check background

.gitignore

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

README.md

@@ -0,0 +1,185 @@
+# 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.

replay_pty.py

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

rfg_adc_plotter/__init__.py

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

rfg_adc_plotter/cli.py

@@ -0,0 +1,120 @@
+"""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

@@ -0,0 +1,17 @@
+"""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
+
+DATA_INVERSION_THRESHOLD = 10.0

rfg_adc_plotter/gui/__init__.py

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

rfg_adc_plotter/io/__init__.py

@@ -0,0 +1,6 @@
+"""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/serial_source.py

@@ -0,0 +1,177 @@
+"""Serial input helpers with pyserial and raw TTY fallbacks."""
+
+from __future__ import annotations
+
+import io
+import os
+import sys
+from typing import Optional
+
+
+def try_open_pyserial(path: str, baud: int, timeout: float):
+    try:
+        import serial  # type: ignore
+    except Exception:
+        return None
+    try:
+        return serial.Serial(path, baudrate=baud, timeout=timeout)
+    except Exception:
+        return None
+
+
+class FDReader:
+    """Buffered wrapper around a raw TTY file descriptor."""
+
+    def __init__(self, fd: int):
+        self._fd = fd
+        raw = os.fdopen(fd, "rb", closefd=False)
+        self._file = raw
+        self._buf = io.BufferedReader(raw, buffer_size=65536)
+
+    def fileno(self) -> int:
+        return self._fd
+
+    def readline(self) -> bytes:
+        return self._buf.readline()
+
+    def close(self) -> None:
+        try:
+            self._buf.close()
+        except Exception:
+            pass
+
+
+def open_raw_tty(path: str, baud: int) -> Optional[FDReader]:
+    """Open a TTY without pyserial and configure it via termios."""
+    try:
+        import termios
+        import tty
+    except Exception:
+        return None
+
+    try:
+        fd = os.open(path, os.O_RDONLY | os.O_NOCTTY)
+    except Exception:
+        return None
+
+    try:
+        attrs = termios.tcgetattr(fd)
+        tty.setraw(fd)
+
+        baud_map = {
+            9600: termios.B9600,
+            19200: termios.B19200,
+            38400: termios.B38400,
+            57600: termios.B57600,
+            115200: termios.B115200,
+            230400: getattr(termios, "B230400", None),
+            460800: getattr(termios, "B460800", None),
+        }
+        speed = baud_map.get(baud) or termios.B115200
+
+        attrs[4] = speed
+        attrs[5] = speed
+        cc = attrs[6]
+        cc[termios.VMIN] = 1
+        cc[termios.VTIME] = 0
+        attrs[6] = cc
+        termios.tcsetattr(fd, termios.TCSANOW, attrs)
+    except Exception:
+        try:
+            os.close(fd)
+        except Exception:
+            pass
+        return None
+
+    return FDReader(fd)
+
+
+class SerialLineSource:
+    """Unified line-oriented wrapper for pyserial and raw TTY readers."""
+
+    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._using = "pyserial" if self._pyserial is not None else "raw"
+        if self._pyserial is None:
+            self._fdreader = open_raw_tty(path, baud)
+            if self._fdreader is None:
+                msg = f"Не удалось открыть порт '{path}' (pyserial и raw TTY не сработали)"
+                if sys.platform.startswith("win"):
+                    msg += ". На Windows нужен pyserial: pip install pyserial"
+                raise RuntimeError(msg)
+
+    def readline(self) -> bytes:
+        if self._pyserial is not None:
+            try:
+                return self._pyserial.readline()
+            except Exception:
+                return b""
+        try:
+            return self._fdreader.readline()  # type: ignore[union-attr]
+        except Exception:
+            return b""
+
+    def close(self) -> None:
+        try:
+            if self._pyserial is not None:
+                self._pyserial.close()
+            elif self._fdreader is not None:
+                self._fdreader.close()
+        except Exception:
+            pass
+
+
+class SerialChunkReader:
+    """Fast non-blocking chunk reader for serial sources."""
+
+    def __init__(self, src: SerialLineSource):
+        self._src = src
+        self._ser = src._pyserial
+        self._fd: Optional[int] = None
+        if self._ser is not None:
+            try:
+                self._ser.timeout = 0
+            except Exception:
+                pass
+        else:
+            try:
+                self._fd = src._fdreader.fileno()  # type: ignore[union-attr]
+                try:
+                    os.set_blocking(self._fd, False)
+                except Exception:
+                    pass
+            except Exception:
+                self._fd = None
+
+    def read_available(self) -> bytes:
+        """Return currently available bytes or b"" when nothing is ready."""
+        if self._ser is not None:
+            try:
+                available = int(getattr(self._ser, "in_waiting", 0))
+            except Exception:
+                available = 0
+            if available > 0:
+                try:
+                    return self._ser.read(available)
+                except Exception:
+                    return b""
+            return b""
+
+        if self._fd is None:
+            return b""
+
+        out = bytearray()
+        while True:
+            try:
+                chunk = os.read(self._fd, 65536)
+                if not chunk:
+                    break
+                out += chunk
+                if len(chunk) < 65536:
+                    break
+            except BlockingIOError:
+                break
+            except Exception:
+                break
+        return bytes(out)

