Modbus-rtu/HostComputer/bin_to_time_freq.py

# -*- coding: utf-8 -*-
"""
.bin 波形文件 -> 时频曲线查看/出图工具（PLSR 项目用）

数据格式: 逻辑分析仪导出的单通道数字采样，每字节 1 个采样点；
          电平 >= 阈值(默认128) 判为高（脉冲），否则为低。
频率定义: 每脉冲频率 = 采样率 / 脉冲周期；
          周期 = 本脉冲高电平起点 -> 下一脉冲高电平起点；
          最后一个脉冲无下一脉冲，按高电平宽度 x2 近似（50% 占空比）。
时间定义: 脉冲时间 = 高电平中点（与低电平/空闲段无关）。

用法:
    python bin_to_time_freq.py <bin文件> [采样率Hz] [选项]
示例:
    python bin_to_time_freq.py "Document/PLSR_document/波形/10段.bin" 6250000
    python bin_to_time_freq.py xxx.bin 6250000 --ymax=12000
    python bin_to_time_freq.py xxx.bin 6250000 --save=out.png
    python bin_to_time_freq.py xxx.bin 6250000 --selftest

选项:
    --thresh=128   电平阈值（默认128）
    --ymax=12000   频率轴上限（默认自适应）
    --save=路径    保存 PNG 后退出（不弹窗）
    --selftest     仅打印统计，不画图

交互（弹窗模式）:
    滚轮           缩放 X 轴（向上放大/向下缩小，以鼠标位置为中心）
    Ctrl+滚轮      缩放 Y 轴（以鼠标 Y 位置为中心）
    左键拖拽       双向平移（上下左右跟随鼠标）
    双击           复位到全图
    鼠标悬停       高亮最近的点并显示其脉冲序号、时间与频率

依赖: pip install numpy matplotlib
"""

import sys
import os

import numpy as np

try:
    import matplotlib
    matplotlib.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei']
    matplotlib.rcParams['axes.unicode_minus'] = False
except Exception:
    pass


def load_time_freq(path, fs, threshold=128):
    """读取 bin，返回 (t_sec, freq_hz, hi_width_sec, meta)。
    t_sec 以第一个脉冲为 0 时刻；hi_width 为每脉冲高电平宽度。"""
    data = np.fromfile(path, dtype=np.uint8)
    if data.size == 0:
        raise ValueError("文件为空: %s" % path)
    samples = (data >= threshold).astype(np.int8)

    # run 级压缩
    changes = np.flatnonzero(np.diff(samples) != 0) + 1
    starts = np.concatenate(([0], changes))
    ends = np.concatenate((changes, [len(samples)]))
    runs = np.column_stack((starts, ends, samples[starts]))

    # 脉冲 = 高电平 run，且其后紧跟低电平 run
    hi = np.flatnonzero(runs[:, 2] == 1)
    hi = hi[hi + 1 < len(runs)]
    lo_ok = runs[hi + 1, 2] == 0
    hi = hi[lo_ok]

    t_start = runs[hi, 0].astype(np.int64)
    hi_width = (runs[hi, 1] - runs[hi, 0]).astype(np.int64)

    # 周期：高到高；末脉冲按 2x 高电平宽度近似
    next_start = np.append(t_start[1:], [t_start[-1] + 2 * hi_width[-1]])
    period = next_start - t_start
    freq = fs / np.maximum(period, 1)

    # 时间：高电平中点，去起始偏移
    t_sec = (t_start + hi_width / 2.0) / fs
    t_sec = t_sec - t_sec[0]

    meta = {
        'total_samples': int(len(samples)),
        'duration': len(samples) / fs,
        'pulses': int(len(freq)),
        'offset_ms': t_start[0] / fs * 1000,
    }
    return t_sec, freq, hi_width / fs, meta


