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修改了显示频谱的脚本 

和s曲线
codex/plsr-2026-minimal
ywh 3 hafta önce
ebeveyn
ee75c7e18d
işleme
dd4a42e3b9
5 değiştirilmiş dosya ile 442 ekleme ve 68 silme
Görünümü Böl
Diff Seçenekleri
İstatistikleri Göster Yama Dosyasını İndir Diff Dosyasını İndir
  1. +5
    -0
      .gitignore
  2. BIN
      HostComputer/__pycache__/bin_to_time_freq.cpython-314.pyc
  3. +119
    -6
      HostComputer/bin_to_time_freq.py
  4. +174
    -35
      HostComputer/bin_to_time_freq1.py
  5. +144
    -27
      PLSR/Src/plsr_planner.c

+ 5
- 0
.gitignore Dosyayı Görüntüle

@@ -30,3 +30,8 @@ tmp/
.codex-tmp/
Document/PLSR_document/波形/
Document/PLSR_document/上位机图片/
.codex_docx_qa_final5/
.codex_docx_qa_final/
.codex_docx_qa_final2/
.codex_docx_qa_final4/
.codex_docx_qa_final3/

BIN
HostComputer/__pycache__/bin_to_time_freq.cpython-314.pyc Dosyayı Görüntüle


+ 119
- 6
HostComputer/bin_to_time_freq.py Dosyayı Görüntüle

@@ -18,14 +18,23 @@
python bin_to_time_freq.py xxx.bin 3125000 --save=out.png
python bin_to_time_freq.py xxx.bin 3125000 --selftest
python bin_to_time_freq.py xxx.bin 3125000 --reverse --cycles=8
python bin_to_time_freq.py xxx.bin 3125000 --d1 --d2
python bin_to_time_freq.py xxx.bin 3125000 --d1 --dwindow-ms=10
python bin_to_time_freq.py xxx.bin 3125000 --d1 --d2 --d1-window-ms=5 --d2-window-ms=15

选项:
--thresh=128 电平阈值（默认128）
--cycles=8 用连续多少个周期估计频率（默认8；1为逐周期）
--reverse 反相电平后再检测脉冲（低电平作为脉冲高电平）
--ymax=12000 频率轴上限（默认自适应）
--d1 另开一张图：频率对时间的一阶导数 df/dt（红）
--d2 另开一张图：频率对时间的二阶导数 d²f/dt²（黑）
--dwindow-ms=5 一、二阶导数的公共拟合窗口，单位 ms（默认5）
--d1-window-ms 原函数 -> 一阶导数的拟合窗口（默认继承 --dwindow-ms）
--d2-window-ms 一阶导数 -> 二阶导数的拟合窗口（默认继承 --dwindow-ms）
--save=路径 保存 PNG 后退出（不弹窗）
--selftest 仅打印统计，不画图
--help, -h 显示这份中文帮助后退出

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）
@@ -166,7 +175,61 @@ def selftest(path, fs, thresh, cycles, reverse=False):
print("波形活动时长: %.1f ms" % (meta['activity_duration'] * 1000))


def plot_show(path, fs, thresh, ymax, cycles, reverse, save_path=None):
def local_linear_derivative(t_sec, values, window_s=5e-3, min_points=7):
"""对非等间隔时间点做局部线性拟合，返回 d(values)/dt。

直接对相邻频率点差分会放大采样量化抖动；局部最小二乘斜率
使用真实时间间隔，并对这种抖动做平均。
"""
t = np.asarray(t_sec, dtype=np.float64)
y = np.asarray(values, dtype=np.float64)
if t.ndim != 1 or y.ndim != 1 or len(t) != len(y):
raise ValueError("t_sec 和 values 必须是等长一维数组")
if len(t) < 2:
raise ValueError("至少需要两个点才能计算导数")
if window_s <= 0:
raise ValueError("导数拟合窗口必须大于0")
if np.any(np.diff(t) <= 0):
raise ValueError("时间点必须严格递增")

n = len(t)
min_points = min(n, max(2, int(min_points)))
half_window = window_s / 2.0
left = np.searchsorted(t, t - half_window, side='left')
right = np.searchsorted(t, t + half_window, side='right')

# 低频段在固定时间窗口内可能点数太少，至少补足 min_points 个点。
idx = np.arange(n)
fallback_left = np.clip(idx - min_points // 2, 0, n - min_points)
fallback_right = fallback_left + min_points
too_few = (right - left) < min_points
left[too_few] = fallback_left[too_few]
right[too_few] = fallback_right[too_few]

# 移动时间原点减少长时采集时前缀和相减的精度损失。
x = t - (t[0] + t[-1]) / 2.0
sx = np.concatenate(([0.0], np.cumsum(x)))
sy = np.concatenate(([0.0], np.cumsum(y)))
sxx = np.concatenate(([0.0], np.cumsum(x * x)))
sxy = np.concatenate(([0.0], np.cumsum(x * y)))
count = (right - left).astype(np.float64)
sum_x = sx[right] - sx[left]
sum_y = sy[right] - sy[left]
sum_xx = sxx[right] - sxx[left]
sum_xy = sxy[right] - sxy[left]
denominator = count * sum_xx - sum_x * sum_x
numerator = count * sum_xy - sum_x * sum_y

derivative = np.empty(n, dtype=np.float64)
good = np.abs(denominator) > np.finfo(np.float64).eps
derivative[good] = numerator[good] / denominator[good]
derivative[~good] = np.gradient(y, t)[~good]
return derivative


