3 freq diversion

.gitignore

@@ -1,8 +1,12 @@
-.venv/
-env/
-__pycache__/
-*.py[cod]
-.pytest_cache/
-.Python
 my_picocom_logfile.txt
-sample_data/
+*pyc
+__pycache__/
+*.log
+*.tmp
+*.bak
+*.swp
+*.swo       
+acm_9
+build
+.venv
+sample_data

README.md

@@ -1,185 +0,0 @@
-# RFG STM32 ADC Receiver GUI
-
-PyQtGraph-приложение для чтения свипов из последовательного порта и отображения:
-
-- текущего свипа
-- водопада по свипам
-- FFT текущего свипа
-- B-scan по FFT
-
-После рефакторинга проект разделен на пакет `rfg_adc_plotter`. Старый запуск через `RFG_ADC_dataplotter.py` сохранен как совместимый wrapper.
-
-## Структура
-
-- `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
-
-## Зависимости
-
-Минимально нужны:
-
-```bash
-python3 -m venv .venv
-. .venv/bin/activate
-pip install numpy pyqtgraph PyQt5
-```
-
-Если `pyserial` не установлен, приложение попробует открыть порт через raw TTY.
-
-## Быстрый старт
-
-Запуск через старый entrypoint:
-
-```bash
-.venv/bin/python RFG_ADC_dataplotter.py /dev/ttyACM0
-```
-
-Запуск напрямую через пакет:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0
-```
-
-Показать справку:
-
-```bash
-.venv/bin/python RFG_ADC_dataplotter.py --help
-```
-
-## Примеры запуска
-
-Обычный запуск с живого порта:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --baud 115200
-```
-
-Больше истории в водопаде и ограничение FPS:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --max-sweeps 400 --max-fps 20
-```
-
-Фиксированный диапазон по оси Y:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --ylim -1000,1000
-```
-
-С включенной нормировкой `simple`:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --norm-type simple
-```
-
-Режим измерения ширины главного пика FFT:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --calibrate
-```
-
-Поиск топ-3 пиков относительно rolling median reference:
-
-```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
-```
-
-Logscale binary с парой `int32`:
-
-```bash
-.venv/bin/python -m rfg_adc_plotter.main /dev/ttyACM0 --logscale
-```
-
-Logscale 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
-```
-
-## 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`.
-
-## 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.

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

@@ -1,7 +1,16 @@
 #!/usr/bin/env python3
-"""Replay a capture file through a pseudo-TTY for local GUI verification."""
+"""
+Эмулятор серийного порта: воспроизводит лог-файл в цикле через PTY.
 
-from __future__ import annotations
+Использование:
+  python3 replay_pty.py my_picocom_logfile.txt
+  python3 replay_pty.py my_picocom_logfile.txt --pty /tmp/ttyVIRT0
+  python3 replay_pty.py my_picocom_logfile.txt --speed 2.0   # в 2 раза быстрее реального
+  python3 replay_pty.py my_picocom_logfile.txt --speed 0     # максимально быстро
+
+Затем в другом терминале:
+  python -m rfg_adc_plotter.main /tmp/ttyVIRT0
+"""
 
 import argparse
 import os
@@ -9,7 +18,7 @@ import sys
 import time
 
 
-def main() -> None:
+def main():
     parser = argparse.ArgumentParser(
         description="Воспроизводит лог-файл через PTY как виртуальный серийный порт."
     )
@@ -34,18 +43,20 @@ def main() -> None:
         "--baud",
         type=int,
         default=115200,
-        help="Скорость (бод) для расчета задержек (по умолчанию 115200)",
+        help="Скорость (бод) для расчёта 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)
+        sys.exit(1)
 
+    # Открываем PTY-пару: master (мы пишем) / slave (GUI читает)
     master_fd, slave_fd = os.openpty()
     slave_path = os.ttyname(slave_fd)
-    os.close(slave_fd)
+    os.close(slave_fd)  # GUI откроет slave сам по симлинку
 
+    # Симлинк с удобным именем
     try:
         os.unlink(args.pty)
     except FileNotFoundError:
@@ -53,25 +64,26 @@ def main() -> None:
     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(f"Симлинк   : {args.pty}  →  {slave_path}")
+    print(f"Запустите : python -m rfg_adc_plotter.main {args.pty}")
     print("Ctrl+C для остановки.\n")
 
+    # Задержка на байт: 10 бит (8N1) / скорость / множитель
     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
+    _CHUNK = 4096
     loop = 0
     try:
         while True:
             loop += 1
             print(f"[loop {loop}] {args.file}")
-            with open(args.file, "rb") as handle:
+            with open(args.file, "rb") as f:
                 while True:
-                    chunk = handle.read(chunk_size)
+                    chunk = f.read(_CHUNK)
                     if not chunk:
                         break
                     os.write(master_fd, chunk)

rfg_adc_plotter/__init__.py

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

rfg_adc_plotter/cli.py

@@ -1,120 +0,0 @@
-"""Command-line parser for the ADC plotter."""
-
-from __future__ import annotations
-
-import argparse
-
-
-def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
-        description=(
-            "Читает свипы из виртуального COM-порта и рисует: "
-            "последний свип и водопад (реалтайм)."
-        )
-    )
-    parser.add_argument(
-        "port",
-        help="Путь к порту, например /dev/ttyACM1 или COM3 (COM10+: \\\\.\\COM10)",
-    )
-    parser.add_argument("--baud", type=int, default=115200, help="Скорость (по умолчанию 115200)")
-    parser.add_argument("--max-sweeps", type=int, default=200, help="Количество видимых свипов в водопаде")
-    parser.add_argument("--max-fps", type=float, default=30.0, help="Лимит частоты отрисовки, кадров/с")
-    parser.add_argument("--cmap", default="viridis", help="Цветовая карта водопада")
-    parser.add_argument(
-        "--spec-clip",
-        default="2,98",
-        help=(
-            "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",
-        action="store_true",
-        help="Заполнять выпавшие точки средними значениями между соседними",
-    )
-    parser.add_argument(
-        "--ylim",
-        type=str,
-        default=None,
-        help="Фиксированные Y-пределы для кривой формата min,max (например -1000,1000). По умолчанию авто",
-    )
-    parser.add_argument(
-        "--backend",
-        choices=["auto", "pg", "mpl"],
-        default="pg",
-        help="Совместимый флаг. Поддерживаются только auto и pg; mpl удален.",
-    )
-    parser.add_argument(
-        "--norm-type",
-        choices=["projector", "simple"],
-        default="projector",
-        help="Тип нормировки: projector (по огибающим в [-1,+1]) или simple (raw/calib)",
-    )
-    parser.add_argument(
-        "--bin",
-        dest="bin_mode",
-        action="store_true",
-        help=(
-            "Бинарный протокол: старт свипа 0xFFFF,0xFFFF,0xFFFF,(CH<<8)|0x0A; "
-            "точки step,uint32(hi16,lo16),0x000A"
-        ),
-    )
-    parser.add_argument(
-        "--logscale",
-        action="store_true",
-        default=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 logscale-протокол c парой int16 (avg_1, avg_2): "
-            "старт 0xFFFF,0xFFFF,0xFFFF,(CH<<8)|0x0A; точка step,avg1_lo16,avg2_lo16,0xFFFF"
-        ),
-    )
-    parser.add_argument(
-        "--parser_test",
-        action="store_true",
-        help=(
-            "Тестовый парсер для формата 16-bit x2: "
-            "одиночный 0xFFFF завершает точку, серия 0xFFFF начинает новый свип"
-        ),
-    )
-    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

rfg_adc_plotter/constants.py

@@ -1,17 +1,21 @@
-"""Shared constants for sweep parsing and visualization."""
-
-WF_WIDTH = 1000
-FFT_LEN = 1024
-BACKGROUND_MEDIAN_SWEEPS = 64
-
-SWEEP_FREQ_MIN_GHZ = 3.3
-SWEEP_FREQ_MAX_GHZ = 14.3
-
-LOG_BASE = 10.0
-LOG_SCALER = 0.001
-LOG_POSTSCALER = 10.0
-LOG_EXP_LIMIT = 300.0
-
-C_M_S = 299_792_458.0
-
+WF_WIDTH = 1000  # максимальное число точек в ряду водопада
+FFT_LEN = 4096   # длина БПФ для спектра/водопада спектров
+LOG_EXP = 2.0    # основание экспоненты для опции --logscale
+# Порог для инверсии сырых данных: если среднее значение свипа ниже порога —
+# считаем, что сигнал «меньше нуля» и домножаем свип на -1
 DATA_INVERSION_THRESHOLD = 10.0
+
+# Частотная сетка рабочего свипа (положительная часть), ГГц
+FREQ_MIN_GHZ = 3.323
+FREQ_MAX_GHZ = 14.323
+
+# Скорость света для перевода времени пролёта в one-way depth
+SPEED_OF_LIGHT_M_S = 299_792_458.0
+
+# Параметры IFFT-спектра (временной профиль из спектра 3.2..14.3 ГГц)
+# Двусторонний спектр формируется как: [нули -14.3..-3.2 | нули -3.2..+3.2 | данные +3.2..+14.3]
+ZEROS_LOW = 758       # нули от -14.3 до -3.2 ГГц
+ZEROS_MID = 437       # нули от -3.2 до +3.2 ГГц
+SWEEP_LEN = 758       # ожидаемая длина свипа (3.2 → 14.3 ГГц)
+FREQ_SPAN_GHZ = 28.6  # полная двусторонняя полоса (-14.3 .. +14.3 ГГц)
+IFFT_LEN = ZEROS_LOW + ZEROS_MID + SWEEP_LEN  # = 1953

