logdetector logic semiapplied

RFG_ADC_dataplotter.py

@@ -36,6 +36,11 @@ FFT_LEN = 1024   # длина БПФ для спектра/водопада сп
 # Порог для инверсии сырых данных: если среднее значение свипа ниже порога —
 # считаем, что сигнал «меньше нуля» и домножаем свип на -1
 DATA_INVERSION_THRASHOLD = 10.0
+LOG_DETECTOR_OFFSET = 0.0
+LOG_DETECTOR_SCALER = -0.001
+LOG_DETECTOR_BASE = 2.0
+LOG_DETECTOR_EXP_MIN = -149.0
+LOG_DETECTOR_EXP_MAX = 128.0
 
 Number = Union[int, float]
 SweepInfo = Dict[str, Any]
@@ -59,7 +64,8 @@ def _format_status_kv(data: Mapping[str, Any]) -> str:
             return f"{fv:.3g}"
         return f"{fv:.3f}".rstrip("0").rstrip(".")
 
-    parts = [f"{k}:{_fmt(v)}" for k, v in data.items()]
+    hidden_keys = {"pre_exp_sweep", "sweep_1", "sweep_2"}
+    parts = [f"{k}:{_fmt(v)}" for k, v in data.items() if k not in hidden_keys]
     return "  ".join(parts)
 
 
@@ -85,6 +91,116 @@ def _parse_spec_clip(spec: Optional[str]) -> Optional[Tuple[float, float]]:
         return None
 
 
+def _normalize_sweep_simple(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
+    """Простая нормировка: поэлементное деление raw/calib."""
+    w = min(raw.size, calib.size)
+    if w <= 0:
+        return raw
+    out = np.full_like(raw, np.nan, dtype=np.float32)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        out[:w] = raw[:w] / calib[:w]
+    out = np.nan_to_num(out, nan=np.nan, posinf=np.nan, neginf=np.nan)
+    return out
+
+
+def _build_calib_envelopes(calib: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """Оценить нижнюю/верхнюю огибающие калибровочной кривой."""
+    n = int(calib.size)
+    if n <= 0:
+        empty = np.zeros((0,), dtype=np.float32)
+        return empty, empty
+
+    y = np.asarray(calib, dtype=np.float32)
+    finite = np.isfinite(y)
+    if not np.any(finite):
+        zeros = np.zeros_like(y, dtype=np.float32)
+        return zeros, zeros
+
+    if not np.all(finite):
+        x = np.arange(n, dtype=np.float32)
+        y = y.copy()
+        y[~finite] = np.interp(x[~finite], x[finite], y[finite]).astype(np.float32)
+
+    if n < 3:
+        return y.copy(), y.copy()
+
+    dy = np.diff(y)
+    s = np.sign(dy).astype(np.int8, copy=False)
+
+    if np.any(s == 0):
+        for i in range(1, s.size):
+            if s[i] == 0:
+                s[i] = s[i - 1]
+        for i in range(s.size - 2, -1, -1):
+            if s[i] == 0:
+                s[i] = s[i + 1]
+        s[s == 0] = 1
+
+    max_idx = np.where((s[:-1] > 0) & (s[1:] < 0))[0] + 1
+    min_idx = np.where((s[:-1] < 0) & (s[1:] > 0))[0] + 1
+
+    x = np.arange(n, dtype=np.float32)
+
+    def _interp_nodes(nodes: np.ndarray) -> np.ndarray:
+        if nodes.size == 0:
+            idx = np.array([0, n - 1], dtype=np.int64)
+        else:
+            idx = np.unique(np.concatenate(([0], nodes, [n - 1]))).astype(np.int64)
+        return np.interp(x, idx.astype(np.float32), y[idx]).astype(np.float32)
+
+    upper = _interp_nodes(max_idx)
+    lower = _interp_nodes(min_idx)
+
+    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:
+    """Нормировка через проекцию между огибающими калибровки в диапазон [-1, +1]."""
+    w = min(raw.size, calib.size)
+    if w <= 0:
+        return raw
+
+    out = np.full_like(raw, np.nan, dtype=np.float32)
+    raw_seg = np.asarray(raw[:w], dtype=np.float32)
+    lower, upper = _build_calib_envelopes(np.asarray(calib[:w], dtype=np.float32))
+    span = upper - lower
+
+    finite_span = span[np.isfinite(span) & (span > 0)]
+    if finite_span.size > 0:
+        eps = max(float(np.median(finite_span)) * 1e-6, 1e-9)
+    else:
+        eps = 1e-9
+
+    valid = (
+        np.isfinite(raw_seg)
+        & np.isfinite(lower)
+        & np.isfinite(upper)
+        & (span > eps)
+    )
+    if np.any(valid):
+        proj = np.empty_like(raw_seg, dtype=np.float32)
+        proj[valid] = ((2.0 * (raw_seg[valid] - lower[valid]) / span[valid]) - 1.0) * 1000.0
+        proj[valid] = np.clip(proj[valid], -1000.0, 1000.0)
+        proj[~valid] = np.nan
+        out[:w] = proj
+
+    return out
+
+
+def _normalize_by_calib(raw: np.ndarray, calib: np.ndarray, norm_type: str) -> np.ndarray:
+    """Нормировка свипа по выбранному алгоритму."""
+    nt = str(norm_type).strip().lower()
+    if nt == "simple":
+        return _normalize_sweep_simple(raw, calib)
+    return _normalize_sweep_projector(raw, calib)
+
+
 def try_open_pyserial(path: str, baud: int, timeout: float):
     try:
         import serial  # type: ignore
@@ -274,6 +390,8 @@ class SweepReader(threading.Thread):
         out_queue: Queue[SweepPacket],
         stop_event: threading.Event,
         fancy: bool = False,
+        bin_mode: bool = False,
+        logdetector: bool = False,
     ):
         super().__init__(daemon=True)
         self._port_path = port_path
@@ -282,12 +400,26 @@ class SweepReader(threading.Thread):
         self._stop = stop_event
         self._src: Optional[SerialLineSource] = None
         self._fancy = bool(fancy)
+        self._bin_mode = bool(bin_mode)
+        self._logdetector = bool(logdetector)
         self._max_width: int = 0
         self._sweep_idx: int = 0
         self._last_sweep_ts: Optional[float] = None
         self._n_valid_hist = deque()
 
