implemented new normalisator mode: projector. It takes upper and lower evenlopes of ref signal and projects raw data from evenlopes scope to +-1000

для управления этим.

RFG_ADC_dataplotter.py

@@ -4,7 +4,7 @@
 
 Формат строк:
   - "Sweep_start" — начало нового свипа (предыдущий считается завершённым)
-  - "s X Y" — точка (индекс X, значение Y), все целые со знаком
+  - "s CH X Y" — точка (номер канала, индекс X, значение Y), все целые со знаком
 
 Отрисовываются два графика:
   - Левый: последний полученный свип (Y vs X)
@@ -38,7 +38,7 @@ FFT_LEN = 1024   # длина БПФ для спектра/водопада сп
 DATA_INVERSION_THRASHOLD = 10.0
 
 Number = Union[int, float]
-SweepInfo = Dict[str, Number]
+SweepInfo = Dict[str, Any]
 SweepPacket = Tuple[np.ndarray, SweepInfo]
 
 
@@ -85,6 +85,116 @@ 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
@@ -287,9 +397,11 @@ class SweepReader(threading.Thread):
         self._last_sweep_ts: Optional[float] = None
         self._n_valid_hist = deque()
 
-    def _finalize_current(self, xs, ys):
+    def _finalize_current(self, xs, ys, channels: Optional[set[int]]):
         if not xs:
             return
+        ch_list = sorted(channels) if channels else [0]
+        ch_primary = ch_list[0] if ch_list else 0
         max_x = max(xs)
         width = max_x + 1
         self._max_width = max(self._max_width, width)
@@ -336,10 +448,14 @@ class SweepReader(threading.Thread):
                 sweep *= -1.0
         except Exception:
             pass
-        sweep -= float(np.nanmean(sweep))
+        #sweep -= float(np.nanmean(sweep))
 
         # Метрики для статусной строки (вид словаря: переменная -> значение)
         self._sweep_idx += 1
+        if len(ch_list) > 1:
+            sys.stderr.write(
+                f"[warn] Sweep {self._sweep_idx}: изменялся номер канала: {ch_list}\n"
+            )
         now = time.time()
         if self._last_sweep_ts is None:
             dt_ms = float("nan")
@@ -363,6 +479,8 @@ class SweepReader(threading.Thread):
             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,
@@ -388,6 +506,8 @@ class SweepReader(threading.Thread):
         # Состояние текущего свипа
         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)
@@ -422,20 +542,37 @@ class SweepReader(threading.Thread):
                         continue
 
                     if line.startswith(b"Sweep_start"):
-                        self._finalize_current(xs, ys)
+                        self._finalize_current(xs, ys, cur_channels)
                         xs.clear()
                         ys.clear()
+                        cur_channel = None
+                        cur_channels.clear()
                         continue
 
-                    # s X Y (оба целые со знаком). Разделяем по любым пробелам/табам.
+                    # 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":
+                        if len(parts) >= 3 and (parts[0].lower() == b"s" or parts[0].lower().startswith(b"s")):
                             try:
-                                x = int(parts[1], 10)
-                                y = int(parts[2], 10)  # поддержка знака: "+…" и "-…"
+                                if parts[0].lower() == b"s":
+                                    if len(parts) >= 4:
+                                        ch = int(parts[1], 10)
+                                        x = int(parts[2], 10)
+                                        y = int(parts[3], 10)  # поддержка знака: "+…" и "-…"
+                                    else:
+                                        ch = 0
+                                        x = int(parts[1], 10)
+                                        y = int(parts[2], 10)  # поддержка знака: "+…" и "-…"
+                                else:
+                                    # формат вида "s0"
+                                    ch = int(parts[0][1:], 10)
+                                    x = int(parts[1], 10)
+                                    y = int(parts[2], 10)  # поддержка знака: "+…" и "-…"
                             except Exception:
                                 continue
+                            if cur_channel is None:
+                                cur_channel = ch
+                            cur_channels.add(ch)
                             xs.append(x)
                             ys.append(y)
 