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/gui/matplotlib_backend.py

@@ -0,0 +1,680 @@
+"""Matplotlib-бэкенд реалтайм-плоттера свипов."""
+
+import sys
+import threading
+from queue import Queue
+from typing import Optional, Tuple
+
+import numpy as np
+
+from rfg_adc_plotter.constants import FFT_LEN, FREQ_MAX_GHZ, FREQ_MIN_GHZ, IFFT_LEN
+from rfg_adc_plotter.io.sweep_reader import SweepReader
+from rfg_adc_plotter.processing.normalizer import build_calib_envelopes
+from rfg_adc_plotter.state.app_state import AppState, format_status
+from rfg_adc_plotter.state.ring_buffer import RingBuffer
+from rfg_adc_plotter.types import SweepPacket
+
+
+def _parse_ylim(ylim_str: Optional[str]) -> Optional[Tuple[float, float]]:
+    if not ylim_str:
+        return None
+    try:
+        y0, y1 = ylim_str.split(",")
+        return (float(y0), float(y1))
+    except Exception:
+        sys.stderr.write("[warn] Некорректный формат --ylim, игнорирую. Ожидалось min,max\n")
+        return None
+
+
+def _parse_spec_clip(spec: Optional[str]) -> Optional[Tuple[float, float]]:
+    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, high = float(p0), float(p1)
+        if not (0.0 <= low < high <= 100.0):
+            return None
+        return (low, high)
+    except Exception:
+        return None
+
+
+def _visible_levels(data: np.ndarray, axis) -> Optional[Tuple[float, float]]:
+    """(vmin, vmax) по текущей видимой области imshow."""
+    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 run_matplotlib(args):
+    try:
+        import matplotlib
+        import matplotlib.pyplot as plt
+        from matplotlib.animation import FuncAnimation
+        from matplotlib.widgets import Button as MplButton
+        from matplotlib.widgets import CheckButtons, RadioButtons, Slider, TextBox
+    except Exception as e:
+        sys.stderr.write(f"[error] Нужны matplotlib и её зависимости: {e}\n")
+        sys.exit(1)
+
+    q: Queue[SweepPacket] = Queue(maxsize=1000)
+    stop_event = threading.Event()
+    reader = SweepReader(
+        args.port,
+        args.baud,
+        q,
+        stop_event,
+        fancy=bool(args.fancy),
+        bin_mode=bool(getattr(args, "bin_mode", False)),
+        logscale=bool(getattr(args, "logscale", False)),
+        debug=bool(getattr(args, "debug", False)),
+    )
+    reader.start()
+
+    max_sweeps = int(max(10, args.max_sweeps))
+    max_fps = max(1.0, float(args.max_fps))
+    interval_ms = int(1000.0 / max_fps)
+    spec_clip = _parse_spec_clip(getattr(args, "spec_clip", None))
+    spec_mean_sec = float(getattr(args, "spec_mean_sec", 0.0))
+    fixed_ylim = _parse_ylim(getattr(args, "ylim", None))
+    norm_type = str(getattr(args, "norm_type", "projector")).strip().lower()
+    logscale_enabled = bool(getattr(args, "logscale", False))
+
+    state = AppState(norm_type=norm_type)
+    state.configure_capture_import(fancy=bool(args.fancy), logscale=bool(getattr(args, "logscale", False)))
+    ring = RingBuffer(max_sweeps)
+    try:
+        ring.set_fft_complex_mode(str(getattr(args, "ifft_complex_mode", "arccos")))
+    except Exception:
+        pass
+
+    # --- Создание фигуры ---
+    fig, axs = plt.subplots(2, 2, figsize=(12, 8))
+    (ax_line, ax_img), (ax_fft, ax_spec) = axs
+    if hasattr(fig.canvas.manager, "set_window_title"):
+        fig.canvas.manager.set_window_title(args.title)
+    fig.subplots_adjust(wspace=0.25, hspace=0.35, left=0.07, right=0.90, top=0.92, bottom=0.22)
+
+    # Статусная строка
+    status_text = fig.text(0.01, 0.01, "", ha="left", va="bottom", fontsize=8, family="monospace")
+    pipeline_text = fig.text(0.01, 0.03, "", ha="left", va="bottom", fontsize=8, family="monospace")
+    ref_text = fig.text(0.01, 0.05, "", ha="left", va="bottom", fontsize=8, family="monospace")
+
+    # График последнего свипа
+    line_obj, = ax_line.plot([], [], lw=1, color="tab:blue")
+    line_norm_obj, = ax_line.plot([], [], lw=1, color="tab:green")
+    line_pre_exp_obj, = ax_line.plot([], [], lw=1, color="tab:red")
+    line_post_exp_obj, = ax_line.plot([], [], lw=1, color="tab:green")
+    line_env_lo, = ax_line.plot([], [], lw=1, color="tab:orange", linestyle="--", alpha=0.7)
+    line_env_hi, = ax_line.plot([], [], lw=1, color="tab:orange", linestyle="--", alpha=0.7)
+    ax_line.set_title("Сырые данные", pad=1)
+    ax_line.set_xlabel("Частота, ГГц")
+    channel_text = ax_line.text(
+        0.98, 0.98, "", transform=ax_line.transAxes,
+        ha="right", va="top", fontsize=9, family="monospace",
+    )
+    if fixed_ylim is not None:
+        ax_line.set_ylim(fixed_ylim)
+
+    # График спектра
+    fft_line_t1, = ax_fft.plot([], [], lw=1, color="tab:blue", label="1/3 (low f)")
+    fft_line_t2, = ax_fft.plot([], [], lw=1, color="tab:orange", label="2/3 (mid f)")
+    fft_line_t3, = ax_fft.plot([], [], lw=1, color="tab:green", label="3/3 (high f)")
+    ax_fft.set_title("FFT", pad=1)
+    ax_fft.set_xlabel("Глубина, м")
+    ax_fft.set_ylabel("Амплитуда")
+    ax_fft.legend(loc="upper right", fontsize=8)
+
+    # Водопад сырых данных
+    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_ylabel("частота")
+    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", pad=12)
+    ax_spec.set_ylabel("Глубина, м")
+    try:
+        ax_spec.tick_params(axis="x", labelbottom=False)
+    except Exception:
+        pass
+
+    # Слайдеры и чекбокс
+    contrast_slider = None
+    try:
+        fft_bins = ring.fft_bins if ring.fft_bins > 0 else IFFT_LEN
+        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])
+        ax_cb = fig.add_axes([0.92, 0.45, 0.08, 0.08])
+        ax_cb_file = fig.add_axes([0.92, 0.36, 0.08, 0.08])
+        ax_line_mode = fig.add_axes([0.92, 0.10, 0.08, 0.08])
+        ax_ifft_mode = fig.add_axes([0.92, 0.01, 0.08, 0.08])
+        ymin_slider = Slider(ax_smin, "Y min", 0, max(1, fft_bins - 1), valinit=0, valstep=1, orientation="vertical")
+        ymax_slider = Slider(ax_smax, "Y max", 0, max(1, fft_bins - 1), valinit=max(1, fft_bins - 1), valstep=1, orientation="vertical")
+        contrast_slider = Slider(ax_sctr, "Int max", 0, 100, valinit=100, valstep=1, orientation="vertical")
+        calib_cb = CheckButtons(ax_cb, ["калибровка"], [False])
+        calib_file_cb = CheckButtons(ax_cb_file, ["из файла"], [False])
+        line_mode_rb = RadioButtons(ax_line_mode, ("raw", "processed"), active=0)
+        ifft_mode_rb = RadioButtons(
+            ax_ifft_mode,
+            ("arccos", "diff"),
+            active=(1 if ring.fft_complex_mode == "diff" else 0),
+        )
+        try:
+            ax_line_mode.set_title("Линия", fontsize=8, pad=2)
+        except Exception:
+            pass
+        try:
+            ax_ifft_mode.set_title("IFFT", fontsize=8, pad=2)
+        except Exception:
+            pass
+        line_mode_state = {"value": "raw"}
+        ifft_mode_state = {"value": str(ring.fft_complex_mode)}
+
+        import os as _os
+        try:
+            import tkinter as _tk
+            from tkinter import filedialog as _tk_filedialog
+            _tk_available = True
+        except Exception:
+            _tk = None
+            _tk_filedialog = None
+            _tk_available = False
+
+        # Нижняя панель путей и кнопок (работает без Qt; выбор файла через tkinter опционален).
+        ax_calib_path = fig.add_axes([0.07, 0.14, 0.40, 0.04])
+        ax_calib_load = fig.add_axes([0.48, 0.14, 0.07, 0.04])
+        ax_calib_pick = fig.add_axes([0.56, 0.14, 0.06, 0.04])
+        ax_calib_sample = fig.add_axes([0.63, 0.14, 0.09, 0.04])
+        ax_calib_save = fig.add_axes([0.73, 0.14, 0.10, 0.04])
+
+        ax_bg_path = fig.add_axes([0.07, 0.09, 0.40, 0.04])
+        ax_bg_load = fig.add_axes([0.48, 0.09, 0.07, 0.04])
+        ax_bg_pick = fig.add_axes([0.56, 0.09, 0.06, 0.04])
+        ax_bg_sample = fig.add_axes([0.63, 0.09, 0.09, 0.04])
+        ax_bg_save2 = fig.add_axes([0.73, 0.09, 0.10, 0.04])
+
+        calib_path_box = TextBox(ax_calib_path, "Калибр", initial=state.calib_envelope_path)
+        bg_path_box = TextBox(ax_bg_path, "Фон", initial=state.background_path)
+
+        calib_load_btn2 = MplButton(ax_calib_load, "Загруз.")
+        calib_pick_btn2 = MplButton(ax_calib_pick, "Файл")
+        calib_sample_btn2 = MplButton(ax_calib_sample, "sample")
+        calib_save_btn2 = MplButton(ax_calib_save, "Сохр env")
+
+        bg_load_btn2 = MplButton(ax_bg_load, "Загруз.")
+        bg_pick_btn2 = MplButton(ax_bg_pick, "Файл")
+        bg_sample_btn2 = MplButton(ax_bg_sample, "sample")
+        bg_save_btn2 = MplButton(ax_bg_save2, "Сохр фон")
+
+        if not _tk_available:
+            try:
+                calib_pick_btn2.label.set_text("Файл-")
+                bg_pick_btn2.label.set_text("Файл-")
+            except Exception:
+                pass
+
+        def _tb_text(tb):
+            try:
+                return str(tb.text).strip()
+            except Exception:
+                return ""
+
+        def _pick_file_dialog(initial_path: str) -> str:
+            if not _tk_available or _tk is None or _tk_filedialog is None:
+                return ""
+            root = None
+            try:
+                root = _tk.Tk()
+                root.withdraw()
+                root.attributes("-topmost", True)
+            except Exception:
+                root = None
+            try:
+                return str(
+                    _tk_filedialog.askopenfilename(
+                        initialdir=_os.path.dirname(initial_path) or ".",
+                        initialfile=_os.path.basename(initial_path) or "",
+                        title="Выбрать файл эталона (.npy или capture)",
+                    )
+                )
+            finally:
+                try:
+                    if root is not None:
+                        root.destroy()
+                except Exception:
+                    pass
+
+        def _sync_path_boxes():
+            try:
+                if _tb_text(calib_path_box) != state.calib_envelope_path:
+                    calib_path_box.set_val(state.calib_envelope_path)
+            except Exception:
+                pass
+            try:
+                if _tb_text(bg_path_box) != state.background_path:
+                    bg_path_box.set_val(state.background_path)
+            except Exception:
+                pass
+
+        def _refresh_status_texts():
+            pipeline_text.set_text(f"{state.format_pipeline_status()} | cplx:{ring.fft_complex_mode}")
+            ref_text.set_text(state.format_reference_status())
+            try:
+                fig.canvas.draw_idle()
+            except Exception:
+                pass
+
+        def _line_mode() -> str:
+            return str(line_mode_state.get("value", "raw"))
+
+        def _refresh_checkboxes():
+            try:
+                # file-mode чекбокс показываем всегда; он активен при наличии пути/данных.
+                ax_cb_file.set_visible(True)
+            except Exception:
+                pass
+
+        def _load_calib_from_ui():
+            p = _tb_text(calib_path_box)
+            if p:
+                state.set_calib_envelope_path(p)
+            ok = state.load_calib_reference()
+            if ok and bool(calib_file_cb.get_status()[0]):
+                state.set_calib_mode("file")
+                state.set_calib_enabled(bool(calib_cb.get_status()[0]))
+            _sync_path_boxes()
+            _refresh_checkboxes()
+            _refresh_status_texts()
+            return ok
+
+        def _load_bg_from_ui():
+            p = _tb_text(bg_path_box)
+            if p:
+                state.set_background_path(p)
+            ok = state.load_background_reference()
+            _sync_path_boxes()
+            _refresh_status_texts()
+            return ok
+
+        def _on_ylim_change(_val):
+            try:
+                y0 = int(min(ymin_slider.val, ymax_slider.val))
+                y1 = int(max(ymin_slider.val, ymax_slider.val))
+                ax_spec.set_ylim(y0, y1)
+                fig.canvas.draw_idle()
+            except Exception:
+                pass
+
+        def _on_calib_file_clicked(_v):
+            use_file = bool(calib_file_cb.get_status()[0])
+            if use_file:
+                ok = _load_calib_from_ui()
+                if ok:
+                    state.set_calib_mode("file")
+                else:
+                    calib_file_cb.set_active(0)  # снять галочку
+            else:
+                state.set_calib_mode("live")
+            state.set_calib_enabled(bool(calib_cb.get_status()[0]))
+            _refresh_status_texts()
+
+        def _on_calib_clicked(_v):
+            state.set_calib_enabled(bool(calib_cb.get_status()[0]))
+            _refresh_checkboxes()
+            _refresh_status_texts()
+
+        ax_btn_bg = fig.add_axes([0.92, 0.27, 0.08, 0.05])
+        ax_cb_bg = fig.add_axes([0.92, 0.20, 0.08, 0.06])
+        save_bg_btn = MplButton(ax_btn_bg, "Сохр. фон")
+        bg_cb = CheckButtons(ax_cb_bg, ["вычет фона"], [False])
+
+        def _on_save_bg(_event):
+            ok = state.save_background()
+            if ok:
+                state.load_background()
+            _sync_path_boxes()
+            _refresh_status_texts()
+
+        def _on_bg_clicked(_v):
+            state.set_background_enabled(bool(bg_cb.get_status()[0]))
+            _refresh_status_texts()
+
+        def _on_calib_load_btn(_event):
+            _load_calib_from_ui()
+
+        def _on_calib_pick_btn(_event):
+            path = _pick_file_dialog(_tb_text(calib_path_box) or state.calib_envelope_path)
+            if not path:
+                return
+            state.set_calib_envelope_path(path)
+            _sync_path_boxes()
+            _refresh_status_texts()
+
+        def _on_calib_sample_btn(_event):
+            state.set_calib_envelope_path(_os.path.join("sample_data", "no_antennas_35dB_attenuators"))
+            _sync_path_boxes()
+            if _load_calib_from_ui() and not bool(calib_file_cb.get_status()[0]):
+                calib_file_cb.set_active(0)
+
+        def _on_calib_save_btn(_event):
+            state.save_calib_envelope()
+            _sync_path_boxes()
+            _refresh_status_texts()
+
+        def _on_bg_load_btn(_event):
+            _load_bg_from_ui()
+
+        def _on_bg_pick_btn(_event):
+            path = _pick_file_dialog(_tb_text(bg_path_box) or state.background_path)
+            if not path:
+                return
+            state.set_background_path(path)
+            _sync_path_boxes()
+            _refresh_status_texts()
+
+        def _on_bg_sample_btn(_event):
+            state.set_background_path(_os.path.join("sample_data", "empty"))
+            _sync_path_boxes()
+            _load_bg_from_ui()
+
+        def _on_bg_save_btn2(_event):
+            ok = state.save_background()
+            if ok:
+                state.load_background()
+            _sync_path_boxes()
+            _refresh_status_texts()
+
+        def _on_line_mode_clicked(label):
+            line_mode_state["value"] = str(label)
+            try:
+                fig.canvas.draw_idle()
+            except Exception:
+                pass
+
+        def _on_ifft_mode_clicked(label):
+            ifft_mode_state["value"] = str(label)
+            try:
+                ring.set_fft_complex_mode(str(label))
+            except Exception:
+                pass
+            fft_line_t1.set_data([], [])
+            fft_line_t2.set_data([], [])
+            fft_line_t3.set_data([], [])
+            _refresh_status_texts()
+            try:
+                fig.canvas.draw_idle()
+            except Exception:
+                pass
+
+        save_bg_btn.on_clicked(_on_save_bg)
+        bg_cb.on_clicked(_on_bg_clicked)
+        calib_load_btn2.on_clicked(_on_calib_load_btn)
+        calib_pick_btn2.on_clicked(_on_calib_pick_btn)
+        calib_sample_btn2.on_clicked(_on_calib_sample_btn)
+        calib_save_btn2.on_clicked(_on_calib_save_btn)
+        bg_load_btn2.on_clicked(_on_bg_load_btn)
+        bg_pick_btn2.on_clicked(_on_bg_pick_btn)
+        bg_sample_btn2.on_clicked(_on_bg_sample_btn)
+        bg_save_btn2.on_clicked(_on_bg_save_btn2)
+        line_mode_rb.on_clicked(_on_line_mode_clicked)
+        ifft_mode_rb.on_clicked(_on_ifft_mode_clicked)
+
+        ymin_slider.on_changed(_on_ylim_change)
+        ymax_slider.on_changed(_on_ylim_change)
+        contrast_slider.on_changed(lambda _v: fig.canvas.draw_idle())
+        calib_cb.on_clicked(_on_calib_clicked)
+        calib_file_cb.on_clicked(_on_calib_file_clicked)
+        _sync_path_boxes()
+        _refresh_checkboxes()
+        _refresh_status_texts()
+    except Exception:
+        calib_cb = None
+        line_mode_state = {"value": "raw"}
+        ifft_mode_state = {"value": str(getattr(ring, "fft_complex_mode", "arccos"))}
+
+    FREQ_MIN = float(FREQ_MIN_GHZ)
+    FREQ_MAX = float(FREQ_MAX_GHZ)
+
+    def _fft_depth_max() -> float:
+        axis = ring.fft_depth_axis_m
+        if axis is None or axis.size == 0:
+            return 1.0
+        try:
+            vmax = float(axis[-1])
+        except Exception:
+            vmax = float(np.nanmax(axis))
+        if not np.isfinite(vmax) or vmax <= 0.0:
+            return 1.0
+        return vmax
+
+    # --- Инициализация imshow при первом свипе ---
+    def _init_imshow_extents():
+        w = ring.width
+        ms = ring.max_sweeps
+        fb = max(1, int(ring.fft_bins))
+        depth_max = _fft_depth_max()
+        img_obj.set_data(np.zeros((w, ms), dtype=np.float32))
+        img_obj.set_extent((0, ms - 1, FREQ_MIN, FREQ_MAX))
+        ax_img.set_xlim(0, ms - 1)
+        ax_img.set_ylim(FREQ_MIN, FREQ_MAX)
+        img_fft_obj.set_data(np.zeros((fb, ms), dtype=np.float32))
+        img_fft_obj.set_extent((0, ms - 1, 0.0, depth_max))
+        ax_spec.set_xlim(0, ms - 1)
+        ax_spec.set_ylim(0.0, depth_max)
+        ax_fft.set_xlim(0.0, depth_max)
+
+    _imshow_initialized = [False]
+
+    def update(_frame):
+        changed = state.drain_queue(q, ring) > 0
+
+        if changed and not _imshow_initialized[0] and ring.is_ready:
+            _init_imshow_extents()
+            _imshow_initialized[0] = True
+
+        # Линейный график свипа
+        if state.current_sweep_raw is not None:
+            raw = state.current_sweep_raw
+            if ring.x_shared is not None and raw.size <= ring.x_shared.size:
+                xs = ring.x_shared[: raw.size]
+            else:
+                xs = np.arange(raw.size, dtype=np.int32)
+            line_mode = str(line_mode_state.get("value", "raw"))
+            main = state.current_sweep_processed if line_mode == "processed" else raw
+            if main is not None:
+                line_obj.set_data(xs[: main.size], main)
+            else:
+                line_obj.set_data([], [])
+            if line_mode == "raw":
+                if state.calib_mode == "file" and state.calib_file_envelope is not None:
+                    upper = np.asarray(state.calib_file_envelope, dtype=np.float32)
+                    n_env = min(xs.size, upper.size)
+                    if n_env > 0:
+                        x_env = xs[:n_env]
+                        y_env = upper[:n_env]
+                        line_env_lo.set_data(x_env, -y_env)
+                        line_env_hi.set_data(x_env, y_env)
+                    else:
+                        line_env_lo.set_data([], [])
+                        line_env_hi.set_data([], [])
+                elif state.last_calib_sweep is not None:
+                    calib = np.asarray(state.last_calib_sweep, dtype=np.float32)
+                    lower, upper = build_calib_envelopes(calib)
+                    n_env = min(xs.size, lower.size, upper.size)
+                    if n_env > 0:
+                        line_env_lo.set_data(xs[:n_env], lower[:n_env])
+                        line_env_hi.set_data(xs[:n_env], upper[:n_env])
+                    else:
+                        line_env_lo.set_data([], [])
+                        line_env_hi.set_data([], [])
+                else:
+                    line_env_lo.set_data([], [])
+                    line_env_hi.set_data([], [])
+            else:
+                line_env_lo.set_data([], [])
+                line_env_hi.set_data([], [])
+            if logscale_enabled:
+                if state.current_sweep_pre_exp is not None:
+                    pre = state.current_sweep_pre_exp
+                    line_pre_exp_obj.set_data(xs[: pre.size], pre)
+                else:
+                    line_pre_exp_obj.set_data([], [])
+
+                post = state.current_sweep_post_exp if state.current_sweep_post_exp is not None else raw
+                line_post_exp_obj.set_data(xs[: post.size], post)
+
+                if line_mode == "processed":
+                    if state.current_sweep_processed is not None:
+                        proc = state.current_sweep_processed
+                        line_obj.set_data(xs[: proc.size], proc)
+                    else:
+                        line_obj.set_data([], [])
+                else:
+                    line_obj.set_data(xs[: raw.size], raw)
+                line_norm_obj.set_data([], [])
+            else:
+                line_pre_exp_obj.set_data([], [])
+                line_post_exp_obj.set_data([], [])
+                if line_mode == "raw" and state.current_sweep_norm is not None:
+                    line_norm_obj.set_data(
+                        xs[: state.current_sweep_norm.size], state.current_sweep_norm
+                    )
+                else:
+                    line_norm_obj.set_data([], [])
+            ax_line.set_xlim(FREQ_MIN, FREQ_MAX)
+            if fixed_ylim is not None:
+                ax_line.set_ylim(fixed_ylim)
+            else:
+                ax_line.relim()
+                ax_line.autoscale_view(scalex=False, scaley=True)
+            ax_line.set_ylabel("Y")
+
+            # Профиль по глубине: три линии для 1/3, 2/3, 3/3 частотного диапазона.
+            third_axes = ring.last_fft_third_axes_m
+            third_vals = ring.last_fft_third_vals
+            lines = (fft_line_t1, fft_line_t2, fft_line_t3)
+            xs_max = []
+            ys_min = []
+            ys_max = []
+            for line_fft, xs_fft, fft_vals in zip(lines, third_axes, third_vals):
+                if xs_fft is None or fft_vals is None:
+                    line_fft.set_data([], [])
+                    continue
+                n = min(int(xs_fft.size), int(fft_vals.size))
+                if n <= 0:
+                    line_fft.set_data([], [])
+                    continue
+                x_seg = xs_fft[:n]
+                y_seg = fft_vals[:n]
+                line_fft.set_data(x_seg, y_seg)
+                xs_max.append(float(x_seg[n - 1]))
+                ys_min.append(float(np.nanmin(y_seg)))
+                ys_max.append(float(np.nanmax(y_seg)))
+
+            if xs_max and ys_min and ys_max:
+                ax_fft.set_xlim(0, float(max(xs_max)))
+                ax_fft.set_ylim(float(min(ys_min)), float(max(ys_max)))
+
+        # Водопад сырых данных
+        if changed and ring.is_ready:
+            disp = ring.get_display_ring()
+            if ring.x_shared is not None:
+                n = ring.x_shared.size
+                disp = disp[:n, :]
+            img_obj.set_data(disp)
+            img_obj.set_extent((0, ring.max_sweeps - 1, FREQ_MIN, FREQ_MAX))
+            ax_img.set_ylim(FREQ_MIN, FREQ_MAX)
+            levels = _visible_levels(disp, ax_img)
+            if levels is not None:
+                img_obj.set_clim(vmin=levels[0], vmax=levels[1])
+
+        # Водопад спектров
+        if changed and ring.is_ready:
+            disp_fft = ring.get_display_ring_fft()
+            disp_fft = ring.subtract_recent_mean_fft(disp_fft, spec_mean_sec)
+            img_fft_obj.set_data(disp_fft)
+            depth_max = _fft_depth_max()
+            img_fft_obj.set_extent((0, ring.max_sweeps - 1, 0.0, depth_max))
+            ax_spec.set_ylim(0.0, depth_max)
+            levels = ring.compute_fft_levels(disp_fft, spec_clip)
+            if levels is not None:
+                try:
+                    c = float(contrast_slider.val) / 100.0 if contrast_slider is not None else 1.0
+                except Exception:
+                    c = 1.0
+                vmax_eff = levels[0] + c * (levels[1] - levels[0])
+                img_fft_obj.set_clim(vmin=levels[0], vmax=vmax_eff)
+
+        # Статус и подпись канала
+        if changed and state.current_info:
+            status_text.set_text(format_status(state.current_info))
+            channel_text.set_text(state.format_channel_label())
+            pipeline_text.set_text(f"{state.format_pipeline_status()} | cplx:{ring.fft_complex_mode}")
+            ref_text.set_text(state.format_reference_status())
+        elif changed:
+            pipeline_text.set_text(f"{state.format_pipeline_status()} | cplx:{ring.fft_complex_mode}")
+            ref_text.set_text(state.format_reference_status())
+
+        return (
+            line_obj,
+            line_norm_obj,
+            line_pre_exp_obj,
+            line_post_exp_obj,
+            line_env_lo,
+            line_env_hi,
+            img_obj,
+            fft_line_t1,
+            fft_line_t2,
+            fft_line_t3,
+            img_fft_obj,
+            status_text,
+            pipeline_text,
+            ref_text,
+            channel_text,
+        )
+
+    ani = FuncAnimation(fig, update, interval=interval_ms, blit=False)
+    plt.show()
+    stop_event.set()
+    reader.join(timeout=1.0)

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/capture_reference_loader.py

