#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""端到端时序图生成器（PD132T 主图，PD136HA 对照）——  纯 stdlib 手写 SVG

输出：docs/img/timing-chain-pd132t.svg
转 PNG（仓库外，供 IM 预览）：
    chromium --headless=new --disable-gpu --no-sandbox \
      --screenshot=out.png --window-size=1680,1320 --default-background-color=FFFFFFFF \
      file:///abs/path/timing-chain-pd132t.svg

图中所有曲线都是按 §9 的公式真算的，不是示意画：
  窗结账 = LPCNT 个 Xn 之和；CAPVD 为 79/256 一阶 IIR；判据 = CAPVD < Origin − floor(Origin·表值/65536)
"""
import math

# ---------- 站点参数（PD132T @ 100kHz） ----------
F_CAP = 3.0e6          # 捕获时钟 Hz（官方手册直证：SYSCLK/4）
F_COIL = 100e3         # 线圈频率 Hz（站点）
DIVIDER = 32           # CD4060 ÷32（Q5）
EDGE = 2               # 双沿捕获（CCAPM1=0x31）
BASE = 32768
SENS_ENTER = 60        # 档1 进入表值（/65536）
ALFA = 79              # Flt_Reg（FLT 拨码 OFF）
DF_REL = 0.5e-3        # 车辆引起的相对频偏（0.5%，实际车型量级）

XN0 = round(DIVIDER * F_CAP / (EDGE * F_COIL))        # 480 tick @100kHz
OUT = 'docs/img/timing-chain-pd132t.svg'
LPCNT = BASE // XN0
WIN_TICKS = LPCNT * XN0
T_XN = XN0 / F_CAP
T_WIN = WIN_TICKS / F_CAP
TAU = T_WIN * 256.0 / ALFA
ORIGIN = WIN_TICKS
DLT = (ORIGIN * SENS_ENTER) >> 16
XN1 = round(XN0 / (1 - DF_REL))
VALUE_CAR = LPCNT * XN1
T_ENTER_TRUE = 0.6e-3   # 车辆真实进入时刻（窗内）

W, H = 1680, 1560
P = []                  # SVG 片段
def add(s): P.append(s)
def esc(t): return t.replace('&', '&amp;').replace('<', '&lt;').replace('>', '&gt;')
def txt(x, y, s, size=15, fill='#111', anchor='start', weight='normal', fam='Noto Serif CJK SC, Noto Sans CJK SC, sans-serif'):
    add('<text x="%.1f" y="%.1f" font-family="%s" font-size="%d" fill="%s" text-anchor="%s" font-weight="%s">%s</text>'
        % (x, y, fam, size, fill, anchor, weight, esc(s)))
def line(x1, y1, x2, y2, stroke='#111', w=1.4, dash=None, op=1.0):
    d = ' stroke-dasharray="%s"' % dash if dash else ''
    add('<line x1="%.1f" y1="%.1f" x2="%.1f" y2="%.1f" stroke="%s" stroke-width="%.1f" opacity="%.2f"%s/>'
        % (x1, y1, x2, y2, stroke, w, op, d))
def poly(pts, stroke='#0a58ca', w=1.8, fill='none', op=1.0):
    s = ' '.join('%.2f,%.2f' % p for p in pts)
    add('<polyline points="%s" fill="%s" stroke="%s" stroke-width="%.1f" opacity="%.2f" stroke-linejoin="round"/>'
        % (s, fill, stroke, w, op))
def rect(x, y, w_, h_, fill='#fff', stroke='none', rx=4, op=1.0, sw=1.2):
    add('<rect x="%.1f" y="%.1f" width="%.1f" height="%.1f" rx="%d" fill="%s" stroke="%s" stroke-width="%.1f" opacity="%.2f"/>'
        % (x, y, w_, h_, rx, fill, stroke, sw, op))
def dot(x, y, r=3.0, fill='#c1121f'):
    add('<circle cx="%.1f" cy="%.1f" r="%.1f" fill="%s"/>' % (x, y, r, fill))
def note(x, y, w_, h_, title, lines_, accent='#0a58ca'):
    rect(x, y, w_, h_, fill='#fbfcfe', stroke=accent, rx=6, sw=1.2)
    txt(x + 12, y + 24, title, 16, accent, weight='bold')
    for i, s in enumerate(lines_):
        txt(x + 12, y + 48 + i * 21, s, 14, '#333')

