From cc38900caea4b310568edc691be7db124503fd21 Mon Sep 17 00:00:00 2001 From: LittleSam129 <1qwe432@gmail.com> Date: Tue, 8 Sep 2026 17:29:52 +0300 Subject: [PATCH] Lab052: suppress false preamble candidates --- PROJECT_LOG.md | 144 + data/processed/lab052/acceptance.json | 27 + data/processed/lab052/allocations.json | 13 + data/processed/lab052/baseline_profile.json | 24914 ++++++++++++++++ data/processed/lab052/baseline_stream.json | 7257 +++++ data/processed/lab052/cpu_profile.json | 7275 +++++ .../lab052/detector_distributions.png | Bin 0 -> 165363 bytes .../lab052/detector_distributions.svg | 5303 ++++ .../processed/lab052/initial_receiver_cpu.txt | 74 + data/processed/lab052/memory.json | 115 + data/processed/lab052/receiver_cpu.txt | 74 + .../lab052/regression20k/result.json | 198 + .../lab052/rejected_nms_comparison.json | 124 + data/processed/lab052/rule_comparison.json | 121 + data/processed/lab052/sensitivity.json | 905 + data/processed/lab052/source_fixture.json | 186 + data/processed/lab052/statistics.json | 107 + data/processed/lab052/stream.json | 7276 +++++ data/processed/lab052/stream_repeat_1.json | 7275 +++++ data/processed/lab052/stream_repeat_2.json | 7275 +++++ data/processed/lab052/stream_repeat_3.json | 7275 +++++ docs/LABS_INDEX.md | 40 + docs/lab052_current_state.md | 390 + experiments/lab052_candidate_detection.py | 287 + experiments/lab052_detection_statistics.py | 157 + experiments/lab052_detector_study.py | 374 + tests/test_lab052_candidate_detection.py | 211 + 27 files changed, 77397 insertions(+) create mode 100644 data/processed/lab052/acceptance.json create mode 100644 data/processed/lab052/allocations.json create mode 100644 data/processed/lab052/baseline_profile.json create mode 100644 data/processed/lab052/baseline_stream.json create mode 100644 data/processed/lab052/cpu_profile.json create mode 100644 data/processed/lab052/detector_distributions.png create mode 100644 data/processed/lab052/detector_distributions.svg create mode 100644 data/processed/lab052/initial_receiver_cpu.txt create mode 100644 data/processed/lab052/memory.json create mode 100644 data/processed/lab052/receiver_cpu.txt create mode 100644 data/processed/lab052/regression20k/result.json create mode 100644 data/processed/lab052/rejected_nms_comparison.json create mode 100644 data/processed/lab052/rule_comparison.json create mode 100644 data/processed/lab052/sensitivity.json create mode 100644 data/processed/lab052/source_fixture.json create mode 100644 data/processed/lab052/statistics.json create mode 100644 data/processed/lab052/stream.json create mode 100644 data/processed/lab052/stream_repeat_1.json create mode 100644 data/processed/lab052/stream_repeat_2.json create mode 100644 data/processed/lab052/stream_repeat_3.json create mode 100644 docs/lab052_current_state.md create mode 100644 experiments/lab052_candidate_detection.py create mode 100644 experiments/lab052_detection_statistics.py create mode 100644 experiments/lab052_detector_study.py create mode 100644 tests/test_lab052_candidate_detection.py diff --git a/PROJECT_LOG.md b/PROJECT_LOG.md index 7694db2..1a63b35 100644 --- a/PROJECT_LOG.md +++ b/PROJECT_LOG.md @@ -1859,3 +1859,147 @@ RF TX=0; HackRF,180 МГц,QPSK,новый FEC/кодек и аппаратны стоимости уникальных фоновых кандидатов в полном режиме5 изображений/с, с неизменными PHY/FEC/CRC и независимыми положительными/отрицательными регрессиями. До отдельного разрешения не выполнять RF или аппаратные500 ксимв/с. + +# Запись 041 + +## Дата + +8 сентября 2026 года + +## Тема + +Lab052 — уменьшение числа ложных кандидатов преамбулы при 500 ксимв/с, +сохранение слабых пакетов и повторная проверка свежести JPEG + +## Выполнено + +Перед новой разработкой проверены root/main/HEAD/status, diff stat/check, +фактический код Lab051, отчёт, индекс и запись 040. Повторные шлюзы Lab051: +quick_gate PASS, pytest 688 passed, 0 failed. Verifier подтвердил scope. +По прямому разрешению нового задания показан staged scope из 13 файлов, +создан коммит 9f33607d3ae82992c595de37dd64e99d7e4dea73: +Lab051: accelerate 500 ksymbol/s streaming receiver. +9695 добавлений; код, тесты, документация и шесть итоговых JSON Lab051. +IQ-окна, промежуточные результаты и посторонние файлы не добавлены; push не было. +Lab052 начата от этого фактического HEAD. + +Рабочий режим сохранён: BPSK, 500000 символов/с, Fs=8000000, 16 отсчётов/символ, +JPEG C 128×96 RGB q25, 1740–1893 байта. Управление ELRS/CRSF остаётся отдельно. +Ни один файл protocol, старые модули PHY/FEC/CRC или формат фрагмента не изменён. + +Уточнена исходная метрика: исторические 147082 Lab051 — все строгие кандидаты, +включая 240 valid; ложных было 146842. Новые timing-окна 12239 также включают +настоящие. Прежняя длинная сетка зависела от несохранённых encode_seconds. +В Lab052 тот же генератор с seed50050/CFO10/фазой0,73/чередованием полярности +использует один сохранённый замер готовности JPEG; SHA всех изображений проверяется. +Все сравниваемые варианты получают одну и ту же новую воспроизводимую сетку. +Точное историческое размещение пакетов не объявляется восстановленным. + +Профиль прежнего Lab051 на 6,044416 с IQ:1675842 локальных пика, +392785 выше первого порога,147005 строгих проверок,240true/146765false. +24281,089 ложных проверок на секунду IQ. Ложные кандидаты дают11387 уникальных +уточнённых позиций и135378 повторных. Низкий нормированный порог пропускает шум +независимо от его абсолютной амплитуды; повторная PRBS7 создаёт сильные боковые пики. + +Построены распределения нормированной корреляции, median/MAD и квантили по блокам. +Медиана false score0,035464; главный true peak0,841691. В357 блоках чистого фона, +выделенных только после независимых решений, медиана блочных медиан0,016353, +MAD0,006184; фон не идеально стационарен. Адаптивный median/MAD-порог рассмотрен, +но не выбран: он не устраняет структуру повторных пиков, а чувствительность +при смене фона требует отдельного доказательства. Нормировка энергией каждой +половины использует только наблюдаемый IQ, без TX reference. + +Первый вариант подавлял меньшие пики в радиусе преамбулы и давал0false на основном +потоке, но adversary воспроизвёл потерю слабого VALID рядом с сильной ложной +преамбулой без правильного заголовка. Этот вариант отвергнут. Исправление: +сначала общие дешёвые корреляции всех локальных кандидатов по частям, затем +ограниченная полная ширина группы; FAIL представителя сохраняет альтернативы. +Прежний контрпример и его перенос через границы блоков больше не теряют пакет. + +Основное правило: обе половины преамбулы проходят нормированную корреляцию +не ниже0,08474038063392896. Порог выбран заранее из номинальной модели +Beta(1,N−1),N640,alpha0,01; точная вероятность после фильтрации/отбора пиков +не утверждается. Две FFT частей общие на блок, per-candidate timing не запускается +на отклонённом шуме. Исходный первый порог0.030964473262429237 сохранён. +Фаза/полярность неизвестны, используется модуль; новый фазовый CFO-gate не введён. + +Группа имеет полную ширину≤20480 отсчётов от первого пика, без цепочного расширения. +Минимальный допустимый кадр26720 отсчётов;20480<26720−2×2008=22704. +Это разделяет центральные кандидаты двух последовательных неперекрывающихся +кадров в диапазоне прежней точности локализации. Представитель — наибольшая +корреляция, tie→правый; при отказе проверяется следующая альтернатива. +Только настоящий PHY/FEC/CRC PASS разрешает пропуск принятого физического кадра. +Граница IQ-блока может добавить проверки, но не скрывает следующий кандидат. + +Сравнены0/1/2/3 части. Ложных строгих проверок145863/332/15/6, во всех вариантах +сохранены те же240 уникальных уточнённых стартов и30 JPEG SHA MATCH. +Выбраны две части: минимальная проверка распределённой структуры,9784,33× +сокращение false. Третья часть уменьшает15до6, но дороже общего этапа. +Группировка без дешёвого фильтра и с корректным возвратом альтернатив почти +не снижает нагрузку распределённого шума. Глобальный peak/second не используется. + +Обнаружены734 лишних построения неизменной опорной формы на каждый проход. +Введены локальные постоянные кэши опоры,RRC taps,PRBS bits,171140 байт. +Массивы побитно сравнены с прежними, глобальные функции возвращаются в finally. +Сами PHY,16 samples/symbol,все1601 timing-offset,soft Viterbi и CRC не менялись. + +Три чистых полных прохода:processing/RF1,257688/1,219199/1,219273, +в каждом240/240 пакетов,30/30 точных JPEG,15false,0 потерь. Итог — целиком +медианный третий проход:7,369795 с обработки /6,044416 с IQ=1,219273. +Свежий прежний Lab051 без наблюдателя на той же сетке41,633312 с,ratio6,887897; +полное ускорение5,649×, относительно исторической Lab051 —5,242×. + +Итоговые счётчики:392785 первого этапа,388407 отклонены дешёво,4378 сохранены, +384 группы,243 строгих представителя+12 альтернатив,15false/240true. +4123 кандидата пропущены внутри уже принятого физического кадра. +Строгих проверок42,187699/с IQ,ложных2,481629/с; новых soft-окон488. +Настоящие строгие проверки3,996005 с,среднее16,650 мс; ложные всего28,884 мс. +Общая децимация/корреляция1,790240 с,проверка частей0,446616 с. + +INPUT5fps/30images;RECEIVED30;STALE30;DISPLAYED=FRESH DISPLAYED=0;FRESH FPS=0. +Возраст декодированных, затем отброшенных JPEG234,520/834,077/1549,595 мс. +MAX_VIDEO_AGE200 мс. Реальная измеренная стоимость каждого push и сборки/decode +использована в прежней событийной шкале. Нулевая стоимость RX не подставлялась. +Цель реального времени и5 свежих кадров/с НЕ достигнута. Теперь ограничение — +настоящие пакеты вместе с децимацией,FFT и копированием IQ,а не ложные кандидаты. +Финальный отдельный cProfile:FFT1,650 с,FIR1,295 с,Viterbi1,397 с собственного +времени. Вложенные matched/refine не суммируются повторно. + +Камера/экран UNKNOWN;capture lookup применяется после JPEG только для отчётной +модели вывода. В неизменном fragment format нет capture timestamp, независимая +live-проверка возраста не реализована. Отставание1,426779 с соответствовало бы +91313848 байтам внешнего IQ backlog. Такой буфер не хранится офлайн-источником; +bounded state не объявляется готовой live-очередью без overflow/gap policy. + +Пик удерживаемого IQ3177728 байт(3,031 МиБ),проверяемый предел3398912 байт. +Инженерная оценка рабочих массивов136,161 МиБ,не универсальный предел C-аллокаций. +RSS выбранного процесса после потока149393408 байт,peak168804352(160,984 МиБ). +Короткий отдельный replay измерил6754 наблюдаемых ndarray(6681 владельцев), +37378942 суммарных байта владельцев иtracemalloc peak11586792 байта. +Число массивов — нижняя граница Python-наблюдения; tracing не входит в CPU benchmark. + +Длинный тест2048 блоков,16,777216 с распределённого шума со сменой амплитуды: +4783206 пиков,1068038 кандидатов,одна строгая ложная проверка,0 принятых пакетов. +Во всех контрольных точках после прогрева IQ2653440 байт,децимация397216, +сохранённых кандидатов0. RSS после прогрева кэшей стабилизируется. +После finish IQ-буферы освобождены. Основной поток:не более23 eligible пиков +и3 групп на блок; история наблюдателя существует только вне рабочего RX. + +45 слабых сценариев Eb/N0=0/1/2/3/7 дБ,CFO−100/+10/+100 Гц,триseed/масштаба: +старый и новый правильно приняли37/45,0 дополнительных потерь;8 не приняты обоими. +Старая Lab047-запись20 ксимв/с штатным Lab048:36/36,CRC PASS,JPEG SHA MATCH, +19 ошибок до FEC,0 после,0 байтовых ошибок. Аппаратных запусков не было. + +30 целевых тестов PASS,verifier иadversary независимо подтвердили исправление +ложного подавления,границы блоков,плохую CRC,эквивалентность частей и finally. +ФинальныеJSON,выбор медианного прохода,UTF-8,ссылки и график проверены независимо. +quick_gate PASS:718 тестов,20 функциональных проверок,13 штатных пропусков. +Полный pytest:718 passed за77,15 с,0 failed,одно предупреждение .pytest_cache. +Новый helper наблюдения массивов отдельно выполнен;три модуля проверены +py_compile и run_functional_tests после его добавления. git diff --check PASS. +Обязательные регрессии в acceptance.json PASS;real-time/5-fps target явно FAIL. +Lab052 git add/commit иpush не выполнялись;индекс пуст. RF TX=0. + +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. diff --git a/data/processed/lab052/acceptance.json b/data/processed/lab052/acceptance.json new file mode 100644 index 0000000..cb7274d --- /dev/null +++ b/data/processed/lab052/acceptance.json @@ -0,0 +1,27 @@ +{ + "checks": { + "median_whole_timeline": true, + "all_240_packets": true, + "all_30_exact_jpegs": true, + "same_240_unique_refined_starts": true, + "same_first_stage_counts": true, + "false_count_15": true, + "no_sensitivity_losses": true, + "bounded_long_iq": true, + "longer_than_main": true, + "long_noise_accepts_zero": true, + "finish_clears_iq_and_candidates": true, + "old20k_36_crc_and_sha": true, + "measured_cpu_sum": true, + "radio_protocol_files_unchanged": true + }, + "required_regressions_pass": true, + "software_5fps_target_pass": false, + "real_time_capable": false, + "fresh_display_fps": 0.0, + "processing_per_rf_second": 1.2192732766011487, + "speedup_same_fixture": 5.649181959197251, + "false_candidate_reduction": 9784.333333333334, + "rf_tx": 0, + "protocol_check_command": "git status --porcelain --untracked-files=all -- protocol" +} diff --git a/data/processed/lab052/allocations.json b/data/processed/lab052/allocations.json new file mode 100644 index 0000000..a56c960 --- /dev/null +++ b/data/processed/lab052/allocations.json @@ -0,0 +1,13 @@ +{ + "observed_array_objects": 6754, + "observed_owned_arrays": 6681, + "observed_owned_bytes_total": 37378942, + "tracemalloc_retained_bytes": 6461, + "tracemalloc_peak_bytes": 11586792, + "array_count_is_lower_bound": true, + "iq_seconds": 0.063184125, + "packets": 1, + "interpretation": "observed Python ndarray lower bound; tracked RX allocation peak; input IQ prepared before tracing", + "timing_not_used_as_benchmark": true, + "rf_tx": 0 +} diff --git a/data/processed/lab052/baseline_profile.json b/data/processed/lab052/baseline_profile.json new file mode 100644 index 0000000..5bab7b8 --- /dev/null +++ b/data/processed/lab052/baseline_profile.json @@ -0,0 +1,24914 @@ +{ + "scope": "continuous synthetic IQ; measured per-block CPU event replay; camera/display latency UNKNOWN", + "symbol_rate": 500000, + "samples_per_symbol": 16, + "input_fps": 5, + "input_frames": 30, + "observation_seconds": 6.0, + "iq_seconds": 6.044416, + "tx_images": 30, + "tx_physical_packets": 240, + "dropped_before_tx": 0, + "maximum_waiting_frames": 1, + "rx_physical_packets": 240, + "received_images": 30, + "object_jpeg_integrity": true, + "dropped_stale_at_display": 30, + "displayed": 0, + "fresh_displayed": 0, + "raw_received_fps": 0.8333333333333334, + "raw_display_fps": 0.0, + "fresh_display_fps": 0.0, + "drain_fresh_displayed": 0, + "image_age_min_seconds": 0.3170079002233669, + "image_age_median_seconds": 17.101650951461167, + "image_age_max_seconds": 33.83346800185577, + "fresh_image_age_seconds": [], + "max_video_age_seconds": 0.2, + "processing_seconds": 41.47070270194672, + "rx_processing_seconds": 41.45943160180468, + "assembly_seconds": 0.005786900292150676, + "jpeg_decode_seconds": 0.005484199849888682, + "processing_per_rf_second": 6.860994131103273, + "physical_packet_processing_seconds": 0.17279459459144467, + "maximum_cpu_lag_seconds": 35.47346880184809, + "implied_external_iq_backlog_bytes": 2270302008, + "live_external_queue_implemented": false, + "retained_iq_peak_bytes": 3177728, + "working_arrays_upper_estimate_bytes": 142775456, + "object_payload_peak_bytes": 1893, + "process_memory": { + "rss_bytes": 337178624, + "peak_rss_bytes": 397295616 + }, + "rss_samples": [ + { + "samples": 4194304, + "rss_bytes": 292507648, + "peak_rss_bytes": 292511744 + }, + { + "samples": 8388608, + "rss_bytes": 238424064, + "peak_rss_bytes": 333815808 + }, + { + "samples": 12582912, + "rss_bytes": 172843008, + "peak_rss_bytes": 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@@ + 7786130 function calls (7783174 primitive calls) in 12.641 seconds + + Ordered by: internal time + List reduced from 336 to 30 due to restriction <30> + + ncalls tottime percall cumtime percall filename:lineno(function) + 7779 2.401 0.000 2.401 0.000 {built-in method scipy.fft._duccfft.pyduccfft.c2c} + 240 1.355 0.006 1.363 0.006 lab051_streaming_receiver.py:139(fast_viterbi) + 1467 1.290 0.001 1.301 0.001 _upfirdn.py:91(apply_filter) + 312654 1.160 0.000 2.039 0.000 numeric.py:2337(isclose) + 738 0.665 0.001 12.616 0.017 lab050_streaming_receiver.py:133(push_iq_block) + 974 0.519 0.001 2.622 0.003 bpsk_radio.py:1079(root_raised_cosine_taps) + 734 0.515 0.001 3.251 0.004 lab044_hackrf_bpsk_packet.py:189(_scale_and_carrier) + 2746 0.327 0.000 0.327 0.000 {method 'copy' of 'numpy.ndarray' objects} + 1455 0.267 0.000 0.269 0.000 lab043_pluto_to_rtlsdr.py:516(pilot_prbs7) + 1563270 0.251 0.000 0.360 0.000 numeric.py:2429() + 2593 0.228 0.000 3.133 0.001 _signaltools.py:485(_freq_domain_conv) + 1934 0.160 0.000 0.160 0.000 {method 'cumsum' of 'numpy.ndarray' objects} + 7779 0.155 0.000 0.261 0.000 helper.py:146(_fix_shape) + 16722 0.153 0.000 0.153 0.000 {built-in method numpy.zeros} + 1624470 0.152 0.000 0.163 0.000 {built-in method builtins.isinstance} + 29235 0.144 0.000 0.149 0.000 {built-in method numpy.array} + 313134 0.128 0.000 0.251 0.000 _ufunc_config.py:440(__enter__) + 480 0.125 0.000 1.649 0.003 lab051_streaming_receiver.py:258(matched_window) + 12394 0.113 0.000 0.113 0.000 {method 'reduce' of 'numpy.ufunc' objects} + 480 0.095 0.000 2.613 0.005 lab051_streaming_receiver.py:287(extract) + 734 0.093 0.000 0.093 0.000 {built-in method scipy.ndimage._nd_image.min_or_max_filter1d} + 784660 0.076 0.000 0.076 0.000 {built-in method builtins.abs} + 734 0.074 0.000 0.540 0.001 lab043_hackrf_bpsk.py:248(_normalized_sliding_correlation) + 313134 0.074 0.000 0.074 0.000 {built-in method numpy._core._multiarray_umath._make_extobj} + 313134 0.071 0.000 0.124 0.000 _ufunc_config.py:456(__exit__) + 885 0.070 0.000 0.075 0.000 _arraypad_impl.py:86(_pad_simple) + 480 0.070 0.000 0.210 0.000 lab043_hackrf_bpsk.py:530(_evaluate_symbols) + 5894 0.061 0.000 0.061 0.000 {method 'reshape' of 'numpy.ndarray' objects} + 734 0.060 0.000 11.916 0.016 lab052_candidate_detection.py:142(_scan) + 734 0.057 0.000 2.708 0.004 lab043_pluto_to_rtlsdr.py:458(shape_frame_like_lab042) + + + 7786130 function calls (7783174 primitive calls) in 12.641 seconds + + Ordered by: cumulative time + List reduced from 336 to 30 due to restriction <30> + + ncalls tottime percall cumtime percall filename:lineno(function) + 738 0.665 0.001 12.616 0.017 lab050_streaming_receiver.py:133(push_iq_block) + 734 0.060 0.000 11.916 0.016 lab052_candidate_detection.py:142(_scan) + 240 0.022 0.000 5.697 0.024 lab046c_offline_recovery.py:637(decode_candidate_from_memmap) + 734 0.004 0.000 3.548 0.005 lab051_streaming_receiver.py:185(__init__) + 734 0.007 0.000 3.544 0.005 lab044_hackrf_bpsk_packet.py:257(_preamble_reference_samples) + 734 0.515 0.001 3.251 0.004 lab044_hackrf_bpsk_packet.py:189(_scale_and_carrier) + 2593 0.228 0.000 3.133 0.001 _signaltools.py:485(_freq_domain_conv) + 7779 0.012 0.000 2.869 0.000 _backend.py:19(__ua_function__) + 7779 0.020 0.000 2.847 0.000 _basic_backend.py:52(_execute_nD) + 7779 0.030 0.000 2.824 0.000 basic.py:126(c2cn) + 2188 0.036 0.000 2.806 0.001 _signaltools.py:589(fftconvolve) + 734 0.057 0.000 2.708 0.004 lab043_pluto_to_rtlsdr.py:458(shape_frame_like_lab042) + 974 0.519 0.001 2.622 0.003 bpsk_radio.py:1079(root_raised_cosine_taps) + 480 0.095 0.000 2.613 0.005 lab051_streaming_receiver.py:287(extract) + 7779 2.401 0.000 2.401 0.000 {built-in method scipy.fft._duccfft.pyduccfft.c2c} + 734 0.025 0.000 2.057 0.003 lab052_candidate_detection.py:112(coarse_scores) + 312654 1.160 0.000 2.039 0.000 numeric.py:2337(isclose) + 5186 0.005 0.000 1.855 0.000 _basic_backend.py:113(fftn) + 480 0.125 0.000 1.649 0.003 lab051_streaming_receiver.py:258(matched_window) + 240 0.023 0.000 1.508 0.006 lab045_hackrf_bpsk_fec.py:476(analyze_post_preamble_soft) + 240 0.052 0.000 1.497 0.006 lab051_streaming_receiver.py:222(refine) + 734 0.030 0.000 1.415 0.002 lab051_streaming_receiver.py:113(get) + 1467 0.003 0.000 1.383 0.001 lab051_streaming_receiver.py:120(filtered) + 1467 0.026 0.000 1.379 0.001 _signaltools.py:3884(resample_poly) + 240 1.355 0.006 1.363 0.006 lab051_streaming_receiver.py:139(fast_viterbi) + 1467 0.005 0.000 1.331 0.001 _upfirdn.py:108(upfirdn) + 1467 1.290 0.001 1.301 0.001 _upfirdn.py:91(apply_filter) + 2593 0.002 0.000 0.999 0.000 _basic_backend.py:120(ifftn) + 405 0.022 0.000 0.797 0.002 _signaltools.py:850(oaconvolve) + 240 0.001 0.000 0.661 0.003 lab043_hackrf_bpsk.py:366(_matched_filter_taps) diff --git a/data/processed/lab052/memory.json b/data/processed/lab052/memory.json new file mode 100644 index 0000000..f2308a1 --- /dev/null +++ b/data/processed/lab052/memory.json @@ -0,0 +1,115 @@ +{ + "blocks": 2048, + "iq_seconds": 16.777216, + "background_scales": [ + 0.002, + 0.2, + 20, + 0.02 + ], + "accepted_noise_packets": 0, + "peak_retained_iq_bytes": 3177728, + "iq_limit_bytes": 3398912, + "retained_after_finish_bytes": 0, + "constant_template_bytes": 171140, + "candidate_state_after_finish": 0, + "peak_eligible_candidates": 1, + "peak_cluster_count": 1, + "detector": { + "total_local_peaks": 4783206, + "first_stage_candidates": 1068038, + "cheap_stage_checked": 1068038, + "cheap_stage_rejected": 1068037, + "clusters": 1, + "representative_checks": 1 + }, + "strict_candidates": 1, + "processing_seconds": 8.453774801455438, + "checkpoints": [ + { + "block": 256, + "iq_bytes": 2653440, + "decimated_bytes": 397216, + "retained_candidates": 0, + "peak_eligible_candidates": 0, + "counter_keys": 5, + "rss_bytes": 137297920, + "peak_rss_bytes": 168501248 + }, + { + "block": 512, + "iq_bytes": 2653440, + "decimated_bytes": 397216, + "retained_candidates": 0, + "peak_eligible_candidates": 1, + "counter_keys": 7, + "rss_bytes": 139948032, + "peak_rss_bytes": 168501248 + }, + { + "block": 768, + "iq_bytes": 2653440, + "decimated_bytes": 397216, + "retained_candidates": 0, + "peak_eligible_candidates": 1, + "counter_keys": 7, + "rss_bytes": 140001280, + "peak_rss_bytes": 168501248 + }, + { + "block": 1024, + "iq_bytes": 2653440, + "decimated_bytes": 397216, + "retained_candidates": 0, + "peak_eligible_candidates": 1, + "counter_keys": 7, + "rss_bytes": 140533760, + "peak_rss_bytes": 168501248 + }, + { + "block": 1280, + "iq_bytes": 2653440, + "decimated_bytes": 397216, + "retained_candidates": 0, + "peak_eligible_candidates": 1, + "counter_keys": 7, + "rss_bytes": 140533760, + "peak_rss_bytes": 168501248 + }, + { + "block": 1536, + "iq_bytes": 2653440, + "decimated_bytes": 397216, + "retained_candidates": 0, + "peak_eligible_candidates": 1, + "counter_keys": 7, + "rss_bytes": 140541952, + "peak_rss_bytes": 168501248 + }, + { + "block": 1792, + "iq_bytes": 2653440, + "decimated_bytes": 397216, + "retained_candidates": 0, + "peak_eligible_candidates": 1, + "counter_keys": 7, + "rss_bytes": 140550144, + "peak_rss_bytes": 168501248 + }, + { + "block": 2048, + "iq_bytes": 2653440, + "decimated_bytes": 397216, + "retained_candidates": 0, + "peak_eligible_candidates": 1, + "counter_keys": 7, + "rss_bytes": 140550144, + "peak_rss_bytes": 168501248 + } + ], + "process_memory": { + "rss_bytes": 135602176, + "peak_rss_bytes": 168501248 + }, + "rf_tx": 0 +} diff --git a/data/processed/lab052/receiver_cpu.txt b/data/processed/lab052/receiver_cpu.txt new file mode 100644 index 0000000..e2e8c35 --- /dev/null +++ b/data/processed/lab052/receiver_cpu.txt @@ -0,0 +1,74 @@ + 1173653 function calls (1170697 primitive calls) in 8.087 seconds + + Ordered by: internal time + List reduced from 324 to 30 due to restriction <30> + + ncalls tottime percall cumtime percall filename:lineno(function) + 10251 1.650 0.000 1.650 0.000 {built-in method scipy.fft._duccfft.pyduccfft.c2c} + 240 1.397 0.006 1.405 0.006 lab051_streaming_receiver.py:139(fast_viterbi) + 1467 1.295 0.001 1.308 0.001 _upfirdn.py:91(apply_filter) + 738 0.553 0.001 8.065 0.011 lab050_streaming_receiver.py:133(push_iq_block) + 2701 0.313 0.000 0.313 0.000 {method 'copy' of 'numpy.ndarray' objects} + 14414 0.167 0.000 0.167 0.000 {built-in method numpy.zeros} + 3417 0.157 0.000 2.311 0.001 _signaltools.py:485(_freq_domain_conv) + 734 0.152 0.000 7.483 0.010 lab052_candidate_detection.py:181(_scan) + 2687 0.145 0.000 0.145 0.000 {method 'cumsum' of 'numpy.ndarray' objects} + 11504 0.113 0.000 0.113 0.000 {method 'reduce' of 'numpy.ufunc' objects} + 488 0.098 0.000 0.865 0.002 lab051_streaming_receiver.py:258(matched_window) + 734 0.095 0.000 0.531 0.001 lab052_candidate_detection.py:125(split_scores) + 35184 0.091 0.000 0.097 0.000 {built-in method numpy.array} + 495 0.085 0.000 1.733 0.004 lab051_streaming_receiver.py:287(extract) + 488 0.075 0.000 0.225 0.000 lab043_hackrf_bpsk.py:530(_evaluate_symbols) + 734 0.069 0.000 7.321 0.010 lab052_candidate_detection.py:185(_scan_with_templates) + 10251 0.068 0.000 0.222 0.000 helper.py:146(_fix_shape) + 734 0.062 0.000 0.372 0.001 lab043_hackrf_bpsk.py:248(_normalized_sliding_correlation) + 240 0.059 0.000 0.083 0.000 bit_interleaver.py:41(build_permutation) + 972 0.052 0.000 0.055 0.000 _arraypad_impl.py:86(_pad_simple) + 488 0.051 0.000 0.533 0.001 lab051_streaming_receiver.py:41(fast_preamble_timing) + 734 0.049 0.000 1.457 0.002 lab051_streaming_receiver.py:113(get) + 255 0.049 0.000 1.084 0.004 lab051_streaming_receiver.py:222(refine) + 