@@ -0,0 +1,227 @@
+"""Загрузка эталонов (калибровка/фон) из .npy или бинарных capture-файлов."""
+
+from __future__ import annotations
+
+from collections import Counter
+from dataclasses import dataclass
+import os
+from typing import Iterable, List, Optional, Tuple
+
+import numpy as np
+
+from rfg_adc_plotter.io.sweep_parser_core import BinaryRecordStreamParser, SweepAssembler
+from rfg_adc_plotter.types import SweepPacket
+
+
+@dataclass(frozen=True)
+class CaptureParseSummary:
+    path: str
+    format: str  # "npy" | "bin_capture"
+    sweeps_total: int
+    sweeps_valid: int
+    channels_seen: Tuple[int, ...]
+    dominant_width: Optional[int]
+    dominant_n_valid: Optional[int]
+    aggregation: str
+    warnings: Tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class ReferenceLoadResult:
+    vector: np.ndarray
+    summary: CaptureParseSummary
+    kind: str  # "calibration_envelope" | "background_raw" | "background_processed"
+    source_type: str  # "npy" | "capture"
+
+
+def detect_reference_file_format(path: str) -> Optional[str]:
+    """Определить формат файла эталона: .npy или бинарный capture."""
+    p = str(path).strip()
+    if not p or not os.path.isfile(p):
+        return None
+    if p.lower().endswith(".npy"):
+        return "npy"
+
+    try:
+        size = os.path.getsize(p)
+    except Exception:
+        return None
+    if size <= 0 or (size % 8) != 0:
+        return None
+
+    try:
+        with open(p, "rb") as f:
+            sample = f.read(min(size, 8 * 2048))
+    except Exception:
+        return None
+
+    if len(sample) < 8:
+        return None
+
+    # Быстрый sniff aligned-записей: в валидных записях байт 6 == 0x0A.
+    recs = len(sample) // 8
+    if recs <= 0:
+        return None
+    marker_hits = 0
+    start_hits = 0
+    for i in range(0, recs * 8, 8):
+        b = sample[i : i + 8]
+        if b[6] == 0x0A:
+            marker_hits += 1
+            if b[:6] == b"\xff\xff\xff\xff\xff\xff":
+                start_hits += 1
+    if marker_hits >= max(4, int(recs * 0.8)) and start_hits >= 1:
+        return "bin_capture"
+    return None
+
+
+def load_capture_sweeps(path: str, *, fancy: bool = False, logscale: bool = False) -> List[SweepPacket]:
+    """Загрузить свипы из бинарного capture-файла в формате --bin."""
+    parser = BinaryRecordStreamParser()
+    assembler = SweepAssembler(fancy=fancy, logscale=logscale, debug=False)
+    sweeps: List[SweepPacket] = []
+
+    with open(path, "rb") as f:
+        while True:
+            chunk = f.read(65536)
+            if not chunk:
+                break
+            events = parser.feed(chunk)
+            for ev in events:
+                packets = assembler.consume_binary_event(ev)
+                if packets:
+                    sweeps.extend(packets)
+    tail = assembler.finalize_current()
+    if tail is not None:
+        sweeps.append(tail)
+
+    return sweeps
+
+
+def _mode_int(values: Iterable[int]) -> Optional[int]:
+    vals = [int(v) for v in values]
+    if not vals:
+        return None
+    ctr = Counter(vals)
+    return int(max(ctr.items(), key=lambda kv: (kv[1], kv[0]))[0])
+
+
+def aggregate_capture_reference(
+    sweeps: List[SweepPacket],
+    *,
+    channel: int = 0,
+    method: str = "median",
+    path: str = "",
+) -> Tuple[np.ndarray, CaptureParseSummary]:
+    """Отфильтровать и агрегировать свипы из capture в один эталонный вектор."""
+    ch_target = int(channel)
+    meth = str(method).strip().lower() or "median"
+    warnings: list[str] = []
+
+    if meth != "median":
+        warnings.append(f"aggregation '{meth}' не поддерживается, использую median")
+        meth = "median"
+
+    channels_seen: set[int] = set()
+    candidate_rows: list[np.ndarray] = []
+    widths: list[int] = []
+    n_valids: list[int] = []
+
+    for sweep, info in sweeps:
+        chs = info.get("chs") if isinstance(info, dict) else None
+        ch_set: set[int] = set()
+        if isinstance(chs, (list, tuple, set)):
+            for v in chs:
+                try:
+                    ch_set.add(int(v))
+                except Exception:
+                    pass
+        else:
+            try:
+                ch_set.add(int(info.get("ch", 0)))  # type: ignore[union-attr]
+            except Exception:
+                pass
+        channels_seen.update(ch_set)
+        if ch_target not in ch_set:
+            continue
+
+        row = np.asarray(sweep, dtype=np.float32).reshape(-1)
+        candidate_rows.append(row)
+        widths.append(int(row.size))
+        n_valids.append(int(np.count_nonzero(np.isfinite(row))))
+
+    sweeps_total = len(sweeps)
+    if not candidate_rows:
+        summary = CaptureParseSummary(
+            path=path,
+            format="bin_capture",
+            sweeps_total=sweeps_total,
+            sweeps_valid=0,
+            channels_seen=tuple(sorted(channels_seen)),
+            dominant_width=None,
+            dominant_n_valid=None,
+            aggregation=meth,
+            warnings=tuple(warnings + [f"канал ch{ch_target} не найден"]),
+        )
+        raise ValueError(summary.warnings[-1])
+
+    dominant_width = _mode_int(widths)
+    dominant_n_valid = _mode_int(n_valids)
+    if dominant_width is None or dominant_n_valid is None:
+        summary = CaptureParseSummary(
+            path=path,
+            format="bin_capture",
+            sweeps_total=sweeps_total,
+            sweeps_valid=0,
+            channels_seen=tuple(sorted(channels_seen)),
+            dominant_width=dominant_width,
+            dominant_n_valid=dominant_n_valid,
+            aggregation=meth,
+            warnings=tuple(warnings + ["не удалось определить доминирующие параметры свипа"]),
+        )
+        raise ValueError(summary.warnings[-1])
+
+    valid_rows: list[np.ndarray] = []
+    n_valid_threshold = max(1, int(np.floor(0.95 * dominant_n_valid)))
+    for row in candidate_rows:
+        if row.size != dominant_width:
+            continue
+        n_valid = int(np.count_nonzero(np.isfinite(row)))
+        if n_valid < n_valid_threshold:
+            continue
+        valid_rows.append(row)
+
+    if not valid_rows:
+        warnings.append("после фильтрации не осталось валидных свипов")
+        summary = CaptureParseSummary(
+            path=path,
+            format="bin_capture",
+            sweeps_total=sweeps_total,
+            sweeps_valid=0,
+            channels_seen=tuple(sorted(channels_seen)),
+            dominant_width=dominant_width,
+            dominant_n_valid=dominant_n_valid,
+            aggregation=meth,
+            warnings=tuple(warnings),
+        )
+        raise ValueError(summary.warnings[-1])
+
+    # Детерминированная агрегация: медиана по валидным свипам.
+    stack = np.stack(valid_rows, axis=0).astype(np.float32, copy=False)
+    vector = np.nanmedian(stack, axis=0).astype(np.float32, copy=False)
+
+    if len(valid_rows) < len(candidate_rows):
+        warnings.append(f"отфильтровано {len(candidate_rows) - len(valid_rows)} неполных/нестандартных свипов")
+
+    summary = CaptureParseSummary(
+        path=path,
+        format="bin_capture",
+        sweeps_total=sweeps_total,
+        sweeps_valid=len(valid_rows),
+        channels_seen=tuple(sorted(channels_seen)),
+        dominant_width=dominant_width,
+        dominant_n_valid=dominant_n_valid,
+        aggregation=meth,
+        warnings=tuple(warnings),
+    )
+    return vector, summary

rfg_adc_plotter/io/serial_source.py

@@ -1,6 +1,4 @@
-"""Serial input helpers with pyserial and raw TTY fallbacks."""
-
-from __future__ import annotations
+"""Источники последовательного ввода: обёртки над pyserial и raw TTY."""
 
 import io
 import os
@@ -14,13 +12,14 @@ def try_open_pyserial(path: str, baud: int, timeout: float):
     except Exception:
         return None
     try:
-        return serial.Serial(path, baudrate=baud, timeout=timeout)
+        ser = serial.Serial(path, baudrate=baud, timeout=timeout)
+        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):
         self._fd = fd
@@ -34,7 +33,7 @@ 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 +41,10 @@ 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
@@ -67,14 +69,17 @@ 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 +92,11 @@ 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 +112,13 @@ 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()
@@ -123,7 +129,7 @@ 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):
         self._src = src
@@ -145,22 +151,20 @@ 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:
+                n = 0
+            if n > 0:
                 try:
-                    return self._ser.read(available)
+                    return self._ser.read(n)
                 except Exception:
                     return b""
             return b""
-
         if self._fd is None:
             return b""
-
         out = bytearray()
         while True:
             try:

rfg_adc_plotter/io/sweep_parser_core.py

@@ -1,399 +1,210 @@
-"""Reusable sweep parsers and sweep assembly helpers."""
+"""Переиспользуемые компоненты парсинга бинарных свипов и сборки SweepPacket."""
 
 from __future__ import annotations
 
-import math
-import time
 from collections import deque
-from typing import List, Optional, Sequence, Set
+import time
+from typing import Iterable, List, Optional, Sequence, Set, Tuple
 
 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, StartEvent, SweepAuxCurves, SweepInfo, SweepPacket
+from rfg_adc_plotter.constants import DATA_INVERSION_THRESHOLD, LOG_EXP
+from rfg_adc_plotter.types import SweepInfo, SweepPacket
+
+# Binary parser events:
+#   ("start", ch)
+#   ("point", ch, x, y)
+BinaryEvent = Tuple[str, int] | Tuple[str, int, int, int]
 
 
-def u32_to_i32(value: int) -> int:
-    return value - 0x1_0000_0000 if (value & 0x8000_0000) else value
+def u32_to_i32(v: int) -> int:
+    """Преобразование 32-bit слова в знаковое значение."""
+    return v - 0x1_0000_0000 if (v & 0x8000_0000) else v
 
 
-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
-
-
-class AsciiSweepParser:
-    """Incremental parser for ASCII sweep streams."""
+class BinaryRecordStreamParser:
+    """Инкрементальный парсер бинарных записей протокола (по 8 байт)."""
 
     def __init__(self):
         self._buf = bytearray()
+        self.bytes_consumed: int = 0
+        self.start_count: int = 0
+        self.point_count: int = 0
+        self.desync_count: int = 0
 
-    def feed(self, data: bytes) -> List[ParserEvent]:
+    def feed(self, data: bytes) -> List[BinaryEvent]:
         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())
+        events: List[BinaryEvent] = []
+        buf = self._buf
+
+        while len(buf) >= 8:
+            w0 = int(buf[0]) | (int(buf[1]) << 8)
+            w1 = int(buf[2]) | (int(buf[3]) << 8)
+            w2 = int(buf[4]) | (int(buf[5]) << 8)
+
+            if w0 == 0xFFFF and w1 == 0xFFFF and w2 == 0xFFFF and buf[6] == 0x0A:
+                ch = int(buf[7])
+                events.append(("start", ch))
+                del buf[:8]
+                self.bytes_consumed += 8
+                self.start_count += 1
                 continue
 
-            parts = line.split()
-            if len(parts) < 3:
+            if buf[6] == 0x0A:
+                ch = int(buf[7])
+                value_u32 = (w1 << 16) | w2
+                events.append(("point", ch, int(w0), u32_to_i32(value_u32)))
+                del buf[:8]
+                self.bytes_consumed += 8
+                self.point_count += 1
                 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)))
+
+            del buf[:1]
+            self.bytes_consumed += 1
+            self.desync_count += 1
+
         return events
 
+    def buffered_size(self) -> int:
+        return len(self._buf)
 
-class LegacyBinaryParser:
-    """Byte-resynchronizing parser for legacy 8-byte binary records."""
-
-    def __init__(self):
-        self._buf = bytearray()
-
-    @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:
-            w0 = self._u16_at(self._buf, 0)
-            w1 = self._u16_at(self._buf, 2)
-            w2 = self._u16_at(self._buf, 4)
-            if w0 == 0xFFFF and w1 == 0xFFFF and w2 == 0xFFFF and self._buf[6] == 0x0A:
-                events.append(StartEvent(ch=int(self._buf[7])))
-                del self._buf[:8]
-                continue
-            if self._buf[6] == 0x0A:
-                ch = int(self._buf[7])
-                value = u32_to_i32((w1 << 16) | w2)
-                events.append(PointEvent(ch=ch, x=int(w0), y=float(value)))
-                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()
-
-    @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:
-                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)
-                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
-
-    @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)
-                events.append(StartEvent(ch=self._current_channel))
-                del self._buf[:8]
-                continue
-            if words[3] == 0xFFFF and words[0] != 0xFFFF:
-                avg_1 = u16_to_i16(words[1])
-                avg_2 = u16_to_i16(words[2])
-                events.append(
-                    PointEvent(
-                        ch=self._current_channel,
-                        x=int(words[0]),
-                        y=log_pair_to_sweep(avg_1, avg_2),
-                        aux=(float(avg_1), float(avg_2)),
-                    )
-                )
-                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
-        avg_1 = u16_to_i16(int(self._point_buf[1]))
-        avg_2 = u16_to_i16(int(self._point_buf[2]))
-        self._expected_step = step + 1
-        return PointEvent(
-            ch=self._current_channel,
-            x=step,
-            y=log_pair_to_sweep(avg_1, avg_2),
-            aux=(float(avg_1), float(avg_2)),
-        )
-
-    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
+    def clear_buffer_keep_tail(self, max_tail: int = 262_144):
+        if len(self._buf) > max_tail:
+            del self._buf[:-max_tail]
 
 
 class SweepAssembler:
-    """Collect parser events into sweep packets matching runtime expectations."""
+    """Собирает точки в свип и применяет ту же постобработку, что realtime parser."""
 
-    def __init__(self, fancy: bool = False, apply_inversion: bool = True):
+    def __init__(self, fancy: bool = False, logscale: bool = False, debug: bool = False):
         self._fancy = bool(fancy)
-        self._apply_inversion = bool(apply_inversion)
-        self._max_width = 0
-        self._sweep_idx = 0
+        self._logscale = bool(logscale)
+        self._debug = bool(debug)
+
+        self._max_width: int = 0
+        self._sweep_idx: int = 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._ys: list[int] = []
         self._cur_channel: Optional[int] = None
         self._cur_channels: set[int] = set()
 
-    def _reset_current(self) -> None:
+    def reset_current(self):
         self._xs.clear()
         self._ys.clear()
-        self._aux_1.clear()
-        self._aux_2.clear()
         self._cur_channel = 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_channels.add(int(event.ch))
-            return packet
-
+    def add_point(self, ch: int, x: int, y: int):
         if self._cur_channel is None:
-            self._cur_channel = int(event.ch)
-        self._cur_channels.add(int(event.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 None
+            self._cur_channel = int(ch)
+        self._cur_channels.add(int(ch))
+        self._xs.append(int(x))
+        self._ys.append(int(y))
 
-    def finalize_current(self) -> Optional[SweepPacket]:
-        if not self._xs:
+    def start_new_sweep(self, ch: int, now_ts: Optional[float] = None) -> Optional[SweepPacket]:
+        packet = self.finalize_current(now_ts=now_ts)
+        self.reset_current()
+        self._cur_channel = int(ch)
+        self._cur_channels.add(int(ch))
+        return packet
+
+    def consume_binary_event(self, event: BinaryEvent, now_ts: Optional[float] = None) -> List[SweepPacket]:
+        out: List[SweepPacket] = []
+        tag = event[0]
+        if tag == "start":
+            packet = self.start_new_sweep(int(event[1]), now_ts=now_ts)
+            if packet is not None:
+                out.append(packet)
+            return out
+        # point
+        _tag, ch, x, y = event  # type: ignore[misc]
+        self.add_point(int(ch), int(x), int(y))
+        return out
+
+    def finalize_arrays(
+        self,
+        xs: Sequence[int],
+        ys: Sequence[int],
+        channels: Optional[Set[int]],
+        now_ts: Optional[float] = None,
+    ) -> Optional[SweepPacket]:
+        if self._debug:
+            if not xs:
+                import sys
+                sys.stderr.write("[debug] _finalize_current: xs пуст — свип пропущен\n")
+            else:
+                import sys
+                sys.stderr.write(
+                    f"[debug] _finalize_current: {len(xs)} точек → свип #{self._sweep_idx + 1}\n"
+                )
+        if not xs:
             return None
 
-        ch_list = sorted(self._cur_channels) if self._cur_channels else [0]
+        ch_list = sorted(channels) if channels else [0]
         ch_primary = ch_list[0] if ch_list else 0
-        width = max(int(max(self._xs)) + 1, 1)
+        max_x = max(int(v) for v in xs)
+        width = max_x + 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),
-            )
+        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:
+            for x, y in zip(xs, ys):
+                xi = int(x)
+                if 0 <= xi < target_width:
+                    sweep[xi] = float(y)
 
         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
+                known = ~np.isnan(sweep)
+                if np.any(known):
+                    known_idx = np.nonzero(known)[0]
+                    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:
+                pass
+
+        try:
+            m = float(np.nanmean(sweep))
+            if np.isfinite(m) and m < DATA_INVERSION_THRESHOLD:
+                sweep *= -1.0
+        except Exception:
+            pass
+
+        pre_exp_sweep = None
+        if self._logscale:
+            try:
+                pre_exp_sweep = sweep.copy()
+                with np.errstate(over="ignore", invalid="ignore"):
+                    sweep = np.power(LOG_EXP, np.asarray(sweep, dtype=np.float64)).astype(np.float32)
+                sweep[~np.isfinite(sweep)] = np.nan
             except Exception:
                 pass
 
         self._sweep_idx += 1
-        now = time.time()
+        if len(ch_list) > 1:
+            import sys
+            sys.stderr.write(f"[warn] Sweep {self._sweep_idx}: изменялся номер канала: {ch_list}\n")
+
+        now = float(time.time() if now_ts is None else now_ts)
         if self._last_sweep_ts is None:
             dt_ms = float("nan")
         else:
@@ -403,7 +214,10 @@ class SweepAssembler:
         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 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))
@@ -424,4 +238,10 @@ class SweepAssembler:
             "std": std,
             "dt_ms": dt_ms,
         }
-        return (sweep, info, aux_curves)
+        if pre_exp_sweep is not None:
+            info["pre_exp_sweep"] = pre_exp_sweep
+
+        return (sweep, info)
+
+    def finalize_current(self, now_ts: Optional[float] = None) -> Optional[SweepPacket]:
+        return self.finalize_arrays(self._xs, self._ys, self._cur_channels, now_ts=now_ts)

rfg_adc_plotter/io/sweep_reader.py

@@ -1,26 +1,18 @@
-"""Background sweep reader thread."""
-
-from __future__ import annotations
+"""Фоновый поток чтения и парсинга свипов из последовательного порта."""
 
 import sys
 import threading
 import time
 from queue import Full, Queue
+from typing import Optional
 
+from rfg_adc_plotter.io.sweep_parser_core import BinaryRecordStreamParser, SweepAssembler
 from rfg_adc_plotter.io.serial_source import SerialChunkReader, SerialLineSource
-from rfg_adc_plotter.io.sweep_parser_core import (
-    AsciiSweepParser,
-    LegacyBinaryParser,
-    LogScale16BitX2BinaryParser,
-    LogScaleBinaryParser32,
-    ParserTestStreamParser,
-    SweepAssembler,
-)
 from rfg_adc_plotter.types import SweepPacket
 
 
 class SweepReader(threading.Thread):
-    """Read a serial source in the background and emit completed sweep packets."""
+    """Фоновый поток: читает строки, формирует завершённые свипы и кладёт в очередь."""
 
     def __init__(
         self,
@@ -31,69 +23,220 @@ class SweepReader(threading.Thread):
         fancy: bool = False,
         bin_mode: bool = False,
         logscale: bool = False,
-        parser_16_bit_x2: bool = False,
-        parser_test: bool = False,
+        debug: bool = False,
     ):
         super().__init__(daemon=True)
         self._port_path = port_path
-        self._baud = int(baud)
-        self._queue = out_queue
+        self._baud = baud
+        self._q = out_queue
         self._stop = stop_event
+        self._src: Optional[SerialLineSource] = None
         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._src: SerialLineSource | None = None
+        self._debug = bool(debug)
+        self._assembler = SweepAssembler(fancy=self._fancy, logscale=self._logscale, debug=self._debug)
 