-    def _finalize_current(self, xs, ys, channels: Optional[set[int]]):
+    @staticmethod
+    def _u32_to_i32(v: int) -> int:
+        """Преобразование 32-bit слова в знаковое значение."""
+        return v - 0x1_0000_0000 if (v & 0x8000_0000) else v
+
+    def _finalize_current(
+        self,
+        xs,
+        ys,
+        channels: Optional[set[int]],
+        ys1: Optional[list[int]] = None,
+        ys2: Optional[list[int]] = None,
+    ):
         if not xs:
             return
         ch_list = sorted(channels) if channels else [0]
@@ -296,17 +428,26 @@ class SweepReader(threading.Thread):
         width = max_x + 1
         self._max_width = max(self._max_width, width)
         target_width = self._max_width if self._fancy else width
-        # Быстрый векторизованный путь
-        sweep = np.full((target_width,), np.nan, dtype=np.float32)
+        def _build_sweep(values) -> np.ndarray:
+            arr = np.full((target_width,), np.nan, dtype=np.float32)
             try:
                 idx = np.asarray(xs, dtype=np.int64)
-            vals = np.asarray(ys, dtype=np.float32)
-            sweep[idx] = vals
+                vals = np.asarray(values, dtype=np.float32)
+                arr[idx] = vals
             except Exception:
-            # Запасной путь
-            for x, y in zip(xs, ys):
+                for x, y in zip(xs, values):
                     if 0 <= x < target_width:
-                    sweep[x] = float(y)
+                        arr[x] = float(y)
+            return arr
+
+        sweep_1: Optional[np.ndarray] = None
+        sweep_2: Optional[np.ndarray] = None
+        if ys1 is not None and ys2 is not None and len(ys1) == len(xs) and len(ys2) == len(xs):
+            sweep_1 = _build_sweep(ys1)
+            sweep_2 = _build_sweep(ys2)
+            sweep = sweep_1 - sweep_2
+        else:
+            sweep = _build_sweep(ys)
         # Метрики валидных точек до заполнения пропусков
         finite_pre = np.isfinite(sweep)
         n_valid_cur = int(np.count_nonzero(finite_pre))
@@ -331,13 +472,45 @@ class SweepReader(threading.Thread):
             except Exception:
                 # В случае ошибки просто оставляем как есть
                 pass
+        '''
         # Инверсия данных при «отрицательном» уровне (среднее ниже порога)
+
+
         try:
             m = float(np.nanmean(sweep))
             if np.isfinite(m) and m < DATA_INVERSION_THRASHOLD:
                 sweep *= -1.0
         except Exception:
             pass
+        '''
+        
+        pre_exp_sweep: Optional[np.ndarray] = None
+        if self._logdetector:
+            try:
+                if sweep_1 is not None and sweep_2 is not None:
+                    s1_pre = (sweep_1 - LOG_DETECTOR_OFFSET) * LOG_DETECTOR_SCALER
+                    s2_pre = (sweep_2 - LOG_DETECTOR_OFFSET) * LOG_DETECTOR_SCALER
+                    s1_pre = np.clip(s1_pre, LOG_DETECTOR_EXP_MIN, LOG_DETECTOR_EXP_MAX)
+                    s2_pre = np.clip(s2_pre, LOG_DETECTOR_EXP_MIN, LOG_DETECTOR_EXP_MAX)
+#                    with np.errstate(over="ignore", invalid="ignore"):
+#                        sweep_1 = np.power(LOG_DETECTOR_BASE, np.asarray(s1_pre, dtype=np.float64)).astype(np.float32)
+#                        sweep_2 = np.power(LOG_DETECTOR_BASE, np.asarray(s2_pre, dtype=np.float64)).astype(np.float32)
+                    sweep_1 = np.power(LOG_DETECTOR_BASE, np.asarray(s1_pre, dtype=np.float64)).astype(np.float32)
+                    sweep_2 = np.power(LOG_DETECTOR_BASE, np.asarray(s2_pre, dtype=np.float64)).astype(np.float32)
+                    sweep_1[~np.isfinite(sweep_1)] = np.nan
+                    sweep_2[~np.isfinite(sweep_2)] = np.nan
+                    sweep = sweep_1 - sweep_2
+                else:
+                    sweep = (sweep - LOG_DETECTOR_OFFSET) * LOG_DETECTOR_SCALER
+                    sweep = np.clip(sweep, LOG_DETECTOR_EXP_MIN, LOG_DETECTOR_EXP_MAX)
+                    pre_exp_sweep = sweep.copy()
+                    with np.errstate(over="ignore", invalid="ignore"):
+                        sweep = np.power(LOG_DETECTOR_BASE, np.asarray(sweep, dtype=np.float64)).astype(np.float32)
+                    sweep[~np.isfinite(sweep)] = np.nan
+            except Exception:
+                pass
+
+        #print(sweep)
         #sweep -= float(np.nanmean(sweep))
 
