detection.phy.lab045.CRC_PASS and outcome.frame is not None: + self.true_strict_seconds += row["seconds"] + self.accepted_until = self.base_sample + outcome.next_sample + self.packet_count += 1 + emitted.append(detection.previous.previous.ReceivedPacket( + self.base_sample + outcome.refined_start_sample, self.accepted_until, + self.total_samples, bytes(outcome.frame.reception.payload_bytes))) + else: + self.false_strict_seconds += row["seconds"] + self.rejected_reasons[outcome.reason] += 1 + finally: + active_error = sys.exc_info()[0] is not None + cleanup_error = None + try: + for future in futures.values(): + try: + unused, row = future.result() + self.discarded_speculative_checks += 1 + self.discarded_speculative_seconds += row["seconds"] + except BaseException as error: + if cleanup_error is None: + cleanup_error = error + finally: + self.pending_jobs = self.candidate_state_count = 0 + if active_error or cleanup_error is not None: + self.close() + if not active_error and cleanup_error is not None: + raise cleanup_error + self.blocks_processed += 1 + self.scan_sample = owned_end + retain_start = max(0, self.scan_sample - self.margin) + trim = retain_start - self.base_sample + if trim > 0: + self.base_sample = retain_start + start = self.base_sample % self.ring_capacity + self.buffer = self.ring[start:start + self.total_samples - self.base_sample] + return emitted + + def push_iq_block(self, samples): + preparation_started = time.perf_counter() + if self.closed: + raise RuntimeError("Поток уже закрыт") + samples = np.asarray(samples, dtype=np.complex64) + if samples.ndim != 1 or samples.size > self.block_samples or not np.all(np.isfinite(samples)): + raise ValueError("Нужен конечный одномерный IQ-блок не больше block_samples") + self.last_packet_rows = [] + started = time.perf_counter() + if self.total_samples + samples.size - self.base_sample > self.buffer_limit_samples: + raise RuntimeError("Превышен предел буфера IQ") + # Две копии только НОВОГО блока дают непрерывный view через стык кольца. + # Старое окно не переносится. Все строгие jobs предыдущего push завершены. + position = self.total_samples % self.ring_capacity + first = min(samples.size, self.ring_capacity - position) + self.ring[position:position + first] = samples[:first] + self.ring[position + self.ring_capacity:position + self.ring_capacity + first] = samples[:first] + remaining = samples.size - first + if remaining: + self.ring[:remaining] = samples[first:] + self.ring[self.ring_capacity:self.ring_capacity + remaining] = samples[first:] + self.total_samples += samples.size + start = self.base_sample % self.ring_capacity + self.buffer = self.ring[start:start + self.total_samples - self.base_sample] + self.peak_buffer_samples = max(self.peak_buffer_samples, self.buffer.size) + if self.buffer.size > self.buffer_limit_samples: + raise RuntimeError("Превышен предел буфера IQ") + emitted = [] + self.input_preparation_seconds += time.perf_counter() - preparation_started + while self.total_samples >= self.scan_sample + self.core_samples + self.lookahead: + emitted.extend(self._scan(self.scan_sample + self.core_samples)) + self.processing_seconds += time.perf_counter() - started + return emitted + + def finish(self): + if self.closed: + raise RuntimeError("Поток уже закрыт") + self.last_packet_rows = [] + started = time.perf_counter() + try: + return self._scan(self.total_samples) if self.scan_sample < self.total_samples else [] + finally: + self.close() + self.processing_seconds += time.perf_counter() - started + + def close(self): + """Дождаться всех выполняющихся jobs до освобождения IQ и счётчиков.""" + if self.executor: + self.executor.shutdown(wait=True, cancel_futures=True) + self.pending_jobs = self.candidate_state_count = 0 + self.buffer = np.empty(0, np.complex64) + self.ring = np.empty(0, np.complex64) + self.decimator.values = np.empty(0, np.complex64) + self.closed = True + + +def run_functional_tests() -> None: + assert StreamingReceiver().finish() == [] + + +def main() -> None: + run_functional_tests() + print("Lab053 receiver functional checks PASS; RF TX=0") + + +if __name__ == "__main__": + main() diff --git a/experiments/lab053_viterbi.py b/experiments/lab053_viterbi.py new file mode 100644 index 0000000..83d4ce7 --- /dev/null +++ b/experiments/lab053_viterbi.py @@ -0,0 +1,98 @@ +"""Lab053: те же переходы soft Viterbi, циклы компилируются только для CPU.""" + +from __future__ import annotations + +from pathlib import Path +import sys +import time + +import numpy as np + +if __package__ in (None, ""): + sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from experiments import lab051_streaming_receiver as previous + +runtime = Path(__file__).resolve().parents[1] / ".lab053_cpu_runtime/packages" +if runtime.is_dir(): + sys.path.insert(0, str(runtime)) +try: + from numba import njit +except ImportError: + njit = None + +conv = previous.lab045.conv +symbols = conv._BRANCH_SYMBOLS +CODES = (2 * (symbols[:, :, 0] > 0) + (symbols[:, :, 1] > 0)).astype(np.intp) +PREDECESSORS = conv._PREDECESSORS.copy() +CODES.flags.writeable = PREDECESSORS.flags.writeable = False + + +def trellis_decode(metrics, predecessors, codes): + """Порядок сложений исходный; строгое < сохраняет tie в ветвь 0. + + Не объединяем несколько шагов: это могло бы изменить решение при + равных округлённых суммах. Старт/финиш 0, 64 состояния, хвост 6 бит. + """ + step_count = metrics.shape[0] + path = np.full(64, 1e18, dtype=np.float64) + path[0] = 0.0 + next_path = np.empty(64, np.float64) + choices = np.empty((step_count, 64), np.uint8) + for step in range(step_count): + for state in range(64): + zero = path[predecessors[state, 0]] + metrics[step, codes[state, 0]] + one = path[predecessors[state, 1]] + metrics[step, codes[state, 1]] + take_one = one < zero + choices[step, state] = take_one + next_path[state] = one if take_one else zero + path, next_path = next_path, path + decoded = np.empty(step_count, np.uint8) + state = 0 + for step in range(step_count - 1, -1, -1): + decoded[step] = state >> 5 + state = predecessors[state, choices[step, state]] + assert state == 0 + return decoded[:-6] + + +compiled_trellis = njit(cache=True, nogil=True, fastmath=False)(trellis_decode) if njit is not None else None + + +def viterbi_decode_soft(soft_values, information_bit_count): + if compiled_trellis is None: + return previous.fast_viterbi(soft_values, information_bit_count) + if not isinstance(information_bit_count, (int, np.integer)) or information_bit_count <= 0: + return previous.ORIGINAL_VITERBI(soft_values, information_bit_count) + values = np.asarray(soft_values, dtype=np.float64) + if (values.ndim != 1 or values.size != conv.encoded_bit_count(information_bit_count) + or not np.all(np.isfinite(values))): + return previous.ORIGINAL_VITERBI(soft_values, information_bit_count) + received = values.reshape(int(information_bit_count) + 6, 2) + negative = (received + 1.0) ** 2 + positive = (received - 1.0) ** 2 + metrics = np.column_stack((negative[:, 0] + negative[:, 1], negative[:, 0] + positive[:, 1], + positive[:, 0] + negative[:, 1], positive[:, 0] + positive[:, 1])) + return compiled_trellis(metrics, PREDECESSORS, CODES) + + +def warmup() -> dict: + started = time.perf_counter() + values = np.ones(conv.encoded_bit_count(8), np.float64) + assert np.array_equal(viterbi_decode_soft(values, 8), previous.fast_viterbi(values, 8)) + return {"backend": "NUMBA_CPU" if compiled_trellis is not None else "NUMPY_LAB051", + "warmup_seconds": time.perf_counter() - started, "fastmath": False, "gpu_used": False} + + +def run_functional_tests() -> None: + values = np.zeros(conv.encoded_bit_count(8), np.float64) + assert np.array_equal(viterbi_decode_soft(values, 8), previous.fast_viterbi(values, 8)) + + +def main() -> None: + print(warmup()) + run_functional_tests() + + +if __name__ == "__main__": + main() diff --git a/requirements-lab053-cpu.txt b/requirements-lab053-cpu.txt new file mode 100644 index 0000000..01de364 --- /dev/null +++ b/requirements-lab053-cpu.txt @@ -0,0 +1,4 @@ +# Дополнительный CPU JIT Lab053. Базовые зависимости — requirements.txt. +# Проверено с Python 3.13.1 и NumPy 2.2.6. GPU не используется. +numba==0.61.2 +llvmlite==0.44.0 diff --git a/tests/test_lab053_packet_pipeline.py b/tests/test_lab053_packet_pipeline.py new file mode 100644 index 0000000..45db959 --- /dev/null +++ b/tests/test_lab053_packet_pipeline.py @@ -0,0 +1,345 @@ +"""Регрессии изменённых вычислений/очередей Lab053, без файлов экспериментов.""" + +from __future__ import annotations + +from concurrent.futures import ThreadPoolExecutor +from threading import Event, Lock, Thread +import time +from types import SimpleNamespace + +import numpy as np +import pytest + +from experiments import lab052_candidate_detection as baseline +from experiments import lab053_packet_processing as packet +from experiments import lab053_streaming_receiver as stream +from experiments import lab053_viterbi as viterbi +from experiments.lab050_streaming_receiver import streaming_mode +from experiments.lab053_pipeline import BoundedQueue + + +def waveform(payload, seed=53001, cfo=10, phase=.73, ebn0=7, bad_crc=False): + physical = baseline.phy.lab045 + raw_packet = physical.build_packet(payload, physical.MESSAGE_TYPE, physical.SEQUENCE_NUMBER) + if bad_crc: + raw_packet = raw_packet[:-1] + bytes([raw_packet[-1] ^ 1]) + coded = physical.conv.encode(physical.lab044.bytes_to_bits(raw_packet)) + permutation = physical.interleaver.build_permutation(len(coded), physical.INTERLEAVER_COLUMNS) + bits = np.concatenate((physical.lab044.preamble_bits(), physical.encode_phy_header(len(raw_packet)), + physical.interleaver.interleave(coded, permutation))) + raw = baseline.phy.build_parameterized_tx_cs8(SimpleNamespace(frame_bits=bits, frame_symbol_count=len(bits)), baseline.phy.PhyMode(500000)) + components = np.frombuffer(raw, np.int8).astype(np.float32) + values = components[0::2] + 1j * components[1::2] + values *= np.exp(1j * (phase + 2*np.pi*cfo*np.arange(len(values))/8000000)).astype(np.complex64) + random_generator = np.random.default_rng(seed) + sigma = np.sqrt(float(np.sum(np.abs(values)**2)) / len(bits) / (2 * 10**(ebn0/10))) + values += sigma * (random_generator.normal(size=len(values)) + 1j*random_generator.normal(size=len(values))) + return values.astype(np.complex64) + + +def receive(values, receiver): + packets = [] + try: + for offset in range(0, len(values), receiver.block_samples): + packets.extend(receiver.push_iq_block(values[offset:offset + receiver.block_samples])) + packets.extend(receiver.finish()) + return [(item.start_sample, item.end_sample, item.payload) for item in packets] + finally: + if hasattr(receiver, "close"): + receiver.close() + + +@pytest.mark.parametrize("count", [1, 7, 96, 2152, 8192]) +@pytest.mark.parametrize("kind", ["noise", "zeros", "near_tie", "large"]) +def test_viterbi_bits_match_both_previous_decoders(count, kind): + physical_count = viterbi.conv.encoded_bit_count(count) + values = np.random.default_rng(53002 + count).normal(size=physical_count) + if kind == "zeros": + values[:] = 0 + elif kind == "near_tie": + values *= 1e-12 + elif kind == "large": + values *= 1e100 + assert len(values) == physical_count + original = viterbi.previous.ORIGINAL_VITERBI(values, count) + assert np.array_equal(viterbi.previous.fast_viterbi(values, count), original) + assert np.array_equal(viterbi.viterbi_decode_soft(values, count), original) + + +@pytest.mark.parametrize("values,count,error", [(np.ones(4), 0, ValueError), (np.ones(4), 1.5, TypeError), + (np.ones((2, 2)), 1, ValueError), (np.ones(4), 1, ValueError), (np.full(14, np.nan), 1, ValueError)]) +def