-    def _build_parser(self):
-        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)
-
-    def _enqueue(self, packet: SweepPacket) -> None:
+    def _finalize_current(self, xs, ys, channels: Optional[set]):
+        packet = self._assembler.finalize_arrays(xs, ys, channels)
+        if packet is None:
+            return
+        sweep, info = packet
         try:
-            self._queue.put_nowait(packet)
+            self._q.put_nowait((sweep, info))
         except Full:
             try:
-                _ = self._queue.get_nowait()
+                _ = self._q.get_nowait()
             except Exception:
                 pass
             try:
-                self._queue.put_nowait(packet)
+                self._q.put_nowait((sweep, info))
             except Exception:
                 pass
 
-    def run(self) -> None:
+    def _run_ascii_stream(self, chunk_reader: SerialChunkReader):
+        xs: list[int] = []
+        ys: list[int] = []
+        cur_channel: Optional[int] = None
+        cur_channels: set[int] = set()
+
+        buf = bytearray()
+        _dbg_line_count = 0
+        _dbg_match_count = 0
+        _dbg_sweep_count = 0
+        while not self._stop.is_set():
+            data = chunk_reader.read_available()
+            if data:
+                buf += data
+            else:
+                time.sleep(0.0005)
+                continue
+
+            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:
+                    continue
+
+                _dbg_line_count += 1
+
+                if line.startswith(b"Sweep_start"):
+                    if self._debug:
+                        sys.stderr.write(f"[debug] ASCII строка #{_dbg_line_count}: Sweep_start → финализация свипа\n")
+                        _dbg_sweep_count += 1
+                    self._finalize_current(xs, ys, cur_channels)
+                    xs.clear()
+                    ys.clear()
+                    cur_channel = None
+                    cur_channels.clear()
+                    continue
+
+                if len(line) >= 3:
+                    parts = line.split()
+                    if len(parts) >= 3 and (parts[0].lower() == b"s" or parts[0].lower().startswith(b"s")):
+                        try:
+                            if parts[0].lower() == 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)
+                            else:
+                                ch = int(parts[0][1:], 10)
+                                x = int(parts[1], 10)
+                                y = int(parts[2], 10)
+                        except Exception:
+                            if self._debug and _dbg_line_count <= 5:
+                                hex_repr = " ".join(f"{b:02x}" for b in line[:16])
+                                sys.stderr.write(
+                                    f"[debug] ASCII строка #{_dbg_line_count} ({len(line)} байт): {hex_repr}"
+                                    f"{'...' if len(line) > 16 else ''}  → похожа на 's', но не парсится\n"
+                                )
+                            continue
+                        _dbg_match_count += 1
+                        if self._debug and _dbg_match_count <= 3:
+                            sys.stderr.write(f"[debug] ASCII точка: ch={ch} x={x} y={y}\n")
+                        if cur_channel is None:
+                            cur_channel = ch
+                        cur_channels.add(ch)
+                        xs.append(x)
+                        ys.append(y)
+                        continue
+
+                if self._debug and _dbg_line_count <= 5:
+                    hex_repr = " ".join(f"{b:02x}" for b in line[:16])
+                    sys.stderr.write(
+                        f"[debug] ASCII строка #{_dbg_line_count} ({len(line)} байт): {hex_repr}"
+                        f"{'...' if len(line) > 16 else ''}  → нет совпадения\n"
+                    )
+
+                if self._debug and _dbg_line_count % 100 == 0:
+                    sys.stderr.write(
+                        f"[debug] ASCII статистика: строк={_dbg_line_count}, "
+                        f"совпадений={_dbg_match_count}, свипов={_dbg_sweep_count}\n"
+                    )
+
+            if len(buf) > 1_000_000:
+                del buf[:-262144]
+
+        self._finalize_current(xs, ys, cur_channels)
+
+    def _run_binary_stream(self, chunk_reader: SerialChunkReader):
+        xs: list[int] = []
+        ys: list[int] = []
+        cur_channel: Optional[int] = None
+        cur_channels: set[int] = set()
+        parser = BinaryRecordStreamParser()
+
+        # Бинарный протокол (4 слова LE u16 = 8 байт на запись):
+        #   старт свипа: 0xFFFF, 0xFFFF, 0xFFFF, (ch<<8)|0x0A
+        #     Байты на проводе: ff ff  ff ff  ff ff  0a [ch]
+        #     ch=0 → последнее слово=0x000A; ch=1 → 0x010A; и т.д.
+        #   точка данных: step_u16, value_hi_u16, value_lo_u16, (ch<<8)|0x0A
+        #     Байты на проводе: [step_lo step_hi] [hi_lo hi_hi] [lo_lo lo_hi] 0a [ch]
+        #     value_i32 = sign_extend((value_hi<<16)|value_lo)
+        #   Признак записи: байт 6 == 0x0A, байт 7 — номер канала.
+        #   При десинхронизации сдвигаемся на 1 БАЙТ (не слово) для самосинхронизации.
+
+        _dbg_byte_count = 0
+        _dbg_desync_count = 0
+        _dbg_sweep_count = 0
+        _dbg_point_count = 0
+        while not self._stop.is_set():
+            data = chunk_reader.read_available()
+            if data:
+                events = parser.feed(data)
+            else:
+                time.sleep(0.0005)
+                continue
+
+            for ev in events:
+                tag = ev[0]
+                if tag == "start":
+                    ch_new = int(ev[1])
+                    if self._debug:
+                        sys.stderr.write(f"[debug] BIN: старт свипа, ch={ch_new}\n")
+                        _dbg_sweep_count += 1
+                    self._finalize_current(xs, ys, cur_channels)
+                    xs.clear()
+                    ys.clear()
+                    cur_channels.clear()
+                    cur_channel = ch_new
+                    cur_channels.add(cur_channel)
+                    continue
+
+                _tag, ch_from_term, step, value_i32 = ev  # type: ignore[misc]
+                if cur_channel is None:
+                    cur_channel = int(ch_from_term)
+                cur_channels.add(int(cur_channel))
+                xs.append(int(step))
+                ys.append(int(value_i32))
+                _dbg_point_count += 1
+                if self._debug and _dbg_point_count <= 3:
+                    sys.stderr.write(
+                        f"[debug] BIN точка: step={int(step)} ch={int(ch_from_term)} → value={int(value_i32)}\n"
+                    )
+
+            _dbg_byte_count = parser.bytes_consumed
+            _dbg_desync_count = parser.desync_count
+
+            if self._debug and _dbg_byte_count > 0 and _dbg_byte_count % 4000 < 8:
+                sys.stderr.write(
+                    f"[debug] BIN статистика: байт={_dbg_byte_count}, "
+                    f"десинхронизаций={_dbg_desync_count}, точек={_dbg_point_count}, свипов={_dbg_sweep_count}\n"
+                )
+
+            if parser.buffered_size() > 1_000_000:
+                parser.clear_buffer_keep_tail(262_144)
+
+        self._finalize_current(xs, ys, cur_channels)
+
+    def run(self):
+
         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 exc:
-            sys.stderr.write(f"[error] {exc}\n")
+        except Exception as e:
+            sys.stderr.write(f"[error] {e}\n")
             return
 
-        parser, assembler = self._build_parser()
-
         try:
             chunk_reader = SerialChunkReader(self._src)
-            while not self._stop.is_set():
-                data = chunk_reader.read_available()
-                if not data:
-                    time.sleep(0.0005)
-                    continue
-                for event in parser.feed(data):
-                    packet = assembler.consume(event)
-                    if packet is not None:
-                        self._enqueue(packet)
-            packet = assembler.finalize_current()
-            if packet is not None:
-                self._enqueue(packet)
+            if self._debug:
+                mode_str = "бинарный (--bin)" if self._bin_mode else "ASCII (по умолчанию)"
+                sys.stderr.write(f"[debug] Режим парсера: {mode_str}\n")
+            if self._bin_mode:
+                self._run_binary_stream(chunk_reader)
+            else:
+                self._run_ascii_stream(chunk_reader)
         finally:
             try:
                 if self._src is not None:

rfg_adc_plotter/main.py

@@ -1,25 +1,137 @@
-"""Main entrypoint for the modularized ADC plotter."""
+#!/usr/bin/env python3
+"""
+Реалтайм-плоттер для свипов из виртуального COM-порта.
 
-from __future__ import annotations
+Формат строк:
+  - "Sweep_start" — начало нового свипа (предыдущий считается завершённым)
+  - "s CH X Y" — точка (номер канала, индекс X, значение Y), все целые со знаком
 
+Отрисовываются четыре графика:
+  - Сырые данные: последний полученный свип (Y vs X)
+  - Водопад сырых данных: последние N свипов
+  - FFT текущего свипа
+  - B-scan: водопад FFT-строк
+
+Зависимости: numpy. PySerial опционален — при его отсутствии
+используется сырой доступ к TTY через termios.
+GUI: matplotlib (совместимый) или pyqtgraph (быстрый).
+"""
+
+import argparse
 import sys
 
-from rfg_adc_plotter.cli import build_parser
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Читает свипы из виртуального COM-порта и рисует: "
+            "последний свип и водопад (реалтайм)."
+        )
+    )
+    parser.add_argument(
+        "port",
+        help="Путь к порту, например /dev/ttyACM1 или COM3 (COM10+: \\\\.\\COM10)",
+    )
+    parser.add_argument("--baud", type=int, default=115200, help="Скорость (по умолчанию 115200)")
+    parser.add_argument("--max-sweeps", type=int, default=200, help="Количество видимых свипов в водопаде")
+    parser.add_argument("--max-fps", type=float, default=30.0, help="Лимит частоты отрисовки, кадров/с")
+    parser.add_argument("--cmap", default="viridis", help="Цветовая карта водопада")
+    parser.add_argument(
+        "--spec-clip",
+        default="2,98",
+        help=(
+            "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",
+        action="store_true",
+        help="Заполнять выпавшие точки средними значениями между соседними",
+    )
+    parser.add_argument(
+        "--ylim",
+        type=str,
+        default=None,
+        help="Фиксированные Y-пределы для кривой формата min,max (например -1000,1000). По умолчанию авто",
+    )
+    parser.add_argument(
+        "--backend",
+        choices=["auto", "pg", "mpl"],
+        default="auto",
+        help="Графический бэкенд: pyqtgraph (pg) — быстрее; matplotlib (mpl) — совместимый. По умолчанию auto",
+    )
+    parser.add_argument(
+        "--norm-type",
+        choices=["projector", "simple"],
+        default="projector",
+        help="Тип нормировки: projector (по огибающим в [-1000,+1000]) или simple (raw/calib)",
+    )
+    parser.add_argument(
+        "--ifft-complex-mode",
+        choices=["arccos", "diff"],
+        default="arccos",
+        help=(
+            "Режим реконструкции комплексного спектра перед IFFT: "
+            "arccos (phi=arccos(x), unwrap) или diff (sin(phi) через численную производную)"
+        ),
+    )
+    parser.add_argument(
+        "--bin",
+        dest="bin_mode",
+        action="store_true",
+        help=(
+            "Бинарный протокол (8 байт на запись, LE u16 слова): "
+            "старт свипа ff ff ff ff ff ff 0a [ch]; "
+            "точка step_u16 hi_u16 lo_u16 0a [ch]; "
+            "value=sign_ext((hi<<16)|lo); ch=0..N в старшем байте маркера"
+        ),
+    )
+    parser.add_argument(
+        "--logscale",
+        action="store_true",
+        help="После поправки знака применять экспоненту LOG_EXP**x (LOG_EXP=2)",
+    )
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="Отладочный вывод парсера: показывает принятые строки/слова и причины отсутствия свипов",
+    )
+    return parser
 
 
-def main() -> None:
+def main():
     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
+    if args.backend == "pg":
+        from rfg_adc_plotter.gui.pyqtgraph_backend import run_pyqtgraph
+        try:
+            run_pyqtgraph(args)
+        except Exception as e:
+            sys.stderr.write(f"[error] PyQtGraph бэкенд недоступен: {e}\n")
+            sys.exit(1)
+        return
 
-    try:
-        run_pyqtgraph(args)
-    except Exception as exc:
-        sys.stderr.write(f"[error] PyQtGraph бэкенд недоступен: {exc}\n")
-        raise SystemExit(1) from exc
+    if args.backend == "auto":
+        try:
+            from rfg_adc_plotter.gui.pyqtgraph_backend import run_pyqtgraph
+            run_pyqtgraph(args)
+            return
+        except Exception:
+            pass  # Откатываемся на matplotlib
+
+    from rfg_adc_plotter.gui.matplotlib_backend import run_matplotlib
+    run_matplotlib(args)
 
 
 if __name__ == "__main__":

rfg_adc_plotter/processing/__init__.py

@@ -1,67 +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,
-    calibrate_freqs,
-    get_calibration_base,
-    get_calibration_coeffs,
-    load_calib_envelope,
-    recalculate_calibration_c,
-    save_calib_envelope,
-    set_calibration_base_value,
-)
-from rfg_adc_plotter.processing.fft import (
-    compute_distance_axis,
-    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,
-    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",
-    "calibrate_freqs",
-    "compute_auto_ylim",
-    "compute_distance_axis",
-    "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_fft_background",
-    "normalize_by_envelope",
-    "normalize_by_calib",
-    "parse_spec_clip",
-    "recalculate_calibration_c",
-    "rolling_median_ref",
-    "save_calib_envelope",
-    "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

@@ -1,124 +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 = np.asarray(sweep["I"], dtype=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)
-        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 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 _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)

rfg_adc_plotter/processing/fft.py

@@ -1,267 +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 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_seg = np.asarray(sweep[:take_fft], dtype=np.float32)
-    fallback = np.nan_to_num(sweep_seg, nan=0.0).astype(np.float32, 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 = np.interp(x_uniform, x_unique, y_unique).astype(np.float32)
-    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_seg = np.asarray(fft_seg[:band_len], dtype=np.float32)
-        if fft_seg.size < band_len:
-            padded = np.zeros((band_len,), dtype=np.float32)
-            padded[: fft_seg.size] = fft_seg
-            fft_seg = padded
-
-    window = np.hanning(band_len).astype(np.float32)
-    band = np.nan_to_num(fft_seg, nan=0.0).astype(np.float32, copy=False) * window
-
-    spectrum = np.zeros((int(fft_len),), dtype=np.float32)
-    spectrum[pos_idx] = band
-    spectrum[neg_idx] = 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_seg = np.asarray(fft_seg[:band_len], dtype=np.float32)
-        if fft_seg.size < band_len:
-            padded = np.zeros((band_len,), dtype=np.float32)
-            padded[: fft_seg.size] = fft_seg
-            fft_seg = padded
-
-    window = np.hanning(band_len).astype(np.float32)
-    band = np.nan_to_num(fft_seg, nan=0.0).astype(np.float32, copy=False) * window
-
-    spectrum = np.zeros((int(fft_len),), dtype=np.float32)
-    spectrum[pos_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_mag_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, dtype=np.float32)
-
-    fft_seg, take_fft = prepared
-    fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
-    window = np.hanning(take_fft).astype(np.float32)
-    fft_in[:take_fft] = fft_seg * window
-    spec = np.fft.ifft(fft_in)
-    mag = np.abs(spec).astype(np.float32)
-    if mag.shape[0] != bins:
-        mag = mag[:bins]
-    return mag
-
-
-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"
-    raise ValueError(f"Unsupported FFT mode: {mode!r}")
-
-
-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."""
-    if bins <= 0:
-        return np.zeros((0,), dtype=np.float32)
-
-    fft_mode = _normalize_fft_mode(mode, symmetric)
-    if fft_mode == "direct":
-        return _compute_fft_mag_row_direct(sweep, freqs, bins)
-
-    if fft_mode == "positive_only":
-        spectrum_centered = build_positive_only_centered_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-    else:
-        spectrum_centered = build_symmetric_ifft_spectrum(sweep, freqs, fft_len=FFT_LEN)
-    if spectrum_centered is None:
-        return np.full((bins,), np.nan, dtype=np.float32)
-
-    spec = np.fft.ifft(np.fft.ifftshift(spectrum_centered))
-    mag = np.abs(spec).astype(np.float32)
-    if mag.shape[0] != bins:
-        mag = mag[:bins]
-    return mag
-
-
-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 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/fourier.py