         # Метрики для статусной строки (вид словаря: переменная -> значение)
@@ -378,6 +551,11 @@ class SweepReader(threading.Thread):
             "std": std,
             "dt_ms": dt_ms,
         }
+        if pre_exp_sweep is not None:
+            info["pre_exp_sweep"] = pre_exp_sweep
+        if sweep_1 is not None and sweep_2 is not None:
+            info["sweep_1"] = sweep_1
+            info["sweep_2"] = sweep_2
 
         # Кладём готовый свип (если очередь полна — выбрасываем самый старый)
         try:
@@ -392,34 +570,21 @@ class SweepReader(threading.Thread):
             except Exception:
                 pass
 
-    def run(self):
-        # Состояние текущего свипа
+    def _run_ascii_stream(self, chunk_reader: SerialChunkReader):
         xs: list[int] = []
         ys: list[int] = []
         cur_channel: Optional[int] = None
         cur_channels: set[int] = set()
 
-        try:
-            self._src = SerialLineSource(self._port_path, self._baud, timeout=1.0)
-            sys.stderr.write(f"[info] Открыл порт {self._port_path} ({self._src._using})\n")
-        except Exception as e:
-            sys.stderr.write(f"[error] {e}\n")
-            return
-
-        try:
-            # Быстрый неблокирующий дренаж порта с разбором по байтам
-            chunk_reader = SerialChunkReader(self._src)
         buf = bytearray()
         while not self._stop.is_set():
             data = chunk_reader.read_available()
             if data:
                 buf += data
             else:
-                    # Короткая уступка CPU, если нет новых данных
                 time.sleep(0.0005)
                 continue
 
-                # Обрабатываем все полные строки
             while True:
                 nl = buf.find(b"\n")
                 if nl == -1:
@@ -439,7 +604,6 @@ class SweepReader(threading.Thread):
                     cur_channels.clear()
                     continue
 
-                    # sCH X Y  или  s CH X Y  (все целые со знаком). Разделяем по любым пробелам/табам.
                 if len(line) >= 3:
                     parts = line.split()
                     if len(parts) >= 3 and (parts[0].lower() == b"s" or parts[0].lower().startswith(b"s")):
@@ -448,16 +612,15 @@ class SweepReader(threading.Thread):
                                 if len(parts) >= 4:
                                     ch = int(parts[1], 10)
                                     x = int(parts[2], 10)
-                                        y = int(parts[3], 10)  # поддержка знака: "+…" и "-…"
+                                    y = int(parts[3], 10)
                                 else:
                                     ch = 0
                                     x = int(parts[1], 10)
-                                        y = int(parts[2], 10)  # поддержка знака: "+…" и "-…"
+                                    y = int(parts[2], 10)
                             else:
-                                    # формат вида "s0"
                                 ch = int(parts[0][1:], 10)
                                 x = int(parts[1], 10)
-                                    y = int(parts[2], 10)  # поддержка знака: "+…" и "-…"
+                                y = int(parts[2], 10)
                         except Exception:
                             continue
                         if cur_channel is None:
@@ -466,15 +629,104 @@ class SweepReader(threading.Thread):
                         xs.append(x)
                         ys.append(y)
 
