1853 lines
74 KiB
Python
Executable File
1853 lines
74 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Реалтайм-плоттер для свипов из виртуального COM-порта.
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Формат строк:
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- "Sweep_start" — начало нового свипа (предыдущий считается завершённым)
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- "s CH X Y" — точка (номер канала, индекс X, значение Y), все целые со знаком
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Отрисовываются два графика:
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- Левый: последний полученный свип (Y vs X)
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- Правый: водопад (последние N свипов во времени)
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Оптимизации для скорости:
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- Парсинг и чтение в фоновой нити
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- Анимация с обновлением только данных (без лишнего пересоздания фигур)
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- Кольцевой буфер под водопад с фиксированным числом свипов
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Зависимости: matplotlib, numpy. PySerial опционален — при его отсутствии
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используется сырой доступ к TTY через termios.
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"""
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import argparse
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import io
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import os
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import sys
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import threading
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import time
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from collections import deque
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from queue import Queue, Empty, Full
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from typing import Any, Dict, Mapping, Optional, Tuple, Union
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import numpy as np
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WF_WIDTH = 1000 # максимальное число точек в ряду водопада
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FFT_LEN = 1024 # длина БПФ для спектра/водопада спектров
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LOG_BASE = 10.0
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LOG_SCALER = 0.001 # int32 значения приходят в fixed-point лог-шкале с шагом 1e-3
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LOG_POSTSCALER = 1000
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LOG_EXP_LIMIT = 300.0 # запас до переполнения float64 при возведении LOG_BASE в степень
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# Порог для инверсии сырых данных: если среднее значение свипа ниже порога —
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# считаем, что сигнал «меньше нуля» и домножаем свип на -1
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DATA_INVERSION_THRASHOLD = 10.0
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Number = Union[int, float]
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SweepInfo = Dict[str, Any]
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SweepAuxCurves = Optional[Tuple[np.ndarray, np.ndarray]]
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SweepPacket = Tuple[np.ndarray, SweepInfo, SweepAuxCurves]
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def _format_status_kv(data: Mapping[str, Any]) -> str:
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"""Преобразовать словарь метрик в одну строку 'k:v'."""
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def _fmt(v: Any) -> str:
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if v is None:
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return "NA"
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try:
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fv = float(v)
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except Exception:
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return str(v)
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if not np.isfinite(fv):
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return "nan"
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# Достаточно компактно для статус-строки.
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if abs(fv) >= 1000 or (0 < abs(fv) < 0.01):
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return f"{fv:.3g}"
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return f"{fv:.3f}".rstrip("0").rstrip(".")
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parts = [f"{k}:{_fmt(v)}" for k, v in data.items()]
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return " ".join(parts)
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def _parse_spec_clip(spec: Optional[str]) -> Optional[Tuple[float, float]]:
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"""Разобрать строку вида "low,high" процентов для контрастного отображения водопада спектров.
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Возвращает пару (low, high) или None для отключения. Допустимы значения 0..100, low < high.
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Ключевые слова отключения: "off", "none", "no".
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"""
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if not spec:
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return None
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s = str(spec).strip().lower()
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if s in ("off", "none", "no"):
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return None
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try:
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p0, p1 = s.replace(";", ",").split(",")
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low = float(p0)
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high = float(p1)
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if not (0.0 <= low < high <= 100.0):
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return None
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return (low, high)
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except Exception:
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return None
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def _log_value_to_linear(value: int) -> float:
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"""Преобразовать fixed-point логарифмическое значение в линейную шкалу."""
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exponent = max(-LOG_EXP_LIMIT, min(LOG_EXP_LIMIT, float(value) * LOG_SCALER))
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return float(LOG_BASE ** exponent)
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def _log_pair_to_sweep(avg_1: int, avg_2: int) -> float:
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"""Разность двух логарифмических усреднений в линейной шкале."""
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return (_log_value_to_linear(avg_1) - _log_value_to_linear(avg_2))*LOG_POSTSCALER
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def _compute_auto_ylim(*series_list: Optional[np.ndarray]) -> Optional[Tuple[float, float]]:
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"""Общий Y-диапазон по всем переданным кривым с небольшим запасом."""
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y_min: Optional[float] = None
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y_max: Optional[float] = None
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for series in series_list:
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if series is None:
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continue
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arr = np.asarray(series)
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if arr.size == 0:
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continue
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finite = arr[np.isfinite(arr)]
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if finite.size == 0:
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continue
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cur_min = float(np.min(finite))
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cur_max = float(np.max(finite))
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y_min = cur_min if y_min is None else min(y_min, cur_min)
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y_max = cur_max if y_max is None else max(y_max, cur_max)
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if y_min is None or y_max is None:
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return None
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if y_min == y_max:
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pad = max(1.0, abs(y_min) * 0.05)
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else:
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pad = 0.05 * (y_max - y_min)
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return (y_min - pad, y_max + pad)
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def _normalize_sweep_simple(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
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"""Простая нормировка: поэлементное деление raw/calib."""
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w = min(raw.size, calib.size)
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if w <= 0:
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return raw
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out = np.full_like(raw, np.nan, dtype=np.float32)
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with np.errstate(divide="ignore", invalid="ignore"):
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out[:w] = raw[:w] / calib[:w]
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out = np.nan_to_num(out, nan=np.nan, posinf=np.nan, neginf=np.nan)
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return out
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def _build_calib_envelopes(calib: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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"""Оценить нижнюю/верхнюю огибающие калибровочной кривой."""