@@ -445,7 +582,7 @@ class SweepReader(threading.Thread):
         finally:
             try:
                 # Завершаем оставшийся свип
-                self._finalize_current(xs, ys)
+                self._finalize_current(xs, ys, cur_channels)
             except Exception:
                 pass
             try:
@@ -478,6 +615,15 @@ def main():
             "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",
@@ -496,6 +642,12 @@ 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)",
+    )
 
     args = parser.parse_args()
 
@@ -513,7 +665,7 @@ def main():
         import matplotlib
         import matplotlib.pyplot as plt
         from matplotlib.animation import FuncAnimation
-        from matplotlib.widgets import Slider
+        from matplotlib.widgets import Slider, CheckButtons
     except Exception as e:
         sys.stderr.write(f"[error] Нужны matplotlib и ее зависимости: {e}\n")
         sys.exit(1)
@@ -532,7 +684,9 @@ def main():
     fig.subplots_adjust(wspace=0.25, hspace=0.35, left=0.07, right=0.90, top=0.92, bottom=0.08)
 
     # Состояние для отображения
-    current_sweep: Optional[np.ndarray] = None
+    current_sweep_raw: Optional[np.ndarray] = None
+    current_sweep_norm: Optional[np.ndarray] = None
+    last_calib_sweep: Optional[np.ndarray] = None
     current_info: Optional[SweepInfo] = None
     x_shared: Optional[np.ndarray] = None
     width: Optional[int] = None
@@ -548,10 +702,14 @@ def main():
     freq_shared: Optional[np.ndarray] = None
     # Параметры контраста водопада спектров
     spec_clip = _parse_spec_clip(getattr(args, "spec_clip", None))
+    spec_mean_sec = float(getattr(args, "spec_mean_sec", 0.0))
     # Ползунки управления Y для B-scan и контрастом
     ymin_slider = None
     ymax_slider = None
     contrast_slider = None
+    calib_enabled = False
+    norm_type = str(getattr(args, "norm_type", "projector")).strip().lower()
+    cb = None
 
     # Статусная строка (внизу окна)
     status_text = fig.text(
@@ -565,10 +723,22 @@ def main():
     )
 
     # Линейный график последнего свипа
-    line_obj, = ax_line.plot([], [], lw=1)
+    line_obj, = ax_line.plot([], [], lw=1, color="tab:blue")
+    line_calib_obj, = ax_line.plot([], [], lw=1, color="tab:red")
+    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_ylabel("")
+    channel_text = ax_line.text(
+        0.98,
+        0.98,
+        "",
+        transform=ax_line.transAxes,
+        ha="right",
+        va="top",
+        fontsize=9,
+        family="monospace",
+    )
 
     # Линейный график спектра текущего свипа
     fft_line_obj, = ax_fft.plot([], [], lw=1)
@@ -621,14 +791,31 @@ def main():
         ax_spec.tick_params(axis="x", labelbottom=False)
     except Exception:
         pass
+
+    def _normalize_sweep(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
+        return _normalize_by_calib(raw, calib, norm_type=norm_type)
+
+    def _set_calib_enabled():
+        nonlocal calib_enabled, current_sweep_norm
+        try:
+            calib_enabled = bool(cb.get_status()[0]) if cb is not None else False
+        except Exception:
+            calib_enabled = False
+        if calib_enabled and current_sweep_raw is not None and last_calib_sweep is not None:
+            current_sweep_norm = _normalize_sweep(current_sweep_raw, last_calib_sweep)
+        else:
+            current_sweep_norm = None
+
     # Слайдеры для управления осью Y B-scan (мин/макс) и контрастом
     try:
         ax_smin = fig.add_axes([0.92, 0.55, 0.02, 0.35])
         ax_smax = fig.add_axes([0.95, 0.55, 0.02, 0.35])
         ax_sctr = fig.add_axes([0.98, 0.55, 0.02, 0.35])
+        ax_cb = fig.add_axes([0.92, 0.45, 0.08, 0.08])
         ymin_slider = Slider(ax_smin, "Y min", 0, max(1, fft_bins - 1), valinit=0, valstep=1, orientation="vertical")
         ymax_slider = Slider(ax_smax, "Y max", 0, max(1, fft_bins - 1), valinit=max(1, fft_bins - 1), valstep=1, orientation="vertical")
         contrast_slider = Slider(ax_sctr, "Int max", 0, 100, valinit=100, valstep=1, orientation="vertical")
+        cb = CheckButtons(ax_cb, ["калибровка"], [False])
 
         def _on_ylim_change(_val):
             try:
@@ -643,6 +830,7 @@ def main():
         ymax_slider.on_changed(_on_ylim_change)
         # Контраст влияет на верхнюю границу цветовой шкалы (процент от авто-диапазона)
         contrast_slider.on_changed(lambda _v: fig.canvas.draw_idle())
+        cb.on_clicked(lambda _v: _set_calib_enabled())
     except Exception:
         pass
 