-                # Защита от переполнения буфера при отсутствии переводов строки
             if len(buf) > 1_000_000:
                 del buf[:-262144]
-        finally:
-            try:
-                # Завершаем оставшийся свип
+
         self._finalize_current(xs, ys, cur_channels)
-            except Exception:
-                pass
+
+    def _run_binary_stream(self, chunk_reader: SerialChunkReader):
+        xs: list[int] = []
+        ys: list[int] = []
+        ys1: list[int] = []
+        ys2: list[int] = []
+        cur_channel: Optional[int] = None
+        cur_channels: set[int] = set()
+        words = deque()
+
+        buf = bytearray()
+        while not self._stop.is_set():
+            data = chunk_reader.read_available()
+            if data:
+                buf += data
+            else:
+                time.sleep(0.0005)
+                continue
+
+            usable = len(buf) & ~1
+            if usable == 0:
+                continue
+
+            i = 0
+            while i < usable:
+                w = int(buf[i]) | (int(buf[i + 1]) << 8)
+                words.append(w)
+                i += 2
+
+            # Новый бинарный формат:
+            # - старт: FFFF,FFFF,FFFF,FFFF,FFFF,(CH<<8)|0x0A
+            # - точка: X,avg1_hi,avg1_lo,avg2_hi,avg2_lo,0x000A
+            while len(words) >= 6:
+                w0 = int(words[0])
+                w1 = int(words[1])
+                w2 = int(words[2])
+                w3 = int(words[3])
+                w4 = int(words[4])
+                w5 = int(words[5])
+
+                if (
+                    w0 == 0xFFFF and w1 == 0xFFFF and w2 == 0xFFFF
+                    and w3 == 0xFFFF and w4 == 0xFFFF and (w5 & 0x00FF) == 0x000A
+                ):
+                    self._finalize_current(xs, ys, cur_channels, ys1=ys1, ys2=ys2)
+                    xs.clear()
+                    ys.clear()
+                    ys1.clear()
+                    ys2.clear()
+                    cur_channels.clear()
+                    cur_channel = (w5 >> 8) & 0x00FF
+                    cur_channels.add(cur_channel)
+                    for _ in range(6):
+                        words.popleft()
+                    continue
+
+                if w5 == 0x000A:
+                    if cur_channel is not None:
+                        cur_channels.add(cur_channel)
+                    xs.append(w0)
+                    avg1_u32 = (w1 << 16) | w2
+                    avg2_u32 = (w3 << 16) | w4
+                    avg1 = self._u32_to_i32(avg1_u32)
+                    avg2 = self._u32_to_i32(avg2_u32)
+                    ys1.append(avg1)
+                    ys2.append(avg2)
+                    ys.append(avg1 - avg2)
+                    for _ in range(6):
+                        words.popleft()
+                    continue
+
+                words.popleft()
+
+            del buf[:usable]
+            if len(buf) > 1_000_000:
+                del buf[:-262144]
+
+        self._finalize_current(xs, ys, cur_channels, ys1=ys1, ys2=ys2)
+
+    def run(self):
+        try:
+            self._src = SerialLineSource(self._port_path, self._baud, timeout=1.0)
+            sys.stderr.write(f"[info] Открыл порт {self._port_path} ({self._src._using})\n")
+        except Exception as e:
+            sys.stderr.write(f"[error] {e}\n")
+            return
+
+        try:
+            chunk_reader = SerialChunkReader(self._src)
+            if self._bin_mode:
+                self._run_binary_stream(chunk_reader)
+            else:
+                self._run_ascii_stream(chunk_reader)
+        finally:
             try:
                 if self._src is not None:
                     self._src.close()
@@ -501,7 +753,7 @@ def main():
         "--spec-clip",
         default="2,98",
         help=(
-            "Процентильная обрезка уровней водопада спектров, % (min,max). "
+            "Процентильная обрезка уровней водопада спектров, %% (min,max). "
             "Напр. 2,98. 'off' — отключить"
         ),
     )
@@ -532,6 +784,27 @@ def main():
         default="auto",
         help="Графический бэкенд: pyqtgraph (pg) — быстрее; matplotlib (mpl) — совместимый. По умолчанию auto",
     )
+    parser.add_argument(
+        "--norm-type",
+        choices=["projector", "simple"],
+        default="projector",
+        help="Тип нормировки: projector (по огибающим в [-1,+1]) или simple (raw/calib)",
+    )
+    parser.add_argument(
+        "--bin",
+        dest="bin_mode",
+        action="store_true",
+        default=True,
+        help=(
+            "Бинарный протокол: старт FFFFx5,(CH<<8)|0x0A; "
+            "точки X,avg1_hi,avg1_lo,avg2_hi,avg2_lo,0x000A (sweep=avg1-avg2)"
+        ),
+    )
+    parser.add_argument(
+        "--logdetector",
+        action="store_true",
+        help="Лог-детектор: после инверсии ((sweep-OFFSET)*SCALER) и затем BASE**sweep",
+    )
 
     args = parser.parse_args()
 
@@ -557,7 +830,15 @@ def main():
     # Очередь завершённых свипов и поток чтения
     q: Queue[SweepPacket] = Queue(maxsize=1000)
     stop_event = threading.Event()
-    reader = SweepReader(args.port, args.baud, q, stop_event, fancy=bool(args.fancy))
+    reader = SweepReader(
+        args.port,
+        args.baud,
+        q,
+        stop_event,
+        fancy=bool(args.fancy),
+        bin_mode=bool(getattr(args, "bin_mode", False)),
+        logdetector=bool(getattr(args, "logdetector", False)),
+    )
     reader.start()
 