def plot_show(path, fs, thresh, ymax, cycles, reverse, save_path=None,
draw_d1=False, draw_d2=False, d1_window_ms=5.0,
d2_window_ms=None):
t, freq, hi_w, meta = load_time_freq(path, fs, thresh, cycles, reverse)

import matplotlib.pyplot as plt
@@ -183,8 +246,8 @@ def plot_show(path, fs, thresh, ymax, cycles, reverse, save_path=None):
y_full = freq
ax.set_xlim(0.0, meta['activity_duration'] * 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 个测量点，%d 周期平均，活动时长 %.0f ms）" % (
os.path.basename(path), meta['pulses'], meta['cycles'],
ax.set_title("%s 时频曲线（检测到脉冲 %d，频率测量点 %d，%d 周期平均，活动时长 %.0f ms）" % (
os.path.basename(path), meta['detected_pulses'], meta['pulses'], meta['cycles'],
meta['activity_duration'] * 1000))
ax.set_xlabel("时间 (ms)")
ax.set_ylabel("频率 (Hz)")
@@ -195,10 +258,53 @@ def plot_show(path, fs, thresh, ymax, cycles, reverse, save_path=None):
ax.grid(True, which='minor', alpha=0.15)
ax.minorticks_on()

fig_der = None
draw_d1 = draw_d1 and (len(freq) >= 2)
draw_d2 = draw_d2 and (len(freq) >= 3)
if draw_d1 or draw_d2:
f64 = freq.astype(np.float64)
if d2_window_ms is None:
d2_window_ms = d1_window_ms
d1_full = local_linear_derivative(t, f64, d1_window_ms * 1e-3)
d2_full = (local_linear_derivative(t, d1_full, d2_window_ms * 1e-3)
if draw_d2 else None)
plot_count = int(draw_d1) + int(draw_d2)
fig_der, axes_der = plt.subplots(plot_count, 1, figsize=(15, 4.5 * plot_count),
sharex=True, squeeze=False)
fig_der.subplots_adjust(bottom=0.14, top=0.90)
row = 0
if draw_d1:
ax_der = axes_der[row, 0]
ax_der.plot(x_full, d1_full, '-', lw=0.9, color='red', label='df/dt')
ax_der.set_ylabel("一阶导数 (Hz/s)")
ax_der.legend(loc='upper right')
row += 1
if draw_d2:
ax_der = axes_der[row, 0]
ax_der.plot(x_full, d2_full, '-', lw=0.9, color='black', label='d²f/dt²')
ax_der.set_ylabel("二阶导数 (Hz/s²)")
ax_der.legend(loc='upper right')
for ax_der in axes_der[:, 0]:
ax_der.set_xlim(0.0, meta['activity_duration'] * 1000 * 1.02)
ax_der.grid(True, which='major', alpha=0.35)
ax_der.minorticks_on()
axes_der[-1, 0].set_xlabel("时间 (ms)")
axes_der[0, 0].set_title(
"时频曲线导数（一阶窗口 %.3g ms / 二阶窗口 %.3g ms）" %
(d1_window_ms, d2_window_ms))

if save_path:
scatter.set_data(x_full, y_full)
root, ext = os.path.splitext(save_path)
fig.savefig(save_path, dpi=130)
print("已保存: %s" % save_path)
if fig_der is not None:
der_path = root + "_d" + ext
fig_der.savefig(der_path, dpi=130)
print("已保存: %s" % der_path)
plt.close(fig)
if fig_der is not None:
plt.close(fig_der)
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
@@ -323,8 +429,9 @@ def plot_show(path, fs, thresh, ymax, cycles, reverse, save_path=None):
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("测量点 %d 个，使用 %d 周期平均，活动时长 %.1f ms" % (
meta['pulses'], meta['cycles'], meta['activity_duration'] * 1000))
print("检测到脉冲 %d，频率测量点 %d，使用 %d 周期平均，活动时长 %.1f ms" % (
meta['detected_pulses'], meta['pulses'], meta['cycles'],
meta['activity_duration'] * 1000))
plt.show()


@@ -344,6 +451,11 @@ def main():
reverse = '--reverse' in opts
ymax = float(opts.get('--ymax', 0))
save_path = opts.get('--save', '')
draw_d1 = '--d1' in opts
draw_d2 = '--d2' in opts
derivative_window_ms = float(opts.get('--dwindow-ms', 5.0))
d1_window_ms = float(opts.get('--d1-window-ms', derivative_window_ms))
d2_window_ms = float(opts.get('--d2-window-ms', derivative_window_ms))

if not os.path.isfile(path):
print("错误：文件不存在 - %s" % path)
@@ -352,7 +464,8 @@ def main():
if '--selftest' in opts:
selftest(path, fs, thresh, cycles, reverse)
else:
plot_show(path, fs, thresh, ymax, cycles, reverse, save_path or None)
plot_show(path, fs, thresh, ymax, cycles, reverse, save_path or None,
draw_d1, draw_d2, d1_window_ms, d2_window_ms)


if __name__ == '__main__':