# ================= 真算：窗序列 / CAPVD / 判据 =================
def sim(df_rel, n_windows=60):
    """df_rel: 车辆引起的相对频升（>0）。返回 (t_s, Value, CAPVD) 逐窗列表。"""
    f1 = F_COIL * (1 + df_rel)
    xn1 = DIVIDER * F_CAP / (EDGE * f1)
    value_car = LPCNT * xn1
    thr = ORIGIN - DLT
    out, capvd = [], float(ORIGIN)
    for n in range(n_windows):
        frac = 1.0 if n >= 1 else max(0.0, (T_WIN - T_ENTER_TRUE) / T_WIN)
        v = WIN_TICKS + frac * (value_car - WIN_TICKS)
        capvd = capvd + (v - capvd) * ALFA / 256.0
        out.append(((n + 1) * T_WIN, v, capvd))
    cross = next((i for i, (_, _, c) in enumerate(out) if c < thr), None)
    return out, thr, (None if cross is None else (cross + 1) * T_WIN)

big, THR, T_CROSS = sim(0.005)      # 大信号 Δf/f = +0.5%
sml, _, T_CROSS_S = sim(0.001)      # 小信号 Δf/f = +0.1%

# ================= 画布与坐标 =================
X0, X1 = 250.0, 1470.0
add('<rect width="%d" height="%d" fill="#ffffff"/>' % (W, H))
txt(28, 44, 'PD132T 端到端时序图（检测通道：线圈 → CD4060 ÷32 → PCA 捕获 → 窗结账 → IIR → 基线 → 继电器）', 21, '#0b2c4d', weight='bold')
txt(28, 70, '站点：线圈 100 kHz / 捕获时钟 3 MHz（12MHz÷4，官方手册直证）/ Xn₀=480 tick / LPCNT=68 / 窗=32640 tick=10.88 ms / α=79(τ=35.3 ms) / 进入档1=60(0.0916%Δf/f)', 14, '#555')
note(1170, 20, 470, 62, '三段横轴（跨 6 个数量级，非线性）',
     ['① 微观 0~11.2 ms（一个窗）　② 中观 0~0.6 s（检测到输出）', '③ 宏观 0~12 s（基线块平均 / 上电学习）'])

def axis(y, t0, t1, label, ticks, sub=None):
    line(X0, y, X1, y, '#888', 1.2)
    for t, s in ticks:
        x = X0 + (t - t0) / (t1 - t0) * (X1 - X0)
        line(x, y, x, y + 7, '#888', 1.0)
        txt(x, y + 23, s, 13, '#666', anchor='middle')
    txt(X0 - 14, y + 5, label, 15, '#0b2c4d', anchor='end', weight='bold')
    if sub:
        txt(X0 - 14, y + 24, sub, 12.5, '#777', anchor='end')
    return lambda t: X0 + (t - t0) / (t1 - t0) * (X1 - X0)

# ---------------- 面板 A：一个窗（微观） ----------------
ya, TA = 108.0, 11.2e-3
txt(28, ya + 12, '① 微观：一个测量窗（0 ~ 11.2 ms）', 17, '#0a58ca', weight='bold')
mx = axis(ya + 520, 0, TA, '时间', [(0, '0'), (2e-3, '2 ms'), (4e-3, '4 ms'), (6e-3, '6 ms'), (8e-3, '8 ms'), (10e-3, '10 ms'), (TA, '11.2 ms')])

# A1 线圈频率（车辆 ⇒ 频率升高）
yb = ya + 60
poly([(X0, yb + 26), (mx(T_ENTER_TRUE), yb + 26), (mx(T_ENTER_TRUE), yb + 14), (X1, yb + 14)], '#c1121f', 2.0)
line(X0 - 14, yb + 26, X0 + 4, yb + 26, '#c1121f', 1.0, dash='3,3')
txt(X1 + 6, yb + 20, '线圈谐振频率', 13.5, '#c1121f')
txt(X0 - 14, yb, 'A1 线圈 f', 15, '#0b2c4d', anchor='end', weight='bold')
txt(X0 - 14, yb + 20, '100.000 kHz', 12.5, '#777', anchor='end')
txt(mx(T_ENTER_TRUE) + 10, yb - 4, '车辆进入（0.6 ms）：L↓ ⇒ f↑ +0.5% ⇒ 周期↓、Xn↓', 14, '#c1121f')

