implemented new binary mode (--logscale): 2 32-bit values: avg_1, avg_2. Also implemented log-detector mode: avg_1,2 are processed as lg(signal_power) in def _log_pair_to_sweep. Tuning variables: LOG_BASE, LOG_SCALER, LOG_POSTSCALER.

RFG_ADC_dataplotter.py

@@ -33,13 +33,18 @@ import numpy as np
 
 WF_WIDTH = 1000  # максимальное число точек в ряду водопада
 FFT_LEN = 1024   # длина БПФ для спектра/водопада спектров
+LOG_BASE = 10.0
+LOG_SCALER = 0.001  # int32 значения приходят в fixed-point лог-шкале с шагом 1e-3
+LOG_POSTSCALER = 1000
+LOG_EXP_LIMIT = 300.0  # запас до переполнения float64 при возведении LOG_BASE в степень
 # 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
 
 Number = Union[int, float]
 SweepInfo = Dict[str, Any]
-SweepPacket = Tuple[np.ndarray, SweepInfo]
+SweepAuxCurves = Optional[Tuple[np.ndarray, np.ndarray]]
+SweepPacket = Tuple[np.ndarray, SweepInfo, SweepAuxCurves]
 
 
 def _format_status_kv(data: Mapping[str, Any]) -> str:
@@ -85,6 +90,44 @@ def _parse_spec_clip(spec: Optional[str]) -> Optional[Tuple[float, float]]:
         return None
 
 
+def _log_value_to_linear(value: int) -> float:
+    """Преобразовать fixed-point логарифмическое значение в линейную шкалу."""
+    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:
+    """Разность двух логарифмических усреднений в линейной шкале."""
+    return (_log_value_to_linear(avg_1) - _log_value_to_linear(avg_2))*LOG_POSTSCALER
+
+
+def _compute_auto_ylim(*series_list: Optional[np.ndarray]) -> Optional[Tuple[float, float]]:
+    """Общий Y-диапазон по всем переданным кривым с небольшим запасом."""
+    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)
+
+
 def _normalize_sweep_simple(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
     """Простая нормировка: поэлементное деление raw/calib."""
     w = min(raw.size, calib.size)
@@ -385,6 +428,7 @@ class SweepReader(threading.Thread):
         stop_event: threading.Event,
         fancy: bool = False,
         bin_mode: bool = False,
+        logscale: bool = False,
     ):
         super().__init__(daemon=True)
         self._port_path = port_path
@@ -394,6 +438,7 @@ class SweepReader(threading.Thread):
         self._src: Optional[SerialLineSource] = None
         self._fancy = bool(fancy)
         self._bin_mode = bool(bin_mode)
+        self._logscale = bool(logscale)
         self._max_width: int = 0
         self._sweep_idx: int = 0
         self._last_sweep_ts: Optional[float] = None
@@ -404,7 +449,14 @@ class SweepReader(threading.Thread):
         """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]]):
+    def _finalize_current(
+        self,
+        xs,
+        ys,
+        channels: Optional[set[int]],
+        raw_curves: Optional[Tuple[list[int], list[int]]] = None,
+        apply_inversion: bool = True,
+    ):
         if not xs:
             return
         ch_list = sorted(channels) if channels else [0]
@@ -413,17 +465,43 @@ 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
+
+        def _scatter(values, dtype) -> np.ndarray:
+            series = np.full((target_width,), np.nan, dtype=dtype)
+            try:
+                idx = np.asarray(xs, dtype=np.int64)
+                vals = np.asarray(values, dtype=dtype)
+                series[idx] = vals
+            except Exception:
+                for x, y in zip(xs, values):
+                    if 0 <= x < target_width:
+                        series[x] = y
+            return series
+
+        def _fill_missing(series: np.ndarray):
+            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]
+
         # Быстрый векторизованный путь
-        sweep = np.full((target_width,), np.nan, dtype=np.float32)
+        sweep = _scatter(ys, np.float32)
-        try:
+        aux_curves: SweepAuxCurves = None
-            idx = np.asarray(xs, dtype=np.int64)
+        if raw_curves is not None:
-            vals = np.asarray(ys, dtype=np.float32)
+            aux_curves = (
-            sweep[idx] = vals
+                _scatter(raw_curves[0], np.float32),
-        except Exception:
+                _scatter(raw_curves[1], np.float32),
-            # Запасной путь
+            )
-            for x, y in zip(xs, ys):
-                if 0 <= x < target_width:
-                    sweep[x] = float(y)
         # Метрики валидных точек до заполнения пропусков
         finite_pre = np.isfinite(sweep)
         n_valid_cur = int(np.count_nonzero(finite_pre))
@@ -431,31 +509,21 @@ class SweepReader(threading.Thread):
         # Дополнительная обработка пропусков: при --fancy заполняем внутренние разрывы, края и дотягиваем до максимальной длины
         if self._fancy:
             try:
-                known = ~np.isnan(sweep)
+                _fill_missing(sweep)
-                if np.any(known):
+                if aux_curves is not None:
-                    known_idx = np.nonzero(known)[0]
+                    _fill_missing(aux_curves[0])
-                    # Для каждой пары соседних известных индексов заполним промежуток средним значением
+                    _fill_missing(aux_curves[1])
-                    for i0, i1 in zip(known_idx[:-1], known_idx[1:]):
-                        if i1 - i0 > 1:
-                            avg = (sweep[i0] + sweep[i1]) * 0.5
-                            sweep[i0 + 1 : i1] = avg
-                    first_idx = int(known_idx[0])
-                    last_idx = int(known_idx[-1])
-                    if first_idx > 0:
-                        sweep[:first_idx] = sweep[first_idx]
-                    if last_idx < sweep.size - 1:
-                        sweep[last_idx + 1 :] = sweep[last_idx]
             except Exception:
                 # В случае ошибки просто оставляем как есть
                 pass
         # Инверсия данных при «отрицательном» уровне (среднее ниже порога)
-        try:
+        if apply_inversion:
-            m = float(np.nanmean(sweep))
+            try:
-            if np.isfinite(m) and m < DATA_INVERSION_THRASHOLD:
+                m = float(np.nanmean(sweep))
-                sweep *= -1.0
+                if np.isfinite(m) and m < DATA_INVERSION_THRASHOLD:
-                
+                    sweep *= -1.0
-        except Exception:
+            except Exception:
-            pass
+                pass
         #sweep = np.abs(sweep)
         #sweep -= float(np.nanmean(sweep))
 