+ 174
- 35
HostComputer/bin_to_time_freq1.py Dosyayı Görüntüle

@@ -4,24 +4,38 @@

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。
--reverse 时先把高低电平对调，再按同样规则找脉冲。
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）。
仅使用实际检测到的相邻上升沿，不猜测末脉冲周期。
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]
python bin_to_time_freq1.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
python bin_to_time_freq1.py "Document/PLSR_document/波形/10段.bin" 6250000
python bin_to_time_freq1.py xxx.bin 6250000 --ymax=12000
python bin_to_time_freq1.py xxx.bin 6250000 --line
python bin_to_time_freq1.py xxx.bin 6250000 --reverse
python bin_to_time_freq1.py xxx.bin 6250000 --d1 --d2
python bin_to_time_freq1.py xxx.bin 6250000 --d1 --dwindow-ms=10
python bin_to_time_freq1.py xxx.bin 6250000 --d1 --d2 --d1-window-ms=5 --d2-window-ms=15
python bin_to_time_freq1.py xxx.bin 6250000 --save=out.png
python bin_to_time_freq1.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）
--line 将脉冲点连成折线（默认只画散点）
--reverse 高低电平对调后再算频率点（低电平当脉冲）
--d1 另开一张图：频率对时间的一阶导数 df/dt（红）
--d2 另开一张图：频率对时间的二阶导数 d²f/dt²（黑）
--dwindow-ms=5 一、二阶导数的公共拟合窗口，单位 ms（默认5）
--d1-window-ms 原函数 -> 一阶导数的拟合窗口（默认继承 --dwindow-ms）
--d2-window-ms 一阶导数 -> 二阶导数的拟合窗口（默认继承 --dwindow-ms）
--save=路径 保存 PNG 后退出（不弹窗）
--selftest 仅打印统计，不画图
--help, -h 显示这份中文帮助后退出

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）
@@ -46,13 +60,20 @@ except Exception:
pass


def load_time_freq(path, fs, threshold=128):
def load_time_freq(path, fs, threshold=128, reverse=False):
"""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。"""
t_sec 以第一个脉冲为 0 时刻；hi_width 为每脉冲高电平宽度。
reverse=True 时先把高低电平对调，再按高电平找脉冲。"""
data = np.fromfile(path, dtype=np.uint8)
if data.size == 0:
raise ValueError("文件为空: %s" % path)
if fs <= 0:
raise ValueError("采样率必须大于0")
if not 0 <= threshold <= 255:
raise ValueError("阈值必须在0到255之间")
samples = (data >= threshold).astype(np.int8)
if reverse:
samples = 1 - samples

# run 级压缩
changes = np.flatnonzero(np.diff(samples) != 0) + 1
@@ -60,68 +81,132 @@ def load_time_freq(path, fs, threshold=128):
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
# 每个高电平 run 的起点都是一个上升沿。如果采集从高电平开始，
# 第一个 run 的真实上升沿在文件外，不能用来测周期。
hi = np.flatnonzero(runs[:, 2] == 1)
hi = hi[hi + 1 < len(runs)]
lo_ok = runs[hi + 1, 2] == 0
hi = hi[lo_ok]
if hi.size and runs[hi[0], 0] == 0:
hi = hi[1:]
if hi.size < 2:
raise ValueError("至少需要两个完整的上升沿才能计算频率: %s" % path)

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)
# 只用相邻实测上升沿求周期，不根据占空比猜测末脉冲频率。
period = np.diff(t_start)
freq = float(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_start[:-1] + hi_width[:-1] / 2.0) / fs
t_sec = t_sec - t_sec[0]

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


def selftest(path, fs, thresh):
return t_sec, freq, hi_width[:-1] / fs, meta


def local_linear_derivative(t_sec, values, window_s=5e-3, min_points=7):
"""对非等间隔时间点做局部线性拟合，返回 d(values)/dt。

直接对相邻单周期频率做差分会放大整数采样点带来的量化抖动；
局部最小二乘斜率使用真实时间间隔，并对这种抖动做平均。
"""
t = np.asarray(t_sec, dtype=np.float64)
y = np.asarray(values, dtype=np.float64)
if t.ndim != 1 or y.ndim != 1 or len(t) != len(y):
raise ValueError("t_sec 和 values 必须是等长一维数组")
if len(t) < 2:
raise ValueError("至少需要两个点才能计算导数")
if window_s <= 0:
raise ValueError("导数拟合窗口必须大于0")
if np.any(np.diff(t) <= 0):
raise ValueError("时间点必须严格递增")

n = len(t)
min_points = min(n, max(2, int(min_points)))
half_window = window_s / 2.0
left = np.searchsorted(t, t - half_window, side='left')
right = np.searchsorted(t, t + half_window, side='right')

# 低频段在固定时间窗口内可能点数太少，至少补足 min_points 个点。
idx = np.arange(n)
fallback_left = np.clip(idx - min_points // 2, 0, n - min_points)
fallback_right = fallback_left + min_points
too_few = (right - left) < min_points
left[too_few] = fallback_left[too_few]
right[too_few] = fallback_right[too_few]

# 前缀和使每个窗口的最小二乘斜率可以 O(1) 计算。
# 先把时间原点移到数据中心，降低长时采集时前缀和相减的精度损失。
x = t - (t[0] + t[-1]) / 2.0
sx = np.concatenate(([0.0], np.cumsum(x)))
sy = np.concatenate(([0.0], np.cumsum(y)))
sxx = np.concatenate(([0.0], np.cumsum(x * x)))
sxy = np.concatenate(([0.0], np.cumsum(x * y)))
count = (right - left).astype(np.float64)
sum_x = sx[right] - sx[left]
sum_y = sy[right] - sy[left]
sum_xx = sxx[right] - sxx[left]
sum_xy = sxy[right] - sxy[left]
denominator = count * sum_xx - sum_x * sum_x
numerator = count * sum_xy - sum_x * sum_y

derivative = np.empty(n, dtype=np.float64)
good = np.abs(denominator) > np.finfo(np.float64).eps
derivative[good] = numerator[good] / denominator[good]
derivative[~good] = np.gradient(y, t)[~good]
return derivative


def selftest(path, fs, thresh, reverse=False):
import statistics
t, freq, hi_w, meta = load_time_freq(path, fs, thresh)
t, freq, hi_w, meta = load_time_freq(path, fs, thresh, reverse)
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'])
print("检测到脉冲: %d，频率测量点: %d" % (
meta['detected_pulses'], 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" % (
print("末测量点: 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)
def plot_show(path, fs, thresh, ymax, save_path=None, draw_line=False,
reverse=False, draw_d1=False, draw_d2=False,
d1_window_ms=5.0, d2_window_ms=None):
t, freq, hi_w, meta = load_time_freq(path, fs, thresh, reverse)