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n = int(calib.size)
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if n <= 0:
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empty = np.zeros((0,), dtype=np.float32)
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return empty, empty
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y = np.asarray(calib, dtype=np.float32)
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finite = np.isfinite(y)
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if not np.any(finite):
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zeros = np.zeros_like(y, dtype=np.float32)
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return zeros, zeros
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if not np.all(finite):
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x = np.arange(n, dtype=np.float32)
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y = y.copy()
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y[~finite] = np.interp(x[~finite], x[finite], y[finite]).astype(np.float32)
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if n < 3:
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return y.copy(), y.copy()
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dy = np.diff(y)
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s = np.sign(dy).astype(np.int8, copy=False)
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if np.any(s == 0):
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for i in range(1, s.size):
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if s[i] == 0:
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s[i] = s[i - 1]
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for i in range(s.size - 2, -1, -1):
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if s[i] == 0:
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s[i] = s[i + 1]
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s[s == 0] = 1
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max_idx = np.where((s[:-1] > 0) & (s[1:] < 0))[0] + 1
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min_idx = np.where((s[:-1] < 0) & (s[1:] > 0))[0] + 1
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x = np.arange(n, dtype=np.float32)
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def _interp_nodes(nodes: np.ndarray) -> np.ndarray:
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if nodes.size == 0:
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idx = np.array([0, n - 1], dtype=np.int64)
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else:
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idx = np.unique(np.concatenate(([0], nodes, [n - 1]))).astype(np.int64)
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return np.interp(x, idx.astype(np.float32), y[idx]).astype(np.float32)
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upper = _interp_nodes(max_idx)
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lower = _interp_nodes(min_idx)
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swap = lower > upper
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if np.any(swap):
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tmp = upper[swap].copy()
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upper[swap] = lower[swap]
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lower[swap] = tmp
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return lower, upper
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def _normalize_sweep_projector(raw: np.ndarray, calib: np.ndarray) -> np.ndarray:
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"""Нормировка через проекцию между огибающими калибровки в диапазон [-1, +1]."""
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w = min(raw.size, calib.size)
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if w <= 0:
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return raw
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out = np.full_like(raw, np.nan, dtype=np.float32)
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raw_seg = np.asarray(raw[:w], dtype=np.float32)
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lower, upper = _build_calib_envelopes(np.asarray(calib[:w], dtype=np.float32))
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span = upper - lower
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finite_span = span[np.isfinite(span) & (span > 0)]
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if finite_span.size > 0:
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eps = max(float(np.median(finite_span)) * 1e-6, 1e-9)
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else:
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eps = 1e-9
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valid = (
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np.isfinite(raw_seg)
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& np.isfinite(lower)
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& np.isfinite(upper)
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& (span > eps)
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)
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if np.any(valid):
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proj = np.empty_like(raw_seg, dtype=np.float32)
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proj[valid] = ((2.0 * (raw_seg[valid] - lower[valid]) / span[valid]) - 1.0) * 1000.0
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proj[valid] = np.clip(proj[valid], -1000.0, 1000.0)
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proj[~valid] = np.nan
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out[:w] = proj
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return out
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def _normalize_by_calib(raw: np.ndarray, calib: np.ndarray, norm_type: str) -> np.ndarray:
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"""Нормировка свипа по выбранному алгоритму."""
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nt = str(norm_type).strip().lower()
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if nt == "simple":
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return _normalize_sweep_simple(raw, calib)
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return _normalize_sweep_projector(raw, calib)
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def try_open_pyserial(path: str, baud: int, timeout: float):
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try:
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import serial # type: ignore
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except Exception:
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return None
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try:
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ser = serial.Serial(path, baudrate=baud, timeout=timeout)
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return ser
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except Exception:
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return None
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class FDReader:
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"""Простой враппер чтения строк из файлового дескриптора TTY."""
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def __init__(self, fd: int):
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# Отдельно буферизуем для корректной readline()
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self._fd = fd
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raw = os.fdopen(fd, "rb", closefd=False)
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self._file = raw
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self._buf = io.BufferedReader(raw, buffer_size=65536)
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def fileno(self) -> int:
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return self._fd
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def readline(self) -> bytes:
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return self._buf.readline()
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def close(self):
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try:
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self._buf.close()
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except Exception:
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||
pass
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def open_raw_tty(path: str, baud: int) -> Optional[FDReader]:
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"""Открыть TTY без pyserial и настроить порт через termios.
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Возвращает FDReader или None при ошибке.
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||
"""
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try:
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import termios
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import tty
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except Exception:
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||
return None
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||
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try:
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fd = os.open(path, os.O_RDONLY | os.O_NOCTTY)
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except Exception:
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||
return None
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||
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||
try:
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attrs = termios.tcgetattr(fd)
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# Установим «сырое» состояние
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||
tty.setraw(fd)
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||
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||
# Скорость
|
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baud_map = {
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9600: termios.B9600,
|
||
19200: termios.B19200,
|
||
38400: termios.B38400,
|
||
57600: termios.B57600,
|
||
115200: termios.B115200,
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||
230400: getattr(termios, "B230400", None),
|
||
460800: getattr(termios, "B460800", None),
|
||
}
|
||
b = baud_map.get(baud) or termios.B115200
|
||
|
||
attrs[4] = b # ispeed
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||
attrs[5] = b # ospeed
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||
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||
# VMIN=1, VTIME=0 — блокирующее чтение по байту
|
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cc = attrs[6]
|
||
cc[termios.VMIN] = 1
|
||
cc[termios.VTIME] = 0
|
||
attrs[6] = cc
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||
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||
termios.tcsetattr(fd, termios.TCSANOW, attrs)
|
||
except Exception:
|
||
try:
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os.close(fd)
|
||
except Exception:
|
||
pass
|
||
return None
|
||
|
||
return FDReader(fd)
|
||
|
||
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||
class SerialLineSource:
|
||
"""Единый интерфейс для чтения строк из порта (pyserial или raw TTY)."""