@@ -651,6 +839,7 @@ def main():
     interval_ms = int(1000.0 / max_fps)
     frames_since_ylim_update = 0
 
+
     def ensure_buffer(_w: int):
         nonlocal ring, width, head, x_shared, ring_fft, freq_shared, ring_time
         if ring is not None:
@@ -753,7 +942,7 @@ def main():
                 y_max_fft = float(fr_max)
 
     def drain_queue():
-        nonlocal current_sweep, current_info
+        nonlocal current_sweep_raw, current_sweep_norm, current_info, last_calib_sweep
         drained = 0
         while True:
             try:
@@ -761,10 +950,26 @@ def main():
             except Empty:
                 break
             drained += 1
-            current_sweep = s
+            current_sweep_raw = s
             current_info = info
+            ch = 0
+            try:
+                ch = int(info.get("ch", 0)) if isinstance(info, dict) else 0
+            except Exception:
+                ch = 0
+            if ch == 0:
+                last_calib_sweep = s
+                current_sweep_norm = None
+                sweep_for_proc = s
+            else:
+                if calib_enabled and last_calib_sweep is not None:
+                    current_sweep_norm = _normalize_sweep(s, last_calib_sweep)
+                    sweep_for_proc = current_sweep_norm
+                else:
+                    current_sweep_norm = None
+                    sweep_for_proc = s
             ensure_buffer(s.size)
-            push_sweep(s)
+            push_sweep(sweep_for_proc)
         return drained
 
     def make_display_ring():
@@ -780,6 +985,24 @@ def main():
         base_t = ring_time if head == 0 else np.roll(ring_time, -head)
         return base_t
 
+    def _subtract_recent_mean_fft(disp_fft: np.ndarray) -> np.ndarray:
+        """Вычесть среднее по каждой частоте за последние spec_mean_sec секунд."""
+        if spec_mean_sec <= 0.0:
+            return disp_fft
+        disp_times = make_display_times()
+        if disp_times is None:
+            return disp_fft
+        now_t = time.time()
+        mask = np.isfinite(disp_times) & (disp_times >= (now_t - spec_mean_sec))
+        if not np.any(mask):
+            return disp_fft
+        try:
+            mean_spec = np.nanmean(disp_fft[:, mask], axis=1)
+        except Exception:
+            return disp_fft
+        mean_spec = np.nan_to_num(mean_spec, nan=0.0)
+        return disp_fft - mean_spec[:, None]
+
     def make_display_ring_fft():
         if ring_fft is None:
             return np.zeros((1, 1), dtype=np.float32)
@@ -791,18 +1014,26 @@ def main():
         changed = drain_queue() > 0
 
         # Обновление линии последнего свипа
-        if current_sweep is not None:
-            if x_shared is not None and current_sweep.size <= x_shared.size:
-                xs = x_shared[: current_sweep.size]
+        if current_sweep_raw is not None:
+            if x_shared is not None and current_sweep_raw.size <= x_shared.size:
+                xs = x_shared[: current_sweep_raw.size]
             else:
-                xs = np.arange(current_sweep.size, dtype=np.int32)
-            line_obj.set_data(xs, current_sweep)
+                xs = np.arange(current_sweep_raw.size, dtype=np.int32)
+            line_obj.set_data(xs, current_sweep_raw)
+            if last_calib_sweep is not None:
+                line_calib_obj.set_data(xs[: last_calib_sweep.size], last_calib_sweep)
+            else:
+                line_calib_obj.set_data([], [])
+            if current_sweep_norm is not None:
+                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.size - 1))
+            ax_line.set_xlim(0, max(1, current_sweep_raw.size - 1))
             # Адаптивные Y-лимиты (если не задан --ylim)
             if fixed_ylim is None:
-                y0 = float(np.nanmin(current_sweep))
-                y1 = float(np.nanmax(current_sweep))
+                y0 = float(np.nanmin(current_sweep_raw))
+                y1 = float(np.nanmax(current_sweep_raw))
                 if np.isfinite(y0) and np.isfinite(y1):
                     if y0 == y1:
                         pad = max(1.0, abs(y0) * 0.05)
@@ -815,10 +1046,11 @@ def main():
                     ax_line.set_ylim(y0, y1)
 