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

draw_d1 = draw_d1 and (len(freq) >= 2)
draw_d2 = draw_d2 and (len(freq) >= 3)
fig, ax = plt.subplots(figsize=(15, 7))
fig.subplots_adjust(bottom=0.10, top=0.92)

# 全脉冲散点：复用 artist，重绘时按可见范围抽稀（大文件流畅）
MAX_VISIBLE_POINTS = 5000 # 可见范围内最多绘制的点数，超过则等间隔抽稀
(scatter,) = ax.plot([], [], '.', ms=1.5, color='C0', alpha=0.5, zorder=1)
(scatter,) = ax.plot([], [], '.', ms=1.5, color='C0', alpha=0.5, zorder=2)
line = None
if draw_line:
(line,) = ax.plot([], [], '-', lw=0.9, color='C0', alpha=0.85, zorder=1)
tx_ms = t * 1000
x_full = tx_ms
y_full = freq
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))
title_extra = "，高低对调" if reverse else ""
ax.set_title("%s 时频曲线（检测到脉冲 %d，频率测量点 %d，活动时长 %.0f ms%s）" % (
os.path.basename(path), meta['detected_pulses'], meta['pulses'],
(t[-1] + hi_w[-1] / 2) * 1000, title_extra))
ax.set_xlabel("时间 (ms)")
ax.set_ylabel("频率 (Hz)")
# 更密的刻度
@@ -131,10 +216,53 @@ def plot_show(path, fs, thresh, ymax, save_path=None):
ax.grid(True, which='minor', alpha=0.15)
ax.minorticks_on()

fig_der = None
if draw_d1 or draw_d2:
f64 = freq.astype(np.float64)
if d2_window_ms is None:
d2_window_ms = d1_window_ms
d1_full = local_linear_derivative(t, f64, d1_window_ms * 1e-3)
d2_full = (local_linear_derivative(t, d1_full, d2_window_ms * 1e-3)
if draw_d2 else None)
plot_count = int(draw_d1) + int(draw_d2)
fig_der, axes_der = plt.subplots(plot_count, 1, figsize=(15, 4.5 * plot_count),
sharex=True, squeeze=False)
fig_der.subplots_adjust(bottom=0.14, top=0.90)
row = 0
if draw_d1:
ax_der = axes_der[row, 0]
ax_der.plot(x_full, d1_full, '-', lw=0.9, color='red', label='df/dt')
ax_der.set_ylabel("一阶导数 (Hz/s)")
ax_der.legend(loc='upper right')
row += 1
if draw_d2:
ax_der = axes_der[row, 0]
ax_der.plot(x_full, d2_full, '-', lw=0.9, color='black', label='d²f/dt²')
ax_der.set_ylabel("二阶导数 (Hz/s²)")
ax_der.legend(loc='upper right')
for ax_der in axes_der[:, 0]:
ax_der.set_xlim(0.0, (t[-1] + hi_w[-1]) * 1000 * 1.02)
ax_der.grid(True, which='major', alpha=0.35)
ax_der.minorticks_on()
axes_der[-1, 0].set_xlabel("时间 (ms)")
axes_der[0, 0].set_title(
"时频曲线导数（一阶窗口 %.3g ms / 二阶窗口 %.3g ms）" %
(d1_window_ms, d2_window_ms))

if save_path:
scatter.set_data(x_full, y_full)
if line is not None:
line.set_data(x_full, y_full)
root, ext = os.path.splitext(save_path)
fig.savefig(save_path, dpi=130)
print("已保存: %s" % save_path)
if fig_der is not None:
der_path = root + "_d" + ext
fig_der.savefig(der_path, dpi=130)
print("已保存: %s" % der_path)
plt.close(fig)
if fig_der is not None:
plt.close(fig_der)
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
@@ -161,6 +289,8 @@ def plot_show(path, fs, thresh, ymax, save_path=None):
else:
idx = np.flatnonzero(mask)
scatter.set_data(x_full[idx], y_full[idx])
if line is not None:
line.set_data(x_full[idx], y_full[idx])

def on_scroll(event):
if event.inaxes is not ax or event.xdata is None:
@@ -259,8 +389,9 @@ def plot_show(path, fs, thresh, ymax, save_path=None):
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))
print("检测到脉冲 %d，频率测量点 %d，活动时长 %.1f ms" % (
meta['detected_pulses'], meta['pulses'],
(t[-1] + hi_w[-1] / 2) * 1000))
plt.show()


@@ -269,7 +400,7 @@ def main():
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:
if len(args) < 1 or '--help' in opts or '-h' in sys.argv[1:]:
print(__doc__)
sys.exit(0)

@@ -278,15 +409,23 @@ def main():
thresh = int(opts.get('--thresh', 128))
ymax = float(opts.get('--ymax', 0))
save_path = opts.get('--save', '')
draw_line = '--line' in opts
reverse = '--reverse' in opts
draw_d1 = '--d1' in opts
draw_d2 = '--d2' in opts
derivative_window_ms = float(opts.get('--dwindow-ms', 5.0))
d1_window_ms = float(opts.get('--d1-window-ms', derivative_window_ms))
d2_window_ms = float(opts.get('--d2-window-ms', derivative_window_ms))

