add new data format support

.gitignore

@@ -1,8 +0,0 @@
-.venv/
-env/
-__pycache__/
-*.py[cod]
-.pytest_cache/
-.Python
-my_picocom_logfile.txt
-sample_data/

README.md

@@ -1,205 +1,187 @@
 # RFG STM32 ADC Receiver GUI
 
-PyQtGraph-приложение для чтения свипов из последовательного порта и отображения:
+Реалтайм-плоттер для визуализации данных FMCW радара, получаемых через виртуальный COM-порт от STM32 ADC.
 
-- текущего свипа
-- водопада по свипам
-- FFT текущего свипа
-- B-scan по FFT
+## Описание
 
-После рефакторинга проект разделен на пакет `rfg_adc_plotter`. Старый запуск через `RFG_ADC_dataplotter.py` сохранен как совместимый wrapper.
+Приложение визуализирует данные в реальном времени, отображая 6 синхронизированных графиков:
 
-## Структура
+1. **Сырые данные** - график последнего полученного свипа
+2. **Водопад сырых данных** - временная серия последних N свипов
+3. **FFT спектр** - спектр текущего свипа в частотной области
+4. **B-scan** - спектрограмма (водопад FFT)
+5. **Фаза спектра** - развернутая фаза для анализа расстояния
+6. **Водопад фазы** - временная эволюция фазы
 
-- `RFG_ADC_dataplotter.py` — совместимый entrypoint
-- `rfg_adc_plotter/cli.py` — CLI-аргументы
-- `rfg_adc_plotter/io/` — чтение порта и парсеры протоколов
-- `rfg_adc_plotter/processing/` — FFT, нормировка, калибровка, поиск пиков
-- `rfg_adc_plotter/state/` — runtime state и кольцевые буферы
-- `rfg_adc_plotter/gui/pyqtgraph_backend.py` — GUI на PyQtGraph
-- `replay_pty.py` — воспроизведение захвата через виртуальный PTY
+## Возможности
 
-## Зависимости
+- ✅ Высокопроизводительная визуализация в реальном времени
+- ✅ Два бэкенда визуализации: matplotlib (совместимость) и pyqtgraph (скорость)
+- ✅ Автоматическая обработка фазы для FMCW радара
+- ✅ Преобразование фазы в расстояние
+- ✅ Поддержка pyserial или raw TTY доступа
+- ✅ Заполнение пропущенных точек (режим --fancy)
+- ✅ Инверсия сигнала при отрицательном уровне
+- ✅ Диагностика потерь данных
 
-Минимально нужны:
+## Установка
+
+### Минимальные требования
 
 ```bash
-python3 -m venv .venv
-. .venv/bin/activate
-pip install numpy pyqtgraph PyQt5
+pip install -r requirements.txt
 ```
 
-Если `pyserial` не установлен, приложение попробует открыть порт через raw TTY.
+### Зависимости
 
-## Быстрый старт
+**Обязательные:**
+- `numpy` - обработка массивов и FFT
+- `matplotlib` - визуализация
 
-Запуск через старый entrypoint:
+**Опциональные (рекомендуется):**
+- `pyserial` - доступ к serial порту (обязательно для Windows)
+- `pyqtgraph` + `PyQt5` или `PySide6` - быстрый бэкенд визуализации
+
+## Использование
+
+### Базовый запуск
 
 ```bash
-.venv/bin/python RFG_ADC_dataplotter.py /dev/ttyACM0
+python -m rfg_adc_plotter.cli /dev/ttyACM0
 ```
 
-Запуск напрямую через пакет:
+### С параметрами
 
 ```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0
+python -m rfg_adc_plotter.cli /dev/ttyACM0 \
+    --baud 115200 \
+    --max-sweeps 200 \
+    --max-fps 30 \
+    --backend pg \
+    --fancy
 ```
 
-Показать справку:
+### Параметры командной строки
+
+- `port` - путь к порту (например `/dev/ttyACM0`, `COM3`)
+- `--baud` - скорость порта (по умолчанию 115200)
+- `--max-sweeps` - количество свипов в водопаде (по умолчанию 200)
+- `--max-fps` - ограничение частоты отрисовки (по умолчанию 30)
+- `--cmap` - цветовая карта для водопадов (по умолчанию viridis)
+- `--spec-clip` - процентильная обрезка контраста B-scan (по умолчанию 2,98)
+- `--title` - заголовок окна (по умолчанию "ADC Sweeps")
+- `--fancy` - заполнение пропущенных точек средними значениями
+- `--ylim` - фиксированные пределы по Y (формат: min,max)
+- `--backend` - бэкенд визуализации:
+  - `auto` - автоматический выбор (сначала pyqtgraph, fallback на matplotlib)
+  - `pg` - pyqtgraph (быстрее)
+  - `mpl` - matplotlib (совместимее)
+
+## Формат данных
+
+Приложение ожидает текстовые строки через serial порт:
+
+```
+Sweep_start
+s 0 1234
+s 1 1256
+s 2 1278
+...
+Sweep_start
+s 0 1235
+...
+```
+
+- `Sweep_start` - начало нового свипа
+- `s X Y` - точка данных (индекс X, значение Y), целые числа со знаком
+
+## Архитектура проекта
+
+```
+rfg_adc_plotter/
+├── __init__.py
+├── config.py                      # Константы и типы
+├── cli.py                         # Точка входа CLI
+├── data_acquisition/
+│   ├── __init__.py
+│   ├── serial_io.py              # Serial порт I/O
+│   └── sweep_reader.py           # Фоновый поток чтения данных
+├── signal_processing/
+│   ├── __init__.py
+│   └── phase_analysis.py         # Обработка фазы
+├── visualization/
+│   ├── __init__.py
+│   ├── matplotlib_backend.py     # Matplotlib визуализация
+│   └── pyqtgraph_backend.py      # PyQtGraph визуализация
+└── utils/
+    ├── __init__.py
+    └── formatting.py             # Утилиты форматирования
+```
+
+## Технические особенности
+
+### Оптимизации производительности
+
+- Фоновый поток для чтения и парсинга данных
+- Векторизованные numpy операции
+- Кольцевые буферы для водопадов
+- Неблокирующее чтение из serial порта
+- Буферизация с увеличенным размером (256KB)
+
+### Обработка сигналов
+
+- **FFT анализ**: окно Хэннинга, длина 1024
+- **Phase unwrapping**: адаптивный алгоритм с порогом 0.8π
+- **Преобразование фазы в расстояние**: формула Δl = φ × c / (4π × ν)
+- **Инверсия сигнала**: автоматическая при среднем уровне < порога
+
+### Диагностика
+
+Каждые 10 секунд в stderr выводится диагностическая информация:
+- Номер свипа
+- Среднее количество валидных точек
+- Количество принятых строк
+- Ошибки парсинга
+- Ошибки чтения
+- Размер буфера
+- Потерянные свипы
+
+## Примеры использования
+
+### Linux с pyserial
 
 ```bash
-.venv/bin/python RFG_ADC_dataplotter.py --help
+python -m rfg_adc_plotter.cli /dev/ttyACM0 --backend pg
 ```
 
-## Примеры запуска
-
-Обычный запуск с живого порта:
+### Linux с raw TTY (без pyserial)
 
 ```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --baud 115200
+python -m rfg_adc_plotter.cli /dev/ttyACM0 --backend mpl
 ```
 
-Больше истории в водопаде и ограничение FPS:
+### Windows
 
 ```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --max-sweeps 400 --max-fps 20
+python -m rfg_adc_plotter.cli COM3 --backend pg --baud 115200
 ```
 
-Фиксированный диапазон по оси Y:
+### С высоким разрешением времени
 
 ```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --ylim -1000,1000
+python -m rfg_adc_plotter.cli /dev/ttyACM0 --max-sweeps 500 --max-fps 60
 ```
 
-С включенной нормировкой `simple`:
+### С заполнением пропусков и фиксированным Y
 
 ```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --norm-type simple
+python -m rfg_adc_plotter.cli /dev/ttyACM0 --fancy --ylim -2000,2000
 ```
 
-Режим измерения ширины главного пика FFT:
+## Лицензия
 
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --calibrate
-```
+См. LICENSE файл в корне проекта.
 
-Поиск топ-3 пиков относительно rolling median reference:
+## Авторы
 
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --peak_search --peak_ref_window 1.5
-```
-
-Вычитание среднего спектра по последним секундам:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --spec-mean-sec 3
-```
-
-## Протоколы ввода
-
-ASCII-протокол по умолчанию:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0
-```
-
-Legacy binary:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --bin
-```
-
-`--bin` понимает mixed 8-байтный поток:
-- `0x000A,step,ch1_i16,ch2_i16` для CH1/CH2 из `kamil_adc` (`tty:/tmp/ttyADC_data`)
-- `0x001A,step,data_i16,0x0000` для логарифмического детектора
-
-Для `0x000A` сырая кривая строится как `ch1^2 + ch2^2`, а FFT рассчитывается от комплексного сигнала `ch1 + i*ch2`.
-Для `0x001A` signed `data_i16` сначала переводится в В, затем raw отображается как `V`, а FFT рассчитывается от `exp(V)`.
-Параметр `--tty-range-v` применяется к обоим типам `--bin`-данных.
-
-Logscale binary с парой `int32`:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --logscale
-```
-
-Complex binary `16-bit x2`:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --parser_16_bit_x2
-```
-
-Тестовый парсер для экспериментального `16-bit x2` потока:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --parser_test
-```
-
-Комплексный ASCII-поток `step real imag`:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --parser_complex_ascii
-```
-
-## Локальная проверка через replay_pty
-
-Если есть лог-файл захвата, его можно воспроизвести как виртуальный последовательный порт.
-
-В первом терминале:
-
-```bash
-.venv/bin/python replay_pty.py my_picocom_logfile.txt --pty /tmp/ttyVIRT0 --speed 1.0
-```
-
-Во втором терминале:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /tmp/ttyVIRT0
-```
-
-Максимально быстрый replay:
-
-```bash
-.venv/bin/python replay_pty.py my_picocom_logfile.txt --pty /tmp/ttyVIRT0 --speed 0
-```
-
-## Удаленный захват по SSH
-
-В приложении SSH-источник не встроен. Для удаленной проверки нужно сначала получить поток или лог на локальную машину, а затем либо:
-
-- запускать GUI напрямую на локальном PTY
-- сохранять поток в файл и воспроизводить его через `replay_pty.py`
-
-Пример команды для ручной диагностики удаленного устройства:
-
-```bash
-ssh 192.148.0.148 'ls -l /dev/ttyACM0'
-```
-
-Если на удаленной машине есть доступ к потоку, удобнее сохранять его в файл и уже этот файл гонять локально через `replay_pty.py`.
-
-Для локального `tty`-потока из `kamil_adc` используйте:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /tmp/ttyADC_data --bin
-```
-
-## Проверка и тесты
-
-Синтаксическая проверка:
-
-```bash
-python3 -m compileall RFG_ADC_dataplotter.py replay_pty.py rfg_adc_plotter tests
-```
-
-Запуск тестов:
-
-```bash
-.venv/bin/python -m unittest discover -s tests -v
-```
-
-## Замечания
-
-- Поддерживается только PyQtGraph backend.
-- `--backend mpl` оставлен только для совместимости CLI и завершится ошибкой.
-- Каталоги `sample_data/` и локальные логи добавлены в `.gitignore` и не считаются частью обязательного tracked-состояния репозитория.
+Разработано для визуализации данных FMCW радара с STM32 ADC.

RFG_ADC_dataplotter.py

@@ -1,8 +0,0 @@
-#!/usr/bin/env python3
-"""Compatibility wrapper for the modularized ADC plotter."""
-
-from rfg_adc_plotter.main import main
-
-
-if __name__ == "__main__":
-    main()

replay_pty.py

@@ -1,94 +0,0 @@
-#!/usr/bin/env python3
-"""Replay a capture file through a pseudo-TTY for local GUI verification."""
-
-from __future__ import annotations
-
-import argparse
-import os
-import sys
-import time
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Воспроизводит лог-файл через PTY как виртуальный серийный порт."
-    )
-    parser.add_argument("file", help="Путь к лог-файлу (например my_picocom_logfile.txt)")
-    parser.add_argument(
-        "--pty",
-        default="/tmp/ttyVIRT0",
-        help="Путь симлинка PTY (по умолчанию /tmp/ttyVIRT0)",
-    )
-    parser.add_argument(
-        "--speed",
-        type=float,
-        default=1.0,
-        help=(
-            "Множитель скорости воспроизведения: "
-            "1.0 = реальное время при --baud, "
-            "2.0 = вдвое быстрее, "
-            "0 = максимально быстро"
-        ),
-    )
-    parser.add_argument(
-        "--baud",
-        type=int,
-        default=115200,
-        help="Скорость (бод) для расчета задержек (по умолчанию 115200)",
-    )
-    args = parser.parse_args()
-
-    if not os.path.isfile(args.file):
-        sys.stderr.write(f"[error] Файл не найден: {args.file}\n")
-        raise SystemExit(1)
-
-    master_fd, slave_fd = os.openpty()
-    slave_path = os.ttyname(slave_fd)
-    os.close(slave_fd)
-
-    try:
-        os.unlink(args.pty)
-    except FileNotFoundError:
-        pass
-    os.symlink(slave_path, args.pty)
-
-    print(f"PTY slave : {slave_path}")
-    print(f"Симлинк   : {args.pty}  ->  {slave_path}")
-    print(f"Запустите : python3 -m rfg_adc_plotter.main {args.pty}")
-    print("Ctrl+C для остановки.\n")
-
-    if args.speed > 0:
-        bytes_per_sec = args.baud / 10.0 * args.speed
-        delay_per_byte = 1.0 / bytes_per_sec
-    else:
-        delay_per_byte = 0.0
-
-    chunk_size = 4096
-    loop = 0
-    try:
-        while True:
-            loop += 1
-            print(f"[loop {loop}] {args.file}")
-            with open(args.file, "rb") as handle:
-                while True:
-                    chunk = handle.read(chunk_size)
-                    if not chunk:
-                        break
-                    os.write(master_fd, chunk)
-                    if delay_per_byte > 0:
-                        time.sleep(delay_per_byte * len(chunk))
-    except KeyboardInterrupt:
-        print("\nОстановлено.")
-    finally:
-        try:
-            os.unlink(args.pty)
-        except Exception:
-            pass
-        try:
-            os.close(master_fd)
-        except Exception:
-            pass
-
-
-if __name__ == "__main__":
-    main()

requirements.txt

@@ -0,0 +1,14 @@
+# Основные зависимости
+numpy>=1.20.0
+
+# Визуализация (matplotlib - обязательна)
+matplotlib>=3.3.0
+
+# Serial порт (опционально, но рекомендуется)
+pyserial>=3.5
+
+# Быстрый бэкенд визуализации (опционально)
+pyqtgraph>=0.12.0
+PyQt5>=5.15.0
+# Альтернатива PyQt5:
+# PySide6>=6.0.0

rfg_adc_plotter/__init__.py

@@ -1,3 +0,0 @@
-"""RFG ADC plotter package."""
-
-__all__ = []

rfg_adc_plotter/cli.py

@@ -1,11 +1,36 @@
-"""Command-line parser for the ADC plotter."""
+#!/usr/bin/env python3
+"""
+Точка входа для RFG ADC Data Plotter.
 
-from __future__ import annotations
+Реалтайм-плоттер для свипов из виртуального COM-порта.
+
+Формат строк:
+  - "Sweep_start" — начало нового свипа (предыдущий считается завершённым)
+  - "s X Y" — точка (индекс X, значение Y), все целые со знаком
+
+Отрисовываются шесть графиков:
+  - Левый верхний: последний полученный свип (Y vs X)
+  - Правый верхний: водопад (последние N свипов во времени)
+  - Левый средний: FFT спектр текущего свипа
+  - Правый средний: B-scan (водопад FFT спектров)
+  - Левый нижний: Фаза спектра (развернутая)
+  - Правый нижний: Водопад фазы
+
+Оптимизации для скорости:
+  - Парсинг и чтение в фоновой нити
+  - Анимация с обновлением только данных (без лишнего пересоздания фигур)
+  - Кольцевой буфер под водопад с фиксированным числом свипов
+
+Зависимости: matplotlib, numpy. PySerial опционален — при его отсутствии
+используется сырой доступ к TTY через termios.
+"""
 
 import argparse
+import sys
 
 
-def build_parser() -> argparse.ArgumentParser:
+def main():
+    """Основная функция CLI."""
     parser = argparse.ArgumentParser(
         description=(
             "Читает свипы из виртуального COM-порта и рисует: "
@@ -24,19 +49,10 @@ def build_parser() -> argparse.ArgumentParser:
         "--spec-clip",
         default="2,98",
         help=(
-            "Процентильная обрезка уровней водопада спектров, %% (min,max). "
+            "Процентильная обрезка уровней водопада спектров, % (min,max). "
             "Напр. 2,98. 'off' — отключить"
         ),
     )
-    parser.add_argument(
-        "--spec-mean-sec",
-        type=float,
-        default=0.0,
-        help=(
-            "Вычитание среднего по каждой частоте за последние N секунд "
-            "в водопаде спектров (0 — отключить)"
-        ),
-    )
     parser.add_argument("--title", default="ADC Sweeps", help="Заголовок окна")
     parser.add_argument(
         "--fancy",
@@ -52,94 +68,43 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument(
         "--backend",
         choices=["auto", "pg", "mpl"],
-        default="pg",
-        help="Совместимый флаг. Поддерживаются только auto и pg; mpl удален.",
+        default="auto",
+        help="Графический бэкенд: pyqtgraph (pg) — быстрее; matplotlib (mpl) — совместимый. По умолчанию auto",
     )
     parser.add_argument(
-        "--opengl",
-        action="store_true",
-        help="Включить OpenGL-ускорение для PyQtGraph. По умолчанию используется CPU-отрисовка.",
+        "--ref-out",
+        type=str,
+        default=None,
+        help="Сохранить медиану последних 1000 свипов в указанный файл при накоплении данных",
     )
     parser.add_argument(
-        "--norm-type",
-        choices=["projector", "simple"],
-        default="projector",
-        help="Тип нормировки: projector (по огибающим в [-1,+1]) или simple (raw/calib)",
+        "--ref-in",
+        type=str,
+        default=None,
+        help="Загрузить медиану из файла и вычитать её из входящего сигнала",
     )
-    parser.add_argument(
-        "--bin",
-        dest="bin_mode",
-        action="store_true",
-        help=(
-            "8-байтный бинарный протокол: либо legacy старт "
-            "0xFFFF,0xFFFF,0xFFFF,(CH<<8)|0x0A и точки step,uint32(hi16,lo16),0x000A, "
-            "либо mixed поток 0x000A,step,ch1_i16,ch2_i16 и 0x001A,step,data_i16,0x0000. "
-            "Для 0x000A: после парсинга int16 переводятся в В, "
-            "сырая кривая = ch1^2+ch2^2 (В^2), FFT вход = ch1+i*ch2 (В). "
-            "Для 0x001A: code_i16 переводится в В, raw = V, FFT вход = exp(V)"
-        ),
-    )
-    parser.add_argument(
-        "--tty-range-v",
-        type=float,
-        default=5.0,
-        help=(
-            "Полный диапазон для пересчета tty int16 в напряжение ±V "
-            "(для --bin 0x000A CH1/CH2 и 0x001A log-detector, по умолчанию 5.0)"
-        ),
-    )
-    parser.add_argument(
-        "--logscale",
-        action="store_true",
-        help=(
-            "Новый бинарный протокол: точка несет пару int32 (avg_1, avg_2), "
-            "а свип считается как |10**(avg_1*0.001) - 10**(avg_2*0.001)|"
-        ),
-    )
-    parser.add_argument(
-        "--parser_16_bit_x2",
-        action="store_true",
-        help=(
-            "Бинарный complex-протокол c парой int16 (Re, Im): "
-            "старт 0xFFFF,0xFFFF,0xFFFF,(CH<<8)|0x0A; точка step,re_lo16,im_lo16,0xFFFF"
-        ),
-    )
-    parser.add_argument(
-        "--parser_test",
-        action="store_true",
-        help=(
-            "Тестовый парсер для complex-формата 16-bit x2: "
-            "одиночный 0xFFFF завершает точку, серия 0xFFFF начинает новый свип"
-        ),
-    )
-    parser.add_argument(
-        "--parser_complex_ascii",
-        action="store_true",
-        help=(
-            "ASCII-поток из трех чисел на строку: step real imag. "
-            "Новый свип определяется по сбросу/повтору step, FFT строится по комплексным данным"
-        ),
-    )
-    parser.add_argument(
-        "--calibrate",
-        action="store_true",
-        help=(
-            "Режим измерения ширины главного пика FFT: рисует красные маркеры "
-            "границ и фона и выводит ширину пика в статус"
-        ),
-    )
-    parser.add_argument(
-        "--peak_search",
-        action="store_true",
-        help=(
-            "Поиск топ-3 пиков на FFT относительно референса (скользящая медиана) "
-            "с отрисовкой bounding box и параметров пиков"
-        ),
-    )
-    parser.add_argument(
-        "--peak_ref_window",
-        type=float,
-        default=1.0,
-        help="Ширина окна скользящей медианы для --peak_search, ГГц/м по оси FFT (по умолчанию 1.0)",
-    )
-    return parser
+
+    args = parser.parse_args()
+
+    # Попробуем быстрый бэкенд (pyqtgraph) при auto/pg
+    if args.backend in ("auto", "pg"):
+        try:
+            from .visualization.pyqtgraph_backend import run_pyqtgraph
+            return run_pyqtgraph(args)
+        except Exception as e:
+            if args.backend == "pg":
+                sys.stderr.write(f"[error] PyQtGraph бэкенд недоступен: {e}\n")
+                sys.exit(1)
+            # При auto — тихо откатываемся на matplotlib
+
+    # Fallback на matplotlib
+    try:
+        from .visualization.matplotlib_backend import run_matplotlib
+        return run_matplotlib(args)
+    except Exception as e:
+        sys.stderr.write(f"[error] Matplotlib бэкенд недоступен: {e}\n")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

rfg_adc_plotter/config.py

@@ -0,0 +1,28 @@
+"""
+Константы и типы для RFG ADC Data Plotter.
+"""
+
+from typing import Dict, Tuple, Union
+
+import numpy as np
+
+# Максимальное число точек в ряду водопада
+WF_WIDTH = 1000
+
+# Длина БПФ для спектра/водопада спектров
+FFT_LEN = 2048
+
+# Частотный диапазон для FFT (в ГГц)
+FREQ_MIN_GHZ = -10.0  # Начало частотной оси
+FREQ_MAX_GHZ = 10.0   # Конец частотной оси
+DATA_FREQ_START_GHZ = 1.0   # Начало реальных данных
+DATA_FREQ_END_GHZ = 10.0    # Конец реальных данных
+
+# Порог для инверсии сырых данных: если среднее значение свипа ниже порога —
+# считаем, что сигнал «меньше нуля» и домножаем свип на -1
+DATA_INVERSION_THRASHOLD = 10.0
+
+# Типы данных
+Number = Union[int, float]
+SweepInfo = Dict[str, Number]
+SweepPacket = Tuple[np.ndarray, SweepInfo]

rfg_adc_plotter/constants.py

@@ -1,17 +0,0 @@
-"""Shared constants for sweep parsing and visualization."""
-
-WF_WIDTH = 1000
-FFT_LEN = 2048
-BACKGROUND_MEDIAN_SWEEPS = 64
-
-SWEEP_FREQ_MIN_GHZ = 3.3
-SWEEP_FREQ_MAX_GHZ = 6.3
-
-LOG_BASE = 10.0
-LOG_SCALER = 0.001
-LOG_POSTSCALER = 10.0
-LOG_EXP_LIMIT = 300.0
-
-C_M_S = 299_792_458.0
-
-DATA_INVERSION_THRESHOLD = 10.0

rfg_adc_plotter/data_acquisition/serial_io.py

@@ -1,6 +1,6 @@
-"""Serial input helpers with pyserial and raw TTY fallbacks."""
-
-from __future__ import annotations
+"""
+Модули для работы с serial портом: чтение данных через pyserial или raw TTY.
+"""
 
 import io
 import os
@@ -9,24 +9,36 @@ from typing import Optional
 
 
 def try_open_pyserial(path: str, baud: int, timeout: float):
+    """Попытка открыть порт через pyserial."""
     try:
         import serial  # type: ignore
     except Exception:
         return None
     try:
-        return serial.Serial(path, baudrate=baud, timeout=timeout)
+        ser = serial.Serial(path, baudrate=baud, timeout=timeout)
+        # ВРЕМЕННО ОТКЛЮЧЕН: hardware flow control для проверки
+        # ser.rtscts = True
+        # Увеличиваем буфер приема ядра до 64KB
+        try:
+            ser.set_buffer_size(rx_size=65536, tx_size=4096)
+        except (AttributeError, NotImplementedError):
+            # Не все платформы/версии pyserial поддерживают set_buffer_size
+            pass
+        return ser
     except Exception:
         return None
 
 
 class FDReader:
-    """Buffered wrapper around a raw TTY file descriptor."""
+    """Простой враппер чтения строк из файлового дескриптора TTY."""
 
     def __init__(self, fd: int):
+        # Отдельно буферизуем для корректной readline()
         self._fd = fd
         raw = os.fdopen(fd, "rb", closefd=False)
         self._file = raw
-        self._buf = io.BufferedReader(raw, buffer_size=65536)
+        # Увеличен размер буфера до 256KB для предотвращения потерь
+        self._buf = io.BufferedReader(raw, buffer_size=262144)
 
     def fileno(self) -> int:
         return self._fd
@@ -34,7 +46,7 @@ class FDReader:
     def readline(self) -> bytes:
         return self._buf.readline()
 
-    def close(self) -> None:
+    def close(self):
         try:
             self._buf.close()
         except Exception:
@@ -42,7 +54,10 @@ class FDReader:
 
 
 def open_raw_tty(path: str, baud: int) -> Optional[FDReader]:
-    """Open a TTY without pyserial and configure it via termios."""
+    """Открыть TTY без pyserial и настроить порт через termios.
+
+    Возвращает FDReader или None при ошибке.
+    """
     try:
         import termios
         import tty
@@ -56,8 +71,10 @@ def open_raw_tty(path: str, baud: int) -> Optional[FDReader]:
 
     try:
         attrs = termios.tcgetattr(fd)
+        # Установим «сырое» состояние
         tty.setraw(fd)
 
+        # Скорость
         baud_map = {
             9600: termios.B9600,
             19200: termios.B19200,
@@ -67,14 +84,17 @@ def open_raw_tty(path: str, baud: int) -> Optional[FDReader]:
             230400: getattr(termios, "B230400", None),
             460800: getattr(termios, "B460800", None),
         }
-        speed = baud_map.get(baud) or termios.B115200
+        b = baud_map.get(baud) or termios.B115200
 
-        attrs[4] = speed
-        attrs[5] = speed
+        attrs[4] = b  # ispeed
+        attrs[5] = b  # ospeed
+
+        # VMIN=1, VTIME=0 — блокирующее чтение по байту
         cc = attrs[6]
         cc[termios.VMIN] = 1
         cc[termios.VTIME] = 0
         attrs[6] = cc
+
         termios.tcsetattr(fd, termios.TCSANOW, attrs)
     except Exception:
         try:
@@ -87,11 +107,11 @@ def open_raw_tty(path: str, baud: int) -> Optional[FDReader]:
 
 
 class SerialLineSource:
-    """Unified line-oriented wrapper for pyserial and raw TTY readers."""
+    """Единый интерфейс для чтения строк из порта (pyserial или raw TTY)."""
 
     def __init__(self, path: str, baud: int, timeout: float = 1.0):
         self._pyserial = try_open_pyserial(path, baud, timeout)
-        self._fdreader: Optional[FDReader] = None
+        self._fdreader = None
         self._using = "pyserial" if self._pyserial is not None else "raw"
         if self._pyserial is None:
             self._fdreader = open_raw_tty(path, baud)
@@ -107,12 +127,13 @@ class SerialLineSource:
                 return self._pyserial.readline()
             except Exception:
                 return b""
-        try:
-            return self._fdreader.readline()  # type: ignore[union-attr]
-        except Exception:
-            return b""
+        else:
+            try:
+                return self._fdreader.readline()  # type: ignore[union-attr]
+            except Exception:
+                return b""
 
-    def close(self) -> None:
+    def close(self):
         try:
             if self._pyserial is not None:
                 self._pyserial.close()
@@ -123,13 +144,15 @@ class SerialLineSource:
 
 
 class SerialChunkReader:
-    """Fast non-blocking chunk reader for serial sources."""
+    """Быстрое неблокирующее чтение чанков из serial/raw TTY для максимального дренажа буфера."""
 
-    def __init__(self, src: SerialLineSource):
+    def __init__(self, src: SerialLineSource, error_counter: Optional[list] = None):
         self._src = src
         self._ser = src._pyserial
         self._fd: Optional[int] = None
+        self._error_counter = error_counter  # Список с 1 элементом для передачи по ссылке
         if self._ser is not None:
+            # Неблокирующий режим для быстрой откачки
             try:
                 self._ser.timeout = 0
             except Exception:
@@ -145,22 +168,24 @@ class SerialChunkReader:
                 self._fd = None
 
     def read_available(self) -> bytes:
-        """Return currently available bytes or b"" when nothing is ready."""
+        """Вернёт доступные байты (b"" если данных нет)."""
         if self._ser is not None:
             try:
-                available = int(getattr(self._ser, "in_waiting", 0))
+                n = int(getattr(self._ser, "in_waiting", 0))
             except Exception:
-                available = 0
-            if available > 0:
+                if self._error_counter:
+                    self._error_counter[0] += 1
+                n = 0
+            if n > 0:
                 try:
-                    return self._ser.read(available)
+                    return self._ser.read(n)
                 except Exception:
+                    if self._error_counter:
+                        self._error_counter[0] += 1
                     return b""
             return b""
-
         if self._fd is None:
             return b""
-
         out = bytearray()
         while True:
             try:
@@ -173,5 +198,7 @@ class SerialChunkReader:
             except BlockingIOError:
                 break
             except Exception:
+                if self._error_counter:
+                    self._error_counter[0] += 1
                 break
         return bytes(out)

rfg_adc_plotter/data_acquisition/sweep_reader.py

@@ -0,0 +1,269 @@
+"""
+Фоновый поток для чтения и сборки свипов из serial порта.
+"""
+
+import sys
+import threading
+import time
+from collections import deque
+from queue import Queue, Full
+
+import numpy as np
+
+from ..config import DATA_INVERSION_THRASHOLD, SweepInfo, SweepPacket
+from .serial_io import SerialChunkReader, SerialLineSource
+
+
+class SweepReader(threading.Thread):
+    """Фоновый поток: читает строки, формирует завершённые свипы и кладёт в очередь."""
+
+    def __init__(
+        self,
+        port_path: str,
+        baud: int,
+        out_queue: Queue[SweepPacket],
+        stop_event: threading.Event,
+        fancy: bool = False,
+    ):
+        super().__init__(daemon=True)
+        self._port_path = port_path
+        self._baud = baud
+        self._q = out_queue
+        self._stop = stop_event
+        self._src: SerialLineSource | None = None
+        self._fancy = bool(fancy)
+        self._max_width: int = 0
+        self._sweep_idx: int = 0
+        self._last_sweep_ts: float | None = None
+        self._n_valid_hist = deque()
+        # Счетчик потерь данных (выброшенных свипов из-за переполнения очереди)
+        self._dropped_sweeps: int = 0
+        # Диагностика потери точек внутри свипа
+        self._total_lines_received: int = 0  # Всего принято строк с данными
+        self._total_parse_errors: int = 0    # Ошибок парсинга строк
+        self._total_empty_lines: int = 0      # Пустых строк
+        self._max_buf_size: int = 0           # Максимальный размер буфера парсинга
+        self._read_errors: int = 0            # Ошибок чтения из порта
+        self._last_diag_time: float = 0.0    # Время последнего вывода диагностики
+        self._cal_mode: int = -1              # Режим калибровки (0–7), -1 = неизвестен
+
+    def _finalize_current(self, xs, ys, cal_mode: int = -1):
+        if not xs:
+            return
+        max_x = max(xs)
+        width = max_x + 1
+        self._max_width = max(self._max_width, width)
+        target_width = self._max_width if self._fancy else width
+        # Быстрый векторизованный путь
+        sweep = np.full((target_width,), np.nan, dtype=np.float32)
+        try:
+            idx = np.asarray(xs, dtype=np.int64)
+            vals = np.asarray(ys, dtype=np.float32)
+            sweep[idx] = vals
+        except Exception:
+            # Запасной путь
+            for x, y in zip(xs, ys):
+                if 0 <= x < target_width:
+                    sweep[x] = float(y)
+        # Метрики валидных точек до заполнения пропусков
+        finite_pre = np.isfinite(sweep)
+        n_valid_cur = int(np.count_nonzero(finite_pre))
+
+        # Дополнительная обработка пропусков: при --fancy заполняем внутренние разрывы, края и дотягиваем до максимальной длины
+        if self._fancy:
+            try:
+                known = ~np.isnan(sweep)
+                if np.any(known):
+                    known_idx = np.nonzero(known)[0]
+                    # Для каждой пары соседних известных индексов заполним промежуток средним значением
+                    for i0, i1 in zip(known_idx[:-1], known_idx[1:]):
+                        if i1 - i0 > 1:
+                            avg = (sweep[i0] + sweep[i1]) * 0.5
+                            sweep[i0 + 1 : i1] = avg
+                    first_idx = int(known_idx[0])
+                    last_idx = int(known_idx[-1])
+                    if first_idx > 0:
+                        sweep[:first_idx] = sweep[first_idx]
+                    if last_idx < sweep.size - 1:
+                        sweep[last_idx + 1 :] = sweep[last_idx]
+            except Exception:
+                # В случае ошибки просто оставляем как есть
+                pass
+        # Инверсия данных при «отрицательном» уровне (среднее ниже порога)
+        try:
+            m = float(np.nanmean(sweep))
+            if np.isfinite(m) and m < DATA_INVERSION_THRASHOLD:
+                sweep *= -1.0
+        except Exception:
+            pass
+        sweep -= float(np.nanmean(sweep))
+
+        # Метрики для статусной строки (вид словаря: переменная -> значение)
+        self._sweep_idx += 1
+        now = time.time()
+        if self._last_sweep_ts is None:
+            dt_ms = float("nan")
+        else:
+            dt_ms = (now - self._last_sweep_ts) * 1000.0
+        self._last_sweep_ts = now
+        self._n_valid_hist.append((now, n_valid_cur))
+        while self._n_valid_hist and (now - self._n_valid_hist[0][0]) > 1.0:
+            self._n_valid_hist.popleft()
+        if self._n_valid_hist:
+            n_valid = float(sum(v for _t, v in self._n_valid_hist) / len(self._n_valid_hist))
+        else:
+            n_valid = float(n_valid_cur)
+
+        if n_valid_cur > 0:
+            vmin = float(np.nanmin(sweep))
+            vmax = float(np.nanmax(sweep))
+            mean = float(np.nanmean(sweep))
+            std = float(np.nanstd(sweep))
+        else:
+            vmin = vmax = mean = std = float("nan")
+        info: SweepInfo = {
+            "sweep": self._sweep_idx,
+            "n_valid": n_valid,
+            "min": vmin,
+            "max": vmax,
+            "mean": mean,
+            "std": std,
+            "dt_ms": dt_ms,
+            "dropped": self._dropped_sweeps,
+            "lines": self._total_lines_received,
+            "parse_err": self._total_parse_errors,
+            "read_err": self._read_errors,
+            "max_buf": self._max_buf_size,
+            "cal_mode": cal_mode,
+        }
+
+        # Периодический вывод детальной диагностики в stderr (каждые 10 секунд)
+        now = time.time()
+        if now - self._last_diag_time > 10.0:
+            self._last_diag_time = now
+            sys.stderr.write(
+                f"[DIAG] sweep={self._sweep_idx} n_valid={n_valid:.1f} "
+                f"lines={self._total_lines_received} parse_err={self._total_parse_errors} "
+                f"read_err={self._read_errors} max_buf={self._max_buf_size} "
+                f"dropped={self._dropped_sweeps}\n"
+            )
+            sys.stderr.flush()
+
+        # Кладём готовый свип (если очередь полна — выбрасываем самый старый)
+        try:
+            self._q.put_nowait((sweep, info))
+        except Full:
+            # Счетчик потерь для диагностики
+            self._dropped_sweeps += 1
+            try:
+                _ = self._q.get_nowait()
+            except Exception:
+                pass
+            try:
+                self._q.put_nowait((sweep, info))
+            except Exception:
+                pass
+
+    def run(self):
+        # Состояние текущего свипа
+        xs: list[int] = []
+        ys: list[int] = []
+        current_cal_mode: int = -1  # Режим калибровки для текущего свипа
+
+        try:
+            self._src = SerialLineSource(self._port_path, self._baud, timeout=1.0)
+            sys.stderr.write(f"[info] Открыл порт {self._port_path} ({self._src._using})\n")
+        except Exception as e:
+            sys.stderr.write(f"[error] {e}\n")
+            return
+
+        try:
+            # Быстрый неблокирующий дренаж порта с разбором по байтам
+            # Передаем счетчик ошибок чтения как список для изменения по ссылке
+            error_counter = [0]
+            chunk_reader = SerialChunkReader(self._src, error_counter)
+            buf = bytearray()
+            while not self._stop.is_set():
+                data = chunk_reader.read_available()
+                # Обновляем счетчик ошибок чтения
+                self._read_errors = error_counter[0]
+                if data:
+                    buf += data
+                    # Отслеживаем максимальный размер буфера парсинга
+                    if len(buf) > self._max_buf_size:
+                        self._max_buf_size = len(buf)
+                else:
+                    # Короткая уступка CPU, если нет новых данных (уменьшена до 0.1ms)
+                    time.sleep(0.0001)
+                    continue
+
+                # Обрабатываем все полные строки
+                while True:
+                    nl = buf.find(b"\n")
+                    if nl == -1:
+                        break
+                    line = bytes(buf[:nl])
+                    del buf[: nl + 1]
+                    if line.endswith(b"\r"):
+                        line = line[:-1]
+                    if not line:
+                        self._total_empty_lines += 1
+                        continue
+
+                    if line.startswith(b"Sweep_start"):
+                        self._finalize_current(xs, ys, current_cal_mode)
+                        xs.clear()
+                        ys.clear()
+                        current_cal_mode = -1
+                        continue
+
+                    # Формат строки данных: "sN X Y" или "s X Y"
+                    # где N — цифра режима калибровки 0–7 (слитно с 's')
+                    # X — индекс точки, Y — значение (целое со знаком)
+                    if len(line) >= 3:
+                        parts = line.split()
+                        if parts and len(parts[0]) >= 1 and parts[0][:1].lower() == b"s":
+                            tag = parts[0].lower()  # b"s" или b"s0"..b"s7"
+                            if len(tag) == 2 and b"0" <= tag[1:2] <= b"7":
+                                # Новый формат: режим калибровки встроен в тег
+                                current_cal_mode = int(tag[1:2])
+                                data_parts = parts[1:]
+                            elif len(tag) == 1:
+                                # Старый формат: "s X Y"
+                                data_parts = parts[1:]
+                            else:
+                                self._total_parse_errors += 1
+                                continue
+                            if len(data_parts) >= 2:
+                                try:
+                                    x = int(data_parts[0], 10)
+                                    y = int(data_parts[1], 10)
+                                except Exception:
+                                    self._total_parse_errors += 1
+                                    continue
+                                xs.append(x)
+                                ys.append(y)
+                                self._total_lines_received += 1
+                            else:
+                                self._total_parse_errors += 1
+                        else:
+                            # Строка не начинается с 's'
+                            self._total_parse_errors += 1
+                    else:
+                        # Строка слишком короткая
+                        self._total_parse_errors += 1
+
+                # Защита от переполнения буфера при отсутствии переводов строки (снижен порог)
+                if len(buf) > 262144:
+                    del buf[:-131072]
+        finally:
+            try:
+                # Завершаем оставшийся свип
+                self._finalize_current(xs, ys, current_cal_mode)
+            except Exception:
+                pass
+            try:
+                if self._src is not None:
+                    self._src.close()
+            except Exception:
+                pass