def selftest(path, fs, thresh):
    import statistics
    t, freq, hi_w, meta = load_time_freq(path, fs, thresh)
    print("文件: %s" % path)
    print("总采样: %d  (%.3f s @ %.2f MS/s)" % (meta['total_samples'], meta['duration'], fs / 1e6))
    print("起始采集偏移: %.1f ms" % meta['offset_ms'])
    print("脉冲总数: %d" % meta['pulses'])
    if meta['pulses']:
        print("频率 min=%.0f max=%.0f 中位=%.0f Hz" % (
            freq.min(), freq.max(), statistics.median(freq)))
        print("末脉冲: t=%.3f ms, 高电平 %.3f ms, %.0f Hz" % (
            t[-1] * 1000, hi_w[-1] * 1000, freq[-1]))
        print("波形活动时长: %.1f ms" % ((t[-1] + hi_w[-1] / 2) * 1000))


def plot_show(path, fs, thresh, ymax, save_path=None):
    t, freq, hi_w, meta = load_time_freq(path, fs, thresh)

    import matplotlib.pyplot as plt
    from matplotlib.ticker import MaxNLocator

    fig, ax = plt.subplots(figsize=(15, 7))
    fig.subplots_adjust(bottom=0.10, top=0.92)

    # 全脉冲散点
    ax.plot(t * 1000, freq, '.', ms=1.5, color='C0', alpha=0.5, zorder=1)
    ax.set_xlim(0.0, (t[-1] + hi_w[-1]) * 1000 * 1.02)
    ax.set_ylim(0, ymax if ymax > 0 else freq.max() * 1.08)
    ax.set_title("%s 时频曲线（%d 脉冲，活动时长 %.0f ms）" % (
        os.path.basename(path), meta['pulses'],
        (t[-1] + hi_w[-1] / 2) * 1000))
    ax.set_xlabel("时间 (ms)")
    ax.set_ylabel("频率 (Hz)")
    # 更密的刻度
    ax.xaxis.set_major_locator(MaxNLocator(nbins=20))
    ax.yaxis.set_major_locator(MaxNLocator(nbins=15))
    ax.grid(True, which='major', alpha=0.35)
    ax.grid(True, which='minor', alpha=0.15)
    ax.minorticks_on()

    if save_path:
        fig.savefig(save_path, dpi=130)
        print("已保存: %s" % save_path)
        plt.close(fig)
        return

    # 交互：滚轮缩放 X（Ctrl+滚轮缩放 Y）/ 左键双向拖拽平移 / 双击复位 / 悬停高亮
    state = {'press_x': None, 'press_y': None,
             'press_xlim': None, 'press_ylim': None}

    # 悬停高亮：一个红点标记 + 一个带框文本
    (hl_marker,) = ax.plot([], [], 'o', ms=9, mfc='red', mec='white',
                           mew=1.0, zorder=5, visible=False)
    hl_text = ax.text(0, 0, '', fontsize=10, color='black',
                      bbox=dict(boxstyle='round,pad=0.3', fc='yellow', ec='red', alpha=0.9),
                      zorder=6, visible=False)
    hover_last = {'idx': -1, 'visible': False}

    def on_scroll(event):
        if event.inaxes is not ax or event.xdata is None:
            return
        zoom_in = event.button == 'up'
        factor = 1.0 / 1.5 if zoom_in else 1.5
        if event.key in ('control', 'ctrl'):
            # Ctrl+滚轮：缩放 Y 轴，以鼠标 Y 位置为锚点（锚点数据点不动）
            y0, y1 = ax.get_ylim()
            cy = event.ydata
            n0 = cy - (cy - y0) * factor
            n1 = cy + (y1 - cy) * factor
            if n1 - n0 < 1.0:
                return
            ax.set_ylim(n0, n1)
        else:
            # 普通滚轮：缩放 X 轴，以鼠标 X 位置为锚点（锚点数据点不动）
            x0, x1 = ax.get_xlim()
            n0 = event.xdata - (event.xdata - x0) * factor
            n1 = event.xdata + (x1 - event.xdata) * factor
            if n1 - n0 < 1e-6:
                return
            ax.set_xlim(n0, n1)
        fig.canvas.draw_idle()

    def on_press(event):
        if event.inaxes is ax and event.button == 1:
            state['press_x'] = event.xdata
            state['press_y'] = event.ydata
            state['press_xlim'] = ax.get_xlim()
            state['press_ylim'] = ax.get_ylim()

    def on_motion(event):
        if event.inaxes is not ax or event.xdata is None:
            return
        if state['press_x'] is not None:
            # 拖拽平移：X/Y 双向跟随鼠标
            x0, x1 = state['press_xlim']
            y0, y1 = state['press_ylim']
            dx = event.xdata - state['press_x']
            dy = event.ydata - state['press_y']
            ax.set_xlim(x0 - dx, x1 - dx)
            ax.set_ylim(y0 - dy, y1 - dy)
            fig.canvas.draw_idle()
            return
        # 悬停：找可见范围内距鼠标最近的脉冲点（屏幕像素距离 < 20px 才高亮）
        x0, x1 = ax.get_xlim()
        tx_ms = t * 1000
        mask = (tx_ms >= x0) & (tx_ms <= x1)
        if not np.any(mask):
            hl_marker.set_visible(False)
            hl_text.set_visible(False)
            hover_last['visible'] = False
            fig.canvas.draw_idle()
            return
        px, py = ax.transData.transform(np.column_stack([tx_ms[mask], freq[mask]])).T
        dist = np.hypot(px - event.x, py - event.y)
        k = int(np.argmin(dist))
        if dist[k] <= 20.0:
            idx = int(np.flatnonzero(mask)[k])
            hl_marker.set_data([tx_ms[idx]], [freq[idx]])
            hl_text.set_text("脉冲 #%d\nt = %.3f ms\nf = %.0f Hz"
                             % (idx + 1, tx_ms[idx], freq[idx]))
            hl_text.set_position((tx_ms[idx] + (x1 - x0) * 0.01,
                                  freq[idx] + (ax.get_ylim()[1] - ax.get_ylim()[0]) * 0.02))
            hl_marker.set_visible(True)
            hl_text.set_visible(True)
            if hover_last['idx'] != idx or not hover_last['visible']:
                fig.canvas.draw_idle()
            hover_last['idx'] = idx
            hover_last['visible'] = True
        else:
            if hover_last['visible']:
                hl_marker.set_visible(False)
                hl_text.set_visible(False)
                fig.canvas.draw_idle()
            hover_last['visible'] = False

    def on_release(event):
        state['press_x'] = None

    def on_double(event):
        if event.dblclick:
            ax.set_xlim(0.0, (t[-1] + hi_w[-1]) * 1000 * 1.02)
            ax.set_ylim(0, ymax if ymax > 0 else freq.max() * 1.08)
            fig.canvas.draw_idle()

    fig.canvas.mpl_connect('scroll_event', on_scroll)
    fig.canvas.mpl_connect('button_press_event', on_press)
    fig.canvas.mpl_connect('button_release_event', on_release)
    fig.canvas.mpl_connect('motion_notify_event', on_motion)
    fig.canvas.mpl_connect('button_press_event', on_double)

    print("打开窗口：滚轮缩放X / Ctrl+滚轮缩放Y / 左键双向拖拽平移 / 双击复位")
    print("脉冲 %d 个，活动时长 %.1f ms" % (
        meta['pulses'], (t[-1] + hi_w[-1] / 2) * 1000))
    plt.show()


def main():
    args = [a for a in sys.argv[1:] if not a.startswith('--')]
    opts = {a.split('=', 1)[0]: (a.split('=', 1)[1] if '=' in a else True)
            for a in sys.argv[1:] if a.startswith('--')}

    if len(args) < 1 or '--help' in opts or '-h' in opts:
        print(__doc__)
        sys.exit(0)

    path = args[0]
    fs = float(args[1]) if len(args) > 1 else 6.25e6
    thresh = int(opts.get('--thresh', 128))
    ymax = float(opts.get('--ymax', 0))
    save_path = opts.get('--save', '')

    if not os.path.isfile(path):
        print("错误：文件不存在 - %s" % path)
        sys.exit(1)

    if '--selftest' in opts:
        selftest(path, fs, thresh)
    else:
        plot_show(path, fs, thresh, ymax, save_path or None)


if __name__ == '__main__':
    main()