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

RFG_ADC_dataplotter.py

@@ -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
@@ -532,6 +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()
 
@@ -592,6 +708,7 @@ def main():
     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)
@@ -674,15 +791,9 @@ 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
@@ -1146,6 +1257,7 @@ 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()
     # Диапазон по Y: авто по умолчанию (поддерживает отрицательные значения)
     fixed_ylim: Optional[Tuple[float, float]] = None
     if args.ylim:
@@ -1158,14 +1270,7 @@ 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