13668 0.045 0.000 0.107 0.000 helper.py:47(_init_nd_shape_and_axes) + 488 0.043 0.000 0.119 0.000 lab043_hackrf_bpsk.py:454(_estimate_cfo_gain) + 2933 0.040 0.000 2.010 0.001 _signaltools.py:589(fftconvolve) + 31006 0.040 0.000 0.040 0.000 {built-in method numpy.asarray} + 5366 0.038 0.000 0.038 0.000 {method 'reshape' of 'numpy.ndarray' objects} + 10251 0.034 0.000 2.061 0.000 basic.py:126(c2cn) + 1467 0.032 0.000 1.401 0.001 _signaltools.py:3884(resample_poly) + + + 1173653 function calls (1170697 primitive calls) in 8.087 seconds + + Ordered by: cumulative time + List reduced from 324 to 30 due to restriction <30> + + ncalls tottime percall cumtime percall filename:lineno(function) + 738 0.553 0.001 8.065 0.011 lab050_streaming_receiver.py:133(push_iq_block) + 734 0.152 0.000 7.483 0.010 lab052_candidate_detection.py:181(_scan) + 734 0.069 0.000 7.321 0.010 lab052_candidate_detection.py:185(_scan_with_templates) + 255 0.026 0.000 4.417 0.017 lab046c_offline_recovery.py:637(decode_candidate_from_memmap) + 3417 0.157 0.000 2.311 0.001 _signaltools.py:485(_freq_domain_conv) + 10251 0.014 0.000 2.110 0.000 _backend.py:19(__ua_function__) + 10251 0.021 0.000 2.086 0.000 _basic_backend.py:52(_execute_nD) + 10251 0.034 0.000 2.061 0.000 basic.py:126(c2cn) + 2933 0.040 0.000 2.010 0.001 _signaltools.py:589(fftconvolve) + 734 0.025 0.000 1.932 0.003 lab052_candidate_detection.py:111(coarse_scores) + 495 0.085 0.000 1.733 0.004 lab051_streaming_receiver.py:287(extract) + 10251 1.650 0.000 1.650 0.000 {built-in method scipy.fft._duccfft.pyduccfft.c2c} + 240 0.026 0.000 1.558 0.006 lab045_hackrf_bpsk_fec.py:476(analyze_post_preamble_soft) + 734 0.049 0.000 1.457 0.002 lab051_streaming_receiver.py:113(get) + 1467 0.004 0.000 1.406 0.001 lab051_streaming_receiver.py:120(filtered) + 240 1.397 0.006 1.405 0.006 lab051_streaming_receiver.py:139(fast_viterbi) + 1467 0.032 0.000 1.401 0.001 _signaltools.py:3884(resample_poly) + 6834 0.005 0.000 1.360 0.000 _basic_backend.py:113(fftn) + 1467 0.005 0.000 1.342 0.001 _upfirdn.py:108(upfirdn) + 1467 1.295 0.001 1.308 0.001 _upfirdn.py:91(apply_filter) + 255 0.049 0.000 1.084 0.004 lab051_streaming_receiver.py:222(refine) + 488 0.098 0.000 0.865 0.002 lab051_streaming_receiver.py:258(matched_window) + 3417 0.002 0.000 0.733 0.000 _basic_backend.py:120(ifftn) + 484 0.022 0.000 0.712 0.001 _signaltools.py:850(oaconvolve) + 488 0.002 0.000 0.535 0.001 lab051_streaming_receiver.py:283(timing) + 488 0.051 0.000 0.533 0.001 lab051_streaming_receiver.py:41(fast_preamble_timing) + 734 0.095 0.000 0.531 0.001 lab052_candidate_detection.py:125(split_scores) + 734 0.062 0.000 0.372 0.001 lab043_hackrf_bpsk.py:248(_normalized_sliding_correlation) + 2701 0.313 0.000 0.313 0.000 {method 'copy' of 'numpy.ndarray' objects} + 488 0.075 0.000 0.225 0.000 lab043_hackrf_bpsk.py:530(_evaluate_symbols) diff --git a/data/processed/lab052/regression20k/result.json b/data/processed/lab052/regression20k/result.json new file mode 100644 index 0000000..f0dcc85 --- /dev/null +++ b/data/processed/lab052/regression20k/result.json @@ -0,0 +1,198 @@ +{ + "stage": "LAB048_OFFLINE_RECEIVER_PERFORMANCE", + "baseline": { + "capture_seconds": 15.0, + "full_processing_seconds": 37.423910000012256, + "strict_stage_seconds": 32.45996900013415, + "average_strict_candidate_seconds": 0.47043433333527757, + "approximate_peak_bytes": 1545755352 + }, + "optimized": { + "capture_seconds": 15.0, + "full_processing_seconds": 13.213345700060017, + "coarse_search_seconds": 4.5940155000425875, + "strict_stage_seconds": 8.609627199941315, + "average_strict_candidate_seconds": 0.12477720579625094, + "approximate_peak_bytes": 1545755352, + "stage_measurements": { + "cs8_to_complex64": { + "calls": 113, + "cumulative_seconds": 1.3091708993306383 + }, + "refine_start": { + "calls": 69, + "cumulative_seconds": 2.8543391003040597 + }, + "matched_filter": { + "calls": 105, + "cumulative_seconds": 3.088835699832998 + }, + "timing": { + "calls": 105, + "cumulative_seconds": 1.2946644995827228 + }, + "cfo_phase_soft": { + "calls": 105, + "cumulative_seconds": 0.0416537004057318 + }, + "phy_header": { + "calls": 105, + "cumulative_seconds": 0.004346300149336457 + }, + "post_preamble_fec_crc": { + "calls": 36, + 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+ }, + "phy_changed": false, + "fec_changed": false, + "bpsk_changed": false, + "rf_tx": 0 +} \ No newline at end of file diff --git a/data/processed/lab052/rejected_nms_comparison.json b/data/processed/lab052/rejected_nms_comparison.json new file mode 100644 index 0000000..49b6255 --- /dev/null +++ b/data/processed/lab052/rejected_nms_comparison.json @@ -0,0 +1,124 @@ +{ + "rules": [ + { + "rule": { + "parts": 0, + "radius_samples": 20480, + "part_threshold": null + }, + "received_images": 30, + "object_jpeg_integrity": true, + "true_candidates_lost": 0, + "false_candidate_reduction": 263.9658273381295, + "detector": { + "total_local_peaks": 1675842, + "first_stage_candidates": 390639, + "clusters": 900, + "cheap_stage_checked": 796, + "inside_accepted_frame": 104, + "strict_true_candidates": 240, + "strict_false_candidates": 556, + "strict_checks_per_rf_second": 131.691796196688, + "cheap_seconds": 0.0009583993814885616, + "cluster_seconds": 0.15876410226337612, + 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42.187698530346026, + "cheap_seconds": 0.4466161011951044, + "cluster_seconds": 0.003113199840299785, + "true_strict_seconds": 3.9960052993847057, + "false_strict_seconds": 0.028884499799460173, + "peak_cluster_count": 3, + "peak_eligible_count": 23, + "candidate_state_after_finish": 0, + "detection_arrays_peak_estimate_bytes": 1480664 + }, + "timing_includes_diagnostic_observer": false +} diff --git a/docs/LABS_INDEX.md b/docs/LABS_INDEX.md index a36341c..f2827d8 100644 --- a/docs/LABS_INDEX.md +++ b/docs/LABS_INDEX.md @@ -308,6 +308,46 @@ кандидатов в полном потоке 5 кадров/с с тем же PHY/FEC/CRC. - **RF / Git:** RF TX=0; Lab051 git add/commit и push не выполнялись. STOP. +## Lab052 + +- **Цель:** снизить число ложных строгих проверок на 500 ксимв/с при сохранении + BPSK, PRBS7, PHY/FEC/CRC, фрагментации и всех принимаемых настоящих пакетов. +- **База:** Lab051 закреплена разрешённым коммитом + `9f33607d3ae82992c595de37dd64e99d7e4dea73`; 13 файлов, без push. +- **Профиль:** 1675842 локальных пика, 392785 кандидатов первого каскада; + прежний RX выполнил 147005 строгих проверок: 146765 ложных и 240 истинных. + Исторические 147082 Lab051 означали все кандидаты, а не только ложные. +- **Алгоритм:** общий дешёвый расчёт корреляций двух половин преамбулы, + локальная нормировка каждой половины; ограниченная полная ширина группы + 20480 отсчётов, максимум первым, альтернативы сохраняются после FAIL. + Первый порог прежний; VALID только после штатного PHY/FEC/CRC. +- **Отвергнутый вариант:** безусловное подавление соседних меньших пиков + теряло слабый пакет рядом с сильной ложной преамбулой. Контрпример adversary + исправлен и закреплён тестами, включая границы блоков и плохую CRC. +- **Кандидаты:** 388407 отклонены дешёво, 384 группы, 243 представителя +12 + альтернативных строгих проверок; 15 ложных, 240 настоящих, 0 потерь. + Сокращение ложных строгих проверок 9784,33×. Все 240 уникальных стартов совпали. +- **Полный поток:** C/5 JPEG в секунду, 30 изображений, 240/240 пакетов, + 30/30 JPEG SHA MATCH. Медианный полный проход 7,369795 с /6,044416 с IQ + =1,219273. Возраст 234,520/834,077/1549,595 мс, все 30 просрочены, + **FRESH DISPLAY FPS=0; REAL-TIME CAPABLE=NO**. +- **Остаточная нагрузка:** настоящие пакеты 3,996005 с, общая децимация/ + корреляция 1,790240 с. Все ложные проверки вместе лишь 28,884 мс. +- **Память:** удерживаемый IQ 3,031 МиБ, инженерная оценка массивов 136,161 МиБ, + измеренный process RSS peak 160,984 МиБ. Шумовой тест 16,78 с не накапливает + IQ/кандидатов; внешний live-backlog остаётся нереализованным. +- **Регрессии:** 37/45 слабых сценариев у обоих приёмников, 0 дополнительных + потерь. Старая запись 20 ксимв/с: 36/36, CRC PASS, JPEG SHA MATCH. +- **Код:** [обнаружитель](../experiments/lab052_candidate_detection.py), + [опыты](../experiments/lab052_detector_study.py), + [статистика](../experiments/lab052_detection_statistics.py), + [30 тестов](../tests/test_lab052_candidate_detection.py). +- **Отчёт:** [состояние](lab052_current_state.md), + [основной поток](../data/processed/lab052/stream.json). Журнал: запись 041. +- **Далее:** отдельное исследование истинных пакетов, общей обработки IQ и + задержки JPEG-пачки; текущий этап завершить на программных результатах. +- **RF / Git:** RF TX=0; Lab052 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/lab052_current_state.md b/docs/lab052_current_state.md new file mode 100644 index 0000000..915c97c --- /dev/null +++ b/docs/lab052_current_state.md @@ -0,0 +1,390 @@ +# Lab052 — снижение числа ложных кандидатов при 500 ксимв/с + +Дата: 8 сентября 2026 года. SOFTWARE ONLY. RF TX = 0. + +## Результат + +Ложные строгие проверки сокращены с **146765 до 15**, в **9784,33 раза** +(99,9898%). Сохранены все **240 уникальных физических пакетов**, получены +**30/30 JPEG с точными байтами**. Программное исследование завершено. + +**Реальное время и 5 свежих изображений/с не достигнуты.** Медианный из трёх +полных проходов: **7,369795 с обработки / 6,044416 с IQ = 1,219273**. +Возраст декодированных изображений: **234,520 / 834,077 / 1549,595 мс** +(min / median / max). При MAX_VIDEO_AGE=200 мс все 30 просрочены, +**DISPLAYED=FRESH DISPLAYED=0, FRESH DISPLAY FPS=0**. + +В этих условиях ложные проверки уже не определяют скорость: они занимают +28,884 мс за весь опыт, 0,392% полной обработки. Дальнейшее удаление только +этих проверок не закроет оставшееся отставание. + +Рабочая точка: BPSK, 500000 символов/с, Fs=8000000 отсчётов/с, 16 отсчётов/символ. +JPEG C: 128×96 RGB, quality 25, 1740–1893 байта. PRBS7, PHY, FEC, CRC, +интерливер, формат пакета и фрагмента не менялись. Файлы protocol не изменены. +Управление RadioMaster → ExpressLRS → штатный ELRS RX → CRSF → контроллер +остаётся отдельным физическим каналом; в SDR-поток управление не добавлялось. +Временные подмены старых функций рассчитаны на один последовательный RX-worker; +потокобезопасность и одновременная работа нескольких таких приёмников не реализованы. + +## Git и аудит + +Проверены root/main/HEAD/status, diff stat/check, отчёт Lab051, запись 040, +индекс лабораторных, три новых вычислительных модуля, тесты и итоговые JSON. +До новой реализации quick_gate PASS; pytest **688 passed, 0 failed**. +Verifier независимо подтвердил состав и согласованность данных. + +По прямому разрешению пункта 0 задания показаны staged stat/status и создан: + +`9f33607d3ae82992c595de37dd64e99d7e4dea73` + +`Lab051: accelerate 500 ksymbol/s streaming receiver` + +Ровно 13 файлов, 9695 добавлений: код/тесты/отчёт Lab051, журнал и индекс, +baseline_profile.json, benchmark.json, stream.json, memory.json, replay.json, +regression20k/result.json. IQ-окна, промежуточные замеры, JPEG и посторонние +файлы не добавлялись. Push не выполнялся. Это committed HEAD всей Lab052. + +## Что означают исходные 147 тысяч + +В исходном JSON Lab051 **147082 — все строгие кандидаты**, включая 240 принятых; +ложных было **146842**. Число 12239 — новые soft/timing-окна, включая настоящие. +Исторический processing/RF=6,391045, fresh FPS=0. Называть 147082 ложными неверно. + +Lab051 не сохраняла измеренные encode_seconds каждого JPEG. Её длинная временная +сетка немного менялась между запусками. Поэтому в Lab052 тот же генератор, +noise seed=50050, coded-bit Eb/N0=7 дБ, CFO=+10 Гц, фаза 0,73, чередование +полярности и CS8-квантование используют **однократно измеренную и сохранённую +готовность JPEG** в source_fixture.json. Проверяются SHA всех 30 JPEG. +Все новые варианты получают одну и ту же последовательность IQ; сравнение +потерь и кандидатов ниже относится к этой общей сетке. Историческая сетка +Lab051 не объявляется побитно воспроизведённой. + +Свежий профиль прежнего Lab051, после независимых решений: + +| Метрика | Значение | +|---|---:| +| IQ / Fs | 6,044416 с / 8 Мвыб/с | +| TOTAL LOCAL PEAKS | 1675842 | +| FIRST-STAGE CANDIDATES | 392785 | +| STRICT CANDIDATES | 147005 | +| TRUE CANDIDATES | 240 | +| FALSE CANDIDATES | 146765 | +| CANDIDATES / RF SECOND | 24320,795 | +| FALSE CANDIDATES / RF SECOND | 24281,089 | +| Уникальных уточнённых позиций ложных кандидатов | 11387 | +| Повторных уточнённых позиций | 135378 | + +Отдельный свежий проход старого Lab051 **без наблюдателя**, на этой же +зафиксированной сетке: **41,633312 с, processing/RF=6,887897**, 240/240 пакетов +и 30/30 точных JPEG. Сравнение с итоговыми 7,369795 с даёт около **5,65×** +ускорения всего приёмника. Относительно исторических 38,630132 