|
||
|
||
def __init__(self, path: str, baud: int, timeout: float = 1.0):
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||
self._pyserial = try_open_pyserial(path, baud, timeout)
|
||
self._fdreader = None
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||
self._using = "pyserial" if self._pyserial is not None else "raw"
|
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if self._pyserial is None:
|
||
self._fdreader = open_raw_tty(path, baud)
|
||
if self._fdreader is None:
|
||
msg = f"Не удалось открыть порт '{path}' (pyserial и raw TTY не сработали)"
|
||
if sys.platform.startswith("win"):
|
||
msg += ". На Windows нужен pyserial: pip install pyserial"
|
||
raise RuntimeError(msg)
|
||
|
||
def readline(self) -> bytes:
|
||
if self._pyserial is not None:
|
||
try:
|
||
return self._pyserial.readline()
|
||
except Exception:
|
||
return b""
|
||
else:
|
||
try:
|
||
return self._fdreader.readline() # type: ignore[union-attr]
|
||
except Exception:
|
||
return b""
|
||
|
||
def close(self):
|
||
try:
|
||
if self._pyserial is not None:
|
||
self._pyserial.close()
|
||
elif self._fdreader is not None:
|
||
self._fdreader.close()
|
||
except Exception:
|
||
pass
|
||
|
||
|
||
class SerialChunkReader:
|
||
"""Быстрое неблокирующее чтение чанков из serial/raw TTY для максимального дренажа буфера."""
|
||
|
||
def __init__(self, src: SerialLineSource):
|
||
self._src = src
|
||
self._ser = src._pyserial
|
||
self._fd: Optional[int] = None
|
||
if self._ser is not None:
|
||
# Неблокирующий режим для быстрой откачки
|
||
try:
|
||
self._ser.timeout = 0
|
||
except Exception:
|
||
pass
|
||
else:
|
||
try:
|
||
self._fd = src._fdreader.fileno() # type: ignore[union-attr]
|
||
try:
|
||
os.set_blocking(self._fd, False)
|
||
except Exception:
|
||
pass
|
||
except Exception:
|
||
self._fd = None
|
||
|
||
def read_available(self) -> bytes:
|
||
"""Вернёт доступные байты (b"" если данных нет)."""
|
||
if self._ser is not None:
|
||
try:
|
||
n = int(getattr(self._ser, "in_waiting", 0))
|
||
except Exception:
|
||
n = 0
|
||
if n > 0:
|
||
try:
|
||
return self._ser.read(n)
|
||
except Exception:
|
||
return b""
|
||
return b""
|
||
if self._fd is None:
|
||
return b""
|
||
out = bytearray()
|
||
while True:
|
||
try:
|
||
chunk = os.read(self._fd, 65536)
|
||
if not chunk:
|
||
break
|
||
out += chunk
|
||
if len(chunk) < 65536:
|
||
break
|
||
except BlockingIOError:
|
||
break
|
||
except Exception:
|
||
break
|
||
return bytes(out)
|
||
|
||
|
||
class SweepReader(threading.Thread):
|
||
"""Фоновый поток: читает строки, формирует завершённые свипы и кладёт в очередь."""
|
||
|
||
def __init__(
|
||
self,
|
||
port_path: str,
|
||
baud: int,
|
||
out_queue: Queue[SweepPacket],
|
||
stop_event: threading.Event,
|
||
fancy: bool = False,
|
||
bin_mode: bool = False,
|
||
logscale: bool = False,
|
||
):
|
||
super().__init__(daemon=True)
|
||
self._port_path = port_path
|
||
self._baud = baud
|
||
self._q = out_queue
|
||
self._stop = stop_event
|
||
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
|
||
self._n_valid_hist = deque()
|
||
|
||
@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]],
|
||
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]
|
||
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)
|
||
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 = _scatter(ys, np.float32)
|
||
aux_curves: SweepAuxCurves = None
|
||
if raw_curves is not None:
|
||
aux_curves = (
|
||
_scatter(raw_curves[0], np.float32),
|
||
_scatter(raw_curves[1], np.float32),
|
||
)
|
||
# Метрики валидных точек до заполнения пропусков
|
||
finite_pre = np.isfinite(sweep)
|
||
n_valid_cur = int(np.count_nonzero(finite_pre))
|
||
|
||
# Дополнительная обработка пропусков: при --fancy заполняем внутренние разрывы, края и дотягиваем до максимальной длины
|
||
if self._fancy:
|
||
try:
|
||
_fill_missing(sweep)
|
||
if aux_curves is not None:
|
||
_fill_missing(aux_curves[0])
|
||
_fill_missing(aux_curves[1])
|
||
except Exception:
|
||
# В случае ошибки просто оставляем как есть
|
||
pass
|
||
# Инверсия данных при «отрицательном» уровне (среднее ниже порога)
|
||
if apply_inversion:
|
||
try:
|
||
m = float(np.nanmean(sweep))
|
||
if np.isfinite(m) and m < DATA_INVERSION_THRASHOLD:
|
||
sweep *= -1.0
|
||
except Exception:
|
||
pass
|
||
#sweep = np.abs(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")
|
||
else:
|
||
dt_ms = (now - self._last_sweep_ts) * 1000.0
|
||
self._last_sweep_ts = now
|
||
self._n_valid_hist.append((now, n_valid_cur))
|
||
while self._n_valid_hist and (now - self._n_valid_hist[0][0]) > 1.0:
|
||
self._n_valid_hist.popleft()
|
||
if self._n_valid_hist:
|
||
n_valid = float(sum(v for _t, v in self._n_valid_hist) / len(self._n_valid_hist))
|
||
else:
|
||
n_valid = float(n_valid_cur)
|
||
|
||
if n_valid_cur > 0:
|
||
vmin = float(np.nanmin(sweep))
|
||
vmax = float(np.nanmax(sweep))