@@ -0,0 +1,300 @@
+"""Преобразование свипа в IFFT-профиль по глубине (м).
+
+Поддерживает несколько режимов восстановления комплексного спектра перед IFFT:
+- ``arccos``: phi = arccos(x), continuous unwrap, z = exp(1j*phi)
+- ``diff``:   x ~= cos(phi), diff(x) -> sin(phi), z = cos + 1j*sin (с проекцией на единичную окружность)
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Optional
+
+import numpy as np
+
+from rfg_adc_plotter.constants import (
+    FREQ_MAX_GHZ,
+    FREQ_MIN_GHZ,
+    FREQ_SPAN_GHZ,
+    IFFT_LEN,
+    SPEED_OF_LIGHT_M_S,
+)
+
+
+logger = logging.getLogger(__name__)
+
+_EPS = 1e-12
+_TWO_PI = float(2.0 * np.pi)
+_VALID_COMPLEX_MODES = {"arccos", "diff"}
+
+
+def _fallback_depth_response(
+    size: int,
+    values: Optional[np.ndarray] = None,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Безопасный fallback для GUI/ring: всегда возвращает ненулевую длину."""
+    n = max(1, int(size))
+    depth = np.linspace(0.0, 1.0, n, dtype=np.float32)
+    if values is None:
+        return depth, np.zeros((n,), dtype=np.float32)
+
+    arr = np.asarray(values)
+    if arr.size == 0:
+        return depth, np.zeros((n,), dtype=np.float32)
+
+    if np.iscomplexobj(arr):
+        src = np.abs(arr)
+    else:
+        src = np.abs(np.nan_to_num(arr, nan=0.0, posinf=0.0, neginf=0.0))
+    src = np.asarray(src, dtype=np.float32).ravel()
+
+    out = np.zeros((n,), dtype=np.float32)
+    take = min(n, src.size)
+    if take > 0:
+        out[:take] = src[:take]
+    return depth, out
+
+
+def _normalize_complex_mode(mode: str) -> str:
+    m = str(mode).strip().lower()
+    if m not in _VALID_COMPLEX_MODES:
+        raise ValueError(f"Invalid complex reconstruction mode: {mode!r}")
+    return m
+
+
+def build_ifft_time_axis_ns() -> np.ndarray:
+    """Legacy helper: старая временная ось IFFT в наносекундах (фиксированная длина)."""
+    return (
+        np.arange(IFFT_LEN, dtype=np.float64) / (FREQ_SPAN_GHZ * 1e9) * 1e9
+    ).astype(np.float32)
+
+
+def build_frequency_axis_hz(sweep_width: int) -> np.ndarray:
+    """Построить частотную сетку (Гц) для текущей длины свипа."""
+    n = int(sweep_width)
+    if n <= 0:
+        return np.zeros((0,), dtype=np.float64)
+    if n == 1:
+        return np.array([FREQ_MIN_GHZ * 1e9], dtype=np.float64)
+    return np.linspace(FREQ_MIN_GHZ * 1e9, FREQ_MAX_GHZ * 1e9, n, dtype=np.float64)
+
+
+def normalize_trace_unit_range(x: np.ndarray) -> np.ndarray:
+    """Signed-нормировка массива по max(abs(.)) в диапазон около [-1, 1]."""
+    arr = np.asarray(x, dtype=np.float64).ravel()
+    if arr.size == 0:
+        return arr
+    arr = np.nan_to_num(arr, nan=0.0, posinf=0.0, neginf=0.0)
+    amax = float(np.max(np.abs(arr)))
+    if (not np.isfinite(amax)) or amax <= _EPS:
+        return np.zeros_like(arr, dtype=np.float64)
+    return arr / amax
+
+
+def normalize_sweep_for_phase(sweep: np.ndarray) -> np.ndarray:
+    """Совместимый alias: нормировка свипа перед восстановлением фазы."""
+    return normalize_trace_unit_range(sweep)
+
+
+def unwrap_arccos_phase_continuous(x_norm: np.ndarray) -> np.ndarray:
+    """Непрерывно развернуть фазу, восстановленную через arccos.
+
+    Для каждой точки рассматриваются ветви ±phi + 2πk и выбирается кандидат,
+    ближайший к предыдущей фазе (nearest continuous).
+    """
+    x = np.asarray(x_norm, dtype=np.float64).ravel()
+    if x.size == 0:
+        return np.zeros((0,), dtype=np.float64)
+    x = np.nan_to_num(x, nan=0.0, posinf=1.0, neginf=-1.0)
+    x = np.clip(x, -1.0, 1.0)
+    phi0 = np.arccos(x)
+
+    out = np.empty_like(phi0, dtype=np.float64)
+    out[0] = float(phi0[0])
+    for i in range(1, phi0.size):
+        base_phi = float(phi0[i])
+        prev = float(out[i - 1])
+
+        best_cand: Optional[float] = None
+        best_key: Optional[tuple[float, float]] = None
+
+        for sign in (1.0, -1.0):
+            base = sign * base_phi
+            k_center = int(np.round((prev - base) / _TWO_PI))
+            for k in (k_center - 1, k_center, k_center + 1):
+                cand = base + _TWO_PI * float(k)
+                step = abs(cand - prev)
+                # Tie-break: при равенстве шага предпочесть больший кандидат.
+                key = (step, -cand)
+                if best_key is None or key < best_key:
+                    best_key = key
+                    best_cand = cand
+
+        out[i] = prev if best_cand is None else float(best_cand)
+    return out
+
+
+def reconstruct_complex_spectrum_arccos(sweep: np.ndarray) -> np.ndarray:
+    """Режим arccos: cos(phi) -> phi -> exp(i*phi)."""
+    x_norm = normalize_trace_unit_range(sweep)
+    if x_norm.size == 0:
+        return np.zeros((0,), dtype=np.complex128)
+    phi = unwrap_arccos_phase_continuous(np.clip(x_norm, -1.0, 1.0))
+    return np.exp(1j * phi).astype(np.complex128, copy=False)
+
+
+def reconstruct_complex_spectrum_diff(sweep: np.ndarray) -> np.ndarray:
+    """Режим diff: x~=cos(phi), diff(x)->sin(phi), z=cos+i*sin с проекцией на единичную окружность."""
+    cos_phi = normalize_trace_unit_range(sweep)
+    if cos_phi.size == 0:
+        return np.zeros((0,), dtype=np.complex128)
+    cos_phi = np.clip(cos_phi, -1.0, 1.0)
+
+    if cos_phi.size < 2:
+        sin_est = np.zeros_like(cos_phi, dtype=np.float64)
+    else:
+        d = np.gradient(cos_phi)
+        sin_est = normalize_trace_unit_range(d)
+        sin_est = np.clip(sin_est, -1.0, 1.0)
+    sin_est = normalize_trace_unit_range(d)
+    # mag = np.abs(sin_est)
+    # mask = mag > _EPS
+    # if np.any(mask):
+    #     sin_est[mask] = sin_est[mask] / mag[mask]
+    z = cos_phi.astype(np.complex128, copy=False)  + 1j * sin_est.astype(np.complex128, copy=False)
+    mag = np.abs(z)
+    z_unit = np.ones_like(z, dtype=np.complex128)
+    mask = mag > _EPS
+    if np.any(mask):
+        z_unit[mask] = z[mask] / mag[mask]
+    return z_unit
+
+
+def reconstruct_complex_spectrum_from_real_trace(
+    sweep: np.ndarray,
+    *,
+    complex_mode: str = "arccos",
+) -> np.ndarray:
+    """Восстановить комплексный спектр из вещественного свипа в выбранном режиме."""
+    mode = _normalize_complex_mode(complex_mode)
+    if mode == "arccos":
+        return reconstruct_complex_spectrum_arccos(sweep)
+    if mode == "diff":
+        return reconstruct_complex_spectrum_diff(sweep)
+    raise ValueError(f"Unsupported complex reconstruction mode: {complex_mode!r}")
+
+
+def perform_ifft_depth_response(
+    s_array: np.ndarray,
+    frequencies_hz: np.ndarray,
+    *,
+    axis: str = "abs",
+    start_hz: float | None = None,
+    stop_hz: float | None = None,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Frequency-to-depth conversion with zero-padding and frequency offset handling."""
+    try:
+        s_in = np.asarray(s_array, dtype=np.complex128).ravel()
+        f_in = np.asarray(frequencies_hz, dtype=np.float64).ravel()
+        m = min(s_in.size, f_in.size)
+        if m < 2:
+            raise ValueError("Not enough points")
+
+        s = s_in[:m]
+        f = f_in[:m]
+
+        lo = float(FREQ_MIN_GHZ * 1e9 if start_hz is None else start_hz)
+        hi = float(FREQ_MAX_GHZ * 1e9 if stop_hz is None else stop_hz)
+        if hi < lo:
+            lo, hi = hi, lo
+
+        mask = (
+            np.isfinite(f)
+            & np.isfinite(np.real(s))
+            & np.isfinite(np.imag(s))
+            & (f >= lo)
+            & (f <= hi)
+        )
+        f = f[mask]
+        s = s[mask]
+
+        n = int(f.size)
+        if n < 2:
+            raise ValueError("Not enough frequency points after filtering")
+        if np.any(np.diff(f) <= 0.0):
+            raise ValueError("Non-increasing frequency grid")
+
+        df = float((f[-1] - f[0]) / (n - 1))
+        if not np.isfinite(df) or df <= 0.0:
+            raise ValueError("Invalid frequency step")
+
+        k0 = int(np.round(float(f[0]) / df))
+        if k0 < 0:
+            raise ValueError("Negative frequency offset index")
+
+        min_len = int(2 * (k0 + n - 1))
+        if min_len <= 0:
+            raise ValueError("Invalid FFT length")
+        n_fft = 1 << int(np.ceil(np.log2(float(min_len))))
+
+        dt = 1.0 / (n_fft * df)
+        t_sec = np.arange(n_fft, dtype=np.float64) * dt
+
+        h = np.zeros((n_fft,), dtype=np.complex128)
+        end = k0 + n
+        if end > n_fft:
+            raise ValueError("Spectrum placement exceeds FFT buffer")
+        h[k0:end] = s
+
+        y = np.fft.ifft(h)
+        depth_m = t_sec * SPEED_OF_LIGHT_M_S
+
+        axis_name = str(axis).strip().lower()
+        if axis_name == "abs":
+            y_fin = np.abs(y)
+        elif axis_name == "real":
+            y_fin = np.real(y)
+        elif axis_name == "imag":
+            y_fin = np.imag(y)
+        elif axis_name == "phase":
+            y_fin = np.angle(y)
+        else:
+            raise ValueError(f"Invalid axis parameter: {axis!r}")
+
+        return depth_m.astype(np.float32, copy=False), np.asarray(y_fin, dtype=np.float32)
+
+    except Exception as exc:  # noqa: BLE001
+        logger.error("IFFT depth response failed: %r", exc)
+        return _fallback_depth_response(np.asarray(s_array).size, np.asarray(s_array))
+
+
+def compute_ifft_profile_from_sweep(
+    sweep: Optional[np.ndarray],
+    *,
+    complex_mode: str = "arccos",
+) -> tuple[np.ndarray, np.ndarray]:
+    """Высокоуровневый pipeline: sweep -> complex spectrum -> IFFT(abs) depth profile."""
+    if sweep is None:
+        return _fallback_depth_response(1, None)
+
+    try:
+        s = np.asarray(sweep, dtype=np.float64).ravel()
+        if s.size == 0:
+            return _fallback_depth_response(1, None)
+
+        freqs_hz = build_frequency_axis_hz(s.size)
+        s_complex = reconstruct_complex_spectrum_from_real_trace(s, complex_mode=complex_mode)
+        depth_m, y = perform_ifft_depth_response(s_complex, freqs_hz, axis="abs")
+        n = min(depth_m.size, y.size)
+        if n <= 0:
+            return _fallback_depth_response(s.size, s)
+        return depth_m[:n].astype(np.float32, copy=False), np.maximum(y[:n], 1e-12).astype(np.float32, copy=False)   # log10 для лучшей визуализации в водопаде  
+    except Exception as exc:  # noqa: BLE001
+        logger.error("compute_ifft_profile_from_sweep failed: %r", exc)
+        return _fallback_depth_response(np.asarray(sweep).size if sweep is not None else 1, sweep)
+
+
+def compute_ifft_db_profile(sweep: Optional[np.ndarray]) -> np.ndarray:
+    """Legacy wrapper (deprecated name): возвращает линейный |IFFT| профиль."""
+    _depth_m, y = compute_ifft_profile_from_sweep(sweep, complex_mode="arccos")
+    return y

rfg_adc_plotter/processing/normalization.py

@@ -1,173 +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 normalize_by_envelope(raw: np.ndarray, envelope: np.ndarray) -> np.ndarray:
-    """Normalize a sweep by an envelope with safe resampling and zero protection."""
-    raw_arr = np.asarray(raw, dtype=np.float32).reshape(-1)
-    if raw_arr.size == 0:
-        return raw_arr.copy()
-
-    env = resample_envelope(envelope, raw_arr.size)
-    out = np.full_like(raw_arr, np.nan, dtype=np.float32)
-    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
-    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/normalizer.py

@@ -0,0 +1,149 @@
+"""Алгоритмы нормировки свипов по калибровочной кривой."""
+
+from typing import Tuple
+
+import numpy as np
+
+
+def normalize_simple(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
+    """Простая нормировка: поэлементное деление raw/calib."""
+    w = min(raw.size, calib.size)
+    if w <= 0:
+        return raw
+    out = np.full_like(raw, np.nan, dtype=np.float32)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        out[:w] = raw[:w] / calib[:w]
+    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]:
+    """Оценить огибающую по модулю сигнала.
+
+    Возвращает (lower, upper) = (-envelope, +envelope), где envelope —
+    интерполяция через локальные максимумы |calib|.
+    """
+    n = int(calib.size)
+    if n <= 0:
+        empty = np.zeros((0,), dtype=np.float32)
+        return empty, empty
+
+    y = np.asarray(calib, dtype=np.float32)
+    finite = np.isfinite(y)
+    if not np.any(finite):
+        zeros = np.zeros_like(y, dtype=np.float32)
+        return zeros, zeros
+
+    if not np.all(finite):
+        x = np.arange(n, dtype=np.float32)
+        y = y.copy()
+        y[~finite] = np.interp(x[~finite], x[finite], y[finite]).astype(np.float32)
+
+    a = np.abs(y)
+
+    if n < 3:
+        env = a.copy()
+        return -env, env
+
+    da = np.diff(a)
+    s = np.sign(da).astype(np.int8, copy=False)
+
+    if np.any(s == 0):
+        for i in range(1, s.size):
+            if s[i] == 0:
+                s[i] = s[i - 1]
+        for i in range(s.size - 2, -1, -1):
+            if s[i] == 0:
+                s[i] = s[i + 1]
+        s[s == 0] = 1
+
+    max_idx = np.where((s[:-1] > 0) & (s[1:] < 0))[0] + 1
+
+    x = np.arange(n, dtype=np.float32)
+
+    if max_idx.size == 0:
+        idx = np.array([0, n - 1], dtype=np.int64)
+    else:
+        idx = np.unique(np.concatenate(([0], max_idx, [n - 1]))).astype(np.int64)
+    env = np.interp(x, idx.astype(np.float32), a[idx]).astype(np.float32)
+
+    return -env, env
+
+
+def normalize_projector(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
+    """Нормировка через проекцию между огибающими калибровки в диапазон [-1000, +1000]."""
+    w = min(raw.size, calib.size)
+    if w <= 0:
+        return raw
+
+    out = np.full_like(raw, np.nan, dtype=np.float32)
+    raw_seg = np.asarray(raw[:w], dtype=np.float32)
+    lower, upper = build_calib_envelopes(np.asarray(calib[:w], 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[:w] = proj
+
+    return out
+
+
+def normalize_by_calib(raw: np.ndarray, calib: np.ndarray, norm_type: str) -> np.ndarray:
+    """Нормировка свипа по выбранному алгоритму."""
+    nt = str(norm_type).strip().lower()
+    if nt == "simple":
+        return normalize_simple(raw, calib)
+    return normalize_projector(raw, calib)
+
+
+def normalize_by_envelope(raw: np.ndarray, envelope: np.ndarray) -> np.ndarray:
+    """Нормировка свипа через проекцию на огибающую из файла.
+
+    Воспроизводит логику normalize_projector: проецирует raw в [-1000, +1000]
+    используя готовую верхнюю огибающую (upper = envelope, lower = -envelope).
+    """
+    w = min(raw.size, envelope.size)
+    if w <= 0:
+        return raw
+
+    out = np.full_like(raw, np.nan, dtype=np.float32)
+    raw_seg = np.asarray(raw[:w], dtype=np.float32)
+    upper = np.asarray(envelope[:w], dtype=np.float32)
+    lower = -upper
+    span = upper - lower  # = 2 * upper
+
+    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[:w] = proj
+
+    return out