@@ -500,14 +568,14 @@ class SweepReader(threading.Thread):
 
         # Кладём готовый свип (если очередь полна — выбрасываем самый старый)
         try:
-            self._q.put_nowait((sweep, info))
+            self._q.put_nowait((sweep, info, aux_curves))
         except Full:
             try:
                 _ = self._q.get_nowait()
             except Exception:
                 pass
             try:
-                self._q.put_nowait((sweep, info))
+                self._q.put_nowait((sweep, info, aux_curves))
             except Exception:
                 pass
 
@@ -653,6 +721,99 @@ class SweepReader(threading.Thread):
 
         self._finalize_current(xs, ys, cur_channels)
 
+    def _run_logscale_binary_stream(self, chunk_reader: SerialChunkReader):
+        xs: list[int] = []
+        ys: list[float] = []
+        avg_1_vals: list[int] = []
+        avg_2_vals: 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
+
+            # Бинарный logscale-протокол:
+            #   старт свипа: 0xFFFF x5, затем (ch<<8)|0x0A
+            #   точка: step, 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,
+                        raw_curves=(avg_1_vals, avg_2_vals),
+                        apply_inversion=False,
+                    )
+                    xs.clear()
+                    ys.clear()
+                    avg_1_vals.clear()
+                    avg_2_vals.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)
+                    avg_1 = self._u32_to_i32((w1 << 16) | w2)
+                    avg_2 = self._u32_to_i32((w3 << 16) | w4)
+                    xs.append(w0)
+                    avg_1_vals.append(avg_1)
+                    avg_2_vals.append(avg_2)
+                    ys.append(_log_pair_to_sweep(avg_1, avg_2))
+                    #ys.append(LOG_BASE**(avg_1/LOG_SCALER) - LOG_BASE**(avg_2/LOG_SCALER))
+                    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,
+            raw_curves=(avg_1_vals, avg_2_vals),
+            apply_inversion=False,
+        )
+
     def run(self):
         try:
             self._src = SerialLineSource(self._port_path, self._baud, timeout=1.0)
@@ -663,7 +824,9 @@ class SweepReader(threading.Thread):
 