             # Обновление спектра текущего свипа
-            take_fft = min(int(current_sweep.size), FFT_LEN)
+            sweep_for_fft = current_sweep_norm if current_sweep_norm is not None else current_sweep_raw
+            take_fft = min(int(sweep_for_fft.size), FFT_LEN)
             if take_fft > 0 and freq_shared is not None:
                 fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
-                seg = np.nan_to_num(current_sweep[:take_fft], nan=0.0).astype(np.float32, copy=False)
+                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)
@@ -847,6 +1079,7 @@ def main():
         # Обновление водопада спектров
         if changed and ring_fft is not None:
             disp_fft = make_display_ring_fft()
+            disp_fft = _subtract_recent_mean_fft(disp_fft)
             # Новые данные справа: без реверса
             img_fft_obj.set_data(disp_fft)
             # Подписи времени не обновляем динамически (оставляем авто-тики)
@@ -881,9 +1114,33 @@ def main():
 
         if changed and current_info:
             status_text.set_text(_format_status_kv(current_info))
+            chs = current_info.get("chs") if isinstance(current_info, dict) else None
+            if chs is None:
+                chs = current_info.get("ch") if isinstance(current_info, dict) else None
+            if chs is None:
+                channel_text.set_text("")
+            else:
+                try:
+                    if isinstance(chs, (list, tuple, set)):
+                        ch_list = sorted(int(v) for v in chs)
+                        ch_text_val = ", ".join(str(v) for v in ch_list)
+                    else:
+                        ch_text_val = str(int(chs))
+                    channel_text.set_text(f"chs {ch_text_val}")
+                except Exception:
+                    channel_text.set_text(f"chs {chs}")
 
         # Возвращаем обновлённые артисты
-        return (line_obj, img_obj, fft_line_obj, img_fft_obj, status_text)
+        return (
+            line_obj,
+            line_calib_obj,
+            line_norm_obj,
+            img_obj,
+            fft_line_obj,
+            img_fft_obj,
+            status_text,
+            channel_text,
+        )
 
     ani = FuncAnimation(fig, update, interval=interval_ms, blit=False)
 
@@ -929,8 +1186,13 @@ 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_norm = p_line.plot(pen=pg.mkPen((60, 180, 90), width=1))
     p_line.setLabel("bottom", "X")
     p_line.setLabel("left", "Y")
+    ch_text = pg.TextItem("", anchor=(1, 1))
+    ch_text.setZValue(10)
+    p_line.addItem(ch_text)
 
     # Водопад (справа-сверху)
     p_img = win.addPlot(row=0, col=1, title="Сырые данные водопад")
@@ -965,16 +1227,25 @@ def run_pyqtgraph(args):
     img_fft = pg.ImageItem()
     p_spec.addItem(img_fft)
 
+    # Чекбокс калибровки
+    calib_cb = QtWidgets.QCheckBox("калибровка")
+    cb_proxy = QtWidgets.QGraphicsProxyWidget()
+    cb_proxy.setWidget(calib_cb)
+    win.addItem(cb_proxy, row=2, col=1)
+
     # Статусная строка (внизу окна)
     status = pg.LabelItem(justify="left")
-    win.addItem(status, row=2, col=0, colspan=2)
+    win.addItem(status, row=3, col=0, colspan=2)
 