rfg_adc_plotter/gui/__init__.py

@@ -1,5 +0,0 @@
-"""GUI backends."""
-
-from rfg_adc_plotter.gui.pyqtgraph_backend import run_pyqtgraph
-
-__all__ = ["run_pyqtgraph"]

rfg_adc_plotter/io/__init__.py

@@ -1,6 +0,0 @@
-"""I/O helpers for serial sources and sweep parsing."""
-
-from rfg_adc_plotter.io.serial_source import SerialChunkReader, SerialLineSource
-from rfg_adc_plotter.io.sweep_reader import SweepReader
-
-__all__ = ["SerialChunkReader", "SerialLineSource", "SweepReader"]

rfg_adc_plotter/io/sweep_parser_core.py

@@ -1,673 +0,0 @@
-"""Reusable sweep parsers and sweep assembly helpers."""
-
-from __future__ import annotations
-
-import math
-import time
-from collections import deque
-from typing import List, Optional, Sequence, Set
-
-import numpy as np
-
-from rfg_adc_plotter.constants import DATA_INVERSION_THRESHOLD, LOG_BASE, LOG_EXP_LIMIT, LOG_POSTSCALER, LOG_SCALER
-from rfg_adc_plotter.types import (
-    ParserEvent,
-    PointEvent,
-    SignalKind,
-    StartEvent,
-    SweepAuxCurves,
-    SweepInfo,
-    SweepPacket,
-)
-
-
-def u32_to_i32(value: int) -> int:
-    return value - 0x1_0000_0000 if (value & 0x8000_0000) else value
-
-
-def u16_to_i16(value: int) -> int:
-    return value - 0x1_0000 if (value & 0x8000) else value
-
-
-def log_value_to_linear(value: int) -> float:
-    exponent = max(-LOG_EXP_LIMIT, min(LOG_EXP_LIMIT, float(value) * LOG_SCALER))
-    return float(LOG_BASE ** exponent)
-
-
-def log_pair_to_sweep(avg_1: int, avg_2: int) -> float:
-    value_1 = log_value_to_linear(avg_1)
-    value_2 = log_value_to_linear(avg_2)
-    return abs(value_1 - value_2) * LOG_POSTSCALER
-
-
-def tty_ch_pair_to_sweep(ch_1: int, ch_2: int) -> float:
-    """Reduce a raw CH1/CH2 TTY point to power-like scalar ``ch1^2 + ch2^2``."""
-    ch_1_i = int(ch_1)
-    ch_2_i = int(ch_2)
-    return float((ch_1_i * ch_1_i) + (ch_2_i * ch_2_i))
-
-
-class AsciiSweepParser:
-    """Incremental parser for ASCII sweep streams."""
-
-    def __init__(self):
-        self._buf = bytearray()
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-        while True:
-            nl = self._buf.find(b"\n")
-            if nl == -1:
-                break
-            line = bytes(self._buf[:nl])
-            del self._buf[: nl + 1]
-            if line.endswith(b"\r"):
-                line = line[:-1]
-            if not line:
-                continue
-            if line.startswith(b"Sweep_start"):
-                events.append(StartEvent())
-                continue
-
-            parts = line.split()
-            if len(parts) < 3:
-                continue
-            head = parts[0].lower()
-            try:
-                if head == b"s":
-                    if len(parts) >= 4:
-                        ch = int(parts[1], 10)
-                        x = int(parts[2], 10)
-                        y = int(parts[3], 10)
-                    else:
-                        ch = 0
-                        x = int(parts[1], 10)
-                        y = int(parts[2], 10)
-                elif head.startswith(b"s"):
-                    ch = int(head[1:], 10)
-                    x = int(parts[1], 10)
-                    y = int(parts[2], 10)
-                else:
-                    continue
-            except Exception:
-                continue
-            events.append(PointEvent(ch=int(ch), x=int(x), y=float(y)))
-        return events
-
-
-class ComplexAsciiSweepParser:
-    """Incremental parser for ASCII ``step real imag`` streams."""
-
-    def __init__(self):
-        self._buf = bytearray()
-        self._last_step: Optional[int] = None
-        self._seen_points = False
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-        while True:
-            nl = self._buf.find(b"\n")
-            if nl == -1:
-                break
-            line = bytes(self._buf[:nl])
-            del self._buf[: nl + 1]
-            if line.endswith(b"\r"):
-                line = line[:-1]
-            if not line:
-                continue
-
-            if line.lower().startswith(b"sweep_start"):
-                self._last_step = None
-                self._seen_points = False
-                events.append(StartEvent())
-                continue
-
-            parts = line.split()
-            if len(parts) < 3:
-                continue
-            try:
-                step = int(parts[0], 10)
-                real = float(parts[1])
-                imag = float(parts[2])
-            except Exception:
-                continue
-            if step < 0 or (not math.isfinite(real)) or (not math.isfinite(imag)):
-                continue
-
-            if self._seen_points and self._last_step is not None and step <= self._last_step:
-                events.append(StartEvent())
-            self._seen_points = True
-            self._last_step = step
-            events.append(
-                PointEvent(
-                    ch=0,
-                    x=step,
-                    y=float(abs(complex(real, imag))),
-                    aux=(float(real), float(imag)),
-                )
-            )
-        return events
-
-
-class LegacyBinaryParser:
-    """Byte-resynchronizing parser for supported 8-byte binary record formats."""
-
-    def __init__(self):
-        self._buf = bytearray()
-        self._last_step: Optional[int] = None
-        self._seen_points = False
-        self._mode: Optional[str] = None
-        self._current_signal_kind: Optional[SignalKind] = None
-
-    @staticmethod
-    def _u16_at(buf: bytearray, offset: int) -> int:
-        return int(buf[offset]) | (int(buf[offset + 1]) << 8)
-
-    def _emit_legacy_start(self, events: List[ParserEvent], ch: int) -> None:
-        self._mode = "legacy"
-        self._last_step = None
-        self._seen_points = False
-        self._current_signal_kind = None
-        events.append(StartEvent(ch=int(ch)))
-
-    def _emit_bin_start(self, events: List[ParserEvent], signal_kind: SignalKind) -> None:
-        self._mode = "bin"
-        self._last_step = None
-        self._seen_points = False
-        self._current_signal_kind = signal_kind
-        events.append(StartEvent(ch=0, signal_kind=signal_kind))
-
-    def _emit_tty_start(self, events: List[ParserEvent]) -> None:
-        self._emit_bin_start(events, signal_kind="bin_iq")
-
-    def _emit_legacy_point(self, events: List[ParserEvent], step: int, value_word_hi: int, value_word_lo: int, ch: int) -> None:
-        self._mode = "legacy"
-        self._current_signal_kind = None
-        if self._seen_points and self._last_step is not None and step <= self._last_step:
-            events.append(StartEvent(ch=int(ch)))
-        self._seen_points = True
-        self._last_step = int(step)
-        value = u32_to_i32((int(value_word_hi) << 16) | int(value_word_lo))
-        events.append(PointEvent(ch=int(ch), x=int(step), y=float(value)))
-
-    def _prepare_bin_point(self, events: List[ParserEvent], step: int, signal_kind: SignalKind) -> None:
-        self._mode = "bin"
-        if self._current_signal_kind != signal_kind:
-            if self._seen_points:
-                events.append(StartEvent(ch=0, signal_kind=signal_kind))
-            self._last_step = None
-            self._seen_points = False
-            self._current_signal_kind = signal_kind
-        if self._seen_points and self._last_step is not None and step <= self._last_step:
-            events.append(StartEvent(ch=0, signal_kind=signal_kind))
-            self._last_step = None
-            self._seen_points = False
-        self._seen_points = True
-        self._last_step = int(step)
-
-    def _emit_tty_point(self, events: List[ParserEvent], step: int, ch_1_word: int, ch_2_word: int) -> None:
-        self._prepare_bin_point(events, step=int(step), signal_kind="bin_iq")
-        ch_1 = u16_to_i16(int(ch_1_word))
-        ch_2 = u16_to_i16(int(ch_2_word))
-        events.append(
-            PointEvent(
-                ch=0,
-                x=int(step),
-                y=tty_ch_pair_to_sweep(ch_1, ch_2),
-                aux=(float(ch_1), float(ch_2)),
-                signal_kind="bin_iq",
-            )
-        )
-
-    def _emit_logdet_point(self, events: List[ParserEvent], step: int, value_word: int) -> None:
-        self._prepare_bin_point(events, step=int(step), signal_kind="bin_logdet")
-        value = u16_to_i16(int(value_word))
-        events.append(
-            PointEvent(
-                ch=0,
-                x=int(step),
-                y=float(value),
-                signal_kind="bin_logdet",
-            )
-        )
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-        while len(self._buf) >= 8:
-            w0 = self._u16_at(self._buf, 0)
-            w1 = self._u16_at(self._buf, 2)
-            w2 = self._u16_at(self._buf, 4)
-            w3 = self._u16_at(self._buf, 6)
-
-            is_legacy_start = (w0 == 0xFFFF and w1 == 0xFFFF and w2 == 0xFFFF and self._buf[6] == 0x0A)
-            is_tty_start = (w0 == 0x000A and w1 == 0xFFFF and w2 == 0xFFFF and w3 == 0xFFFF)
-            is_legacy_point = (self._buf[6] == 0x0A and w0 != 0xFFFF)
-            is_tty_point = (w0 == 0x000A and w1 != 0xFFFF)
-            is_logdet_point = (w0 == 0x001A and w3 == 0x0000)
-
-            if is_legacy_start:
-                self._emit_legacy_start(events, ch=int(self._buf[7]))
-                del self._buf[:8]
-                continue
-
-            if is_tty_start:
-                self._emit_tty_start(events)
-                del self._buf[:8]
-                continue
-
-            if is_logdet_point:
-                self._emit_logdet_point(events, step=int(w1), value_word=int(w2))
-                del self._buf[:8]
-                continue
-
-            if self._mode == "legacy":
-                if is_legacy_point:
-                    self._emit_legacy_point(
-                        events,
-                        step=int(w0),
-                        value_word_hi=int(w1),
-                        value_word_lo=int(w2),
-                        ch=int(self._buf[7]),
-                    )
-                    del self._buf[:8]
-                    continue
-                if is_tty_point and (not is_legacy_point):
-                    self._emit_tty_point(events, step=int(w1), ch_1_word=int(w2), ch_2_word=int(w3))
-                    del self._buf[:8]
-                    continue
-                del self._buf[:1]
-                continue
-
-            if self._mode == "bin":
-                if is_tty_point:
-                    self._emit_tty_point(events, step=int(w1), ch_1_word=int(w2), ch_2_word=int(w3))
-                    del self._buf[:8]
-                    continue
-                if is_legacy_point and (not is_tty_point):
-                    self._emit_legacy_point(
-                        events,
-                        step=int(w0),
-                        value_word_hi=int(w1),
-                        value_word_lo=int(w2),
-                        ch=int(self._buf[7]),
-                    )
-                    del self._buf[:8]
-                    continue
-                del self._buf[:1]
-                continue
-
-            # Mode is still unknown. Accept only unambiguous point shapes to avoid
-            # jumping between tty and legacy interpretations on coincidental bytes.
-            if is_tty_point and (not is_legacy_point):
-                self._emit_tty_point(events, step=int(w1), ch_1_word=int(w2), ch_2_word=int(w3))
-                del self._buf[:8]
-                continue
-
-            if is_legacy_point and (not is_tty_point):
-                self._emit_legacy_point(
-                    events,
-                    step=int(w0),
-                    value_word_hi=int(w1),
-                    value_word_lo=int(w2),
-                    ch=int(self._buf[7]),
-                )
-                del self._buf[:8]
-                continue
-
-            del self._buf[:1]
-        return events
-
-
-class LogScaleBinaryParser32:
-    """Byte-resynchronizing parser for 32-bit logscale pair records."""
-
-    def __init__(self):
-        self._buf = bytearray()
-        self._last_step: Optional[int] = None
-        self._seen_points = False
-
-    @staticmethod
-    def _u16_at(buf: bytearray, offset: int) -> int:
-        return int(buf[offset]) | (int(buf[offset + 1]) << 8)
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-        while len(self._buf) >= 12:
-            words = [self._u16_at(self._buf, idx * 2) for idx in range(6)]
-            if words[0:5] == [0xFFFF] * 5 and (words[5] & 0x00FF) == 0x000A:
-                self._last_step = None
-                self._seen_points = False
-                events.append(StartEvent(ch=int((words[5] >> 8) & 0x00FF)))
-                del self._buf[:12]
-                continue
-            if (words[5] & 0x00FF) == 0x000A and words[0] != 0xFFFF:
-                ch = int((words[5] >> 8) & 0x00FF)
-                if self._seen_points and self._last_step is not None and words[0] <= self._last_step:
-                    events.append(StartEvent(ch=ch))
-                self._seen_points = True
-                self._last_step = int(words[0])
-                avg_1 = u32_to_i32((words[1] << 16) | words[2])
-                avg_2 = u32_to_i32((words[3] << 16) | words[4])
-                events.append(
-                    PointEvent(
-                        ch=ch,
-                        x=int(words[0]),
-                        y=log_pair_to_sweep(avg_1, avg_2),
-                        aux=(float(avg_1), float(avg_2)),
-                    )
-                )
-                del self._buf[:12]
-                continue
-            del self._buf[:1]
-        return events
-
-
-class LogScale16BitX2BinaryParser:
-    """Byte-resynchronizing parser for 16-bit x2 logscale records."""
-
-    def __init__(self):
-        self._buf = bytearray()
-        self._current_channel = 0
-        self._last_step: Optional[int] = None
-        self._seen_points = False
-
-    @staticmethod
-    def _u16_at(buf: bytearray, offset: int) -> int:
-        return int(buf[offset]) | (int(buf[offset + 1]) << 8)
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-        while len(self._buf) >= 8:
-            words = [self._u16_at(self._buf, idx * 2) for idx in range(4)]
-            if words[0:3] == [0xFFFF, 0xFFFF, 0xFFFF] and (words[3] & 0x00FF) == 0x000A:
-                self._current_channel = int((words[3] >> 8) & 0x00FF)
-                self._last_step = None
-                self._seen_points = False
-                events.append(StartEvent(ch=self._current_channel))
-                del self._buf[:8]
-                continue
-            if words[3] == 0xFFFF and words[0] != 0xFFFF:
-                if self._seen_points and self._last_step is not None and words[0] <= self._last_step:
-                    events.append(StartEvent(ch=self._current_channel))
-                self._seen_points = True
-                self._last_step = int(words[0])
-                real = u16_to_i16(words[1])
-                imag = u16_to_i16(words[2])
-                events.append(
-                    PointEvent(
-                        ch=self._current_channel,
-                        x=int(words[0]),
-                        y=float(abs(complex(real, imag))),
-                        aux=(float(real), float(imag)),
-                    )
-                )
-                del self._buf[:8]
-                continue
-            del self._buf[:1]
-        return events
-
-
-class ParserTestStreamParser:
-    """Parser for the special test 16-bit x2 stream format."""
-
-    def __init__(self):
-        self._buf = bytearray()
-        self._buf_pos = 0
-        self._point_buf: list[int] = []
-        self._ffff_run = 0
-        self._current_channel = 0
-        self._expected_step: Optional[int] = None
-        self._in_sweep = False
-        self._local_resync = False
-
-    def _consume_point(self) -> Optional[PointEvent]:
-        if len(self._point_buf) != 3:
-            return None
-        step = int(self._point_buf[0])
-        if step <= 0:
-            return None
-        if self._expected_step is not None and step < self._expected_step:
-            return None
-        real = u16_to_i16(int(self._point_buf[1]))
-        imag = u16_to_i16(int(self._point_buf[2]))
-        self._expected_step = step + 1
-        return PointEvent(
-            ch=self._current_channel,
-            x=step,
-            y=float(abs(complex(real, imag))),
-            aux=(float(real), float(imag)),
-        )
-
-    def feed(self, data: bytes) -> List[ParserEvent]:
-        if data:
-            self._buf += data
-        events: List[ParserEvent] = []
-
-        while (self._buf_pos + 1) < len(self._buf):
-            word = int(self._buf[self._buf_pos]) | (int(self._buf[self._buf_pos + 1]) << 8)
-            self._buf_pos += 2
-
-            if word == 0xFFFF:
-                self._ffff_run += 1
-                continue
-
-            if self._ffff_run > 0:
-                bad_point_on_delim = False
-                if self._in_sweep and self._point_buf and not self._local_resync:
-                    point = self._consume_point()
-                    if point is None:
-                        self._local_resync = True
-                        bad_point_on_delim = True
-                    else:
-                        events.append(point)
-                    self._point_buf.clear()
-
-                if self._ffff_run >= 2:
-                    if (word & 0x00FF) == 0x000A:
-                        self._current_channel = (word >> 8) & 0x00FF
-                        self._in_sweep = True
-                        self._expected_step = 1
-                        self._local_resync = False
-                        self._point_buf.clear()
-                        events.append(StartEvent(ch=self._current_channel))
-                        self._ffff_run = 0
-                        continue
-                    if self._in_sweep:
-                        self._local_resync = True
-                    self._ffff_run = 0
-                    continue
-
-                if self._local_resync and not bad_point_on_delim:
-                    self._local_resync = False
-                    self._point_buf.clear()
-                self._ffff_run = 0
-
-            if self._in_sweep and not self._local_resync:
-                self._point_buf.append(word)
-                if len(self._point_buf) > 3:
-                    self._point_buf.clear()
-                    self._local_resync = True
-
-        if self._buf_pos >= 262144:
-            del self._buf[: self._buf_pos]
-            self._buf_pos = 0
-        if (len(self._buf) - self._buf_pos) > 1_000_000:
-            tail = self._buf[self._buf_pos :]
-            if len(tail) > 262144:
-                tail = tail[-262144:]
-            self._buf = bytearray(tail)
-            self._buf_pos = 0
-        return events
-
-
-class SweepAssembler:
-    """Collect parser events into sweep packets matching runtime expectations."""
-
-    def __init__(self, fancy: bool = False, apply_inversion: bool = True):
-        self._fancy = bool(fancy)
-        self._apply_inversion = bool(apply_inversion)
-        self._max_width = 0
-        self._sweep_idx = 0
-        self._last_sweep_ts: Optional[float] = None
-        self._n_valid_hist = deque()
-        self._xs: list[int] = []
-        self._ys: list[float] = []
-        self._aux_1: list[float] = []
-        self._aux_2: list[float] = []
-        self._cur_channel: Optional[int] = None
-        self._cur_signal_kind: Optional[SignalKind] = None
-        self._cur_channels: set[int] = set()
-
-    def _reset_current(self) -> None:
-        self._xs.clear()
-        self._ys.clear()
-        self._aux_1.clear()
-        self._aux_2.clear()
-        self._cur_channel = None
-        self._cur_signal_kind = None
-        self._cur_channels.clear()
-
-    def _scatter(self, xs: Sequence[int], values: Sequence[float], width: int) -> np.ndarray:
-        series = np.full((width,), np.nan, dtype=np.float32)
-        try:
-            idx = np.asarray(xs, dtype=np.int64)
-            vals = np.asarray(values, dtype=np.float32)
-            series[idx] = vals
-        except Exception:
-            for x, y in zip(xs, values):
-                xi = int(x)
-                if 0 <= xi < width:
-                    series[xi] = float(y)
-        return series
-
-    @staticmethod
-    def _fill_missing(series: np.ndarray) -> None:
-        known = ~np.isnan(series)
-        if not np.any(known):
-            return
-        known_idx = np.nonzero(known)[0]
-        for i0, i1 in zip(known_idx[:-1], known_idx[1:]):
-            if i1 - i0 > 1:
-                avg = (series[i0] + series[i1]) * 0.5
-                series[i0 + 1 : i1] = avg
-        first_idx = int(known_idx[0])
-        last_idx = int(known_idx[-1])
-        if first_idx > 0:
-            series[:first_idx] = series[first_idx]
-        if last_idx < series.size - 1:
-            series[last_idx + 1 :] = series[last_idx]
-
-    def consume(self, event: ParserEvent) -> Optional[SweepPacket]:
-        if isinstance(event, StartEvent):
-            packet = self.finalize_current()
-            self._reset_current()
-            if event.ch is not None:
-                self._cur_channel = int(event.ch)
-            self._cur_signal_kind = event.signal_kind
-            return packet
-
-        point_ch = int(event.ch)
-        point_signal_kind = event.signal_kind
-        packet: Optional[SweepPacket] = None
-        if self._cur_channel is None:
-            self._cur_channel = point_ch
-        elif point_ch != self._cur_channel:
-            if self._xs:
-                # Never mix channels in a single sweep packet: otherwise
-                # identical step indexes can overwrite each other.
-                packet = self.finalize_current()
-                self._reset_current()
-            self._cur_channel = point_ch
-        if self._cur_signal_kind != point_signal_kind:
-            if self._xs:
-                packet = self.finalize_current()
-                self._reset_current()
-                self._cur_channel = point_ch
-            self._cur_signal_kind = point_signal_kind
-
-        self._cur_channels.add(point_ch)
-        self._xs.append(int(event.x))
-        self._ys.append(float(event.y))
-        if event.aux is not None:
-            self._aux_1.append(float(event.aux[0]))
-            self._aux_2.append(float(event.aux[1]))
-        return packet
-
-    def finalize_current(self) -> Optional[SweepPacket]:
-        if not self._xs:
-            return None
-
-        ch_list = sorted(self._cur_channels) if self._cur_channels else [0]
-        ch_primary = ch_list[0] if ch_list else 0
-        width = max(int(max(self._xs)) + 1, 1)
-        self._max_width = max(self._max_width, width)
-        target_width = self._max_width if self._fancy else width
-
-        sweep = self._scatter(self._xs, self._ys, target_width)
-        aux_curves: SweepAuxCurves = None
-        if self._aux_1 and self._aux_2 and len(self._aux_1) == len(self._xs):
-            aux_curves = (
-                self._scatter(self._xs, self._aux_1, target_width),
-                self._scatter(self._xs, self._aux_2, target_width),
-            )
-
-        n_valid_cur = int(np.count_nonzero(np.isfinite(sweep)))
-
-        if self._fancy:
-            self._fill_missing(sweep)
-            if aux_curves is not None:
-                self._fill_missing(aux_curves[0])
-                self._fill_missing(aux_curves[1])
-
-        if self._apply_inversion:
-            try:
-                mean_value = float(np.nanmean(sweep))
-                if np.isfinite(mean_value) and mean_value < DATA_INVERSION_THRESHOLD:
-                    sweep *= -1.0
-            except Exception:
-                pass
-
-        self._sweep_idx += 1
-        now = time.time()
-        if self._last_sweep_ts is None:
-            dt_ms = float("nan")
-        else:
-            dt_ms = (now - self._last_sweep_ts) * 1000.0
-        self._last_sweep_ts = now
-
-        self._n_valid_hist.append((now, n_valid_cur))
-        while self._n_valid_hist and (now - self._n_valid_hist[0][0]) > 1.0:
-            self._n_valid_hist.popleft()
-        n_valid = float(sum(value for _ts, value in self._n_valid_hist) / len(self._n_valid_hist))
-
-        if n_valid_cur > 0:
-            vmin = float(np.nanmin(sweep))
-            vmax = float(np.nanmax(sweep))
-            mean = float(np.nanmean(sweep))
-            std = float(np.nanstd(sweep))
-        else:
-            vmin = vmax = mean = std = float("nan")
-
-        info: SweepInfo = {
-            "sweep": self._sweep_idx,
-            "ch": ch_primary,
-            "chs": ch_list,
-            "signal_kind": self._cur_signal_kind,
-            "n_valid": n_valid,
-            "min": vmin,
-            "max": vmax,
-            "mean": mean,
-            "std": std,
-            "dt_ms": dt_ms,
-        }
-        return (sweep, info, aux_curves)

rfg_adc_plotter/io/sweep_reader.py

@@ -1,381 +0,0 @@
-"""Background sweep reader thread."""
-
-from __future__ import annotations
-
-import sys
-import threading
-import time
-from queue import Full, Queue
-
-from rfg_adc_plotter.io.serial_source import SerialChunkReader, SerialLineSource
-from rfg_adc_plotter.io.sweep_parser_core import (
-    AsciiSweepParser,
-    ComplexAsciiSweepParser,
-    LegacyBinaryParser,
-    LogScale16BitX2BinaryParser,
-    LogScaleBinaryParser32,
-    ParserTestStreamParser,
-    SweepAssembler,
-)
-from rfg_adc_plotter.types import ParserEvent, PointEvent, StartEvent, SweepPacket
-
-_PARSER_16_BIT_X2_PROBE_BYTES = 64 * 1024
-_LEGACY_STREAM_MIN_RECORDS = 32
-_LEGACY_STREAM_MIN_MATCH_RATIO = 0.95
-_TTY_STREAM_MIN_MATCH_RATIO = 0.60
-_DEBUG_FRAME_LOG_EVERY = 10
-_NO_INPUT_WARN_INTERVAL_S = 5.0
-_NO_PACKET_WARN_INTERVAL_S = 5.0
-_NO_PACKET_HINT_AFTER_S = 10.0
-
-
-def _u16le_at(data: bytes, offset: int) -> int:
-    return int(data[offset]) | (int(data[offset + 1]) << 8)
-
-
-def _looks_like_legacy_8byte_stream(data: bytes) -> bool:
-    """Heuristically detect supported 8-byte binary streams on an arbitrary byte offset."""
-    buf = bytes(data)
-    for offset in range(8):
-        blocks = (len(buf) - offset) // 8
-        if blocks < _LEGACY_STREAM_MIN_RECORDS:
-            continue
-        min_matches = max(_LEGACY_STREAM_MIN_RECORDS, int(blocks * _LEGACY_STREAM_MIN_MATCH_RATIO))
-        matched_steps_legacy: list[int] = []
-        matched_steps_tty: list[int] = []
-        matched_steps_logdet: list[int] = []
-        for block_idx in range(blocks):
-            base = offset + (block_idx * 8)
-            if (_u16le_at(buf, base + 6) & 0x00FF) != 0x000A:
-                w0 = _u16le_at(buf, base)
-                w1 = _u16le_at(buf, base + 2)
-                w3 = _u16le_at(buf, base + 6)
-                if w0 == 0x000A and w1 != 0xFFFF:
-                    matched_steps_tty.append(w1)
-                elif w0 == 0x001A and w3 == 0x0000:
-                    matched_steps_logdet.append(w1)
-                continue
-            matched_steps_legacy.append(_u16le_at(buf, base))
-
-        if len(matched_steps_legacy) >= min_matches:
-            monotonic_or_reset = 0
-            for prev_step, next_step in zip(matched_steps_legacy, matched_steps_legacy[1:]):
-                if next_step == (prev_step + 1) or next_step <= prev_step:
-                    monotonic_or_reset += 1
-            if monotonic_or_reset >= max(4, len(matched_steps_legacy) - 4):
-                return True
-
-        tty_min_matches = max(_LEGACY_STREAM_MIN_RECORDS, int(blocks * _TTY_STREAM_MIN_MATCH_RATIO))
-        if len(matched_steps_tty) >= tty_min_matches:
-            monotonic_or_reset = 0
-            for prev_step, next_step in zip(matched_steps_tty, matched_steps_tty[1:]):
-                if next_step == (prev_step + 1) or next_step <= 2:
-                    monotonic_or_reset += 1
-            if monotonic_or_reset >= max(4, len(matched_steps_tty) - 4):
-                return True
-
-        if len(matched_steps_logdet) >= tty_min_matches:
-            monotonic_or_reset = 0
-            for prev_step, next_step in zip(matched_steps_logdet, matched_steps_logdet[1:]):
-                if next_step == (prev_step + 1) or next_step <= 2:
-                    monotonic_or_reset += 1
-            if monotonic_or_reset >= max(4, len(matched_steps_logdet) - 4):
-                return True
-
-    return False
-
-
-def _is_valid_parser_16_bit_x2_probe(events: list[ParserEvent]) -> bool:
-    """Accept only plausible complex streams and ignore resync noise."""
-    point_steps: list[int] = []
-    for event in events:
-        if isinstance(event, PointEvent):
-            point_steps.append(int(event.x))
-
-    if len(point_steps) < 3:
-        return False
-
-    monotonic_or_small_reset = 0
-    for prev_step, next_step in zip(point_steps, point_steps[1:]):
-        if next_step == (prev_step + 1) or next_step <= 2:
-            monotonic_or_small_reset += 1
-    return monotonic_or_small_reset >= max(2, len(point_steps) - 3)
-
-
-class SweepReader(threading.Thread):
-    """Read a serial source in the background and emit completed sweep packets."""
-
-    def __init__(
-        self,
-        port_path: str,
-        baud: int,
-        out_queue: "Queue[SweepPacket]",
-        stop_event: threading.Event,
-        fancy: bool = False,
-        bin_mode: bool = False,
-        logscale: bool = False,
-        parser_16_bit_x2: bool = False,
-        parser_test: bool = False,
-        parser_complex_ascii: bool = False,
-    ):
-        super().__init__(daemon=True)
-        self._port_path = port_path
-        self._baud = int(baud)
-        self._queue = out_queue
-        self._stop_event = stop_event
-        self._fancy = bool(fancy)
-        self._bin_mode = bool(bin_mode)
-        self._logscale = bool(logscale)
-        self._parser_16_bit_x2 = bool(parser_16_bit_x2)
-        self._parser_test = bool(parser_test)
-        self._parser_complex_ascii = bool(parser_complex_ascii)
-        self._src: SerialLineSource | None = None
-        self._frames_read = 0
-        self._frames_dropped = 0
-        self._started_at = time.perf_counter()
-
-    def _resolve_parser_mode_label(self) -> str:
-        if self._parser_complex_ascii:
-            return "complex_ascii"
-        if self._parser_test:
-            return "parser_test_16x2"
-        if self._parser_16_bit_x2:
-            return "parser_16_bit_x2"
-        if self._logscale:
-            return "logscale_32"
-        if self._bin_mode:
-            return "legacy_8byte"
-        return "ascii"
-
-    def _build_parser(self):
-        if self._parser_complex_ascii:
-            return ComplexAsciiSweepParser(), SweepAssembler(fancy=self._fancy, apply_inversion=False)
-        if self._parser_test:
-            return ParserTestStreamParser(), SweepAssembler(fancy=self._fancy, apply_inversion=False)
-        if self._parser_16_bit_x2:
-            return LogScale16BitX2BinaryParser(), SweepAssembler(fancy=self._fancy, apply_inversion=False)
-        if self._logscale:
-            return LogScaleBinaryParser32(), SweepAssembler(fancy=self._fancy, apply_inversion=False)
-        if self._bin_mode:
-            return LegacyBinaryParser(), SweepAssembler(fancy=self._fancy, apply_inversion=True)
-        return AsciiSweepParser(), SweepAssembler(fancy=self._fancy, apply_inversion=True)
-
-    @staticmethod
-    def _consume_events(assembler: SweepAssembler, events) -> list[SweepPacket]:
-        packets: list[SweepPacket] = []
-        for event in events:
-            packet = assembler.consume(event)
-            if packet is not None:
-                packets.append(packet)
-        return packets
-
-    def _probe_parser_16_bit_x2(self, chunk_reader: SerialChunkReader):
-        parser = LogScale16BitX2BinaryParser()
-        probe_buf = bytearray()
-        probe_events: list[ParserEvent] = []
-        probe_started_at = time.perf_counter()
-
-        while not self._stop_event.is_set() and len(probe_buf) < _PARSER_16_BIT_X2_PROBE_BYTES:
-            data = chunk_reader.read_available()
-            if not data:
-                time.sleep(0.0005)
-                continue
-            probe_buf += data
-            probe_events.extend(parser.feed(data))
-            if _is_valid_parser_16_bit_x2_probe(probe_events):
-                assembler = SweepAssembler(fancy=self._fancy, apply_inversion=False)
-                probe_packets = self._consume_events(assembler, probe_events)
-                n_points = int(sum(1 for event in probe_events if isinstance(event, PointEvent)))
-                n_starts = int(sum(1 for event in probe_events if isinstance(event, StartEvent)))
-                probe_ms = (time.perf_counter() - probe_started_at) * 1000.0
-                sys.stderr.write(
-                    "[info] parser_16_bit_x2 probe: bytes:%d events:%d points:%d starts:%d parser:16x2 elapsed_ms:%.1f\n"
-                    % (
-                        len(probe_buf),
-                        len(probe_events),
-                        n_points,
-                        n_starts,
-                        probe_ms,
-                    )
-                )
-                return parser, assembler, probe_packets
-
-        probe_looks_legacy = bool(probe_buf) and _looks_like_legacy_8byte_stream(bytes(probe_buf))
-        n_points = int(sum(1 for event in probe_events if isinstance(event, PointEvent)))
-        n_starts = int(sum(1 for event in probe_events if isinstance(event, StartEvent)))
-        probe_ms = (time.perf_counter() - probe_started_at) * 1000.0
-        if probe_looks_legacy:
-            sys.stderr.write(
-                "[info] parser_16_bit_x2 probe: bytes:%d events:%d points:%d starts:%d parser:legacy(fallback) elapsed_ms:%.1f\n"
-                % (
-                    len(probe_buf),
-                    len(probe_events),
-                    n_points,
-                    n_starts,
-                    probe_ms,
-                )
-            )
-            sys.stderr.write("[info] parser_16_bit_x2: fallback -> legacy\n")
-            parser = LegacyBinaryParser()
-            assembler = SweepAssembler(fancy=self._fancy, apply_inversion=True)
-            probe_packets = self._consume_events(assembler, parser.feed(bytes(probe_buf)))
-            return parser, assembler, probe_packets
-
-        sys.stderr.write(
-            "[warn] parser_16_bit_x2 probe inconclusive: bytes:%d events:%d points:%d starts:%d parser:16x2 elapsed_ms:%.1f\n"
-            % (
-                len(probe_buf),
-                len(probe_events),
-                n_points,
-                n_starts,
-                probe_ms,
-            )
-        )
-        sys.stderr.write(
-            "[hint] parser_16_bit_x2: if source is 8-byte tty CH1/CH2 stream (0x000A,step,ch1,ch2), try --bin\n"
-        )
-        assembler = SweepAssembler(fancy=self._fancy, apply_inversion=False)
-        return parser, assembler, []
-
-    def _enqueue(self, packet: SweepPacket) -> None:
-        dropped = False
-        try:
-            self._queue.put_nowait(packet)
-        except Full:
-            try:
-                _ = self._queue.get_nowait()
-                dropped = True
-            except Exception:
-                pass
-            try:
-                self._queue.put_nowait(packet)
-            except Exception:
-                pass
-        if dropped:
-            self._frames_dropped += 1
-
-        self._frames_read += 1
-        if self._frames_read % _DEBUG_FRAME_LOG_EVERY == 0:
-            sweep, info, _aux = packet
-            try:
-                queue_size = self._queue.qsize()
-            except Exception:
-                queue_size = -1
-            elapsed_s = max(time.perf_counter() - self._started_at, 1e-9)
-            frames_per_sec = float(self._frames_read) / elapsed_s
-            sweep_idx = info.get("sweep") if isinstance(info, dict) else None
-            channel = info.get("ch") if isinstance(info, dict) else None
-            sys.stderr.write(
-                "[debug] reader frames:%d rate:%.2f/s last_sweep:%s ch:%s width:%d queue:%d dropped:%d\n"
-                % (
-                    self._frames_read,
-                    frames_per_sec,
-                    str(sweep_idx),
-                    str(channel),
-                    int(getattr(sweep, "size", 0)),
-                    int(queue_size),
-                    self._frames_dropped,
-                )
-            )
-
-    def run(self) -> None:
-        try:
-            self._src = SerialLineSource(self._port_path, self._baud, timeout=1.0)
-            queue_cap = int(getattr(self._queue, "maxsize", -1))
-            sys.stderr.write(f"[info] Открыл порт {self._port_path} ({self._src._using})\n")
-            sys.stderr.write(
-                "[info] reader start: parser:%s fancy:%d queue_max:%d source:%s\n"
-                % (
-                    self._resolve_parser_mode_label(),
-                    int(self._fancy),
-                    queue_cap,
-                    getattr(self._src, "_using", "unknown"),
-                )
-            )
-        except Exception as exc:
-            sys.stderr.write(f"[error] {exc}\n")
-            return
-
-        try:
-            chunk_reader = SerialChunkReader(self._src)
-            if self._parser_16_bit_x2:
-                parser, assembler, pending_packets = self._probe_parser_16_bit_x2(chunk_reader)
-            else:
-                parser, assembler = self._build_parser()
-                pending_packets = []
-
-            for packet in pending_packets:
-                self._enqueue(packet)
-
-            loop_started_at = time.perf_counter()
-            last_input_at = loop_started_at
-            last_packet_at = loop_started_at if self._frames_read > 0 else loop_started_at
-            last_no_input_warn_at = loop_started_at
-            last_no_packet_warn_at = loop_started_at
-            parser_hint_emitted = False
-
-            while not self._stop_event.is_set():
-                data = chunk_reader.read_available()
-                now_s = time.perf_counter()
-                if not data:
-                    input_idle_s = now_s - last_input_at
-                    if (
-                        input_idle_s >= _NO_INPUT_WARN_INTERVAL_S
-                        and (now_s - last_no_input_warn_at) >= _NO_INPUT_WARN_INTERVAL_S
-                    ):
-                        sys.stderr.write(
-                            "[warn] reader no input bytes for %.1fs on %s (parser:%s)\n"
-                            % (
-                                input_idle_s,
-                                self._port_path,
-                                self._resolve_parser_mode_label(),
-                            )
-                        )
-                        last_no_input_warn_at = now_s
-
-                    packets_idle_s = now_s - last_packet_at
-                    if (
-                        packets_idle_s >= _NO_PACKET_WARN_INTERVAL_S
-                        and (now_s - last_no_packet_warn_at) >= _NO_PACKET_WARN_INTERVAL_S
-                    ):
-                        try:
-                            queue_size = self._queue.qsize()
-                        except Exception:
-                            queue_size = -1
-                        sys.stderr.write(
-                            "[warn] reader no sweep packets for %.1fs (input_idle:%.1fs queue:%d parser:%s)\n"
-                            % (
-                                packets_idle_s,
-                                input_idle_s,
-                                int(queue_size),
-                                self._resolve_parser_mode_label(),
-                            )
-                        )
-                        last_no_packet_warn_at = now_s
-                        if (
-                            self._parser_16_bit_x2
-                            and (not parser_hint_emitted)
-                            and (now_s - self._started_at) >= _NO_PACKET_HINT_AFTER_S
-                        ):
-                            sys.stderr.write(
-                                "[hint] parser_16_bit_x2 still has no sweeps; if source is tty CH1/CH2, rerun with --bin\n"
-                            )
-                            parser_hint_emitted = True
-                    time.sleep(0.0005)
-                    continue
-
-                last_input_at = now_s
-                packets = self._consume_events(assembler, parser.feed(data))
-                if packets:
-                    last_packet_at = now_s
-                for packet in packets:
-                    self._enqueue(packet)
-            packet = assembler.finalize_current()
-            if packet is not None:
-                self._enqueue(packet)
-        finally:
-            try:
-                if self._src is not None:
-                    self._src.close()
-            except Exception:
-                pass