rfg_adc_plotter/io/sweep_parser_core.py

@@ -0,0 +1,427 @@
+"""Reusable sweep parsers and sweep assembly helpers."""
+
+from __future__ import annotations
+
+import math
+import time
+from collections import deque
+from typing import List, Optional, Sequence, Set
+
+import numpy as np
+
+from rfg_adc_plotter.constants import DATA_INVERSION_THRESHOLD, LOG_BASE, LOG_EXP_LIMIT, LOG_POSTSCALER, LOG_SCALER
+from rfg_adc_plotter.types import ParserEvent, PointEvent, StartEvent, SweepAuxCurves, SweepInfo, SweepPacket
+
+
+def u32_to_i32(value: int) -> int:
+    return value - 0x1_0000_0000 if (value & 0x8000_0000) else value
+
+
+def u16_to_i16(value: int) -> int:
+    return value - 0x1_0000 if (value & 0x8000) else value
+
+
+def log_value_to_linear(value: int) -> float:
+    exponent = max(-LOG_EXP_LIMIT, min(LOG_EXP_LIMIT, float(value) * LOG_SCALER))
+    return float(LOG_BASE ** exponent)
+
+
+def log_pair_to_sweep(avg_1: int, avg_2: int) -> float:
+    value_1 = log_value_to_linear(avg_1)
+    value_2 = log_value_to_linear(avg_2)
+    return abs(value_1 - value_2) * LOG_POSTSCALER
+
+
+class AsciiSweepParser:
+    """Incremental parser for ASCII sweep streams."""
+
+    def __init__(self):
+        self._buf = bytearray()
+
+    def feed(self, data: bytes) -> List[ParserEvent]:
+        if data:
+            self._buf += data
+        events: List[ParserEvent] = []
+        while True:
+            nl = self._buf.find(b"\n")
+            if nl == -1:
+                break
+            line = bytes(self._buf[:nl])
+            del self._buf[: nl + 1]
+            if line.endswith(b"\r"):
+                line = line[:-1]
+            if not line:
+                continue
+            if line.startswith(b"Sweep_start"):
+                events.append(StartEvent())
+                continue
+
+            parts = line.split()
+            if len(parts) < 3:
+                continue
+            head = parts[0].lower()
+            try:
+                if head == b"s":
+                    if len(parts) >= 4:
+                        ch = int(parts[1], 10)
+                        x = int(parts[2], 10)
+                        y = int(parts[3], 10)
+                    else:
+                        ch = 0
+                        x = int(parts[1], 10)
+                        y = int(parts[2], 10)
+                elif head.startswith(b"s"):
+                    ch = int(head[1:], 10)
+                    x = int(parts[1], 10)
+                    y = int(parts[2], 10)
+                else:
+                    continue
+            except Exception:
+                continue
+            events.append(PointEvent(ch=int(ch), x=int(x), y=float(y)))
+        return events
+
+
+class 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
+
+
+class SweepAssembler:
+    """Collect parser events into sweep packets matching runtime expectations."""
+
+    def __init__(self, fancy: bool = False, apply_inversion: bool = True):
+        self._fancy = bool(fancy)
+        self._apply_inversion = bool(apply_inversion)
+        self._max_width = 0
+        self._sweep_idx = 0
+        self._last_sweep_ts: Optional[float] = None
+        self._n_valid_hist = deque()
+        self._xs: list[int] = []
+        self._ys: list[float] = []
+        self._aux_1: list[float] = []
+        self._aux_2: list[float] = []
+        self._cur_channel: Optional[int] = None
+        self._cur_channels: set[int] = set()
+
+    def _reset_current(self) -> None:
+        self._xs.clear()
+        self._ys.clear()
+        self._aux_1.clear()
+        self._aux_2.clear()
+        self._cur_channel = None
+        self._cur_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
+
+        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
+
+    def finalize_current(self) -> Optional[SweepPacket]:
+        if not self._xs:
+            return None
+
+        ch_list = sorted(self._cur_channels) if self._cur_channels else [0]
+        ch_primary = ch_list[0] if ch_list else 0
+        width = max(int(max(self._xs)) + 1, 1)
+        self._max_width = max(self._max_width, width)
+        target_width = self._max_width if self._fancy else width
+
+        sweep = self._scatter(self._xs, self._ys, target_width)
+        aux_curves: SweepAuxCurves = None
+        if self._aux_1 and self._aux_2 and len(self._aux_1) == len(self._xs):
+            aux_curves = (
+                self._scatter(self._xs, self._aux_1, target_width),
+                self._scatter(self._xs, self._aux_2, target_width),
+            )
+
+        n_valid_cur = int(np.count_nonzero(np.isfinite(sweep)))
+
+        if self._fancy:
+            self._fill_missing(sweep)
+            if aux_curves is not None:
+                self._fill_missing(aux_curves[0])
+                self._fill_missing(aux_curves[1])
+
+        if self._apply_inversion:
+            try:
+                mean_value = float(np.nanmean(sweep))
+                if np.isfinite(mean_value) and mean_value < DATA_INVERSION_THRESHOLD:
+                    sweep *= -1.0
+            except Exception:
+                pass
+
+        self._sweep_idx += 1
+        now = time.time()
+        if self._last_sweep_ts is None:
+            dt_ms = float("nan")
+        else:
+            dt_ms = (now - self._last_sweep_ts) * 1000.0
+        self._last_sweep_ts = now
+
+        self._n_valid_hist.append((now, n_valid_cur))
+        while self._n_valid_hist and (now - self._n_valid_hist[0][0]) > 1.0:
+            self._n_valid_hist.popleft()
+        n_valid = float(sum(value for _ts, value in self._n_valid_hist) / len(self._n_valid_hist))
+
+        if n_valid_cur > 0:
+            vmin = float(np.nanmin(sweep))
+            vmax = float(np.nanmax(sweep))
+            mean = float(np.nanmean(sweep))
+            std = float(np.nanstd(sweep))
+        else:
+            vmin = vmax = mean = std = float("nan")
+
+        info: SweepInfo = {
+            "sweep": self._sweep_idx,
+            "ch": ch_primary,
+            "chs": ch_list,
+            "n_valid": n_valid,
+            "min": vmin,
+            "max": vmax,
+            "mean": mean,
+            "std": std,
+            "dt_ms": dt_ms,
+        }
+        return (sweep, info, aux_curves)