|
||
mean = float(np.nanmean(sweep))
|
||
std = float(np.nanstd(sweep))
|
||
else:
|
||
vmin = vmax = mean = std = float("nan")
|
||
info: SweepInfo = {
|
||
"sweep": self._sweep_idx,
|
||
"ch": ch_primary,
|
||
"chs": ch_list,
|
||
"n_valid": n_valid,
|
||
"min": vmin,
|
||
"max": vmax,
|
||
"mean": mean,
|
||
"std": std,
|
||
"dt_ms": dt_ms,
|
||
}
|
||
|
||
# Кладём готовый свип (если очередь полна — выбрасываем самый старый)
|
||
try:
|
||
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, aux_curves))
|
||
except Exception:
|
||
pass
|
||
|
||
def _run_ascii_stream(self, chunk_reader: SerialChunkReader):
|
||
xs: list[int] = []
|
||
ys: list[int] = []
|
||
cur_channel: Optional[int] = None
|
||
cur_channels: set[int] = set()
|
||
buf = bytearray()
|
||
while not self._stop.is_set():
|
||
data = chunk_reader.read_available()
|
||
if data:
|
||
buf += data
|
||
else:
|
||
time.sleep(0.0005)
|
||
continue
|
||
|
||
while True:
|
||
nl = buf.find(b"\n")
|
||
if nl == -1:
|
||
break
|
||
line = bytes(buf[:nl])
|
||
del buf[: nl + 1]
|
||
if line.endswith(b"\r"):
|
||
line = line[:-1]
|
||
if not line:
|
||
continue
|
||
|
||
if line.startswith(b"Sweep_start"):
|
||
self._finalize_current(xs, ys, cur_channels)
|
||
xs.clear()
|
||
ys.clear()
|
||
cur_channel = None
|
||
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")):
|
||
try:
|
||
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)
|
||
|
||
if len(buf) > 1_000_000:
|
||
del buf[:-262144]
|
||
|
||
self._finalize_current(xs, ys, cur_channels)
|
||
|
||
def _run_binary_stream(self, chunk_reader: SerialChunkReader):
|
||
xs: list[int] = []
|
||
ys: 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 протокол:
|
||
# старт свипа (актуальный): 0xFFFF, 0xFFFF, 0xFFFF, (ch<<8)|0x0A
|
||
# старт свипа (legacy): 0xFFFF, 0xFFFF, channel, 0x0A0A
|
||
# точка: step, value_hi, value_lo, 0x000A
|
||
while len(words) >= 4:
|
||
w0 = int(words[0])
|
||
w1 = int(words[1])
|
||
w2 = int(words[2])
|
||
w3 = int(words[3])
|
||
|
||
if w0 == 0xFFFF and w1 == 0xFFFF and w2 == 0xFFFF and (w3 & 0x00FF) == 0x000A:
|
||
self._finalize_current(xs, ys, cur_channels)
|
||
xs.clear()
|
||
ys.clear()
|
||
cur_channels.clear()
|
||
cur_channel = (w3 >> 8) & 0x00FF
|
||
cur_channels.add(cur_channel)
|
||
for _ in range(4):
|
||
words.popleft()
|
||
continue
|
||
|
||
if w0 == 0xFFFF and w1 == 0xFFFF and w3 == 0x0A0A:
|
||
self._finalize_current(xs, ys, cur_channels)
|
||
xs.clear()
|
||
ys.clear()
|
||
cur_channels.clear()
|
||
cur_channel = w2
|
||
cur_channels.add(cur_channel)
|
||
for _ in range(4):
|
||
words.popleft()
|
||
continue
|
||
|
||
if w3 == 0x000A:
|
||
if cur_channel is not None:
|
||
cur_channels.add(cur_channel)
|
||
xs.append(w0)
|
||
value_u32 = (w1 << 16) | w2
|
||
ys.append(self._u32_to_i32(value_u32))
|
||
for _ in range(4):
|
||
words.popleft()
|
||
continue
|
||
|
||
# Поток может начаться с середины пакета; сдвигаемся по слову до ресинхронизации.
|
||
words.popleft()
|
||
|
||
del buf[:usable]
|
||
if len(buf) > 1_000_000:
|
||
del buf[:-262144]
|
||
|
||
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)
|
||
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._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)
|
||
finally:
|
||
try:
|
||
if self._src is not None:
|
||
self._src.close()
|
||
except Exception:
|
||
pass
|
||
|
||
|
||
def main():
|
||
parser = argparse.ArgumentParser(
|
||
description=(
|
||
"Читает свипы из виртуального COM-порта и рисует: "
|
||
"последний свип и водопад (реалтайм)."
|
||
)
|
||
)
|
||
parser.add_argument(
|
||
"port",
|
||
help="Путь к порту, например /dev/ttyACM1 или COM3 (COM10+: \\\\.\\COM10)",
|
||
)
|
||
parser.add_argument("--baud", type=int, default=115200, help="Скорость (по умолчанию 115200)")
|
||
parser.add_argument("--max-sweeps", type=int, default=200, help="Количество видимых свипов в водопаде")
|
||
parser.add_argument("--max-fps", type=float, default=30.0, help="Лимит частоты отрисовки, кадров/с")
|
||
parser.add_argument("--cmap", default="viridis", help="Цветовая карта водопада")
|
||
parser.add_argument(
|
||
"--spec-clip",
|
||
default="2,98",
|
||
help=(
|
||
"Процентильная обрезка уровней водопада спектров, % (min,max). "
|
||
"Напр. 2,98. 'off' — отключить"
|
||
),
|
||
)
|
||
parser.add_argument(
|
||
"--spec-mean-sec",
|
||
type=float,
|
||
default=0.0,
|
||
help=(
|
||
"Вычитание среднего по каждой частоте за последние N секунд "
|
||
"в водопаде спектров (0 — отключить)"
|
||
),
|
||
)
|
||
parser.add_argument("--title", default="ADC Sweeps", help="Заголовок окна")
|
||
parser.add_argument(
|
||
"--fancy",
|
||
action="store_true",
|
||
help="Заполнять выпавшие точки средними значениями между соседними",
|
||
)
|
||
parser.add_argument(
|
||
"--ylim",
|
||
type=str,
|
||
default=None,
|
||
help="Фиксированные Y-пределы для кривой формата min,max (например -1000,1000). По умолчанию авто",