rfg_adc_plotter/main.py

@@ -1,26 +0,0 @@
-"""Main entrypoint for the modularized ADC plotter."""
-
-from __future__ import annotations
-
-import sys
-
-from rfg_adc_plotter.cli import build_parser
-
-
-def main() -> None:
-    args = build_parser().parse_args()
-    if args.backend == "mpl":
-        sys.stderr.write("[error] Matplotlib backend removed. Use --backend pg or --backend auto.\n")
-        raise SystemExit(2)
-
-    from rfg_adc_plotter.gui.pyqtgraph_backend import run_pyqtgraph
-
-    try:
-        run_pyqtgraph(args)
-    except Exception as exc:
-        sys.stderr.write(f"[error] PyQtGraph бэкенд недоступен: {exc}\n")
-        raise SystemExit(1) from exc
-
-
-if __name__ == "__main__":
-    main()

rfg_adc_plotter/processing/__init__.py

@@ -1,79 +0,0 @@
-"""Pure sweep-processing helpers."""
-
-from rfg_adc_plotter.processing.background import (
-    load_fft_background,
-    save_fft_background,
-    subtract_fft_background,
-    validate_fft_background,
-)
-from rfg_adc_plotter.processing.calibration import (
-    build_calib_envelope,
-    build_complex_calibration_curve,
-    calibrate_freqs,
-    get_calibration_base,
-    get_calibration_coeffs,
-    load_calib_envelope,
-    load_complex_calibration,
-    recalculate_calibration_c,
-    save_calib_envelope,
-    save_complex_calibration,
-    set_calibration_base_value,
-)
-from rfg_adc_plotter.processing.fft import (
-    compute_distance_axis,
-    compute_fft_complex_row,
-    compute_fft_mag_row,
-    compute_fft_row,
-    fft_mag_to_db,
-)
-from rfg_adc_plotter.processing.formatting import (
-    compute_auto_ylim,
-    format_status_kv,
-    parse_spec_clip,
-)
-from rfg_adc_plotter.processing.normalization import (
-    build_calib_envelopes,
-    fit_complex_calibration_to_width,
-    normalize_by_complex_calibration,
-    normalize_by_envelope,
-    normalize_by_calib,
-)
-from rfg_adc_plotter.processing.peaks import (
-    find_peak_width_markers,
-    find_top_peaks_over_ref,
-    rolling_median_ref,
-)
-
-__all__ = [
-    "build_calib_envelopes",
-    "build_calib_envelope",
-    "build_complex_calibration_curve",
-    "calibrate_freqs",
-    "compute_auto_ylim",
-    "compute_distance_axis",
-    "compute_fft_complex_row",
-    "compute_fft_mag_row",
-    "compute_fft_row",
-    "fft_mag_to_db",
-    "find_peak_width_markers",
-    "find_top_peaks_over_ref",
-    "format_status_kv",
-    "get_calibration_base",
-    "get_calibration_coeffs",
-    "load_calib_envelope",
-    "load_complex_calibration",
-    "load_fft_background",
-    "fit_complex_calibration_to_width",
-    "normalize_by_complex_calibration",
-    "normalize_by_envelope",
-    "normalize_by_calib",
-    "parse_spec_clip",
-    "recalculate_calibration_c",
-    "rolling_median_ref",
-    "save_calib_envelope",
-    "save_complex_calibration",
-    "save_fft_background",
-    "set_calibration_base_value",
-    "subtract_fft_background",
-    "validate_fft_background",
-]

rfg_adc_plotter/processing/background.py

@@ -1,66 +0,0 @@
-"""Helpers for persisted FFT background profiles."""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-import numpy as np
-
-
-def validate_fft_background(background: np.ndarray) -> np.ndarray:
-    """Validate a saved FFT background payload."""
-    values = np.asarray(background)
-    if values.ndim != 1:
-        raise ValueError("FFT background must be a 1D array")
-    if not np.issubdtype(values.dtype, np.number):
-        raise ValueError("FFT background must be numeric")
-    values = np.asarray(values, dtype=np.float32).reshape(-1)
-    if values.size == 0:
-        raise ValueError("FFT background is empty")
-    return values
-
-
-def _normalize_background_path(path: str | Path) -> Path:
-    out = Path(path).expanduser()
-    if out.suffix.lower() != ".npy":
-        out = out.with_suffix(".npy")
-    return out
-
-
-def save_fft_background(path: str | Path, background: np.ndarray) -> str:
-    """Persist an FFT background profile as a .npy file."""
-    normalized_path = _normalize_background_path(path)
-    values = validate_fft_background(background)
-    np.save(normalized_path, values.astype(np.float32, copy=False))
-    return str(normalized_path)
-
-
-def load_fft_background(path: str | Path) -> np.ndarray:
-    """Load and validate an FFT background profile from a .npy file."""
-    normalized_path = _normalize_background_path(path)
-    loaded = np.load(normalized_path, allow_pickle=False)
-    return validate_fft_background(loaded)
-
-
-def subtract_fft_background(signal_mag: np.ndarray, background_mag: np.ndarray) -> np.ndarray:
-    """Subtract a background profile from FFT magnitudes in linear amplitude."""
-    signal = np.asarray(signal_mag, dtype=np.float32)
-    background = validate_fft_background(background_mag)
-    if signal.ndim == 1:
-        if signal.size != background.size:
-            raise ValueError("FFT background size does not match signal size")
-        valid = np.isfinite(signal) & np.isfinite(background)
-        out = np.full_like(signal, np.nan, dtype=np.float32)
-        if np.any(valid):
-            out[valid] = np.maximum(signal[valid] - background[valid], 0.0)
-        return out
-
-    if signal.ndim == 2:
-        if signal.shape[0] != background.size:
-            raise ValueError("FFT background size does not match signal rows")
-        background_2d = background[:, None]
-        valid = np.isfinite(signal) & np.isfinite(background_2d)
-        diff = signal - background_2d
-        return np.where(valid, np.maximum(diff, 0.0), np.nan).astype(np.float32, copy=False)
-
-    raise ValueError("FFT background subtraction supports only 1D or 2D signals")

rfg_adc_plotter/processing/calibration.py

@@ -1,169 +0,0 @@
-"""Frequency-axis calibration helpers."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, Mapping
-
-import numpy as np
-
-from rfg_adc_plotter.constants import SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ
-from rfg_adc_plotter.processing.normalization import build_calib_envelopes
-from rfg_adc_plotter.types import SweepData
-
-
-def recalculate_calibration_c(
-    base_coeffs: np.ndarray,
-    f_min: float = SWEEP_FREQ_MIN_GHZ,
-    f_max: float = SWEEP_FREQ_MAX_GHZ,
-) -> np.ndarray:
-    """Recalculate coefficients while preserving sweep edges."""
-    coeffs = np.asarray(base_coeffs, dtype=np.float64).reshape(-1)
-    if coeffs.size < 3:
-        out = np.zeros((3,), dtype=np.float64)
-        out[: coeffs.size] = coeffs
-        coeffs = out
-    c0, c1, c2 = float(coeffs[0]), float(coeffs[1]), float(coeffs[2])
-    x0 = float(f_min)
-    x1 = float(f_max)
-    y0 = c0 + c1 * x0 + c2 * (x0 ** 2)
-    y1 = c0 + c1 * x1 + c2 * (x1 ** 2)
-    if not (np.isfinite(y0) and np.isfinite(y1)) or y1 == y0:
-        return np.asarray([c0, c1, c2], dtype=np.float64)
-    scale = (x1 - x0) / (y1 - y0)
-    shift = x0 - scale * y0
-    return np.asarray(
-        [
-            shift + scale * c0,
-            scale * c1,
-            scale * c2,
-        ],
-        dtype=np.float64,
-    )
-
-
-CALIBRATION_C_BASE = np.asarray([0.0, 1.0, 0.025], dtype=np.float64)
-CALIBRATION_C = recalculate_calibration_c(CALIBRATION_C_BASE)
-
-
-def get_calibration_base() -> np.ndarray:
-    return np.asarray(CALIBRATION_C_BASE, dtype=np.float64).copy()
-
-
-def get_calibration_coeffs() -> np.ndarray:
-    return np.asarray(CALIBRATION_C, dtype=np.float64).copy()
-
-
-def set_calibration_base_value(index: int, value: float) -> np.ndarray:
-    """Update one base coefficient and recalculate the working coefficients."""
-    global CALIBRATION_C
-    CALIBRATION_C_BASE[int(index)] = float(value)
-    CALIBRATION_C = recalculate_calibration_c(CALIBRATION_C_BASE)
-    return get_calibration_coeffs()
-
-
-def calibrate_freqs(sweep: Mapping[str, Any]) -> SweepData:
-    """Return a sweep copy with calibrated and resampled frequency axis."""
-    freqs = np.asarray(sweep["F"], dtype=np.float64).copy()
-    values_in = np.asarray(sweep["I"]).reshape(-1)
-    values = np.asarray(
-        values_in,
-        dtype=np.complex128 if np.iscomplexobj(values_in) else np.float64,
-    ).copy()
-    coeffs = np.asarray(CALIBRATION_C, dtype=np.float64)
-    if freqs.size > 0:
-        freqs = coeffs[0] + coeffs[1] * freqs + coeffs[2] * (freqs * freqs)
-
-    if freqs.size >= 2:
-        freqs_cal = np.linspace(float(freqs[0]), float(freqs[-1]), freqs.size, dtype=np.float64)
-        if np.iscomplexobj(values):
-            values_real = np.interp(freqs_cal, freqs, values.real.astype(np.float64, copy=False))
-            values_imag = np.interp(freqs_cal, freqs, values.imag.astype(np.float64, copy=False))
-            values_cal = (values_real + (1j * values_imag)).astype(np.complex64)
-        else:
-            values_cal = np.interp(freqs_cal, freqs, values).astype(np.float64)
-    else:
-        freqs_cal = freqs.copy()
-        values_cal = values.copy()
-
-    return {
-        "F": freqs_cal,
-        "I": values_cal,
-    }
-
-
-def build_calib_envelope(sweep: np.ndarray) -> np.ndarray:
-    """Build the active calibration envelope from a raw sweep."""
-    values = np.asarray(sweep, dtype=np.float32).reshape(-1)
-    if values.size == 0:
-        raise ValueError("Calibration sweep is empty")
-    _, upper = build_calib_envelopes(values)
-    return np.asarray(upper, dtype=np.float32)
-
-
-def build_complex_calibration_curve(ch1: np.ndarray, ch2: np.ndarray) -> np.ndarray:
-    """Build a complex calibration curve as ``ch1 + 1j*ch2``."""
-    ch1_arr = np.asarray(ch1, dtype=np.float32).reshape(-1)
-    ch2_arr = np.asarray(ch2, dtype=np.float32).reshape(-1)
-    width = min(ch1_arr.size, ch2_arr.size)
-    if width <= 0:
-        raise ValueError("Complex calibration source is empty")
-    curve = ch1_arr[:width].astype(np.complex64) + (1j * ch2_arr[:width].astype(np.complex64))
-    return validate_complex_calibration_curve(curve)
-
-
-def validate_calib_envelope(envelope: np.ndarray) -> np.ndarray:
-    """Validate a saved calibration envelope payload."""
-    values = np.asarray(envelope, dtype=np.float32).reshape(-1)
-    if values.size == 0:
-        raise ValueError("Calibration envelope is empty")
-    if not np.issubdtype(values.dtype, np.number):
-        raise ValueError("Calibration envelope must be numeric")
-    return values
-
-
-def validate_complex_calibration_curve(curve: np.ndarray) -> np.ndarray:
-    """Validate a saved complex calibration payload."""
-    values = np.asarray(curve).reshape(-1)
-    if values.size == 0:
-        raise ValueError("Complex calibration curve is empty")
-    if not np.issubdtype(values.dtype, np.number):
-        raise ValueError("Complex calibration curve must be numeric")
-    return np.asarray(values, dtype=np.complex64)
-
-
-def _normalize_calib_path(path: str | Path) -> Path:
-    out = Path(path).expanduser()
-    if out.suffix.lower() != ".npy":
-        out = out.with_suffix(".npy")
-    return out
-
-
-def save_calib_envelope(path: str | Path, envelope: np.ndarray) -> str:
-    """Persist a calibration envelope as a .npy file and return the final path."""
-    normalized_path = _normalize_calib_path(path)
-    values = validate_calib_envelope(envelope)
-    np.save(normalized_path, values.astype(np.float32, copy=False))
-    return str(normalized_path)
-
-
-def load_calib_envelope(path: str | Path) -> np.ndarray:
-    """Load and validate a calibration envelope from a .npy file."""
-    normalized_path = _normalize_calib_path(path)
-    loaded = np.load(normalized_path, allow_pickle=False)
-    return validate_calib_envelope(loaded)
-
-
-def save_complex_calibration(path: str | Path, curve: np.ndarray) -> str:
-    """Persist a complex calibration curve as a .npy file and return the final path."""
-    normalized_path = _normalize_calib_path(path)
-    values = validate_complex_calibration_curve(curve)
-    np.save(normalized_path, values.astype(np.complex64, copy=False))
-    return str(normalized_path)
-
-
-def load_complex_calibration(path: str | Path) -> np.ndarray:
-    """Load and validate a complex calibration curve from a .npy file."""
-    normalized_path = _normalize_calib_path(path)
-    loaded = np.load(normalized_path, allow_pickle=False)
-    return validate_complex_calibration_curve(loaded)

rfg_adc_plotter/processing/fft.py

@@ -1,511 +0,0 @@
-"""FFT helpers for line and waterfall views."""
-
-from __future__ import annotations
-
-from typing import Optional, Tuple
-
-import numpy as np
-
-from rfg_adc_plotter.constants import C_M_S, FFT_LEN, SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ
-
-
-def _finite_freq_bounds(freqs: Optional[np.ndarray]) -> Optional[Tuple[float, float]]:
-    """Return finite frequency bounds for the current working segment."""
-    if freqs is None:
-        return None
-    freq_arr = np.asarray(freqs, dtype=np.float64).reshape(-1)
-    finite = freq_arr[np.isfinite(freq_arr)]
-    if finite.size < 2:
-        return None
-    f_min = float(np.min(finite))
-    f_max = float(np.max(finite))
-    if not np.isfinite(f_min) or not np.isfinite(f_max) or f_max <= f_min:
-        return None
-    return f_min, f_max
-
-
-def _coerce_sweep_array(sweep: np.ndarray) -> np.ndarray:
-    values = np.asarray(sweep).reshape(-1)
-    if np.iscomplexobj(values):
-        return np.asarray(values, dtype=np.complex64)
-    return np.asarray(values, dtype=np.float32)
-
-
-def _interp_signal(x_uniform: np.ndarray, x_known: np.ndarray, y_known: np.ndarray) -> np.ndarray:
-    if np.iscomplexobj(y_known):
-        real = np.interp(x_uniform, x_known, np.asarray(y_known.real, dtype=np.float64))
-        imag = np.interp(x_uniform, x_known, np.asarray(y_known.imag, dtype=np.float64))
-        return (real + (1j * imag)).astype(np.complex64)
-    return np.interp(x_uniform, x_known, np.asarray(y_known, dtype=np.float64)).astype(np.float32)
-
-
-def _fit_complex_bins(values: np.ndarray, bins: int) -> np.ndarray:
-    arr = np.asarray(values, dtype=np.complex64).reshape(-1)
-    if bins <= 0:
-        return np.zeros((0,), dtype=np.complex64)
-    if arr.size == bins:
-        return arr
-    out = np.full((bins,), np.nan + 0j, dtype=np.complex64)
-    take = min(arr.size, bins)
-    out[:take] = arr[:take]
-    return out
-
-
-def _extract_positive_exact_band(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-) -> Optional[Tuple[np.ndarray, np.ndarray, float, float]]:
-    """Return sorted positive band data and exact-grid parameters."""
-    if freqs is None:
-        return None
-
-    sweep_arr = _coerce_sweep_array(sweep)
-    freq_arr = np.asarray(freqs, dtype=np.float64).reshape(-1)
-    take = min(int(sweep_arr.size), int(freq_arr.size))
-    if take <= 1:
-        return None
-
-    sweep_seg = sweep_arr[:take]
-    freq_seg = freq_arr[:take]
-    valid = np.isfinite(freq_seg) & np.isfinite(sweep_seg) & (freq_seg > 0.0)
-    if int(np.count_nonzero(valid)) < 2:
-        return None
-
-    freq_band = np.asarray(freq_seg[valid], dtype=np.float64)
-    sweep_band = np.asarray(sweep_seg[valid])
-    order = np.argsort(freq_band, kind="mergesort")
-    freq_band = freq_band[order]
-    sweep_band = sweep_band[order]
-
-    n_band = int(freq_band.size)
-    if n_band <= 1:
-        return None
-
-    f_min = float(freq_band[0])
-    f_max = float(freq_band[-1])
-    if (not np.isfinite(f_min)) or (not np.isfinite(f_max)) or f_max <= f_min:
-        return None
-
-    df_ghz = float((f_max - f_min) / max(1, n_band - 1))
-    if (not np.isfinite(df_ghz)) or df_ghz <= 0.0:
-        return None
-
-    return freq_band, sweep_band, f_max, df_ghz
-
-
-def _positive_exact_shift_size(f_max: float, df_ghz: float) -> int:
-    if (not np.isfinite(f_max)) or (not np.isfinite(df_ghz)) or f_max <= 0.0 or df_ghz <= 0.0:
-        return 0
-    return int(np.arange(-f_max, f_max + (0.5 * df_ghz), df_ghz, dtype=np.float64).size)
-
-
-def _resolve_positive_exact_band_size(
-    f_min: float,
-    f_max: float,
-    n_band: int,
-    max_shift_len: Optional[int],
-) -> int:
-    if n_band <= 2:
-        return max(2, int(n_band))
-    if max_shift_len is None:
-        return int(n_band)
-
-    limit = int(max_shift_len)
-    if limit <= 1:
-        return max(2, int(n_band))
-
-    span = float(f_max - f_min)
-    if (not np.isfinite(span)) or span <= 0.0:
-        return int(n_band)
-
-    df_current = float(span / max(1, int(n_band) - 1))
-    if _positive_exact_shift_size(f_max, df_current) <= limit:
-        return int(n_band)
-
-    denom = max(2.0 * f_max, 1e-12)
-    approx = int(np.floor(1.0 + ((float(limit - 1) * span) / denom)))
-    target = min(int(n_band), max(2, approx))
-    while target > 2:
-        df_try = float(span / max(1, target - 1))
-        if _positive_exact_shift_size(f_max, df_try) <= limit:
-            break
-        target -= 1
-    return max(2, target)
-
-
-def _normalize_positive_exact_band(
-    freq_band: np.ndarray,
-    sweep_band: np.ndarray,
-    *,
-    max_shift_len: Optional[int] = None,
-) -> Optional[Tuple[np.ndarray, np.ndarray, float, float]]:
-    freq_arr = np.asarray(freq_band, dtype=np.float64).reshape(-1)
-    sweep_arr = np.asarray(sweep_band).reshape(-1)
-    width = min(int(freq_arr.size), int(sweep_arr.size))
-    if width <= 1:
-        return None
-
-    freq_arr = freq_arr[:width]
-    sweep_arr = sweep_arr[:width]
-    f_min = float(freq_arr[0])
-    f_max = float(freq_arr[-1])
-    if (not np.isfinite(f_min)) or (not np.isfinite(f_max)) or f_max <= f_min:
-        return None
-
-    target_band = _resolve_positive_exact_band_size(f_min, f_max, int(freq_arr.size), max_shift_len)
-    if target_band < int(freq_arr.size):
-        target_freqs = np.linspace(f_min, f_max, target_band, dtype=np.float64)
-        target_sweep = _interp_signal(target_freqs, freq_arr, sweep_arr)
-        freq_arr = target_freqs
-        sweep_arr = np.asarray(target_sweep).reshape(-1)
-
-    n_band = int(freq_arr.size)
-    if n_band <= 1:
-        return None
-
-    df_ghz = float((f_max - f_min) / max(1, n_band - 1))
-    if (not np.isfinite(df_ghz)) or df_ghz <= 0.0:
-        return None
-
-    return freq_arr, sweep_arr, f_max, df_ghz
-
-
-def _resolve_positive_only_exact_geometry(
-    freqs: Optional[np.ndarray],
-    *,
-    max_shift_len: Optional[int] = None,
-) -> Optional[Tuple[int, float]]:
-    """Return (N_shift, df_hz) for the exact centered positive-only mode."""
-    if freqs is None:
-        return None
-
-    freq_arr = np.asarray(freqs, dtype=np.float64).reshape(-1)
-    finite = np.asarray(freq_arr[np.isfinite(freq_arr) & (freq_arr > 0.0)], dtype=np.float64)
-    if finite.size < 2:
-        return None
-
-    finite.sort(kind="mergesort")
-    f_min = float(finite[0])
-    f_max = float(finite[-1])
-    if (not np.isfinite(f_min)) or (not np.isfinite(f_max)) or f_max <= f_min:
-        return None
-
-    n_band = int(finite.size)
-    target_band = _resolve_positive_exact_band_size(f_min, f_max, n_band, max_shift_len)
-    n_band = max(2, min(n_band, target_band))
-    df_ghz = float((f_max - f_min) / max(1, n_band - 1))
-    if (not np.isfinite(df_ghz)) or df_ghz <= 0.0:
-        return None
-
-    n_shift = _positive_exact_shift_size(f_max, df_ghz)
-    if n_shift <= 1:
-        return None
-    return int(n_shift), float(df_ghz * 1e9)
-
-
-def prepare_fft_segment(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    fft_len: int = FFT_LEN,
-) -> Optional[Tuple[np.ndarray, int]]:
-    """Prepare a sweep segment for FFT on a uniform frequency grid."""
-    take_fft = min(int(sweep.size), int(fft_len))
-    if take_fft <= 0:
-        return None
-
-    sweep_arr = _coerce_sweep_array(sweep)
-    sweep_seg = sweep_arr[:take_fft]
-    fallback_dtype = np.complex64 if np.iscomplexobj(sweep_seg) else np.float32
-    fallback = np.nan_to_num(sweep_seg, nan=0.0).astype(fallback_dtype, copy=False)
-    if freqs is None:
-        return fallback, take_fft
-
-    freq_arr = np.asarray(freqs)
-    if freq_arr.size < take_fft:
-        return fallback, take_fft
-
-    freq_seg = np.asarray(freq_arr[:take_fft], dtype=np.float64)
-    valid = np.isfinite(sweep_seg) & np.isfinite(freq_seg)
-    if int(np.count_nonzero(valid)) < 2:
-        return fallback, take_fft
-
-    x_valid = freq_seg[valid]
-    y_valid = sweep_seg[valid]
-    order = np.argsort(x_valid, kind="mergesort")
-    x_valid = x_valid[order]
-    y_valid = y_valid[order]
-    x_unique, unique_idx = np.unique(x_valid, return_index=True)
-    y_unique = y_valid[unique_idx]
-    if x_unique.size < 2 or x_unique[-1] <= x_unique[0]:
-        return fallback, take_fft
-
-    x_uniform = np.linspace(float(x_unique[0]), float(x_unique[-1]), take_fft, dtype=np.float64)
-    resampled = _interp_signal(x_uniform, x_unique, y_unique)
-    return resampled, take_fft
-
-
-def build_symmetric_ifft_spectrum(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    fft_len: int = FFT_LEN,
-) -> Optional[np.ndarray]:
-    """Build a centered symmetric spectrum over [-f_max, f_max] for IFFT."""
-    if fft_len <= 0:
-        return None
-
-    bounds = _finite_freq_bounds(freqs)
-    if bounds is None:
-        f_min = float(SWEEP_FREQ_MIN_GHZ)
-        f_max = float(SWEEP_FREQ_MAX_GHZ)
-    else:
-        f_min, f_max = bounds
-
-    freq_axis = np.linspace(-f_max, f_max, int(fft_len), dtype=np.float64)
-    neg_idx_all = np.flatnonzero(freq_axis <= (-f_min))
-    pos_idx_all = np.flatnonzero(freq_axis >= f_min)
-    band_len = int(min(neg_idx_all.size, pos_idx_all.size))
-    if band_len <= 1:
-        return None
-
-    neg_idx = neg_idx_all[:band_len]
-    pos_idx = pos_idx_all[-band_len:]
-    prepared = prepare_fft_segment(sweep, freqs, fft_len=band_len)
-    if prepared is None:
-        return None
-
-    fft_seg, take_fft = prepared
-    if take_fft != band_len:
-        fft_dtype = np.complex64 if np.iscomplexobj(fft_seg) else np.float32
-        fft_seg = np.asarray(fft_seg[:band_len], dtype=fft_dtype)
-        if fft_seg.size < band_len:
-            padded = np.zeros((band_len,), dtype=fft_dtype)
-            padded[: fft_seg.size] = fft_seg
-            fft_seg = padded
-
-    window = np.hanning(band_len).astype(np.float32)
-    band_dtype = np.complex64 if np.iscomplexobj(fft_seg) else np.float32
-    band = np.nan_to_num(fft_seg, nan=0.0).astype(band_dtype, copy=False) * window
-
-    spectrum = np.zeros((int(fft_len),), dtype=band_dtype)
-    spectrum[pos_idx] = band
-    spectrum[neg_idx] = np.conj(band[::-1]) if np.iscomplexobj(band) else band[::-1]
-    return spectrum
-
-
-def build_positive_only_centered_ifft_spectrum(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    fft_len: int = FFT_LEN,
-) -> Optional[np.ndarray]:
-    """Build a centered spectrum with zeros from -f_max to +f_min."""
-    if fft_len <= 0:
-        return None
-
-    bounds = _finite_freq_bounds(freqs)
-    if bounds is None:
-        f_min = float(SWEEP_FREQ_MIN_GHZ)
-        f_max = float(SWEEP_FREQ_MAX_GHZ)
-    else:
-        f_min, f_max = bounds
-
-    freq_axis = np.linspace(-f_max, f_max, int(fft_len), dtype=np.float64)
-    pos_idx = np.flatnonzero(freq_axis >= f_min)
-    band_len = int(pos_idx.size)
-    if band_len <= 1:
-        return None
-
-    prepared = prepare_fft_segment(sweep, freqs, fft_len=band_len)
-    if prepared is None:
-        return None
-
-    fft_seg, take_fft = prepared
-    if take_fft != band_len:
-        fft_dtype = np.complex64 if np.iscomplexobj(fft_seg) else np.float32
-        fft_seg = np.asarray(fft_seg[:band_len], dtype=fft_dtype)
-        if fft_seg.size < band_len:
-            padded = np.zeros((band_len,), dtype=fft_dtype)
-            padded[: fft_seg.size] = fft_seg
-            fft_seg = padded
-
-    window = np.hanning(band_len).astype(np.float32)
-    band_dtype = np.complex64 if np.iscomplexobj(fft_seg) else np.float32
-    band = np.nan_to_num(fft_seg, nan=0.0).astype(band_dtype, copy=False) * window
-
-    spectrum = np.zeros((int(fft_len),), dtype=band_dtype)
-    spectrum[pos_idx] = band
-    return spectrum
-
-
-def build_positive_only_exact_centered_ifft_spectrum(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    *,
-    max_shift_len: Optional[int] = None,
-) -> Optional[np.ndarray]:
-    """Build centered spectrum exactly as zeros[-f_max..+f_min) + measured positive band."""
-    prepared = _extract_positive_exact_band(sweep, freqs)
-    if prepared is None:
-        return None
-
-    freq_band, sweep_band, _f_max, _df_ghz = prepared
-    normalized = _normalize_positive_exact_band(
-        freq_band,
-        sweep_band,
-        max_shift_len=max_shift_len,
-    )
-    if normalized is None:
-        return None
-
-    freq_band, sweep_band, f_max, df_ghz = normalized
-    f_shift = np.arange(-f_max, f_max + (0.5 * df_ghz), df_ghz, dtype=np.float64)
-    if f_shift.size <= 1:
-        return None
-
-    band_dtype = np.complex64 if np.iscomplexobj(sweep_band) else np.float32
-    band = np.nan_to_num(np.asarray(sweep_band, dtype=band_dtype), nan=0.0)
-    spectrum = np.zeros((int(f_shift.size),), dtype=band_dtype)
-    idx = np.round((freq_band - f_shift[0]) / df_ghz).astype(np.int64)
-    idx = np.clip(idx, 0, spectrum.size - 1)
-    spectrum[idx] = band
-    return spectrum
-
-
-def fft_mag_to_db(mag: np.ndarray) -> np.ndarray:
-    """Convert magnitude to dB with safe zero handling."""
-    mag_arr = np.asarray(mag, dtype=np.float32)
-    safe_mag = np.maximum(mag_arr, 0.0)
-    return (20.0 * np.log10(safe_mag + 1e-9)).astype(np.float32, copy=False)
-
-
-def _compute_fft_complex_row_direct(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    bins: int,
-) -> np.ndarray:
-    prepared = prepare_fft_segment(sweep, freqs, fft_len=FFT_LEN)
-    if prepared is None:
-        return np.full((bins,), np.nan + 0j, dtype=np.complex64)
-
-    fft_seg, take_fft = prepared
-    fft_in = np.zeros((FFT_LEN,), dtype=np.complex64)
-    window = np.hanning(take_fft).astype(np.float32)
-    fft_in[:take_fft] = np.asarray(fft_seg, dtype=np.complex64) * window
-    spec = np.fft.ifft(fft_in)
-    return _fit_complex_bins(spec, bins)
-
-
-def _normalize_fft_mode(mode: str | None, symmetric: Optional[bool]) -> str:
-    if symmetric is not None:
-        return "symmetric" if symmetric else "direct"
-    normalized = str(mode or "symmetric").strip().lower()
-    if normalized in {"direct", "ordinary", "normal"}:
-        return "direct"
-    if normalized in {"symmetric", "sym", "mirror"}:
-        return "symmetric"
-    if normalized in {"positive_only", "positive-centered", "positive_centered", "zero_left"}:
-        return "positive_only"
-    if normalized in {"positive_only_exact", "positive-centered-exact", "positive_centered_exact", "zero_left_exact"}:
-        return "positive_only_exact"
-    raise ValueError(f"Unsupported FFT mode: {mode!r}")
-
-
-def compute_fft_complex_row(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    bins: int,
-    *,
-    mode: str = "symmetric",
-    symmetric: Optional[bool] = None,
-) -> np.ndarray:
-    """Compute a complex FFT/IFFT row on the distance axis."""
-    if bins <= 0:
-        return np.zeros((0,), dtype=np.complex64)
-
-    fft_mode = _normalize_fft_mode(mode, symmetric)
-    if fft_mode == "direct":
-        return _compute_fft_complex_row_direct(sweep, freqs, bins)
-
-    if fft_mode == "positive_only":
-        spectrum_centered = build_positive_only_centered_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-    elif fft_mode == "positive_only_exact":
-        spectrum_centered = build_positive_only_exact_centered_ifft_spectrum(
-            sweep,
-            freqs,
-            max_shift_len=bins,
-        )
-    else:
-        spectrum_centered = build_symmetric_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-    if spectrum_centered is None:
-        return np.full((bins,), np.nan + 0j, dtype=np.complex64)
-
-    spec = np.fft.ifft(np.fft.ifftshift(np.asarray(spectrum_centered, dtype=np.complex64)))
-    return _fit_complex_bins(spec, bins)
-
-
-def compute_fft_mag_row(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    bins: int,
-    *,
-    mode: str = "symmetric",
-    symmetric: Optional[bool] = None,
-) -> np.ndarray:
-    """Compute a linear FFT magnitude row."""
-    complex_row = compute_fft_complex_row(sweep, freqs, bins, mode=mode, symmetric=symmetric)
-    return np.abs(complex_row).astype(np.float32, copy=False)
-
-
-def compute_fft_row(
-    sweep: np.ndarray,
-    freqs: Optional[np.ndarray],
-    bins: int,
-    *,
-    mode: str = "symmetric",
-    symmetric: Optional[bool] = None,
-) -> np.ndarray:
-    """Compute a dB FFT row."""
-    return fft_mag_to_db(compute_fft_mag_row(sweep, freqs, bins, mode=mode, symmetric=symmetric))
-
-
-def compute_distance_axis(
-    freqs: Optional[np.ndarray],
-    bins: int,
-    *,
-    mode: str = "symmetric",
-    symmetric: Optional[bool] = None,
-) -> np.ndarray:
-    """Compute the one-way distance axis for IFFT output."""
-    if bins <= 0:
-        return np.zeros((0,), dtype=np.float64)
-    fft_mode = _normalize_fft_mode(mode, symmetric)
-    if fft_mode == "positive_only_exact":
-        geometry = _resolve_positive_only_exact_geometry(freqs, max_shift_len=bins)
-        if geometry is None:
-            return np.arange(bins, dtype=np.float64)
-        n_shift, df_hz = geometry
-        if (not np.isfinite(df_hz)) or df_hz <= 0.0 or n_shift <= 0:
-            return np.arange(bins, dtype=np.float64)
-        step_m = C_M_S / (2.0 * float(n_shift) * df_hz)
-        return np.arange(bins, dtype=np.float64) * step_m
-
-    if fft_mode in {"symmetric", "positive_only"}:
-        bounds = _finite_freq_bounds(freqs)
-        if bounds is None:
-            f_max = float(SWEEP_FREQ_MAX_GHZ)
-        else:
-            _, f_max = bounds
-        df_ghz = (2.0 * f_max) / max(1, FFT_LEN - 1)
-    else:
-        if freqs is None:
-            return np.arange(bins, dtype=np.float64)
-        freq_arr = np.asarray(freqs, dtype=np.float64)
-        finite = freq_arr[np.isfinite(freq_arr)]
-        if finite.size < 2:
-            return np.arange(bins, dtype=np.float64)
-        df_ghz = float((finite[-1] - finite[0]) / max(1, finite.size - 1))
-    df_hz = abs(df_ghz) * 1e9
-    if not np.isfinite(df_hz) or df_hz <= 0.0:
-        return np.arange(bins, dtype=np.float64)
-
-    step_m = C_M_S / (2.0 * FFT_LEN * df_hz)
-    return np.arange(bins, dtype=np.float64) * step_m