с — **5,24×**. +Инструментированный профиль и чистый контроль хранятся раздельно; настольная +нагрузка и прогрев влияют на временные числа. + +Сначала детектор получает каждый локальный максимум выше низкого фиксированного +порога. Только затем пропускает область уже принятого пакета. На продолжительном +шуме число проб растёт с числом входных отсчётов. Нормировка на энергию означает, +что малая абсолютная мощность шума сама по себе не уменьшает корреляционную +статистику. Более 135 тысяч проверок вновь приходят к уже встречавшимся позициям. + +PRBS7 повторяется через 127 символов, то есть через 2032 отсчёта в рабочем режиме. +Преамбула содержит 1280 символов. Поэтому рядом с ней возможны сильные боковые +корреляционные пики, помимо случайных шумовых максимумов. Только проверка +структуры и последующий PHY/FEC/CRC различают их надёжнее одного большого пика. + +## Распределения и фон + +Истинные главные пики определены после CRC PASS: берётся максимум в прежней +области уточнения около восстановленного старта. Это диагностическая метрика, +не дополнительный вход алгоритма. Числа истинных кадров также сверены по +множеству всех 240 уникальных уточнённых стартов, а не только по счётчику. + +| Корреляционная статистика | min | median | p99 | max | +|---|---:|---:|---:|---:| +| Главный пик настоящей преамбулы, 240 | 0,830055 | 0,841691 | 0,848389 | 0,849945 | +| Ложные строгие кандидаты, 146765 | 0,030964 | 0,035464 | 0,055803 | 0,805951 | +| Меньшая корреляция половин у принятых кандидатов | 0,825038 | 0,837340 | 0,845914 | 0,846226 | + +Отдельно после приёма выделены 357 блоков, чьи корреляционные окна не пересекают +ни одного декодированного физического кадра. Медиана блочных медиан =0,016353, +медиана MAD =0,006184, медиана квантилей 99% =0,041875. +Диапазон медиан 0,006451–0,017422 показывает, что фон не идеально стационарен. +MAD — медиана абсолютного отклонения от медианы; здесь это мера разброса. +Полные min/median/MAD/p90/p99/p999/max по блокам сохранены в JSON. + +Адаптацию порога по median/MAD рассмотрели, но не выбрали: она не устраняет +повторные структурные пики и потребовала бы отдельно доказывать чувствительность +при смене фона. Основное правило использует **локальную энергию каждой половины** +наблюдаемого IQ. Глобальный median/second или TX reference в решении не участвуют. +На меняющемся фоне и в слабых положительных регрессиях дополнительные потери +не наблюдались; универсальная чувствительность при любом нестационарном канале +этим конечным исследованием не доказана. + +![Распределения обнаружителя](../data/processed/lab052/detector_distributions.png) + +## Выбранное правило + +1. Прежняя грубая PRBS7-корреляция на двух отсчётах/символ и прежний первый + порог **0.030964473262429237**. Все локальные пики считаются одинаково с baseline. +2. На уже децимированном общем входе блока считаются корреляции двух половин + опорной формы. Полная форма содержит 2644 отсчёта, каждая половина — 1322. + Для каждой позиции обе величины нормируются **своими** энергиями IQ и опоры. + Требуются обе корреляции ≥**0.08474038063392896**. Одиночная сильная половина + этого условия не выполняет. Неизвестная фаза/полярность сохраняется через + модуль; согласование фаз половин, сужающее CFO, не вводилось. +3. Прошедшие пики объединяются в группы с полной шириной ≤20480 отсчётов + **от первого пика**, без продления по цепочке следующих соседей. На границе + блока группа обрывается: это может добавить работу, но не скрывает следующий пик. +4. Представитель — максимум нормированной корреляции; при равенстве правый пик. + Если он не проходит строгий тракт, проверяется следующая альтернатива группы. + Только реальный CRC PASS разрешает прежний пропуск принятого физического кадра. +5. VALID остаётся исключительно прежним **PHY → FEC → CRC PASS**. + +Корреляции частей — общие FFT на блок, не новое timing-окно для каждого пика. +Это дополнительные согласованные фильтры частей преамбулы на децимированном +сигнале. Повторно использовать полный RRC-выход для предварительного решения +не потребовалось: его создание на шумовых блоках дороже этой проверки. +Полный RRC/timing и все 1601 прежних смещения остались в строгом тракте. + +Порог половины выбран до положительных сравнений из номинальной шумовой модели +`|rho|² ~ Beta(1,N−1)`, N=640, alpha=0,01 на часть: +`sqrt(1−alpha**(1/(N−1)))`. Это инженерная исходная настройка, **не** доказанная +вероятность ложного принятия после фильтрации и выбора максимумов. Корреляция +отсчётов и отбор пиков нарушают независимость; фактические отказы измерены отдельно. +Ни порог, ни ширина не выбираются по TX offsets. + +Обоснование ширины для двух разных последовательных физических кадров: + +`MIN_FRAME_SAMPLES=(1280+144+2*(12*8+6)+32+10)*16=26720`. + +Точность прежнего локального уточнения ±2000 плюс сетка децимации 8: +`20480 < 26720−2*2008 = 22704`. +Поэтому два центральных кандидата неперекрывающихся минимальных кадров, +локализованных в этом диапазоне, не попадают в одну группу. Это обоснование +для последовательного потока, не обещание разрешить любые радиоколлизии. + +## Почему первый вариант был отвергнут + +Изначальное подавление всех меньших пиков в радиусе одной преамбулы дало +0 ложных строгих проверок на основном опыте. Adversary воспроизвёл потерю: +ложная сильная преамбула на отсчёте 10000 без правильного заголовка подавляла +настоящий слабый пакет на 30000. Lab051 его принимала, новый вариант — нет. +Одного обоснования минимальной дистанцией двух **настоящих** кадров недостаточно +для случая сильного ложного пика. Этот вариант **отвергнут**. + +Исправление: дешёвая проверка всех локальных кандидатов до выбора представителя, +ограниченная полная ширина группы и сохранение альтернатив после FAIL. +Контрпример, его сдвиги через границу IQ-блока и сильный пакет с плохой CRC +перепроверены независимо. Корректный слабый пакет теперь сохраняется. +Итог Lab052 — **15**, а не предварительные 0 ложных проверок. + +## Сравнение числа частей + +Во всех четырёх вариантах сохранены те же 240 уникальных стартов и 30 точных JPEG. +Наблюдатель собирал полные распределения; время этих проходов не является +основным измерением производительности. + +| Правило | Порог части | Ложные строгие проверки | Сокращение | Потеря настоящих | +|---|---:|---:|---:|---:| +| Только группы, с альтернативами после FAIL | — | 145863 | 1,006× | 0 | +| Одна часть, контроль скалярного порога | 0,059951 | 332 | 442,06× | 0 | +| **Две части, основной вариант** | **0,084740** | **15** | **9784,33×** | **0** | +| Три части | 0,103813 | 6 | 24460,83× | 0 | + +Одна часть — только сравнительный контроль, не принятый ответ «поднять порог». +Третья часть убирает ещё девять проверок, но увеличивает стоимость общего +этапа: 0,531 → 0,819 с в инструментированных проходах. Две части — минимальный +вариант с проверкой распределённой структуры и большим запасом к ориентиру 50×. +Группировка сама по себе с корректным возвратом альтернатив почти не помогает +на распределённом шуме: отказы нельзя использовать для безусловного удаления соседей. + +## Окончательные вычисления и свежесть + +Три отдельных полных прохода без наблюдателя: processing/RF +**1,257688 / 1,219199 / 1,219273**, в каждом 240/240 пакетов, 30/30 JPEG, +15 ложных проверок и fresh FPS=0. Выбран целиком третий проход — медианный +по суммарному времени, без смешивания временных отметок разных запусков. + +| Стадия / счётчик | Итог | +|---|---:| +| TOTAL LOCAL PEAKS | 1675842 | +| FIRST-STAGE / CHEAP CHECKED | 392785 | +| CHEAP REJECTED | 388407 | +| CHEAP RETAINED | 4378 | +| CLUSTERS, включая группы внутри уже принятых кадров | 384 | +| REPRESENTATIVE / ALTERNATIVE STRICT CHECKS | 243 / 12 | +| Пропущено внутри принятого физического кадра | 4123 | +| STRICT FALSE / TRUE | 15 / 240 | +| STRICT CHECKS / RF SECOND | 42,187699 | +| FALSE CHECKS / RF SECOND | 2,481629 | +| Новые soft/timing-окна | 488 | +| Грубая корреляция и децимация | 1,790240 с | +| Общие проверки частей | 0,446616 с | +| Группировка | 0,003113 с | +| Строгие настоящие / ложные | 3,996005 / 0,028884 с | +| Среднее настоящего кандидата | 16,650 мс | +| Суммарно RX / сборка / JPEG decode | 7,358399 / 0,006008 / 0,005388 с | +| Полная обработка | 7,369795 с | +| PROCESSING / RF | 1,219273 | +| INPUT FPS / FRAMES | 5 / 30 | +| RX PACKETS / IMAGES | 240 / 30 | +| DROPPED BEFORE TX / STALE AFTER DECODE | 0 / 30 | +| DISPLAYED / FRESH DISPLAYED / FRESH FPS | 0 / 0 / 0 | +| RAW RECEIVED FPS в первые 6 с шкалы работника | 4 | +| Возраст декодированных JPEG min / median / max | 234,520 / 834,077 / 1549,595 мс | +| MAX_VIDEO_AGE | 200 мс | + +После устранения основной массы ложных кандидатов обнаружена лишняя подготовка: +старый адаптер очищал кэш и 734 раза строил ту же опорную форму. В начальном +cProfile это занимало 3,544 с. В Lab052 три неизменных массива — опора, RRC taps +и PRBS7 bits — хранятся в самом приёмнике. Они побитно сравнены со старым +расчётом; функции возвращаются в finally до выхода старого контекста режима. +Это не новая PHY и не передача данных TX в RX. + +Финальный отдельный cProfile только вокруг push/finish показывает следующие +узкие места: FFT 1,650 с собственного времени, FIR-децимация 1,295 с, +Viterbi 1,397 с; уточнение 1,084 с и matched 0,865 с включают свои FFT, +поэтому эти строки нельзя складывать повторно. Копирование ndarray — 0,313 с, +прочие расходы push — также измерены. Данные cProfile не заменяют чистый замер. + +Теперь основное ограничение — **настоящие пакеты вместе с общей обработкой IQ**. +40 пакетов/с ×16,650 мс требуют около 666 мс обработки в секунду; к ним добавляются +децимация/корреляция, общий промежуточный этап, копирование и организация блоков. +Во время JPEG-пачки пакет приходит каждые 11,532 мс, быстрее средней строгой +обработки настоящего пакета. Нужны и дальнейшее снижение общей стоимости, +и проверка задержки пачки. Одного среднего processing/RF<1 недостаточно для +доказательства MAX_VIDEO_AGE=200 мс при неравномерной нагрузке. + +Событийная шкала повторно использует Lab051: +`worker_start=max(previous_worker_end, block_arrival); worker_end=start+measured_push`. +Сборка/JPEG decode добавляют измеренные затраты. RF и CPU перекрываются; +стоимость RX не заменяется нулём. Генерация IQ, сравнение SHA и JSON I/O исключены. +Восстановленные байты сверяются с TX только после принятия, сборки и JPEG decode. + +Возраст относится к **декодированным, затем отброшенным** изображениям. +Камера и физический экран не измерялись, их задержки UNKNOWN. Capture lookup +используется после вердикта для отчётной модели вывода. В неизменном формате +фрагмента нет capture timestamp; независимый live-контроль возраста не реализован. +Максимальное отставание 1,426779 с соответствует примерно 91313848 байтам +внешнего complex64 IQ. Этот backlog вычислен, но не хранится офлайн-источником. +REAL-TIME CAPABLE=NO; 5 FPS SOFTWARE REAL-TIME TARGET=FAIL. + +## Память и длительное состояние + +- Пик удерживаемого IQ: **3177728 байт = 3,031 МиБ**; проверяемый предел 3398912. +- Инженерная оценка рабочих массивов: **142775456 байт = 136,161 МиБ**. + Это прежний запас Lab051, включающий буферы; не доказанный универсальный + предел C-аллокаций и не отдельная величина для сложения с RSS. +- Оценка массивов именно обнаружителя в опыте: **1480664 байта**; + постоянные новые шаблоны — **171140 байт**. Временные FFT отдельно целиком + не перечислены, они покрываются общей инженерной оценкой с запасом. +- RSS выбранного полного прохода: **149393408 байт** после потока; + process RSS peak **168804352 байта = 160,984 МиБ**. Это весь процесс Python, + включая библиотеки, генератор и аллокатор, а не только массивы RX. +- В основном опыте одновременно максимум **23** прошедших дешёвый этап пика + и **3** группы на блок; между вызовами и после finish состояние кандидатов =0. +- Предел числа кандидатов задаётся числом локальных максимумов ограниченного + IQ-окна. Списки групп и альтернатив уничтожаются на выходе, не копятся в сессии. + +Дополнительно измерен отдельный RX replay длительностью 0,063184 с IQ с одним +независимо восстановленным пакетом. Наблюдаются 6754 ndarray, из них 6681 владеют +данными; суммарные байты наблюдаемых владельцев за проход — 37378942 (не пик). +**tracemalloc peak=11586792 байта**, после выхода остаётся 6461 байт отслеживаемых +выделений. Входной IQ подготовлен до tracing. Это измерение отслеживаемой памяти +RX, включая Python, и нижняя граница числа массивов в локальных переменных; +временные C/SciPy-аллокации полностью не перечислены. Короткий replay не заменяет +RSS полного опыта. Подсчёт ndarray и tracemalloc выполняются в разных проходах; +tracemalloc измеряет следующий прогретый проход. Его tracing-время не используется +для оценки скорости, а пик не объявляется верхней границей