docs(measurement): 测量节拍是区间 —— 周期累积法的窗口量化（用户提出）+ 全仓回灌
- 新文件 docs/measurement-window-quantization.md：窗长 = ⌊BASE/Xn₀⌋·Xn₁ tick（LPCNT 整数除法 + 学习后冻结）
· 标称 BASE/f_cap（10.92/21.85ms）是【上限】，实际 = 标称 ×(BASE−r₀)/BASE × f₀/f_now
· 站点偏差：100kHz −0.39%（PD132T 10.88ms / PD136HA 21.76ms）、60kHz −2.34%、带内最差 −6.20%（Xn₀=2049）
· 深占压（频偏 −5%）窗再 +5.3%；"检测阈值"量级（≤0.46%）可忽略
· ✅ Δf/f 口径不受影响：Value/Origin = Xn₁/Xn₀ 精确（余数完全约掉）—— 灵敏度/交付结论全部保持
· ⚠ 新发现：loop1_LPCNT 为 INT8U(≤255)，同族靠 406<Xn<2050 保证 ≤80；
PD136HA 该窗口已注释 ⇒ Xn<129 时 8 位截断 ⇒ 异常线圈可能报"堵车"而非"故障"（建议恢复或加钳位）
- 新增 tools/window_calc.py（内置六型号参数，实跑验证文档全部数字）
- 回灌：cross-model 总表（新增脚注 + 测频路线说明 + 待补证1条）、PD132T↔PD136HA 对比报告（§0/§3.1/§3.2/§4.3）、
两份交付对齐报告（"测量窗列 = 标称上限" + 区间脚注）、filter-coefficient 专题（M1H 8.19ms 勘误 + τ 复算 + 脚注）、
README（新增"专题文档"段 + 目录树）
- 表格校验 0 异常
This commit is contained in:
wangfq
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""测量窗量化计算器（周期累积法）
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背景：检测器不是"累加固定 tick 数"，而是"累加固定个数的周期"：
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LPCNT = floor(BASE / Xn0) # 整数除法，学习时算一次后【冻结】
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窗长 = LPCNT * Xn1 # 单位：捕获时钟 tick ← 不是恒定 BASE！
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窗时长 = 窗长 / f_cap
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其中 Xn0 = 学习时的周期计数、Xn1 = 当前周期计数（Xn 与线圈频率成反比）。
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结论（详见 docs/measurement-window-quantization.md）：
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* 表中 BASE/f_cap（如 10.92ms / 21.85ms）是【标称上限】，实际窗 = BASE - (BASE mod Xn0) tick
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* 站点偏差 = r0/BASE（r0 = BASE mod Xn0），带内 0 ~ -6.2%
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* 深占压（线圈频偏 -d%）时窗时长再放大 1/(1-d)
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* 【比值口径不受影响】：Value/Origin = Xn1/Xn0 精确（余数完全约掉）
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用法：
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python3 window_calc.py # 打印内置型号对照表
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python3 window_calc.py --model PD132T --f-coil 100k
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python3 window_calc.py --f-cap 3M --base 32768 --edge 2 --divider 32 --f-coil 60k
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参数：--f-coil 线圈频率(Hz 或 100k/60k)、--f-cap 捕获时钟、--base 归一化基数、
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--edge 捕获沿系数(双沿=2 / 单沿=1)、--divider 前端分频比(CD4060 ÷32 或直连=1)
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"""
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import argparse
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import sys
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# 内置型号参数: (BASE, f_cap, edge_factor, front_divider, note)
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MODELS = {
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"M1H": (32768, 4.0e6, 2, 32, "STC12C5202, PCA 双沿, ÷32(推断), 4MHz"),
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"PD132L": (32768, 1.2e6, 1, 32, "STM8S003, TIM1_CC3 下降沿, ÷32, 1.2MHz"),
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"PD132T": (32768, 3.0e6, 2, 32, "MA801, PCA 双沿, ÷32(Q5), 3MHz"),
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"PD136HA": (32768, 1.5e6, 1, 32, "HC32F003C4, ADTIM4 单沿, ÷32, 1.5MHz"),
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"DLD154V4B": (131072, 120e6, 1, 1, "AT32F421, TIM3 直连振荡(1 Xn = 1 原始周期), 120MHz"),
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"DLD154Pro": (524288, 60e6, 1, 1, "CH591R, TMR0 下降沿直连, 60MHz 实测"),
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}
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def parse_freq(text):
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"""'100k' / '3M' / '1.5e6' / '480' -> float Hz"""
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t = str(text).strip()
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mult = 1.0
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if t[-1] in "kK":
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mult, t = 1e3, t[:-1]
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elif t[-1] in "mM":
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mult, t = 1e6, t[:-1]
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return float(t) * mult
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def window(base, f_cap, edge, divider, f_coil, f_now=None):
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"""返回 dict：Xn、LPCNT、窗长(tick)、窗时长(s)、偏差、口径验证"""
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f_now = f_coil if f_now is None else f_now
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C = divider * f_cap / float(edge) # 判据常数: Xn = C / f_coil
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xn0 = C / f_coil
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lpcnt = int(base // xn0)
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if lpcnt == 0: # 固件兜底: Xn > BASE 时 LPCNT=1
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lpcnt = 1
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xn_float0 = xn0
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xn0 = round(xn0) # 固件里 Xn 是整数计数
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xn1 = round(C / f_now)
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window_ticks = lpcnt * xn0 # 学习时/稳态
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window_ticks_now = lpcnt * xn1 # 当前频率下
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r0 = base % xn0 if xn0 <= base else base
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return {
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"C": C, "Xn0": xn0, "LPCNT": lpcnt, "r0": r0,
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"window_ticks": window_ticks,