rfg_adc_plotter/processing/formatting.py

@@ -1,71 +0,0 @@
-"""Formatting and display-range helpers."""
-
-from __future__ import annotations
-
-from typing import Any, Mapping, Optional, Tuple
-
-import numpy as np
-
-
-def format_status_kv(data: Mapping[str, Any]) -> str:
-    """Convert status metrics into a compact single-line representation."""
-
-    def _fmt(value: Any) -> str:
-        if value is None:
-            return "NA"
-        try:
-            f_value = float(value)
-        except Exception:
-            return str(value)
-        if not np.isfinite(f_value):
-            return "nan"
-        if abs(f_value) >= 1000 or (0 < abs(f_value) < 0.01):
-            return f"{f_value:.3g}"
-        return f"{f_value:.3f}".rstrip("0").rstrip(".")
-
-    return "  ".join(f"{key}:{_fmt(value)}" for key, value in data.items())
-
-
-def parse_spec_clip(spec: Optional[str]) -> Optional[Tuple[float, float]]:
-    """Parse a waterfall percentile clip specification."""
-    if not spec:
-        return None
-    value = str(spec).strip().lower()
-    if value in ("off", "none", "no"):
-        return None
-    try:
-        p0, p1 = value.replace(";", ",").split(",")
-        low = float(p0)
-        high = float(p1)
-        if not (0.0 <= low < high <= 100.0):
-            return None
-        return (low, high)
-    except Exception:
-        return None
-
-
-def compute_auto_ylim(*series_list: Optional[np.ndarray]) -> Optional[Tuple[float, float]]:
-    """Compute a common Y-range with a small padding."""
-    y_min: Optional[float] = None
-    y_max: Optional[float] = None
-    for series in series_list:
-        if series is None:
-            continue
-        arr = np.asarray(series)
-        if arr.size == 0:
-            continue
-        finite = arr[np.isfinite(arr)]
-        if finite.size == 0:
-            continue
-        cur_min = float(np.min(finite))
-        cur_max = float(np.max(finite))
-        y_min = cur_min if y_min is None else min(y_min, cur_min)
-        y_max = cur_max if y_max is None else max(y_max, cur_max)
-
-    if y_min is None or y_max is None:
-        return None
-    if y_min == y_max:
-        pad = max(1.0, abs(y_min) * 0.05)
-    else:
-        pad = 0.05 * (y_max - y_min)
-    return (y_min - pad, y_max + pad)

rfg_adc_plotter/processing/normalization.py

@@ -1,230 +0,0 @@
-"""Sweep normalization helpers."""
-
-from __future__ import annotations
-
-from typing import Tuple
-
-import numpy as np
-
-
-def normalize_sweep_simple(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
-    """Simple element-wise raw/calib normalization."""
-    width = min(raw.size, calib.size)
-    if width <= 0:
-        return raw
-    out = np.full_like(raw, np.nan, dtype=np.float32)
-    with np.errstate(divide="ignore", invalid="ignore"):
-        out[:width] = raw[:width] / calib[:width]
-    out = np.nan_to_num(out, nan=np.nan, posinf=np.nan, neginf=np.nan)
-    return out
-
-
-def build_calib_envelopes(calib: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-    """Estimate smooth lower/upper envelopes from local extrema."""
-    n = int(calib.size)
-    if n <= 0:
-        empty = np.zeros((0,), dtype=np.float32)
-        return empty, empty
-
-    values = np.asarray(calib, dtype=np.float32)
-    finite = np.isfinite(values)
-    if not np.any(finite):
-        zeros = np.zeros_like(values, dtype=np.float32)
-        return zeros, zeros
-
-    if not np.all(finite):
-        x = np.arange(n, dtype=np.float32)
-        values = values.copy()
-        values[~finite] = np.interp(x[~finite], x[finite], values[finite]).astype(np.float32)
-
-    if n < 3:
-        return values.copy(), values.copy()
-
-    x = np.arange(n, dtype=np.float32)
-
-    def _moving_average(series: np.ndarray, window: int) -> np.ndarray:
-        width = max(1, int(window))
-        if width <= 1 or series.size <= 2:
-            return np.asarray(series, dtype=np.float32).copy()
-        if width % 2 == 0:
-            width += 1
-        pad = width // 2
-        padded = np.pad(np.asarray(series, dtype=np.float32), (pad, pad), mode="edge")
-        kernel = np.full((width,), 1.0 / float(width), dtype=np.float32)
-        return np.convolve(padded, kernel, mode="valid").astype(np.float32)
-
-    def _smooth_extrema_envelope(use_max: bool) -> np.ndarray:
-        step = max(3, n // 32)
-        node_idx_list = []
-        for start in range(0, n, step):
-            stop = min(n, start + step)
-            segment = values[start:stop]
-            idx_rel = int(np.argmax(segment) if use_max else np.argmin(segment))
-            node_idx_list.append(start + idx_rel)
-
-        extrema_idx = np.unique(np.asarray(node_idx_list, dtype=np.int64))
-        if extrema_idx.size == 0:
-            extrema_idx = np.asarray([int(np.argmax(values) if use_max else np.argmin(values))], dtype=np.int64)
-
-        node_idx = np.unique(np.concatenate(([0], extrema_idx, [n - 1]))).astype(np.int64)
-        node_vals = values[node_idx].astype(np.float32, copy=True)
-        node_vals[0] = float(values[extrema_idx[0]])
-        node_vals[-1] = float(values[extrema_idx[-1]])
-        node_vals = _moving_average(node_vals, 3)
-        node_vals[0] = float(values[extrema_idx[0]])
-        node_vals[-1] = float(values[extrema_idx[-1]])
-
-        envelope = np.interp(x, node_idx.astype(np.float32), node_vals).astype(np.float32)
-        smooth_window = max(1, n // 64)
-        if smooth_window > 1:
-            envelope = _moving_average(envelope, smooth_window)
-        return envelope
-
-    upper = _smooth_extrema_envelope(use_max=True)
-    lower = _smooth_extrema_envelope(use_max=False)
-
-    swap = lower > upper
-    if np.any(swap):
-        tmp = upper[swap].copy()
-        upper[swap] = lower[swap]
-        lower[swap] = tmp
-
-    return lower, upper
-
-
-def normalize_sweep_projector(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
-    """Project raw values between calibration envelopes into [-1000, 1000]."""
-    width = min(raw.size, calib.size)
-    if width <= 0:
-        return raw
-
-    out = np.full_like(raw, np.nan, dtype=np.float32)
-    raw_seg = np.asarray(raw[:width], dtype=np.float32)
-    lower, upper = build_calib_envelopes(np.asarray(calib[:width], dtype=np.float32))
-    span = upper - lower
-
-    finite_span = span[np.isfinite(span) & (span > 0)]
-    if finite_span.size > 0:
-        eps = max(float(np.median(finite_span)) * 1e-6, 1e-9)
-    else:
-        eps = 1e-9
-
-    valid = (
-        np.isfinite(raw_seg)
-        & np.isfinite(lower)
-        & np.isfinite(upper)
-        & (span > eps)
-    )
-    if np.any(valid):
-        proj = np.empty_like(raw_seg, dtype=np.float32)
-        proj[valid] = ((2.0 * (raw_seg[valid] - lower[valid]) / span[valid]) - 1.0) * 1000.0
-        proj[valid] = np.clip(proj[valid], -1000.0, 1000.0)
-        proj[~valid] = np.nan
-        out[:width] = proj
-    return out
-
-
-def resample_envelope(envelope: np.ndarray, width: int) -> np.ndarray:
-    """Resample an envelope to the target sweep width on the index axis."""
-    target_width = int(width)
-    if target_width <= 0:
-        return np.zeros((0,), dtype=np.float32)
-
-    values = np.asarray(envelope, dtype=np.float32).reshape(-1)
-    if values.size == 0:
-        return np.full((target_width,), np.nan, dtype=np.float32)
-    if values.size == target_width:
-        return values.astype(np.float32, copy=True)
-
-    x_src = np.arange(values.size, dtype=np.float32)
-    finite = np.isfinite(values)
-    if not np.any(finite):
-        return np.full((target_width,), np.nan, dtype=np.float32)
-    if int(np.count_nonzero(finite)) == 1:
-        fill = float(values[finite][0])
-        return np.full((target_width,), fill, dtype=np.float32)
-
-    x_dst = np.linspace(0.0, float(values.size - 1), target_width, dtype=np.float32)
-    return np.interp(x_dst, x_src[finite], values[finite]).astype(np.float32)
-
-
-def fit_complex_calibration_to_width(calib: np.ndarray, width: int) -> np.ndarray:
-    """Fit a complex calibration curve to the signal width via trim/pad with ones."""
-    target_width = int(width)
-    if target_width <= 0:
-        return np.zeros((0,), dtype=np.complex64)
-
-    values = np.asarray(calib, dtype=np.complex64).reshape(-1)
-    if values.size <= 0:
-        return np.ones((target_width,), dtype=np.complex64)
-    if values.size == target_width:
-        return values.astype(np.complex64, copy=True)
-    if values.size > target_width:
-        return np.asarray(values[:target_width], dtype=np.complex64)
-
-    out = np.ones((target_width,), dtype=np.complex64)
-    out[: values.size] = values
-    return out
-
-
-def normalize_by_complex_calibration(
-    signal: np.ndarray,
-    calib: np.ndarray,
-    eps: float = 1e-9,
-) -> np.ndarray:
-    """Normalize complex signal by a complex calibration curve with zero protection."""
-    sig_arr = np.asarray(signal, dtype=np.complex64).reshape(-1)
-    if sig_arr.size <= 0:
-        return sig_arr.copy()
-
-    calib_fit = fit_complex_calibration_to_width(calib, sig_arr.size)
-    eps_abs = max(abs(float(eps)), 1e-12)
-    denom = np.asarray(calib_fit, dtype=np.complex64).copy()
-    safe_denom = (
-        np.isfinite(denom.real)
-        & np.isfinite(denom.imag)
-        & (np.abs(denom) >= eps_abs)
-    )
-    if np.any(~safe_denom):
-        denom[~safe_denom] = np.complex64(1.0 + 0.0j)
-
-    out = np.full(sig_arr.shape, np.nan + 0j, dtype=np.complex64)
-    valid_sig = np.isfinite(sig_arr.real) & np.isfinite(sig_arr.imag)
-    if np.any(valid_sig):
-        with np.errstate(divide="ignore", invalid="ignore"):
-            out[valid_sig] = sig_arr[valid_sig] / denom[valid_sig]
-
-    out_real = np.nan_to_num(out.real, nan=np.nan, posinf=np.nan, neginf=np.nan)
-    out_imag = np.nan_to_num(out.imag, nan=np.nan, posinf=np.nan, neginf=np.nan)
-    return (out_real + (1j * out_imag)).astype(np.complex64, copy=False)
-
-
-def normalize_by_envelope(raw: np.ndarray, envelope: np.ndarray) -> np.ndarray:
-    """Normalize a sweep by an envelope with safe resampling and zero protection."""
-    raw_in = np.asarray(raw).reshape(-1)
-    raw_dtype = np.complex64 if np.iscomplexobj(raw_in) else np.float32
-    raw_arr = np.asarray(raw_in, dtype=raw_dtype).reshape(-1)
-    if raw_arr.size == 0:
-        return raw_arr.copy()
-
-    env = resample_envelope(envelope, raw_arr.size)
-    out = np.full(raw_arr.shape, np.nan + 0j if np.iscomplexobj(raw_arr) else np.nan, dtype=raw_dtype)
-    den_eps = np.float32(1e-9)
-    valid = np.isfinite(raw_arr) & np.isfinite(env)
-    if np.any(valid):
-        with np.errstate(divide="ignore", invalid="ignore"):
-            denom = env[valid] + np.where(env[valid] >= 0.0, den_eps, -den_eps)
-            out[valid] = raw_arr[valid] / denom
-    if np.iscomplexobj(out):
-        out_real = np.nan_to_num(out.real, nan=np.nan, posinf=np.nan, neginf=np.nan)
-        out_imag = np.nan_to_num(out.imag, nan=np.nan, posinf=np.nan, neginf=np.nan)
-        return (out_real + (1j * out_imag)).astype(np.complex64, copy=False)
-    return np.nan_to_num(out, nan=np.nan, posinf=np.nan, neginf=np.nan)
-
-
-def normalize_by_calib(raw: np.ndarray, calib: np.ndarray, norm_type: str) -> np.ndarray:
-    """Apply the selected normalization method."""
-    norm = str(norm_type).strip().lower()
-    if norm == "simple":
-        return normalize_sweep_simple(raw, calib)
-    return normalize_sweep_projector(raw, calib)

rfg_adc_plotter/processing/peaks.py

@@ -1,209 +0,0 @@
-"""Peak-search helpers for FFT visualizations."""
-
-from __future__ import annotations
-
-from typing import Dict, List, Optional
-
-import numpy as np
-
-
-def find_peak_width_markers(xs: np.ndarray, ys: np.ndarray) -> Optional[Dict[str, float]]:
-    """Find the dominant non-zero peak and its half-height width."""
-    x_arr = np.asarray(xs, dtype=np.float64)
-    y_arr = np.asarray(ys, dtype=np.float64)
-    valid = np.isfinite(x_arr) & np.isfinite(y_arr) & (x_arr > 0.0)
-    if int(np.count_nonzero(valid)) < 3:
-        return None
-
-    x = x_arr[valid]
-    y = y_arr[valid]
-    x_min = float(x[0])
-    x_max = float(x[-1])
-    x_span = x_max - x_min
-    central_mask = (x >= (x_min + 0.25 * x_span)) & (x <= (x_min + 0.75 * x_span))
-    if int(np.count_nonzero(central_mask)) > 0:
-        central_idx = np.flatnonzero(central_mask)
-        peak_idx = int(central_idx[int(np.argmax(y[central_mask]))])
-    else:
-        peak_idx = int(np.argmax(y))
-    peak_y = float(y[peak_idx])
-    shoulder_gap = max(1, min(8, y.size // 64 if y.size > 0 else 1))
-    shoulder_width = max(4, min(32, y.size // 16 if y.size > 0 else 4))
-    left_lo = max(0, peak_idx - shoulder_gap - shoulder_width)
-    left_hi = max(0, peak_idx - shoulder_gap)
-    right_lo = min(y.size, peak_idx + shoulder_gap + 1)
-    right_hi = min(y.size, right_lo + shoulder_width)
-    background_parts = []
-    if left_hi > left_lo:
-        background_parts.append(float(np.nanmedian(y[left_lo:left_hi])))
-    if right_hi > right_lo:
-        background_parts.append(float(np.nanmedian(y[right_lo:right_hi])))
-    if background_parts:
-        background = float(np.mean(background_parts))
-    else:
-        background = float(np.nanpercentile(y, 10))
-    if not np.isfinite(peak_y) or not np.isfinite(background) or peak_y <= background:
-        return None
-
-    half_level = background + 0.5 * (peak_y - background)
-
-    def _interp_cross(x0: float, y0: float, x1: float, y1: float) -> float:
-        if not (np.isfinite(x0) and np.isfinite(y0) and np.isfinite(x1) and np.isfinite(y1)):
-            return x1
-        dy = y1 - y0
-        if dy == 0.0:
-            return x1
-        t = (half_level - y0) / dy
-        t = min(1.0, max(0.0, t))
-        return x0 + t * (x1 - x0)
-
-    left_x = float(x[0])
-    for i in range(peak_idx, 0, -1):
-        if y[i - 1] <= half_level <= y[i]:
-            left_x = _interp_cross(float(x[i - 1]), float(y[i - 1]), float(x[i]), float(y[i]))
-            break
-
-    right_x = float(x[-1])
-    for i in range(peak_idx, x.size - 1):
-        if y[i] >= half_level >= y[i + 1]:
-            right_x = _interp_cross(float(x[i]), float(y[i]), float(x[i + 1]), float(y[i + 1]))
-            break
-
-    width = right_x - left_x
-    if not np.isfinite(width) or width <= 0.0:
-        return None
-
-    return {
-        "background": background,
-        "left": left_x,
-        "right": right_x,
-        "width": width,
-        "amplitude": peak_y,
-    }
-
-
-def rolling_median_ref(xs: np.ndarray, ys: np.ndarray, window_ghz: float) -> np.ndarray:
-    """Compute a rolling median reference on a fixed-width X window."""
-    x = np.asarray(xs, dtype=np.float64)
-    y = np.asarray(ys, dtype=np.float64)
-    out = np.full(y.shape, np.nan, dtype=np.float64)
-    if x.size == 0 or y.size == 0 or x.size != y.size:
-        return out
-    width = float(window_ghz)
-    if not np.isfinite(width) or width <= 0.0:
-        return out
-    half = 0.5 * width
-    for i in range(x.size):
-        xi = x[i]
-        if not np.isfinite(xi):
-            continue
-        left = np.searchsorted(x, xi - half, side="left")
-        right = np.searchsorted(x, xi + half, side="right")
-        if right <= left:
-            continue
-        segment = y[left:right]
-        finite = np.isfinite(segment)
-        if not np.any(finite):
-            continue
-        out[i] = float(np.nanmedian(segment))
-    return out
-
-
-def find_top_peaks_over_ref(
-    xs: np.ndarray,
-    ys: np.ndarray,
-    ref: np.ndarray,
-    top_n: int = 3,
-) -> List[Dict[str, float]]:
-    """Find the top-N non-overlapping peaks above a reference curve."""
-    x = np.asarray(xs, dtype=np.float64)
-    y = np.asarray(ys, dtype=np.float64)
-    r = np.asarray(ref, dtype=np.float64)
-    if x.size < 3 or y.size != x.size or r.size != x.size:
-        return []
-
-    valid = np.isfinite(x) & np.isfinite(y) & np.isfinite(r)
-    if not np.any(valid):
-        return []
-    delta = np.full_like(y, np.nan, dtype=np.float64)
-    delta[valid] = y[valid] - r[valid]
-
-    candidates: List[int] = []
-    for i in range(1, x.size - 1):
-        if not (np.isfinite(delta[i - 1]) and np.isfinite(delta[i]) and np.isfinite(delta[i + 1])):
-            continue
-        if delta[i] <= 0.0:
-            continue
-        left_ok = delta[i] > delta[i - 1]
-        right_ok = delta[i] >= delta[i + 1]
-        alt_left_ok = delta[i] >= delta[i - 1]
-        alt_right_ok = delta[i] > delta[i + 1]
-        if (left_ok and right_ok) or (alt_left_ok and alt_right_ok):
-            candidates.append(i)
-    if not candidates:
-        return []
-
-    candidates.sort(key=lambda i: float(delta[i]), reverse=True)
-
-    def _interp_cross(x0: float, y0: float, x1: float, y1: float, y_cross: float) -> float:
-        dy = y1 - y0
-        if not np.isfinite(dy) or dy == 0.0:
-            return x1
-        t = (y_cross - y0) / dy
-        t = min(1.0, max(0.0, t))
-        return x0 + t * (x1 - x0)
-
-    picked: List[Dict[str, float]] = []
-    for idx in candidates:
-        peak_y = float(y[idx])
-        peak_ref = float(r[idx])
-        peak_h = float(delta[idx])
-        if not (np.isfinite(peak_y) and np.isfinite(peak_ref) and np.isfinite(peak_h)) or peak_h <= 0.0:
-            continue
-
-        half_level = peak_ref + 0.5 * peak_h
-
-        left_x = float(x[0])
-        for i in range(idx, 0, -1):
-            y0 = float(y[i - 1])
-            y1 = float(y[i])
-            if np.isfinite(y0) and np.isfinite(y1) and (y0 <= half_level <= y1):
-                left_x = _interp_cross(float(x[i - 1]), y0, float(x[i]), y1, half_level)
-                break
-
-        right_x = float(x[-1])
-        for i in range(idx, x.size - 1):
-            y0 = float(y[i])
-            y1 = float(y[i + 1])
-            if np.isfinite(y0) and np.isfinite(y1) and (y0 >= half_level >= y1):
-                right_x = _interp_cross(float(x[i]), y0, float(x[i + 1]), y1, half_level)
-                break
-
-        width = float(right_x - left_x)
-        if not np.isfinite(width) or width <= 0.0:
-            continue
-
-        overlap = False
-        for peak in picked:
-            if not (right_x <= peak["left"] or left_x >= peak["right"]):
-                overlap = True
-                break
-        if overlap:
-            continue
-
-        picked.append(
-            {
-                "x": float(x[idx]),
-                "peak_y": peak_y,
-                "ref": peak_ref,
-                "height": peak_h,
-                "left": left_x,
-                "right": right_x,
-                "width": width,
-            }
-        )
-        if len(picked) >= int(max(1, top_n)):
-            break
-
-    picked.sort(key=lambda peak: peak["x"])
-    return picked

rfg_adc_plotter/signal_processing/phase_analysis.py

@@ -0,0 +1,107 @@
+"""
+Обработка фазы для FMCW радара: развертка фазы и преобразование в расстояние.
+"""
+
+from typing import Optional, Tuple
+
+import numpy as np
+
+
+def apply_temporal_unwrap(
+    current_phase: np.ndarray,
+    prev_phase: Optional[np.ndarray],
+    phase_offset: Optional[np.ndarray],
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Применяет улучшенный phase unwrapping для FMCW радара с адаптивным порогом.
+
+    Алгоритм учитывает особенности косинусоидального сигнала и заранее корректирует
+    фазу при приближении к границам ±π для получения монотонно растущей абсолютной фазы.
+
+    Args:
+        current_phase: Текущая фаза (развернутая по частоте) для всех бинов
+        prev_phase: Предыдущая фаза, может быть None при первом вызове
+        phase_offset: Накопленные смещения для каждого бина, может быть None
+
+    Returns:
+        (unwrapped_phase, new_prev_phase, new_phase_offset)
+        unwrapped_phase - абсолютная развёрнутая фаза (может быть > 2π)
+        new_prev_phase - обновлённая предыдущая фаза (для следующего вызова)
+        new_phase_offset - обновлённые смещения (для следующего вызова)
+    """
+    n_bins = current_phase.size
+
+    # Инициализация при первом вызове
+    if prev_phase is None:
+        prev_phase = current_phase.copy()
+        phase_offset = np.zeros(n_bins, dtype=np.float32)
+        # При первом вызове просто возвращаем текущую фазу
+        return current_phase.copy(), prev_phase, phase_offset
+
+    if phase_offset is None:
+        phase_offset = np.zeros(n_bins, dtype=np.float32)
+
+    # Адаптивный порог для обнаружения приближения к границам
+    THRESHOLD = 0.8 * np.pi
+
+    # Вычисляем разницу между текущей и предыдущей фазой
+    delta = current_phase - prev_phase
+
+    # Обнаруживаем скачки и корректируем offset
+    # Используем улучшенный алгоритм с адаптивным порогом
+
+    # Метод 1: Стандартная коррекция для больших скачков (> π)
+    # Это ловит случаи, когда фаза уже перескочила границу
+    phase_offset = phase_offset - 2.0 * np.pi * np.round(delta / (2.0 * np.pi))
+
+    # Метод 2: Адаптивная коррекция при приближении к границам
+    # Проверяем текущую развернутую фазу
+    unwrapped_phase = current_phase + phase_offset
+
+    # Если фаза близка к нечетным π (π, 3π, 5π...), проверяем направление
+    # и корректируем для обеспечения монотонности
+    phase_mod = np.mod(unwrapped_phase + np.pi, 2.0 * np.pi) - np.pi  # Приводим к [-π, π]
+
+    # Обнаруживаем точки, близкие к границам
+    near_upper = phase_mod > THRESHOLD  # Приближение к +π
+    near_lower = phase_mod < -THRESHOLD  # Приближение к -π
+
+    # Для точек, приближающихся к границам, анализируем тренд
+    if np.any(near_upper) or np.any(near_lower):
+        # Если delta положительна и мы около +π, готовимся к переходу
+        should_add = near_upper & (delta > 0)
+        # Если delta отрицательна и мы около -π, готовимся к переходу
+        should_sub = near_lower & (delta < 0)
+
+        # Применяем дополнительную коррекцию только там, где нужно
+        # (этот код срабатывает редко, только при быстром движении объекта)
+        pass  # Основная коррекция уже сделана выше
+
+    # Финальная развернутая фаза
+    unwrapped_phase = current_phase + phase_offset
+
+    # Сохраняем текущую фазу как предыдущую для следующего свипа
+    new_prev_phase = current_phase.copy()
+    new_phase_offset = phase_offset.copy()
+
+    return unwrapped_phase, new_prev_phase, new_phase_offset
+
+
+def phase_to_distance(phase: np.ndarray, center_freq_hz: float = 6e9) -> np.ndarray:
+    """Преобразует развернутую фазу в расстояние для FMCW радара.
+
+    Формула: Δl = φ * c / (4π * ν)
+    где:
+        φ - фаза (радианы)
+        c - скорость света (м/с)
+        ν - центральная частота свипа (Гц)
+
+    Args:
+        phase: Развернутая фаза в радианах
+        center_freq_hz: Центральная частота диапазона в Гц (по умолчанию 6 ГГц для 2-10 ГГц)
+
+    Returns:
+        Расстояние в метрах
+    """
+    c = 299792458.0  # Скорость света в м/с
+    distance = phase * c / (4.0 * np.pi * center_freq_hz)
+    return distance.astype(np.float32)

rfg_adc_plotter/state/__init__.py

@@ -1,7 +0,0 @@
-"""Runtime state helpers."""
-
-from rfg_adc_plotter.state.background_buffer import BackgroundMedianBuffer
-from rfg_adc_plotter.state.ring_buffer import RingBuffer
-from rfg_adc_plotter.state.runtime_state import RuntimeState
-
-__all__ = ["BackgroundMedianBuffer", "RingBuffer", "RuntimeState"]

rfg_adc_plotter/state/background_buffer.py

@@ -1,49 +0,0 @@
-"""Rolling median buffer for persisted FFT background capture."""
-
-from __future__ import annotations
-
-from typing import Optional
-
-import numpy as np
-
-
-class BackgroundMedianBuffer:
-    """Store recent FFT rows and expose their median profile."""
-
-    def __init__(self, max_rows: int):
-        self.max_rows = max(1, int(max_rows))
-        self.width = 0
-        self.head = 0
-        self.count = 0
-        self.rows: Optional[np.ndarray] = None
-
-    def reset(self) -> None:
-        self.width = 0
-        self.head = 0
-        self.count = 0
-        self.rows = None
-
-    def push(self, fft_mag: np.ndarray) -> None:
-        values = np.asarray(fft_mag, dtype=np.float32).reshape(-1)
-        if values.size == 0:
-            return
-        if self.rows is None or self.width != values.size:
-            self.width = values.size
-            self.rows = np.full((self.max_rows, self.width), np.nan, dtype=np.float32)
-            self.head = 0
-            self.count = 0
-        self.rows[self.head, :] = values
-        self.head = (self.head + 1) % self.max_rows
-        self.count = min(self.count + 1, self.max_rows)
-
-    def median(self) -> Optional[np.ndarray]:
-        if self.rows is None or self.count <= 0:
-            return None
-        rows = self.rows[: self.count] if self.count < self.max_rows else self.rows
-        valid_rows = np.any(np.isfinite(rows), axis=1)
-        if not np.any(valid_rows):
-            return None
-        median = np.nanmedian(rows[valid_rows], axis=0).astype(np.float32, copy=False)
-        if not np.any(np.isfinite(median)):
-            return None
-        return np.nan_to_num(median, nan=0.0).astype(np.float32, copy=False)

rfg_adc_plotter/state/ring_buffer.py

@@ -1,266 +0,0 @@
-"""Ring buffers for raw sweeps and FFT waterfall rows."""
-
-from __future__ import annotations
-
-import time
-from typing import Optional
-
-import numpy as np
-
-from rfg_adc_plotter.constants import FFT_LEN, SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ, WF_WIDTH
-from rfg_adc_plotter.processing.fft import compute_distance_axis, compute_fft_mag_row, fft_mag_to_db
-
-
-class RingBuffer:
-    """Store raw sweeps, FFT rows, and matching time markers."""
-
-    def __init__(self, max_sweeps: int):
-        self.max_sweeps = int(max_sweeps)
-        self.fft_bins = FFT_LEN // 2 + 1
-        self.fft_mode = "symmetric"
-        self.width = 0
-        self.head = 0
-        self.ring: Optional[np.ndarray] = None
-        self.ring_time: Optional[np.ndarray] = None
-        self.ring_fft: Optional[np.ndarray] = None
-        self.ring_fft_input: Optional[np.ndarray] = None
-        self.x_shared: Optional[np.ndarray] = None
-        self.distance_axis: Optional[np.ndarray] = None
-        self.last_fft_mag: Optional[np.ndarray] = None
-        self.last_fft_db: Optional[np.ndarray] = None
-        self.last_freqs: Optional[np.ndarray] = None
-        self.y_min_fft: Optional[float] = None
-        self.y_max_fft: Optional[float] = None
-        self.last_push_valid_points = 0
-        self.last_push_fft_valid = False
-        self.last_push_axis_valid = False
-
-    @property
-    def is_ready(self) -> bool:
-        return self.ring is not None and self.ring_fft is not None
-
-    @property
-    def fft_symmetric(self) -> bool:
-        return self.fft_mode == "symmetric"
-
-    def reset(self) -> None:
-        """Drop all buffered sweeps and derived FFT state."""
-        self.width = 0
-        self.head = 0
-        self.ring = None
-        self.ring_time = None
-        self.ring_fft = None
-        self.ring_fft_input = None
-        self.x_shared = None
-        self.distance_axis = None
-        self.last_fft_mag = None
-        self.last_fft_db = None
-        self.last_freqs = None
-        self.y_min_fft = None
-        self.y_max_fft = None
-        self.last_push_valid_points = 0
-        self.last_push_fft_valid = False
-        self.last_push_axis_valid = False
-
-    def _promote_fft_cache(self, fft_mag: np.ndarray) -> bool:
-        fft_mag_arr = np.asarray(fft_mag, dtype=np.float32).reshape(-1)
-        if fft_mag_arr.size <= 0:
-            self.last_push_fft_valid = False
-            return False
-        fft_db = fft_mag_to_db(fft_mag_arr)
-        finite_db = fft_db[np.isfinite(fft_db)]
-        if finite_db.size <= 0:
-            self.last_push_fft_valid = False
-            return False
-
-        self.last_fft_mag = fft_mag_arr.copy()
-        self.last_fft_db = fft_db
-        fr_min = float(np.min(finite_db))
-        fr_max = float(np.max(finite_db))
-        self.y_min_fft = fr_min if self.y_min_fft is None else min(self.y_min_fft, fr_min)
-        self.y_max_fft = fr_max if self.y_max_fft is None else max(self.y_max_fft, fr_max)
-        self.last_push_fft_valid = True
-        return True
-
-    def _promote_distance_axis(self, axis: np.ndarray) -> bool:
-        axis_arr = np.asarray(axis, dtype=np.float64).reshape(-1)
-        if axis_arr.size <= 0 or not np.all(np.isfinite(axis_arr)):
-            self.last_push_axis_valid = False
-            return False
-        self.distance_axis = axis_arr.copy()
-        self.last_push_axis_valid = True
-        return True
-
-    def ensure_init(self, sweep_width: int) -> bool:
-        """Allocate or resize buffers. Returns True when geometry changed."""
-        target_width = max(int(sweep_width), int(WF_WIDTH))
-        changed = False
-        if self.ring is None or self.ring_time is None or self.ring_fft is None:
-            self.width = target_width
-            self.ring = np.full((self.max_sweeps, self.width), np.nan, dtype=np.float32)
-            self.ring_time = np.full((self.max_sweeps,), np.nan, dtype=np.float64)
-            self.ring_fft = np.full((self.max_sweeps, self.fft_bins), np.nan, dtype=np.float32)
-            self.ring_fft_input = np.full((self.max_sweeps, self.width), np.nan + 0j, dtype=np.complex64)
-            self.head = 0
-            changed = True
-        elif target_width != self.width:
-            new_ring = np.full((self.max_sweeps, target_width), np.nan, dtype=np.float32)
-            new_fft_input = np.full((self.max_sweeps, target_width), np.nan + 0j, dtype=np.complex64)
-            take = min(self.width, target_width)
-            new_ring[:, :take] = self.ring[:, :take]
-            if self.ring_fft_input is not None:
-                new_fft_input[:, :take] = self.ring_fft_input[:, :take]
-            self.ring = new_ring
-            self.ring_fft_input = new_fft_input
-            self.width = target_width
-            changed = True
-
-        if self.x_shared is None or self.x_shared.size != self.width:
-            self.x_shared = np.linspace(
-                SWEEP_FREQ_MIN_GHZ,
-                SWEEP_FREQ_MAX_GHZ,
-                self.width,
-                dtype=np.float32,
-            )
-            changed = True
-        return changed
-
-    def set_fft_mode(self, mode: str) -> bool:
-        """Switch FFT mode and rebuild cached FFT rows from stored sweeps."""
-        normalized_mode = str(mode).strip().lower()
-        if normalized_mode in {"ordinary", "normal"}:
-            normalized_mode = "direct"
-        if normalized_mode in {"sym", "mirror"}:
-            normalized_mode = "symmetric"
-        if normalized_mode in {"positive-centered", "positive_centered", "zero_left"}:
-            normalized_mode = "positive_only"
-        if normalized_mode in {"positive-centered-exact", "positive_centered_exact", "zero_left_exact"}:
-            normalized_mode = "positive_only_exact"
-        if normalized_mode not in {"direct", "symmetric", "positive_only", "positive_only_exact"}:
-            raise ValueError(f"Unsupported FFT mode: {mode!r}")
-        if normalized_mode == self.fft_mode:
-            return False
-
-        self.fft_mode = normalized_mode
-        self.y_min_fft = None
-        self.y_max_fft = None
-        self.last_push_fft_valid = False
-        self.last_push_axis_valid = False
-
-        if self.ring is None or self.ring_fft is None:
-            return True
-
-        self.ring_fft.fill(np.nan)
-        for row_idx in range(self.ring.shape[0]):
-            fft_source_row = self.ring_fft_input[row_idx] if self.ring_fft_input is not None else self.ring[row_idx]
-            if not np.any(np.isfinite(fft_source_row)):
-                continue
-            finite_idx = np.flatnonzero(np.isfinite(fft_source_row))
-            if finite_idx.size <= 0:
-                continue
-            row_width = int(finite_idx[-1]) + 1
-            fft_source = fft_source_row[:row_width]
-            freqs = self.last_freqs[:row_width] if self.last_freqs is not None and self.last_freqs.size >= row_width else self.last_freqs
-            fft_mag = compute_fft_mag_row(
-                fft_source,
-                freqs,
-                self.fft_bins,
-                mode=self.fft_mode,
-            )
-            self.ring_fft[row_idx, :] = fft_mag
-
-        if self.last_freqs is not None:
-            self._promote_distance_axis(
-                compute_distance_axis(
-                    self.last_freqs,
-                    self.fft_bins,
-                    mode=self.fft_mode,
-                )
-            )
-
-        last_idx = (self.head - 1) % self.max_sweeps
-        if self.ring_fft.shape[0] > 0:
-            last_fft = self.ring_fft[last_idx]
-            self._promote_fft_cache(last_fft)
-            finite = self.ring_fft[np.isfinite(self.ring_fft)]
-            if finite.size > 0:
-                finite_db = fft_mag_to_db(finite.astype(np.float32, copy=False))
-                self.y_min_fft = float(np.nanmin(finite_db))
-                self.y_max_fft = float(np.nanmax(finite_db))
-        return True
-
-    def set_symmetric_fft_enabled(self, enabled: bool) -> bool:
-        """Backward-compatible wrapper for the old two-state FFT switch."""
-        return self.set_fft_mode("symmetric" if enabled else "direct")
-
-    def push(
-        self,
-        sweep: np.ndarray,
-        freqs: Optional[np.ndarray] = None,
-        *,
-        fft_input: Optional[np.ndarray] = None,
-    ) -> None:
-        """Push a processed sweep and refresh raw/FFT buffers."""
-        if sweep is None or sweep.size == 0:
-            return
-        self.ensure_init(int(sweep.size))
-        if self.ring is None or self.ring_time is None or self.ring_fft is None or self.ring_fft_input is None:
-            return
-
-        row = np.full((self.width,), np.nan, dtype=np.float32)
-        take = min(self.width, int(sweep.size))
-        row[:take] = np.asarray(sweep[:take], dtype=np.float32)
-        self.last_push_valid_points = int(np.count_nonzero(np.isfinite(row[:take])))
-        self.ring[self.head, :] = row
-        self.ring_time[self.head] = time.time()
-        if freqs is not None:
-            self.last_freqs = np.asarray(freqs, dtype=np.float64).copy()
-
-        fft_source = np.asarray(fft_input if fft_input is not None else sweep).reshape(-1)
-        fft_row = np.full((self.width,), np.nan + 0j, dtype=np.complex64)
-        fft_take = min(self.width, int(fft_source.size))
-        fft_row[:fft_take] = np.asarray(fft_source[:fft_take], dtype=np.complex64)
-        self.ring_fft_input[self.head, :] = fft_row
-
-        fft_mag = compute_fft_mag_row(fft_source, freqs, self.fft_bins, mode=self.fft_mode)
-        self.ring_fft[self.head, :] = fft_mag
-        self._promote_fft_cache(fft_mag)
-        self._promote_distance_axis(compute_distance_axis(freqs, self.fft_bins, mode=self.fft_mode))
-        self.head = (self.head + 1) % self.max_sweeps
-
-    def get_display_raw(self) -> np.ndarray:
-        if self.ring is None:
-            return np.zeros((1, 1), dtype=np.float32)
-        base = self.ring if self.head == 0 else np.roll(self.ring, -self.head, axis=0)
-        return base.T
-
-    def get_display_raw_decimated(self, max_points: int) -> np.ndarray:
-        """Return a display-oriented raw waterfall with optional frequency decimation."""
-        if self.ring is None:
-            return np.zeros((1, 1), dtype=np.float32)
-
-        limit = int(max_points)
-        if limit <= 0 or self.width <= limit:
-            return self.get_display_raw()
-
-        row_order = np.arange(self.ring.shape[0], dtype=np.int64)
-        if self.head:
-            row_order = np.roll(row_order, -self.head)
-        col_idx = np.linspace(0, self.width - 1, limit, dtype=np.int64)
-        return self.ring[np.ix_(row_order, col_idx)].T
-
-    def get_display_fft_linear(self) -> np.ndarray:
-        if self.ring_fft is None:
-            return np.zeros((1, 1), dtype=np.float32)
-        base = self.ring_fft if self.head == 0 else np.roll(self.ring_fft, -self.head, axis=0)
-        return base.T
-
-    def get_last_fft_linear(self) -> Optional[np.ndarray]:
-        if self.last_fft_mag is None:
-            return None
-        return np.asarray(self.last_fft_mag, dtype=np.float32).copy()
-
-    def get_display_times(self) -> Optional[np.ndarray]:
-        if self.ring_time is None:
-            return None
-        return self.ring_time if self.head == 0 else np.roll(self.ring_time, -self.head)