     # Графика
@@ -569,6 +850,9 @@ def main():
 
     # Состояние для отображения
     current_sweep_raw: Optional[np.ndarray] = None
+    current_sweep_1: Optional[np.ndarray] = None
+    current_sweep_2: Optional[np.ndarray] = None
+    current_sweep_pre_exp: Optional[np.ndarray] = None
     current_sweep_norm: Optional[np.ndarray] = None
     last_calib_sweep: Optional[np.ndarray] = None
     current_info: Optional[SweepInfo] = None
@@ -592,6 +876,8 @@ def main():
     ymax_slider = None
     contrast_slider = None
     calib_enabled = False
+    norm_type = str(getattr(args, "norm_type", "projector")).strip().lower()
+    logdetector_enabled = bool(getattr(args, "logdetector", False))
     cb = None
 
     # Статусная строка (внизу окна)
@@ -607,10 +893,10 @@ def main():
 
     # Линейный график последнего свипа
     line_obj, = ax_line.plot([], [], lw=1, color="tab:blue")
-    line_calib_obj, = ax_line.plot([], [], lw=1, color="tab:red")
+    line_calib_obj, = ax_line.plot([], [], lw=1, color="gold")
     line_norm_obj, = ax_line.plot([], [], lw=1, color="tab:green")
     ax_line.set_title("Сырые данные", pad=1)
-    ax_line.set_xlabel("F")
+    ax_line.set_xlabel("ГГц")
     ax_line.set_ylabel("")
     channel_text = ax_line.text(
         0.98,
@@ -626,8 +912,8 @@ def main():
     # Линейный график спектра текущего свипа
     fft_line_obj, = ax_fft.plot([], [], lw=1)
     ax_fft.set_title("FFT", pad=1)
-    ax_fft.set_xlabel("X")
-    ax_fft.set_ylabel("Амплитуда, дБ")
+    ax_fft.set_xlabel("Время")
+    ax_fft.set_ylabel("дБ")
 
     # Диапазон по Y для последнего свипа: авто по умолчанию (поддерживает отрицательные значения)
     fixed_ylim: Optional[Tuple[float, float]] = None
@@ -651,7 +937,7 @@ def main():
     )
     ax_img.set_title("Сырые данные", pad=12)
     ax_img.set_xlabel("")
-    ax_img.set_ylabel("частота")
+    ax_img.set_ylabel("ГГц")
     # Не показываем численные значения по времени на водопаде сырых данных
     try:
         ax_img.tick_params(axis="x", labelbottom=False)
@@ -674,15 +960,9 @@ def main():
         ax_spec.tick_params(axis="x", labelbottom=False)
     except Exception:
         pass
+
     def _normalize_sweep(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
-        w = min(raw.size, calib.size)
-        if w <= 0:
-            return raw
-        out = np.full_like(raw, np.nan, dtype=np.float32)
-        with np.errstate(divide="ignore", invalid="ignore"):
-            out[:w] = raw[:w] / calib[:w]
-        out = np.nan_to_num(out, nan=np.nan, posinf=np.nan, neginf=np.nan)
-        return out
+        return _normalize_by_calib(raw, calib, norm_type=norm_type)
 
     def _set_calib_enabled():
         nonlocal calib_enabled, current_sweep_norm
@@ -734,15 +1014,15 @@ def main():
         if ring is not None:
             return
         width = WF_WIDTH
-        x_shared = np.arange(width, dtype=np.int32)
+        x_shared = np.linspace(3.3, 14.3, width, dtype=np.float32)
         ring = np.full((max_sweeps, width), np.nan, dtype=np.float32)
         ring_time = np.full((max_sweeps,), np.nan, dtype=np.float64)
         head = 0
         # Обновляем изображение под новые размеры: время по X (горизонталь), X по Y
         img_obj.set_data(np.zeros((width, max_sweeps), dtype=np.float32))
-        img_obj.set_extent((0, max_sweeps - 1, 0, width - 1 if width > 0 else 1))
+        img_obj.set_extent((0, max_sweeps - 1, 3.3, 14.3))
         ax_img.set_xlim(0, max_sweeps - 1)
-        ax_img.set_ylim(0, max(1, width - 1))
+        ax_img.set_ylim(3.3, 14.3)
         # FFT буферы: время по X, бин по Y
         ring_fft = np.full((max_sweeps, fft_bins), np.nan, dtype=np.float32)
         img_fft_obj.set_data(np.zeros((fft_bins, max_sweeps), dtype=np.float32))
@@ -814,7 +1094,7 @@ def main():
                 # Окно Хэннинга
                 win = np.hanning(take_fft).astype(np.float32)
                 fft_in[:take_fft] = seg * win
-                spec = np.fft.rfft(fft_in)
+                spec = np.fft.ifft(fft_in)
                 mag = np.abs(spec).astype(np.float32)
                 fft_row = 20.0 * np.log10(mag + 1e-9)
                 if fft_row.shape[0] != bins:
@@ -831,7 +1111,7 @@ def main():
                 y_max_fft = float(fr_max)
 