|
||
)
|
||
parser.add_argument(
|
||
"--backend",
|
||
choices=["auto", "pg", "mpl"],
|
||
default="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",
|
||
help=(
|
||
"Бинарный протокол: старт свипа 0xFFFF,0xFFFF,0xFFFF,(CH<<8)|0x0A; "
|
||
"точки step,uint32(hi16,lo16),0x000A"
|
||
),
|
||
)
|
||
parser.add_argument(
|
||
"--logscale",
|
||
action="store_true",
|
||
default=True,
|
||
help=(
|
||
"Новый бинарный протокол: точка 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()
|
||
|
||
# Попробуем быстрый бэкенд (pyqtgraph) при auto/pg
|
||
if args.backend in ("pg"):
|
||
try:
|
||
return run_pyqtgraph(args)
|
||
except Exception as e:
|
||
if args.backend == "pg":
|
||
sys.stderr.write(f"[error] PyQtGraph бэкенд недоступен: {e}\n")
|
||
sys.exit(1)
|
||
# При auto — тихо откатываемся на matplotlib
|
||
|
||
try:
|
||
import matplotlib
|
||
import matplotlib.pyplot as plt
|
||
from matplotlib.animation import FuncAnimation
|
||
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)
|
||
|
||
# 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),
|
||
bin_mode=bool(args.bin_mode),
|
||
logscale=bool(args.logscale),
|
||
)
|
||
reader.start()
|
||
|
||
# Графика
|
||
fig, axs = plt.subplots(2, 2, figsize=(12, 8))
|
||
(ax_line, ax_img), (ax_fft, ax_spec) = axs
|
||
fig.canvas.manager.set_window_title(args.title) if hasattr(fig.canvas.manager, "set_window_title") else None
|
||
# Увеличим расстояния и оставим место справа под ползунки оси Y B-scan
|
||
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_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
|
||
x_shared: Optional[np.ndarray] = None
|
||
width: Optional[int] = None
|
||
max_sweeps = int(max(10, args.max_sweeps))
|
||
ring = None # type: Optional[np.ndarray]
|
||
ring_time = None # type: Optional[np.ndarray]
|
||
head = 0
|
||
# Авто-уровни цветовой шкалы водопада сырых данных пересчитываются по видимой области.
|
||
# FFT состояние
|
||
fft_bins = FFT_LEN // 2 + 1
|
||
ring_fft = None # type: Optional[np.ndarray]
|
||
y_min_fft, y_max_fft = None, None
|
||
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(
|
||
0.01,
|
||
0.01,
|
||
"",
|
||
ha="left",
|
||
va="bottom",
|
||
fontsize=8,
|
||
family="monospace",
|
||
)
|
||
|
||
# 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")
|
||
ax_line.set_title("Сырые данные", pad=1)
|
||
ax_line.set_xlabel("ГГц")
|
||
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)
|
||
ax_fft.set_title("FFT", pad=1)
|
||
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
|
||
# CLI переопределение при необходимости
|
||
if args.ylim:
|
||
try:
|
||
y0, y1 = args.ylim.split(",")
|
||
fixed_ylim = (float(y0), float(y1))
|
||
except Exception:
|
||
sys.stderr.write("[warn] Некорректный формат --ylim, игнорирую. Ожидалось min,max\n")
|
||
if fixed_ylim is not None:
|
||
ax_line.set_ylim(fixed_ylim)
|
||
|
||
# Водопад (будет инициализирован при первом свипе)
|
||
img_obj = ax_img.imshow(
|
||
np.zeros((1, 1), dtype=np.float32),
|
||
aspect="auto",
|
||
interpolation="nearest",
|
||
origin="lower",
|
||
cmap=args.cmap,
|
||
)
|
||
ax_img.set_title("Сырые данные", pad=12)
|
||
ax_img.set_xlabel("")
|
||
ax_img.set_ylabel("ГГц")
|
||
# Не показываем численные значения по времени на водопаде сырых данных
|
||
try:
|
||
ax_img.tick_params(axis="x", labelbottom=False)
|
||
except Exception:
|
||
pass
|
||
|
||
# Водопад спектров
|
||
img_fft_obj = ax_spec.imshow(
|
||
np.zeros((1, 1), dtype=np.float32),
|
||
aspect="auto",
|
||
interpolation="nearest",
|
||
origin="lower",
|
||
cmap=args.cmap,
|
||
)
|
||
ax_spec.set_title("B-scan (дБ)", pad=12)
|
||
ax_spec.set_xlabel("")
|
||
ax_spec.set_ylabel("расстояние")
|
||
# Не показываем численные значения по времени на B-scan
|
||
try:
|
||
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:
|
||
y0 = int(min(ymin_slider.val, ymax_slider.val))
|
||
y1 = int(max(ymin_slider.val, ymax_slider.val))
|
||
ax_spec.set_ylim(y0, y1)
|
||
fig.canvas.draw_idle()
|
||
except Exception:
|
||
pass
|
||
|
||
ymin_slider.on_changed(_on_ylim_change)
|
||
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
|
||
|
||
# Для контроля частоты обновления
|
||
max_fps = max(1.0, float(args.max_fps))
|
||
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:
|
||
return
|
||
width = WF_WIDTH
|
||
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, 3.3, 14.3))
|
||
ax_img.set_xlim(0, max_sweeps - 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))
|
||
img_fft_obj.set_extent((0, max_sweeps - 1, 0, fft_bins - 1))
|
||
ax_spec.set_xlim(0, max_sweeps - 1)
|
||
ax_spec.set_ylim(0, max(1, fft_bins - 1))
|
||
freq_shared = np.arange(fft_bins, dtype=np.int32)
|
||
|
||
def _visible_levels_matplotlib(data: np.ndarray, axis) -> Optional[Tuple[float, float]]:
|
||
"""(vmin, vmax) по центральным 90% значений в видимой области imshow."""