rfg_adc_plotter/io/sweep_reader.py

@@ -0,0 +1,102 @@
+"""Background sweep reader thread."""
+
+from __future__ import annotations
+
+import sys
+import threading
+import time
+from queue import Full, Queue
+
+from rfg_adc_plotter.io.serial_source import SerialChunkReader, SerialLineSource
+from rfg_adc_plotter.io.sweep_parser_core import (
+    AsciiSweepParser,
+    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."""
+
+    def __init__(
+        self,
+        port_path: str,
+        baud: int,
+        out_queue: "Queue[SweepPacket]",
+        stop_event: threading.Event,
+        fancy: bool = False,
+        bin_mode: bool = False,
+        logscale: bool = False,
+        parser_16_bit_x2: bool = False,
+        parser_test: bool = False,
+    ):
+        super().__init__(daemon=True)
+        self._port_path = port_path
+        self._baud = int(baud)
+        self._queue = out_queue
+        self._stop = stop_event
+        self._fancy = bool(fancy)
+        self._bin_mode = bool(bin_mode)
+        self._logscale = bool(logscale)
+        self._parser_16_bit_x2 = bool(parser_16_bit_x2)
+        self._parser_test = bool(parser_test)
+        self._src: SerialLineSource | None = None
+
+    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:
+        try:
+            self._queue.put_nowait(packet)
+        except Full:
+            try:
+                _ = self._queue.get_nowait()
+            except Exception:
+                pass
+            try:
+                self._queue.put_nowait(packet)
+            except Exception:
+                pass
+
+    def run(self) -> None:
+        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")
+            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)
+        finally:
+            try:
+                if self._src is not None:
+                    self._src.close()
+            except Exception:
+                pass

rfg_adc_plotter/main.py

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

rfg_adc_plotter/processing/__init__.py

@@ -0,0 +1,67 @@
+"""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

@@ -0,0 +1,66 @@
+"""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

@@ -0,0 +1,124 @@
+"""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

@@ -0,0 +1,267 @@
+"""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

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

rfg_adc_plotter/processing/normalization.py

@@ -0,0 +1,173 @@
+"""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/peaks.py