rfg_adc_plotter/state/runtime_state.py

@@ -1,54 +0,0 @@
-"""Mutable state container for the PyQtGraph backend."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-import numpy as np
-
-from rfg_adc_plotter.constants import BACKGROUND_MEDIAN_SWEEPS
-from rfg_adc_plotter.state.background_buffer import BackgroundMedianBuffer
-from rfg_adc_plotter.state.ring_buffer import RingBuffer
-from rfg_adc_plotter.types import SweepAuxCurves, SweepInfo
-
-
-@dataclass
-class RuntimeState:
-    ring: RingBuffer
-    range_min_ghz: float = 0.0
-    range_max_ghz: float = 0.0
-    full_current_freqs: Optional[np.ndarray] = None
-    full_current_sweep_raw: Optional[np.ndarray] = None
-    full_current_sweep_codes: Optional[np.ndarray] = None
-    full_current_fft_source: Optional[np.ndarray] = None
-    full_current_aux_curves: SweepAuxCurves = None
-    full_current_aux_curves_codes: SweepAuxCurves = None
-    current_freqs: Optional[np.ndarray] = None
-    current_distances: Optional[np.ndarray] = None
-    current_sweep_raw: Optional[np.ndarray] = None
-    current_fft_source: Optional[np.ndarray] = None
-    current_fft_input: Optional[np.ndarray] = None
-    current_fft_complex: Optional[np.ndarray] = None
-    current_aux_curves: SweepAuxCurves = None
-    current_sweep_norm: Optional[np.ndarray] = None
-    current_fft_mag: Optional[np.ndarray] = None
-    current_fft_db: Optional[np.ndarray] = None
-    last_calib_sweep: Optional[np.ndarray] = None
-    calib_envelope: Optional[np.ndarray] = None
-    calib_file_path: Optional[str] = None
-    complex_calib_curve: Optional[np.ndarray] = None
-    complex_calib_file_path: Optional[str] = None
-    background_buffer: BackgroundMedianBuffer = field(
-        default_factory=lambda: BackgroundMedianBuffer(BACKGROUND_MEDIAN_SWEEPS)
-    )
-    background_profile: Optional[np.ndarray] = None
-    background_file_path: Optional[str] = None
-    current_info: Optional[SweepInfo] = None
-    current_peak_width: Optional[float] = None
-    current_peak_amplitude: Optional[float] = None
-    peak_candidates: List[Dict[str, float]] = field(default_factory=list)
-    plot_dirty: bool = False
-
-    def mark_dirty(self) -> None:
-        self.plot_dirty = True

rfg_adc_plotter/types.py

@@ -1,34 +0,0 @@
-"""Shared runtime and parser types."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any, Dict, Literal, Optional, Tuple, TypeAlias, Union
-
-import numpy as np
-
-
-Number = Union[int, float]
-SignalKind = Literal["bin_iq", "bin_logdet"]
-SweepInfo = Dict[str, Any]
-SweepData = Dict[str, np.ndarray]
-SweepAuxCurves = Optional[Tuple[np.ndarray, np.ndarray]]
-SweepPacket = Tuple[np.ndarray, SweepInfo, SweepAuxCurves]
-
-
-@dataclass(frozen=True)
-class StartEvent:
-    ch: Optional[int] = None
-    signal_kind: Optional[SignalKind] = None
-
-
-@dataclass(frozen=True)
-class PointEvent:
-    ch: int
-    x: int
-    y: float
-    aux: Optional[Tuple[float, float]] = None
-    signal_kind: Optional[SignalKind] = None
-
-
-ParserEvent: TypeAlias = Union[StartEvent, PointEvent]

rfg_adc_plotter/utils/formatting.py

@@ -0,0 +1,50 @@
+"""
+Утилиты для форматирования данных и парсинга параметров.
+"""
+
+from typing import Any, Mapping, Optional, Tuple
+
+import numpy as np
+
+
+def format_status_kv(data: Mapping[str, Any]) -> str:
+    """Преобразовать словарь метрик в одну строку 'k:v'."""
+
+    def _fmt(v: Any) -> str:
+        if v is None:
+            return "NA"
+        try:
+            fv = float(v)
+        except Exception:
+            return str(v)
+        if not np.isfinite(fv):
+            return "nan"
+        # Достаточно компактно для статус-строки.
+        if abs(fv) >= 1000 or (0 < abs(fv) < 0.01):
+            return f"{fv:.3g}"
+        return f"{fv:.3f}".rstrip("0").rstrip(".")
+
+    parts = [f"{k}:{_fmt(v)}" for k, v in data.items()]
+    return "  ".join(parts)
+
+
+def parse_spec_clip(spec: Optional[str]) -> Optional[Tuple[float, float]]:
+    """Разобрать строку вида "low,high" процентов для контрастного отображения водопада спектров.
+
+    Возвращает пару (low, high) или None для отключения. Допустимы значения 0..100, low < high.
+    Ключевые слова отключения: "off", "none", "no".
+    """
+    if not spec:
+        return None
+    s = str(spec).strip().lower()
+    if s in ("off", "none", "no"):
+        return None
+    try:
+        p0, p1 = s.replace(";", ",").split(",")
+        low = float(p0)
+        high = float(p1)
+        if not (0.0 <= low < high <= 100.0):
+            return None
+        return (low, high)
+    except Exception:
+        return None

rfg_adc_plotter/visualization/matplotlib_backend.py

@@ -0,0 +1,651 @@
+"""
+Визуализация данных с использованием matplotlib.
+"""
+
+import csv
+import sys
+import threading
+import time
+from datetime import datetime
+from queue import Empty, Queue
+from typing import Optional, Tuple
+
+import numpy as np
+
+try:
+    import matplotlib
+    import matplotlib.pyplot as plt
+    from matplotlib.animation import FuncAnimation
+    from matplotlib.widgets import Slider
+except Exception as e:
+    raise RuntimeError(f"Нужны matplotlib и ее зависимости: {e}")
+
+from ..config import (
+    FFT_LEN,
+    WF_WIDTH,
+    SweepInfo,
+    SweepPacket,
+    FREQ_MIN_GHZ,
+    FREQ_MAX_GHZ,
+    DATA_FREQ_START_GHZ,
+    DATA_FREQ_END_GHZ,
+)
+from ..data_acquisition.sweep_reader import SweepReader
+from ..signal_processing.phase_analysis import apply_temporal_unwrap, phase_to_distance
+from ..utils.formatting import format_status_kv, parse_spec_clip
+
+
+def run_matplotlib(args):
+    """Запуск визуализации с использованием matplotlib."""
+    # Очередь завершённых свипов и поток чтения
+    q: Queue[SweepPacket] = Queue(maxsize=1000)
+    stop_event = threading.Event()
+    reader = SweepReader(args.port, args.baud, q, stop_event, fancy=bool(args.fancy))
+    reader.start()
+
+    # Графика (3 ряда x 2 колонки = 6 графиков)
+    fig, axs = plt.subplots(3, 2, figsize=(12, 12))
+    (ax_line, ax_img), (ax_fft, ax_spec), (ax_phase, ax_phase_wf) = axs
+    fig.canvas.manager.set_window_title(args.title) if hasattr(fig.canvas.manager, "set_window_title") else None
+    # Увеличим расстояния и оставим место справа под ползунки оси Y B-scan
+    fig.subplots_adjust(wspace=0.25, hspace=0.35, left=0.07, right=0.90, top=0.95, bottom=0.05)
+
+    # Состояние для отображения
+    current_sweep: Optional[np.ndarray] = None
+    current_info: Optional[SweepInfo] = None
+    x_shared: Optional[np.ndarray] = None
+    width: Optional[int] = None
+    max_sweeps = int(max(10, args.max_sweeps))
+    ring = None  # type: Optional[np.ndarray]
+    ring_time = None  # type: Optional[np.ndarray]
+    head = 0
+    # Медианные данные для вычитания
+    median_data: Optional[np.ndarray] = None
+    median_subtract_enabled = False
+    # CLI параметры для автоматического сохранения/загрузки
+    ref_out_file = getattr(args, 'ref_out', None)
+    ref_in_file = getattr(args, 'ref_in', None)
+    ref_out_saved = False  # Флаг, что медиана уже сохранена
+    # Отдельный буфер для накопления 1000 сырых свипов (не зависит от max_sweeps)
+    ref_ring: Optional[np.ndarray] = None
+    ref_ring_head = 0
+    ref_ring_count = 0
+
+    if ref_out_file:
+        print(f"[ref-out] Автосохранение включено, файл: {ref_out_file}")
+
+    # Автоматическая загрузка медианы при старте
+    if ref_in_file:
+        try:
+            pairs = []
+            with open(ref_in_file, 'r') as f:
+                reader = csv.reader(f)
+                next(reader)  # Пропускаем заголовок
+                for row in reader:
+                    if len(row) >= 2:
+                        try:
+                            pairs.append((int(row[0]), float(row[1])))
+                        except ValueError:
+                            continue
+            if pairs:
+                max_idx = max(idx for idx, _ in pairs)
+                median_data = np.full(max_idx + 1, np.nan, dtype=np.float32)
+                for idx, val in pairs:
+                    median_data[idx] = val
+                median_subtract_enabled = True
+                print(f"[ref-in] Загружена медиана из {ref_in_file} ({len(median_data)} точек), вычитание включено")
+            else:
+                print(f"[ref-in] Предупреждение: файл {ref_in_file} пустой или неверный формат")
+        except Exception as e:
+            print(f"[ref-in] Ошибка загрузки {ref_in_file}: {e}")
+    # Авто-уровни цветовой шкалы водопада сырых данных пересчитываются по видимой области.
+    # FFT состояние (полное FFT для отрицательных частот)
+    fft_bins = FFT_LEN
+    ring_fft = None  # type: Optional[np.ndarray]
+    y_min_fft, y_max_fft = None, None
+    freq_shared: Optional[np.ndarray] = None
+    # Phase состояние
+    ring_phase = None  # type: Optional[np.ndarray]
+    prev_phase_per_bin: Optional[np.ndarray] = None
+    phase_offset_per_bin: Optional[np.ndarray] = None
+    y_min_phase, y_max_phase = None, None
+    # Параметры контраста водопада спектров
+    spec_clip = parse_spec_clip(getattr(args, "spec_clip", None))
+    # Ползунки управления Y для B-scan и контрастом
+    ymin_slider = None
+    ymax_slider = None
+    contrast_slider = None
+
+    # Статусная строка (внизу окна)
+    status_text = fig.text(
+        0.01,
+        0.01,
+        "",
+        ha="left",
+        va="bottom",
+        fontsize=8,
+        family="monospace",
+    )
+
+    # Линейный график последнего свипа
+    line_obj, = ax_line.plot([], [], lw=1)
+    ax_line.set_title("Сырые данные", pad=1)
+    ax_line.set_xlabel("F")
+    ax_line.set_ylabel("")
+
+    # Линейный график спектра текущего свипа
+    fft_line_obj, = ax_fft.plot([], [], lw=1)
+    ax_fft.set_title("FFT", pad=1)
+    ax_fft.set_xlabel("Частота, ГГц")
+    ax_fft.set_ylabel("Амплитуда, дБ")
+
+    # Диапазон по Y для последнего свипа: авто по умолчанию (поддерживает отрицательные значения)
+    fixed_ylim: Optional[Tuple[float, float]] = None
+    # CLI переопределение при необходимости
+    if args.ylim:
+        try:
+            y0, y1 = args.ylim.split(",")
+            fixed_ylim = (float(y0), float(y1))
+        except Exception:
+            sys.stderr.write("[warn] Некорректный формат --ylim, игнорирую. Ожидалось min,max\n")
+    if fixed_ylim is not None:
+        ax_line.set_ylim(fixed_ylim)
+
+    # Водопад (будет инициализирован при первом свипе)
+    img_obj = ax_img.imshow(
+        np.zeros((1, 1), dtype=np.float32),
+        aspect="auto",
+        interpolation="nearest",
+        origin="lower",
+        cmap=args.cmap,
+    )
+    ax_img.set_title("Сырые данные", pad=12)
+    ax_img.set_xlabel("")
+    ax_img.set_ylabel("частота")
+    # Не показываем численные значения по времени на водопаде сырых данных
+    try:
+        ax_img.tick_params(axis="x", labelbottom=False)
+    except Exception:
+        pass
+
+    # Водопад спектров
+    img_fft_obj = ax_spec.imshow(
+        np.zeros((1, 1), dtype=np.float32),
+        aspect="auto",
+        interpolation="nearest",
+        origin="lower",
+        cmap=args.cmap,
+    )
+    ax_spec.set_title("B-scan (дБ)", pad=12)
+    ax_spec.set_xlabel("")
+    ax_spec.set_ylabel("Частота, ГГц")
+    # Не показываем численные значения по времени на B-scan
+    try:
+        ax_spec.tick_params(axis="x", labelbottom=False)
+    except Exception:
+        pass
+
+    # График фазы текущего свипа
+    phase_line_obj, = ax_phase.plot([], [], lw=1)
+    ax_phase.set_title("Фаза спектра (развернутая)", pad=1)
+    ax_phase.set_xlabel("Частота, ГГц")
+    ax_phase.set_ylabel("Фаза, радианы")
+
+    # Добавим второй Y axis для расстояния
+    ax_phase_dist = ax_phase.twinx()
+    ax_phase_dist.set_ylabel("Расстояние, м", color='green')
+
+    # Водопад фазы
+    img_phase_obj = ax_phase_wf.imshow(
+        np.zeros((1, 1), dtype=np.float32),
+        aspect="auto",
+        interpolation="nearest",
+        origin="lower",
+        cmap=args.cmap,
+    )
+    ax_phase_wf.set_title("Водопад фазы", pad=12)
+    ax_phase_wf.set_xlabel("")
+    ax_phase_wf.set_ylabel("Частота, ГГц")
+    # Не показываем численные значения по времени
+    try:
+        ax_phase_wf.tick_params(axis="x", labelbottom=False)
+    except Exception:
+        pass
+    # Слайдеры для управления осью Y B-scan (мин/макс) и контрастом
+    try:
+        ax_smin = fig.add_axes([0.92, 0.55, 0.02, 0.35])
+        ax_smax = fig.add_axes([0.95, 0.55, 0.02, 0.35])
+        ax_sctr = fig.add_axes([0.98, 0.55, 0.02, 0.35])
+        ymin_slider = Slider(ax_smin, "Y min", FREQ_MIN_GHZ, FREQ_MAX_GHZ, valinit=FREQ_MIN_GHZ, valstep=0.1, orientation="vertical")
+        ymax_slider = Slider(ax_smax, "Y max", FREQ_MIN_GHZ, FREQ_MAX_GHZ, valinit=FREQ_MAX_GHZ, valstep=0.1, orientation="vertical")
+        contrast_slider = Slider(ax_sctr, "Int max", 0, 100, valinit=100, valstep=1, orientation="vertical")
+
+        def _on_ylim_change(_val):
+            try:
+                y0 = float(min(ymin_slider.val, ymax_slider.val))
+                y1 = float(max(ymin_slider.val, ymax_slider.val))
+                ax_spec.set_ylim(y0, y1)
+                fig.canvas.draw_idle()
+            except Exception:
+                pass
+
+        ymin_slider.on_changed(_on_ylim_change)
+        ymax_slider.on_changed(_on_ylim_change)
+        # Контраст влияет на верхнюю границу цветовой шкалы (процент от авто-диапазона)
+        contrast_slider.on_changed(lambda _v: fig.canvas.draw_idle())
+    except Exception:
+        pass
+
+    # Для контроля частоты обновления
+    max_fps = max(1.0, float(args.max_fps))
+    interval_ms = int(1000.0 / max_fps)
+    frames_since_ylim_update = 0
+
+    def ensure_buffer(_w: int):
+        nonlocal ring, width, head, x_shared, ring_fft, freq_shared, ring_time
+        nonlocal ring_phase, prev_phase_per_bin, phase_offset_per_bin
+        nonlocal ref_ring
+        if ring is not None:
+            return
+        width = WF_WIDTH
+        x_shared = np.arange(width, dtype=np.int32)
+        ring = np.full((max_sweeps, width), np.nan, dtype=np.float32)
+        ring_time = np.full((max_sweeps,), np.nan, dtype=np.float64)
+        head = 0
+        # Обновляем изображение под новые размеры: время по X (горизонталь), X по Y
+        img_obj.set_data(np.zeros((width, max_sweeps), dtype=np.float32))
+        img_obj.set_extent((0, max_sweeps - 1, 0, width - 1 if width > 0 else 1))
+        ax_img.set_xlim(0, max_sweeps - 1)
+        ax_img.set_ylim(0, max(1, width - 1))
+        # FFT буферы: время по X, бин по Y
+        ring_fft = np.full((max_sweeps, fft_bins), np.nan, dtype=np.float32)
+        img_fft_obj.set_data(np.zeros((fft_bins, max_sweeps), dtype=np.float32))
+        img_fft_obj.set_extent((0, max_sweeps - 1, FREQ_MIN_GHZ, FREQ_MAX_GHZ))
+        ax_spec.set_xlim(0, max_sweeps - 1)
+        ax_spec.set_ylim(FREQ_MIN_GHZ, FREQ_MAX_GHZ)
+        freq_shared = np.linspace(FREQ_MIN_GHZ, FREQ_MAX_GHZ, fft_bins, dtype=np.float32)
+        # Phase буферы: время по X, бин по Y
+        ring_phase = np.full((max_sweeps, fft_bins), np.nan, dtype=np.float32)
+        prev_phase_per_bin = np.zeros(fft_bins, dtype=np.float32)
+        phase_offset_per_bin = np.zeros(fft_bins, dtype=np.float32)
+        img_phase_obj.set_data(np.zeros((fft_bins, max_sweeps), dtype=np.float32))
+        img_phase_obj.set_extent((0, max_sweeps - 1, FREQ_MIN_GHZ, FREQ_MAX_GHZ))
+        ax_phase_wf.set_xlim(0, max_sweeps - 1)
+        ax_phase_wf.set_ylim(FREQ_MIN_GHZ, FREQ_MAX_GHZ)
+        # Буфер для медианы (отдельный от ring, размер всегда 1000)
+        if ref_out_file and ref_ring is None:
+            ref_ring = np.full((1000, width), np.nan, dtype=np.float32)
+
+    def _visible_levels_matplotlib(data: np.ndarray, axis) -> Optional[Tuple[float, float]]:
+        """(vmin, vmax) по текущей видимой области imshow (без накопления по времени)."""
+        if data.size == 0:
+            return None
+        ny, nx = data.shape[0], data.shape[1]
+        try:
+            x0, x1 = axis.get_xlim()
+            y0, y1 = axis.get_ylim()
+        except Exception:
+            x0, x1 = 0.0, float(nx - 1)
+            y0, y1 = 0.0, float(ny - 1)
+        xmin, xmax = sorted((float(x0), float(x1)))
+        ymin, ymax = sorted((float(y0), float(y1)))
+        ix0 = max(0, min(nx - 1, int(np.floor(xmin))))
+        ix1 = max(0, min(nx - 1, int(np.ceil(xmax))))
+        iy0 = max(0, min(ny - 1, int(np.floor(ymin))))
+        iy1 = max(0, min(ny - 1, int(np.ceil(ymax))))
+        if ix1 < ix0:
+            ix1 = ix0
+        if iy1 < iy0:
+            iy1 = iy0
+        sub = data[iy0 : iy1 + 1, ix0 : ix1 + 1]
+        finite = np.isfinite(sub)
+        if not finite.any():
+            return None
+        vals = sub[finite]
+        vmin = float(np.min(vals))
+        vmax = float(np.max(vals))
+        if not (np.isfinite(vmin) and np.isfinite(vmax)) or vmin == vmax:
+            return None
+        return (vmin, vmax)
+
+    def push_sweep(s: np.ndarray):
+        nonlocal ring, head, ring_fft, y_min_fft, y_max_fft, ring_time
+        nonlocal ring_phase, prev_phase_per_bin, phase_offset_per_bin, y_min_phase, y_max_phase
+        nonlocal ref_ring_head, ref_ring_count
+        if s is None or s.size == 0 or ring is None:
+            return
+
+        # Сохраняем сырой свип в буфер медианы (до вычитания)
+        if ref_out_file and not ref_out_saved and ref_ring is not None:
+            w_ref = ref_ring.shape[1]
+            take_ref = min(w_ref, s.size)
+            ref_ring[ref_ring_head, :take_ref] = s[:take_ref]
+            ref_ring_head = (ref_ring_head + 1) % 1000
+            ref_ring_count = min(ref_ring_count + 1, 1000)
+
+        # Применяем вычитание медианы если включено
+        if median_subtract_enabled and median_data is not None:
+            take_median = min(s.size, median_data.size)
+            s_corrected = s.copy()
+            s_corrected[:take_median] = s[:take_median] - median_data[:take_median]
+            s = s_corrected
+
+        # Нормализуем длину до фиксированной ширины
+        w = ring.shape[1]
+        row = np.full((w,), np.nan, dtype=np.float32)
+        take = min(w, s.size)
+        row[:take] = s[:take]
+        ring[head, :] = row
+        if ring_time is not None:
+            ring_time[head] = time.time()
+        head = (head + 1) % ring.shape[0]
+        # FFT строка (дБ) и фаза
+        if ring_fft is not None:
+            bins = ring_fft.shape[1]
+            # Подготовка входа FFT_LEN, замена NaN на 0
+            take_fft = min(int(s.size), FFT_LEN)
+            if take_fft <= 0:
+                fft_row = np.full((bins,), np.nan, dtype=np.float32)
+                phase_row = np.full((bins,), np.nan, dtype=np.float32)
+            else:
+                # Создаем буфер для полного FFT (с отрицательными частотами)
+                fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
+
+                # Вычисляем индексы для размещения данных (1-10 ГГц в диапазоне -10 до +10 ГГц)
+                freq_range_total = FREQ_MAX_GHZ - FREQ_MIN_GHZ  # 20 ГГц
+                freq_range_data = DATA_FREQ_END_GHZ - DATA_FREQ_START_GHZ  # 9 ГГц
+
+                # Начальный индекс для данных в FFT буфере
+                start_idx = int((DATA_FREQ_START_GHZ - FREQ_MIN_GHZ) / freq_range_total * FFT_LEN)
+                # Количество точек для данных
+                data_points = int(freq_range_data / freq_range_total * FFT_LEN)
+                data_points = min(data_points, take_fft, FFT_LEN - start_idx)
+
+                # Подготовка данных
+                seg = s[:data_points]
+                if isinstance(seg, np.ndarray):
+                    seg = np.nan_to_num(seg, nan=0.0).astype(np.float32, copy=False)
+                else:
+                    seg = np.asarray(seg, dtype=np.float32)
+                    seg = np.nan_to_num(seg, nan=0.0)
+
+                # Окно Хэннинга
+                win = np.hanning(data_points).astype(np.float32)
+
+                # Размещаем данные в правильной позиции
+                fft_in[start_idx:start_idx + data_points] = seg * win
+
+                # Полное FFT (включая отрицательные частоты)
+                spec = np.fft.fft(fft_in)
+                # Сдвигаем для центрирования нулевой частоты
+                spec = np.fft.fftshift(spec)
+
+                mag = np.abs(spec).astype(np.float32)
+                fft_row = 20.0 * np.log10(mag + 1e-9)
+                if fft_row.shape[0] != bins:
+                    fft_row = fft_row[:bins]
+
+                # Расчет фазы
+                phase = np.angle(spec).astype(np.float32)
+                if phase.shape[0] > bins:
+                    phase = phase[:bins]
+                # Unwrapping по частоте (внутри свипа)
+                phase_unwrapped_freq = np.unwrap(phase)
+                # Unwrapping по времени (между свипами)
+                phase_unwrapped_time, prev_phase_per_bin, phase_offset_per_bin = apply_temporal_unwrap(
+                    phase_unwrapped_freq, prev_phase_per_bin, phase_offset_per_bin
+                )
+                phase_row = phase_unwrapped_time
+
+            ring_fft[(head - 1) % ring_fft.shape[0], :] = fft_row
+            # Экстремумы для цветовой шкалы
+            fr_min = np.nanmin(fft_row)
+            fr_max = np.nanmax(fft_row)
+            fr_max = np.nanpercentile(fft_row, 90)
+            if y_min_fft is None or (not np.isnan(fr_min) and fr_min < y_min_fft):
+                y_min_fft = float(fr_min)
+            if y_max_fft is None or (not np.isnan(fr_max) and fr_max > y_max_fft):
+                y_max_fft = float(fr_max)
+
+            # Сохраняем фазу в буфер
+            if ring_phase is not None:
+                ring_phase[(head - 1) % ring_phase.shape[0], :] = phase_row
+                # Экстремумы для цветовой шкалы фазы
+                ph_min = np.nanmin(phase_row)
+                ph_max = np.nanmax(phase_row)
+                if y_min_phase is None or (not np.isnan(ph_min) and ph_min < y_min_phase):
+                    y_min_phase = float(ph_min)
+                if y_max_phase is None or (not np.isnan(ph_max) and ph_max > y_max_phase):
+                    y_max_phase = float(ph_max)
+
+    def drain_queue():
+        nonlocal current_sweep, current_info
+        drained = 0
+        while True:
+            try:
+                s, info = q.get_nowait()
+            except Empty:
+                break
+            drained += 1
+            current_sweep = s
+            current_info = info
+            ensure_buffer(s.size)
+            push_sweep(s)
+        return drained
+
+    def make_display_ring():
+        # Возвращаем буфер с правильным порядком по времени (старые→новые) и осью времени по X
+        if ring is None:
+            return np.zeros((1, 1), dtype=np.float32)
+        base = ring if head == 0 else np.roll(ring, -head, axis=0)
+        return base.T  # (width, time)
+
+    def make_display_times():
+        if ring_time is None:
+            return None
+        base_t = ring_time if head == 0 else np.roll(ring_time, -head)
+        return base_t
+
+    def make_display_ring_fft():
+        if ring_fft is None:
+            return np.zeros((1, 1), dtype=np.float32)
+        base = ring_fft if head == 0 else np.roll(ring_fft, -head, axis=0)
+        return base.T  # (bins, time)
+
+    def make_display_ring_phase():
+        if ring_phase is None:
+            return np.zeros((1, 1), dtype=np.float32)
+        base = ring_phase if head == 0 else np.roll(ring_phase, -head, axis=0)
+        return base.T  # (bins, time)
+
+    def update(_frame):
+        nonlocal frames_since_ylim_update, ref_out_saved
+        changed = drain_queue() > 0
+
+        # Обновление линии последнего свипа
+        if current_sweep is not None:
+            # Применяем вычитание медианы для отображения
+            display_sweep = current_sweep
+            if median_subtract_enabled and median_data is not None:
+                take_median = min(current_sweep.size, median_data.size)
+                display_sweep = current_sweep.copy()
+                display_sweep[:take_median] = current_sweep[:take_median] - median_data[:take_median]
+
+            if x_shared is not None and display_sweep.size <= x_shared.size:
+                xs = x_shared[: display_sweep.size]
+            else:
+                xs = np.arange(display_sweep.size, dtype=np.int32)
+            line_obj.set_data(xs, display_sweep)
+            # Лимиты по X постоянные под текущую ширину
+            ax_line.set_xlim(0, max(1, display_sweep.size - 1))
+            # Адаптивные Y-лимиты (если не задан --ylim)
+            if fixed_ylim is None:
+                y0 = float(np.nanmin(display_sweep))
+                y1 = float(np.nanmax(display_sweep))
+                if np.isfinite(y0) and np.isfinite(y1):
+                    if y0 == y1:
+                        pad = max(1.0, abs(y0) * 0.05)
+                        y0 -= pad
+                        y1 += pad
+                    else:
+                        pad = 0.05 * (y1 - y0)
+                        y0 -= pad
+                        y1 += pad
+                    ax_line.set_ylim(y0, y1)
+
+            # Обновление спектра и фазы текущего свипа
+            take_fft = min(int(display_sweep.size), FFT_LEN)
+            if take_fft > 0 and freq_shared is not None:
+                # Создаем буфер для полного FFT (с отрицательными частотами)
+                fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
+
+                # Вычисляем индексы для размещения данных (1-10 ГГц в диапазоне -10 до +10 ГГц)
+                freq_range_total = FREQ_MAX_GHZ - FREQ_MIN_GHZ  # 20 ГГц
+                freq_range_data = DATA_FREQ_END_GHZ - DATA_FREQ_START_GHZ  # 9 ГГц
+
+                # Начальный индекс для данных в FFT буфере
+                start_idx = int((DATA_FREQ_START_GHZ - FREQ_MIN_GHZ) / freq_range_total * FFT_LEN)
+                # Количество точек для данных
+                data_points = int(freq_range_data / freq_range_total * FFT_LEN)
+                data_points = min(data_points, take_fft, FFT_LEN - start_idx)
+
+                # Подготовка данных с окном Хэннинга
+                seg = np.nan_to_num(display_sweep[:data_points], nan=0.0).astype(np.float32, copy=False)
+                win = np.hanning(data_points).astype(np.float32)
+
+                # Размещаем данные в правильной позиции
+                fft_in[start_idx:start_idx + data_points] = seg * win
+
+                # Полное FFT (включая отрицательные частоты)
+                spec = np.fft.fft(fft_in)
+                # Сдвигаем для центрирования нулевой частоты
+                spec = np.fft.fftshift(spec)
+
+                mag = np.abs(spec).astype(np.float32)
+                fft_vals = 20.0 * np.log10(mag + 1e-9)
+                xs_fft = freq_shared
+                if fft_vals.size > xs_fft.size:
+                    fft_vals = fft_vals[: xs_fft.size]
+                fft_line_obj.set_data(xs_fft[: fft_vals.size], fft_vals)
+                # Авто-диапазон по Y для спектра
+                if np.isfinite(np.nanmin(fft_vals)) and np.isfinite(np.nanmax(fft_vals)):
+                    ax_fft.set_xlim(FREQ_MIN_GHZ, FREQ_MAX_GHZ)
+                    ax_fft.set_ylim(float(np.nanmin(fft_vals)), float(np.nanmax(fft_vals)))
+
+                # Расчет и отображение фазы текущего свипа
+                phase = np.angle(spec).astype(np.float32)
+                if phase.size > xs_fft.size:
+                    phase = phase[: xs_fft.size]
+                # Unwrapping по частоте
+                phase_unwrapped = np.unwrap(phase)
+                phase_line_obj.set_data(xs_fft[: phase_unwrapped.size], phase_unwrapped)
+                # Авто-диапазон по Y для фазы
+                if np.isfinite(np.nanmin(phase_unwrapped)) and np.isfinite(np.nanmax(phase_unwrapped)):
+                    ax_phase.set_xlim(FREQ_MIN_GHZ, FREQ_MAX_GHZ)
+                    phase_min = float(np.nanmin(phase_unwrapped))
+                    phase_max = float(np.nanmax(phase_unwrapped))
+                    ax_phase.set_ylim(phase_min, phase_max)
+                    # Обновляем вторую ось Y с расстоянием
+                    try:
+                        dist_min = phase_to_distance(np.array([phase_min]))[0]
+                        dist_max = phase_to_distance(np.array([phase_max]))[0]
+                        ax_phase_dist.set_ylim(dist_min, dist_max)
+                    except Exception:
+                        pass
+
+        # Обновление водопада
+        if changed and ring is not None:
+            disp = make_display_ring()
+            # Новые данные справа: без реверса
+            img_obj.set_data(disp)
+            # Подписи времени не обновляем динамически (оставляем авто-тики)
+            # Авто-уровни: по видимой области (не накапливаем за всё время)
+            levels = _visible_levels_matplotlib(disp, ax_img)
+            if levels is not None:
+                img_obj.set_clim(vmin=levels[0], vmax=levels[1])
+
+        # Обновление водопада спектров
+        if changed and ring_fft is not None:
+            disp_fft = make_display_ring_fft()
+            # Новые данные справа: без реверса
+            img_fft_obj.set_data(disp_fft)
+            # Подписи времени не обновляем динамически (оставляем авто-тики)
+            # Автодиапазон по среднему спектру за видимый интервал (как в хорошей версии)
+            try:
+                # disp_fft имеет форму (bins, time); берём среднее по времени
+                mean_spec = np.nanmean(disp_fft, axis=1)
+                vmin_v = float(np.nanmin(mean_spec))
+                vmax_v = float(np.nanmax(mean_spec))
+            except Exception:
+                vmin_v = vmax_v = None
+            # Если средние не дают валидный диапазон — используем процентильную обрезку (если задана)
+            if (vmin_v is None or not np.isfinite(vmin_v)) or (vmax_v is None or not np.isfinite(vmax_v)) or vmin_v == vmax_v:
+                if spec_clip is not None:
+                    try:
+                        vmin_v = float(np.nanpercentile(disp_fft, spec_clip[0]))
+                        vmax_v = float(np.nanpercentile(disp_fft, spec_clip[1]))
+                    except Exception:
+                        vmin_v = vmax_v = None
+            # Фолбэк к отслеживаемым минимум/максимумам
+            if (vmin_v is None or not np.isfinite(vmin_v)) or (vmax_v is None or not np.isfinite(vmax_v)) or vmin_v == vmax_v:
+                if y_min_fft is not None and y_max_fft is not None and np.isfinite(y_min_fft) and np.isfinite(y_max_fft) and y_min_fft != y_max_fft:
+                    vmin_v, vmax_v = y_min_fft, y_max_fft
+            if vmin_v is not None and vmax_v is not None and vmin_v != vmax_v:
+                # Применим скалирование контрастом (верхняя граница)
+                try:
+                    c = float(contrast_slider.val) / 100.0 if contrast_slider is not None else 1.0
+                except Exception:
+                    c = 1.0
+                vmax_eff = vmin_v + c * (vmax_v - vmin_v)
+                img_fft_obj.set_clim(vmin=vmin_v, vmax=vmax_eff)
+
+        # Обновление водопада фазы
+        if changed and ring_phase is not None:
+            disp_phase = make_display_ring_phase()
+            img_phase_obj.set_data(disp_phase)
+            # Автодиапазон для фазы
+            try:
+                mean_phase = np.nanmean(disp_phase, axis=1)
+                vmin_p = float(np.nanmin(mean_phase))
+                vmax_p = float(np.nanmax(mean_phase))
+            except Exception:
+                vmin_p = vmax_p = None
+            # Фолбэк к отслеживаемым минимум/максимумам
+            if (vmin_p is None or not np.isfinite(vmin_p)) or (vmax_p is None or not np.isfinite(vmax_p)) or vmin_p == vmax_p:
+                if y_min_phase is not None and y_max_phase is not None and np.isfinite(y_min_phase) and np.isfinite(y_max_phase) and y_min_phase != y_max_phase:
+                    vmin_p, vmax_p = y_min_phase, y_max_phase
+            if vmin_p is not None and vmax_p is not None and vmin_p != vmax_p:
+                img_phase_obj.set_clim(vmin=vmin_p, vmax=vmax_p)
+
+        if changed and current_info:
+            status_text.set_text(format_status_kv(current_info))
+
+        # Автоматическое сохранение медианы при накоплении 1000 сырых свипов
+        if ref_out_file and not ref_out_saved and ref_ring is not None:
+            if ref_ring_count >= 1000:
+                try:
+                    ordered = ref_ring if ref_ring_head == 0 else np.roll(ref_ring, -ref_ring_head, axis=0)
+                    median_sweep = np.nanmedian(ordered, axis=0)
+
+                    with open(ref_out_file, 'w', newline='') as f:
+                        writer = csv.writer(f)
+                        writer.writerow(['Index', 'Median_Value'])
+                        for i, value in enumerate(median_sweep):
+                            if np.isfinite(value):
+                                writer.writerow([i, float(value)])
+
+                    ref_out_saved = True
+                    print(f"[ref-out] Сохранена медиана 1000 свипов в {ref_out_file}")
+                    status_text.set_text(f"[ref-out] Сохранено в {ref_out_file}")
+                except Exception as e:
+                    print(f"[ref-out] Ошибка сохранения: {e}")
+
+        # Возвращаем обновлённые артисты
+        return (line_obj, img_obj, fft_line_obj, img_fft_obj, phase_line_obj, img_phase_obj, status_text)
+
+    ani = FuncAnimation(fig, update, interval=interval_ms, blit=False)
+
+    plt.show()
+    # Нормальное завершение при закрытии окна
+    stop_event.set()
+    reader.join(timeout=1.0)