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)
selftest(path, fs, thresh, reverse)
else:
plot_show(path, fs, thresh, ymax, save_path or None)
plot_show(path, fs, thresh, ymax, save_path or None, draw_line,
reverse, draw_d1, draw_d2, d1_window_ms, d2_window_ms)


if __name__ == '__main__':


+ 144
- 27
PLSR/Src/plsr_planner.c Dosyayı Görüntüle

@@ -63,34 +63,150 @@ void PlsrPlannerTimingReset(void)
}
#endif

static const uint32_t PlsrPlannerSmoothIntegralQ24[65] =
#define PLSR_PLANNER_CURVE_TABLE_BITS (9U)
#define PLSR_PLANNER_CURVE_TABLE_INTERVALS (1UL << PLSR_PLANNER_CURVE_TABLE_BITS)
#define PLSR_PLANNER_CURVE_TABLE_SIZE (PLSR_PLANNER_CURVE_TABLE_INTERVALS + 1UL)
#define PLSR_PLANNER_CURVE_DERIVATIVE_SHIFT (8U + PLSR_PLANNER_CURVE_TABLE_BITS)

/* curveMode 1: fixed seven-section jerk profile. Each entry/exit ramp
uses a 1:2:1 constant-jerk/constant-acceleration/constant-jerk ratio.
The table stores the integral of the normalized frequency blend. */
static const uint32_t PlsrPlannerSmoothIntegralQ24[PLSR_PLANNER_CURVE_TABLE_SIZE] =
{
0UL, 64UL, 504UL, 1688UL, 3968UL, 7688UL, 13176UL, 20752UL,
30720UL, 43376UL, 59000UL, 77864UL, 100224UL, 126328UL, 156408UL,
190688UL, 229376UL, 272672UL, 320760UL, 373816UL, 432000UL,
495464UL, 564344UL, 638768UL, 718848UL, 804688UL, 896376UL,
993992UL, 1097600UL, 1207256UL, 1323000UL, 1444864UL, 1572864UL,
1707008UL, 1847288UL, 1993688UL, 2146176UL, 2304712UL, 2469240UL,
2639696UL, 2816000UL, 2998064UL, 3185784UL, 3379048UL, 3577728UL,
3781688UL, 3990776UL, 4204832UL, 4423680UL, 4647136UL, 4875000UL,
5107064UL, 5343104UL, 5582888UL, 5826168UL, 6072688UL, 6322176UL,
6574352UL, 6828920UL, 7085576UL, 7344000UL, 7603864UL, 7864824UL,
8126528UL, 8388608UL
0UL, 0UL, 1UL, 3UL, 7UL, 14UL, 24UL, 38UL,
57UL, 81UL, 111UL, 148UL, 192UL, 244UL, 305UL, 375UL,
455UL, 546UL, 648UL, 762UL, 889UL, 1029UL, 1183UL, 1352UL,
1536UL, 1736UL, 1953UL, 2187UL, 2439UL, 2710UL, 3000UL, 3310UL,
3641UL, 3993UL, 4367UL, 4764UL, 5184UL, 5628UL, 6097UL, 6591UL,
7111UL, 7658UL, 8232UL, 8834UL, 9465UL, 10125UL, 10815UL, 11536UL,
12288UL, 13072UL, 13889UL, 14739UL, 15623UL, 16542UL, 17496UL, 18486UL,
19513UL, 20577UL, 21679UL, 22820UL, 24000UL, 25220UL, 26481UL, 27783UL,
29127UL, 30514UL, 31944UL, 33418UL, 34937UL, 36501UL, 38111UL, 39768UL,
41472UL, 43224UL, 45025UL, 46875UL, 48775UL, 50726UL, 52728UL, 54782UL,
56889UL, 59049UL, 61263UL, 63532UL, 65856UL, 68236UL, 70673UL, 73167UL,
75719UL, 78330UL, 81000UL, 83730UL, 86521UL, 89373UL, 92287UL, 95264UL,
98304UL, 101408UL, 104577UL, 107811UL, 111111UL, 114478UL, 117912UL, 121414UL,
124985UL, 128625UL, 132335UL, 136116UL, 139968UL, 143892UL, 147889UL, 151959UL,
156103UL, 160322UL, 164616UL, 168986UL, 173433UL, 177957UL, 182559UL, 187240UL,
192000UL, 196840UL, 201761UL, 206763UL, 211847UL, 217014UL, 222264UL, 227598UL,
233017UL, 238521UL, 244110UL, 249785UL, 255545UL, 261390UL, 267321UL, 273337UL,
279438UL, 285625UL, 291897UL, 298254UL, 304697UL, 311225UL, 317838UL, 324537UL,
331321UL, 338190UL, 345145UL, 352185UL, 359310UL, 366521UL, 373817UL, 381198UL,
388665UL, 396217UL, 403854UL, 411577UL, 419385UL, 427278UL, 435257UL, 443321UL,
451470UL, 459705UL, 468025UL, 476430UL, 484921UL, 493497UL, 502158UL, 510905UL,
519737UL, 528654UL, 537657UL, 546745UL, 555918UL, 565177UL, 574521UL, 583950UL,
593465UL, 603065UL, 612750UL, 622521UL, 632377UL, 642318UL, 652345UL, 662457UL,
672654UL, 682937UL, 693305UL, 703758UL, 714297UL, 724921UL, 735630UL, 746425UL,