|
||
if data.size == 0:
|
||
return None
|
||
ny, nx = data.shape[0], data.shape[1]
|
||
try:
|
||
x0, x1 = axis.get_xlim()
|
||
y0, y1 = axis.get_ylim()
|
||
except Exception:
|
||
x0, x1 = 0.0, float(nx - 1)
|
||
y0, y1 = 0.0, float(ny - 1)
|
||
xmin, xmax = sorted((float(x0), float(x1)))
|
||
ymin, ymax = sorted((float(y0), float(y1)))
|
||
ix0 = max(0, min(nx - 1, int(np.floor(xmin))))
|
||
ix1 = max(0, min(nx - 1, int(np.ceil(xmax))))
|
||
iy0 = max(0, min(ny - 1, int(np.floor(ymin))))
|
||
iy1 = max(0, min(ny - 1, int(np.ceil(ymax))))
|
||
if ix1 < ix0:
|
||
ix1 = ix0
|
||
if iy1 < iy0:
|
||
iy1 = iy0
|
||
sub = data[iy0 : iy1 + 1, ix0 : ix1 + 1]
|
||
finite = np.isfinite(sub)
|
||
if not finite.any():
|
||
return None
|
||
vals = sub[finite]
|
||
vmin = float(np.nanpercentile(vals, 5))
|
||
vmax = float(np.nanpercentile(vals, 95))
|
||
if not (np.isfinite(vmin) and np.isfinite(vmax)) or vmin == vmax:
|
||
return None
|
||
return (vmin, vmax)
|
||
|
||
def push_sweep(s: np.ndarray):
|
||
nonlocal ring, head, ring_fft, y_min_fft, y_max_fft, ring_time
|
||
if s is None or s.size == 0 or ring is None:
|
||
return
|
||
# Нормализуем длину до фиксированной ширины
|
||
w = ring.shape[1]
|
||
row = np.full((w,), np.nan, dtype=np.float32)
|
||
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:
|
||
bins = ring_fft.shape[1]
|
||
# Подготовка входа FFT_LEN, замена NaN на 0
|
||
take_fft = min(int(s.size), FFT_LEN)
|
||
if take_fft <= 0:
|
||
fft_row = np.full((bins,), np.nan, dtype=np.float32)
|
||
else:
|
||
fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
|
||
seg = s[:take_fft]
|
||
if isinstance(seg, np.ndarray):
|
||
seg = np.nan_to_num(seg, nan=0.0).astype(np.float32, copy=False)
|
||
else:
|
||
seg = np.asarray(seg, dtype=np.float32)
|
||
seg = np.nan_to_num(seg, nan=0.0)
|
||
# Окно Хэннинга
|
||
win = np.hanning(take_fft).astype(np.float32)
|
||
fft_in[:take_fft] = seg * win
|
||
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:
|
||
# rfft длиной FFT_LEN даёт bins == FFT_LEN//2+1
|
||
fft_row = fft_row[:bins]
|
||
ring_fft[(head - 1) % ring_fft.shape[0], :] = fft_row
|
||
# Экстремумы для цветовой шкалы
|
||
fr_min = np.nanmin(fft_row)
|
||
fr_max = np.nanmax(fft_row)
|
||
fr_max = np.nanpercentile(fft_row, 90)
|
||
if y_min_fft is None or (not np.isnan(fr_min) and fr_min < y_min_fft):
|
||
y_min_fft = float(fr_min)
|
||
if y_max_fft is None or (not np.isnan(fr_max) and fr_max > y_max_fft):
|
||
y_max_fft = float(fr_max)
|
||
|
||
def drain_queue():
|
||
nonlocal current_sweep_raw, current_aux_curves, current_sweep_norm, current_info, last_calib_sweep
|
||
drained = 0
|
||
while True:
|
||
try:
|
||
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:
|
||
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(sweep_for_proc)
|
||
return drained
|
||
|
||
def make_display_ring():
|
||
# Возвращаем буфер с правильным порядком по времени (старые→новые) и осью времени по X
|
||
if ring is None:
|
||
return np.zeros((1, 1), dtype=np.float32)
|
||
base = ring if head == 0 else np.roll(ring, -head, axis=0)
|
||
return base.T # (width, time)
|
||
|
||
def make_display_times():
|
||
if ring_time is None:
|
||
return None
|
||
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)
|
||
base = ring_fft if head == 0 else np.roll(ring_fft, -head, axis=0)
|
||
return base.T # (bins, time)
|
||
|
||
def update(_frame):
|
||
nonlocal frames_since_ylim_update
|
||
changed = drain_queue() > 0
|
||
|
||
# Обновление линии последнего свипа
|
||
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_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:
|
||
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: 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:
|
||
y_series = [current_sweep_raw, last_calib_sweep, current_sweep_norm]
|
||
if current_aux_curves is not None:
|
||
y_series.extend(current_aux_curves)
|
||
y_limits = _compute_auto_ylim(*y_series)
|
||
if y_limits is not None:
|
||
ax_line.set_ylim(y_limits[0], y_limits[1])
|
||
|
||
# Обновление спектра текущего свипа
|
||
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(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.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]
|
||
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) * 1.5)
|
||
ax_fft.set_ylim(float(np.nanmin(fft_vals)), float(np.nanmax(fft_vals)))
|
||
|
||
# Обновление водопада
|
||
if changed and ring is not None:
|
||
disp = make_display_ring()
|
||
# Новые данные справа: без реверса
|
||
img_obj.set_data(disp)
|
||
# Подписи времени не обновляем динамически (оставляем авто-тики)
|
||
# Авто-уровни: по видимой области (не накапливаем за всё время)
|
||
levels = _visible_levels_matplotlib(disp, ax_img)
|
||
if levels is not None:
|
||
img_obj.set_clim(vmin=levels[0], vmax=levels[1])
|
||
|
||
# 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)
|
||
# Автодиапазон по среднему спектру за видимый интервал (как в хорошей версии)
|
||
try:
|
||