@@ -0,0 +1,209 @@
+"""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/state/__init__.py

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

rfg_adc_plotter/state/background_buffer.py

@@ -0,0 +1,49 @@
+"""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

@@ -0,0 +1,193 @@
+"""Ring buffers for raw sweeps and FFT waterfall rows."""
+
+from __future__ import annotations
+
+import time
+from typing import Optional
+
+import numpy as np
+
+from rfg_adc_plotter.constants import FFT_LEN, SWEEP_FREQ_MAX_GHZ, SWEEP_FREQ_MIN_GHZ, WF_WIDTH
+from rfg_adc_plotter.processing.fft import compute_distance_axis, compute_fft_mag_row, fft_mag_to_db
+
+
+class RingBuffer:
+    """Store raw sweeps, FFT rows, and matching time markers."""
+
+    def __init__(self, max_sweeps: int):
+        self.max_sweeps = int(max_sweeps)
+        self.fft_bins = FFT_LEN // 2 + 1
+        self.fft_mode = "symmetric"
+        self.width = 0
+        self.head = 0
+        self.ring: Optional[np.ndarray] = None
+        self.ring_time: Optional[np.ndarray] = None
+        self.ring_fft: Optional[np.ndarray] = None
+        self.x_shared: Optional[np.ndarray] = None
+        self.distance_axis: Optional[np.ndarray] = None
+        self.last_fft_mag: Optional[np.ndarray] = None
+        self.last_fft_db: Optional[np.ndarray] = None
+        self.last_freqs: Optional[np.ndarray] = None
+        self.y_min_fft: Optional[float] = None
+        self.y_max_fft: Optional[float] = None
+
+    @property
+    def is_ready(self) -> bool:
+        return self.ring is not None and self.ring_fft is not None
+
+    @property
+    def fft_symmetric(self) -> bool:
+        return self.fft_mode == "symmetric"
+
+    def reset(self) -> None:
+        """Drop all buffered sweeps and derived FFT state."""
+        self.width = 0
+        self.head = 0
+        self.ring = None
+        self.ring_time = None
+        self.ring_fft = None
+        self.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 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:
+            return False
+
+        self.fft_mode = normalized_mode
+        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 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:
+            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)
+        self.ring[self.head, :] = row
+        self.ring_time[self.head] = time.time()
+        if freqs is not None:
+            self.last_freqs = np.asarray(freqs, dtype=np.float64).copy()
+
+        fft_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)
+
+        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)
+
+        self.distance_axis = compute_distance_axis(freqs, self.fft_bins, mode=self.fft_mode)
+        self.head = (self.head + 1) % self.max_sweeps
+
+    def get_display_raw(self) -> np.ndarray:
+        if self.ring is None:
+            return np.zeros((1, 1), dtype=np.float32)
+        base = self.ring if self.head == 0 else np.roll(self.ring, -self.head, axis=0)
+        return base.T
+
+    def get_display_fft_linear(self) -> np.ndarray:
+        if self.ring_fft is None:
+            return np.zeros((1, 1), dtype=np.float32)
+        base = self.ring_fft if self.head == 0 else np.roll(self.ring_fft, -self.head, axis=0)
+        return base.T
+
+    def get_last_fft_linear(self) -> Optional[np.ndarray]:
+        if self.last_fft_mag is None:
+            return None
+        return np.asarray(self.last_fft_mag, dtype=np.float32).copy()
+
+    def get_display_times(self) -> Optional[np.ndarray]:
+        if self.ring_time is None:
+            return None
+        return self.ring_time if self.head == 0 else np.roll(self.ring_time, -self.head)

rfg_adc_plotter/state/runtime_state.py

@@ -0,0 +1,46 @@
+"""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

@@ -0,0 +1,31 @@
+"""Shared runtime and parser types."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Tuple, TypeAlias, 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]

tests/test_background_buffer.py

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

tests/test_cli.py

@@ -0,0 +1,42 @@
+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_processing.py

@@ -0,0 +1,275 @@
+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

@@ -0,0 +1,90 @@
+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_sweep_parser_core.py

@@ -0,0 +1,159 @@
+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()