         try:
             chunk_reader = SerialChunkReader(self._src)
-            if self._bin_mode:
+            if self._logscale:
+                self._run_logscale_binary_stream(chunk_reader)
+            elif self._bin_mode:
                 self._run_binary_stream(chunk_reader)
             else:
                 self._run_ascii_stream(chunk_reader)
@@ -740,6 +903,14 @@ def main():
             "точки step,uint32(hi16,lo16),0x000A"
         ),
     )
+    parser.add_argument(
+        "--logscale",
+        action="store_true",
+        help=(
+            "Новый бинарный протокол: точка несёт пару int32 (avg_1, avg_2), "
+            "а свип считается как 10**(avg_1*0.001) - 10**(avg_2*0.001)"
+        ),
+    )
 
     args = parser.parse_args()
 
@@ -772,6 +943,7 @@ def main():
         stop_event,
         fancy=bool(args.fancy),
         bin_mode=bool(args.bin_mode),
+        logscale=bool(args.logscale),
     )
     reader.start()
 
@@ -784,6 +956,7 @@ def main():
 
     # Состояние для отображения
     current_sweep_raw: Optional[np.ndarray] = None
+    current_aux_curves: SweepAuxCurves = None
     current_sweep_norm: Optional[np.ndarray] = None
     last_calib_sweep: Optional[np.ndarray] = None
     current_info: Optional[SweepInfo] = None
@@ -822,6 +995,8 @@ def main():
     )
 
     # Линейный график последнего свипа
+    line_avg1_obj, = ax_line.plot([], [], lw=1, color="0.65")
+    line_avg2_obj, = ax_line.plot([], [], lw=1, color="0.45")
     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")
@@ -1041,15 +1216,16 @@ 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_aux_curves, current_sweep_norm, current_info, last_calib_sweep
         drained = 0
         while True:
             try:
-                s, info = q.get_nowait()
+                s, info, aux_curves = q.get_nowait()
             except Empty:
                 break
             drained += 1
             current_sweep_raw = s
+            current_aux_curves = aux_curves
             current_info = info
             ch = 0
             try:
@@ -1119,6 +1295,13 @@ def main():
             else:
                 xs = np.arange(current_sweep_raw.size, dtype=np.int32)
             line_obj.set_data(xs, current_sweep_raw)
+            if current_aux_curves is not None:
+                avg_1_curve, avg_2_curve = current_aux_curves
+                line_avg1_obj.set_data(xs[: avg_1_curve.size], avg_1_curve)
+                line_avg2_obj.set_data(xs[: avg_2_curve.size], avg_2_curve)
+            else:
+                line_avg1_obj.set_data([], [])
+                line_avg2_obj.set_data([], [])
             if last_calib_sweep is not None:
                 line_calib_obj.set_data(xs[: last_calib_sweep.size], last_calib_sweep)
             else:
@@ -1131,18 +1314,12 @@ def main():
             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))
+                y_series = [current_sweep_raw, last_calib_sweep, current_sweep_norm]
-                y1 = float(np.nanmax(current_sweep_raw))
+                if current_aux_curves is not None:
-                if np.isfinite(y0) and np.isfinite(y1):
+                    y_series.extend(current_aux_curves)
-                    if y0 == y1:
+                y_limits = _compute_auto_ylim(*y_series)
-                        pad = max(1.0, abs(y0) * 0.05)
+                if y_limits is not None:
-                        y0 -= pad
+                    ax_line.set_ylim(y_limits[0], y_limits[1])
-                        y1 += pad
-                    else:
-                        pad = 0.05 * (y1 - y0)
-                        y0 -= pad
-                        y1 += pad
-                    ax_line.set_ylim(y0, y1)
 