# A2 P23 方波 + 捕获点
yc = ya + 150
t_in0, t_in1 = XN0 / F_CAP * EDGE / EDGE, XN0 / F_CAP      # 半周期 = 160µs
pts, t, lvl = [(X0, yc + 26)], 0.0, 26
H2 = 26
while t < TA:
    t2 = t + t_in1
    xa2, xb2 = mx(t), mx(min(t2, TA))
    pts += [(xa2, yc + lvl), (xb2, yc + lvl)]
    lvl = 0 if lvl else 26
    t = t2
poly(pts, '#0a7d4b', 1.6)
ct = 0.0
while ct <= TA:
    dot(mx(ct), yc + (26 if int(round(ct / t_in1)) % 2 == 0 else 0), 2.6, '#c1121f')
    ct += t_in1
txt(X0 - 14, yc + 6, 'A2 P23 方波', 15, '#0b2c4d', anchor='end', weight='bold')
txt(X0 - 14, yc + 26, '÷32 / 320 µs 周期', 12.5, '#777', anchor='end')
txt(X1 + 6, yc + 14, '绿=P23，红点=捕获点（双沿）', 13.5, '#0a7d4b')
line(mx(0.8e-3), yc + 30, mx(2.4e-3), yc + 30, '#c1121f', 1.2)
line(mx(0.8e-3), yc + 26, mx(0.8e-3), yc + 34, '#c1121f', 1.2)
line(mx(2.4e-3), yc + 26, mx(2.4e-3), yc + 34, '#c1121f', 1.2)
txt(mx(1.6e-3), yc + 50, 'Xn = 相邻捕获差 = 480 tick = 160 µs（= Xn/f_cap）', 13.5, '#c1121f', anchor='middle')

# A3 CapSum 累加台阶
yd0, yd1 = ya + 380, ya + 240
txt(X0 - 14, yd0, 'A3 CapSum', 15, '#0b2c4d', anchor='end', weight='bold')
txt(X0 - 14, yd0 + 18, 'Σ Xn（tick）', 12.5, '#777', anchor='end')
line(X0, yd0, X1, yd0, '#aaa', 1.0, dash='4,4')
line(X0, yd1, X1, yd1, '#aaa', 1.0, dash='4,4')
txt(X1 + 6, yd1 + 4, '32768（BASE 标称）', 12.5, '#777')
txt(X1 + 6, yd0 + 4, '0', 12.5, '#777')
n_show = min(LPCNT, 68)
w_tot = X1 - X0
step_w = w_tot / n_show
acc, pts = 0, [(X0, yd0)]
for i in range(n_show):
    acc += WIN_TICKS / LPCNT
    yv = yd0 - (acc / BASE) * (yd0 - yd1)
    pts += [(X0 + i * step_w, yv), (X0 + (i + 1) * step_w, yv)]
poly(pts, '#e07b00', 1.8)
line(X0 + w_tot * 0.02, yd0 + 30, X0 + w_tot * 0.96, yd0 + 30, '#e07b00', 1.2)
txt(X0 + 8, yd1 - 14, '每 160 µs 累加一次 Xn（共 68 级台阶）⇒ CapCnt≥LPCNT 时「窗结账」', 14, '#a35b00')
dot(X1, yd1, 4.5, '#c1121f')
txt(X0 + 8, ya + 506, '窗结账：Value = Σ(68 个 Xn) = 32640 ⇒ CAP_OK=1 ⇒ 主循环随即调用 VD1_TASK()', 14, '#c1121f')

# A4 Value 台阶
ye = ya + 440
poly([(X0, ye), (X1 * 0 + mx(10.88e-3), ye), (mx(10.88e-3), ye + 34), (X1, ye + 34)], '#0a58ca', 2.0)
line(X0, ye, X1, ye, '#aaa', 1.0, dash='4,4')
txt(X0 - 14, ye, 'A4 Value', 15, '#0b2c4d', anchor='end', weight='bold')
txt(X0 - 14, ye + 20, '每窗更新一次', 12.5, '#777', anchor='end')
txt(X1 + 6, ye + 4, 'Origin 基准 32640', 12.5, '#0a58ca')
txt(X1 + 6, ye + 38, '车后 32486（−0.5%）', 12.5, '#0a58ca')
txt(mx(10.88e-3) - 12, ye - 12, '窗边界 10.88 ms（本窗只含 0.6~10.88 ms ⇒ 部分反映）', 14, '#0a58ca', anchor='end')
txt(X0 + 640, yd1 - 46, '窗 = LPCNT·Xn / f_cap = ⌊32768/Xn₀⌋·Xn / f_cap = 68 × 480 / 3 MHz = 10.880 ms（标称上限）', 14, '#0b2c4d', weight='bold')