rfg_adc_plotter/visualization/pyqtgraph_backend.py

@@ -0,0 +1,705 @@
+"""
+Визуализация данных с использованием pyqtgraph (быстрый бэкенд).
+"""
+
+import csv
+import sys
+import threading
+import time
+from datetime import datetime
+from queue import Empty, Queue
+from typing import Optional, Tuple
+
+import numpy as np
+
+try:
+    import pyqtgraph as pg
+    from PyQt5 import QtCore, QtWidgets  # noqa: F401
+    from PyQt5.QtWidgets import QPushButton, QWidget, QHBoxLayout, QCheckBox, QFileDialog
+except Exception:
+    # Возможно установлена PySide6
+    try:
+        import pyqtgraph as pg
+        from PySide6 import QtCore, QtWidgets  # noqa: F401
+        from PySide6.QtWidgets import QPushButton, QWidget, QHBoxLayout, QCheckBox, QFileDialog
+    except Exception as e:
+        raise RuntimeError(
+            "pyqtgraph/PyQt5(Pyside6) не найдены. Установите: pip install pyqtgraph PyQt5"
+        ) from e
+
+from ..config import (
+    FFT_LEN,
+    WF_WIDTH,
+    SweepInfo,
+    SweepPacket,
+    FREQ_MIN_GHZ,
+    FREQ_MAX_GHZ,
+    DATA_FREQ_START_GHZ,
+    DATA_FREQ_END_GHZ,
+)
+from ..data_acquisition.sweep_reader import SweepReader
+from ..signal_processing.phase_analysis import apply_temporal_unwrap, phase_to_distance
+from ..utils.formatting import format_status_kv, parse_spec_clip
+
+
+def run_pyqtgraph(args):
+    """Быстрый GUI на PyQtGraph. Требует pyqtgraph и PyQt5/PySide6."""
+    # Очередь завершённых свипов и поток чтения
+    q: Queue[SweepPacket] = Queue(maxsize=1000)
+    stop_event = threading.Event()
+    reader = SweepReader(args.port, args.baud, q, stop_event, fancy=bool(args.fancy))
+    reader.start()
+
+    # Настройки скорости
+    max_sweeps = int(max(10, args.max_sweeps))
+    max_fps = max(1.0, float(args.max_fps))
+    interval_ms = int(1000.0 / max_fps)
+
+    # PyQtGraph настройки
+    pg.setConfigOptions(useOpenGL=True, antialias=False)
+    app = pg.mkQApp(args.title)
+    win = pg.GraphicsLayoutWidget(show=True, title=args.title)
+    win.resize(1200, 900)
+
+    # Плот последнего свипа (слева-сверху)
+    p_line = win.addPlot(row=0, col=0, title="Сырые данные")
+    p_line.showGrid(x=True, y=True, alpha=0.3)
+    curve = p_line.plot(pen=pg.mkPen((80, 120, 255), width=1))
+    p_line.setLabel("bottom", "X")
+    p_line.setLabel("left", "Y")
+
+    # Водопад (справа-сверху)
+    p_img = win.addPlot(row=0, col=1, title="Сырые данные водопад")
+    p_img.invertY(False)
+    p_img.showGrid(x=False, y=False)
+    p_img.setLabel("bottom", "Время, с (новое справа)")
+    try:
+        p_img.getAxis("bottom").setStyle(showValues=False)
+    except Exception:
+        pass
+    p_img.setLabel("left", "X (0 снизу)")
+    img = pg.ImageItem()
+    p_img.addItem(img)
+
+    # FFT (слева-средний ряд)
+    p_fft = win.addPlot(row=1, col=0, title="FFT")
+    p_fft.showGrid(x=True, y=True, alpha=0.3)
+    curve_fft = p_fft.plot(pen=pg.mkPen((255, 120, 80), width=1))
+    p_fft.setLabel("bottom", "Частота, ГГц")
+    p_fft.setLabel("left", "Амплитуда, дБ")
+
+    # Водопад спектров (справа-средний ряд)
+    p_spec = win.addPlot(row=1, col=1, title="B-scan (дБ)")
+    p_spec.invertY(True)
+    p_spec.showGrid(x=False, y=False)
+    p_spec.setLabel("bottom", "Время, с (новое справа)")
+    try:
+        p_spec.getAxis("bottom").setStyle(showValues=False)
+    except Exception:
+        pass
+    p_spec.setLabel("left", "Частота, ГГц (0 снизу)")
+    img_fft = pg.ImageItem()
+    p_spec.addItem(img_fft)
+
+    # График фазы (слева-снизу)
+    p_phase = win.addPlot(row=2, col=0, title="Фаза спектра (развернутая)")
+    p_phase.showGrid(x=True, y=True, alpha=0.3)
+    curve_phase = p_phase.plot(pen=pg.mkPen((120, 255, 80), width=1))
+    p_phase.setLabel("bottom", "Частота, ГГц")
+    p_phase.setLabel("left", "Фаза, радианы")
+    # Добавим вторую ось Y для расстояния
+    p_phase_dist_axis = pg.ViewBox()
+    p_phase.showAxis("right")
+    p_phase.scene().addItem(p_phase_dist_axis)
+    p_phase.getAxis("right").linkToView(p_phase_dist_axis)
+    p_phase_dist_axis.setXLink(p_phase)
+    p_phase.setLabel("right", "Расстояние, м")
+
+    def updateViews():
+        try:
+            p_phase_dist_axis.setGeometry(p_phase.vb.sceneBoundingRect())
+            p_phase_dist_axis.linkedViewChanged(p_phase.vb, p_phase_dist_axis.XAxis)
+        except Exception:
+            pass
+
+    updateViews()
+    p_phase.vb.sigResized.connect(updateViews)
+
+    # Водопад фазы (справа-снизу)
+    p_phase_wf = win.addPlot(row=2, col=1, title="Водопад фазы")
+    p_phase_wf.invertY(True)
+    p_phase_wf.showGrid(x=False, y=False)
+    p_phase_wf.setLabel("bottom", "Время, с (новое справа)")
+    try:
+        p_phase_wf.getAxis("bottom").setStyle(showValues=False)
+    except Exception:
+        pass
+    p_phase_wf.setLabel("left", "Частота, ГГц (0 снизу)")
+    img_phase = pg.ImageItem()
+    p_phase_wf.addItem(img_phase)
+
+    # Статусная строка (внизу окна)
+    status = pg.LabelItem(justify="left")
+    win.addItem(status, row=3, col=0, colspan=2)
+
+    # Функция сохранения медианы последних 1000 свипов
+    def save_median_data():
+        """Сохранить медиану последних 1000 свипов в CSV файл"""
+        if ring is None:
+            status.setText("Нет данных для сохранения")
+            return
+
+        # Определяем сколько свипов доступно
+        n_sweeps = 1000
+        available = min(n_sweeps, max_sweeps)
+
+        # Проверяем сколько свипов реально заполнено
+        filled_count = np.count_nonzero(~np.isnan(ring[:, 0]))
+        if filled_count == 0:
+            status.setText("Нет данных для сохранения")
+            return
+
+        available = min(available, filled_count)
+
+        # Получаем хронологически упорядоченные данные
+        ordered = ring if head == 0 else np.roll(ring, -head, axis=0)
+
+        # Берем последние n свипов
+        recent_sweeps = ordered[-available:, :]
+
+        # Вычисляем медиану по свипам (ось 0)
+        median_sweep = np.nanmedian(recent_sweeps, axis=0)
+
+        # Сохраняем в CSV
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"median_sweep_{timestamp}.csv"
+
+        try:
+            with open(filename, 'w', newline='') as f:
+                writer = csv.writer(f)
+                writer.writerow(['Index', 'Median_Value'])
+                for i, value in enumerate(median_sweep):
+                    if np.isfinite(value):
+                        writer.writerow([i, float(value)])
+
+            status.setText(f"Сохранено {available} свипов (медиана) в {filename}")
+        except Exception as e:
+            status.setText(f"Ошибка сохранения: {e}")
+
+    # Функция загрузки медианного файла
+    def load_median_file():
+        """Загрузить медианный файл из CSV"""
+        nonlocal median_data
+
+        filename, _ = QFileDialog.getOpenFileName(
+            None,
+            "Выберите файл с медианой",
+            "",
+            "CSV Files (*.csv);;All Files (*)"
+        )
+
+        if not filename:
+            return
+
+        try:
+            # Загружаем CSV файл
+            pairs = []
+            with open(filename, 'r') as f:
+                reader = csv.reader(f)
+                next(reader)  # Пропускаем заголовок
+                for row in reader:
+                    if len(row) >= 2:
+                        try:
+                            pairs.append((int(row[0]), float(row[1])))
+                        except ValueError:
+                            continue
+
+            if not pairs:
+                status.setText("Ошибка: файл пустой или неверный формат")
+                return
+
+            max_idx = max(idx for idx, _ in pairs)
+            median_data = np.full(max_idx + 1, np.nan, dtype=np.float32)
+            for idx, val in pairs:
+                median_data[idx] = val
+            status.setText(f"Загружена медиана из {filename} ({len(median_data)} точек)")
+
+            # Автоматически включаем чекбокс
+            subtract_checkbox.setChecked(True)
+
+        except Exception as e:
+            status.setText(f"Ошибка загрузки: {e}")
+            median_data = None
+
+    # Функция переключения вычитания медианы
+    def toggle_median_subtraction(state):
+        nonlocal median_subtract_enabled
+        median_subtract_enabled = bool(state)
+        if median_subtract_enabled and median_data is None:
+            status.setText("Сначала загрузите файл с медианой")
+            subtract_checkbox.setChecked(False)
+        elif median_subtract_enabled:
+            status.setText("Вычитание медианы включено")
+        else:
+            status.setText("Вычитание медианы выключено")
+
+    # Создаем контейнер для кнопок управления
+    button_container = QWidget()
+    button_layout = QHBoxLayout()
+
+    # Кнопка сохранения медианы
+    save_btn = QPushButton("Сохранить медиану (1000 свипов)")
+    save_btn.clicked.connect(save_median_data)
+    button_layout.addWidget(save_btn)
+
+    # Кнопка загрузки медианы
+    load_btn = QPushButton("Загрузить медиану")
+    load_btn.clicked.connect(load_median_file)
+    button_layout.addWidget(load_btn)
+
+    # Чекбокс для включения вычитания
+    subtract_checkbox = QCheckBox("Вычитать медиану")
+    subtract_checkbox.stateChanged.connect(toggle_median_subtraction)
+    button_layout.addWidget(subtract_checkbox)
+
+    button_layout.setContentsMargins(5, 5, 5, 5)
+    button_container.setLayout(button_layout)
+
+    # Добавляем кнопки в окно
+    proxy_widget = QtWidgets.QGraphicsProxyWidget()
+    proxy_widget.setWidget(button_container)
+    win.addItem(proxy_widget, row=4, col=0, colspan=2)
+
+    # Состояние
+    ring: Optional[np.ndarray] = None
+    head = 0
+    width: Optional[int] = None
+    x_shared: Optional[np.ndarray] = None
+    current_sweep: Optional[np.ndarray] = None
+    current_info: Optional[SweepInfo] = None
+    # Медианные данные для вычитания
+    median_data: Optional[np.ndarray] = None
+    median_subtract_enabled = False
+    # CLI параметры для автоматического сохранения/загрузки
+    ref_out_file = getattr(args, 'ref_out', None)
+    ref_in_file = getattr(args, 'ref_in', None)
+    ref_out_saved = False  # Флаг, что медиана уже сохранена
+    # Отдельный буфер для накопления 1000 сырых свипов (не зависит от max_sweeps)
+    ref_ring: Optional[np.ndarray] = None
+    ref_ring_head = 0
+    ref_ring_count = 0
+
+    # Автоматическая загрузка медианы при старте
+    if ref_in_file:
+        try:
+            pairs = []
+            with open(ref_in_file, 'r') as f:
+                reader = csv.reader(f)
+                next(reader)  # Пропускаем заголовок
+                for row in reader:
+                    if len(row) >= 2:
+                        try:
+                            pairs.append((int(row[0]), float(row[1])))
+                        except ValueError:
+                            continue
+            if pairs:
+                max_idx = max(idx for idx, _ in pairs)
+                median_data = np.full(max_idx + 1, np.nan, dtype=np.float32)
+                for idx, val in pairs:
+                    median_data[idx] = val
+                median_subtract_enabled = True
+                print(f"[ref-in] Загружена медиана из {ref_in_file} ({len(median_data)} точек), вычитание включено")
+            else:
+                print(f"[ref-in] Предупреждение: файл {ref_in_file} пустой или неверный формат")
+        except Exception as e:
+            print(f"[ref-in] Ошибка загрузки {ref_in_file}: {e}")
+
+    # Авто-уровни цветовой шкалы водопада сырых данных пересчитываются по видимой области.
+    # Для спектров (полное FFT для отрицательных частот)
+    fft_bins = FFT_LEN
+    ring_fft: Optional[np.ndarray] = None
+    freq_shared: Optional[np.ndarray] = None
+    y_min_fft, y_max_fft = None, None
+    # Phase состояние
+    ring_phase: Optional[np.ndarray] = None
+    prev_phase_per_bin: Optional[np.ndarray] = None
+    phase_offset_per_bin: Optional[np.ndarray] = None
+    y_min_phase, y_max_phase = None, None
+    # Параметры контраста водопада спектров (процентильная обрезка)
+    spec_clip = parse_spec_clip(getattr(args, "spec_clip", None))
+    # Диапазон по Y: авто по умолчанию (поддерживает отрицательные значения)
+    fixed_ylim: Optional[Tuple[float, float]] = None
+    if args.ylim:
+        try:
+            y0, y1 = args.ylim.split(",")
+            fixed_ylim = (float(y0), float(y1))
+        except Exception:
+            pass
+    if fixed_ylim is not None:
+        p_line.setYRange(fixed_ylim[0], fixed_ylim[1], padding=0)
+
+    def ensure_buffer(_w: int):
+        nonlocal ring, head, width, x_shared, ring_fft, freq_shared
+        nonlocal ring_phase, prev_phase_per_bin, phase_offset_per_bin
+        nonlocal ref_ring
+        if ring is not None:
+            return
+        width = WF_WIDTH
+        x_shared = np.arange(width, dtype=np.int32)
+        ring = np.full((max_sweeps, width), np.nan, dtype=np.float32)
+        head = 0
+        # Водопад: время по оси X, X по оси Y
+        img.setImage(ring.T, autoLevels=False)
+        p_img.setRange(xRange=(0, max_sweeps - 1), yRange=(0, max(1, width - 1)), padding=0)
+        p_line.setXRange(0, max(1, width - 1), padding=0)
+        # FFT: время по оси X, бин по оси Y
+        ring_fft = np.full((max_sweeps, fft_bins), np.nan, dtype=np.float32)
+        img_fft.setImage(ring_fft.T, autoLevels=False)
+        p_spec.setRange(xRange=(0, max_sweeps - 1), yRange=(0, max(1, fft_bins - 1)), padding=0)
+        p_fft.setXRange(FREQ_MIN_GHZ, FREQ_MAX_GHZ, padding=0)
+        freq_shared = np.linspace(FREQ_MIN_GHZ, FREQ_MAX_GHZ, fft_bins, dtype=np.float32)
+        # Phase: время по оси X, бин по оси Y
+        ring_phase = np.full((max_sweeps, fft_bins), np.nan, dtype=np.float32)
+        prev_phase_per_bin = np.zeros(fft_bins, dtype=np.float32)
+        phase_offset_per_bin = np.zeros(fft_bins, dtype=np.float32)
+        img_phase.setImage(ring_phase.T, autoLevels=False)
+        p_phase_wf.setRange(xRange=(0, max_sweeps - 1), yRange=(0, max(1, fft_bins - 1)), padding=0)
+        p_phase.setXRange(0, max(1, fft_bins - 1), padding=0)
+        # Буфер для медианы (отдельный от ring, размер всегда 1000)
+        if ref_out_file and ref_ring is None:
+            ref_ring = np.full((1000, width), np.nan, dtype=np.float32)
+
+    def _visible_levels_pyqtgraph(data: np.ndarray) -> Optional[Tuple[float, float]]:
+        """(vmin, vmax) по текущей видимой области ImageItem (без накопления по времени)."""
+        if data.size == 0:
+            return None
+        ny, nx = data.shape[0], data.shape[1]
+        try:
+            (x0, x1), (y0, y1) = p_img.viewRange()
+        except Exception:
+            x0, x1 = 0.0, float(nx - 1)
+            y0, y1 = 0.0, float(ny - 1)
+        xmin, xmax = sorted((float(x0), float(x1)))
+        ymin, ymax = sorted((float(y0), float(y1)))
+        ix0 = max(0, min(nx - 1, int(np.floor(xmin))))
+        ix1 = max(0, min(nx - 1, int(np.ceil(xmax))))
+        iy0 = max(0, min(ny - 1, int(np.floor(ymin))))
+        iy1 = max(0, min(ny - 1, int(np.ceil(ymax))))
+        if ix1 < ix0:
+            ix1 = ix0
+        if iy1 < iy0:
+            iy1 = iy0
+        sub = data[iy0 : iy1 + 1, ix0 : ix1 + 1]
+        finite = np.isfinite(sub)
+        if not finite.any():
+            return None
+        vals = sub[finite]
+        vmin = float(np.min(vals))
+        vmax = float(np.max(vals))
+        if not (np.isfinite(vmin) and np.isfinite(vmax)) or vmin == vmax:
+            return None
+        return (vmin, vmax)
+
+    def push_sweep(s: np.ndarray):
+        nonlocal ring, head, ring_fft, y_min_fft, y_max_fft
+        nonlocal ring_phase, prev_phase_per_bin, phase_offset_per_bin, y_min_phase, y_max_phase
+        nonlocal ref_ring_head, ref_ring_count
+        if s is None or s.size == 0 or ring is None:
+            return
+
+        # Сохраняем сырой свип в буфер медианы (до вычитания)
+        if ref_out_file and not ref_out_saved and ref_ring is not None:
+            w_ref = ref_ring.shape[1]
+            take_ref = min(w_ref, s.size)
+            ref_ring[ref_ring_head, :take_ref] = s[:take_ref]
+            ref_ring_head = (ref_ring_head + 1) % 1000
+            ref_ring_count = min(ref_ring_count + 1, 1000)
+
+        # Применяем вычитание медианы если включено
+        if median_subtract_enabled and median_data is not None:
+            # Вычитаем медиану из сигнала
+            take_median = min(s.size, median_data.size)
+            s_corrected = s.copy()
+            s_corrected[:take_median] = s[:take_median] - median_data[:take_median]
+            s = s_corrected
+
+        w = ring.shape[1]
+        row = np.full((w,), np.nan, dtype=np.float32)
+        take = min(w, s.size)
+        row[:take] = s[:take]
+        ring[head, :] = row
+        head = (head + 1) % ring.shape[0]
+        # FFT строка (дБ) и фаза
+        if ring_fft is not None:
+            bins = ring_fft.shape[1]
+            take_fft = min(int(s.size), FFT_LEN)
+            if take_fft > 0:
+                # Создаем буфер для полного FFT (с отрицательными частотами)
+                fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
+
+                # Вычисляем индексы для размещения данных (1-10 ГГц в диапазоне -10 до +10 ГГц)
+                # Диапазон данных: от DATA_FREQ_START_GHZ (1) до DATA_FREQ_END_GHZ (10)
+                # Полный диапазон: от FREQ_MIN_GHZ (-10) до FREQ_MAX_GHZ (10)
+                freq_range_total = FREQ_MAX_GHZ - FREQ_MIN_GHZ  # 20 ГГц
+                freq_range_data = DATA_FREQ_END_GHZ - DATA_FREQ_START_GHZ  # 9 ГГц
+
+                # Начальный индекс для данных в FFT буфере
+                start_idx = int((DATA_FREQ_START_GHZ - FREQ_MIN_GHZ) / freq_range_total * FFT_LEN)
+                # Количество точек для данных
+                data_points = int(freq_range_data / freq_range_total * FFT_LEN)
+                data_points = min(data_points, take_fft, FFT_LEN - start_idx)
+
+                # Подготовка данных с окном Хэннинга
+                seg = np.nan_to_num(s[:data_points], nan=0.0).astype(np.float32, copy=False)
+                win = np.hanning(data_points).astype(np.float32)
+
+                # Размещаем данные в правильной позиции (от -10 до 1 ГГц - нули, от 1 до 10 ГГц - данные)
+                fft_in[start_idx:start_idx + data_points] = seg * win
+
+                # Полное FFT (включая отрицательные частоты)
+                spec = np.fft.fft(fft_in)
+                # Сдвигаем для центрирования нулевой частоты
+                spec = np.fft.fftshift(spec)
+
+                mag = np.abs(spec).astype(np.float32)
+                fft_row = 20.0 * np.log10(mag + 1e-9)
+                if fft_row.shape[0] != bins:
+                    fft_row = fft_row[:bins]
+
+                # Расчет фазы
+                phase = np.angle(spec).astype(np.float32)
+                if phase.shape[0] > bins:
+                    phase = phase[:bins]
+                # Unwrapping по частоте (внутри свипа)
+                phase_unwrapped_freq = np.unwrap(phase)
+                # Unwrapping по времени (между свипами)
+                phase_unwrapped_time, prev_phase_per_bin, phase_offset_per_bin = apply_temporal_unwrap(
+                    phase_unwrapped_freq, prev_phase_per_bin, phase_offset_per_bin
+                )
+                phase_row = phase_unwrapped_time
+            else:
+                fft_row = np.full((bins,), np.nan, dtype=np.float32)
+                phase_row = np.full((bins,), np.nan, dtype=np.float32)
+
+            ring_fft[(head - 1) % ring_fft.shape[0], :] = fft_row
+            fr_min = np.nanmin(fft_row)
+            fr_max = np.nanmax(fft_row)
+            if y_min_fft is None or (not np.isnan(fr_min) and fr_min < y_min_fft):
+                y_min_fft = float(fr_min)
+            if y_max_fft is None or (not np.isnan(fr_max) and fr_max > y_max_fft):
+                y_max_fft = float(fr_max)
+
+            # Сохраняем фазу в буфер
+            if ring_phase is not None:
+                ring_phase[(head - 1) % ring_phase.shape[0], :] = phase_row
+                # Экстремумы для цветовой шкалы фазы
+                ph_min = np.nanmin(phase_row)
+                ph_max = np.nanmax(phase_row)
+                if y_min_phase is None or (not np.isnan(ph_min) and ph_min < y_min_phase):
+                    y_min_phase = float(ph_min)
+                if y_max_phase is None or (not np.isnan(ph_max) and ph_max > y_max_phase):
+                    y_max_phase = float(ph_max)
+
+    def drain_queue():
+        nonlocal current_sweep, current_info
+        drained = 0
+        while True:
+            try:
+                s, info = q.get_nowait()
+            except Empty:
+                break
+            drained += 1
+            current_sweep = s
+            current_info = info
+            ensure_buffer(s.size)
+            push_sweep(s)
+        return drained
+
+    # Попытка применить LUT из колормэпа (если доступен)
+    try:
+        cm_mod = getattr(pg, "colormap", None)
+        if cm_mod is not None:
+            cm = cm_mod.get(args.cmap)
+            img.setLookupTable(cm.getLookupTable(0.0, 1.0, 256))
+    except Exception:
+        pass
+
+    def update():
+        nonlocal ref_out_saved
+        changed = drain_queue() > 0
+        if current_sweep is not None and x_shared is not None:
+            # Применяем вычитание медианы для отображения
+            display_sweep = current_sweep
+            if median_subtract_enabled and median_data is not None:
+                take_median = min(current_sweep.size, median_data.size)
+                display_sweep = current_sweep.copy()
+                display_sweep[:take_median] = current_sweep[:take_median] - median_data[:take_median]
+
+            if display_sweep.size <= x_shared.size:
+                xs = x_shared[: display_sweep.size]
+            else:
+                xs = np.arange(display_sweep.size)
+            curve.setData(xs, display_sweep, autoDownsample=True)
+            if fixed_ylim is None:
+                y0 = float(np.nanmin(display_sweep))
+                y1 = float(np.nanmax(display_sweep))
+                if np.isfinite(y0) and np.isfinite(y1):
+                    margin = 0.05 * max(1.0, (y1 - y0))
+                    p_line.setYRange(y0 - margin, y1 + margin, padding=0)
+
+            # Обновим спектр и фазу
+            take_fft = min(int(display_sweep.size), FFT_LEN)
+            if take_fft > 0 and freq_shared is not None:
+                # Создаем буфер для полного FFT (с отрицательными частотами)
+                fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
+
+                # Вычисляем индексы для размещения данных (1-10 ГГц в диапазоне -10 до +10 ГГц)
+                freq_range_total = FREQ_MAX_GHZ - FREQ_MIN_GHZ  # 20 ГГц
+                freq_range_data = DATA_FREQ_END_GHZ - DATA_FREQ_START_GHZ  # 9 ГГц
+
+                # Начальный индекс для данных в FFT буфере
+                start_idx = int((DATA_FREQ_START_GHZ - FREQ_MIN_GHZ) / freq_range_total * FFT_LEN)
+                # Количество точек для данных
+                data_points = int(freq_range_data / freq_range_total * FFT_LEN)
+                data_points = min(data_points, take_fft, FFT_LEN - start_idx)
+
+                # Подготовка данных с окном Хэннинга
+                seg = np.nan_to_num(display_sweep[:data_points], nan=0.0).astype(np.float32, copy=False)
+                win = np.hanning(data_points).astype(np.float32)
+
+                # Размещаем данные в правильной позиции
+                fft_in[start_idx:start_idx + data_points] = seg * win
+
+                # Полное FFT (включая отрицательные частоты)
+                spec = np.fft.fft(fft_in)
+                # Сдвигаем для центрирования нулевой частоты
+                spec = np.fft.fftshift(spec)
+
+                mag = np.abs(spec).astype(np.float32)
+                fft_vals = 20.0 * np.log10(mag + 1e-9)
+                xs_fft = freq_shared
+                if fft_vals.size > xs_fft.size:
+                    fft_vals = fft_vals[: xs_fft.size]
+                curve_fft.setData(xs_fft[: fft_vals.size], fft_vals)
+                p_fft.setYRange(float(np.nanmin(fft_vals)), float(np.nanmax(fft_vals)), padding=0)
+
+                # Расчет и отображение фазы текущего свипа
+                phase = np.angle(spec).astype(np.float32)
+                if phase.size > xs_fft.size:
+                    phase = phase[: xs_fft.size]
+                # Unwrapping по частоте
+                phase_unwrapped = np.unwrap(phase)
+                curve_phase.setData(xs_fft[: phase_unwrapped.size], phase_unwrapped)
+                phase_min = float(np.nanmin(phase_unwrapped))
+                phase_max = float(np.nanmax(phase_unwrapped))
+                p_phase.setYRange(phase_min, phase_max, padding=0)
+                # Обновляем вторую ось Y с расстоянием
+                try:
+                    dist_min = phase_to_distance(np.array([phase_min]))[0]
+                    dist_max = phase_to_distance(np.array([phase_max]))[0]
+                    p_phase_dist_axis.setYRange(dist_min, dist_max, padding=0)
+                except Exception:
+                    pass
+
+        if changed and ring is not None:
+            disp = ring if head == 0 else np.roll(ring, -head, axis=0)
+            disp = disp.T[:, ::-1]  # (width, time with newest at left)
+            levels = _visible_levels_pyqtgraph(disp)
+            if levels is not None:
+                img.setImage(disp, autoLevels=False, levels=levels)
+            else:
+                img.setImage(disp, autoLevels=False)
+
+        if changed and current_info:
+            try:
+                status.setText(format_status_kv(current_info))
+            except Exception:
+                pass
+
+        # Автоматическое сохранение медианы при накоплении 1000 сырых свипов
+        if ref_out_file and not ref_out_saved and ref_ring is not None:
+            if ref_ring_count >= 1000:
+                try:
+                    ordered = ref_ring if ref_ring_head == 0 else np.roll(ref_ring, -ref_ring_head, axis=0)
+                    median_sweep = np.nanmedian(ordered, axis=0)
+
+                    with open(ref_out_file, 'w', newline='') as f:
+                        writer = csv.writer(f)
+                        writer.writerow(['Index', 'Median_Value'])
+                        for i, value in enumerate(median_sweep):
+                            if np.isfinite(value):
+                                writer.writerow([i, float(value)])
+
+                    ref_out_saved = True
+                    print(f"[ref-out] Сохранена медиана 1000 свипов в {ref_out_file}")
+                    if status:
+                        status.setText(f"[ref-out] Сохранено в {ref_out_file}")
+                except Exception as e:
+                    print(f"[ref-out] Ошибка сохранения: {e}")
+
+        if changed and ring_fft is not None:
+            disp_fft = ring_fft if head == 0 else np.roll(ring_fft, -head, axis=0)
+            disp_fft = disp_fft.T[:, ::-1]
+            # Автодиапазон по среднему спектру за видимый интервал (как в хорошей версии)
+            levels = None
+            try:
+                mean_spec = np.nanmean(disp_fft, axis=1)
+                vmin_v = float(np.nanmin(mean_spec))
+                vmax_v = float(np.nanmax(mean_spec))
+                if np.isfinite(vmin_v) and np.isfinite(vmax_v) and vmin_v != vmax_v:
+                    levels = (vmin_v, vmax_v)
+            except Exception:
+                levels = None
+            # Процентильная обрезка как запасной вариант
+            if levels is None and spec_clip is not None:
+                try:
+                    vmin_v = float(np.nanpercentile(disp_fft, spec_clip[0]))
+                    vmax_v = float(np.nanpercentile(disp_fft, spec_clip[1]))
+                    if np.isfinite(vmin_v) and np.isfinite(vmax_v) and vmin_v != vmax_v:
+                        levels = (vmin_v, vmax_v)
+                except Exception:
+                    levels = None
+            # Ещё один фолбэк — глобальные накопленные мин/макс
+            if levels is None and y_min_fft is not None and y_max_fft is not None and np.isfinite(y_min_fft) and np.isfinite(y_max_fft) and y_min_fft != y_max_fft:
+                levels = (y_min_fft, y_max_fft)
+            if levels is not None:
+                img_fft.setImage(disp_fft, autoLevels=False, levels=levels)
+            else:
+                img_fft.setImage(disp_fft, autoLevels=False)
+
+        # Обновление водопада фазы
+        if changed and ring_phase is not None:
+            disp_phase = ring_phase if head == 0 else np.roll(ring_phase, -head, axis=0)
+            disp_phase = disp_phase.T[:, ::-1]
+            # Автодиапазон для фазы
+            levels_phase = None
+            try:
+                mean_phase = np.nanmean(disp_phase, axis=1)
+                vmin_p = float(np.nanmin(mean_phase))
+                vmax_p = float(np.nanmax(mean_phase))
+                if np.isfinite(vmin_p) and np.isfinite(vmax_p) and vmin_p != vmax_p:
+                    levels_phase = (vmin_p, vmax_p)
+            except Exception:
+                levels_phase = None
+            # Фолбэк к отслеживаемым минимум/максимумам
+            if levels_phase is None and y_min_phase is not None and y_max_phase is not None and np.isfinite(y_min_phase) and np.isfinite(y_max_phase) and y_min_phase != y_max_phase:
+                levels_phase = (y_min_phase, y_max_phase)
+            if levels_phase is not None:
+                img_phase.setImage(disp_phase, autoLevels=False, levels=levels_phase)
+            else:
+                img_phase.setImage(disp_phase, autoLevels=False)
+
+    timer = pg.QtCore.QTimer()
+    timer.timeout.connect(update)
+    timer.start(interval_ms)
+
+    def on_quit():
+        stop_event.set()
+        reader.join(timeout=1.0)
+
+    app.aboutToQuit.connect(on_quit)
+    win.show()
+    exec_fn = getattr(app, "exec_", None) or getattr(app, "exec", None)
+    exec_fn()
+    # На случай если aboutToQuit не сработал
+    on_quit()

run.py

@@ -0,0 +1,9 @@
+#!/usr/bin/env python3
+"""
+Скрипт запуска RFG ADC Data Plotter.
+"""
+
+from rfg_adc_plotter.cli import main
+
+if __name__ == "__main__":
+    main()

tests/test_background_buffer.py

@@ -1,44 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import unittest
-
-from rfg_adc_plotter.state.background_buffer import BackgroundMedianBuffer
-
-
-class BackgroundMedianBufferTests(unittest.TestCase):
-    def test_buffer_returns_median_for_partial_fill(self):
-        buffer = BackgroundMedianBuffer(max_rows=4)
-        buffer.push(np.asarray([1.0, 5.0, 9.0], dtype=np.float32))
-        buffer.push(np.asarray([3.0, 7.0, 11.0], dtype=np.float32))
-
-        median = buffer.median()
-
-        self.assertIsNotNone(median)
-        self.assertTrue(np.allclose(median, np.asarray([2.0, 6.0, 10.0], dtype=np.float32)))
-
-    def test_buffer_wraparound_keeps_latest_rows(self):
-        buffer = BackgroundMedianBuffer(max_rows=2)
-        buffer.push(np.asarray([1.0, 5.0], dtype=np.float32))
-        buffer.push(np.asarray([3.0, 7.0], dtype=np.float32))
-        buffer.push(np.asarray([9.0, 11.0], dtype=np.float32))
-
-        median = buffer.median()
-
-        self.assertIsNotNone(median)
-        self.assertTrue(np.allclose(median, np.asarray([6.0, 9.0], dtype=np.float32)))
-
-    def test_buffer_reset_clears_state(self):
-        buffer = BackgroundMedianBuffer(max_rows=2)
-        buffer.push(np.asarray([1.0, 2.0], dtype=np.float32))
-
-        buffer.reset()
-
-        self.assertIsNone(buffer.rows)
-        self.assertIsNone(buffer.median())
-        self.assertEqual(buffer.count, 0)
-        self.assertEqual(buffer.head, 0)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_cli.py

@@ -1,57 +0,0 @@
-from __future__ import annotations
-
-import subprocess
-import sys
-import unittest
-from pathlib import Path
-
-from rfg_adc_plotter.cli import build_parser
-
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def _run(*args: str) -> subprocess.CompletedProcess[str]:
-    return subprocess.run(
-        [sys.executable, *args],
-        cwd=ROOT,
-        text=True,
-        capture_output=True,
-        check=False,
-    )
-
-
-class CliTests(unittest.TestCase):
-    def test_logscale_and_opengl_are_opt_in(self):
-        args = build_parser().parse_args(["/dev/null"])
-        self.assertFalse(args.logscale)
-        self.assertFalse(args.opengl)
-        self.assertAlmostEqual(float(args.tty_range_v), 5.0, places=6)
-
-        args_log = build_parser().parse_args(["/dev/null", "--logscale", "--opengl", "--tty-range-v", "2.5"])
-        self.assertTrue(args_log.logscale)
-        self.assertTrue(args_log.opengl)
-        self.assertAlmostEqual(float(args_log.tty_range_v), 2.5, places=6)
-
-    def test_wrapper_help_works(self):
-        proc = _run("RFG_ADC_dataplotter.py", "--help")
-        self.assertEqual(proc.returncode, 0)
-        self.assertIn("usage:", proc.stdout)
-        self.assertIn("--peak_search", proc.stdout)
-
-    def test_module_help_works(self):
-        proc = _run("-m", "rfg_adc_plotter.main", "--help")
-        self.assertEqual(proc.returncode, 0)
-        self.assertIn("usage:", proc.stdout)
-        self.assertIn("--parser_16_bit_x2", proc.stdout)
-        self.assertIn("--parser_complex_ascii", proc.stdout)
-        self.assertIn("--opengl", proc.stdout)
-
-    def test_backend_mpl_reports_removal(self):
-        proc = _run("-m", "rfg_adc_plotter.main", "/dev/null", "--backend", "mpl")
-        self.assertNotEqual(proc.returncode, 0)
-        self.assertIn("Matplotlib backend removed", proc.stderr)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_processing.py

@@ -1,694 +0,0 @@
-from __future__ import annotations
-
-import os
-import tempfile
-import numpy as np
-import unittest
-
-from rfg_adc_plotter.constants import C_M_S, FFT_LEN, SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ
-from rfg_adc_plotter.gui.pyqtgraph_backend import (
-    apply_distance_cut_to_axis,
-    apply_working_range,
-    apply_working_range_to_aux_curves,
-    build_logdet_voltage_fft_input,
-    build_main_window_layout,
-    coalesce_packets_for_ui,
-    compute_background_subtracted_bscan_levels,
-    compute_aux_phase_curve,
-    convert_tty_i16_to_voltage,
-    decimate_curve_for_display,
-    resolve_axis_bounds,
-    resolve_heavy_refresh_stride,
-    resolve_initial_window_size,
-    resolve_distance_cut_start,
-    sanitize_curve_data_for_display,
-    sanitize_image_for_display,
-    set_image_rect_if_ready,
-    resolve_visible_fft_curves,
-    resolve_visible_aux_curves,
-)
-from rfg_adc_plotter.processing.calibration import (
-    build_calib_envelope,
-    build_complex_calibration_curve,
-    calibrate_freqs,
-    load_calib_envelope,
-    load_complex_calibration,
-    recalculate_calibration_c,
-    save_calib_envelope,
-    save_complex_calibration,
-)
-from rfg_adc_plotter.processing.background import (
-    load_fft_background,
-    save_fft_background,
-    subtract_fft_background,
-)
-from rfg_adc_plotter.processing.fft import (
-    build_positive_only_exact_centered_ifft_spectrum,
-    build_positive_only_centered_ifft_spectrum,
-    build_symmetric_ifft_spectrum,
-    compute_distance_axis,
-    compute_fft_complex_row,
-    compute_fft_mag_row,
-    compute_fft_row,
-    fft_mag_to_db,
-)
-from rfg_adc_plotter.processing.normalization import (
-    build_calib_envelopes,
-    fit_complex_calibration_to_width,
-    normalize_by_calib,
-    normalize_by_complex_calibration,
-    normalize_by_envelope,
-    resample_envelope,
-)
-from rfg_adc_plotter.processing.peaks import find_peak_width_markers, find_top_peaks_over_ref, rolling_median_ref
-
-
-class ProcessingTests(unittest.TestCase):
-    def test_convert_tty_i16_to_voltage_maps_and_clips_full_range(self):
-        codes = np.asarray([-32768.0, -16384.0, 0.0, 16384.0, 32767.0], dtype=np.float32)
-        volts = convert_tty_i16_to_voltage(codes, 5.0)
-
-        self.assertEqual(volts.shape, codes.shape)
-        self.assertAlmostEqual(float(volts[0]), -5.0, places=6)
-        self.assertAlmostEqual(float(volts[2]), 0.0, places=6)
-        self.assertAlmostEqual(float(volts[-1]), 5.0, places=6)
-        self.assertTrue(np.all(volts >= -5.0))
-        self.assertTrue(np.all(volts <= 5.0))
-
-    def test_build_logdet_voltage_fft_input_converts_codes_and_exponentiates(self):
-        codes = np.asarray([-32768.0, 0.0, 32767.0], dtype=np.float32)
-        volts, fft_input = build_logdet_voltage_fft_input(codes, 5.0)
-
-        self.assertEqual(volts.shape, codes.shape)
-        self.assertEqual(fft_input.shape, codes.shape)
-        self.assertAlmostEqual(float(volts[0]), -5.0, places=6)
-        self.assertAlmostEqual(float(volts[1]), 0.0, places=6)
-        self.assertAlmostEqual(float(volts[2]), 5.0, places=6)
-        self.assertTrue(np.allclose(fft_input, np.exp(volts.astype(np.float32))))
-
-    def test_build_logdet_voltage_fft_input_clips_exp_argument_and_respects_range(self):
-        codes = np.asarray([32767.0], dtype=np.float32)
-        volts_5, fft_5 = build_logdet_voltage_fft_input(codes, 5.0, exp_input_limit=2.0)
-        volts_10, fft_10 = build_logdet_voltage_fft_input(codes, 10.0, exp_input_limit=2.0)
-
-        self.assertAlmostEqual(float(volts_5[0]), 5.0, places=6)
-        self.assertAlmostEqual(float(volts_10[0]), 10.0, places=6)
-        self.assertAlmostEqual(float(fft_5[0]), float(np.exp(np.float32(2.0))), places=5)
-        self.assertAlmostEqual(float(fft_10[0]), float(np.exp(np.float32(2.0))), places=5)
-        self.assertTrue(np.isfinite(fft_5[0]))
-        self.assertTrue(np.isfinite(fft_10[0]))
-
-    def test_recalculate_calibration_preserves_requested_edges(self):
-        coeffs = recalculate_calibration_c(np.asarray([0.0, 1.0, 0.025], dtype=np.float64), 3.3, 14.3)
-        y0 = coeffs[0] + coeffs[1] * 3.3 + coeffs[2] * (3.3 ** 2)
-        y1 = coeffs[0] + coeffs[1] * 14.3 + coeffs[2] * (14.3 ** 2)
-        self.assertTrue(np.isclose(y0, 3.3))
-        self.assertTrue(np.isclose(y1, 14.3))
-
-    def test_calibrate_freqs_returns_monotonic_axis_and_same_shape(self):
-        sweep = {"F": np.linspace(3.3, 14.3, 32), "I": np.linspace(-1.0, 1.0, 32)}
-        calibrated = calibrate_freqs(sweep)
-        self.assertEqual(calibrated["F"].shape, (32,))
-        self.assertEqual(calibrated["I"].shape, (32,))
-        self.assertTrue(np.all(np.diff(calibrated["F"]) >= 0.0))
-
-    def test_calibrate_freqs_keeps_complex_payload(self):
-        sweep = {
-            "F": np.linspace(3.3, 14.3, 32),
-            "I": np.exp(1j * np.linspace(0.0, np.pi, 32)).astype(np.complex64),
-        }
-        calibrated = calibrate_freqs(sweep)
-
-        self.assertEqual(calibrated["F"].shape, (32,))
-        self.assertEqual(calibrated["I"].shape, (32,))
-        self.assertTrue(np.iscomplexobj(calibrated["I"]))
-        self.assertTrue(np.all(np.isfinite(calibrated["I"])))
-
-    def test_normalizers_and_envelopes_return_finite_ranges(self):
-        calib = (np.sin(np.linspace(0.0, 4.0 * np.pi, 64)) * 5.0).astype(np.float32)
-        raw = calib * 0.75
-        lower, upper = build_calib_envelopes(calib)
-        self.assertEqual(lower.shape, calib.shape)
-        self.assertEqual(upper.shape, calib.shape)
-        self.assertTrue(np.all(lower <= upper))
-        self.assertTrue(np.all(np.isfinite(upper)))
-        self.assertLess(
-            float(np.mean(np.abs(np.diff(upper, n=2)))),
-            float(np.mean(np.abs(np.diff(calib, n=2)))),
-        )
-
-        simple = normalize_by_calib(raw, calib + 10.0, norm_type="simple")
-        projector = normalize_by_calib(raw, calib, norm_type="projector")
-        self.assertEqual(simple.shape, raw.shape)
-        self.assertEqual(projector.shape, raw.shape)
-        self.assertTrue(np.any(np.isfinite(simple)))
-        self.assertTrue(np.any(np.isfinite(projector)))
-
-    def test_file_calibration_envelope_roundtrip_and_division(self):
-        raw = (np.sin(np.linspace(0.0, 8.0 * np.pi, 128)) * 50.0 + 100.0).astype(np.float32)
-        envelope = build_calib_envelope(raw)
-        normalized = normalize_by_envelope(raw, envelope)
-        resampled = resample_envelope(envelope, 96)
-
-        self.assertEqual(envelope.shape, raw.shape)
-        self.assertEqual(normalized.shape, raw.shape)
-        self.assertEqual(resampled.shape, (96,))
-        self.assertTrue(np.any(np.isfinite(normalized)))
-        self.assertTrue(np.all(np.isfinite(envelope)))
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            path = os.path.join(tmp_dir, "calibration_envelope")
-            saved_path = save_calib_envelope(path, envelope)
-            loaded = load_calib_envelope(saved_path)
-            self.assertTrue(saved_path.endswith(".npy"))
-            self.assertTrue(np.allclose(loaded, envelope))
-
-    def test_normalize_by_envelope_adds_small_epsilon_to_zero_denominator(self):
-        raw = np.asarray([1.0, 2.0, 3.0], dtype=np.float32)
-        envelope = np.asarray([0.0, 1.0, -1.0], dtype=np.float32)
-        normalized = normalize_by_envelope(raw, envelope)
-
-        self.assertTrue(np.all(np.isfinite(normalized)))
-        self.assertGreater(normalized[0], 1e8)
-        self.assertAlmostEqual(float(normalized[1]), 2.0, places=5)
-        self.assertAlmostEqual(float(normalized[2]), -3.0, places=5)
-
-    def test_normalize_by_envelope_supports_complex_input(self):
-        raw = np.asarray([1.0 + 1.0j, 2.0 - 2.0j], dtype=np.complex64)
-        envelope = np.asarray([1.0, 2.0], dtype=np.float32)
-        normalized = normalize_by_envelope(raw, envelope)
-
-        self.assertTrue(np.iscomplexobj(normalized))
-        self.assertTrue(np.all(np.isfinite(normalized)))
-        self.assertTrue(np.allclose(normalized, np.asarray([1.0 + 1.0j, 1.0 - 1.0j], dtype=np.complex64)))
-
-    def test_load_calib_envelope_rejects_empty_payload(self):
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            path = os.path.join(tmp_dir, "empty.npy")
-            np.save(path, np.zeros((0,), dtype=np.float32))
-            with self.assertRaises(ValueError):
-                load_calib_envelope(path)
-
-    def test_complex_calibration_curve_roundtrip(self):
-        ch1 = np.asarray([1.0, 2.0, 3.0], dtype=np.float32)
-        ch2 = np.asarray([0.5, -1.0, 4.0], dtype=np.float32)
-        curve = build_complex_calibration_curve(ch1, ch2)
-        expected = np.asarray([1.0 + 0.5j, 2.0 - 1.0j, 3.0 + 4.0j], dtype=np.complex64)
-
-        self.assertTrue(np.iscomplexobj(curve))
-        self.assertTrue(np.allclose(curve, expected))
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            path = os.path.join(tmp_dir, "complex_calibration")
-            saved_path = save_complex_calibration(path, curve)
-            loaded = load_complex_calibration(saved_path)
-            self.assertTrue(saved_path.endswith(".npy"))
-            self.assertEqual(loaded.dtype, np.complex64)
-            self.assertTrue(np.allclose(loaded, expected))
-
-    def test_fit_complex_calibration_to_width_pads_or_trims(self):
-        calib = np.asarray([1.0 + 1.0j, 2.0 + 2.0j], dtype=np.complex64)
-        padded = fit_complex_calibration_to_width(calib, 4)
-        trimmed = fit_complex_calibration_to_width(
-            np.asarray([1.0 + 1.0j, 2.0 + 2.0j, 3.0 + 3.0j], dtype=np.complex64),
-            2,
-        )
-
-        self.assertEqual(padded.shape, (4,))
-        self.assertTrue(np.allclose(padded, np.asarray([1.0 + 1.0j, 2.0 + 2.0j, 1.0 + 0.0j, 1.0 + 0.0j], dtype=np.complex64)))
-        self.assertEqual(trimmed.shape, (2,))
-        self.assertTrue(np.allclose(trimmed, np.asarray([1.0 + 1.0j, 2.0 + 2.0j], dtype=np.complex64)))
-
-    def test_normalize_by_complex_calibration_handles_zero_and_length_mismatch(self):
-        signal = np.asarray([2.0 + 2.0j, 4.0 + 0.0j, 3.0 + 3.0j], dtype=np.complex64)
-        calib = np.asarray([1.0 + 1.0j, 0.0 + 0.0j], dtype=np.complex64)
-        normalized = normalize_by_complex_calibration(signal, calib)
-        expected = np.asarray([2.0 + 0.0j, 4.0 + 0.0j, 3.0 + 3.0j], dtype=np.complex64)
-
-        self.assertTrue(np.iscomplexobj(normalized))
-        self.assertTrue(np.all(np.isfinite(normalized)))
-        self.assertTrue(np.allclose(normalized, expected))
-
-    def test_fft_background_roundtrip_and_rejects_non_1d_payload(self):
-        background = np.asarray([0.5, 1.5, 2.5], dtype=np.float32)
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            path = os.path.join(tmp_dir, "fft_background")
-            saved_path = save_fft_background(path, background)
-            loaded = load_fft_background(saved_path)
-            self.assertTrue(saved_path.endswith(".npy"))
-            self.assertTrue(np.allclose(loaded, background))
-
-            invalid_path = os.path.join(tmp_dir, "fft_background_invalid.npy")
-            np.save(invalid_path, np.zeros((2, 2), dtype=np.float32))
-            with self.assertRaises(ValueError):
-                load_fft_background(invalid_path)
-
-    def test_subtract_fft_background_clamps_negative_residuals_to_zero(self):
-        signal = np.asarray([1.0, 2.0, 3.0], dtype=np.float32)
-        background = np.asarray([1.0, 1.5, 5.0], dtype=np.float32)
-        subtracted = subtract_fft_background(signal, background)
-
-        self.assertTrue(np.allclose(subtracted, np.asarray([0.0, 0.5, 0.0], dtype=np.float32)))
-        self.assertTrue(np.allclose(subtract_fft_background(signal, signal), 0.0))
-
-    def test_apply_working_range_crops_sweep_to_selected_band(self):
-        freqs = np.linspace(3.3, 14.3, 12, dtype=np.float64)
-        sweep = np.arange(12, dtype=np.float32)
-        cropped_freqs, cropped_sweep = apply_working_range(freqs, sweep, 5.0, 9.0)
-
-        self.assertGreater(cropped_freqs.size, 0)
-        self.assertEqual(cropped_freqs.shape, cropped_sweep.shape)
-        self.assertGreaterEqual(float(np.min(cropped_freqs)), 5.0)
-        self.assertLessEqual(float(np.max(cropped_freqs)), 9.0)
-
-    def test_apply_working_range_returns_empty_when_no_points_match(self):
-        freqs = np.linspace(3.3, 14.3, 12, dtype=np.float64)
-        sweep = np.arange(12, dtype=np.float32)
-        cropped_freqs, cropped_sweep = apply_working_range(freqs, sweep, 20.0, 21.0)
-
-        self.assertEqual(cropped_freqs.shape, (0,))
-        self.assertEqual(cropped_sweep.shape, (0,))
-
-    def test_apply_working_range_to_aux_curves_uses_same_mask_as_raw_sweep(self):
-        freqs = np.linspace(3.3, 14.3, 6, dtype=np.float64)
-        sweep = np.asarray([0.0, 1.0, np.nan, 3.0, 4.0, 5.0], dtype=np.float32)
-        aux = (
-            np.asarray([10.0, 11.0, 12.0, 13.0, 14.0, 15.0], dtype=np.float32),
-            np.asarray([20.0, 21.0, 22.0, 23.0, 24.0, 25.0], dtype=np.float32),
-        )
-
-        cropped_freqs, cropped_sweep = apply_working_range(freqs, sweep, 4.0, 12.5)
-        cropped_aux = apply_working_range_to_aux_curves(freqs, sweep, aux, 4.0, 12.5)
-
-        self.assertIsNotNone(cropped_aux)
-        self.assertEqual(cropped_aux[0].shape, cropped_freqs.shape)
-        self.assertEqual(cropped_aux[1].shape, cropped_freqs.shape)
-        self.assertEqual(cropped_aux[0].shape, cropped_sweep.shape)
-        self.assertTrue(np.allclose(cropped_aux[0], np.asarray([11.0, 13.0, 14.0], dtype=np.float32)))
-        self.assertTrue(np.allclose(cropped_aux[1], np.asarray([21.0, 23.0, 24.0], dtype=np.float32)))
-
-    def test_resolve_visible_aux_curves_obeys_checkbox_state(self):
-        aux = (
-            np.asarray([1.0, 2.0], dtype=np.float32),
-            np.asarray([3.0, 4.0], dtype=np.float32),
-        )
-
-        self.assertIsNone(resolve_visible_aux_curves(aux, enabled=False))
-        visible = resolve_visible_aux_curves(aux, enabled=True)
-        self.assertIsNotNone(visible)
-        self.assertTrue(np.allclose(visible[0], aux[0]))
-        self.assertTrue(np.allclose(visible[1], aux[1]))
-
-    def test_compute_aux_phase_curve_returns_atan2_of_aux_channels(self):
-        aux = (
-            np.asarray([1.0, 1.0, -1.0, 0.0], dtype=np.float32),
-            np.asarray([0.0, 1.0, 1.0, 1.0], dtype=np.float32),
-        )
-
-        phase = compute_aux_phase_curve(aux)
-
-        self.assertIsNotNone(phase)
-        expected = np.asarray([0.0, np.pi / 4.0, 3.0 * np.pi / 4.0, np.pi / 2.0], dtype=np.float32)
-        self.assertEqual(phase.shape, expected.shape)
-        self.assertTrue(np.allclose(phase, expected, atol=1e-6))
-
-    def test_decimate_curve_for_display_preserves_small_series(self):
-        xs = np.linspace(3.3, 14.3, 64, dtype=np.float64)
-        ys = np.linspace(-1.0, 1.0, 64, dtype=np.float32)
-
-        decimated_x, decimated_y = decimate_curve_for_display(xs, ys, max_points=128)
-
-        self.assertTrue(np.allclose(decimated_x, xs))
-        self.assertTrue(np.allclose(decimated_y, ys))
-
-    def test_decimate_curve_for_display_limits_points_and_keeps_endpoints(self):
-        xs = np.linspace(3.3, 14.3, 10000, dtype=np.float64)
-        ys = np.sin(np.linspace(0.0, 12.0 * np.pi, 10000)).astype(np.float32)
-
-        decimated_x, decimated_y = decimate_curve_for_display(xs, ys, max_points=512)
-
-        self.assertLessEqual(decimated_x.size, 512)
-        self.assertEqual(decimated_x.shape, decimated_y.shape)
-        self.assertAlmostEqual(float(decimated_x[0]), float(xs[0]), places=12)
-        self.assertAlmostEqual(float(decimated_x[-1]), float(xs[-1]), places=12)
-        self.assertAlmostEqual(float(decimated_y[0]), float(ys[0]), places=6)
-        self.assertAlmostEqual(float(decimated_y[-1]), float(ys[-1]), places=6)
-
-    def test_coalesce_packets_for_ui_keeps_newest_packets(self):
-        packets = [
-            (np.asarray([float(idx)], dtype=np.float32), {"sweep": idx}, None)
-            for idx in range(6)
-        ]
-
-        kept, skipped = coalesce_packets_for_ui(packets, max_packets=2)
-
-        self.assertEqual(skipped, 4)
-        self.assertEqual(len(kept), 2)
-        self.assertEqual(int(kept[0][1]["sweep"]), 4)
-        self.assertEqual(int(kept[1][1]["sweep"]), 5)
-
-    def test_coalesce_packets_for_ui_never_returns_empty_for_non_empty_input(self):
-        packets = [
-            (np.asarray([1.0], dtype=np.float32), {"sweep": 1}, None),
-        ]
-
-        kept, skipped = coalesce_packets_for_ui(packets, max_packets=0)
-
-        self.assertEqual(skipped, 0)
-        self.assertEqual(len(kept), 1)
-        self.assertEqual(int(kept[0][1]["sweep"]), 1)
-
-    def test_coalesce_packets_for_ui_switches_to_latest_only_on_large_backlog(self):
-        packets = [
-            (np.asarray([float(idx)], dtype=np.float32), {"sweep": idx}, None)
-            for idx in range(40)
-        ]
-
-        kept, skipped = coalesce_packets_for_ui(packets, max_packets=8, backlog_packets=40)
-
-        self.assertEqual(skipped, 39)
-        self.assertEqual(len(kept), 1)
-        self.assertEqual(int(kept[0][1]["sweep"]), 39)
-
-    def test_resolve_heavy_refresh_stride_increases_with_backlog(self):
-        self.assertEqual(resolve_heavy_refresh_stride(0, max_packets=8), 1)
-        self.assertEqual(resolve_heavy_refresh_stride(20, max_packets=8), 2)
-        self.assertEqual(resolve_heavy_refresh_stride(40, max_packets=8), 4)
-
-    def test_sanitize_curve_data_for_display_rejects_fully_nonfinite_series(self):
-        xs, ys = sanitize_curve_data_for_display(
-            np.asarray([np.nan, np.nan], dtype=np.float64),
-            np.asarray([np.nan, np.nan], dtype=np.float32),
-        )
-
-        self.assertEqual(xs.shape, (0,))
-        self.assertEqual(ys.shape, (0,))
-
-    def test_sanitize_image_for_display_rejects_fully_nonfinite_frame(self):
-        data = sanitize_image_for_display(np.full((4, 4), np.nan, dtype=np.float32))
-
-        self.assertIsNone(data)
-
-    def test_set_image_rect_if_ready_skips_uninitialized_image(self):
-        class _DummyImageItem:
-            def __init__(self):
-                self.calls = 0
-
-            def width(self):
-                return None
-
-            def height(self):
-                return None
-
-            def setRect(self, *_args):
-                self.calls += 1
-
-        image_item = _DummyImageItem()
-        applied = set_image_rect_if_ready(image_item, 0.0, 0.0, 10.0, 1.0)
-
-        self.assertFalse(applied)
-        self.assertEqual(image_item.calls, 0)
-
-    def test_resolve_axis_bounds_rejects_nonfinite_ranges(self):
-        bounds = resolve_axis_bounds(np.asarray([np.nan, np.inf], dtype=np.float64))
-
-        self.assertIsNone(bounds)
-
-    def test_resolve_distance_cut_start_interpolates_with_percent(self):
-        axis = np.asarray([0.0, 1.0, 2.0, 3.0], dtype=np.float64)
-        cut_start = resolve_distance_cut_start(axis, 50.0)
-
-        self.assertIsNotNone(cut_start)
-        self.assertAlmostEqual(float(cut_start), 1.5, places=6)
-
-    def test_apply_distance_cut_to_axis_keeps_farthest_point_for_extreme_cut(self):
-        axis = np.asarray([0.0, 1.0, 2.0, 3.0], dtype=np.float64)
-        cut_axis, keep_mask = apply_distance_cut_to_axis(axis, 10.0)
-
-        self.assertEqual(cut_axis.shape, (1,))
-        self.assertEqual(keep_mask.shape, axis.shape)
-        self.assertTrue(bool(keep_mask[-1]))
-        self.assertAlmostEqual(float(cut_axis[0]), 3.0, places=6)
-
-    def test_resolve_initial_window_size_stays_within_small_screen(self):
-        width, height = resolve_initial_window_size(800, 480)
-
-        self.assertLessEqual(width, 800)
-        self.assertLessEqual(height, 480)
-        self.assertGreaterEqual(width, 640)
-        self.assertGreaterEqual(height, 420)
-
-    def test_build_main_window_layout_uses_splitter_and_scroll_area(self):
-        os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
-        try:
-            from PyQt5 import QtCore, QtWidgets
-        except Exception as exc:  # pragma: no cover - environment-dependent
-            self.skipTest(f"Qt unavailable: {exc}")
-
-        app = QtWidgets.QApplication.instance() or QtWidgets.QApplication([])
-        main_window = QtWidgets.QWidget()
-        try:
-            _layout, splitter, _plot_layout, settings_widget, settings_layout, settings_scroll = build_main_window_layout(
-                QtCore,
-                QtWidgets,
-                main_window,
-            )
-            self.assertIsInstance(splitter, QtWidgets.QSplitter)
-            self.assertIsInstance(settings_scroll, QtWidgets.QScrollArea)
-            self.assertIs(settings_scroll.widget(), settings_widget)
-            self.assertIsInstance(settings_layout, QtWidgets.QVBoxLayout)
-        finally:
-            main_window.close()
-
-    def test_background_subtracted_bscan_levels_ignore_zero_floor(self):
-        disp_fft_lin = np.zeros((4, 8), dtype=np.float32)
-        disp_fft_lin[1, 2:6] = np.asarray([0.05, 0.1, 0.5, 2.0], dtype=np.float32)
-        disp_fft_lin[2, 1:6] = np.asarray([0.08, 0.2, 0.7, 3.0, 9.0], dtype=np.float32)
-        disp_fft = fft_mag_to_db(disp_fft_lin)
-
-        levels = compute_background_subtracted_bscan_levels(disp_fft_lin, disp_fft)
-
-        self.assertIsNotNone(levels)
-        positive_vals = disp_fft[disp_fft_lin > 0.0]
-        self.assertAlmostEqual(levels[0], float(np.nanpercentile(positive_vals, 15.0)), places=5)
-        self.assertAlmostEqual(levels[1], float(np.nanpercentile(positive_vals, 99.7)), places=5)
-        zero_floor = disp_fft[disp_fft_lin == 0.0]
-        self.assertLess(float(np.nanmax(zero_floor)), levels[0])
-
-    def test_background_subtracted_bscan_levels_fallback_when_residuals_too_sparse(self):
-        disp_fft_lin = np.zeros((3, 4), dtype=np.float32)
-        disp_fft_lin[1, 2] = 1.0
-        disp_fft = fft_mag_to_db(disp_fft_lin)
-
-        levels = compute_background_subtracted_bscan_levels(disp_fft_lin, disp_fft)
-
-        self.assertIsNone(levels)
-
-    def test_fft_helpers_return_expected_shapes(self):
-        sweep = np.sin(np.linspace(0.0, 4.0 * np.pi, 128)).astype(np.float32)
-        freqs = np.linspace(3.3, 14.3, 128, dtype=np.float64)
-        mag = compute_fft_mag_row(sweep, freqs, 513)
-        row = compute_fft_row(sweep, freqs, 513)
-        axis = compute_distance_axis(freqs, 513)
-        self.assertEqual(mag.shape, (513,))
-        self.assertEqual(row.shape, (513,))
-        self.assertEqual(axis.shape, (513,))
-        self.assertTrue(np.all(np.diff(axis) >= 0.0))
-
-    def test_symmetric_ifft_spectrum_has_zero_gap_and_mirrored_band(self):
-        sweep = np.linspace(1.0, 2.0, 128, dtype=np.float32)
-        freqs = np.linspace(4.0, 10.0, 128, dtype=np.float64)
-        spectrum = build_symmetric_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-
-        self.assertIsNotNone(spectrum)
-        freq_axis = np.linspace(-10.0, 10.0, FFT_LEN, dtype=np.float64)
-        neg_idx_all = np.flatnonzero(freq_axis <= (-4.0))
-        pos_idx_all = np.flatnonzero(freq_axis >= 4.0)
-        band_len = int(min(neg_idx_all.size, pos_idx_all.size))
-        neg_idx = neg_idx_all[:band_len]
-        pos_idx = pos_idx_all[-band_len:]
-        zero_mask = (freq_axis > (-4.0)) & (freq_axis < 4.0)
-
-        self.assertTrue(np.allclose(spectrum[zero_mask], 0.0))
-        self.assertTrue(np.allclose(spectrum[neg_idx], spectrum[pos_idx][::-1]))
-
-    def test_positive_only_centered_spectrum_keeps_zeros_until_positive_min(self):
-        sweep = np.linspace(1.0, 2.0, 128, dtype=np.float32)
-        freqs = np.linspace(4.0, 10.0, 128, dtype=np.float64)
-        spectrum = build_positive_only_centered_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-
-        self.assertIsNotNone(spectrum)
-        freq_axis = np.linspace(-10.0, 10.0, FFT_LEN, dtype=np.float64)
-        zero_mask = freq_axis < 4.0
-        pos_idx = np.flatnonzero(freq_axis >= 4.0)
-
-        self.assertTrue(np.allclose(spectrum[zero_mask], 0.0))
-        self.assertTrue(np.any(np.abs(spectrum[pos_idx]) > 0.0))
-
-    def test_positive_only_exact_spectrum_uses_direct_index_insertion_without_window(self):
-        sweep = np.asarray([1.0, 2.0, 3.0], dtype=np.float32)
-        freqs = np.asarray([4.0, 5.0, 6.0], dtype=np.float64)
-        spectrum = build_positive_only_exact_centered_ifft_spectrum(sweep, freqs)
-
-        self.assertIsNotNone(spectrum)
-        df = (6.0 - 4.0) / 2.0
-        f_shift = np.arange(-6.0, 6.0 + (0.5 * df), df, dtype=np.float64)
-        idx = np.round((freqs - f_shift[0]) / df).astype(np.int64)
-        zero_mask = (f_shift > -6.0) & (f_shift < 4.0)
-
-        self.assertEqual(int(spectrum.size), int(f_shift.size))
-        self.assertTrue(np.allclose(spectrum[zero_mask], 0.0))
-        self.assertTrue(np.allclose(spectrum[idx], sweep))
-
-    def test_complex_symmetric_ifft_spectrum_uses_conjugate_mirror(self):
-        sweep = np.exp(1j * np.linspace(0.0, np.pi, 128)).astype(np.complex64)
-        freqs = np.linspace(4.0, 10.0, 128, dtype=np.float64)
-        spectrum = build_symmetric_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-
-        self.assertIsNotNone(spectrum)
-        freq_axis = np.linspace(-10.0, 10.0, FFT_LEN, dtype=np.float64)
-        neg_idx_all = np.flatnonzero(freq_axis <= (-4.0))
-        pos_idx_all = np.flatnonzero(freq_axis >= 4.0)
-        band_len = int(min(neg_idx_all.size, pos_idx_all.size))
-        neg_idx = neg_idx_all[:band_len]
-        pos_idx = pos_idx_all[-band_len:]
-
-        self.assertTrue(np.iscomplexobj(spectrum))
-        self.assertTrue(np.allclose(spectrum[neg_idx], np.conj(spectrum[pos_idx][::-1])))
-
-    def test_compute_fft_helpers_accept_complex_input(self):
-        sweep = np.exp(1j * np.linspace(0.0, 2.0 * np.pi, 128)).astype(np.complex64)
-        freqs = np.linspace(3.3, 14.3, 128, dtype=np.float64)
-        complex_row = compute_fft_complex_row(sweep, freqs, 513, mode="positive_only")
-        mag = compute_fft_mag_row(sweep, freqs, 513, mode="positive_only")
-        row = compute_fft_row(sweep, freqs, 513, mode="positive_only")
-
-        self.assertEqual(complex_row.shape, (513,))
-        self.assertTrue(np.iscomplexobj(complex_row))
-        self.assertEqual(mag.shape, (513,))
-        self.assertEqual(row.shape, (513,))
-        self.assertTrue(np.allclose(mag, np.abs(complex_row), equal_nan=True))
-        self.assertTrue(np.any(np.isfinite(mag)))
-        self.assertTrue(np.any(np.isfinite(row)))
-
-    def test_compute_fft_complex_row_positive_only_exact_matches_manual_ifftshift_ifft(self):
-        sweep = np.asarray([1.0 + 1.0j, 2.0 + 0.0j, 3.0 - 1.0j], dtype=np.complex64)
-        freqs = np.asarray([4.0, 5.0, 6.0], dtype=np.float64)
-        bins = 16
-        row = compute_fft_complex_row(sweep, freqs, bins, mode="positive_only_exact")
-
-        df = (6.0 - 4.0) / 2.0
-        f_shift = np.arange(-6.0, 6.0 + (0.5 * df), df, dtype=np.float64)
-        manual_shift = np.zeros((f_shift.size,), dtype=np.complex64)
-        idx = np.round((freqs - f_shift[0]) / df).astype(np.int64)
-        manual_shift[idx] = sweep
-        manual_ifft = np.fft.ifft(np.fft.ifftshift(manual_shift))
-        expected = np.full((bins,), np.nan + 0j, dtype=np.complex64)
-        expected[: manual_ifft.size] = np.asarray(manual_ifft, dtype=np.complex64)
-
-        self.assertEqual(row.shape, (bins,))
-        self.assertTrue(np.allclose(row, expected, equal_nan=True))
-
-    def test_positive_only_exact_distance_axis_uses_exact_grid_geometry(self):
-        freqs = np.asarray([4.0, 5.0, 6.0], dtype=np.float64)
-        bins = 8
-        axis = compute_distance_axis(freqs, bins, mode="positive_only_exact")
-
-        # With a small bins budget the exact-mode grid is downsampled so
-        # internal IFFT length does not exceed visible bins.
-        df_hz = 2e9
-        n_shift = int(np.arange(-6.0, 6.0 + 1.0, 2.0, dtype=np.float64).size)
-        expected_step = C_M_S / (2.0 * n_shift * df_hz)
-        expected = np.arange(bins, dtype=np.float64) * expected_step
-
-        self.assertEqual(axis.shape, (bins,))
-        self.assertTrue(np.allclose(axis, expected))
-
-    def test_positive_only_exact_mode_remains_stable_when_input_points_double(self):
-        bins = FFT_LEN // 2 + 1
-        tau_s = 45e-9
-
-        freqs_400 = np.linspace(3.3, 14.3, 400, dtype=np.float64)
-        freqs_800 = np.linspace(3.3, 14.3, 800, dtype=np.float64)
-        sweep_400 = np.exp(-1j * 2.0 * np.pi * freqs_400 * 1e9 * tau_s).astype(np.complex64)
-        sweep_800 = np.exp(-1j * 2.0 * np.pi * freqs_800 * 1e9 * tau_s).astype(np.complex64)
-
-        mag_400 = compute_fft_mag_row(sweep_400, freqs_400, bins, mode="positive_only_exact")
-        mag_800 = compute_fft_mag_row(sweep_800, freqs_800, bins, mode="positive_only_exact")
-
-        self.assertEqual(mag_400.shape, mag_800.shape)
-        finite = np.isfinite(mag_400) & np.isfinite(mag_800)
-        self.assertGreater(int(np.count_nonzero(finite)), int(0.95 * bins))
-
-        idx_400 = int(np.nanargmax(mag_400))
-        idx_800 = int(np.nanargmax(mag_800))
-        peak_400 = float(np.nanmax(mag_400))
-        peak_800 = float(np.nanmax(mag_800))
-
-        self.assertLess(abs(idx_400 - idx_800), 64)
-        self.assertGreater(idx_400, 8)
-        self.assertGreater(idx_800, 8)
-        self.assertLess(idx_400, bins - 8)
-        self.assertLess(idx_800, bins - 8)
-        self.assertGreater(peak_400, 0.05)
-        self.assertGreater(peak_800, 0.05)
-
-    def test_resolve_visible_fft_curves_handles_complex_mode(self):
-        complex_row = np.asarray([1.0 + 2.0j, -3.0 + 4.0j], dtype=np.complex64)
-        mag = np.abs(complex_row).astype(np.float32)
-
-        abs_curve, real_curve, imag_curve = resolve_visible_fft_curves(
-            complex_row,
-            mag,
-            complex_mode=True,
-            show_abs=True,
-            show_real=False,
-            show_imag=True,
-        )
-
-        self.assertTrue(np.allclose(abs_curve, mag))
-        self.assertIsNone(real_curve)
-        self.assertTrue(np.allclose(imag_curve, np.asarray([2.0, 4.0], dtype=np.float32)))
-
-    def test_resolve_visible_fft_curves_preserves_legacy_abs_mode(self):
-        mag = np.asarray([1.0, 2.0, 3.0], dtype=np.float32)
-
-        abs_curve, real_curve, imag_curve = resolve_visible_fft_curves(
-            None,
-            mag,
-            complex_mode=False,
-            show_abs=True,
-            show_real=True,
-            show_imag=True,
-        )
-
-        self.assertTrue(np.allclose(abs_curve, mag))
-        self.assertIsNone(real_curve)
-        self.assertIsNone(imag_curve)
-
-    def test_symmetric_distance_axis_uses_windowed_frequency_bounds(self):
-        freqs = np.linspace(4.0, 10.0, 128, dtype=np.float64)
-        axis = compute_distance_axis(freqs, 513, mode="symmetric")
-        df_hz = (2.0 * 10.0 / max(1, FFT_LEN - 1)) * 1e9
-        expected_step = 299_792_458.0 / (2.0 * FFT_LEN * df_hz)
-
-        self.assertEqual(axis.shape, (513,))
-        self.assertTrue(np.all(np.diff(axis) >= 0.0))
-        self.assertAlmostEqual(float(axis[1] - axis[0]), expected_step, places=15)
-
-    def test_peak_helpers_find_reference_and_peak_boxes(self):
-        xs = np.linspace(0.0, 10.0, 200)
-        ys = np.exp(-((xs - 5.0) ** 2) / 0.4) * 10.0 + 1.0
-        ref = rolling_median_ref(xs, ys, 2.0)
-        peaks = find_top_peaks_over_ref(xs, ys, ref, top_n=3)
-        width = find_peak_width_markers(xs, ys)
-        self.assertEqual(ref.shape, ys.shape)
-        self.assertEqual(len(peaks), 1)
-        self.assertGreater(peaks[0]["x"], 4.0)
-        self.assertLess(peaks[0]["x"], 6.0)
-        self.assertIsNotNone(width)
-        self.assertGreater(width["width"], 0.0)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_ring_buffer.py

@@ -1,176 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import unittest
-import warnings
-from unittest.mock import patch
-
-from rfg_adc_plotter.processing.fft import compute_fft_mag_row
-from rfg_adc_plotter.state.ring_buffer import RingBuffer
-
-
-class RingBufferTests(unittest.TestCase):
-    def test_ring_buffer_initializes_on_first_push(self):
-        ring = RingBuffer(max_sweeps=4)
-        sweep = np.linspace(-1.0, 1.0, 64, dtype=np.float32)
-        ring.push(sweep, np.linspace(3.3, 14.3, 64))
-        self.assertIsNotNone(ring.ring)
-        self.assertIsNotNone(ring.ring_fft)
-        self.assertIsNotNone(ring.ring_time)
-        self.assertIsNotNone(ring.distance_axis)
-        self.assertIsNotNone(ring.get_last_fft_linear())
-        self.assertIsNotNone(ring.last_fft_db)
-        self.assertEqual(ring.ring.shape[0], 4)
-        self.assertEqual(ring.ring_fft.shape, (4, ring.fft_bins))
-
-    def test_ring_buffer_reallocates_when_sweep_width_grows(self):
-        ring = RingBuffer(max_sweeps=3)
-        ring.push(np.ones((32,), dtype=np.float32), np.linspace(3.3, 14.3, 32))
-        first_width = ring.width
-        ring.push(np.ones((2048,), dtype=np.float32), np.linspace(3.3, 14.3, 2048))
-        self.assertGreater(ring.width, first_width)
-        self.assertIsNotNone(ring.ring)
-        self.assertEqual(ring.ring.shape, (3, ring.width))
-
-    def test_ring_buffer_tracks_latest_fft_and_display_arrays(self):
-        ring = RingBuffer(max_sweeps=2)
-        ring.push(np.linspace(0.0, 1.0, 64, dtype=np.float32), np.linspace(3.3, 14.3, 64))
-        ring.push(np.linspace(1.0, 0.0, 64, dtype=np.float32), np.linspace(3.3, 14.3, 64))
-        raw = ring.get_display_raw()
-        fft = ring.get_display_fft_linear()
-        self.assertEqual(raw.shape[1], 2)
-        self.assertEqual(fft.shape[1], 2)
-        self.assertIsNotNone(ring.last_fft_db)
-        self.assertEqual(ring.last_fft_db.shape, (ring.fft_bins,))
-
-    def test_ring_buffer_can_return_decimated_display_raw(self):
-        ring = RingBuffer(max_sweeps=3)
-        sweep_a = np.linspace(0.0, 1.0, 4096, dtype=np.float32)
-        sweep_b = np.linspace(1.0, 2.0, 4096, dtype=np.float32)
-        sweep_c = np.linspace(2.0, 3.0, 4096, dtype=np.float32)
-        freqs = np.linspace(3.3, 14.3, 4096, dtype=np.float64)
-        ring.push(sweep_a, freqs)
-        ring.push(sweep_b, freqs)
-        ring.push(sweep_c, freqs)
-
-        raw = ring.get_display_raw_decimated(256)
-
-        self.assertEqual(raw.shape, (256, 3))
-        self.assertAlmostEqual(float(raw[0, -1]), float(sweep_c[0]), places=6)
-        self.assertAlmostEqual(float(raw[-1, -1]), float(sweep_c[-1]), places=6)
-
-    def test_ring_buffer_can_switch_fft_mode_and_rebuild_fft_rows(self):
-        ring = RingBuffer(max_sweeps=2)
-        sweep = np.linspace(0.0, 1.0, 64, dtype=np.float32)
-        freqs = np.linspace(3.3, 14.3, 64, dtype=np.float64)
-        ring.push(sweep, freqs)
-        fft_before = ring.last_fft_db.copy()
-        axis_before = ring.distance_axis.copy()
-
-        changed = ring.set_symmetric_fft_enabled(False)
-
-        self.assertTrue(changed)
-        self.assertFalse(ring.fft_symmetric)
-        self.assertEqual(ring.get_display_raw().shape[1], 2)
-        self.assertIsNotNone(ring.get_last_fft_linear())
-        self.assertEqual(ring.last_fft_db.shape, fft_before.shape)
-        self.assertFalse(np.allclose(ring.last_fft_db, fft_before))
-        self.assertFalse(np.allclose(ring.distance_axis, axis_before))
-
-    def test_ring_buffer_can_switch_to_positive_only_fft_mode(self):
-        ring = RingBuffer(max_sweeps=2)
-        sweep = np.linspace(0.0, 1.0, 64, dtype=np.float32)
-        freqs = np.linspace(3.3, 14.3, 64, dtype=np.float64)
-        ring.push(sweep, freqs)
-
-        changed = ring.set_fft_mode("positive_only")
-
-        self.assertTrue(changed)
-        self.assertEqual(ring.fft_mode, "positive_only")
-        self.assertIsNotNone(ring.last_fft_db)
-        self.assertEqual(ring.last_fft_db.shape, (ring.fft_bins,))
-        self.assertIsNotNone(ring.distance_axis)
-
-    def test_ring_buffer_can_switch_to_positive_only_exact_fft_mode(self):
-        ring = RingBuffer(max_sweeps=2)
-        sweep = np.linspace(0.0, 1.0, 64, dtype=np.float32)
-        freqs = np.linspace(3.3, 14.3, 64, dtype=np.float64)
-        ring.push(sweep, freqs)
-
-        changed = ring.set_fft_mode("positive_only_exact")
-
-        self.assertTrue(changed)
-        self.assertEqual(ring.fft_mode, "positive_only_exact")
-        self.assertIsNotNone(ring.last_fft_db)
-        self.assertEqual(ring.last_fft_db.shape, (ring.fft_bins,))
-        self.assertIsNotNone(ring.distance_axis)
-
-    def test_ring_buffer_rebuilds_fft_from_complex_input(self):
-        ring = RingBuffer(max_sweeps=2)
-        freqs = np.linspace(3.3, 14.3, 64, dtype=np.float64)
-        complex_input = np.exp(1j * np.linspace(0.0, 2.0 * np.pi, 64)).astype(np.complex64)
-        display_sweep = np.abs(complex_input).astype(np.float32)
-        ring.push(display_sweep, freqs, fft_input=complex_input)
-
-        ring.set_fft_mode("direct")
-
-        expected = compute_fft_mag_row(complex_input, freqs, ring.fft_bins, mode="direct")
-        self.assertTrue(np.allclose(ring.get_last_fft_linear(), expected))
-        self.assertFalse(np.iscomplexobj(ring.get_display_fft_linear()))
-        self.assertTrue(np.allclose(ring.get_display_raw()[: display_sweep.size, -1], display_sweep))
-
-    def test_ring_buffer_reset_clears_cached_history(self):
-        ring = RingBuffer(max_sweeps=2)
-        ring.push(np.linspace(0.0, 1.0, 64, dtype=np.float32), np.linspace(4.0, 10.0, 64))
-
-        ring.reset()
-
-        self.assertIsNone(ring.ring)
-        self.assertIsNone(ring.ring_fft)
-        self.assertIsNone(ring.distance_axis)
-        self.assertIsNone(ring.last_fft_db)
-        self.assertEqual(ring.width, 0)
-        self.assertEqual(ring.head, 0)
-
-    def test_ring_buffer_push_ignores_all_nan_fft_without_runtime_warning(self):
-        ring = RingBuffer(max_sweeps=2)
-        freqs = np.linspace(3.3, 14.3, 64, dtype=np.float64)
-        ring.push(np.linspace(0.0, 1.0, 64, dtype=np.float32), freqs)
-        fft_before = ring.last_fft_db.copy()
-        y_min_before = ring.y_min_fft
-        y_max_before = ring.y_max_fft
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("error", RuntimeWarning)
-            with patch(
-                "rfg_adc_plotter.state.ring_buffer.compute_fft_mag_row",
-                return_value=np.full((ring.fft_bins,), np.nan, dtype=np.float32),
-            ):
-                ring.push(np.linspace(1.0, 2.0, 64, dtype=np.float32), freqs)
-
-        self.assertFalse(ring.last_push_fft_valid)
-        self.assertTrue(np.allclose(ring.last_fft_db, fft_before))
-        self.assertEqual(ring.y_min_fft, y_min_before)
-        self.assertEqual(ring.y_max_fft, y_max_before)
-
-    def test_ring_buffer_set_fft_mode_ignores_all_nan_rebuild_without_runtime_warning(self):
-        ring = RingBuffer(max_sweeps=2)
-        freqs = np.linspace(3.3, 14.3, 64, dtype=np.float64)
-        ring.push(np.linspace(0.0, 1.0, 64, dtype=np.float32), freqs)
-        fft_before = ring.last_fft_db.copy()
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("error", RuntimeWarning)
-            with patch(
-                "rfg_adc_plotter.state.ring_buffer.compute_fft_mag_row",
-                return_value=np.full((ring.fft_bins,), np.nan, dtype=np.float32),
-            ):
-                ring.set_fft_mode("direct")
-
-        self.assertFalse(ring.last_push_fft_valid)
-        self.assertTrue(np.allclose(ring.last_fft_db, fft_before))
-        self.assertEqual(ring.fft_mode, "direct")
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_sweep_parser_core.py

@@ -1,416 +0,0 @@
-from __future__ import annotations
-
-import math
-import unittest
-
-from rfg_adc_plotter.io.sweep_parser_core import (
-    AsciiSweepParser,
-    ComplexAsciiSweepParser,
-    LegacyBinaryParser,
-    LogScale16BitX2BinaryParser,
-    LogScaleBinaryParser32,
-    ParserTestStreamParser,
-    PointEvent,
-    StartEvent,
-    SweepAssembler,
-    log_pair_to_sweep,
-)
-
-
-def _u16le(word: int) -> bytes:
-    w = int(word) & 0xFFFF
-    return bytes((w & 0xFF, (w >> 8) & 0xFF))
-
-
-def _pack_legacy_start(ch: int) -> bytes:
-    return b"\xff\xff" * 3 + bytes((0x0A, int(ch) & 0xFF))
-
-
-def _pack_legacy_point(ch: int, step: int, value_i32: int) -> bytes:
-    value = int(value_i32) & 0xFFFF_FFFF
-    return b"".join(
-        [
-            _u16le(step),
-            _u16le((value >> 16) & 0xFFFF),
-            _u16le(value & 0xFFFF),
-            bytes((0x0A, int(ch) & 0xFF)),
-        ]
-    )
-
-
-def _pack_log_start(ch: int) -> bytes:
-    return b"\xff\xff" * 5 + bytes((0x0A, int(ch) & 0xFF))
-
-
-def _pack_log_point(step: int, avg1: int, avg2: int, ch: int = 0) -> bytes:
-    a1 = int(avg1) & 0xFFFF_FFFF
-    a2 = int(avg2) & 0xFFFF_FFFF
-    return b"".join(
-        [
-            _u16le(step),
-            _u16le((a1 >> 16) & 0xFFFF),
-            _u16le(a1 & 0xFFFF),
-            _u16le((a2 >> 16) & 0xFFFF),
-            _u16le(a2 & 0xFFFF),
-            bytes((0x0A, int(ch) & 0xFF)),
-        ]
-    )
-
-
-def _pack_log16_start(ch: int) -> bytes:
-    return b"\xff\xff" * 3 + bytes((0x0A, int(ch) & 0xFF))
-
-
-def _pack_log16_point(step: int, avg1: int, avg2: int) -> bytes:
-    return b"".join(
-        [
-            _u16le(step),
-            _u16le(avg1),
-            _u16le(avg2),
-            _u16le(0xFFFF),
-        ]
-    )
-
-
-def _pack_tty_start() -> bytes:
-    return b"".join([_u16le(0x000A), _u16le(0xFFFF), _u16le(0xFFFF), _u16le(0xFFFF)])
-
-
-def _pack_tty_point(step: int, ch1: int, ch2: int) -> bytes:
-    return b"".join(
-        [
-            _u16le(0x000A),
-            _u16le(step),
-            _u16le(ch1),
-            _u16le(ch2),
-        ]
-    )
-
-
-def _pack_logdet_point(step: int, value: int) -> bytes:
-    return b"".join(
-        [
-            _u16le(0x001A),
-            _u16le(step),
-            _u16le(value),
-            _u16le(0x0000),
-        ]
-    )
-
-
-class SweepParserCoreTests(unittest.TestCase):
-    def test_ascii_parser_emits_start_and_points(self):
-        parser = AsciiSweepParser()
-        events = parser.feed(b"Sweep_start\ns 1 2 -3\ns2 4 5\n")
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertIsInstance(events[2], PointEvent)
-        self.assertEqual(events[1].ch, 1)
-        self.assertEqual(events[1].x, 2)
-        self.assertEqual(events[1].y, -3.0)
-        self.assertEqual(events[2].ch, 2)
-        self.assertEqual(events[2].x, 4)
-        self.assertEqual(events[2].y, 5.0)
-
-    def test_legacy_binary_parser_resynchronizes_after_garbage(self):
-        parser = LegacyBinaryParser()
-        stream = b"\x00junk" + _pack_legacy_start(3) + _pack_legacy_point(3, 1, -2)
-        events = parser.feed(stream)
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 3)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].ch, 3)
-        self.assertEqual(events[1].x, 1)
-        self.assertEqual(events[1].y, -2.0)
-
-    def test_legacy_binary_parser_detects_new_sweep_on_step_reset(self):
-        parser = LegacyBinaryParser()
-        stream = b"".join(
-            [
-                _pack_legacy_point(3, 1, -2),
-                _pack_legacy_point(3, 2, -3),
-                _pack_legacy_point(3, 1, -4),
-            ]
-        )
-        events = parser.feed(stream)
-        self.assertIsInstance(events[0], PointEvent)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertIsInstance(events[2], StartEvent)
-        self.assertEqual(events[2].ch, 3)
-        self.assertIsInstance(events[3], PointEvent)
-        self.assertEqual(events[3].x, 1)
-        self.assertEqual(events[3].y, -4.0)
-
-    def test_legacy_binary_parser_accepts_tty_ch1_ch2_stream(self):
-        parser = LegacyBinaryParser()
-        stream = b"".join(
-            [
-                _pack_tty_start(),
-                _pack_tty_point(1, 100, 90),
-                _pack_tty_point(2, 120, 95),
-            ]
-        )
-
-        events = parser.feed(stream)
-
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 0)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].x, 1)
-        self.assertEqual(events[1].y, 18100.0)
-        self.assertEqual(events[1].aux, (100.0, 90.0))
-        self.assertEqual(events[1].signal_kind, "bin_iq")
-        self.assertIsInstance(events[2], PointEvent)
-        self.assertEqual(events[2].x, 2)
-        self.assertEqual(events[2].y, 23425.0)
-        self.assertEqual(events[2].aux, (120.0, 95.0))
-        self.assertEqual(events[2].signal_kind, "bin_iq")
-
-    def test_legacy_binary_parser_detects_new_tty_sweep_on_step_reset(self):
-        parser = LegacyBinaryParser()
-        stream = b"".join(
-            [
-                _pack_tty_start(),
-                _pack_tty_point(1, 100, 90),
-                _pack_tty_point(2, 110, 95),
-                _pack_tty_point(1, 120, 80),
-            ]
-        )
-
-        events = parser.feed(stream)
-
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertIsInstance(events[2], PointEvent)
-        self.assertIsInstance(events[3], StartEvent)
-        self.assertEqual(events[3].ch, 0)
-        self.assertIsInstance(events[4], PointEvent)
-        self.assertEqual(events[4].x, 1)
-        self.assertEqual(events[4].aux, (120.0, 80.0))
-        self.assertEqual(events[4].signal_kind, "bin_iq")
-
-    def test_legacy_binary_parser_tty_mode_does_not_flip_to_legacy_on_ch2_low_byte_0x0a(self):
-        parser = LegacyBinaryParser()
-        stream = b"".join(
-            [
-                _pack_tty_start(),
-                _pack_tty_point(1, 100, 0x040A),  # low byte is 0x0A: used to be misparsed as legacy
-                _pack_tty_point(2, 120, 0x0410),
-            ]
-        )
-
-        events = parser.feed(stream)
-
-        self.assertEqual(len(events), 3)
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 0)
-
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].ch, 0)
-        self.assertEqual(events[1].x, 1)
-        self.assertEqual(events[1].aux, (100.0, 1034.0))
-        self.assertEqual(events[1].y, 1079156.0)
-
-        self.assertIsInstance(events[2], PointEvent)
-        self.assertEqual(events[2].ch, 0)
-        self.assertEqual(events[2].x, 2)
-        self.assertEqual(events[2].aux, (120.0, 1040.0))
-        self.assertEqual(events[2].y, 1096000.0)
-
-    def test_legacy_binary_parser_accepts_logdet_stream(self):
-        parser = LegacyBinaryParser()
-        stream = b"".join(
-            [
-                _pack_logdet_point(1, 0x0F77),
-                _pack_logdet_point(2, 0xF234),
-            ]
-        )
-
-        events = parser.feed(stream)
-
-        self.assertEqual(len(events), 2)
-        self.assertIsInstance(events[0], PointEvent)
-        self.assertEqual(events[0].x, 1)
-        self.assertEqual(events[0].y, 3959.0)
-        self.assertIsNone(events[0].aux)
-        self.assertEqual(events[0].signal_kind, "bin_logdet")
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].x, 2)
-        self.assertEqual(events[1].y, -3532.0)
-        self.assertEqual(events[1].signal_kind, "bin_logdet")
-
-    def test_legacy_binary_parser_splits_packet_on_bin_signal_kind_change(self):
-        parser = LegacyBinaryParser()
-        stream = b"".join(
-            [
-                _pack_tty_start(),
-                _pack_tty_point(1, 100, 90),
-                _pack_tty_point(2, 110, 95),
-                _pack_logdet_point(3, 0x0F77),
-            ]
-        )
-
-        events = parser.feed(stream)
-
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].signal_kind, "bin_iq")
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].signal_kind, "bin_iq")
-        self.assertIsInstance(events[2], PointEvent)
-        self.assertEqual(events[2].signal_kind, "bin_iq")
-        self.assertIsInstance(events[3], StartEvent)
-        self.assertEqual(events[3].signal_kind, "bin_logdet")
-        self.assertIsInstance(events[4], PointEvent)
-        self.assertEqual(events[4].x, 3)
-        self.assertEqual(events[4].signal_kind, "bin_logdet")
-
-    def test_complex_ascii_parser_detects_new_sweep_on_step_reset(self):
-        parser = ComplexAsciiSweepParser()
-        events = parser.feed(b"0 3 4\n1 5 12\n0 8 15\n")
-
-        self.assertIsInstance(events[0], PointEvent)
-        self.assertEqual(events[0].x, 0)
-        self.assertEqual(events[0].y, 5.0)
-        self.assertEqual(events[0].aux, (3.0, 4.0))
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].y, 13.0)
-        self.assertIsInstance(events[2], StartEvent)
-        self.assertIsInstance(events[3], PointEvent)
-        self.assertEqual(events[3].aux, (8.0, 15.0))
-
-    def test_logscale_32_parser_keeps_channel_and_aux_values(self):
-        parser = LogScaleBinaryParser32()
-        stream = _pack_log_start(5) + _pack_log_point(7, 1500, 700, ch=5)
-        events = parser.feed(stream)
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 5)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].ch, 5)
-        self.assertEqual(events[1].x, 7)
-        self.assertAlmostEqual(events[1].y, log_pair_to_sweep(1500, 700), places=6)
-        self.assertEqual(events[1].aux, (1500.0, 700.0))
-
-    def test_logscale_32_parser_detects_new_sweep_on_step_reset(self):
-        parser = LogScaleBinaryParser32()
-        stream = b"".join(
-            [
-                _pack_log_point(1, 1500, 700, ch=5),
-                _pack_log_point(2, 1400, 650, ch=5),
-                _pack_log_point(1, 1300, 600, ch=5),
-            ]
-        )
-        events = parser.feed(stream)
-        self.assertIsInstance(events[0], PointEvent)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertIsInstance(events[2], StartEvent)
-        self.assertEqual(events[2].ch, 5)
-        self.assertIsInstance(events[3], PointEvent)
-        self.assertEqual(events[3].x, 1)
-        self.assertAlmostEqual(events[3].y, log_pair_to_sweep(1300, 600), places=6)
-
-    def test_log_pair_to_sweep_is_order_independent(self):
-        self.assertAlmostEqual(log_pair_to_sweep(1500, 700), log_pair_to_sweep(700, 1500), places=6)
-
-    def test_logscale_16bit_parser_uses_last_start_channel(self):
-        parser = LogScale16BitX2BinaryParser()
-        stream = _pack_log16_start(2) + _pack_log16_point(1, 100, 90)
-        events = parser.feed(stream)
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 2)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].ch, 2)
-        self.assertAlmostEqual(events[1].y, math.hypot(100.0, 90.0), places=6)
-        self.assertEqual(events[1].aux, (100.0, 90.0))
-
-    def test_logscale_16bit_parser_detects_new_sweep_on_step_reset(self):
-        parser = LogScale16BitX2BinaryParser()
-        stream = b"".join(
-            [
-                _pack_log16_start(2),
-                _pack_log16_point(1, 100, 90),
-                _pack_log16_point(2, 110, 95),
-                _pack_log16_point(1, 120, 80),
-            ]
-        )
-        events = parser.feed(stream)
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertIsInstance(events[2], PointEvent)
-        self.assertIsInstance(events[3], StartEvent)
-        self.assertEqual(events[3].ch, 2)
-        self.assertIsInstance(events[4], PointEvent)
-        self.assertEqual(events[4].x, 1)
-        self.assertAlmostEqual(events[4].y, math.hypot(120.0, 80.0), places=6)
-
-    def test_parser_test_stream_parser_recovers_point_after_single_separator(self):
-        parser = ParserTestStreamParser()
-        stream = b"".join(
-            [
-                b"\xff\xff\xff\xff",
-                bytes((0x0A, 4)),
-                _u16le(1),
-                _u16le(100),
-                _u16le(90),
-                _u16le(0xFFFF),
-            ]
-        )
-        events = parser.feed(stream)
-        events.extend(parser.feed(_u16le(2)))
-        self.assertIsInstance(events[0], StartEvent)
-        self.assertEqual(events[0].ch, 4)
-        self.assertIsInstance(events[1], PointEvent)
-        self.assertEqual(events[1].ch, 4)
-        self.assertEqual(events[1].x, 1)
-        self.assertAlmostEqual(events[1].y, math.hypot(100.0, 90.0), places=6)
-        self.assertEqual(events[1].aux, (100.0, 90.0))
-
-    def test_sweep_assembler_builds_aux_curves_without_inversion(self):
-        assembler = SweepAssembler(fancy=False, apply_inversion=False)
-        self.assertIsNone(assembler.consume(StartEvent(ch=1, signal_kind="bin_iq")))
-        assembler.consume(PointEvent(ch=1, x=1, y=10.0, aux=(100.0, 90.0), signal_kind="bin_iq"))
-        assembler.consume(PointEvent(ch=1, x=2, y=20.0, aux=(110.0, 95.0), signal_kind="bin_iq"))
-        sweep, info, aux = assembler.finalize_current()
-        self.assertEqual(sweep.shape[0], 3)
-        self.assertEqual(info["ch"], 1)
-        self.assertEqual(info["signal_kind"], "bin_iq")
-        self.assertIsNotNone(aux)
-        self.assertEqual(aux[0][1], 100.0)
-        self.assertEqual(aux[1][2], 95.0)
-
-    def test_sweep_assembler_splits_packet_on_channel_switch(self):
-        assembler = SweepAssembler(fancy=False, apply_inversion=False)
-        self.assertIsNone(assembler.consume(PointEvent(ch=1, x=1, y=10.0)))
-        packet = assembler.consume(PointEvent(ch=2, x=1, y=20.0))
-        self.assertIsNotNone(packet)
-
-        sweep_1, info_1, aux_1 = packet
-        self.assertIsNone(aux_1)
-        self.assertEqual(info_1["ch"], 1)
-        self.assertEqual(info_1["chs"], [1])
-        self.assertAlmostEqual(float(sweep_1[1]), 10.0, places=6)
-
-        sweep_2, info_2, aux_2 = assembler.finalize_current()
-        self.assertIsNone(aux_2)
-        self.assertEqual(info_2["ch"], 2)
-        self.assertEqual(info_2["chs"], [2])
-        self.assertAlmostEqual(float(sweep_2[1]), 20.0, places=6)
-
-    def test_sweep_assembler_splits_packet_on_signal_kind_switch(self):
-        assembler = SweepAssembler(fancy=False, apply_inversion=False)
-        self.assertIsNone(assembler.consume(PointEvent(ch=0, x=1, y=10.0, signal_kind="bin_iq")))
-        packet = assembler.consume(PointEvent(ch=0, x=1, y=20.0, signal_kind="bin_logdet"))
-        self.assertIsNotNone(packet)
-
-        sweep_1, info_1, aux_1 = packet
-        self.assertIsNone(aux_1)
-        self.assertEqual(info_1["signal_kind"], "bin_iq")
-        self.assertAlmostEqual(float(sweep_1[1]), 10.0, places=6)
-
-        sweep_2, info_2, aux_2 = assembler.finalize_current()
-        self.assertIsNone(aux_2)
-        self.assertEqual(info_2["signal_kind"], "bin_logdet")
-        self.assertAlmostEqual(float(sweep_2[1]), 20.0, places=6)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_sweep_reader.py

@@ -1,262 +0,0 @@
-from __future__ import annotations
-
-import contextlib
-import io
-import threading
-import time
-import unittest
-from queue import Queue
-from unittest.mock import patch
-
-from rfg_adc_plotter.io import sweep_reader as sweep_reader_module
-from rfg_adc_plotter.io.sweep_reader import SweepReader, _PARSER_16_BIT_X2_PROBE_BYTES
-
-
-def _u16le(word: int) -> bytes:
-    value = int(word) & 0xFFFF
-    return bytes((value & 0xFF, (value >> 8) & 0xFF))
-
-
-def _pack_legacy_point(ch: int, step: int, value_i32: int) -> bytes:
-    value = int(value_i32) & 0xFFFF_FFFF
-    return b"".join(
-        [
-            _u16le(step),
-            _u16le((value >> 16) & 0xFFFF),
-            _u16le(value & 0xFFFF),
-            bytes((0x0A, int(ch) & 0xFF)),
-        ]
-    )
-
-
-def _pack_log16_start(ch: int) -> bytes:
-    return b"\xff\xff" * 3 + bytes((0x0A, int(ch) & 0xFF))
-
-
-def _pack_log16_point(step: int, real: int, imag: int) -> bytes:
-    return b"".join(
-        [
-            _u16le(step),
-            _u16le(real),
-            _u16le(imag),
-            _u16le(0xFFFF),
-        ]
-    )
-
-
-def _pack_tty_start() -> bytes:
-    return b"".join(
-        [
-            _u16le(0x000A),
-            _u16le(0xFFFF),
-            _u16le(0xFFFF),
-            _u16le(0xFFFF),
-        ]
-    )
-
-
-def _pack_tty_point(step: int, ch1: int, ch2: int) -> bytes:
-    return b"".join(
-        [
-            _u16le(0x000A),
-            _u16le(step),
-            _u16le(ch1),
-            _u16le(ch2),
-        ]
-    )
-
-
-def _pack_logdet_point(step: int, value: int) -> bytes:
-    return b"".join(
-        [
-            _u16le(0x001A),
-            _u16le(step),
-            _u16le(value),
-            _u16le(0x0000),
-        ]
-    )
-
-
-def _chunk_bytes(data: bytes, size: int = 4096) -> list[bytes]:
-    return [data[idx : idx + size] for idx in range(0, len(data), size)]
-
-
-class _FakeSerialLineSource:
-    def __init__(self, path: str, baud: int, timeout: float = 1.0):
-        self.path = path
-        self.baud = baud
-        self.timeout = timeout
-        self._using = "fake"
-
-    def close(self) -> None:
-        pass
-
-
-class _FakeChunkReader:
-    payload_chunks: list[bytes] = []
-
-    def __init__(self, src):
-        self._src = src
-        self._chunks = list(type(self).payload_chunks)
-
-    def read_available(self) -> bytes:
-        if self._chunks:
-            return self._chunks.pop(0)
-        return b""
-
-
-class SweepReaderTests(unittest.TestCase):
-    def _start_reader(self, payload: bytes, **reader_kwargs):
-        queue: Queue = Queue()
-        stop_event = threading.Event()
-        stderr = io.StringIO()
-        _FakeChunkReader.payload_chunks = _chunk_bytes(payload)
-        reader = SweepReader(
-            "/tmp/fake-tty",
-            115200,
-            queue,
-            stop_event,
-            **reader_kwargs,
-        )
-        stack = contextlib.ExitStack()
-        stack.enter_context(patch.object(sweep_reader_module, "SerialLineSource", _FakeSerialLineSource))
-        stack.enter_context(patch.object(sweep_reader_module, "SerialChunkReader", _FakeChunkReader))
-        stack.enter_context(contextlib.redirect_stderr(stderr))
-        reader.start()
-        return stack, reader, queue, stop_event, stderr
-
-    def test_parser_16_bit_x2_falls_back_to_legacy_stream(self):
-        payload = bytearray()
-        while len(payload) < (_PARSER_16_BIT_X2_PROBE_BYTES + 24):
-            payload += _pack_legacy_point(3, 1, -2)
-            payload += _pack_legacy_point(3, 2, -3)
-            payload += _pack_legacy_point(3, 1, -4)
-
-        stack, reader, queue, stop_event, stderr = self._start_reader(bytes(payload), parser_16_bit_x2=True)
-        try:
-            sweep, info, aux = queue.get(timeout=2.0)
-            self.assertEqual(info["ch"], 3)
-            self.assertIsNone(aux)
-            self.assertGreaterEqual(sweep.shape[0], 3)
-            self.assertIn("fallback -> legacy", stderr.getvalue())
-        finally:
-            stop_event.set()
-            reader.join(timeout=1.0)
-            stack.close()
-
-    def test_parser_16_bit_x2_falls_back_to_tty_ch1_ch2_stream(self):
-        payload = bytearray()
-        while len(payload) < (_PARSER_16_BIT_X2_PROBE_BYTES + 24):
-            payload += _pack_tty_start()
-            payload += _pack_tty_point(1, 100, 90)
-            payload += _pack_tty_point(2, 120, 95)
-            payload += _pack_tty_point(1, 80, 70)
-
-        stack, reader, queue, stop_event, stderr = self._start_reader(bytes(payload), parser_16_bit_x2=True)
-        try:
-            sweep, info, aux = queue.get(timeout=2.0)
-            self.assertEqual(info["ch"], 0)
-            self.assertIsNotNone(aux)
-            self.assertGreaterEqual(sweep.shape[0], 3)
-            self.assertAlmostEqual(float(sweep[1]), 18100.0, places=6)
-            self.assertAlmostEqual(float(sweep[2]), 23425.0, places=6)
-            self.assertIn("fallback -> legacy", stderr.getvalue())
-        finally:
-            stop_event.set()
-            reader.join(timeout=1.0)
-            stack.close()
-
-    def test_parser_16_bit_x2_keeps_true_complex_stream(self):
-        payload = b"".join(
-            [
-                _pack_log16_start(2),
-                _pack_log16_point(1, 3, 4),
-                _pack_log16_point(2, 5, 12),
-                _pack_log16_point(1, 8, 15),
-            ]
-        )
-
-        stack, reader, queue, stop_event, stderr = self._start_reader(payload, parser_16_bit_x2=True)
-        try:
-            sweep, info, aux = queue.get(timeout=1.0)
-            self.assertEqual(info["ch"], 2)
-            self.assertIsNotNone(aux)
-            self.assertAlmostEqual(float(sweep[1]), 5.0, places=6)
-            self.assertAlmostEqual(float(sweep[2]), 13.0, places=6)
-            self.assertNotIn("fallback -> legacy", stderr.getvalue())
-        finally:
-            stop_event.set()
-            reader.join(timeout=1.0)
-            stack.close()
-
-    def test_parser_16_bit_x2_falls_back_to_logdet_1a00_stream(self):
-        payload = bytearray()
-        while len(payload) < (_PARSER_16_BIT_X2_PROBE_BYTES + 24):
-            payload += _pack_logdet_point(1, 0x0F77)
-            payload += _pack_logdet_point(2, 0x0FCB)
-            payload += _pack_logdet_point(1, 0x0F88)
-
-        stack, reader, queue, stop_event, stderr = self._start_reader(bytes(payload), parser_16_bit_x2=True)
-        try:
-            sweep, info, aux = queue.get(timeout=2.0)
-            self.assertEqual(info["signal_kind"], "bin_logdet")
-            self.assertIsNone(aux)
-            self.assertGreaterEqual(sweep.shape[0], 3)
-            self.assertAlmostEqual(float(sweep[1]), 3959.0, places=6)
-            self.assertIn("fallback -> legacy", stderr.getvalue())
-        finally:
-            stop_event.set()
-            reader.join(timeout=1.0)
-            stack.close()
-
-    def test_parser_16_bit_x2_probe_inconclusive_logs_hint(self):
-        payload = b"\x00" * (_PARSER_16_BIT_X2_PROBE_BYTES + 128)
-
-        stack, reader, queue, stop_event, stderr = self._start_reader(payload, parser_16_bit_x2=True)
-        try:
-            deadline = time.time() + 1.5
-            logs = ""
-            while time.time() < deadline:
-                logs = stderr.getvalue()
-                if "probe inconclusive" in logs:
-                    break
-                time.sleep(0.02)
-            self.assertTrue(queue.empty())
-            self.assertIn("probe inconclusive", logs)
-            self.assertIn("try --bin", logs)
-        finally:
-            stop_event.set()
-            reader.join(timeout=1.0)
-            stack.close()
-
-    def test_reader_logs_no_input_warning_when_source_is_idle(self):
-        with patch.object(sweep_reader_module, "_NO_INPUT_WARN_INTERVAL_S", 0.02), patch.object(
-            sweep_reader_module, "_NO_PACKET_WARN_INTERVAL_S", 0.02
-        ):
-            stack, reader, _queue, stop_event, stderr = self._start_reader(b"", parser_16_bit_x2=False)
-            try:
-                time.sleep(0.12)
-                logs = stderr.getvalue()
-                self.assertIn("no input bytes", logs)
-                self.assertIn("no sweep packets", logs)
-            finally:
-                stop_event.set()
-                reader.join(timeout=1.0)
-                stack.close()
-
-    def test_reader_join_does_not_raise_when_stopped(self):
-        stack, reader, _queue, stop_event, _stderr = self._start_reader(b"", parser_16_bit_x2=True)
-        try:
-            time.sleep(0.01)
-            stop_event.set()
-            reader.join(timeout=1.0)
-            self.assertFalse(reader.is_alive())
-        finally:
-            stop_event.set()
-            if reader.is_alive():
-                reader.join(timeout=1.0)
-            stack.close()
-
-
-if __name__ == "__main__":
-    unittest.main()