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/processing/pipeline.py

@@ -0,0 +1,415 @@
+"""Явный pipeline предобработки свипов перед помещением в RingBuffer."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import os
+from typing import Optional, Tuple
+
+import numpy as np
+
+from rfg_adc_plotter.io.capture_reference_loader import (
+    CaptureParseSummary,
+    aggregate_capture_reference,
+    detect_reference_file_format,
+    load_capture_sweeps,
+)
+from rfg_adc_plotter.processing.normalizer import (
+    build_calib_envelopes,
+    normalize_by_calib,
+    normalize_by_envelope,
+)
+
+DEFAULT_CALIB_ENVELOPE_PATH = "calib_envelope.npy"
+DEFAULT_BACKGROUND_PATH = "background.npy"
+
+
+def _normalize_path(path: str) -> str:
+    return str(path).strip()
+
+
+def _normalize_save_npy_path(path: str) -> str:
+    p = _normalize_path(path)
+    if not p:
+        return p
+    _root, ext = os.path.splitext(p)
+    if ext:
+        return p
+    return f"{p}.npy"
+
+
+def _summary_for_npy(path: str) -> CaptureParseSummary:
+    return CaptureParseSummary(
+        path=path,
+        format="npy",
+        sweeps_total=0,
+        sweeps_valid=0,
+        channels_seen=tuple(),
+        dominant_width=None,
+        dominant_n_valid=None,
+        aggregation="median",
+        warnings=tuple(),
+    )
+
+
+@dataclass(frozen=True)
+class SweepProcessingResult:
+    """Результат предобработки одного свипа."""
+
+    processed_sweep: np.ndarray
+    normalized_sweep: Optional[np.ndarray]
+    calibration_applied: bool
+    background_applied: bool
+    calibration_source: str  # off|live|npy|capture
+    background_source: str   # off|npy|capture(raw)|capture(raw->calib)
+    is_calibration_reference: bool
+    stage_trace: Tuple[str, ...]
+
+
+class SweepPreprocessor:
+    """Управляет калибровкой/фоном и применяет их к входному свипу."""
+
+    def __init__(
+        self,
+        norm_type: str = "projector",
+        calib_envelope_path: str = DEFAULT_CALIB_ENVELOPE_PATH,
+        background_path: str = DEFAULT_BACKGROUND_PATH,
+        auto_save_live_calib_envelope: bool = True,
+    ):
+        self.norm_type = str(norm_type).strip().lower() or "projector"
+        self.calib_enabled = False
+        self.calib_mode = "live"  # live | file
+        self.background_enabled = False
+        self.auto_save_live_calib_envelope = bool(auto_save_live_calib_envelope)
+
+        self.calib_envelope_path = _normalize_path(calib_envelope_path)
+        self.background_path = _normalize_path(background_path)
+
+        self.last_calib_sweep: Optional[np.ndarray] = None
+        self.calib_file_envelope: Optional[np.ndarray] = None
+
+        # background — в текущем домене вычитания (raw или normalized), UI использует для preview/state
+        self.background: Optional[np.ndarray] = None
+        # raw background loaded from capture file; преобразуется на лету при активной калибровке
+        self.background_raw_capture: Optional[np.ndarray] = None
+
+        # Источники и метаданные загрузки
+        self.calib_external_source_type: str = "none"        # none|npy|capture
+        self.background_source_type: str = "none"            # none|npy_processed|capture_raw
+        self.calib_reference_summary: Optional[CaptureParseSummary] = None
+        self.background_reference_summary: Optional[CaptureParseSummary] = None
+        self.last_reference_error: str = ""
+
+        # Параметры офлайн-парсинга capture (должны совпадать с live parser по настройке UI)
+        self.capture_fancy: bool = False
+        self.capture_logscale: bool = False
+        self.reference_aggregation_method: str = "median"
+
+    # ---- Конфигурация ----
+    def set_calib_mode(self, mode: str):
+        m = str(mode).strip().lower()
+        self.calib_mode = "file" if m == "file" else "live"
+
+    def set_calib_enabled(self, enabled: bool):
+        self.calib_enabled = bool(enabled)
+
+    def set_background_enabled(self, enabled: bool):
+        self.background_enabled = bool(enabled)
+
+    def set_capture_parse_options(self, *, fancy: Optional[bool] = None, logscale: Optional[bool] = None):
+        if fancy is not None:
+            self.capture_fancy = bool(fancy)
+        if logscale is not None:
+            self.capture_logscale = bool(logscale)
+
+    def set_calib_envelope_path(self, path: str):
+        p = _normalize_path(path)
+        if p:
+            if p != self.calib_envelope_path:
+                self.calib_file_envelope = None
+                if self.calib_external_source_type in ("npy", "capture"):
+                    self.calib_external_source_type = "none"
+                    self.calib_reference_summary = None
+            self.calib_envelope_path = p
+
+    def set_background_path(self, path: str):
+        p = _normalize_path(path)
+        if p:
+            if p != self.background_path:
+                self.background = None
+                self.background_raw_capture = None
+                self.background_source_type = "none"
+                self.background_reference_summary = None
+            self.background_path = p
+
+    def has_calib_envelope_file(self) -> bool:
+        return bool(self.calib_envelope_path) and os.path.isfile(self.calib_envelope_path)
+
+    def has_background_file(self) -> bool:
+        return bool(self.background_path) and os.path.isfile(self.background_path)
+
+    # ---- Загрузка/сохранение .npy ----
+    def _save_array(self, arr: np.ndarray, current_path: str, path: Optional[str]) -> str:
+        target = _normalize_save_npy_path(path if path is not None else current_path)
+        if not target:
+            raise ValueError("Пустой путь сохранения")
+        np.save(target, arr)
+        return target
+
+    def save_calib_envelope(self, path: Optional[str] = None) -> bool:
+        """Сохранить огибающую из последнего live-калибровочного свипа (экспорт .npy)."""
+        if self.last_calib_sweep is None:
+            return False
+        try:
+            _lower, upper = build_calib_envelopes(self.last_calib_sweep)
+            self.calib_envelope_path = self._save_array(upper, self.calib_envelope_path, path)
+            self.last_reference_error = ""
+            return True
+        except Exception as exc:
+            self.last_reference_error = f"save calib envelope failed: {exc}"
+            return False
+
+    def save_background(self, sweep_for_ring: Optional[np.ndarray], path: Optional[str] = None) -> bool:
+        """Сохранить текущий свип (в текущем домене обработки) как .npy-фон."""
+        if sweep_for_ring is None:
+            return False
+        try:
+            bg = np.asarray(sweep_for_ring, dtype=np.float32).copy()
+            self.background_path = self._save_array(bg, self.background_path, path)
+            self.background = bg
+            self.background_raw_capture = None
+            self.background_source_type = "npy_processed"
+            self.background_reference_summary = _summary_for_npy(self.background_path)
+            self.last_reference_error = ""
+            return True
+        except Exception as exc:
+            self.last_reference_error = f"save background failed: {exc}"
+            return False
+
+    # ---- Загрузка эталонов (.npy или capture) ----
+    def _detect_source_kind(self, path: str, source_kind: str) -> Optional[str]:
+        sk = str(source_kind).strip().lower() or "auto"
+        if sk == "auto":
+            return detect_reference_file_format(path)
+        if sk in ("npy", "bin_capture", "capture"):
+            return "bin_capture" if sk == "capture" else sk
+        return None
+
+    def _load_npy_vector(self, path: str) -> np.ndarray:
+        arr = np.load(path)
+        return np.asarray(arr, dtype=np.float32).reshape(-1)
+
+    def load_calib_reference(
+        self,
+        path: Optional[str] = None,
+        *,
+        source_kind: str = "auto",
+        method: str = "median",
+    ) -> bool:
+        """Загрузить калибровку из .npy (огибающая) или raw capture файла."""
+        if path is not None:
+            self.set_calib_envelope_path(path)
+        p = self.calib_envelope_path
+        if not p or not os.path.isfile(p):
+            self.last_reference_error = f"Файл калибровки не найден: {p}"
+            return False
+
+        fmt = self._detect_source_kind(p, source_kind)
+        if fmt is None:
+            self.last_reference_error = f"Неизвестный формат файла калибровки: {p}"
+            return False
+
+        try:
+            if fmt == "npy":
+                env = self._load_npy_vector(p)
+                self.calib_file_envelope = env
+                self.calib_external_source_type = "npy"
+                self.calib_reference_summary = _summary_for_npy(p)
+                self.last_reference_error = ""
+                return True
+
+            sweeps = load_capture_sweeps(p, fancy=self.capture_fancy, logscale=self.capture_logscale)
+            vec, summary = aggregate_capture_reference(
+                sweeps,
+                channel=0,
+                method=method or self.reference_aggregation_method,
+                path=p,
+            )
+            _lower, upper = build_calib_envelopes(vec)
+            self.calib_file_envelope = np.asarray(upper, dtype=np.float32)
+            self.calib_external_source_type = "capture"
+            self.calib_reference_summary = summary
+            self.last_reference_error = ""
+            return True
+        except Exception as exc:
+            self.last_reference_error = f"Ошибка загрузки калибровки: {exc}"
+            return False
+
+    def load_background_reference(
+        self,
+        path: Optional[str] = None,
+        *,
+        source_kind: str = "auto",
+        method: str = "median",
+    ) -> bool:
+        """Загрузить фон из .npy (готовый домен) или raw capture файла."""
+        if path is not None:
+            self.set_background_path(path)
+        p = self.background_path
+        if not p or not os.path.isfile(p):
+            self.last_reference_error = f"Файл фона не найден: {p}"
+            return False
+
+        fmt = self._detect_source_kind(p, source_kind)
+        if fmt is None:
+            self.last_reference_error = f"Неизвестный формат файла фона: {p}"
+            return False
+
+        try:
+            if fmt == "npy":
+                bg = self._load_npy_vector(p)
+                self.background = bg
+                self.background_raw_capture = None
+                self.background_source_type = "npy_processed"
+                self.background_reference_summary = _summary_for_npy(p)
+                self.last_reference_error = ""
+                return True
+
+            sweeps = load_capture_sweeps(p, fancy=self.capture_fancy, logscale=self.capture_logscale)
+            vec, summary = aggregate_capture_reference(
+                sweeps,
+                channel=0,
+                method=method or self.reference_aggregation_method,
+                path=p,
+            )
+            self.background_raw_capture = np.asarray(vec, dtype=np.float32)
+            # Для UI/preview текущий background отражает текущий домен (пока raw по умолчанию).
+            self.background = self.background_raw_capture
+            self.background_source_type = "capture_raw"
+            self.background_reference_summary = summary
+            self.last_reference_error = ""
+            return True
+        except Exception as exc:
+            self.last_reference_error = f"Ошибка загрузки фона: {exc}"
+            return False
+
+    # Совместимые обертки для старого API (строго .npy)
+    def load_calib_envelope(self, path: Optional[str] = None) -> bool:
+        target = path if path is not None else self.calib_envelope_path
+        return self.load_calib_reference(target, source_kind="npy")
+
+    def load_background(self, path: Optional[str] = None) -> bool:
+        target = path if path is not None else self.background_path
+        return self.load_background_reference(target, source_kind="npy")
+
+    # ---- Нормировка / фон ----
+    def _normalize_against_active_reference(self, raw: np.ndarray) -> Tuple[Optional[np.ndarray], bool, str]:
+        if not self.calib_enabled:
+            return None, False, "off"
+
+        if self.calib_mode == "file":
+            if self.calib_file_envelope is None:
+                return None, False, "off"
+            src = "capture" if self.calib_external_source_type == "capture" else "npy"
+            return normalize_by_envelope(raw, self.calib_file_envelope), True, src
+
+        if self.last_calib_sweep is None:
+            return None, False, "off"
+        return normalize_by_calib(raw, self.last_calib_sweep, self.norm_type), True, "live"
+
+    def _transform_raw_background_for_current_domain(self, calib_applied: bool) -> Optional[np.ndarray]:
+        if self.background_raw_capture is None:
+            return None
+        if not calib_applied:
+            return self.background_raw_capture
+
+        # Порядок pipeline фиксирован: raw -> calibration -> background -> IFFT.
+        # Поэтому raw-фон из capture нужно привести в тот же домен, что и текущий sweep_for_ring.
+        if self.calib_mode == "file" and self.calib_file_envelope is not None:
+            return normalize_by_envelope(self.background_raw_capture, self.calib_file_envelope)
+        if self.calib_mode == "live" and self.last_calib_sweep is not None:
+            return normalize_by_calib(self.background_raw_capture, self.last_calib_sweep, self.norm_type)
+        return None
+
+    def _effective_background(self, calib_applied: bool) -> Tuple[Optional[np.ndarray], str]:
+        if self.background_source_type == "capture_raw":
+            bg = self._transform_raw_background_for_current_domain(calib_applied)
+            if bg is None:
+                return None, "capture(raw->calib:missing-calib)"
+            self.background = np.asarray(bg, dtype=np.float32)
+            return self.background, ("capture(raw->calib)" if calib_applied else "capture(raw)")
+        if self.background_source_type == "npy_processed" and self.background is not None:
+            return self.background, "npy"
+        if self.background is not None:
+            return self.background, "unknown"
+        return None, "off"
+
+    def _subtract_background(self, sweep: np.ndarray, calib_applied: bool) -> Tuple[np.ndarray, bool, str]:
+        if not self.background_enabled:
+            return sweep, False, "off"
+        bg, bg_src = self._effective_background(calib_applied)
+        if bg is None:
+            return sweep, False, f"{bg_src}:missing"
+        out = np.asarray(sweep, dtype=np.float32).copy()
+        w = min(out.size, bg.size)
+        if w > 0:
+            out[:w] -= bg[:w]
+        return out, True, bg_src
+
+    def process(self, sweep: np.ndarray, channel: int, update_references: bool = True) -> SweepProcessingResult:
+        """Применить к свипу калибровку/фон и вернуть явные этапы обработки."""
+        raw = np.asarray(sweep, dtype=np.float32)
+        ch = int(channel)
+
+        if ch == 0:
+            if update_references:
+                self.last_calib_sweep = raw
+                if self.auto_save_live_calib_envelope:
+                    self.save_calib_envelope()
+
+            # ch0 всегда остаётся live-калибровочной ссылкой (raw), но при file-калибровке
+            # можем применять её и к ch0 для отображения/обработки независимо от канала.
+            calib_applied = False
+            calib_source = "off"
+            normalized: Optional[np.ndarray] = None
+            if self.calib_enabled and self.calib_mode == "file":
+                normalized, calib_applied, calib_source = self._normalize_against_active_reference(raw)
+
+            base = normalized if normalized is not None else raw
+            processed, bg_applied, bg_source = self._subtract_background(base, calib_applied=calib_applied)
+
+            stages = ["parsed_sweep", "raw_sweep", "ch0_live_calibration_reference"]
+            stages.append(f"calibration_{calib_source}" if calib_applied else "calibration_off")
+            stages.append(f"background_{bg_source}" if bg_applied else "background_off")
+            stages.extend(["ring_buffer", "ifft_db"])
+
+            return SweepProcessingResult(
+                processed_sweep=processed,
+                normalized_sweep=normalized,
+                calibration_applied=calib_applied,
+                background_applied=bg_applied,
+                calibration_source=calib_source if calib_applied else "off",
+                background_source=bg_source if bg_applied else "off",
+                is_calibration_reference=True,
+                stage_trace=tuple(stages),
+            )
+
+        normalized, calib_applied, calib_source = self._normalize_against_active_reference(raw)
+        base = normalized if normalized is not None else raw
+        processed, bg_applied, bg_source = self._subtract_background(base, calib_applied)
+
+        stages = ["parsed_sweep", "raw_sweep"]
+        stages.append(f"calibration_{calib_source}" if calib_applied else "calibration_off")
+        stages.append(f"background_{bg_source}" if bg_applied else "background_off")
+        stages.extend(["ring_buffer", "ifft_db"])
+
+        return SweepProcessingResult(
+            processed_sweep=processed,
+            normalized_sweep=normalized,
+            calibration_applied=calib_applied,
+            background_applied=bg_applied,
+            calibration_source=calib_source if calib_applied else "off",
+            background_source=bg_source if bg_applied else "off",
+            is_calibration_reference=False,
+            stage_trace=tuple(stages),
+        )

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/app_state.py

@@ -0,0 +1,355 @@
+"""Состояние приложения: текущие свипы и настройки калибровки/нормировки."""
+
+from queue import Empty, Queue
+from typing import Any, Mapping, Optional
+
+import numpy as np
+
+from rfg_adc_plotter.processing.pipeline import (
+    DEFAULT_BACKGROUND_PATH,
+    DEFAULT_CALIB_ENVELOPE_PATH,
+    SweepPreprocessor,
+)
+from rfg_adc_plotter.state.ring_buffer import RingBuffer
+from rfg_adc_plotter.types import SweepInfo, SweepPacket
+
+CALIB_ENVELOPE_PATH = DEFAULT_CALIB_ENVELOPE_PATH
+BACKGROUND_PATH = DEFAULT_BACKGROUND_PATH
+
+
+def format_status(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"
+        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() if k != "pre_exp_sweep"]
+    return "  ".join(parts)
+
+
+class AppState:
+    """Весь изменяемый GUI-state: текущие данные + pipeline предобработки."""
+
+    def __init__(self, norm_type: str = "projector"):
+        self.current_sweep_pre_exp: Optional[np.ndarray] = None
+        self.current_sweep_post_exp: Optional[np.ndarray] = None
+        self.current_sweep_processed: Optional[np.ndarray] = None
+        self.current_sweep_raw: Optional[np.ndarray] = None
+        self.current_sweep_norm: Optional[np.ndarray] = None
+        self.current_info: Optional[SweepInfo] = None
+        self.norm_type: str = str(norm_type).strip().lower()
+        self.preprocessor = SweepPreprocessor(norm_type=self.norm_type)
+        self._last_sweep_for_ring: Optional[np.ndarray] = None
+        self._last_stage_trace: tuple[str, ...] = tuple()
+
+    def configure_capture_import(self, *, fancy: Optional[bool] = None, logscale: Optional[bool] = None):
+        self.preprocessor.set_capture_parse_options(fancy=fancy, logscale=logscale)
+
+    # ---- Свойства pipeline (для совместимости с GUI) ----
+    @property
+    def calib_enabled(self) -> bool:
+        return self.preprocessor.calib_enabled
+
+    @property
+    def calib_mode(self) -> str:
+        return self.preprocessor.calib_mode
+
+    @property
+    def calib_file_envelope(self) -> Optional[np.ndarray]:
+        return self.preprocessor.calib_file_envelope
+
+    @property
+    def last_calib_sweep(self) -> Optional[np.ndarray]:
+        return self.preprocessor.last_calib_sweep
+
+    @property
+    def background(self) -> Optional[np.ndarray]:
+        return self.preprocessor.background
+
+    @property
+    def background_enabled(self) -> bool:
+        return self.preprocessor.background_enabled
+
+    @property
+    def calib_source_type(self) -> str:
+        return self.preprocessor.calib_external_source_type
+
+    @property
+    def background_source_type(self) -> str:
+        return self.preprocessor.background_source_type
+
+    @property
+    def calib_reference_summary(self):
+        return self.preprocessor.calib_reference_summary
+
+    @property
+    def background_reference_summary(self):
+        return self.preprocessor.background_reference_summary
+
+    @property
+    def last_reference_error(self) -> str:
+        return self.preprocessor.last_reference_error
+
+    @property
+    def calib_envelope_path(self) -> str:
+        return self.preprocessor.calib_envelope_path
+
+    @property
+    def background_path(self) -> str:
+        return self.preprocessor.background_path
+
+    # ---- Управление файлами калибровки/фона ----
+    def set_calib_envelope_path(self, path: str):
+        self.preprocessor.set_calib_envelope_path(path)
+        self._refresh_current_processed()
+
+    def set_background_path(self, path: str):
+        self.preprocessor.set_background_path(path)
+        self._refresh_current_processed()
+
+    def has_calib_envelope_file(self) -> bool:
+        return self.preprocessor.has_calib_envelope_file()
+
+    def has_background_file(self) -> bool:
+        return self.preprocessor.has_background_file()
+
+    def save_calib_envelope(self, path: Optional[str] = None) -> bool:
+        return self.preprocessor.save_calib_envelope(path)
+
+    def load_calib_reference(self, path: Optional[str] = None) -> bool:
+        ok = self.preprocessor.load_calib_reference(path)
+        if ok:
+            self._refresh_current_processed()
+        return ok
+
+    def load_calib_envelope(self, path: Optional[str] = None) -> bool:
+        return self.load_calib_reference(path)
+
+    def set_calib_mode(self, mode: str):
+        self.preprocessor.set_calib_mode(mode)
+        self._refresh_current_processed()
+
+    def save_background(self, path: Optional[str] = None) -> bool:
+        return self.preprocessor.save_background(self._last_sweep_for_ring, path)
+
+    def load_background_reference(self, path: Optional[str] = None) -> bool:
+        ok = self.preprocessor.load_background_reference(path)
+        if ok:
+            self._refresh_current_processed()
+        return ok
+
+    def load_background(self, path: Optional[str] = None) -> bool:
+        return self.load_background_reference(path)
+
+    def set_background_enabled(self, enabled: bool):
+        self.preprocessor.set_background_enabled(enabled)
+        self._refresh_current_processed()
+
+    def set_calib_enabled(self, enabled: bool):
+        self.preprocessor.set_calib_enabled(enabled)
+        self._refresh_current_processed()
+
+    # ---- Вспомогательные методы для UI ----
+    def _current_channel(self) -> Optional[int]:
+        if not isinstance(self.current_info, dict):
+            return None
+        try:
+            return int(self.current_info.get("ch", 0))
+        except Exception:
+            return 0
+
+    def _apply_result_to_current(self, result) -> None:
+        self._last_stage_trace = tuple(result.stage_trace)
+        if result.is_calibration_reference:
+            self.current_sweep_norm = None
+        elif result.calibration_applied or result.background_applied:
+            self.current_sweep_norm = result.processed_sweep
+        else:
+            self.current_sweep_norm = None
+        self.current_sweep_processed = result.processed_sweep
+        self._last_sweep_for_ring = result.processed_sweep
+
+    def _refresh_current_processed(self):
+        if self.current_sweep_raw is None:
+            self.current_sweep_norm = None
+            self.current_sweep_processed = None
+            self._last_stage_trace = tuple()
+            return
+        ch = self._current_channel() or 0
+        result = self.preprocessor.process(self.current_sweep_raw, ch, update_references=False)
+        self._apply_result_to_current(result)
+
+    def format_pipeline_status(self) -> str:
+        """Краткое описание pipeline для UI: от распарсенного свипа до IFFT."""
+        ch = self._current_channel()
+        if ch is None:
+            ch_txt = "?"
+        else:
+            ch_txt = str(ch)
+
+        reader_stage = "log-exp" if self.current_sweep_pre_exp is not None else "linear"
+
+        if ch == 0:
+            file_calib_applies = (
+                self.calib_enabled
+                and self.calib_mode == "file"
+                and self.calib_file_envelope is not None
+            )
+            if self.calib_enabled and self.calib_mode == "file":
+                calib_stage = self.format_calib_source_status()
+            else:
+                calib_stage = "calib[off]"
+
+            if not self.background_enabled:
+                bg_stage = "bg[off]"
+            elif self.background_source_type == "capture_raw":
+                if self.background is None:
+                    bg_stage = (
+                        "bg[capture(raw->calib):missing]"
+                        if file_calib_applies
+                        else "bg[capture(raw):missing]"
+                    )
+                else:
+                    bg_stage = "bg[capture(raw->calib)]" if file_calib_applies else "bg[capture(raw)]"
+            elif self.background_source_type == "npy_processed":
+                bg_stage = "bg[npy]" if self.background is not None else "bg[npy:missing]"
+            else:
+                bg_stage = "bg[sub]" if self.background is not None else "bg[missing]"
+            return (
+                f"pipeline ch{ch_txt}: parsed -> {reader_stage} -> raw -> "
+                f"live-calib-ref -> {calib_stage} -> {bg_stage} -> ring -> IFFT(abs, depth_m)"
+            )
+
+        calib_stage = self.format_calib_source_status()
+        bg_stage = self.format_background_source_status()
+
+        return (
+            f"pipeline ch{ch_txt}: parsed -> {reader_stage} -> raw -> "
+            f"{calib_stage} -> {bg_stage} -> ring -> IFFT(abs, depth_m)"
+        )
+
+    def _format_summary(self, summary) -> str:
+        if summary is None:
+            return ""
+        parts: list[str] = []
+        if getattr(summary, "sweeps_valid", 0) or getattr(summary, "sweeps_total", 0):
+            parts.append(f"valid:{summary.sweeps_valid}/{summary.sweeps_total}")
+        if getattr(summary, "dominant_width", None) is not None:
+            parts.append(f"w:{summary.dominant_width}")
+        chs = getattr(summary, "channels_seen", tuple())
+        if chs:
+            parts.append("chs:" + ",".join(str(v) for v in chs))
+        warns = getattr(summary, "warnings", tuple())
+        if warns:
+            parts.append(f"warn:{warns[0]}")
+        return " ".join(parts)
+
+    def format_calib_source_status(self) -> str:
+        if not self.calib_enabled:
+            return "calib[off]"
+        if self.calib_mode == "live":
+            return "calib[live]" if self.last_calib_sweep is not None else "calib[live:no-ref]"
+        if self.calib_file_envelope is None:
+            return "calib[file:missing]"
+        if self.calib_source_type == "capture":
+            return "calib[capture]"
+        if self.calib_source_type == "npy":
+            return "calib[npy]"
+        return "calib[file]"
+
+    def format_background_source_status(self) -> str:
+        if not self.background_enabled:
+            return "bg[off]"
+        src = self.background_source_type
+        if src == "capture_raw":
+            if self.calib_enabled:
+                can_map = (
+                    (self.calib_mode == "file" and self.calib_file_envelope is not None)
+                    or (self.calib_mode == "live" and self.last_calib_sweep is not None)
+                )
+                if not can_map:
+                    return "bg[capture(raw->calib):missing]"
+            if self.background is None:
+                return "bg[capture(raw->calib):missing]"
+            return "bg[capture(raw->calib)]" if self.calib_enabled else "bg[capture(raw)]"
+        if src == "npy_processed":
+            return "bg[npy]" if self.background is not None else "bg[npy:missing]"
+        if self.background is not None:
+            return "bg[sub]"
+        return "bg[missing]"
+
+    def format_reference_status(self) -> str:
+        parts: list[str] = []
+        calib_s = self._format_summary(self.calib_reference_summary)
+        if calib_s:
+            parts.append(f"calib[{calib_s}]")
+        bg_s = self._format_summary(self.background_reference_summary)
+        if bg_s:
+            parts.append(f"bg[{bg_s}]")
+        if self.last_reference_error:
+            parts.append(f"err:{self.last_reference_error}")
+        return " | ".join(parts)
+
+    def format_stage_trace(self) -> str:
+        if not self._last_stage_trace:
+            return ""
+        return " -> ".join(self._last_stage_trace)
+
+    def drain_queue(self, q: "Queue[SweepPacket]", ring: RingBuffer) -> int:
+        """Вытащить все ожидающие свипы из очереди, обновить state и ring.
+
+        Возвращает количество обработанных свипов.
+        """
+        drained = 0
+        while True:
+            try:
+                s, info = q.get_nowait()
+            except Empty:
+                break
+            drained += 1
+
+            self.current_sweep_raw = s
+            self.current_sweep_post_exp = s
+            self.current_info = info
+            pre_exp = info.get("pre_exp_sweep") if isinstance(info, dict) else None
+            self.current_sweep_pre_exp = pre_exp if isinstance(pre_exp, np.ndarray) else None
+
+            try:
+                ch = int(info.get("ch", 0)) if isinstance(info, dict) else 0
+            except Exception:
+                ch = 0
+
+            result = self.preprocessor.process(s, ch, update_references=True)
+            self._apply_result_to_current(result)
+
+            ring.ensure_init(s.size)
+            ring.push(result.processed_sweep)
+        return drained
+
+    def format_channel_label(self) -> str:
+        """Строка с номерами каналов для подписи на графике."""
+        if self.current_info is None:
+            return ""
+        info = self.current_info
+        chs = info.get("chs") if isinstance(info, dict) else None
+        if chs is None:
+            chs = info.get("ch") if isinstance(info, dict) else None
+        if chs is None:
+            return ""
+        try:
+            if isinstance(chs, (list, tuple, set)):
+                ch_list = sorted(int(v) for v in chs)
+                return "chs " + ", ".join(str(v) for v in ch_list)
+            return f"chs {int(chs)}"
+        except Exception:
+            return f"chs {chs}"