холодного RX. + +Длинный тест: **2048 блоков, 16,777216 с** распределённого шума с амплитудными +масштабами 0,002 / 0,2 / 20 / 0,02. Получены 4783206 локальных пиков, +1068038 кандидатов первого каскада; 1068037 отклонены дешёво, один дошёл +до строгого отказа. **Ни одного принятого шумового пакета**. +После прогрева в контрольных точках IQ=2653440 байт, децимация=397216 байт, +сохранённых кандидатов=0. RSS сначала растёт при прогреве кэшей, затем +стабилизируется; последние четыре точки 140533760–140550144 байта. +После finish оба IQ-буфера освобождены, константы ограниченного размера остаются. + +Полная диагностическая история NPZ/JSON принадлежит конечному эксперименту, +не приёмнику. Она выключена в окончательном измерении скорости и длинном тесте. +Ограниченный RX не подменяет отсутствующую живую входную очередь с overflow/gap +policy: аппаратный драйвер и переполнения в этой лабораторной не реализованы. + +## Регрессии и проверка + +45 дополнительных слабых сценариев: Eb/N0=0/1/2/3/7 дБ, CFO=−100/+10/+100 Гц, +три seed, масштабы амплитуды 0,002/0,2/1 и разные фазы/полярности. +Старый и новый RX правильно приняли **37/45**; **0** потерянных пакетов baseline. +Восемь случаев не принимаются обоими, поэтому это не объявляется 45/45 успешным +декодированием. Основной поток отдельно даёт 240/240 и 30/30 SHA MATCH. + +30 быстрых целевых тестов покрывают шумовые группы, равные пики, отсутствие +цепочного расширения, два минимальных соседних кадра, слабый кадр, одну сильную +ложную половину, фазу/масштаб, распределённый и меняющийся шум, границы блоков, +освобождение состояния, эквивалентность общих корреляций отдельным vdot, +ложную преамбулу и восстановление функций. Verifier и adversary независимо +подтвердили исправление воспроизведённой потери; обхода PHY/FEC/CRC не найдено. + +Сохранённая Lab047-запись **20 ксимв/с** повторно разобрана прежним Lab048: +**36/36, CRC PASS, JPEG SHA MATCH**, 19 ошибок до FEC, 0 после, 0 байтовых ошибок. +SHA: `e913dab9ac32795250880a4be67fbbbf89a4f96536842476ab591d1acaafc02a`. +Обработка 15 с старого IQ заняла 13,213346 с. Новый адаптер принимает только +500 ксимв/с; режим 20 ксимв/с, его порог и вычислительный тракт не менялись. + +Итоговый quick_gate PASS: **718 тестов, 20 функциональных проверок, +13 штатных пропусков**. Полный pytest: **718 passed за 77,15 с, 0 failed**; +одно предупреждение доступа к .pytest_cache не влияет на результат тестов. +Дополнительный helper измерения массивов выполнен отдельно, все три модуля +после его добавления проверены py_compile и своими run_functional_tests. +git diff --check PASS. Машинная сверка итоговых JSON — в acceptance.json: +обязательные регрессии PASS, real-time и 5-fps target явно FAIL. + +## Файлы и воспроизведение + +- [Обнаружитель и потоковый адаптер](../experiments/lab052_candidate_detection.py) +- [Генератор сравнений, измерений и регрессий](../experiments/lab052_detector_study.py) +- [Статистика и график](../experiments/lab052_detection_statistics.py) +- [Целевые тесты](../tests/test_lab052_candidate_detection.py) +- [Профиль старого RX](../data/processed/lab052/baseline_profile.json) +- [Старый RX без наблюдателя на том же источнике](../data/processed/lab052/baseline_stream.json) +- [Сравнение правил](../data/processed/lab052/rule_comparison.json) +- [Статистика после решений](../data/processed/lab052/statistics.json) +- [Итоговый полный поток](../data/processed/lab052/stream.json) +- [Длинное состояние](../data/processed/lab052/memory.json) +- [Измеренные массивы и tracemalloc](../data/processed/lab052/allocations.json) +- [Слабые сигналы](../data/processed/lab052/sensitivity.json) +- [Отдельный профиль CPU](../data/processed/lab052/receiver_cpu.txt) +- [Старая запись 20 ксимв/с](../data/processed/lab052/regression20k/result.json) +- [Сверка критериев и отрицательного результата скорости](../data/processed/lab052/acceptance.json) + +```powershell +python experiments/lab052_detector_study.py --stage profile +python experiments/lab052_detector_study.py --stage baseline +python experiments/lab052_detector_study.py --stage rules +python experiments/lab052_detector_study.py --stage stream +python experiments/lab052_detector_study.py --stage cpu +python experiments/lab052_detector_study.py --stage memory +python experiments/lab052_detector_study.py --stage sensitivity +python experiments/lab052_detection_statistics.py +python -m pytest tests/test_lab052_candidate_detection.py -q -p no:cacheprovider +python tools/quick_gate.py +python -m pytest tests/ -q +git diff --check +``` + +Старый IQ: команда Lab048 с параметрами `--run-saved-capture`, +`--capture C:\Users\user\Documents\SDR_Rover_Lab047_raw\20260903_115747\rx_stream.cs8`, +`--original-result C:\Users\user\Documents\SDR_Rover_Lab047_raw\20260903_115747\independent_result.json`, +`--tx-image data/processed/lab047/tx_image.jpg`, +`--output-directory data/processed/lab052/regression20k`. + +Первый запуск фиксирует измеренную готовность JPEG; последующие используют +source_fixture.json. Между запусками меняются фактические CPU timings и RSS, +поэтому временные числа могут отличаться. Основной приём и набор кандидатов +на зафиксированном источнике воспроизводятся. Большой новый IQ-файл не создаётся. + +Lab052 git add/commit и push не выполнялись. RF TX=0; HackRF и другое +оборудование не перечислялись и не запускались. QPSK, 180 МГц, аппаратные +500 ксимв/с и переход к 10 fps не выполнялись. Следующий рекомендуемый этап — +отдельная оптимизация истинных пакетов/общего IQ и задержки JPEG-пачки. STOP. diff --git a/experiments/lab052_candidate_detection.py b/experiments/lab052_candidate_detection.py new file mode 100644 index 0000000..0f8ef5b --- /dev/null +++ b/experiments/lab052_candidate_detection.py @@ -0,0 +1,287 @@ +"""Lab052: локальные группы пиков и дешёвая проверка частей PRBS7. + +Кандидат никогда не становится пакетом без прежних PHY/FEC/CRC. Параметры +заданы длинами PHY и моделью шума, без ожидаемых стартов и содержимого TX. +""" + +from __future__ import annotations + +from collections import Counter +from contextlib import contextmanager +from dataclasses import dataclass +import math +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 scipy.signal import fftconvolve + +from experiments import lab051_streaming_receiver as previous + +phy = previous.phy +offline = previous.offline +SAMPLES_PER_SYMBOL = 16 +PREAMBLE_SAMPLES = phy.lab045.PREAMBLE_BIT_COUNT * SAMPLES_PER_SYMBOL +COARSE_ERROR_SAMPLES = 2000 + 8 # исходное уточнение ±2000 плюс сетка децимации +MIN_FRAME_SAMPLES = (phy.lab045.PREAMBLE_BIT_COUNT + phy.lab045.PHY_HEADER_BIT_COUNT + + phy.lab045.conv.encoded_bit_count(phy.lab045.MIN_PACKET_BYTES * 8) + + 2 * phy.lab045.GUARD_SYMBOL_COUNT + phy.lab045.RRC_SPAN_SYMBOLS) * SAMPLES_PER_SYMBOL +CLUSTER_WIDTH_SAMPLES = PREAMBLE_SAMPLES +PART_FALSE_ALARM = .01 + + +@dataclass(frozen=True) +class DetectorRule: + """parts=0 оставляет только группировку; 1/2/3 нужны для сравнения правил.""" + + parts: int = 2 + cluster_width_samples: int = CLUSTER_WIDTH_SAMPLES + + def __post_init__(self): + if type(self.parts) is not int or not 0 <= self.parts <= 3: + raise ValueError("Нужно от нуля до трёх частей") + if (type(self.cluster_width_samples) is not int or self.cluster_width_samples < 0 + or self.cluster_width_samples % 8 + or self.cluster_width_samples >= MIN_FRAME_SAMPLES - 2 * COARSE_ERROR_SAMPLES): + raise ValueError("Окно должно различать два минимальных физических кадра") + + +def peak_groups(indexes: np.ndarray, width: int): + """Полная ширина от первого пика, без продолжения по цепочке соседей. + + На границе IQ-блока группа обрывается: возможна лишняя проверка, но ни + один следующий пик не теряется из-за представителя из другого блока. + """ + cursor = 0 + while cursor < len(indexes): + end = int(np.searchsorted(indexes, indexes[cursor] + width, side="right")) + yield indexes[cursor:end] + cursor = end + + +def cluster_peak_indexes(scores: np.ndarray, width: int, threshold: float) -> np.ndarray: + """Первые представители ограниченных групп; равенство → правый пик.""" + peaks = offline._all_local_peak_indexes(scores) + peaks = peaks[scores[peaks] >= threshold] + return np.asarray([group[len(group) - 1 - int(np.argmax(scores[group[::-1]]))] + for group in peak_groups(peaks, width)], dtype=np.int64) + + +def part_correlations(values: np.ndarray, reference: np.ndarray, count: int) -> np.ndarray: + """Несколько vdot на уже децимированном согласованном входе, без timing. + + Модуль сохраняет неизвестную фазу/полярность и не вводит ограничения CFO + через согласование фаз между половинами. Каждая часть нормируется своей + энергией, поэтому усиление и смена мощности фона не требуют TX reference. + """ + if values.size != reference.size or count < 1: + return np.zeros(max(count, 0)) + boundaries = np.linspace(0, reference.size, count + 1, dtype=int) + output = np.zeros(count) + for number, (low, high) in enumerate(zip(boundaries[:-1], boundaries[1:])): + part = values[low:high] + expected = reference[low:high] + energy = float(np.vdot(part, part).real) * float(np.vdot(expected, expected).real) + output[number] = abs(np.vdot(expected, part)) / math.sqrt(max(energy, 1e-30)) + return output + + +def part_threshold(count: int) -> float: + """Номинальная H0-модель: независимые комплексные отсчёты по символам. + + |rho|²~Beta(1,N-1), alpha=0.01 на часть. Это исходная инженерная настройка, + не обещание точной вероятности после RRC/выбора пиков: фон проверяется отдельно. + Она фиксируется до положительных опытов и не подгоняется под их offsets. + """ + symbols = phy.lab045.PREAMBLE_BIT_COUNT // count + return math.sqrt(1 - PART_FALSE_ALARM ** (1 / (symbols - 1))) + + +class CandidateFrontEnd(previous.BlockFrontEnd): + """Общая корреляция — согласованный фильтр преамбулы на двух выборках/символ. + + Промежуточному этапу достаточно его исходных данных; полный общий RRC- + выход создаётся прежним строгим трактом только при прошедшем кандидате. + """ + + def coarse_scores(self, samples, reference): + raw = self.decimator.get(samples, self.base) + mean = np.mean(raw, dtype=np.complex128).astype(np.complex64) + needed = math.ceil(self.owned_end / 8) + reference.size + 1 + self.detection_values = raw[:needed].copy() + self.detection_values -= mean + self.detection_values *= previous.detection_rotation(self.detection_values.size, 8) + if self.detection_values.size < reference.size: + return np.empty(0, np.float32) + scores = previous.lab043._normalized_sliding_correlation(self.detection_values, reference).astype(np.float32) + scores[~np.isfinite(scores)] = 0 + np.clip(scores, 0, 1, out=scores) + return scores + + def split_scores(self, reference, count, score_count): + """Общие корреляции частей для всех кандидатов, без per-candidate FFT. + + Проверка выполняется ДО выбора представителя: сильный случайный пик + одной половины не может скрыть согласованный слабый кандидат рядом. + """ + output = np.empty((score_count, count), np.float32) + boundaries = np.linspace(0, reference.size, count + 1, dtype=int) + energy = np.concatenate(([0.], np.cumsum(np.abs(self.detection_values) ** 2, dtype=np.float64))) + for number, (low, high) in enumerate(zip(boundaries[:-1], boundaries[1:])): + expected = reference[low:high] + correlation = fftconvolve(self.detection_values[low:], np.conj(expected[::-1]), mode="valid")[:score_count] + local_energy = energy[high:high + score_count] - energy[low:low + score_count] + denominator = np.sqrt(np.maximum(local_energy * float(np.vdot(expected, expected).real), 1e-30)) + output[:, number] = np.abs(correlation) / denominator + return output + + +class StreamingReceiver(previous.StreamingReceiver): + """Ограниченный IQ и кандидаты только одного блока, API получает только IQ.""" + + def __init__(self, symbol_rate: int = 500000, *, block_samples: int = 65536, + rule: DetectorRule = DetectorRule(), observer=None): + super().