# ---------------- 面板 B：中观（检测与输出） ----------------
ybz = ya + 560
txt(28, ybz + 14, '② 中观：检测延迟与输出（0 ~ 600 ms）　车辆进入 @0.6 ms', 17, '#0a58ca', weight='bold')
TB = 0.6
mb = axis(ybz + 430, 0, TB, '时间', [(0, '0'), (0.1, '100 ms'), (0.2, '200 ms'), (0.3, '300 ms'), (0.4, '400 ms'), (0.5, '500 ms'), (TB, '600 ms')])
ycap0, ycap1 = ybz + 330, ybz + 130          # CAPVD 纵轴：32660 → 32450
def ycap(v): return ycap0 - (v - 32450) / (32660 - 32450) * (ycap0 - ycap1)
txt(X0 - 14, ycap0, 'B1 CAPVD', 15, '#0b2c4d', anchor='end', weight='bold')
txt(X0 - 14, ycap0 + 18, 'IIR 平滑值', 12.5, '#777', anchor='end')
line(X0, ycap(ORIGIN), X1, ycap(ORIGIN), '#0a58ca', 1.2, dash='6,4')
txt(X1 + 6, ycap(ORIGIN) + 4, 'Origin 32640（基线）', 12.5, '#0a58ca')
line(X0, ycap(THR), X1, ycap(THR), '#c1121f', 1.4, dash='6,4')
txt(X1 + 6, ycap(THR) + 4, '判据线 32611（−dlt=29 / 0.0916%）', 12.5, '#c1121f')
def stair(seq, col, wd=1.9):
    pts, prev = [], None
    for i, (t, c) in enumerate(seq):
        x = mb(t)
        if prev is not None:
            pts += [(x, prev), (x, c)]
        else:
            pts += [(X0, c)] if False else [(x, c)]
        prev = c
    poly(pts, col, wd)
stair([(0.0, ORIGIN)] + [(t, c) for t, _, c in big], '#c1121f', 2.0)
stair([(0.0, ORIGIN)] + [(t, c) for t, _, c in sml], '#7aa7d9', 1.6)
txt(mx(0) + 0, 0, '', 1)
if T_CROSS:
    xc = mb(T_CROSS)
    dot(xc, ycap(THR), 5.0, '#c1121f')
    txt(xc + 10, ycap(THR) - 12, '越线 ⇒ 有车（大信号 Δf/f=+0.5%%：第 1 窗即越线，t≈%.1f ms）' % (T_CROSS * 1e3), 14, '#c1121f')
if T_CROSS_S:
    xs = mb(T_CROSS_S)
    dot(xs, ycap(THR), 4.4, '#1f6fb2')
    txt(xs + 12, ycap(THR) + 46, '小信号 Δf/f=+0.1%%：≈2.2τ 才越线（t≈%.0f ms）—— τ 主导' % (T_CROSS_S * 1e3), 14, '#1f6fb2')
note(1020, ybz + 30, 610, 110, '两种检测路径（同一条判据）', [
    '大信号（+0.5%）：第 1 窗越线，窗内平均主导',
    '小信号（+0.1%≈阈值）：≈2.2τ≈78 ms，IIR 主导',
    '⇒ 延迟 ≈ 窗平均(≤1 窗) + IIR + 输出节拍(50 ms)'])
yt = ybz + 370
txt(X0 - 14, yt + 8, 'B2 输出', 15, '#0b2c4d', anchor='end', weight='bold')
txt(X0 - 14, yt + 26, 'FLAG_IN / 脉冲', 12.5, '#777', anchor='end')
line(X0, yt + 34, X1, yt + 34, '#888', 1.0)
tp = 0.0
pts = [(X0, yt + 34)]
while tp < TB:
    x1_, x2_ = mb(tp), mb(min(tp + 0.05, TB))
    pts += [(x1_, yt + 8), (x2_, yt + 8)]
    tp += 0.05
    x3_ = mb(min(tp, TB))
    pts += [(x2_, yt + 8), (x2_, yt + 34)] if x2_ != x3_ else []
    tp += 0.05
poly(pts, '#666', 1.2)
line(X0, yt + 8, mb(T_CROSS or 0.05), yt + 8, '#666', 1.6)
x_out = mb((T_CROSS or 0.05) + 0.05)
poly([(mb(T_CROSS or 0.05), yt + 34), (mb(T_CROSS or 0.05), yt + 6), (mb((T_CROSS or 0.05) + 0.5), yt + 6), (mb((T_CROSS or 0.05) + 0.5), yt + 34)], '#0a7d4b', 2.2)
txt(mb(0.0) + 6, yt + 62, '50 ms 主节拍（T1 ISR）量化：FLAG_IN 在下一拍被消费 ⇒ 输出起点 ≤ 越线+50 ms。绿=继电器脉冲输出（500 ms）', 14, '#0a7d4b')

# ---------------- 面板 C：宏观（基线） ----------------
yc2 = ybz + 500
txt(28, yc2 + 14, '③ 宏观：基线 Origin 的块平均（0 ~ 12 s）', 17, '#0a58ca', weight='bold')
TC = 12.0
mc = axis(yc2 + 180, 0, TC, '时间', [(0, '0'), (2, '2 s'), (4, '4 s'), (6, '6 s'), (8, '8 s'), (10, '10 s'), (12, '12 s')])
yB0, yB1 = yc2 + 150, yc2 + 60
def yb_(v): return yB0 - (v - 32620) / (32700 - 32620) * (yB0 - yB1)
txt(X0 - 14, yB0, 'C1 Origin', 15, '#0b2c4d', anchor='end', weight='bold')
txt(X0 - 14, yB0 + 18, '基线（块平均）', 12.5, '#777', anchor='end')
seqT, x_ = [], 0.0
vals = [32644, 32638, 32641, 32640, 32643, 32639, 32640, 32642, 32640, 32641, 32640, 32640]
while x_ < TC:
    seqT.append((x_, vals[min(int(x_ / 1.088), len(vals) - 1)]))
    x_ += 1.088
stair(seqT, '#0a58ca', 2.0)
seqH, x_ = [], 0.0
valsH = [32650, 32636, 32641]
while x_ < TC:
    seqH.append((x_, valsH[min(int(x_ / 5.571), 2)]))
    x_ += 5.571
stair(seqH, '#999', 1.8)
line(X0, yb_(32640), X1, yb_(32640), '#aaa', 1.0, dash='4,4')
dot(X0, yb_(32644), 4.2, '#c1121f')
txt(X0 + 8, yB0 + 22, '上电：Origin = 首窗单点（噪声最大）', 13.5, '#c1121f')
txt(X1 + 6, yb_(32640) + 4, '真值', 12.5, '#777')
txt(X1 + 6, yb_(32644) - 14, 'PD132T: 100 窗=1.088 s（蓝）', 12.5, '#0a58ca')
txt(X1 + 6, yb_(32644) + 8, 'PD136HA: 256 窗=5.571 s（灰）', 12.5, '#888')
txt(mc(3.5), yB1 - 22, '块平均 ⇒ 基线跟踪是「阶梯式」，块内一直用上一块的均值；有车期间冻结并重新累计', 14, '#0b2c4d', anchor='middle')

# ---------------- 页脚：四条关系 ----------------
note(28, yc2 + 230, 780, 126, '四条关系（讨论快慢时用「窗」最稳）', [
    '① t_Xn = 分频比/(边沿系数·f_coil)：PD132T = 16/f、PD136HA = 32/f（随站点变，不是常数）',
    '② Value = Σ(LPCNT 个 Xn) 是箱式积分（计数域降采样）⇒ Δf/f = 表值/65536 精确',
    '③ τ/T = 256/α ⇒ α79 = 3.24 窗、α64 = 4.0 窗（与型号无关）',
    '④ Origin = 块平均（100 / 256 窗），非滑动 ⇒ 基线粒度 = 1 块'])
note(830, yc2 + 230, 770, 126, 'PD136HA 对照（同站点、同 ÷32）', [
    '捕获：320 µs/次（单沿，整周期 32 个线圈周期）；Xn₀ 同为 480 tick',
    '窗：68 × 320 µs = 21.760 ms（标称上限 21.845）',
    'τ(α64) = 87.0 ms = 4.0 窗；Origin = 256 窗 = 5.571 s',
    '⇒ 全链时间 ×2（基线 ×5.1）—— 响应更慢但积分深度 ×2'])

svg = ('<svg xmlns="http://www.w3.org/2000/svg" width="%d" height="%d" viewBox="0 0 %d %d">' % (W, H, W, H)
       + '\n'.join(P) + '</svg>\n')
open(OUT, 'w', encoding='utf-8').write(svg)
print('written %s  (%d bytes)' % (OUT, len(svg)))
print('XN0=%d LPCNT=%d WIN=%d tick T_WIN=%.3f ms T_XN=%.4f ms TAU=%.2f ms DLT=%d THR=%d T_CROSS=%.3f ms'
      % (XN0, LPCNT, WIN_TICKS, T_WIN * 1e3, T_XN * 1e3, TAU * 1e3, DLT, THR, (T_CROSS or 0) * 1e3))