test_viterbi_invalid_inputs_preserve_validation(values, count, error): + with pytest.raises(error): + viterbi.viterbi_decode_soft(values, count) + + +def test_constant_templates_are_bit_identical_to_lab052(): + old, new = baseline.StreamingReceiver(), packet.PacketProcessor() + assert np.array_equal(old.raw_reference, new.reference) + assert np.array_equal(old.matched_taps, new.taps) + assert np.array_equal(old.preamble_bits, new.bits) + + +@pytest.mark.parametrize("offset", [-799, -37, 0, 513, 799]) +def test_timing_range_equivalence(offset): + processor = packet.PacketProcessor() + random_generator = np.random.default_rng(53003) + values = (random_generator.normal(size=26000) + 1j*random_generator.normal(size=26000)).astype(np.complex64) + nominal = 1000 + values[nominal + offset:nominal + offset + 1280*16:16] += processor.expected * 4 + with streaming_mode(baseline.phy.PhyMode(500000)): + expected = baseline.previous.ORIGINAL_TIMING(values, nominal, processor.expected) + assert processor.timing(values, nominal, processor.expected) == expected == offset + + +@pytest.mark.parametrize("cfo,phase", [(-100, -1.4), (0, 0), (10, .73), (100, np.pi)]) +def test_cfo_phase_polarity_phy_crc_equivalence(cfo, phase): + processor = packet.PacketProcessor() + payload = bytes(range(256)) + values = np.concatenate((np.zeros(10000, np.complex64), waveform(payload, cfo=cfo, phase=phase), np.zeros(5000, np.complex64))) + candidate = baseline.offline.CoarseCandidate(10000, .8) + with streaming_mode(baseline.phy.PhyMode(500000)): + with baseline.previous.optimized_context(baseline.previous.BlockFrontEnd(values, len(values), baseline.Counter())): + old = baseline.offline.decode_candidate_from_memmap(values.view(np.float32), len(values), candidate, 1, + read_iq_window=lambda raw, low, high: raw[2*low:2*high].view(np.complex64)) + new, row = processor.decode(values, candidate) + assert old.accepted and new.accepted + assert new.refined_start_sample == old.refined_start_sample + assert new.next_sample == old.next_sample + assert new.crc == old.crc == "PASS" + assert new.frame.reception.packet_bytes == old.frame.reception.packet_bytes + assert new.frame.timing_offset_samples == old.frame.timing_offset_samples + assert new.frame.bpsk_polarity == old.frame.bpsk_polarity + assert abs(new.frame.estimated_cfo_hz - old.frame.estimated_cfo_hz) < .002 + assert abs(new.frame.estimated_phase_rad - old.frame.estimated_phase_rad) < 2e-5 + + +def test_header_failure_does_not_request_body_or_viterbi(): + processor = packet.PacketProcessor() + values = np.concatenate((np.zeros(10000, np.complex64), processor.reference, np.zeros(100000, np.complex64))) + outcome, row = processor.decode(values, baseline.offline.CoarseCandidate(10000, .8)) + assert not outcome.accepted + assert row["calls"].get("body_processing", 0) == 0 + assert row["calls"].get("viterbi", 0) == 0 + + +def test_bad_crc_is_rejected_after_identical_fec_bits(): + processor = packet.PacketProcessor() + values = np.concatenate((np.zeros(10000, np.complex64), waveform(b"CRC failure", bad_crc=True), np.zeros(5000, np.complex64))) + candidate = baseline.offline.CoarseCandidate(10000, .8) + with streaming_mode(baseline.phy.PhyMode(500000)): + old = baseline.offline.decode_candidate_from_memmap(values.view(np.float32), len(values), candidate, 1, + read_iq_window=lambda raw, low, high: raw[2*low:2*high].view(np.complex64)) + new, unused = processor.decode(values, candidate) + assert not old.accepted and not new.accepted + assert new.crc == old.crc == "FAIL" + assert new.frame.reception.packet_bytes == old.frame.reception.packet_bytes + + +def test_existing_object_assembler_accepts_reverse_fragment_order(): + from experiments.lab051_receiver_performance import video + data = bytes(range(251)) * 5 + frames = video.transport.build_object_frames(data, 42) + assembler = video.transport.ObjectAssembler() + for frame in reversed(frames): + fragment = video.transport.recover_fragment_from_payload(frame.plan.payload) + assembler.offer(fragment) + assert assembler.recovered_object() == data + + +@pytest.mark.parametrize("workers", [1, 2, 4]) +def test_parallel_verdict_and_ring_wrap_match_lab052(workers): + payload = b"ring and worker order" + samples = np.zeros(1100000, np.complex64) + clean = waveform(payload) + for start in (65536 - 7, 410000, 848001): + samples[start:start + len(clean)] += clean + receiver = stream.StreamingReceiver(workers=workers) + old = receive(samples, baseline.StreamingReceiver()) + new = receive(samples, receiver) + assert old == new and len(new) == 3 + assert receiver.candidate_queue_peak <= receiver.candidate_capacity + assert receiver.pending_jobs == 0 and receiver.ring.nbytes == 0 + + +def test_ring_views_preserve_all_samples_without_moving_retained_iq(): + receiver = stream.StreamingReceiver() + source = (np.arange(1800000) % 127).astype(np.complex64) + owner = receiver.ring + def scan(end): + assert np.shares_memory(receiver.buffer, owner) + assert np.array_equal(receiver.buffer, source[receiver.base_sample:receiver.total_samples]) + receiver.scan_sample = end + receiver.base_sample = max(0, end - receiver.margin) + start = receiver.base_sample % receiver.ring_capacity + receiver.buffer = owner[start:start + receiver.total_samples - receiver.base_sample] + return [] + receiver._scan = scan + receive(source, receiver) + + +def test_iq_queue_overflow_is_explicit_and_bounded(): + queue = BoundedQueue(2) + assert queue.offer(np.zeros(65536, np.complex64)) + assert queue.offer(np.zeros(65536, np.complex64)) + assert not queue.offer(np.zeros(65536, np.complex64)) + assert queue.overflows == 1 and queue.peak == queue.qsize() == 2 + + +def test_candidate_queue_overflow_is_explicit(): + release = Event() + class Probe: + def decode(self, *args): + assert release.wait(2) + return None + receiver = stream.StreamingReceiver(workers=2, candidate_capacity=2, processor=Probe()) + try: + receiver.submit(baseline.offline.CoarseCandidate(0, .9)) + receiver.submit(baseline.offline.CoarseCandidate(8, .9)) + with pytest.raises(stream.CandidateQueueOverflow): + receiver.submit(baseline.offline.CoarseCandidate(0, .9)) + assert receiver.candidate_overflow == 1 and receiver.pending_jobs == 2 + finally: + release.set() + receiver.close() + assert receiver.pending_jobs == 0 + + +def test_context_and_fixed_mode_are_isolated_between_threads(): + processor = packet.PacketProcessor() + values = np.concatenate((np.zeros(10000, np.complex64), waveform(b"independent"), np.zeros(10000, np.complex64))) + candidate = baseline.offline.CoarseCandidate(10000, .8) + functions = (packet.lab043._refine_frame_start, packet.physical.conv.viterbi_decode_soft) + def decode(unused): + result = processor.decode(values, candidate) + assert packet.CURRENT.get() is None + return result[0].frame.reception.packet_bytes + with streaming_mode(baseline.phy.PhyMode(20000)), ThreadPoolExecutor(4) as executor: + results = list(executor.map(decode, range(8))) + assert all(item == results[0] for item in results) + assert functions == (packet.lab043._refine_frame_start, packet.physical.conv.viterbi_decode_soft) + + +def test_worker_exception_waits_for_all_jobs_and_preserves_first_error(monkeypatch): + entered, release, lock = Event(), Event(), Lock() + state = {"calls": 0, "active": 0} + class FailingProcessor: + def decode(self, samples, candidate, number=1): + with lock: + state["calls"] += 1 + number = state["calls"] + state["active"] += 1 + try: + if number <= 2: + raise RuntimeError(f"early_{number}") + entered.set() + assert release.wait(2) + raise RuntimeError("late") + finally: + with lock: + state["active"] -= 1 + receiver = stream.StreamingReceiver(workers=2, processor=FailingProcessor()) + receiver.buffer = np.zeros(100000, np.complex64) + receiver.total_samples = len(receiver.buffer) + scores = np.zeros(13000, np.float32) + scores[[100, 2800, 5500, 8200]] = .9 + def coarse(self, samples, reference): + self.detection_values = np.zeros(15000, np.complex64) + return scores + monkeypatch.setattr(stream.DetectionWindow, "coarse_scores", coarse) + monkeypatch.setattr(stream.DetectionWindow, "split_scores", lambda *args: np.ones((len(scores), 2))) + outcome = [] + def run(): + try: + receiver._scan(receiver.total_samples) + except RuntimeError as error: + outcome.append(str(error)) + thread = Thread(target=run) + thread.start() + try: + assert entered.wait(2) + assert thread.is_alive() + finally: + release.set() + thread.join(3) + receiver.close() + assert not thread.is_alive() + assert outcome == ["early_1"] + assert state["active"] == receiver.pending_jobs == receiver.candidate_state_count == 0 + assert receiver.closed + + +def test_alternative_cannot_create_extra_worker_outside_pool(monkeypatch): + busy, release, alternative = Event(), Event(), Event() + lock = Lock() + state = {"active": 0, "peak": 0, "blocked": 0} + class Probe: + def decode(self, samples, candidate, number=1): + with lock: + state["active"] += 1 + state["peak"] = max(state["peak"], state["active"]) + try: + if candidate.sample_offset == 110*8: + alternative.set() + if candidate.sample_offset in (2800*8, 5500*8): + with lock: + state["blocked"] += 1 + if state["blocked"] == 2: + busy.set() + assert release.wait(3) + return SimpleNamespace(accepted=False, reason="FAIL", refined_start_sample=0), {"seconds": .001} + finally: + with lock: + state["active"] -= 1 + receiver = stream.StreamingReceiver(workers=2, processor=Probe()) + receiver.buffer = np.zeros(100000, np.complex64) + receiver.total_samples = len(receiver.buffer) + scores = np.zeros(13000, np.float32) + scores[[100, 110, 2800, 5500]] = [.9, .8, .9, .9] + def coarse(self, samples, reference): + self.detection_values = np.zeros(15000, np.complex64) + return scores + monkeypatch.setattr(stream.DetectionWindow, "coarse_scores", coarse) + monkeypatch.setattr(stream.DetectionWindow, "split_scores", lambda *args: np.ones((len(scores), 2))) + errors = [] + def run(): + try: + receiver._scan(receiver.total_samples) + except BaseException as error: + errors.append(error) + thread = Thread(target=run) + thread.start() + try: + assert busy.wait(3) + assert not alternative.wait(.05) + finally: + release.set() + thread.join(3) + receiver.close() + assert not thread.is_alive() and not errors + assert state["peak"] == 2 and state["active"] == 0 + + +@pytest.mark.parametrize("empty", [False, True]) +def test_source_failure_returns_diagnostics_after_cleanup(tmp_path, monkeypatch, empty): + from experiments import lab053_pipeline as pipeline + monkeypatch.setattr(pipeline.study, "sequence_fixture", lambda: [SimpleNamespace(jpeg=b"")]) + source = tmp_path / "source.c64" + if empty: + source.write_bytes(b"") + result = pipeline.run_pipeline(source=source, paced=False) + assert result["errors"][0]["reason"] == ("EMPTY_IQ_SOURCE" if empty else "SOURCE_EXCEPTION") + assert result["processing_per_rf_second"] is None + assert result["retained_after_close_bytes"] == result["pending_jobs_after_close"] == 0 + + +def test_paced_overflow_stops_without_concatenating_a_gap(tmp_path, monkeypatch): + from experiments import lab053_pipeline as pipeline + monkeypatch.setattr(pipeline.study, "sequence_fixture", lambda: [SimpleNamespace(jpeg=b"")]) + source = tmp_path / "overload.c64" + source.write_bytes(np.zeros(65536 * 20, np.complex64).tobytes()) + result = pipeline.run_pipeline(source=source, paced=True, iq_capacity=1, processing_delay=.04) + assert result["iq_overflow"] == result["iq_queue_peak"] == 1 + assert any(row["reason"] == "IQ_QUEUE_OVERFLOW" for row in result["errors"]) + assert result["consumed_iq_seconds"] < result["iq_seconds"] + assert result["retained_after_close_bytes"] == result["pending_jobs_after_close"] == 0