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"nominal_ticks": base,
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"window_s": window_ticks / f_cap,
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"nominal_s": base / f_cap,
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"dev_pct": (window_ticks / base - 1.0) * 100.0,
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"window_s_now": window_ticks_now / f_cap,
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"dev_now_pct": (window_ticks_now / base - 1.0) * 100.0, # vs 标称上限
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"dev_now_vs_learned_pct": (window_ticks_now / float(window_ticks) - 1.0) * 100.0, # vs 学习时窗
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"ratio_exact": xn1 / float(xn0), # Value/Origin 应为该值
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"ratio_formula": f_now and (f_coil / f_now),
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}
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def main():
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ap = argparse.ArgumentParser(description="周期累积法测量窗量化计算器")
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ap.add_argument("--model", choices=sorted(MODELS), help="内置型号")
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ap.add_argument("--base", type=int, default=32768)
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ap.add_argument("--f-cap", default="3M")
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ap.add_argument("--edge", type=int, choices=(1, 2), default=2, help="捕获沿系数: 双沿=2 单沿=1")
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ap.add_argument("--divider", type=int, default=32, help="前端分频比 (CD4060 ÷32=32, 直连=1)")
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ap.add_argument("--f-coil", default="100k", help="线圈频率 (100k / 3M / 480)")
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ap.add_argument("--f-now", default=None, help="当前频率(默认=f-coil)，用于看频率跟踪分量")
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a = ap.parse_args()
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if a.model:
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base, fcap, edge, div, note = MODELS[a.model]
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print("型号 %s: %s" % (a.model, note))
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else:
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base, fcap, edge, div = a.base, parse_freq(a.f_cap), a.edge, a.divider
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f_coil = parse_freq(a.f_coil)
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f_now = parse_freq(a.f_now) if a.f_now else None
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w = window(base, fcap, edge, div, f_coil, f_now)
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print("参数: BASE=%d f_cap=%.6g Hz 边沿系数=%d 分频=%d 线圈=%.6g Hz"
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% (base, fcap, edge, div, f_coil))
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print("判据常数 C = 分频·f_cap/边沿 = %.4g (Xn = C/f_coil)" % w["C"])
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print("Xn0 = %.2f -> 取整 %d ; LPCNT = floor(%d/%d) = %d ; r0 = BASE mod Xn0 = %d"
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% (w["C"] / f_coil, w["Xn0"], base, w["Xn0"], w["LPCNT"], w["r0"]))
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print("窗长 = LPCNT·Xn0 = %d tick (标称上限 %d tick)" % (w["window_ticks"], w["nominal_ticks"]))
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print("窗时长 = %.4f ms (标称上限 %.4f ms) 偏差 %+.2f%%"
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% (w["window_s"] * 1e3, w["nominal_s"] * 1e3, w["dev_pct"]))
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if f_now and abs(f_now - f_coil) > 1e-9:
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print("频率跟踪: f_now=%.6g Hz -> 窗 %.4f ms（vs 标称 %+.2f%% / vs 学习时窗 %+.2f%%）"
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% (f_now, w["window_s_now"] * 1e3, w["dev_now_pct"], w["dev_now_vs_learned_pct"]))
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print(" 比值口径验证: Value/Origin = %.6f ← 应等于 f0/f_now = %.6f"
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% (w["ratio_exact"], f_coil / f_now))
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print("口径提示: Δf/f = 表值/65536 不受窗长/ BASE 影响（余数约掉）；"
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"量化误差上限 = Xn0/BASE = %.2f%%" % (w["Xn0"] / base * 100.0))
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# 无参时打印族内对照表
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if not any([a.model, a.f_now]) and a.f_coil == "100k":
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print("\n附：内置型号对照（同为 100kHz 线圈，前端均按各自 divider）")
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print("%-11s %8s %6s %5s %8s %11s %8s" % ("型号", "f_cap", "Xn0", "LPCNT", "窗tick", "窗时长", "偏差"))
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for name, (b, fc, e, dv, _n) in MODELS.items():
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C = dv * fc / float(e)
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if not (1 <= C / 1e5 <= b): # 该频率不在其频段内则跳过
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continue
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ww = window(b, fc, e, dv, 1e5)
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print("%-11s %7.3gM %6d %5d %8d %9.4fms %+7.2f%%"
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% (name, fc / 1e6, ww["Xn0"], ww["LPCNT"], ww["window_ticks"],
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ww["window_s"] * 1e3, ww["dev_pct"]))
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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