     # Состояние
     ring: Optional[np.ndarray] = None
+    ring_time: Optional[np.ndarray] = None
     head = 0
     width: Optional[int] = None
     x_shared: Optional[np.ndarray] = None
-    current_sweep: Optional[np.ndarray] = None
+    current_sweep_raw: Optional[np.ndarray] = None
+    current_sweep_norm: Optional[np.ndarray] = None
+    last_calib_sweep: Optional[np.ndarray] = None
     current_info: Optional[SweepInfo] = None
     # Авто-уровни цветовой шкалы водопада сырых данных пересчитываются по видимой области.
     # Для спектров
@@ -984,6 +1255,9 @@ def run_pyqtgraph(args):
     y_min_fft, y_max_fft = None, None
     # Параметры контраста водопада спектров (процентильная обрезка)
     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()
     # Диапазон по Y: авто по умолчанию (поддерживает отрицательные значения)
     fixed_ylim: Optional[Tuple[float, float]] = None
     if args.ylim:
@@ -995,13 +1269,33 @@ def run_pyqtgraph(args):
     if fixed_ylim is not None:
         p_line.setYRange(fixed_ylim[0], fixed_ylim[1], padding=0)
 
+    def _normalize_sweep(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
+        return _normalize_by_calib(raw, calib, norm_type=norm_type)
+
+    def _set_calib_enabled():
+        nonlocal calib_enabled, current_sweep_norm
+        try:
+            calib_enabled = bool(calib_cb.isChecked())
+        except Exception:
+            calib_enabled = False
+        if calib_enabled and current_sweep_raw is not None and last_calib_sweep is not None:
+            current_sweep_norm = _normalize_sweep(current_sweep_raw, last_calib_sweep)
+        else:
+            current_sweep_norm = None
+
+    try:
+        calib_cb.stateChanged.connect(lambda _v: _set_calib_enabled())
+    except Exception:
+        pass
+
     def ensure_buffer(_w: int):
-        nonlocal ring, head, width, x_shared, ring_fft, freq_shared
+        nonlocal ring, ring_time, head, width, x_shared, ring_fft, freq_shared
         if ring is not None:
             return
         width = WF_WIDTH
         x_shared = np.arange(width, dtype=np.int32)
         ring = np.full((max_sweeps, width), np.nan, dtype=np.float32)
+        ring_time = np.full((max_sweeps,), np.nan, dtype=np.float64)
         head = 0
         # Водопад: время по оси X, X по оси Y
         img.setImage(ring.T, autoLevels=False)
@@ -1046,7 +1340,7 @@ def run_pyqtgraph(args):
         return (vmin, vmax)
 
     def push_sweep(s: np.ndarray):
-        nonlocal ring, head, ring_fft, y_min_fft, y_max_fft
+        nonlocal ring, ring_time, head, ring_fft, y_min_fft, y_max_fft
         if s is None or s.size == 0 or ring is None:
             return
         w = ring.shape[1]
@@ -1054,6 +1348,8 @@ def run_pyqtgraph(args):
         take = min(w, s.size)
         row[:take] = s[:take]
         ring[head, :] = row
+        if ring_time is not None:
+            ring_time[head] = time.time()
         head = (head + 1) % ring.shape[0]
         # FFT строка (дБ)
         if ring_fft is not None:
@@ -1080,7 +1376,7 @@ def run_pyqtgraph(args):
                 y_max_fft = float(fr_max)
 
     def drain_queue():
-        nonlocal current_sweep, current_info
+        nonlocal current_sweep_raw, current_sweep_norm, current_info, last_calib_sweep
         drained = 0
         while True:
             try:
@@ -1088,10 +1384,26 @@ def run_pyqtgraph(args):
             except Empty:
                 break
             drained += 1
-            current_sweep = s
+            current_sweep_raw = s
             current_info = info
+            ch = 0
+            try:
+                ch = int(info.get("ch", 0)) if isinstance(info, dict) else 0
+            except Exception:
+                ch = 0
+            if ch == 0:
+                last_calib_sweep = s
+                current_sweep_norm = None
+                sweep_for_proc = s
+            else:
+                if calib_enabled and last_calib_sweep is not None:
+                    current_sweep_norm = _normalize_sweep(s, last_calib_sweep)
+                    sweep_for_proc = current_sweep_norm
+                else:
+                    current_sweep_norm = None
+                    sweep_for_proc = s
             ensure_buffer(s.size)
-            push_sweep(s)
+            push_sweep(sweep_for_proc)
         return drained
 