# disp_fft имеет форму (bins, time); берём среднее по времени
|
||
mean_spec = np.nanmean(disp_fft, axis=1)
|
||
vmin_v = float(np.nanmin(mean_spec))
|
||
vmax_v = float(np.nanmax(mean_spec))
|
||
except Exception:
|
||
vmin_v = vmax_v = None
|
||
# Если средние не дают валидный диапазон — используем процентильную обрезку (если задана)
|
||
if (vmin_v is None or not np.isfinite(vmin_v)) or (vmax_v is None or not np.isfinite(vmax_v)) or vmin_v == vmax_v:
|
||
if spec_clip is not None:
|
||
try:
|
||
vmin_v = float(np.nanpercentile(disp_fft, spec_clip[0]))
|
||
vmax_v = float(np.nanpercentile(disp_fft, spec_clip[1]))
|
||
except Exception:
|
||
vmin_v = vmax_v = None
|
||
# Фолбэк к отслеживаемым минимум/максимумам
|
||
if (vmin_v is None or not np.isfinite(vmin_v)) or (vmax_v is None or not np.isfinite(vmax_v)) or vmin_v == vmax_v:
|
||
if y_min_fft is not None and y_max_fft is not None and np.isfinite(y_min_fft) and np.isfinite(y_max_fft) and y_min_fft != y_max_fft:
|
||
vmin_v, vmax_v = y_min_fft, y_max_fft
|
||
if vmin_v is not None and vmax_v is not None and vmin_v != vmax_v:
|
||
# Применим скалирование контрастом (верхняя граница)
|
||
try:
|
||
c = float(contrast_slider.val) / 100.0 if contrast_slider is not None else 1.0
|
||
except Exception:
|
||
c = 1.0
|
||
vmax_eff = vmin_v + c * (vmax_v - vmin_v)
|
||
img_fft_obj.set_clim(vmin=vmin_v, vmax=vmax_eff)
|
||
|
||
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,
|
||
line_avg1_obj,
|
||
line_avg2_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)
|
||
|
||
plt.show()
|
||
# Нормальное завершение при закрытии окна
|
||
stop_event.set()
|
||
reader.join(timeout=1.0)
|
||
|
||
|
||
def run_pyqtgraph(args):
|
||
"""Быстрый GUI на PyQtGraph. Требует pyqtgraph и PyQt5/PySide6."""
|
||
try:
|
||
import pyqtgraph as pg
|
||
from PyQt5 import QtCore, QtWidgets # noqa: F401
|
||
except Exception:
|
||
# Возможно установлена PySide6
|
||
try:
|
||
import pyqtgraph as pg
|
||
from PySide6 import QtCore, QtWidgets # noqa: F401
|
||
except Exception as e:
|
||
raise RuntimeError(
|
||
"pyqtgraph/PyQt5(Pyside6) не найдены. Установите: pip install pyqtgraph PyQt5"
|
||
) from e
|
||
|
||
# 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),
|
||
bin_mode=bool(args.bin_mode),
|
||
logscale=bool(args.logscale),
|
||
)
|
||
reader.start()
|
||
|
||
# Настройки скорости
|
||
max_sweeps = int(max(10, args.max_sweeps))
|
||
max_fps = max(1.0, float(args.max_fps))
|
||
interval_ms = int(1000.0 / max_fps)
|
||
|
||
# PyQtGraph настройки
|
||
pg.setConfigOptions(useOpenGL=True, antialias=False)
|
||
app = pg.mkQApp(args.title)
|
||
win = pg.GraphicsLayoutWidget(show=True, title=args.title)
|
||
win.resize(1200, 600)
|
||
|
||
# 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))
|
||
p_line.setLabel("bottom", "ГГц")
|
||
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")
|
||
p_img.invertY(False)
|
||
p_img.showGrid(x=False, y=False)
|
||
p_img.setLabel("bottom", "Время, с (новое справа)")
|
||
try:
|
||
p_img.getAxis("bottom").setStyle(showValues=False)
|
||
except Exception:
|
||
pass
|
||
p_img.setLabel("left", "ГГц")
|
||
img = pg.ImageItem()
|
||
p_img.addItem(img)
|
||
|
||
# FFT (слева-снизу)
|
||
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", "Время")
|
||
p_fft.setLabel("left", "дБ")
|
||
|
||
# Водопад спектров (справа-снизу)
|
||
p_spec = win.addPlot(row=1, col=1, title="B-scan (дБ)")
|
||
p_spec.invertY(True)
|
||
p_spec.showGrid(x=False, y=False)
|
||
p_spec.setLabel("bottom", "Время, с (новое справа)")
|
||
try:
|
||
p_spec.getAxis("bottom").setStyle(showValues=False)
|
||
except Exception:
|
||
pass
|
||
p_spec.setLabel("left", "Бин (0 снизу)")
|
||
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=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_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
|
||
# Авто-уровни цветовой шкалы водопада сырых данных пересчитываются по видимой области.
|
||
# Для спектров
|
||
fft_bins = FFT_LEN // 2 + 1
|
||
ring_fft: Optional[np.ndarray] = None
|
||
freq_shared: Optional[np.ndarray] = None
|
||
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:
|
||
try:
|
||
y0, y1 = args.ylim.split(",")
|
||
fixed_ylim = (float(y0), float(y1))
|
||
except Exception:
|
||
pass
|
||
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, ring_time, head, width, x_shared, ring_fft, freq_shared
|
||
if ring is not None:
|
||
return
|
||
width = WF_WIDTH
|
||
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 (ось Y: 3.3..14.3 ГГц)
|
||
img.setImage(ring.T, autoLevels=False)
|
||
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)
|
||
p_spec.setRange(xRange=(0, max_sweeps - 1), yRange=(0, max(1, fft_bins - 1)), padding=0)
|
||
p_fft.setXRange(0, max(1, fft_bins - 1), padding=0)
|
||
freq_shared = np.arange(fft_bins, dtype=np.int32)
|
||
|
||
def _visible_levels_pyqtgraph(data: np.ndarray) -> Optional[Tuple[float, float]]:
|
||
"""(vmin, vmax) по центральным 90% значений в видимой области ImageItem."""