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

@@ -1,193 +1,314 @@
-"""Ring buffers for raw sweeps and FFT waterfall rows."""
-
-from __future__ import annotations
+"""Кольцевой буфер свипов и FFT-строк для водопадного отображения."""
 
 import time
-from typing import Optional
+from typing import Optional, Tuple
 
 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
+from rfg_adc_plotter.constants import (
+    FREQ_MAX_GHZ,
+    FREQ_MIN_GHZ,
+    WF_WIDTH,
+)
+from rfg_adc_plotter.processing.fourier import (
+    build_frequency_axis_hz,
+    compute_ifft_profile_from_sweep,
+    perform_ifft_depth_response,
+    reconstruct_complex_spectrum_from_real_trace,
+)
 
 
 class RingBuffer:
-    """Store raw sweeps, FFT rows, and matching time markers."""
+    """Хранит последние N свипов и соответствующие FFT-строки.
+
+    Все мутабельные поля водопада инкапсулированы здесь,
+    что устраняет необходимость nonlocal в GUI-коде.
+    """
 
     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.max_sweeps = max_sweeps
+        # Размер IFFT-профиля теперь динамический и определяется по первому успешному свипу.
+        self.fft_bins = 0
+        self.fft_complex_mode: str = "arccos"
+
+        # Инициализируются при первом свипе (ensure_init)
+        self.ring: Optional[np.ndarray] = None        # (max_sweeps, WF_WIDTH)
+        self.ring_fft: Optional[np.ndarray] = None    # (max_sweeps, fft_bins)
+        self.ring_time: Optional[np.ndarray] = None   # (max_sweeps,)
+        self.head: int = 0
+        self.width: Optional[int] = 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.fft_depth_axis_m: Optional[np.ndarray] = None  # ось глубины IFFT в метрах
         self.y_min_fft: Optional[float] = None
         self.y_max_fft: Optional[float] = None
+        # FFT последнего свипа (для отображения без повторного вычисления)
+        self.last_fft_vals: Optional[np.ndarray] = None
+        # FFT-профили по третям входного частотного диапазона (для line-plot).
+        self.last_fft_third_axes_m: tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]] = (
+            None,
+            None,
+            None,
+        )
+        self.last_fft_third_vals: tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]] = (
+            None,
+            None,
+            None,
+        )
 
     @property
     def is_ready(self) -> bool:
-        return self.ring is not None and self.ring_fft is not None
+        return self.ring is not None
 
     @property
-    def fft_symmetric(self) -> bool:
-        return self.fft_mode == "symmetric"
+    def fft_time_axis(self) -> Optional[np.ndarray]:
+        """Legacy alias: старое имя поля (раньше было время в нс, теперь глубина в м)."""
+        return self.fft_depth_axis_m
 
-    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.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
+    def set_fft_complex_mode(self, mode: str) -> bool:
+        """Выбрать режим реконструкции комплексного спектра для IFFT.
 
-    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.head = 0
-            changed = True
-        elif target_width != self.width:
-            new_ring = np.full((self.max_sweeps, target_width), np.nan, dtype=np.float32)
-            take = min(self.width, target_width)
-            new_ring[:, :take] = self.ring[:, :take]
-            self.ring = new_ring
-            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 not in {"direct", "symmetric", "positive_only"}:
-            raise ValueError(f"Unsupported FFT mode: {mode!r}")
-        if normalized_mode == self.fft_mode:
+        Возвращает True, если режим изменился (и FFT-буфер был сброшен).
+        """
+        m = str(mode).strip().lower()
+        if m not in ("arccos", "diff"):
+            raise ValueError(f"Unsupported IFFT complex mode: {mode!r}")
+        if m == self.fft_complex_mode:
             return False
-
-        self.fft_mode = normalized_mode
+        self.fft_complex_mode = m
+        # Сбрасываем только FFT-зависимые структуры. Сырые ряды сохраняем.
+        self.ring_fft = None
+        self.fft_depth_axis_m = None
+        self.fft_bins = 0
+        self.last_fft_vals = None
+        self.last_fft_third_axes_m = (None, None, None)
+        self.last_fft_third_vals = (None, None, None)
         self.y_min_fft = None
         self.y_max_fft = None
-
-        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]):
-            sweep_row = self.ring[row_idx]
-            if not np.any(np.isfinite(sweep_row)):
-                continue
-            fft_mag = compute_fft_mag_row(
-                sweep_row,
-                self.last_freqs,
-                self.fft_bins,
-                mode=self.fft_mode,
-            )
-            self.ring_fft[row_idx, :] = fft_mag
-
-        if self.last_freqs is not None:
-            self.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.last_fft_mag = np.asarray(last_fft, dtype=np.float32).copy()
-            self.last_fft_db = fft_mag_to_db(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 ensure_init(self, sweep_width: int):
+        """Инициализировать буферы при первом свипе. Повторные вызовы — no-op (кроме x_shared)."""
+        if self.ring is None:
+            self.width = WF_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.head = 0
+        # Обновляем x_shared если пришёл свип большего размера
+        if self.x_shared is None or sweep_width > self.x_shared.size:
+            self.x_shared = np.linspace(FREQ_MIN_GHZ, FREQ_MAX_GHZ, sweep_width, dtype=np.float32)
 
-    def push(self, sweep: np.ndarray, freqs: Optional[np.ndarray] = None) -> None:
-        """Push a processed sweep and refresh raw/FFT buffers."""
-        if sweep is None or sweep.size == 0:
+    def push(self, s: np.ndarray):
+        """Добавить строку свипа в кольцевой буфер, вычислить FFT-строку."""
+        if s is None or s.size == 0 or self.ring is None:
             return
-        self.ensure_init(int(sweep.size))
-        if self.ring is None or self.ring_time is None or self.ring_fft 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)
+        w = self.ring.shape[1]
+        row = np.full((w,), np.nan, dtype=np.float32)
+        take = min(w, s.size)
+        row[:take] = s[: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()
+        self.head = (self.head + 1) % self.ring.shape[0]
 
-        fft_mag = compute_fft_mag_row(sweep, freqs, self.fft_bins, mode=self.fft_mode)
-        self.ring_fft[self.head, :] = fft_mag
-        self.last_fft_mag = np.asarray(fft_mag, dtype=np.float32).copy()
-        self.last_fft_db = fft_mag_to_db(fft_mag)
+        self._push_fft(s)
 
-        if self.last_fft_db.size > 0:
-            fr_min = float(np.nanmin(self.last_fft_db))
-            fr_max = float(np.nanmax(self.last_fft_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)
+    def _push_fft(self, s: np.ndarray):
+        empty_thirds = (
+            np.zeros((0,), dtype=np.float32),
+            np.zeros((0,), dtype=np.float32),
+            np.zeros((0,), dtype=np.float32),
+        )
+        depth_axis_m, fft_row = compute_ifft_profile_from_sweep(
+            s,
+            complex_mode=self.fft_complex_mode,
+        )
+        fft_row = np.asarray(fft_row, dtype=np.float32).ravel()
+        depth_axis_m = np.asarray(depth_axis_m, dtype=np.float32).ravel()
 
-        self.distance_axis = compute_distance_axis(freqs, self.fft_bins, mode=self.fft_mode)
-        self.head = (self.head + 1) % self.max_sweeps
+        n = min(int(fft_row.size), int(depth_axis_m.size))
+        if n <= 0:
+            self.last_fft_third_axes_m = empty_thirds
+            self.last_fft_third_vals = empty_thirds
+            return
+        if n != fft_row.size:
+            fft_row = fft_row[:n]
+        if n != depth_axis_m.size:
+            depth_axis_m = depth_axis_m[:n]
 
-    def get_display_raw(self) -> np.ndarray:
+        # Для отображения храним только первую половину IFFT-профиля:
+        # вторая половина для текущей схемы симметрична и визуально избыточна.
+        n_keep = max(1, (n + 1) // 2)
+        fft_row = fft_row[:n_keep]
+        depth_axis_m = depth_axis_m[:n_keep]
+        n = n_keep
+
+        needs_reset = (
+            self.ring_fft is None
+            or self.fft_depth_axis_m is None
+            or self.fft_bins != n
+            or self.ring_fft.shape != (self.max_sweeps, n)
+            or self.fft_depth_axis_m.size != n
+        )
+        if (not needs_reset) and n > 0:
+            prev_axis = self.fft_depth_axis_m
+            assert prev_axis is not None
+            if prev_axis.size != n:
+                needs_reset = True
+            else:
+                # Если ось изменилась (например, изменилась длина/частотная сетка), сбрасываем FFT-водопад.
+                if not np.allclose(prev_axis[[0, -1]], depth_axis_m[[0, -1]], rtol=1e-6, atol=1e-9):
+                    needs_reset = True
+
+        if needs_reset:
+            self.fft_bins = n
+            self.ring_fft = np.full((self.max_sweeps, n), np.nan, dtype=np.float32)
+            self.fft_depth_axis_m = depth_axis_m.astype(np.float32, copy=True)
+            self.y_min_fft = None
+            self.y_max_fft = None
+
+        assert self.ring_fft is not None
+        prev_head = (self.head - 1) % self.ring_fft.shape[0]
+        self.ring_fft[prev_head, :] = fft_row
+        self.last_fft_vals = fft_row
+        self.last_fft_third_axes_m, self.last_fft_third_vals = self._compute_fft_thirds(s)
+
+        fr_min = np.nanmin(fft_row)
+        fr_max = float(np.nanpercentile(fft_row, 90))
+        if self.y_min_fft is None or (not np.isnan(fr_min) and fr_min < self.y_min_fft):
+            self.y_min_fft = float(fr_min)
+        if self.y_max_fft is None or (not np.isnan(fr_max) and fr_max > self.y_max_fft):
+            self.y_max_fft = float(fr_max)
+
+    def _compute_fft_thirds(
+        self, s: np.ndarray
+    ) -> tuple[tuple[np.ndarray, np.ndarray, np.ndarray], tuple[np.ndarray, np.ndarray, np.ndarray]]:
+        sweep = np.asarray(s, dtype=np.float64).ravel()
+        total = int(sweep.size)
+
+        def _empty() -> np.ndarray:
+            return np.zeros((0,), dtype=np.float32)
+
+        if total <= 0:
+            return (_empty(), _empty(), _empty()), (_empty(), _empty(), _empty())
+
+        freq_hz = build_frequency_axis_hz(total)
+        edges = np.linspace(0, total, 4, dtype=np.int64)
+
+        axes: list[np.ndarray] = []
+        vals: list[np.ndarray] = []
+
+        for idx in range(3):
+            i0 = int(edges[idx])
+            i1 = int(edges[idx + 1])
+            if i1 - i0 < 2:
+                axes.append(_empty())
+                vals.append(_empty())
+                continue
+
+            seg = sweep[i0:i1]
+            seg_freq = freq_hz[i0:i1]
+            seg_complex = reconstruct_complex_spectrum_from_real_trace(
+                seg,
+                complex_mode=self.fft_complex_mode,
+            )
+            depth_m, seg_fft = perform_ifft_depth_response(seg_complex, seg_freq, axis="abs")
+
+            depth_m = np.asarray(depth_m, dtype=np.float32).ravel()
+            seg_fft = np.asarray(seg_fft, dtype=np.float32).ravel()
+            n = min(int(depth_m.size), int(seg_fft.size))
+            if n <= 0:
+                axes.append(_empty())
+                vals.append(_empty())
+                continue
+
+            depth_m = depth_m[:n]
+            seg_fft = seg_fft[:n]
+
+            n_keep = max(1, (n + 1) // 2)
+            axes.append(depth_m[:n_keep])
+            vals.append(seg_fft[:n_keep])
+
+        return (
+            axes[0],
+            axes[1],
+            axes[2],
+        ), (
+            vals[0],
+            vals[1],
+            vals[2],
+        )
+
+    def get_display_ring(self) -> np.ndarray:
+        """Кольцо в порядке от старого к новому, ось времени по X. Форма: (width, time)."""
         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
+        return base.T  # (width, time)
 
-    def get_display_fft_linear(self) -> np.ndarray:
+    def get_display_ring_fft(self) -> np.ndarray:
+        """FFT-кольцо в порядке от старого к новому. Форма: (bins, time)."""
         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()
+        return base.T  # (bins, time)
 
     def get_display_times(self) -> Optional[np.ndarray]:
+        """Временные метки строк в порядке от старого к новому."""
         if self.ring_time is None:
             return None
         return self.ring_time if self.head == 0 else np.roll(self.ring_time, -self.head)
+
+    def subtract_recent_mean_fft(
+        self, disp_fft: np.ndarray, spec_mean_sec: float
+    ) -> np.ndarray:
+        """Вычесть среднее по каждой частоте за последние spec_mean_sec секунд."""
+        if spec_mean_sec <= 0.0:
+            return disp_fft
+        disp_times = self.get_display_times()
+        if disp_times is None:
+            return disp_fft
+        now_t = time.time()
+        mask = np.isfinite(disp_times) & (disp_times >= (now_t - spec_mean_sec))
+        if not np.any(mask):
+            return disp_fft
+        try:
+            mean_spec = np.nanmean(disp_fft[:, mask], axis=1)
+        except Exception:
+            return disp_fft
+        mean_spec = np.nan_to_num(mean_spec, nan=0.0)
+        return disp_fft - mean_spec[:, None]
+
+    def compute_fft_levels(
+        self, disp_fft: np.ndarray, spec_clip: Optional[Tuple[float, float]]
+    ) -> Optional[Tuple[float, float]]:
+        """Вычислить (vmin, vmax) для отображения водопада спектров."""
+        # 1. По среднему спектру за видимое время
+        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:
+                return (vmin_v, vmax_v)
+        except Exception:
+            pass
+
+        # 2. Процентильная обрезка
+        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]))
+                if np.isfinite(vmin_v) and np.isfinite(vmax_v) and vmin_v != vmax_v:
+                    return (vmin_v, vmax_v)
+            except Exception:
+                pass
+
+        # 3. Глобальные накопленные мин/макс
+        if (
+            self.y_min_fft is not None
+            and self.y_max_fft is not None
+            and np.isfinite(self.y_min_fft)
+            and np.isfinite(self.y_max_fft)
+            and self.y_min_fft != self.y_max_fft
+        ):
+            return (self.y_min_fft, self.y_max_fft)
+
+        return None

rfg_adc_plotter/state/runtime_state.py

@@ -1,46 +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_aux_curves: SweepAuxCurves = None
-    current_freqs: Optional[np.ndarray] = None
-    current_distances: Optional[np.ndarray] = None
-    current_sweep_raw: 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
-    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