             # Обновление спектра текущего свипа
             sweep_for_fft = current_sweep_norm if current_sweep_norm is not None else current_sweep_raw
@@ -1232,6 +1409,8 @@ def main():
         # Возвращаем обновлённые артисты
         return (
             line_obj,
+            line_avg1_obj,
+            line_avg2_obj,
             line_calib_obj,
             line_norm_obj,
             img_obj,
@@ -1274,6 +1453,7 @@ def run_pyqtgraph(args):
         stop_event,
         fancy=bool(args.fancy),
         bin_mode=bool(args.bin_mode),
+        logscale=bool(args.logscale),
     )
     reader.start()
 
@@ -1291,6 +1471,8 @@ def run_pyqtgraph(args):
     # Плот последнего свипа (слева-сверху)
     p_line = win.addPlot(row=0, col=0, title="Сырые данные")
     p_line.showGrid(x=True, y=True, alpha=0.3)
+    curve_avg1 = p_line.plot(pen=pg.mkPen((170, 170, 170), width=1))
+    curve_avg2 = p_line.plot(pen=pg.mkPen((110, 110, 110), width=1))
     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))
@@ -1350,6 +1532,7 @@ def run_pyqtgraph(args):
     width: Optional[int] = None
     x_shared: Optional[np.ndarray] = None
     current_sweep_raw: Optional[np.ndarray] = None
+    current_aux_curves: SweepAuxCurves = None
     current_sweep_norm: Optional[np.ndarray] = None
     last_calib_sweep: Optional[np.ndarray] = None
     current_info: Optional[SweepInfo] = None
@@ -1483,15 +1666,16 @@ 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_aux_curves, current_sweep_norm, current_info, last_calib_sweep
         drained = 0
         while True:
             try:
-                s, info = q.get_nowait()
+                s, info, aux_curves = q.get_nowait()
             except Empty:
                 break
             drained += 1
             current_sweep_raw = s
+            current_aux_curves = aux_curves
             current_info = info
             ch = 0
             try:
@@ -1530,6 +1714,13 @@ def run_pyqtgraph(args):
             else:
                 xs = np.arange(current_sweep_raw.size)
             curve.setData(xs, current_sweep_raw, autoDownsample=True)
+            if current_aux_curves is not None:
+                avg_1_curve, avg_2_curve = current_aux_curves
+                curve_avg1.setData(xs[: avg_1_curve.size], avg_1_curve, autoDownsample=True)
+                curve_avg2.setData(xs[: avg_2_curve.size], avg_2_curve, autoDownsample=True)
+            else:
+                curve_avg1.setData([], [])
+                curve_avg2.setData([], [])
             if last_calib_sweep is not None:
                 curve_calib.setData(xs[: last_calib_sweep.size], last_calib_sweep, autoDownsample=True)
             else:
@@ -1539,11 +1730,12 @@ def run_pyqtgraph(args):
             else:
                 curve_norm.setData([], [])
             if fixed_ylim is None:
-                y0 = float(np.nanmin(current_sweep_raw))
+                y_series = [current_sweep_raw, last_calib_sweep, current_sweep_norm]
-                y1 = float(np.nanmax(current_sweep_raw))
+                if current_aux_curves is not None:
-                if np.isfinite(y0) and np.isfinite(y1):
+                    y_series.extend(current_aux_curves)
-                    margin = 0.05 * max(1.0, (y1 - y0))
+                y_limits = _compute_auto_ylim(*y_series)
-                    p_line.setYRange(y0 - margin, y1 + margin, padding=0)
+                if y_limits is not None:
+                    p_line.setYRange(y_limits[0], y_limits[1], padding=0)
 
             # Обновим спектр
             sweep_for_fft = current_sweep_norm if current_sweep_norm is not None else current_sweep_raw