     # Попытка применить LUT из колормэпа (если доступен)
@@ -1105,24 +1417,33 @@ def run_pyqtgraph(args):
 
     def update():
         changed = drain_queue() > 0
-        if current_sweep is not None and x_shared is not None:
-            if current_sweep.size <= x_shared.size:
-                xs = x_shared[: current_sweep.size]
+        if current_sweep_raw is not None and x_shared is not None:
+            if current_sweep_raw.size <= x_shared.size:
+                xs = x_shared[: current_sweep_raw.size]
             else:
-                xs = np.arange(current_sweep.size)
-            curve.setData(xs, current_sweep, autoDownsample=True)
+                xs = np.arange(current_sweep_raw.size)
+            curve.setData(xs, current_sweep_raw, autoDownsample=True)
+            if last_calib_sweep is not None:
+                curve_calib.setData(xs[: last_calib_sweep.size], last_calib_sweep, autoDownsample=True)
+            else:
+                curve_calib.setData([], [])
+            if current_sweep_norm is not None:
+                curve_norm.setData(xs[: current_sweep_norm.size], current_sweep_norm, autoDownsample=True)
+            else:
+                curve_norm.setData([], [])
             if fixed_ylim is None:
-                y0 = float(np.nanmin(current_sweep))
-                y1 = float(np.nanmax(current_sweep))
+                y0 = float(np.nanmin(current_sweep_raw))
+                y1 = float(np.nanmax(current_sweep_raw))
                 if np.isfinite(y0) and np.isfinite(y1):
                     margin = 0.05 * max(1.0, (y1 - y0))
                     p_line.setYRange(y0 - margin, y1 + margin, padding=0)
 
             # Обновим спектр
-            take_fft = min(int(current_sweep.size), FFT_LEN)
+            sweep_for_fft = current_sweep_norm if current_sweep_norm is not None else current_sweep_raw
+            take_fft = min(int(sweep_for_fft.size), FFT_LEN)
             if take_fft > 0 and freq_shared is not None:
                 fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
-                seg = np.nan_to_num(current_sweep[:take_fft], nan=0.0).astype(np.float32, copy=False)
+                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)
@@ -1148,10 +1469,41 @@ def run_pyqtgraph(args):
                 status.setText(_format_status_kv(current_info))
             except Exception:
                 pass
+            try:
+                chs = current_info.get("chs") if isinstance(current_info, dict) else None
+                if chs is None:
+                    chs = current_info.get("ch") if isinstance(current_info, dict) else None
+                if chs is None:
+                    ch_text.setText("")
+                else:
+                    if isinstance(chs, (list, tuple, set)):
+                        ch_list = sorted(int(v) for v in chs)
+                        ch_text_val = ", ".join(str(v) for v in ch_list)
+                    else:
+                        ch_text_val = str(int(chs))
+                    ch_text.setText(f"chs {ch_text_val}")
+                (x0, x1), (y0, y1) = p_line.viewRange()
+                dx = 0.01 * max(1.0, float(x1 - x0))
+                dy = 0.01 * max(1.0, float(y1 - y0))
+                ch_text.setPos(float(x1 - dx), float(y1 - dy))
+            except Exception:
+                pass
 
         if changed and ring_fft is not None:
             disp_fft = ring_fft if head == 0 else np.roll(ring_fft, -head, axis=0)
             disp_fft = disp_fft.T[:, ::-1]
+            if spec_mean_sec > 0.0 and ring_time is not None:
+                disp_times = ring_time if head == 0 else np.roll(ring_time, -head)
+                disp_times = disp_times[::-1]
+                now_t = time.time()
+                mask = np.isfinite(disp_times) & (disp_times >= (now_t - spec_mean_sec))
+                if np.any(mask):
+                    try:
+                        mean_spec = np.nanmean(disp_fft[:, mask], axis=1)
+                        mean_spec = np.nan_to_num(mean_spec, nan=0.0)
+                        disp_fft = disp_fft - mean_spec[:, None]
+                    except Exception:
+                        pass
             # Автодиапазон по среднему спектру за видимый интервал (как в хорошей версии)
             levels = None
             try: