fig(family): 六型号总览图 + PD132T↔PD136HA 档位对齐图；待实测清单 ⑧
- docs/img/family-overview.{svg,png}（tools/plot_family_overview.py）
面板A 窗长/τ 条形；面板B 检测延迟 vs Δf/f（六型号按各自档0 判据）；
归一化延迟表（@1.1× / @2× / @10× 判据）+ 进入确认与口径脚注
关键：@10×(强信号) 全部=1 窗 ⇒ 8.2/8.7/10.0/10.9/21.9/27.3ms；@1.1×(近阈值) 7~9 窗 ⇒ M1H 57ms ~ PD136HA 197ms
- docs/img/sens-alignment-pd132t-pd136ha.{svg,png}（tools/plot_sens_alignment.py）
四档 进入/离开 双段条形 + 同档偏差(−46%~+69%) + 最近档映射 + 换型 4 步 + 两端档无对应警示
- 待实测清单新增 ⑧ 弱信号复测（0.10% 车：PD132T 档1 ≈76ms 应检出 / PD136HA 档1 应检不到）
- 文档接入：cross-model-comparison.md 新增「六型号总览图」节；comparison §5.4 嵌入对齐图；measurement doc §10 加指引
- README tools 段补 3 个绘图脚本；表格校验 0 异常（16 文件）
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wangfq
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""PD132T ↔ PD136HA 灵敏度档位对齐图（现场调档照着贴）—— 纯 stdlib 手写 SVG
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横轴 = Δf/f（进入/离开阈值的相对频偏，%）；每档画 进入(实心)+离开(浅色) 两段
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偏差 = 同档位号一对一映射的 (H−T)/T；最佳匹配 = 最近的进入阈值
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转 PNG：chromium --headless=new --disable-gpu --no-sandbox \
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--screenshot=docs/img/sens-alignment-pd132t-pd136ha.png --window-size=1500,880 \
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file://$PWD/docs/img/sens-alignment-pd132t-pd136ha.svg
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"""
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W, H = 1500, 1060
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FONT = "'Noto Serif CJK SC','Noto Sans CJK SC',sans-serif"
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K = 65536.0
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out = []
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def esc(s):
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return s.replace('&', '&').replace('<', '<').replace('>', '>')
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def txt(x, y, s, size=14, fill='#222', anchor='start', weight='normal'):
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out.append('<text x="%.1f" y="%.1f" font-size="%s" fill="%s" text-anchor="%s" font-weight="%s" font-family="%s">%s</text>'
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% (x, y, size, fill, anchor, weight, FONT, esc(s)))
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def line(x1, y1, x2, y2, stroke='#999', w=1.0, dash=None):
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d = ' stroke-dasharray="%s"' % dash if dash else ''
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out.append('<line x1="%.1f" y1="%.1f" x2="%.1f" y2="%.1f" stroke="%s" stroke-width="%s"%s/>' % (x1, y1, x2, y2, stroke, w, d))
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def rect(x, y, w, h, fill='none', stroke='none', rx=2, sw=1.0, op=1.0):
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out.append('<rect x="%.1f" y="%.1f" width="%.1f" height="%.1f" rx="%s" fill="%s" fill-opacity="%s" stroke="%s" stroke-width="%s"/>'
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% (x, y, w, h, rx, fill, op, stroke, sw))
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def note(x, y, w, h, title, lines_, color='#0b6'):
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rect(x, y, w, h, '#fbfdff', color, rx=6, sw=1.2, op=0.95)
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txt(x + 12, y + 24, title, 14.5, color, weight='bold')
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for i, s in enumerate(lines_):
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txt(x + 12, y + 48 + i * 21, s, 13, '#333')
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T_IN = [300, 60, 32, 21] # PD132T 进入表（档0..3）
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T_OUT = [60, 32, 21, 14] # PD132T 离开表
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H_IN = [162, 81, 54, 28] # PD136HA 进入表
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H_OUT = [81, 54, 32, 18] # PD136HA 离开表
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ct = lambda v: v / K * 100.0
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out.append('<svg xmlns="http://www.w3.org/2000/svg" width="%d" height="%d" viewBox="0 0 %d %d">' % (W, H, W, H))
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out.append('<rect width="%d" height="%d" fill="#ffffff"/>' % (W, H))
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txt(30, 44, 'PD132T ↔ PD136HA 灵敏度档位对齐图（判据 = 表值/65536 的 Δf/f）', 24, '#111', weight='bold')
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txt(30, 72, '每档画两段：深色 = 离开阈值（回差下沿），浅色 = 进入阈值（回差上沿）；横轴 0~0.5%（线性）', 13, '#555')
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line(30, 86, W - 30, 86, '#ddd', 1.2)
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# ================== 轴 ==================
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AX0, AX1 = 380.0, 1230.0
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VMAX = 0.50
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ax = lambda v: AX0 + (v / VMAX) * (AX1 - AX0)
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GY0, GGH = 118.0, 116.0
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for g in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5]:
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x = ax(g)
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line(x, 112, x, 112 + 4 * GGH, '#ececec', 1.0)
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txt(x, 112 + 4 * GGH + 22, ('%.2f' % g), 12, '#777', anchor='middle')
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txt((AX0 + AX1) / 2, 112 + 4 * GGH + 46, 'Δf/f（%）—— 数值越大越钝（越不易触发）', 13, '#555', anchor='middle')
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# ================== 四档 ==================
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COLT, COLH = '#1565c0', '#e65100'
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for i in range(4):
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gy = GY0 + i * GGH
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viT, voT = ct(T_IN[i]), ct(T_OUT[i])
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viH, voH = ct(H_IN[i]), ct(H_OUT[i])
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dev = (viH - viT) / viT * 100.0
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# 分组背景
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if i % 2 == 0:
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rect(40, gy - 8, W - 80, GGH - 12, '#fafafa', rx=6, sw=0)
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txt(60, gy + 52, '档 %d' % i, 17, '#111', weight='bold')
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# --- PD132T 行 ---
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yT = gy + 6
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rect(AX0, yT, ax(voT) - AX0, 17, COLT, rx=2, op=0.30)
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rect(AX0, yT, ax(viT) - AX0, 17, COLT, rx=2, op=0.85)
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txt(AX0 - 10, yT + 14, 'PD132T', 13.5, COLT, anchor='end', weight='bold')
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txt(ax(viT) + 8, yT + 14, '进入 %.4f%% ／ 离开 %.4f%%' % (viT, voT), 12.5, COLT)
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# --- PD136HA 行 ---
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yH = gy + 52
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rect(AX0, yH, ax(voH) - AX0, 17, COLH, rx=2, op=0.30)
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rect(AX0, yH, ax(viH) - AX0, 17, COLH, rx=2, op=0.85)
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txt(AX0 - 10, yH + 14, 'PD136HA', 13.5, COLH, anchor='end', weight='bold')
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txt(ax(viH) + 8, yH + 14, '进入 %.4f%%' % viH, 12.5, COLH)
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# 同档偏差标注 + 连接线
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line(ax(viT), yT + 17, ax(viH), yH, '#999', 1.2, '4,3')
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badge_col = '#c1121f' if abs(dev) >= 30 else ('#b8860b' if abs(dev) >= 15 else '#0b6')
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txt(AX1 + 16, gy + 40, '同档偏差 %+.0f%%' % dev, 14, badge_col, weight='bold')
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# 最佳匹配说明
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txt(W - 30, GY0 + 4 * GGH + 10, '最佳匹配：档 1 → 档 2（−10.0%）', 13, '#0b6', anchor='end')
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txt(W - 30, GY0 + 4 * GGH + 32, '两端档 0 / 3 无对应档 ⇒ 必须复测', 13, '#c1121f', anchor='end')
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line(AX1 + 4, 112, AX1 + 4, 112 + 4 * GGH, '#ccc', 1.0)
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# ================== 对照表 ==================
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TY = 660.0
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txt(40, TY, '档位对照表（现场照着贴）', 17, '#111', weight='bold')
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COLS = [(60, '档位'), (150, 'PD132T 进入'), (290, 'PD132T 离开'), (430, 'PD136HA 进入'), (570, 'PD136HA 离开'),
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(710, '同档偏差'), (860, '等效替代（最近进入阈值）')]
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for x, s in COLS:
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txt(x, TY + 30, s, 13.5, '#555', weight='bold')
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line(40, TY + 40, W - 40, TY + 40, '#ccc', 1.0)
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for i in range(4):
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y = TY + 66 + i * 36
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viT, voT, viH, voH = ct(T_IN[i]), ct(T_OUT[i]), ct(H_IN[i]), ct(H_OUT[i])
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dev = (viH - viT) / viT * 100.0
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j = min(range(4), key=lambda k: abs(H_IN[k] - T_IN[i]))
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dev2 = (ct(H_IN[j]) - viT) / viT * 100.0
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txt(60, y, '档 %d' % i, 14, '#111', weight='bold')
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txt(150, y, '%.4f %%' % viT, 13, '#1565c0')
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txt(290, y, '%.4f %%' % voT, 13, '#777')
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txt(430, y, '%.4f %%' % viH, 13, '#e65100')
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txt(570, y, '%.4f %%' % voH, 13, '#777')
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txt(710, y, '%+.1f %%' % dev, 13.5, ('#c1121f' if abs(dev) >= 30 else '#b8860b'), weight='bold')
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txt(860, y, 'PD136HA 档 %d（%+.1f%%）' % (j, dev2), 13, '#0b6')
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line(40, TY + 66 + 4 * 36 - 16, W - 40, TY + 66 + 4 * 36 - 16, '#ccc', 1.0)
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# ================== 脚注 ==================
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note(40, TY + 66 + 4 * 36 + 4, 700, 128, '换型动作（4 步）', [
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'① 先算目标：原型号档位 → 百分比 = 表值 / 65536',
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'② 在新型号表里找最接近的档（偏差 ≤15% 可接受；>30% 必须复测）',
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'③ 用 §5.5 公式预估响应：延迟 = (n+1)×窗长（n 由 信号/判据 比决定）',
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'④ 强信号车 + 弱信号车各复测 3 次（记录 判据越线→继电器 实测耗时）',
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], '#c1121f')
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note(760, TY + 66 + 4 * 36 + 4, 700, 128, '两端档位无对应（必须注意）', [
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'档0（最钝）：PD132T 0.4578% ↔ PD136HA 0.2472%（钝 46%）',
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' ⇒ 依赖「最钝档防误触发」的现场，换型后须重新评估误触发率',
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'档3（最灵敏）：PD132T 0.0320% ↔ PD136HA 0.0427%（钝 33%）',
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' ⇒ 依赖「最高灵敏」的现场，PD136HA 可能检不到弱信号车（§5.5 ⑧）',
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], '#e65100')
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OPEN = 'docs/img/sens-alignment-pd132t-pd136ha.svg'
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out.append('</svg>')
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open(OPEN, 'w', encoding='utf-8').write('\n'.join(out))
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print('已写出 %s' % OPEN)
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for i in range(4):
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viT, viH = ct(T_IN[i]), ct(H_IN[i])
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j = min(range(4), key=lambda k: abs(H_IN[k] - T_IN[i]))
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print(' 档%d: PD132T %.4f%% ↔ PD136HA %.4f%%（同档 %+.1f%%）| 最近档 %d（%+.1f%%）'
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% (i, viT, viH, (viH - viT) / viT * 100, j, (ct(H_IN[j]) - viT) / viT * 100))
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