757305UL, 768270UL, 779321UL, 790457UL, 801678UL, 812985UL, 824377UL, 835854UL,
847417UL, 859065UL, 870798UL, 882617UL, 894521UL, 906510UL, 918585UL, 930745UL,
942990UL, 955321UL, 967737UL, 980238UL, 992825UL, 1005497UL, 1018254UL, 1031097UL,
1044025UL, 1057038UL, 1070137UL, 1083321UL, 1096590UL, 1109945UL, 1123385UL, 1136910UL,
1150521UL, 1164217UL, 1177998UL, 1191865UL, 1205817UL, 1219854UL, 1233977UL, 1248185UL,
1262478UL, 1276857UL, 1291321UL, 1305870UL, 1320505UL, 1335225UL, 1350030UL, 1364921UL,
1379897UL, 1394958UL, 1410105UL, 1425337UL, 1440654UL, 1456057UL, 1471545UL, 1487118UL,
1502777UL, 1518521UL, 1534350UL, 1550265UL, 1566265UL, 1582350UL, 1598521UL, 1614777UL,
1631118UL, 1647545UL, 1664057UL, 1680654UL, 1697337UL, 1714105UL, 1730958UL, 1747897UL,
1764921UL, 1782030UL, 1799225UL, 1816505UL, 1833870UL, 1851321UL, 1868857UL, 1886478UL,
1904185UL, 1921977UL, 1939854UL, 1957817UL, 1975865UL, 1993998UL, 2012217UL, 2030521UL,
2048910UL, 2067385UL, 2085945UL, 2104590UL, 2123321UL, 2142137UL, 2161038UL, 2180025UL,
2199097UL, 2218254UL, 2237497UL, 2256825UL, 2276238UL, 2295737UL, 2315321UL, 2334990UL,
2354745UL, 2374585UL, 2394510UL, 2414521UL, 2434617UL, 2454798UL, 2475065UL, 2495417UL,
2515854UL, 2536377UL, 2556985UL, 2577678UL, 2598457UL, 2619321UL, 2640270UL, 2661305UL,
2682425UL, 2703630UL, 2724921UL, 2746297UL, 2767758UL, 2789305UL, 2810937UL, 2832654UL,
2854457UL, 2876345UL, 2898318UL, 2920377UL, 2942521UL, 2964750UL, 2987065UL, 3009465UL,
3031950UL, 3054521UL, 3077177UL, 3099918UL, 3122745UL, 3145657UL, 3168654UL, 3191737UL,
3214905UL, 3238158UL, 3261497UL, 3284921UL, 3308430UL, 3332025UL, 3355705UL, 3379470UL,
3403321UL, 3427257UL, 3451278UL, 3475385UL, 3499577UL, 3523854UL, 3548217UL, 3572665UL,
3597198UL, 3621817UL, 3646521UL, 3671310UL, 3696185UL, 3721145UL, 3746190UL, 3771321UL,
3796537UL, 3821838UL, 3847225UL, 3872697UL, 3898254UL, 3923897UL, 3949625UL, 3975438UL,
4001337UL, 4027321UL, 4053390UL, 4079545UL, 4105785UL, 4132110UL, 4158521UL, 4185017UL,
4211598UL, 4238265UL, 4265017UL, 4291854UL, 4318777UL, 4345785UL, 4372878UL, 4400057UL,
4427321UL, 4454670UL, 4482104UL, 4509622UL, 4537223UL, 4564907UL, 4592673UL, 4620520UL,
4648448UL, 4676456UL, 4704543UL, 4732709UL, 4760953UL, 4789274UL, 4817672UL, 4846146UL,
4874695UL, 4903319UL, 4932017UL, 4960788UL, 4989632UL, 5018548UL, 5047535UL, 5076593UL,
5105721UL, 5134918UL, 5164184UL, 5193518UL, 5222919UL, 5252387UL, 5281921UL, 5311520UL,
5341184UL, 5370912UL, 5400703UL, 5430557UL, 5460473UL, 5490450UL, 5520488UL, 5550586UL,
5580743UL, 5610959UL, 5641233UL, 5671564UL, 5701952UL, 5732396UL, 5762895UL, 5793449UL,
5824057UL, 5854718UL, 5885432UL, 5916198UL, 5947015UL, 5977883UL, 6008801UL, 6039768UL,
6070784UL, 6101848UL, 6132959UL, 6164117UL, 6195321UL, 6226570UL, 6257864UL, 6289202UL,
6320583UL, 6352007UL, 6383473UL, 6414980UL, 6446528UL, 6478116UL, 6509743UL, 6541409UL,
6573113UL, 6604854UL, 6636632UL, 6668446UL, 6700295UL, 6732179UL, 6764097UL, 6796048UL,
6828032UL, 6860048UL, 6892095UL, 6924173UL, 6956281UL, 6988418UL, 7020584UL, 7052778UL,
7084999UL, 7117247UL, 7149521UL, 7181820UL, 7214144UL, 7246492UL, 7278863UL, 7311257UL,
7343673UL, 7376110UL, 7408568UL, 7441046UL, 7473543UL, 7506059UL, 7538593UL, 7571144UL,
7603712UL, 7636296UL, 7668895UL, 7701509UL, 7734137UL, 7766778UL, 7799432UL, 7832098UL,
7864775UL, 7897463UL, 7930161UL, 7962868UL, 7995584UL, 8028308UL, 8061039UL, 8093777UL,
8126521UL, 8159270UL, 8192024UL, 8224782UL, 8257543UL, 8290307UL, 8323073UL, 8355840UL,
8388608UL
};

static const uint32_t PlsrPlannerSineIntegralQ24[65] =
static const uint32_t PlsrPlannerSineIntegralQ24[PLSR_PLANNER_CURVE_TABLE_SIZE] =
{
0UL, 53UL, 421UL, 1420UL, 3362UL, 6560UL, 11321UL, 17949UL,
26744UL, 38000UL, 52007UL, 69047UL, 89393UL, 113314UL, 141066UL,
172899UL, 209052UL, 249753UL, 295221UL, 345662UL, 401269UL,
462225UL, 528698UL, 600845UL, 678806UL, 762711UL, 852672UL,
948789UL, 1051146UL, 1159812UL, 1274841UL, 1396271UL, 1524127UL,
1658415UL, 1799129UL, 1946244UL, 2099722UL, 2259509UL, 2425536UL,
2597719UL, 2775958UL, 2960141UL, 3150138UL, 3345809UL, 3546997UL,
3753534UL, 3965237UL, 4181913UL, 4403356UL, 4629347UL, 4859658UL,
5094050UL, 5332273UL, 5574071UL, 5819175UL, 6067312UL, 6318200UL,
6571549UL, 6827065UL, 7084448UL, 7343394UL, 7603596UL, 7864741UL,
8126517UL, 8388608UL
0UL, 0UL, 1UL, 3UL, 7UL, 13UL, 22UL, 35UL,
53UL, 75UL, 103UL, 137UL, 178UL, 226UL, 282UL, 347UL,
421UL, 505UL, 599UL, 705UL, 822UL, 951UL, 1094UL, 1250UL,
1420UL, 1604UL, 1805UL, 2021UL, 2254UL, 2503UL, 2771UL, 3057UL,
3362UL, 3687UL, 4032UL, 4398UL, 4785UL, 5194UL, 5626UL, 6081UL,
6560UL, 7063UL, 7592UL, 8146UL, 8726UL, 9333UL, 9967UL, 10630UL,
11321UL, 12041UL, 12791UL, 13571UL, 14382UL, 15225UL, 16100UL, 17008UL,
17949UL, 18923UL, 19933UL, 20977UL, 22057UL, 23173UL, 24325UL, 25516UL,
26744UL, 28010UL, 29316UL, 30661UL, 32046UL, 33472UL, 34939UL, 36449UL,
38000UL, 39595UL, 41233UL, 42915UL, 44642UL, 46414UL, 48232UL, 50096UL,
52007UL, 53966UL, 55972UL, 58027UL, 60131UL, 62284UL, 64487UL, 66742UL,
69047UL, 71404UL, 73813UL, 76275UL, 78790UL, 81359UL, 83982UL, 86660UL,
89393UL, 92182UL, 95028UL, 97930UL, 100890UL, 103908UL, 106984UL, 110119UL,
113314UL, 116568UL, 119882UL, 123258UL, 126695UL, 130194UL, 133755UL, 137379UL,
141066UL, 144817UL, 148632UL, 152512UL, 156457UL, 160468UL, 164545UL, 168688UL,
172899UL, 177177UL, 181523UL, 185937UL, 190421UL, 194973UL, 199596UL, 204289UL,
209052UL, 213886UL, 218792UL, 223770UL, 228820UL, 233943UL, 239140UL, 244409UL,
249753UL, 255172UL, 260665UL, 266234UL, 271878UL, 277599UL, 283396UL, 289270UL,
295221UL, 301250UL, 307358UL, 313543UL, 319808UL, 326151UL, 332575UL, 339078UL,
345662UL, 352326UL, 359072UL, 365899UL, 372808UL, 379799UL, 386873UL, 394029UL,
401269UL, 408592UL, 416000UL, 423491UL, 431068UL, 438729UL, 446475UL, 454307UL,
462225UL, 470229UL, 478320UL, 486497UL, 494762UL, 503114UL, 511554UL, 520082UL,
528698UL, 537403UL, 546197UL, 555080UL, 564053UL, 573116UL, 582268UL, 591511UL,
600845UL, 610269UL, 619785UL, 629392UL, 639090UL, 648881UL, 658763UL, 668739UL,
678806UL, 688967UL, 699221UL, 709568UL, 720008UL, 730543UL, 741171UL, 751894UL,
762711UL, 773623UL, 784629UL, 795731UL, 806928UL, 818220UL, 829608UL, 841092UL,
852672UL, 864348UL, 876121UL, 887990UL, 899955UL, 912018UL, 924178UL, 936435UL,
948789UL, 961241UL, 973790UL, 986438UL, 999183UL, 1012026UL, 1024968UL, 1038007UL,
1051146UL, 1064383UL, 1077718UL, 1091153UL, 1104686UL, 1118319UL, 1132051UL, 1145882UL,
1159812UL, 1173841UL, 1187971UL, 1202200UL, 1216528UL, 1230956UL, 1245485UL, 1260113UL,
1274841UL, 1289669UL, 1304597UL, 1319626UL, 1334754UL, 1349983UL, 1365312UL, 1380742UL,
1396271UL, 1411902UL, 1427632UL, 1443464UL, 1459395UL, 1475428UL, 1491560UL, 1507793UL,
1524127UL, 1540561UL, 1557096UL, 1573732UL, 1590467UL, 1607304UL, 1624240UL, 1641278UL,
1658415UL, 1675654UL, 1692992UL, 1710431UL, 1727970UL, 1745610UL, 1763349UL, 1781189UL,
1799129UL, 1817169UL, 1835309UL, 1853548UL, 1871888UL, 1890328UL, 1908867UL, 1927505UL,
1946244UL, 1965082UL, 1984019UL, 2003055UL, 2022190UL, 2041425UL, 2060758UL, 2080191UL,
2099722UL, 2119351UL, 2139080UL, 2158906UL, 2178831UL, 2198854UL, 2218974UL, 2239193UL,
2259509UL, 2279923UL, 2300434UL, 2321042UL, 2341747UL, 2362550UL, 2383449UL, 2404444UL,
2425536UL, 2446724UL, 2468008UL, 2489388UL, 2510864UL, 2532435UL, 2554101UL, 2575863UL,
2597719UL, 2619670UL, 2641715UL, 2663855UL, 2686088UL, 2708416UL, 2730837UL, 2753351UL,
2775958UL, 2798659UL, 2821451UL, 2844337UL, 2867314UL, 2890384UL, 2913545UL, 2936797UL,
2960141UL, 2983575UL, 3007100UL, 3030716UL, 3054421UL, 3078216UL, 3102101UL, 3126075UL,
3150138UL, 3174290UL, 3198530UL, 3222858UL, 3247274UL, 3271777UL, 3296368UL, 3321045UL,
3345809UL, 3370659UL, 3395595UL, 3420617UL, 3445724UL, 3470915UL, 3496192UL, 3521552UL,
3546997UL, 3572525UL, 3598137UL, 3623831UL, 3649608UL, 3675467UL, 3701408UL, 3727430UL,
3753534UL, 3779718UL, 3805983UL, 3832327UL, 3858752UL, 3885255UL, 3911838UL, 3938498UL,
3965237UL, 3992054UL, 4018948UL, 4045919UL, 4072966UL, 4100090UL, 4127289UL, 4154564UL,
4181913UL, 4209337UL, 4236836UL, 4264407UL, 4292052UL, 4319770UL, 4347560UL, 4375422UL,
4403356UL, 4431361UL, 4459436UL, 4487581UL, 4515797UL, 4544081UL, 4572435UL, 4600857UL,
4629347UL, 4657904UL, 4686529UL, 4715220UL, 4743977UL, 4772800UL, 4801688UL, 4830641UL,
4859658UL, 4888739UL, 4917883UL, 4947090UL, 4976359UL, 5005690UL, 5035082UL, 5064536UL,
5094050UL, 5123623UL, 5153256UL, 5182948UL, 5212698UL, 5242506UL, 5272372UL, 5302294UL,
5332273UL, 5362308UL, 5392398UL, 5422543UL, 5452742UL, 5482995UL, 5513301UL, 5543660UL,
5574071UL, 5604534UL, 5635047UL, 5665612UL, 5696227UL, 5726891UL, 5757604UL, 5788366UL,
5819175UL, 5850032UL, 5880936UL, 5911886UL, 5942882UL, 5973923UL, 6005009UL, 6036139UL,
6067312UL, 6098529UL, 6129787UL, 6161088UL, 6192430UL, 6223813UL, 6255236UL, 6286698UL,
6318200UL, 6349740UL, 6381317UL, 6412933UL, 6444585UL, 6476273UL, 6507997UL, 6539755UL,
6571549UL, 6603376UL, 6635236UL, 6667129UL, 6699054UL, 6731011UL, 6762999UL, 6795017UL,
6827065UL, 6859142UL, 6891247UL, 6923381UL, 6955542UL, 6987730UL, 7019944UL, 7052183UL,
7084448UL, 7116737UL, 7149050UL, 7181386UL, 7213745UL, 7246126UL, 7278528UL, 7310951UL,
7343394UL, 7375857UL, 7408339UL, 7440839UL, 7473358UL, 7505893UL, 7538445UL, 7571012UL,
7603596UL, 7636194UL, 7668806UL, 7701431UL, 7734070UL, 7766721UL, 7799383UL, 7832057UL,
7864741UL, 7897435UL, 7930138UL, 7962850UL, 7995570UL, 8028297UL, 8061031UL, 8093771UL,
8126517UL, 8159267UL, 8192022UL, 8224781UL, 8257543UL, 8290307UL, 8323073UL, 8355840UL,
8388608UL
};