__init__(symbol_rate, block_samples=block_samples) + self.rule, self.observer = rule, observer + self.detection_statistics = Counter() + self.cheap_seconds = self.cluster_seconds = 0.0 + self.true_strict_seconds = self.false_strict_seconds = 0.0 + self.peak_cluster_count = self.peak_eligible_count = self.candidate_state_count = 0 + self.working_detection_arrays_peak_bytes = 0 + with previous.previous.streaming_mode(self.mode): + self.raw_reference = phy.lab045.lab044._preamble_reference_samples().copy() + self.matched_taps = previous.lab043._matched_filter_taps().copy() + self.preamble_bits = phy.lab045.lab044.preamble_bits().copy() + for values in (self.raw_reference, self.matched_taps, self.preamble_bits): + values.flags.writeable = False + + @contextmanager + def constant_templates(self): + """Старый адаптер очищает lru на каждом push; эти три константы локальны. + + Возвращаем именно функции до выхода streaming_mode, чтобы его + cache_clear и соседний старый режим работали по прежним правилам. + """ + targets = ((phy.lab045.lab044, "_preamble_reference_samples", lambda: self.raw_reference), + (previous.lab043, "_matched_filter_taps", lambda: self.matched_taps), + (phy.lab045.lab044, "preamble_bits", lambda: self.preamble_bits)) + originals = [(owner, name, getattr(owner, name)) for owner, name, function in targets] + try: + for owner, name, function in targets: + setattr(owner, name, function) + yield + finally: + for owner, name, function in reversed(originals): + setattr(owner, name, function) + + def _scan(self, owned_end): + with self.constant_templates(): + return self._scan_with_templates(owned_end) + + def _scan_with_templates(self, owned_end): + width = self.rule.cluster_width_samples // self.decimation + front = CandidateFrontEnd(self.buffer, owned_end - self.base_sample, + self.front_statistics, decimator=self.decimator, base=self.base_sample) + started = time.perf_counter() + scores = front.coarse_scores(self.buffer, self.reference) + self.coarse_seconds += time.perf_counter() - started + threshold = np.float32(phy.lab047.FIXED_HARDWARE_CANDIDATE_THRESHOLD) + peaks = offline._all_local_peak_indexes(scores) + offsets = self.base_sample + peaks * self.decimation + owned = (offsets >= self.scan_sample) & (offsets < owned_end) + owned_peaks = peaks[owned] + 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 self.rule.parts and len(scores) else np.empty((len(scores), 0))) + passed = (np.all(parts[first_stage] >= part_threshold(self.rule.parts), axis=1) + if self.rule.parts else np.ones(len(first_stage), dtype=bool)) + 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(peak_groups(indexes, width)) + 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) + emitted = [] + try: + with previous.optimized_context(front): + # Внутри группы — самый сильный первым. Отказ не даёт права + # подавить альтернативы: пробуем следующий. Только CRC PASS + # разрешает прежний пропуск уже принятого физического кадра. + ordered = [group[np.argsort(scores[group], kind="stable")[::-1]] for group in groups] + attempted_groups = set() + for group_number, index in ((number, item) for number, group in enumerate(ordered) for item in group): + absolute = self.base_sample + int(index) * self.decimation + # Сетка грубого поиска может округлить следующий стык на + # одну выборку назад. Не терять соседний физический кадр. + 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 = offline.CoarseCandidate(int(index) * self.decimation, float(scores[index])) + started = time.perf_counter() + outcome = offline.decode_candidate_from_memmap( + self.buffer.view(np.float32), self.buffer.size, candidate, self.candidate_count, + read_iq_window=lambda raw, low, high: raw[2 * low:2 * high].view(np.complex64)) + elapsed = time.perf_counter() - started + self.strict_seconds += elapsed + if self.observer is not None: + self.observer.outcome(self.base_sample, candidate, outcome) + if outcome.accepted and outcome.crc == phy.lab045.CRC_PASS and outcome.frame is not None: + self.true_strict_seconds += elapsed + self.accepted_until = self.base_sample + outcome.next_sample + self.packet_count += 1 + emitted.append(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 += elapsed + self.rejected_reasons[outcome.reason] += 1 + finally: + self.candidate_state_count = 0 + 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.buffer = self.buffer[trim:].copy() + self.base_sample = retain_start + return emitted + + +def run_functional_tests() -> None: + assert CLUSTER_WIDTH_SAMPLES < MIN_FRAME_SAMPLES - 2 * COARSE_ERROR_SAMPLES + assert .08 < part_threshold(2) < .09 + assert StreamingReceiver().finish() == [] + + +def main() -> None: + run_functional_tests() + print("Lab052 detector functional checks PASS; RF TX=0") + + +if __name__ == "__main__": + main() diff --git a/experiments/lab052_detection_statistics.py b/experiments/lab052_detection_statistics.py new file mode 100644 index 0000000..ca44b08 --- /dev/null +++ b/experiments/lab052_detection_statistics.py @@ -0,0 +1,157 @@ +"""Lab052: распределения и численные проверки после независимых решений RX.""" + +from __future__ import annotations + +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.lab052_detector_study import OUTPUT, quantiles, write_result +from experiments import lab052_candidate_detection as detection +from experiments.lab051_receiver_profile import array_observation + + +def measure_arrays() -> dict: + """Отдельный короткий RX replay, без смешивания tracing с замером скорости. + + ndarray в Python-locals даёт нижнюю границу наблюдаемых массивов; C/SciPy + могут иметь невидимые временные выделения. tracemalloc peak — отдельная + измеренная величина, не RSS и не универсальная верхняя граница памяти. + """ + physical = detection.phy.lab045 + payload = bytes(range(256)) + plan = physical.build_transmission_plan(payload) + raw = detection.phy.build_parameterized_tx_cs8(plan, detection.phy.PhyMode(500000)) + components = np.frombuffer(raw, np.int8).astype(np.float32) + values = components[0::2] + 1j * components[1::2] + random_generator = np.random.default_rng(52040) + sigma = np.sqrt(float(np.sum(abs(values)**2)) / plan.frame_symbol_count / (2 * 10**.7)) + values += sigma * (random_generator.normal(size=len(values)) + 1j*random_generator.normal(size=len(values))) + samples = .2 * (random_generator.normal(size=63217 + len(values) + 350000) + + 1j * random_generator.normal(size=63217 + len(values) + 350000)) + samples[63217:63217 + len(values)] = values + samples = samples.astype(np.complex64) + + def replay(): + receiver = detection.StreamingReceiver() + packets = [] + 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()) + assert len(packets) == 1 and packets[0].payload == payload + + result = {**array_observation(replay), "iq_seconds": len(samples) / 8000000, + "packets": 1, "interpretation": "observed Python ndarray lower bound; tracked RX allocation peak; input IQ prepared before tracing", + "timing_not_used_as_benchmark": True, "rf_tx": 0} + write_result("allocations.json", result) + return result + + +def analyze() -> dict: + baseline = json.loads((OUTPUT / "baseline_profile.json").read_text(encoding="utf-8")) + optimized = json.loads((OUTPUT / "parts2.json").read_text(encoding="utf-8")) + before = np.load(OUTPUT / "baseline_observations.npz") + after = np.load(OUTPUT / "parts2_observations.npz") + peaks, outcomes = before["peaks"], before["outcomes"] + true = outcomes[outcomes[:, 3] == 1] + false = outcomes[outcomes[:, 3] == 0] + starts = np.sort(true[:, 2].astype(np.int64)) + new_starts = np.sort(after["outcomes"][after["outcomes"][:, 3] == 1, 2].astype(np.int64)) + missing = np.setdiff1d(starts, new_starts) + unexpected = np.setdiff1d(new_starts, starts) + main_scores = [] + for start in starts: + low, high = np.searchsorted(peaks[:, 0], [start - detection.COARSE_ERROR_SAMPLES, + start + detection.COARSE_ERROR_SAMPLES], side="left") + main_scores.append(float(peaks[low:high, 1].max())) + # Окна чистого фона выделяются ТОЛЬКО после независимых принятых кадров. + # В этом опыте decoded PHY packet 268B => waveform 92256 samples. + frame_samples = (detection.phy.lab045.PREAMBLE_BIT_COUNT + detection.phy.lab045.PHY_HEADER_BIT_COUNT + + detection.phy.lab045.conv.encoded_bit_count((256 + detection.phy.lab045.MIN_PACKET_BYTES) * 8) + + 42) * 16 + blocks = baseline["detection_profile"]["blocks"] + noise_blocks = [] + for block in blocks: + low, high = block["start_sample"], block["end_sample"] + (1280 + 42) * 16 + if not np.any((starts < high) & (starts + frame_samples > low)): + noise_blocks.append(block) + noise_statistics = {name: quantiles([block["score_statistics"][name] for block in noise_blocks]) + for name in ("median", "mad", "p90", "p99", "p999")} + cheap = np.load(OUTPUT / "parts2_cheap.npy") + accepted_coarse = after["outcomes"][after["outcomes"][:, 3] == 1, 0] + is_true = np.isin(cheap[:, 0], accepted_coarse) + part_min = cheap[:, 3:].min(axis=1) + false_count = len(false) + new_false = optimized["detector"]["strict_false_candidates"] + result = { + "classification": "strict PHY/FEC/CRC verdict first; no diagnostic feedback into RX", + "baseline_true_unique_starts": len(np.unique(starts)), + "optimized_true_unique_starts": len(np.unique(new_starts)), + "missing_true_starts": missing.tolist(), "unexpected_true_starts": unexpected.tolist(), + "true_main_peak_scores": quantiles(main_scores), "false_strict_scores": quantiles(false[:, 1]), + "baseline_false_refined_positions": len(np.unique(false[:, 2])), + "baseline_false_repeated_refinements": len(false) - len(np.unique(false[:, 2])), + "noise_only_blocks": len(noise_blocks), "noise_block_statistics": noise_statistics, + "split_minimum_true_strict": quantiles(part_min[is_true]), + "split_minimum_other_candidates": quantiles(part_min[~is_true]), + "strict_false_reduction": false_count / new_false, + "strict_false_reduction_fraction": 1 - new_false / false_count, + "first_stage_threshold_unchanged": detection.phy.lab047.FIXED_HARDWARE_CANDIDATE_THRESHOLD, + "part_threshold": detection.part_threshold(2), "rf_tx": 0, + } + write_result("statistics.json", result) + # Научные статические рисунки сохраняются самостоятельными артефактами. + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + figure, axes = plt.subplots(2, 2, figsize=(12, 8), constrained_layout=True) + axis = axes[0, 0] + bins = np.linspace(0, 1, 101) + axis.hist(false[:, 1], bins, histtype="step", label="Ложные строгие кандидаты", log=True) + axis.hist(main_scores, bins, histtype="step", label="Главный пик настоящей преамбулы", log=True) + axis.axvline(result["first_stage_threshold_unchanged"], color="gray", linestyle="--", label="Первый порог") + axis.set(xlabel="Нормированная корреляция", ylabel="Количество", title="Классификация после PHY/FEC/CRC") + axis.legend(fontsize=8) + axis = axes[0, 1] + for name, label in (("median", "Медиана"), ("mad", "MAD"), ("p99", "Квантиль 99%")): + axis.plot([block["start_sample"] / 8000000 for block in noise_blocks], + [block["score_statistics"][name] for block in noise_blocks], ".", markersize=2, label=label) + axis.set(xlabel="Время IQ, с", ylabel="Корреляционная статистика", title="Блоки чистого фона, определённые после RX") + axis.legend(fontsize=8) + axis = axes[1, 0] + axis.hist(part_min[~is_true], bins, histtype="step", log=True, label="Остальные локальные кандидаты") + axis.hist(part_min[is_true], bins, histtype="step", log=True, label="Кандидаты с последующим CRC PASS") + axis.axvline(detection.part_threshold(2), color="gray", linestyle="--", label="Порог каждой половины") + axis.set(xlabel="Меньшая корреляция двух половин", ylabel="Количество", title="Дешёвая проверка обеих частей") + axis.legend(fontsize=8) + axis = axes[1, 1] + rule_data = json.loads((OUTPUT / "rule_comparison.json").read_text(encoding="utf-8"))["rules"] + counts = [row["detector"]["strict_false_candidates"] for row in rule_data] + bars = axis.bar(["Группы", "1 часть", "2 части", "3 части"], counts) + axis.set_yscale("log") + for bar, count in zip(bars, counts): + axis.text(bar.get_x() + bar.get_width()/2, count*1.15, str(count), ha="center", fontsize=9) + axis.set(ylabel="Ложные строгие проверки", title="Все четыре варианта сохранили 240 пакетов") + figure.savefig(OUTPUT / "detector_distributions.png", dpi=160) + figure.savefig(OUTPUT / "detector_distributions.svg") + plt.close(figure) + return result + + +def run_functional_tests() -> None: + assert detection.part_threshold(2) < detection.part_threshold(3) + + +def main() -> None: + result = analyze() + print(json.dumps(result, ensure_ascii=False, indent=2)) + print(json.dumps(measure_arrays(), ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/experiments/lab052_detector_study.py b/experiments/lab052_detector_study.py new file mode 100644 index 0000000..f566386 --- /dev/null +++ b/experiments/lab052_detector_study.py @@ -0,0 +1,374 @@ +"""Lab052: воспроизводимый профиль обнаружителя, только программный IQ. + +Диагностическая история принадлежит эксперименту. Приёмник не получает её +обратно; эталон используется только после независимых решений PHY/FEC/CRC. +""" + +from __future__ import annotations + +import argparse +import cProfile +from collections import Counter +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import sys +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 PIL import Image + +from experiments import lab051_streaming_receiver as previous +from experiments import lab051_receiver_performance as performance +from experiments.lab051_receiver_profile import process_memory + +ROOT = Path(__file__).resolve().parents[1] +OUTPUT = ROOT / "data/processed/lab052" + + +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 sequence_fixture(): + """Тот же генератор Lab051, готовность JPEG фиксируется первым замером. + + Прежний опыт не сохранял encode_seconds, поэтому побитную историческую + временную сетку восстановить нельзя. Все новые сравнения используют одну + сохранённую сетку, один seed и те же байты JPEG, включая реальную encode cost. + """ + with Image.open(ROOT / "data/raw/lab011_scene.jpg") as source: + sequence = performance.video.encode_sequence(source, performance.video.PROFILES[2], 30) + path = OUTPUT / "source_fixture.json" + if path.exists(): + fixture = json.loads(path.read_text(encoding="utf-8")) + for item, recorded in zip(sequence, fixture["images"], strict=True): + assert hashlib.sha256(item.jpeg).hexdigest() == recorded["sha256"] + return [replace(item, encode_seconds=recorded["encode_seconds"]) + for item, recorded in zip(sequence, fixture["images"], strict=True)] + write_result(path.name, {"seed": 50050, "description": "Lab051 generator; frozen measured JPEG readiness", + "images": [{"image_id": item.image_id, "bytes": len(item.jpeg), + "encode_seconds": item.encode_seconds, + "sha256": hashlib.sha256(item.jpeg).hexdigest()} for item in sequence]}) + return sequence + + +def quantiles(values) -> dict: + values = np.asarray(values, dtype=np.float64) + if not values.size: + return {"count": 0} + median = float(np.median(values)) + return {"count": values.size, "min": float(values.min()), "median": median, + "mad": float(np.median(np.abs(values - median))), + "p90": float(np.quantile(values, .9)), "p99": float(np.quantile(values, .99)), + "p999": float(np.quantile(values, .999)), "max": float(values.max())} + + +class DetectionObserver: + """Полная история только для конечного лабораторного профиля, вне RX.""" + + def __init__(self): + self.peaks = [] + self.blocks = [] + self.outcomes = [] + self.cheap_rows = [] + + def scores(self, scores, base, low, high, decimation): + indexes = previous.offline._all_local_peak_indexes(scores) + offsets = base + indexes * decimation + selected = (offsets >= low) & (offsets < high) + self.peaks.append(np.column_stack((offsets[selected], scores[indexes[selected]]))) + first = max(0, (low - base + decimation - 1) // decimation) + last = min(len(scores), (high - base + decimation - 1) // decimation) + self.blocks.append({"start_sample": low, "end_sample": high, + "score_statistics": quantiles(scores[first:last]), + "peak_statistics": quantiles(scores[indexes[selected]])}) + + def outcome(self, base, candidate, outcome): + self.outcomes.append((base + candidate.sample_offset, candidate.score, + base + outcome.refined_start_sample, int(outcome.accepted))) + + def cheap(self, absolute, score, parts, passed): + self.cheap_rows.append([absolute, score, int(passed), *parts]) + + def save(self, name, result): + peaks = np.concatenate(self.peaks) if self.peaks else np.empty((0, 2)) + outcomes = np.asarray(self.outcomes).reshape(-1, 4) + np.savez_compressed(OUTPUT / f"{name}_observations.npz", peaks=peaks, outcomes=outcomes) + if self.cheap_rows: + np.save(OUTPUT / f"{name}_cheap.npy", np.asarray(self.cheap_rows)) + threshold = previous.phy.lab047.FIXED_HARDWARE_CANDIDATE_THRESHOLD + valid = outcomes[:, 3] == 1 + # Сначала строгий вердикт, затем классификация корреляционной статистики. + result["detection_profile"] = { + "total_local_peaks": len(peaks), "first_stage_candidates": int(np.sum(peaks[:, 1] >= threshold)), + "strict_candidates": len(outcomes), "strict_true_candidates": int(np.sum(valid)), + "strict_false_candidates": int(np.sum(~valid)), + "candidates_per_rf_second": len(outcomes) / result["iq_seconds"], + "false_candidates_per_rf_second": int(np.sum(~valid)) / result["iq_seconds"], + "all_local_peaks": quantiles(peaks[:, 1]), + "strict_true_scores": quantiles(outcomes[valid, 1]), + "strict_false_scores": quantiles(outcomes[~valid, 1]), + "blocks": self.blocks, + } + + +class ProfileBaseline(previous.StreamingReceiver): + """Штатный Lab051 с наблюдением после корреляции и после решения.""" + + def __init__(self, observer): + super().__init__() + self.observer = observer + + def _scan(self, owned_end): + front = previous.BlockFrontEnd(self.buffer, owned_end - self.base_sample, self.front_statistics, + decimator=self.decimator, base=self.base_sample) + original_scores = front.coarse_scores + + def scores(samples, reference): + values = original_scores(samples, reference) + self.observer.scores(values, self.base_sample, self.scan_sample, owned_end, self.decimation) + return values + + front.coarse_scores = scores + original_decode = previous.offline.decode_candidate_from_memmap + + def decode(raw, count, candidate, number, **kwargs): + outcome = original_decode(raw, count, candidate, number, **kwargs) + self.observer.outcome(self.base_sample, candidate, outcome) + return outcome + + try: + previous.offline.decode_candidate_from_memmap = decode + with previous.optimized_context(front): + return previous.previous.StreamingReceiver._scan(self, owned_end) + finally: + previous.offline.decode_candidate_from_memmap = original_decode + + +def measured_stream(sequence, factory): + """Повторно использовать исходную событийную шкалу с фактическими push times.""" + instances = [] + + def create(): + receiver = factory() + instances.append(receiver) + return receiver + + original = performance.fast + try: + performance.fast = SimpleNamespace(StreamingReceiver=create) + result = performance.run_stream(sequence) + finally: + performance.fast = original + receiver = instances[0] + result["coarse_seconds"] = receiver.coarse_seconds + result["strict_seconds"] = receiver.strict_seconds + result["peak_candidates_in_block"] = receiver.peak_candidate_count + if hasattr(receiver, "detection_statistics"): + result["detector"] = {**dict(receiver.detection_statistics), + "strict_true_candidates": receiver.packet_count, + "strict_false_candidates": receiver.candidate_count - receiver.packet_count, + "strict_checks_per_rf_second": receiver.candidate_count / result["iq_seconds"], + "cheap_seconds": receiver.cheap_seconds, "cluster_seconds": receiver.cluster_seconds, + "true_strict_seconds": receiver.true_strict_seconds, + "false_strict_seconds": receiver.false_strict_seconds, + "peak_cluster_count": receiver.peak_cluster_count, + "peak_eligible_count": receiver.peak_eligible_count, + "candidate_state_after_finish": receiver.candidate_state_count, + "detection_arrays_peak_estimate_bytes": receiver.working_detection_arrays_peak_bytes} + return result, receiver + + +def profile_baseline(sequence): + observer = DetectionObserver() + result, receiver = measured_stream(sequence, lambda: ProfileBaseline(observer)) + observer.save("baseline", result) + result["timing_includes_diagnostic_observer"] = True + write_result("baseline_profile.json", result) + print(json.dumps({key: value for key, value in result.items() if key in ( + "candidate_count", "received_images", "rx_physical_packets", "processing_per_rf_second", + "coarse_seconds", "strict_seconds")}), flush=True) + print(json.dumps({key: value for key, value in result["detection_profile"].items() if key != "blocks"}), flush=True) + + +def unprofiled_baseline(sequence): + result, receiver = measured_stream(sequence, previous.StreamingReceiver) + result["timing_includes_diagnostic_observer"] = False + write_result("baseline_stream.json", result) + print(json.dumps({key: result[key] for key in ("candidate_count", "processing_seconds", + "processing_per_rf_second", "rx_physical_packets", "received_images", "object_jpeg_integrity")}), flush=True) + + +def run_functional_tests() -> None: + assert quantiles([1, 2, 3])["median"] == 2 + + +def compare_rules(sequence): + from experiments import lab052_candidate_detection as detection + baseline = json.loads((OUTPUT / "baseline_profile.json").read_text(encoding="utf-8")) + results = [] + for parts in (0, 1, 2, 3): + observer = DetectionObserver() + rule = detection.DetectorRule(parts=parts) + result, receiver = measured_stream(sequence, lambda: detection.StreamingReceiver(rule=rule, observer=observer)) + observer.save(f"parts{parts}", result) + result["rule"] = {"parts": parts, "width_samples": rule.cluster_width_samples, + "part_threshold": detection.part_threshold(parts) if parts else None} + result["true_candidates_lost"] = baseline["rx_physical_packets"] - result["rx_physical_packets"] + false_count = result["detector"]["strict_false_candidates"] + baseline_false = baseline["detection_profile"]["strict_false_candidates"] + result["false_candidate_reduction"] = baseline_false / false_count if false_count else None + result["false_candidate_reduction_fraction"] = 1 - false_count / baseline_false + result["timing_includes_diagnostic_observer"] = True + write_result(f"parts{parts}.json", result) + compact = {key: result[key] for key in ("rule", "received_images", "object_jpeg_integrity", + "true_candidates_lost", "false_candidate_reduction", "detector", + "processing_per_rf_second", "fresh_display_fps")} + results.append(compact) + print(json.dumps(compact), flush=True) + write_result("rule_comparison.json", {"rules": results, "rf_tx": 0}) + + +def optimized_stream(sequence): + from experiments import lab052_candidate_detection as detection + results = [] + for number in range(1, 4): + result, receiver = measured_stream(sequence, detection.StreamingReceiver) + result["timing_includes_diagnostic_observer"] = False + write_result(f"stream_repeat_{number}.json", result) + results.append(result) + print(json.dumps({"repeat": number, "ratio": result["processing_per_rf_second"], + "fresh_fps": result["fresh_display_fps"], "packets": result["rx_physical_packets"]}), flush=True) + result = sorted(results, key=lambda row: row["processing_seconds"])[1] + result["selection"] = "median processing time of three complete measured passes; one whole timeline" + write_result("stream.json", result) + print(json.dumps({key: value for key, value in result.items() if key not in ( + "blocks", "images", "rss_samples", "fresh_image_age_seconds")}), flush=True) + + +def profile_cpu(sequence): + from experiments import lab052_candidate_detection as detection + import pstats + from io import StringIO + profiler = cProfile.Profile() + + class ProfiledReceiver(detection.StreamingReceiver): + def push_iq_block(self, samples): + return profiler.runcall(super().push_iq_block, samples) + + def finish(self): + return profiler.runcall(super().finish) + + result, receiver = measured_stream(sequence, ProfiledReceiver) + profiler.dump_stats(str(OUTPUT / "receiver_cpu.prof")) + report = StringIO() + stats = pstats.Stats(profiler, stream=report).strip_dirs() + stats.sort_stats("tottime").print_stats(30) + stats.sort_stats("cumtime").print_stats(30) + (OUTPUT / "receiver_cpu.txt").write_text(report.getvalue(), encoding="utf-8") + result["timing_includes_cprofile"] = True + write_result("cpu_profile.json", result) + print(report.getvalue(), flush=True) + + +def long_state_test(sequence=None, block_count=2048): + """16,78 с распределённого шума, меняющаяся мощность, без истории в RX.""" + from experiments import lab052_candidate_detection as detection + receiver = detection.StreamingReceiver() + random_generator = np.random.default_rng(52030) + checkpoints = [] + accepted = 0 + for number in range(block_count): + components = random_generator.standard_normal((2, receiver.block_samples), dtype=np.float32) + scale = (.002, .2, 20., .02)[(number // 257) % 4] + values = (components[0] + 1j * components[1]) * scale + accepted += len(receiver.push_iq_block(values)) + assert receiver.buffer.size <= receiver.buffer_limit_samples + assert receiver.candidate_state_count == 0 + if (number + 1) % 256 == 0: + checkpoints.append({"block": number + 1, "iq_bytes": receiver.buffer.nbytes, + "decimated_bytes": receiver.decimator.values.nbytes, + "retained_candidates": receiver.candidate_state_count, + "peak_eligible_candidates": receiver.peak_eligible_count, + "counter_keys": len(receiver.detection_statistics) + len(receiver.rejected_reasons), + **process_memory()}) + accepted += len(receiver.finish()) + result = {"blocks": block_count, "iq_seconds": block_count * receiver.block_samples / 8000000, + "background_scales": [.002, .2, 20, .02], "accepted_noise_packets": accepted, + "peak_retained_iq_bytes": receiver.peak_buffer_samples * 8, + "iq_limit_bytes": receiver.buffer_limit_samples * 8, + "retained_after_finish_bytes": receiver.buffer.nbytes + receiver.decimator.values.nbytes, + "constant_template_bytes": receiver.raw_reference.nbytes + receiver.matched_taps.nbytes + receiver.preamble_bits.nbytes, + "candidate_state_after_finish": receiver.candidate_state_count, + "peak_eligible_candidates": receiver.peak_eligible_count, + "peak_cluster_count": receiver.peak_cluster_count, + "detector": dict(receiver.detection_statistics), "strict_candidates": receiver.candidate_count, + "processing_seconds": receiver.processing_seconds, "checkpoints": checkpoints, + "process_memory": process_memory(), "rf_tx": 0} + write_result("memory.json", result) + print(json.dumps(result), flush=True) + + +def sensitivity_test(sequence=None): + """До/после на одинаковом IQ около границы работоспособности FEC.""" + from experiments import lab052_candidate_detection as detection + physical = previous.phy.lab045 + payload = bytes(range(256)) + plan = physical.build_transmission_plan(payload) + raw = previous.phy.build_parameterized_tx_cs8(plan, previous.