     def drain_queue():
-        nonlocal current_sweep_raw, current_sweep_norm, current_info, last_calib_sweep
+        nonlocal current_sweep_raw, current_sweep_1, current_sweep_2, current_sweep_pre_exp, current_sweep_norm, current_info, last_calib_sweep
         drained = 0
         while True:
             try:
@@ -841,6 +1121,12 @@ def main():
             drained += 1
             current_sweep_raw = s
             current_info = info
+            s1 = info.get("sweep_1") if isinstance(info, dict) else None
+            s2 = info.get("sweep_2") if isinstance(info, dict) else None
+            current_sweep_1 = s1 if isinstance(s1, np.ndarray) else None
+            current_sweep_2 = s2 if isinstance(s2, np.ndarray) else None
+            pre = info.get("pre_exp_sweep") if isinstance(info, dict) else None
+            current_sweep_pre_exp = pre if isinstance(pre, np.ndarray) else None
             ch = 0
             try:
                 ch = int(info.get("ch", 0)) if isinstance(info, dict) else 0
@@ -909,6 +1195,16 @@ def main():
             else:
                 xs = np.arange(current_sweep_raw.size, dtype=np.int32)
             line_obj.set_data(xs, current_sweep_raw)
+            if current_sweep_1 is not None and current_sweep_2 is not None:
+                line_calib_obj.set_data(xs[: current_sweep_1.size], current_sweep_1)
+                line_norm_obj.set_data(xs[: current_sweep_2.size], current_sweep_2)
+            elif logdetector_enabled:
+                line_calib_obj.set_data([], [])
+                if current_sweep_pre_exp is not None:
+                    line_norm_obj.set_data(xs[: current_sweep_pre_exp.size], current_sweep_pre_exp)
+                else:
+                    line_norm_obj.set_data([], [])
+            else:
                 if last_calib_sweep is not None:
                     line_calib_obj.set_data(xs[: last_calib_sweep.size], last_calib_sweep)
                 else:
@@ -917,12 +1213,18 @@ def main():
                     line_norm_obj.set_data(xs[: current_sweep_norm.size], current_sweep_norm)
                 else:
                     line_norm_obj.set_data([], [])
-            # Лимиты по X постоянные под текущую ширину
-            ax_line.set_xlim(0, max(1, current_sweep_raw.size - 1))
+            # Лимиты по X: 3.3 ГГц .. 14.3 ГГц
+            ax_line.set_xlim(3.3, 14.3)
             # Адаптивные Y-лимиты (если не задан --ylim)
             if fixed_ylim is None:
-                y0 = float(np.nanmin(current_sweep_raw))
-                y1 = float(np.nanmax(current_sweep_raw))
+                y_candidates = [current_sweep_raw]
+                if current_sweep_1 is not None and current_sweep_2 is not None:
+                    y_candidates.extend([current_sweep_1, current_sweep_2])
+                elif logdetector_enabled and current_sweep_pre_exp is not None:
+                    y_candidates.append(current_sweep_pre_exp)
+                y_concat = np.concatenate([np.asarray(v, dtype=np.float32) for v in y_candidates])
+                y0 = float(np.nanmin(y_concat))
+                y1 = float(np.nanmax(y_concat))
                 if np.isfinite(y0) and np.isfinite(y1):
                     if y0 == y1:
                         pad = max(1.0, abs(y0) * 0.05)
@@ -942,7 +1244,7 @@ def main():
                 seg = np.nan_to_num(sweep_for_fft[:take_fft], nan=0.0).astype(np.float32, copy=False)
                 win = np.hanning(take_fft).astype(np.float32)
                 fft_in[:take_fft] = seg * win
-                spec = np.fft.rfft(fft_in)
+                spec = np.fft.ifft(fft_in)
                 mag = np.abs(spec).astype(np.float32)
                 fft_vals = 20.0 * np.log10(mag + 1e-9)
                 xs_fft = freq_shared
@@ -951,7 +1253,7 @@ def main():
                 fft_line_obj.set_data(xs_fft[: fft_vals.size], fft_vals)
                 # Авто-диапазон по Y для спектра
                 if np.isfinite(np.nanmin(fft_vals)) and np.isfinite(np.nanmax(fft_vals)):
-                    ax_fft.set_xlim(0, max(1, xs_fft.size - 1))
+                    ax_fft.set_xlim(0, max(1, xs_fft.size - 1) * 1.5)
                     ax_fft.set_ylim(float(np.nanmin(fft_vals)), float(np.nanmax(fft_vals)))
 
         # Обновление водопада
@@ -1057,7 +1359,15 @@ def run_pyqtgraph(args):
     # Очередь завершённых свипов и поток чтения
     q: Queue[SweepPacket] = Queue(maxsize=1000)
     stop_event = threading.Event()
-    reader = SweepReader(args.port, args.baud, q, stop_event, fancy=bool(args.fancy))
+    reader = SweepReader(
+        args.port,
+        args.baud,
+        q,
+        stop_event,
+        fancy=bool(args.fancy),
+        bin_mode=bool(getattr(args, "bin_mode", False)),
+        logdetector=bool(getattr(args, "logdetector", False)),
+    )
     reader.start()
 
