Handoff: reconcile docs and tooling with the corrections made this session

Session 2 reversed several of its own conclusions. The docs are append-only, so
a reader could land on a superseded section and act on it. This pass makes the
repo internally consistent.

Defects found and fixed in STATUS.md:
- claimed "Hybrid VQ with k=1024: no" as the answer to the linework question,
  directly contradicting FINDINGS 14, which rejected k=1024. Both profiles are
  k=256.
- malformed profile table (six column separators, five columns).
- next-steps list had two items numbered 3 and listed the full-disc survey
  twice.
- the disk-benchmark section still read CRITICAL-PATH with "if SCSI sustains
  >=800 KB/s, ship pixel-exact". That was written while the bandwidth figure
  was misread as 4 MB/s. At 4 Mbps pixel-exact needs 92-97% of the pipe and is
  not available, and the ring-buffer result means the design no longer hangs on
  the benchmark at all. Rewritten with what it IS still worth doing: confirming
  the 4 Mbps provenance, and confirming DMA is used rather than PIO.

FINDINGS now carries supersession blockquotes on 5, 8, 11, 17 and 18 pointing
at the sections that correct them. 18 is the dangerous one -- its peak-vs-
sustained test is reversed by 21 -- so it is marked DO NOT ACT ON THIS SECTION
while noting the per-frame data itself remains valid.

profile_gen.py had the same problem in code: it defaulted to the superseded
peak sizing and returned lam=25 where the docs say lam=10. The buffered test is
now the default and peak sizing is behind --size-for-peak as a bound only. A
tool that contradicts the findings is worse than no tool.

Also preserves the five measurement scripts that produced this session's
numbers as tools/analysis/05-09, following the session 1 precedent, and adds an
"explicitly abandoned -- do not re-propose" list to STATUS covering entropy
coding, k=1024 codebooks and flat 4x4 VQ.

Claude-Session: https://claude.ai/code/session_01194oWYW8DQXK1SZ2DnChW6

tools/analysis/07_highrate_deflate.py

@@ -0,0 +1,62 @@
+"""How high should the SCSI profile go?
+
+Two questions the current profiles never answered:
+  1. With the payload deflated, where does the hybrid's lam->0 end actually land?
+     (Un-deflated it is 439 KB/s, which is NOT comparable to the 247 KB/s
+      lossless changed-spans+deflate path.)
+  2. Is there any point shipping lossy VQ on SCSI at all, or does the lossless
+     path dominate once both are entropy-coded?
+"""
+import sys, zlib; sys.path.insert(0,'tools/encoder')
+import vq_hybrid as H, vq as VQ, numpy as np, struct
+S=sys.argv[1]
+
+def pack_modes(mode):
+    n=len(mode); out=bytearray((n*2+7)//8)
+    for i,m in enumerate(mode): out[i//4] |= (int(m)&3)<<(6-2*(i%4))
+    return bytes(out)
+
+def payload(mode,l1,l4g,src,nbx):
+    b=bytearray()
+    for i,mo in enumerate(mode):
+        if mo==1: b.append(int(l1[i]))
+        elif mo==2:
+            for j in range(4): b.append(int(l4g[i][j]))
+        elif mo==3:
+            by,bx=divmod(i,nbx); b+=src[by*4:by*4+4,bx*4:bx*4+4].tobytes()
+    return bytes(b)
+
+def spans(a,b,gap=8):
+    out=bytearray()
+    for y in range(a.shape[0]):
+        d=np.nonzero(a[y]!=b[y])[0]
+        if not len(d): continue
+        sp=[];st=d[0];p=d[0]
+        for x in d[1:]:
+            if x-p>gap: sp.append((st,p)); st=x
+            p=x
+        sp.append((st,p))
+        for s0,e0 in sp: out+=bytes([y,s0,e0-s0])+b[y][s0:e0+1].tobytes()
+    return bytes(out)
+
+for s in ['00020','00146']:
+    m=H.build(f'{S}/fr_{s}',k1=256,k4=256,iters=16)
+    pal,idx,W=m['pal'],m['idx'],m['W']; nbx=W//4
+    ceil_db=np.mean([VQ.psnr(o,pal[i]) for o,i in zip(m['rgb'],idx)])
+    # lossless reference, same frames
+    lz=np.mean([len(zlib.compress(spans(idx[i-1],idx[i]),9)) for i in range(1,len(idx))])
+    print(f'=== {s}  palette ceiling {ceil_db:.2f} dB ===',flush=True)
+    print(f'  LOSSLESS changed-spans+deflate : {lz*12/1024:6.1f} KB/s  (pixel-exact)',flush=True)
+    print(f'  {"lam":>6}{"PSNR":>8}{"raw KB/s":>10}{"defl KB/s":>11}{"gain":>7}{"RAW%":>7}',flush=True)
+    for lam in [0.,10.,25.,60.,150.,300.,800.]:
+        e=H.encode(m,lam=lam); r=H.evaluate(m,e)
+        tot_r=tot_d=0
+        for f,im in enumerate(idx):
+            B1=H.blocks_of(im,pal,4,4); l1=VQ.assign(B1,m['C1s'])
+            B4=H.blocks_of(im,pal,2,2); l4=VQ.assign(B4,m['C4s'])
+            q=H._group_2x2_into_4x4(np.arange(len(l4)),W); l4g=l4[q].reshape(-1,4)
+            blob=pack_modes(e['modes'][f])+payload(e['modes'][f],l1,l4g,im,nbx)
+            tot_r+=len(blob); tot_d+=len(zlib.compress(blob,9))
+        n=len(idx)
+        rk=tot_r/n*12/1024; dk=tot_d/n*12/1024
+        print(f'  {lam:6.0f}{r["psnr"]:8.2f}{rk:10.1f}{dk:11.1f}{rk/dk:7.2f}{r["raw"]:7.1f}',flush=True)