|
||
if data.size == 0:
|
||
return None
|
||
ny, nx = data.shape[0], data.shape[1]
|
||
try:
|
||
(x0, x1), (y0, y1) = p_img.viewRange()
|
||
except Exception:
|
||
x0, x1 = 0.0, float(nx - 1)
|
||
y0, y1 = 0.0, float(ny - 1)
|
||
xmin, xmax = sorted((float(x0), float(x1)))
|
||
ymin, ymax = sorted((float(y0), float(y1)))
|
||
ix0 = max(0, min(nx - 1, int(np.floor(xmin))))
|
||
ix1 = max(0, min(nx - 1, int(np.ceil(xmax))))
|
||
iy0 = max(0, min(ny - 1, int(np.floor(ymin))))
|
||
iy1 = max(0, min(ny - 1, int(np.ceil(ymax))))
|
||
if ix1 < ix0:
|
||
ix1 = ix0
|
||
if iy1 < iy0:
|
||
iy1 = iy0
|
||
sub = data[iy0 : iy1 + 1, ix0 : ix1 + 1]
|
||
finite = np.isfinite(sub)
|
||
if not finite.any():
|
||
return None
|
||
vals = sub[finite]
|
||
vmin = float(np.nanpercentile(vals, 5))
|
||
vmax = float(np.nanpercentile(vals, 95))
|
||
if not (np.isfinite(vmin) and np.isfinite(vmax)) or vmin == vmax:
|
||
return None
|
||
return (vmin, vmax)
|
||
|
||
def push_sweep(s: np.ndarray):
|
||
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]
|
||
row = np.full((w,), np.nan, dtype=np.float32)
|
||
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:
|
||
bins = ring_fft.shape[1]
|
||
take_fft = min(int(s.size), FFT_LEN)
|
||
if take_fft > 0:
|
||
fft_in = np.zeros((FFT_LEN,), dtype=np.float32)
|
||
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.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:
|
||
fft_row = fft_row[:bins]
|
||
else:
|
||
fft_row = np.full((bins,), np.nan, dtype=np.float32)
|
||
ring_fft[(head - 1) % ring_fft.shape[0], :] = fft_row
|
||
fr_min = np.nanmin(fft_row)
|
||
fr_max = np.nanmax(fft_row)
|
||
if y_min_fft is None or (not np.isnan(fr_min) and fr_min < y_min_fft):
|
||
y_min_fft = float(fr_min)
|
||
if y_max_fft is None or (not np.isnan(fr_max) and fr_max > y_max_fft):
|
||
y_max_fft = float(fr_max)
|
||
|
||
def drain_queue():
|
||
nonlocal current_sweep_raw, current_aux_curves, current_sweep_norm, current_info, last_calib_sweep
|
||
drained = 0
|
||
while True:
|
||
try:
|
||
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:
|
||
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(sweep_for_proc)
|
||
return drained
|
||
|
||
# Попытка применить LUT из колормэпа (если доступен)
|
||
try:
|
||
cm_mod = getattr(pg, "colormap", None)
|
||
if cm_mod is not None:
|
||
cm = cm_mod.get(args.cmap)
|
||
img.setLookupTable(cm.getLookupTable(0.0, 1.0, 256))
|
||
except Exception:
|
||
pass
|
||
|
||
def update():
|
||
changed = drain_queue() > 0
|
||
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_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:
|
||
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:
|
||
y_series = [current_sweep_raw, last_calib_sweep, current_sweep_norm]
|
||
if current_aux_curves is not None:
|
||
y_series.extend(current_aux_curves)
|
||
y_limits = _compute_auto_ylim(*y_series)
|
||
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
|
||
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(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.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:
|
||
disp = ring if head == 0 else np.roll(ring, -head, axis=0)
|
||
disp = disp.T[:, ::-1] # (width, time with newest at left)
|
||
levels = _visible_levels_pyqtgraph(disp)
|
||
if levels is not None:
|
||
img.setImage(disp, autoLevels=False, levels=levels)
|
||
else:
|
||
img.setImage(disp, autoLevels=False)
|
||
|
||
if changed and current_info:
|
||
try:
|
||
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:
|
||
mean_spec = np.nanmean(disp_fft, axis=1)
|
||
vmin_v = float(np.nanmin(mean_spec))
|
||
vmax_v = float(np.nanmax(mean_spec))
|
||
if np.isfinite(vmin_v) and np.isfinite(vmax_v) and vmin_v != vmax_v:
|
||
levels = (vmin_v, vmax_v)
|
||
except Exception:
|
||
levels = None
|
||
# Процентильная обрезка как запасной вариант
|
||
if levels is None and spec_clip is not None:
|
||
try:
|
||
vmin_v = float(np.nanpercentile(disp_fft, spec_clip[0]))
|
||
vmax_v = float(np.nanpercentile(disp_fft, spec_clip[1]))
|
||
if np.isfinite(vmin_v) and np.isfinite(vmax_v) and vmin_v != vmax_v:
|
||
levels = (vmin_v, vmax_v)
|
||
except Exception:
|
||
levels = None
|
||
# Ещё один фолбэк — глобальные накопленные мин/макс
|
||
if levels is None and y_min_fft is not None and y_max_fft is not None and np.isfinite(y_min_fft) and np.isfinite(y_max_fft) and y_min_fft != y_max_fft:
|
||
levels = (y_min_fft, y_max_fft)
|
||
if levels is not None:
|
||
img_fft.setImage(disp_fft, autoLevels=False, levels=levels)
|
||
else:
|
||
img_fft.setImage(disp_fft, autoLevels=False)
|
||
|
||
timer = pg.QtCore.QTimer()
|
||
timer.timeout.connect(update)
|
||
timer.start(interval_ms)
|
||
|
||
def on_quit():
|
||
stop_event.set()
|
||
reader.join(timeout=1.0)
|
||
|
||
app.aboutToQuit.connect(on_quit)
|
||
win.show()
|
||
exec_fn = getattr(app, "exec_", None) or getattr(app, "exec", None)
|
||
exec_fn()
|
||
# На случай если aboutToQuit не сработал
|
||
on_quit()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|