     # Настройки скорости
@@ -1075,9 +1385,9 @@ def run_pyqtgraph(args):
     p_line = win.addPlot(row=0, col=0, title="Сырые данные")
     p_line.showGrid(x=True, y=True, alpha=0.3)
     curve = p_line.plot(pen=pg.mkPen((80, 120, 255), width=1))
-    curve_calib = p_line.plot(pen=pg.mkPen((220, 60, 60), width=1))
+    curve_calib = p_line.plot(pen=pg.mkPen((220, 200, 60), width=1))
     curve_norm = p_line.plot(pen=pg.mkPen((60, 180, 90), width=1))
-    p_line.setLabel("bottom", "X")
+    p_line.setLabel("bottom", "ГГц")
     p_line.setLabel("left", "Y")
     ch_text = pg.TextItem("", anchor=(1, 1))
     ch_text.setZValue(10)
@@ -1092,7 +1402,7 @@ def run_pyqtgraph(args):
         p_img.getAxis("bottom").setStyle(showValues=False)
     except Exception:
         pass
-    p_img.setLabel("left", "X (0 снизу)")
+    p_img.setLabel("left", "ГГц")
     img = pg.ImageItem()
     p_img.addItem(img)
 
@@ -1100,8 +1410,8 @@ def run_pyqtgraph(args):
     p_fft = win.addPlot(row=1, col=0, title="FFT")
     p_fft.showGrid(x=True, y=True, alpha=0.3)
     curve_fft = p_fft.plot(pen=pg.mkPen((255, 120, 80), width=1))
-    p_fft.setLabel("bottom", "Бин")
-    p_fft.setLabel("left", "Амплитуда, дБ")
+    p_fft.setLabel("bottom", "Время")
+    p_fft.setLabel("left", "дБ")
 
     # Водопад спектров (справа-снизу)
     p_spec = win.addPlot(row=1, col=1, title="B-scan (дБ)")
@@ -1133,6 +1443,9 @@ def run_pyqtgraph(args):
     width: Optional[int] = None
     x_shared: Optional[np.ndarray] = None
     current_sweep_raw: Optional[np.ndarray] = None
+    current_sweep_1: Optional[np.ndarray] = None
+    current_sweep_2: Optional[np.ndarray] = None
+    current_sweep_pre_exp: Optional[np.ndarray] = None
     current_sweep_norm: Optional[np.ndarray] = None
     last_calib_sweep: Optional[np.ndarray] = None
     current_info: Optional[SweepInfo] = None
@@ -1146,6 +1459,8 @@ def run_pyqtgraph(args):
     spec_clip = _parse_spec_clip(getattr(args, "spec_clip", None))
     spec_mean_sec = float(getattr(args, "spec_mean_sec", 0.0))
     calib_enabled = False
+    norm_type = str(getattr(args, "norm_type", "projector")).strip().lower()
+    logdetector_enabled = bool(getattr(args, "logdetector", False))
     # Диапазон по Y: авто по умолчанию (поддерживает отрицательные значения)
     fixed_ylim: Optional[Tuple[float, float]] = None
     if args.ylim:
@@ -1158,14 +1473,7 @@ def run_pyqtgraph(args):
         p_line.setYRange(fixed_ylim[0], fixed_ylim[1], padding=0)
 
     def _normalize_sweep(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
-        w = min(raw.size, calib.size)
-        if w <= 0:
-            return raw
-        out = np.full_like(raw, np.nan, dtype=np.float32)
-        with np.errstate(divide="ignore", invalid="ignore"):
-            out[:w] = raw[:w] / calib[:w]
-        out = np.nan_to_num(out, nan=np.nan, posinf=np.nan, neginf=np.nan)
-        return out
+        return _normalize_by_calib(raw, calib, norm_type=norm_type)
 
     def _set_calib_enabled():
         nonlocal calib_enabled, current_sweep_norm
@@ -1188,14 +1496,15 @@ def run_pyqtgraph(args):
         if ring is not None:
             return
         width = WF_WIDTH
-        x_shared = np.arange(width, dtype=np.int32)
+        x_shared = np.linspace(3.3, 14.3, width, dtype=np.float32)
         ring = np.full((max_sweeps, width), np.nan, dtype=np.float32)
         ring_time = np.full((max_sweeps,), np.nan, dtype=np.float64)
         head = 0
-        # Водопад: время по оси X, X по оси Y
+        # Водопад: время по оси X, X по оси Y (ось Y: 3.3..14.3 ГГц)
         img.setImage(ring.T, autoLevels=False)
-        p_img.setRange(xRange=(0, max_sweeps - 1), yRange=(0, max(1, width - 1)), padding=0)
-        p_line.setXRange(0, max(1, width - 1), padding=0)
+        img.setRect(0, 3.3, max_sweeps, 14.3 - 3.3)
+        p_img.setRange(xRange=(0, max_sweeps - 1), yRange=(3.3, 14.3), padding=0)
+        p_line.setXRange(3.3, 14.3, padding=0)
         # FFT: время по оси X, бин по оси Y
         ring_fft = np.full((max_sweeps, fft_bins), np.nan, dtype=np.float32)
         img_fft.setImage(ring_fft.T, autoLevels=False)
@@ -1255,7 +1564,7 @@ def run_pyqtgraph(args):
                 seg = np.nan_to_num(s[:take_fft], nan=0.0).astype(np.float32, copy=False)
                 win = np.hanning(take_fft).astype(np.float32)
                 fft_in[:take_fft] = seg * win
-                spec = np.fft.rfft(fft_in)
+                spec = np.fft.ifft(fft_in)
                 mag = np.abs(spec).astype(np.float32)
                 fft_row = 20.0 * np.log10(mag + 1e-9)
                 if fft_row.shape[0] != bins:
@@ -1271,7 +1580,7 @@ def run_pyqtgraph(args):
                 y_max_fft = float(fr_max)
 