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))) + outcomes = [] + for constructor in (previous.StreamingReceiver, detection.StreamingReceiver): + receiver = constructor() + packets = [] + 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()) + # Оценка содержимого только после завершения независимого RX. + outcomes.append({"accepted": len(packets), + "payload_match": len(packets) == 1 and packets[0].payload == payload, + "reasons": dict(receiver.rejected_reasons)}) + rows.append({"ebn0_db": ebn0, "cfo_hz": cfo, "seed": seed, "amplitude_scale": scale, + "baseline": outcomes[0], "optimized": outcomes[1], + "lost_baseline_packet": outcomes[0]["payload_match"] and not outcomes[1]["payload_match"]}) + result = {"cases": rows, "count": len(rows), + "baseline_correct": sum(row["baseline"]["payload_match"] for row in rows), + "optimized_correct": sum(row["optimized"]["payload_match"] for row in rows), + "lost_baseline_packets": sum(row["lost_baseline_packet"] for row in 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 main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--stage", choices=("profile", "baseline", "rules", "stream", "cpu", "memory", "sensitivity"), default="profile") + arguments = parser.parse_args() + OUTPUT.mkdir(parents=True, exist_ok=True) + sequence = sequence_fixture() + print(f"Lab052 {arguments.stage}: START; RF TX=0", flush=True) + {"profile": profile_baseline, "baseline": unprofiled_baseline, "rules": compare_rules, "stream": optimized_stream, + "cpu": profile_cpu, "memory": long_state_test, "sensitivity": sensitivity_test}[arguments.stage](sequence) + + +if __name__ == "__main__": + main() diff --git a/tests/test_lab052_candidate_detection.py b/tests/test_lab052_candidate_detection.py new file mode 100644 index 0000000..c37c285 --- /dev/null +++ b/tests/test_lab052_candidate_detection.py @@ -0,0 +1,211 @@ +"""Свойства локального обнаружителя Lab052 без оборудования и внешних данных.""" + +from __future__ import annotations + +import numpy as np +import pytest +from types import SimpleNamespace + +from experiments import lab052_candidate_detection as lab +from experiments.lab050_streaming_receiver import streaming_mode + + +def waveform(payload: bytes, *, seed: int = 52001, ebn0_db: float = 7, phase: float = .73, + cfo_hz: float = 10, scale: float = 1) -> np.ndarray: + random_generator = np.random.default_rng(seed) + # Генератор Lab045 ограничен payload=256, но приёмный PHY допускает + # packet 12..1024. Собираем короткий кадр штатными кодерами для границ. + physical = lab.phy.lab045 + packet = physical.build_packet(payload, physical.MESSAGE_TYPE, physical.SEQUENCE_NUMBER) + coded = physical.conv.encode(physical.lab044.bytes_to_bits(packet)) + permutation = physical.interleaver.build_permutation(len(coded), physical.INTERLEAVER_COLUMNS) + bits = np.concatenate((physical.lab044.preamble_bits(), physical.encode_phy_header(len(packet)), + physical.interleaver.interleave(coded, permutation))) + plan = SimpleNamespace(frame_bits=bits, frame_symbol_count=len(bits)) + raw = lab.phy.build_parameterized_tx_cs8(plan, lab.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_hz * np.arange(len(values)) / 8000000)).astype(np.complex64) + energy = float(np.sum(abs(values) ** 2)) / plan.frame_symbol_count + sigma = np.sqrt(energy / (2 * 10 ** (ebn0_db / 10))) + values += sigma * (random_generator.normal(size=len(values)) + 1j * random_generator.normal(size=len(values))) + return (values * scale).astype(np.complex64) + + +def decode(samples, receiver): + packets = [] + for start in range(0, len(samples), receiver.block_samples): + packets.extend(receiver.push_iq_block(samples[start:start + receiver.block_samples])) + assert receiver.candidate_state_count == 0 if hasattr(receiver, "candidate_state_count") else True + packets.extend(receiver.finish()) + return packets + + +def test_cluster_of_false_peaks_has_one_strongest_representative(): + scores = np.zeros(1600, np.float32) + scores[[600, 620, 654, 700, 730]] = [.05, .07, .12, .06, .04] + assert lab.cluster_peak_indexes(scores, 256, .03).tolist() == [654] + + +def test_two_minimum_frames_do_not_compete_even_at_opposite_coarse_errors(): + distance = lab.MIN_FRAME_SAMPLES - 2 * lab.COARSE_ERROR_SAMPLES + scores = np.zeros(8000, np.float32) + indexes = [1000, 1000 + distance // 8] + scores[indexes] = [.3, .9] + assert lab.cluster_peak_indexes(scores, lab.CLUSTER_WIDTH_SAMPLES // 8, .03).tolist() == indexes + + +def test_equal_peaks_use_reproducible_local_tie(): + scores = np.zeros(1000, np.float32) + scores[[100, 120, 600]] = .2 + assert lab.cluster_peak_indexes(scores, 100, .03).tolist() == [120, 600] + + +def test_equal_peak_chain_does_not_extend_cluster_width(): + scores = np.zeros(500, np.float32) + scores[[100, 180, 260]] = .2 + assert lab.cluster_peak_indexes(scores, 100, .03).tolist() == [180, 260] + + +def test_single_loud_half_cannot_pass_split_check(): + random_generator = np.random.default_rng(52002) + reference = random_generator.choice([-1., 1.], 2560).astype(np.complex64) + samples = (random_generator.normal(size=2560) + 1j * random_generator.normal(size=2560)).astype(np.complex64) + samples[:1280] = reference[:1280] * 100 + assert lab.part_correlations(samples, reference, 1)[0] > lab.part_threshold(1) + assert np.any(lab.part_correlations(samples, reference, 2) < lab.part_threshold(2)) + + +@pytest.mark.parametrize("phase,scale", [(0, .001), (.73, 1), (np.pi, 100), (-1.6, .1)]) +def test_true_preamble_preserves_unknown_gain_phase_and_polarity(phase, scale): + random_generator = np.random.default_rng(52003) + reference = random_generator.choice([-1., 1.], 2560).astype(np.complex64) + samples = reference * np.exp(1j * phase) * scale + assert np.all(lab.part_correlations(samples, reference, 2) >= lab.part_threshold(2)) + + +def test_noise_is_rejected_before_strict_timing(monkeypatch): + random_generator = np.random.default_rng(52004) + samples = (random_generator.normal(size=750000) + 1j * random_generator.normal(size=750000)).astype(np.complex64) + + def forbidden(*args, **kwargs): + pytest.fail("Чистый шум этого seed не должен достигнуть строгого тракта") + + monkeypatch.setattr(lab.offline, "decode_candidate_from_memmap", forbidden) + receiver = lab.StreamingReceiver() + assert decode(samples, receiver) == [] + assert receiver.detection_statistics["cheap_stage_rejected"] > 0 + assert receiver.front_statistics["unique_soft_windows"] == 0 + + +@pytest.mark.parametrize("ebn0_db,scale,cfo", [(7, 1, 10), (2, .02, -100), (1, .2, 100)]) +def test_weak_true_packet_matches_lab051(ebn0_db, scale, cfo): + payload = bytes(range(128)) + values = waveform(payload, seed=52005, ebn0_db=ebn0_db, scale=scale, cfo_hz=cfo) + samples = np.concatenate((np.zeros(63217, np.complex64), values, np.zeros(350000, np.complex64))) + baseline = decode(samples, lab.previous.StreamingReceiver()) + optimized = decode(samples, lab.StreamingReceiver()) + assert [packet.payload for packet in optimized] == [packet.payload for packet in baseline] + assert [packet.payload for packet in baseline] == ([payload] if ebn0_db >= 2 else []) + + +@pytest.mark.parametrize("block_samples,prefix", [(32749, 32749 - 3), (65536, 65536 - 8), + (65536, 65536 + 7)]) +def test_minimum_adjacent_frames_at_block_boundary_are_both_decoded(block_samples, prefix): + # Минимальный packet: пустой payload, заголовок+CRC=12 байт. + first = waveform(b"", seed=52006, phase=.4) + second = waveform(b"x", seed=52007, phase=-2) + assert len(first) == lab.MIN_FRAME_SAMPLES + samples = np.concatenate((np.zeros(prefix, np.complex64), first, second, + np.zeros(350000, np.complex64))) + receiver = lab.StreamingReceiver(block_samples=block_samples) + assert [packet.payload for packet in decode(samples, receiver)] == [b"", b"x"] + + +def test_changing_background_with_weak_packet(): + random_generator = np.random.default_rng(52008) + samples = (random_generator.normal(size=700000) + 1j * random_generator.normal(size=700000)).astype(np.complex64) + samples[:180000] *= .001 + samples[180000:300000] *= 20 + samples[300000:] *= .03 + payload = b"varying local background" + values = waveform(payload, seed=52009, ebn0_db=2, scale=.02) + samples[370013:370013 + len(values)] = values + assert [packet.payload for packet in decode(samples, lab.StreamingReceiver())] == [payload] + + +@pytest.mark.parametrize("true_start", [30000, 65536 - 8, 65536 + 7]) +def test_false_strong_preamble_does_not_hide_weak_valid_packet(true_start): + decoy_start = true_start - 20000 + samples = np.zeros(700000, np.complex64) + decoy = waveform(b"", seed=52022, ebn0_db=100)[:lab.PREAMBLE_SAMPLES + 512] + payload = b"actual valid short packet" + true = waveform(payload, seed=52021, ebn0_db=7, scale=.2) + samples[decoy_start:decoy_start + len(decoy)] += decoy + samples[true_start:true_start + len(true)] += true + expected = decode(samples, lab.previous.StreamingReceiver()) + actual = decode(samples, lab.StreamingReceiver()) + assert [packet.payload for packet in expected] == [payload] + assert [(packet.start_sample, packet.payload) for packet in actual] == [ + (packet.start_sample, packet.payload) for packet in expected] + + +def test_constant_templates_are_exact_and_restored_on_exception(): + receiver = lab.StreamingReceiver() + owner = lab.phy.lab045.lab044 + originals = (owner._preamble_reference_samples, owner.preamble_bits, lab.previous.lab043._matched_filter_taps) + with streaming_mode(lab.phy.PhyMode(500000)): + np.testing.assert_array_equal(receiver.raw_reference, originals[0]()) + np.testing.assert_array_equal(receiver.preamble_bits, originals[1]()) + np.testing.assert_array_equal(receiver.matched_taps, originals[2]()) + with pytest.raises(RuntimeError): + with receiver.constant_templates(): + raise RuntimeError("restore templates") + assert originals == (owner._preamble_reference_samples, owner.preamble_bits, lab.previous.lab043._matched_filter_taps) + + +@pytest.mark.parametrize("count", [1, 2, 3]) +def test_shared_split_scores_equal_individual_vdot(count): + random_generator = np.random.default_rng(52024) + values = (random_generator.normal(size=9000) + 1j * random_generator.normal(size=9000)).astype(np.complex64) + values[4000:] *= .003 + reference = random_generator.choice([-1., 1.], size=2560).astype(np.complex64) + front = object.__new__(lab.CandidateFrontEnd) + front.detection_values = values + scores = front.split_scores(reference, count, len(values) - len(reference) + 1) + for index in (0, 1001, 3999, len(scores) - 1): + np.testing.assert_allclose(scores[index], lab.part_correlations(values[index:index+len(reference)], reference, count), + atol=2e-6, rtol=2e-5) + + +def test_bounded_candidate_state_and_finish(): + random_generator = np.random.default_rng(52010) + receiver = lab.StreamingReceiver() + samples = (random_generator.normal(size=65536) + 1j * random_generator.normal(size=65536)).astype(np.complex64) + for index in range(120): + receiver.push_iq_block(samples * (.01 if index % 2 else 10)) + assert receiver.candidate_state_count == 0 + assert receiver.buffer.size <= receiver.buffer_limit_samples + assert receiver.decimator.values.size <= (receiver.buffer_limit_samples + 7) // 8 + receiver.finish() + assert receiver.buffer.size == receiver.decimator.values.size == receiver.candidate_state_count == 0 + assert receiver.peak_cluster_count <= 20 + assert receiver.observer is None + + +def test_20k_does_not_enter_new_filter_and_mode_is_restored(): + old = lab.previous.lab045.SYMBOL_RATE + with pytest.raises(ValueError): + lab.StreamingReceiver(20000) + lab.run_functional_tests() + assert lab.previous.lab045.SYMBOL_RATE == old + with streaming_mode(lab.phy.PhyMode(20000)): + assert lab.previous.lab045.SAMPLES_PER_SYMBOL == 400 + assert lab.previous.lab045.SYMBOL_RATE == old + + +@pytest.mark.parametrize("rule", [dict(parts=4), dict(parts=-1), dict(cluster_width_samples=26720), + dict(cluster_width_samples=3)]) +def test_invalid_rule_rejected(rule): + with pytest.raises(ValueError): + lab.DetectorRule(**rule)