static uint32_t PlsrPlannerAbsDifference(uint32_t first, uint32_t second)
@@ -294,7 +410,7 @@ static uint64_t PlsrPlannerCurveIntegralQ32(uint64_t progressQ32,
}
table = (curveMode == 1U) ? PlsrPlannerSmoothIntegralQ24
: PlsrPlannerSineIntegralQ24;
scaled = progressQ32 * 64ULL;
scaled = progressQ32 * PLSR_PLANNER_CURVE_TABLE_INTERVALS;
index = (uint32_t)(scaled >> 32U);
fraction = (uint32_t)scaled;
first = (uint64_t)table[index] << 8U;
@@ -365,10 +481,11 @@ static uint32_t PlsrPlannerInstantFrequency(
table = (context->block.curveMode == 1U)
? PlsrPlannerSmoothIntegralQ24
: PlsrPlannerSineIntegralQ24;
scaled = progressQ32 * 64ULL;
scaled = progressQ32 * PLSR_PLANNER_CURVE_TABLE_INTERVALS;
index = (uint32_t)(scaled >> 32U);
curveProgressQ32 =
(uint64_t)(table[index + 1UL] - table[index]) << 14U;
(uint64_t)(table[index + 1UL] - table[index])
<< PLSR_PLANNER_CURVE_DERIVATIVE_SHIFT;
}
if (context->rampToHz >= context->rampFromHz)
{


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