@@ -1,31 +1,7 @@
-"""Shared runtime and parser types."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any, Dict, Optional, Tuple, TypeAlias, Union
+from typing import Any, Dict, Tuple, Union
 
 import numpy as np
 
-
 Number = Union[int, float]
 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
-
-
-@dataclass(frozen=True)
-class PointEvent:
-    ch: int
-    x: int
-    y: float
-    aux: Optional[Tuple[float, float]] = None
-
-
-ParserEvent: TypeAlias = Union[StartEvent, PointEvent]
+SweepPacket = Tuple[np.ndarray, SweepInfo]

run_dataplotter

@@ -0,0 +1,2 @@
+#!/usr/bin/bash
+python3 -m rfg_adc_plotter.main --bin --backend mpl $@

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_capture_reference_loader.py

@@ -0,0 +1,84 @@
+from pathlib import Path
+
+import numpy as np
+
+from rfg_adc_plotter.io.capture_reference_loader import (
+    aggregate_capture_reference,
+    detect_reference_file_format,
+    load_capture_sweeps,
+)
+from rfg_adc_plotter.processing.pipeline import SweepPreprocessor
+
+
+ROOT = Path(__file__).resolve().parents[1]
+SAMPLE_BG = ROOT / "sample_data" / "empty"
+SAMPLE_CALIB = ROOT / "sample_data" / "no_antennas_35dB_attenuators"
+
+
+def test_detect_reference_file_format_for_sample_capture():
+    assert detect_reference_file_format(str(SAMPLE_BG)) == "bin_capture"
+    assert detect_reference_file_format(str(SAMPLE_CALIB)) == "bin_capture"
+
+
+def test_load_capture_sweeps_parses_binary_capture():
+    sweeps = load_capture_sweeps(str(SAMPLE_BG), fancy=False, logscale=False)
+    assert len(sweeps) > 100
+    sweep0, info0 = sweeps[0]
+    assert isinstance(sweep0, np.ndarray)
+    assert "ch" in info0
+    channels = set()
+    for _s, info in sweeps:
+        chs = info.get("chs", [info.get("ch", 0)])
+        channels.update(int(v) for v in chs)
+    assert channels == {0}
+
+
+def test_aggregate_capture_reference_filters_incomplete_sweeps():
+    sweeps = load_capture_sweeps(str(SAMPLE_BG), fancy=False, logscale=False)
+    vector, summary = aggregate_capture_reference(sweeps, channel=0, method="median", path=str(SAMPLE_BG))
+    assert isinstance(vector, np.ndarray)
+    assert vector.dtype == np.float32
+    assert summary.sweeps_total == len(sweeps)
+    assert summary.sweeps_valid > 0
+    assert summary.sweeps_valid < summary.sweeps_total
+    assert summary.dominant_width in (759, 758)  # sample_data starts at x=1..758 => width=759
+
+
+def test_preprocessor_can_load_capture_calib_and_background_and_apply():
+    p = SweepPreprocessor(norm_type="projector", auto_save_live_calib_envelope=False)
+    p.set_capture_parse_options(fancy=False, logscale=False)
+
+    assert p.load_calib_reference(str(SAMPLE_CALIB))
+    p.set_calib_mode("file")
+    p.set_calib_enabled(True)
+    assert p.calib_file_envelope is not None
+    assert p.calib_external_source_type == "capture"
+
+    assert p.load_background_reference(str(SAMPLE_BG))
+    p.set_background_enabled(True)
+    assert p.background_source_type == "capture_raw"
+
+    n = min(758, int(p.calib_file_envelope.size))
+    sweep = np.linspace(-100.0, 100.0, n, dtype=np.float32)
+    res = p.process(sweep, channel=1, update_references=False)
+    assert res.calibration_applied is True
+    assert res.background_applied is True
+    assert res.calibration_source == "capture"
+    assert "background_capture(raw->calib)" in res.stage_trace
+
+
+def test_preprocessor_applies_background_for_ch0_reference_too():
+    p = SweepPreprocessor(norm_type="projector", auto_save_live_calib_envelope=False)
+    p.set_capture_parse_options(fancy=False, logscale=False)
+    assert p.load_background_reference(str(SAMPLE_BG))
+    p.set_background_enabled(True)
+
+    n = min(758, int(p.background.size)) if p.background is not None else 758
+    raw = np.linspace(-10.0, 10.0, n, dtype=np.float32)
+    res = p.process(raw, channel=0, update_references=True)
+
+    assert res.is_calibration_reference is True
+    assert res.background_applied is True
+    assert np.any(np.abs(res.processed_sweep - raw) > 0)
+    assert p.last_calib_sweep is not None
+    assert np.allclose(p.last_calib_sweep[:n], raw[:n], equal_nan=True)

tests/test_cli.py

@@ -1,42 +0,0 @@
-from __future__ import annotations
-
-import subprocess
-import sys
-import unittest
-from pathlib import Path
-
-
-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_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)
-
-    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_fourier_complex_modes.py

@@ -0,0 +1,54 @@
+import numpy as np
+
+from rfg_adc_plotter.processing.fourier import (
+    compute_ifft_profile_from_sweep,
+    reconstruct_complex_spectrum_from_real_trace,
+)
+
+
+def test_reconstruct_complex_spectrum_arccos_mode_returns_complex128():
+    sweep = np.linspace(-3.0, 7.0, 128, dtype=np.float32)
+    z = reconstruct_complex_spectrum_from_real_trace(sweep, complex_mode="arccos")
+    assert z.dtype == np.complex128
+    assert z.shape == sweep.shape
+    assert np.all(np.isfinite(np.real(z)))
+    assert np.all(np.isfinite(np.imag(z)))
+
+
+def test_reconstruct_complex_spectrum_diff_mode_returns_complex128():
+    sweep = np.linspace(-1.0, 1.0, 128, dtype=np.float32)
+    z = reconstruct_complex_spectrum_from_real_trace(sweep, complex_mode="diff")
+    assert z.dtype == np.complex128
+    assert z.shape == sweep.shape
+    assert np.all(np.isfinite(np.real(z)))
+    assert np.all(np.isfinite(np.imag(z)))
+
+
+def test_reconstruct_complex_spectrum_diff_mode_projects_to_unit_circle():
+    sweep = np.sin(np.linspace(0.0, 6.0 * np.pi, 256)).astype(np.float32)
+    z = reconstruct_complex_spectrum_from_real_trace(sweep, complex_mode="diff")
+    mag = np.abs(z)
+    assert np.all(np.isfinite(mag))
+    assert np.allclose(mag, np.ones_like(mag), atol=1e-5, rtol=0.0)
+
+
+def test_compute_ifft_profile_from_sweep_accepts_both_modes():
+    sweep = np.linspace(-5.0, 5.0, 257, dtype=np.float32)
+    d1, y1 = compute_ifft_profile_from_sweep(sweep, complex_mode="arccos")
+    d2, y2 = compute_ifft_profile_from_sweep(sweep, complex_mode="diff")
+
+    assert d1.dtype == np.float32 and y1.dtype == np.float32
+    assert d2.dtype == np.float32 and y2.dtype == np.float32
+    assert d1.size == y1.size and d2.size == y2.size
+    assert d1.size > 0 and d2.size > 0
+    assert np.all(np.diff(d1) >= 0.0)
+    assert np.all(np.diff(d2) >= 0.0)
+
+
+def test_invalid_complex_mode_falls_back_deterministically_in_outer_wrapper():
+    sweep = np.linspace(-1.0, 1.0, 64, dtype=np.float32)
+    depth, y = compute_ifft_profile_from_sweep(sweep, complex_mode="unknown")
+    assert depth.dtype == np.float32
+    assert y.dtype == np.float32
+    assert depth.size == y.size
+    assert depth.size > 0

tests/test_fourier_phase_reconstruction.py

@@ -0,0 +1,75 @@
+import numpy as np
+
+from rfg_adc_plotter.processing.fourier import (
+    build_frequency_axis_hz,
+    compute_ifft_profile_from_sweep,
+    normalize_sweep_for_phase,
+    perform_ifft_depth_response,
+    reconstruct_complex_spectrum_from_real_trace,
+    unwrap_arccos_phase_continuous,
+)
+
+
+def test_normalize_sweep_for_phase_max_abs_and_finite():
+    sweep = np.array([np.nan, -10.0, 5.0, 20.0, -40.0, np.inf, -np.inf], dtype=np.float32)
+    x = normalize_sweep_for_phase(sweep)
+    assert x.dtype == np.float64
+    assert np.all(np.isfinite(x))
+    assert np.max(np.abs(x)) <= 1.0 + 1e-12
+
+
+def test_arccos_unwrap_continuous_recovers_complex_phase_without_large_jumps():
+    phi_true = np.linspace(0.0, 4.0 * np.pi, 1000, dtype=np.float64)
+    x = np.cos(phi_true)
+
+    phi_rec = unwrap_arccos_phase_continuous(x)
+    assert phi_rec.shape == phi_true.shape
+    assert np.max(np.abs(np.diff(phi_rec))) < 0.2
+
+    z_true = np.exp(1j * phi_true)
+    z_rec = np.exp(1j * phi_rec)
+    assert np.allclose(z_rec, z_true, atol=2e-2, rtol=0.0)
+
+
+def test_reconstruct_complex_spectrum_from_real_trace_output_complex128():
+    sweep = np.linspace(-1.0, 1.0, 64, dtype=np.float32)
+    z = reconstruct_complex_spectrum_from_real_trace(sweep)
+    assert z.dtype == np.complex128
+    assert z.shape == sweep.shape
+    assert np.all(np.isfinite(np.real(z)))
+    assert np.all(np.isfinite(np.imag(z)))
+
+
+def test_perform_ifft_depth_response_basic_abs():
+    n = 128
+    freqs = build_frequency_axis_hz(n)
+    s = np.exp(1j * np.linspace(0.0, 2.0 * np.pi, n, dtype=np.float64))
+
+    depth_m, y = perform_ifft_depth_response(s, freqs, axis="abs")
+
+    assert depth_m.dtype == np.float32
+    assert y.dtype == np.float32
+    assert depth_m.ndim == 1 and y.ndim == 1
+    assert depth_m.size == y.size
+    assert depth_m.size >= n
+    assert np.all(np.diff(depth_m) >= 0.0)
+    assert np.all(y >= 0.0)
+
+
+def test_perform_ifft_depth_response_bad_grid_returns_fallback_not_exception():
+    s = np.ones(16, dtype=np.complex128)
+    freqs_desc = np.linspace(10.0, 1.0, 16, dtype=np.float64)
+    depth_m, y = perform_ifft_depth_response(s, freqs_desc, axis="abs")
+    assert depth_m.size == y.size
+    assert depth_m.size == s.size
+    assert np.all(np.isfinite(depth_m))
+
+
+def test_compute_ifft_profile_from_sweep_returns_depth_and_linear_abs():
+    sweep = np.linspace(-5.0, 7.0, 257, dtype=np.float32)
+    depth_m, y = compute_ifft_profile_from_sweep(sweep)
+    assert depth_m.dtype == np.float32
+    assert y.dtype == np.float32
+    assert depth_m.size == y.size
+    assert depth_m.size > 0
+    assert np.all(np.diff(depth_m) >= 0.0)

tests/test_processing.py

@@ -1,275 +0,0 @@
-from __future__ import annotations
-
-import os
-import tempfile
-import numpy as np
-import unittest
-
-from rfg_adc_plotter.constants import FFT_LEN, SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ
-from rfg_adc_plotter.gui.pyqtgraph_backend import (
-    apply_working_range,
-    apply_working_range_to_aux_curves,
-    compute_background_subtracted_bscan_levels,
-    resolve_visible_aux_curves,
-)
-from rfg_adc_plotter.processing.calibration import (
-    build_calib_envelope,
-    calibrate_freqs,
-    load_calib_envelope,
-    recalculate_calibration_c,
-    save_calib_envelope,
-)
-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_centered_ifft_spectrum,
-    build_symmetric_ifft_spectrum,
-    compute_distance_axis,
-    compute_fft_mag_row,
-    compute_fft_row,
-    fft_mag_to_db,
-)
-from rfg_adc_plotter.processing.normalization import (
-    build_calib_envelopes,
-    normalize_by_calib,
-    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_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_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_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_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_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_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

@@ -1,90 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import unittest
-
-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_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_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)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_ring_buffer_fft_axis.py

@@ -0,0 +1,101 @@
+import numpy as np
+
+from rfg_adc_plotter.state.ring_buffer import RingBuffer
+
+
+def test_ring_buffer_allocates_fft_buffers_from_first_push():
+    ring = RingBuffer(max_sweeps=4)
+    ring.ensure_init(64)
+
+    sweep = np.linspace(-1.0, 1.0, 64, dtype=np.float32)
+    ring.push(sweep)
+
+    assert ring.ring_fft is not None
+    assert ring.fft_depth_axis_m is not None
+    assert ring.last_fft_vals is not None
+    assert ring.fft_bins == ring.ring_fft.shape[1]
+    assert ring.fft_bins == ring.fft_depth_axis_m.size
+    assert ring.fft_bins == ring.last_fft_vals.size
+    assert ring.last_fft_third_axes_m != (None, None, None)
+    assert ring.last_fft_third_vals != (None, None, None)
+    for axis, vals in zip(ring.last_fft_third_axes_m, ring.last_fft_third_vals):
+        assert axis is not None
+        assert vals is not None
+        assert axis.dtype == np.float32
+        assert vals.dtype == np.float32
+        assert axis.size == vals.size
+    # Legacy alias kept for compatibility with existing GUI code paths.
+    assert ring.fft_time_axis is ring.fft_depth_axis_m
+
+
+def test_ring_buffer_reallocates_fft_buffers_when_ifft_length_changes():
+    ring = RingBuffer(max_sweeps=4)
+    ring.ensure_init(512)
+
+    ring.push(np.linspace(-1.0, 1.0, 64, dtype=np.float32))
+    first_bins = ring.fft_bins
+    first_shape = None if ring.ring_fft is None else ring.ring_fft.shape
+
+    ring.push(np.linspace(-1.0, 1.0, 512, dtype=np.float32))
+    second_bins = ring.fft_bins
+    second_shape = None if ring.ring_fft is None else ring.ring_fft.shape
+
+    assert ring.ring is not None  # raw ring сохраняется
+    assert first_shape is not None and second_shape is not None
+    assert first_bins != second_bins
+    assert second_shape == (ring.max_sweeps, second_bins)
+    assert ring.fft_depth_axis_m is not None
+    assert ring.fft_depth_axis_m.size == second_bins
+
+
+def test_ring_buffer_mode_switch_resets_fft_buffers_only():
+    ring = RingBuffer(max_sweeps=4)
+    ring.ensure_init(128)
+
+    ring.push(np.linspace(-1.0, 1.0, 128, dtype=np.float32))
+    assert ring.ring is not None
+    assert ring.ring_fft is not None
+    raw_before = ring.ring.copy()
+    assert ring.last_fft_third_axes_m != (None, None, None)
+    assert ring.last_fft_third_vals != (None, None, None)
+
+    changed = ring.set_fft_complex_mode("diff")
+    assert changed is True
+    assert ring.fft_complex_mode == "diff"
+    assert ring.ring is not None
+    assert np.array_equal(ring.ring, raw_before, equal_nan=True)
+    assert ring.ring_fft is None
+    assert ring.fft_depth_axis_m is None
+    assert ring.last_fft_vals is None
+    assert ring.last_fft_third_axes_m == (None, None, None)
+    assert ring.last_fft_third_vals == (None, None, None)
+    assert ring.fft_bins == 0
+
+    ring.push(np.linspace(-1.0, 1.0, 128, dtype=np.float32))
+    assert ring.ring_fft is not None
+    assert ring.fft_depth_axis_m is not None
+    assert ring.last_fft_vals is not None
+    assert ring.last_fft_third_axes_m != (None, None, None)
+    assert ring.last_fft_third_vals != (None, None, None)
+    for axis, vals in zip(ring.last_fft_third_axes_m, ring.last_fft_third_vals):
+        assert axis is not None
+        assert vals is not None
+        assert axis.dtype == np.float32
+        assert vals.dtype == np.float32
+        assert axis.size == vals.size
+
+
+def test_ring_buffer_short_sweeps_keep_third_profiles_well_formed():
+    for n in (1, 2, 3):
+        ring = RingBuffer(max_sweeps=4)
+        ring.ensure_init(n)
+        ring.push(np.linspace(-1.0, 1.0, n, dtype=np.float32))
+
+        assert ring.last_fft_third_axes_m != (None, None, None)
+        assert ring.last_fft_third_vals != (None, None, None)
+        for axis, vals in zip(ring.last_fft_third_axes_m, ring.last_fft_third_vals):
+            assert axis is not None
+            assert vals is not None
+            assert axis.dtype == np.float32
+            assert vals.dtype == np.float32
+            assert axis.size == vals.size

tests/test_sweep_parser_core.py

@@ -1,159 +0,0 @@
-from __future__ import annotations
-
-import math
-import unittest
-
-from rfg_adc_plotter.io.sweep_parser_core import (
-    AsciiSweepParser,
-    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),
-        ]
-    )
-
-
-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_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_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.assertEqual(events[1].aux, (100.0, 90.0))
-
-    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.assertTrue(math.isfinite(events[1].y))
-
-    def test_sweep_assembler_builds_aux_curves_without_inversion(self):
-        assembler = SweepAssembler(fancy=False, apply_inversion=False)
-        self.assertIsNone(assembler.consume(StartEvent(ch=1)))
-        assembler.consume(PointEvent(ch=1, x=1, y=10.0, aux=(100.0, 90.0)))
-        assembler.consume(PointEvent(ch=1, x=2, y=20.0, aux=(110.0, 95.0)))
-        sweep, info, aux = assembler.finalize_current()
-        self.assertEqual(sweep.shape[0], 3)
-        self.assertEqual(info["ch"], 1)
-        self.assertIsNotNone(aux)
-        self.assertEqual(aux[0][1], 100.0)
-        self.assertEqual(aux[1][2], 95.0)
-
-
-if __name__ == "__main__":
-    unittest.main()