     def drain_queue():
-        nonlocal current_sweep_raw, current_sweep_norm, current_info, last_calib_sweep
+        nonlocal current_sweep_raw, current_sweep_1, current_sweep_2, current_sweep_pre_exp, current_sweep_norm, current_info, last_calib_sweep
         drained = 0
         while True:
             try:
@@ -1281,6 +1590,12 @@ def run_pyqtgraph(args):
             drained += 1
             current_sweep_raw = s
             current_info = info
+            s1 = info.get("sweep_1") if isinstance(info, dict) else None
+            s2 = info.get("sweep_2") if isinstance(info, dict) else None
+            current_sweep_1 = s1 if isinstance(s1, np.ndarray) else None
+            current_sweep_2 = s2 if isinstance(s2, np.ndarray) else None
+            pre = info.get("pre_exp_sweep") if isinstance(info, dict) else None
+            current_sweep_pre_exp = pre if isinstance(pre, np.ndarray) else None
             ch = 0
             try:
                 ch = int(info.get("ch", 0)) if isinstance(info, dict) else 0
@@ -1318,6 +1633,16 @@ def run_pyqtgraph(args):
             else:
                 xs = np.arange(current_sweep_raw.size)
             curve.setData(xs, current_sweep_raw, autoDownsample=True)
+            if current_sweep_1 is not None and current_sweep_2 is not None:
+                curve_calib.setData(xs[: current_sweep_1.size], current_sweep_1, autoDownsample=True)
+                curve_norm.setData(xs[: current_sweep_2.size], current_sweep_2, autoDownsample=True)
+            elif logdetector_enabled:
+                curve_calib.setData([], [])
+                if current_sweep_pre_exp is not None:
+                    curve_norm.setData(xs[: current_sweep_pre_exp.size], current_sweep_pre_exp, autoDownsample=True)
+                else:
+                    curve_norm.setData([], [])
+            else:
                 if last_calib_sweep is not None:
                     curve_calib.setData(xs[: last_calib_sweep.size], last_calib_sweep, autoDownsample=True)
                 else:
@@ -1327,8 +1652,14 @@ def run_pyqtgraph(args):
                 else:
                     curve_norm.setData([], [])
             if fixed_ylim is None:
-                y0 = float(np.nanmin(current_sweep_raw))
-                y1 = float(np.nanmax(current_sweep_raw))
+                y_candidates = [current_sweep_raw]
+                if current_sweep_1 is not None and current_sweep_2 is not None:
+                    y_candidates.extend([current_sweep_1, current_sweep_2])
+                elif logdetector_enabled and current_sweep_pre_exp is not None:
+                    y_candidates.append(current_sweep_pre_exp)
+                y_concat = np.concatenate([np.asarray(v, dtype=np.float32) for v in y_candidates])
+                y0 = float(np.nanmin(y_concat))
+                y1 = float(np.nanmax(y_concat))
                 if np.isfinite(y0) and np.isfinite(y1):
                     margin = 0.05 * max(1.0, (y1 - y0))
                     p_line.setYRange(y0 - margin, y1 + margin, padding=0)
@@ -1341,13 +1672,14 @@ def run_pyqtgraph(args):
                 seg = np.nan_to_num(sweep_for_fft[:take_fft], nan=0.0).astype(np.float32, copy=False)
                 win = np.hanning(take_fft).astype(np.float32)
                 fft_in[:take_fft] = seg * win
-                spec = np.fft.rfft(fft_in)
+                spec = np.fft.ifft(fft_in)
                 mag = np.abs(spec).astype(np.float32)
                 fft_vals = 20.0 * np.log10(mag + 1e-9)
                 xs_fft = freq_shared
                 if fft_vals.size > xs_fft.size:
                     fft_vals = fft_vals[: xs_fft.size]
                 curve_fft.setData(xs_fft[: fft_vals.size], fft_vals)
+                p_fft.setXRange(0, max(1, xs_fft.size - 1) * 1.5, padding=0)
                 p_fft.setYRange(float(np.nanmin(fft_vals)), float(np.nanmax(fft_vals)), padding=0)
 
         if changed and ring is not None: