# Kingst VIS 压缩边沿流分析: 数字波形按占空比签名自动分段, 输出行结构/占空比/消隐明细
# 用法: python la_wave_analyzer.py <capture.csv>
#   CSV 列: Time[s], 通道A, 通道B (时间分辨率 10ns; 默认抓 B 通道 D0/D1)
# 判读基线见 doc/architecture/rgb_bit_swap_test.md §1.1
import csv, statistics, time, sys

PATH = sys.argv[1] if len(sys.argv) > 1 else \
    r"E:\Hulk-Coding\LT8619C_DLPC3421_HDMI\tools\wave\B-D0-D7.csv"
CLK_NS = 25.0          # 40MHz 像素时钟

t0 = time.time()
rows = []
with open(PATH, encoding='utf-8-sig', errors='replace') as f:
    r = csv.reader(f)
    next(r)  # header
    for row in r:
        if len(row) < 3:
            continue
        rows.append((float(row[0]) * 1e9, int(row[1]), int(row[2])))  # ns

n = len(rows)
t_start, t_stop = rows[0][0], rows[-1][0] + 1000.0  # 末行状态延续按1us估
print(f"rows={n}  span={t_stop - t_start / 1e9:.9f}s")

# ---- 按行区间累计高低时间, 50ms 窗口计算占空比 ----
W = 50_000_000.0  # ns
win = {'t0': t_start, 'hi0': 0.0, 'hi1': 0.0}
wins = []
for i in range(n - 1):
    t, d0, d1 = rows[i]
    dt = min(rows[i + 1][0], t_stop) - t
    if dt <= 0:
        continue
    win['hi0'] += dt if d0 else 0.0
    win['hi1'] += dt if d1 else 0.0
    if rows[i + 1][0] - win['t0'] >= W:
        span = rows[i + 1][0] - win['t0']
        wins.append((win['t0'], win['hi0'] / span, win['hi1'] / span))
        win = {'t0': rows[i + 1][0], 'hi0': 0.0, 'hi1': 0.0}

# ---- 窗口分类: 依 (d0duty, d1duty) 签名 ----
def classify(a, b):
    hi = lambda x: x > 0.5
    if hi(a) and hi(b):
        return 'FF'
    if hi(a) and not hi(b):
        return '55'
    if not hi(a) and hi(b):
        return 'AA'
    return '00'

segs = []
for t, a, b in wins:
    c = classify(a, b)
    if segs and segs[-1][0] == c:
        segs[-1][1].append((t, a, b))
    else:
        segs.append((c, [(t, a, b)]))

print(f"\n{'段':<4}{'签名':<6}{'时长(s)':<10}{'D0占空':<10}{'D1占空':<10}")
for i, (c, ws) in enumerate(segs):
    dur = (ws[-1][0] - ws[0][0]) / 1e9
    a = statistics.mean(x[1] for x in ws)
    b = statistics.mean(x[2] for x in ws)
    print(f"{i:<6}{c:<8}{dur:<12.2f}{a:<12.1%}{b:<12.1%}")

# ---- 每段: 行周期 + 有效区宽度(长高电平段) ----
print(f"\n按段结构分析 (时钟数 @40MHz):")
for i, (c, ws) in enumerate(segs):
    seg_t0 = ws[0][0] - W / 2
    seg_t1 = ws[-1][0] + W / 2
    ch = 0 if c in ('FF', '55') else 1          # 选有效区为高的通道
    # 找长高电平段(>=15us)及其起点
    starts, widths = [], []
    for j in range(n - 1):
        t, d0, d1 = rows[j]
        if t < seg_t0 or t > seg_t1:
            continue
        lvl = d0 if ch == 0 else d1
        if lvl and rows[j + 1][0] - t >= 15_000.0:
            starts.append(t)
            widths.append(rows[j + 1][0] - t)
    if len(starts) < 3:
        print(f"  段{i} [{c}]: 线段不足({len(starts)}), 跳过")
        continue
    periods = [b - a for a, b in zip(starts, starts[1:]) if 20_000 < b - a < 35_000]
    mp = statistics.median(periods)
    mw = statistics.median(widths)
    print(f"  段{i} [{c}]: 行数~{len(starts)}  行周期={mp:.0f}ns={mp/CLK_NS:.1f}clk  "
          f"有效高={mw:.0f}ns={mw/CLK_NS:.1f}clk  消隐={(mp-mw)/CLK_NS:.1f}clk")

# ---- 每段抽一行: 消隐区的边沿明细 ----
print(f"\n消隐区边沿明细(每段第一行, ns 相对行起点, clk=时钟数):")
for i, (c, ws) in enumerate(segs):
    seg_t0 = ws[0][0] - W / 2
    ch = 0 if c in ('FF', '55') else 1
    line_start = None
    for j in range(n - 1):
        t, d0, d1 = rows[j]
        if t < seg_t0:
            continue
        lvl = d0 if ch == 0 else d1
        if lvl and rows[j + 1][0] - t >= 15_000.0:
            line_start = t
            break
    if line_start is None:
        continue
    print(f"  段{i} [{c}] 行起点+有效区之后:")
    j0 = next(k for k in range(n - 1) if rows[k][0] >= line_start)
    out = []
    for k in range(j0, min(j0 + 30, n - 1)):
        t, d0, d1 = rows[k]
        dt = rows[k + 1][0] - t
        rel = t - line_start
        out.append(f"    +{rel/1000:8.2f}us  D0={d0} D1={d1}  持续{dt/CLK_NS:6.1f}clk")
        if rel > 30_000:
            break
    print('\n'.join(out[:18]))

print(f"\n耗时 {time.time()-t0:.1f}s")