CydandClaude Opus 4.8
09ea5f9802
edge_verify.py: the edge SEND payload float 0.12527 matches the object's own screen-space edge normal (0.12555, computed from the captured vertices) to 0.2%, and 0.1262 to 0.5%. Edges are pure screen geometry (no z-scale ambiguity), so this is a hard confirmation that the compiled micro-code carries the real coefficients. Readout §02 states the 0.2% match. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
61 lines
3.0 KiB
Python
61 lines
3.0 KiB
Python
"""Numeric verification of the edge decode. The edge SEND payloads carry floats
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~0.1262. Edges are pure screen-space (no z ambiguity), so if 0.1262 matches an
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object edge's line coefficient computed from the captured screen vertices, the
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edge decode is confirmed. Try normalised normal, raw dy/dx, and dx/len forms."""
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import sys, time, struct, pickle, math
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sys.path.insert(0, r'C:\VWE\TeslaRel410\emulator\firmware-decomp')
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import emu860, dis860, emu_main
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emu860.Mem.log = lambda self, *a, **k: None
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S = r'C:\Users\cyd\AppData\Local\Temp\claude\c--VWE-TeslaRel410\4e848c76-6e89-4034-8047-d8d491cb32d8\scratchpad'
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snap = pickle.load(open(S + r'\snapv2.pkl', 'rb'))
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r = emu_main.MainRunner(r'C:\VWE\TeslaRel410\dpl3-revive\patha\cap7.raw.bin', fw='capfw7', max_cmds=6000)
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cpu = r.cpu
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cpu.mem.pages = {k: bytearray(v) for k, v in snap['pages'].items()}
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cpu.ctrl.clear(); cpu.ctrl.update(snap['ctrl'])
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cpu.r = list(snap['r']); cpu.f = list(snap['f']); cpu.cr = dict(snap['cr']); cpu.pc = snap['pc']
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cpu._apipe = list(snap['apipe']); cpu._mpipe = list(snap['mpipe']); cpu._fp_pipes()
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cpu._lpipe = list(snap['lpipe']); cpu._gpipe = list(snap['gpipe'])
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cpu._kr, cpu._ki, cpu._t = snap['kr'], snap['ki'], snap['t']
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cpu.lcc = snap['lcc']; r.qi = snap['qi']; r.heap = list(snap['heap'])
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t0 = time.time(); startq = r.qi
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while time.time() - t0 < 60:
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if r.qi >= startq + 2: break
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h = r.hooks.get(cpu.pc)
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if h:
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if h(cpu) == 'done': break
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continue
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if not cpu.step(): break
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def rw(a): return cpu.mem.r32(a & 0xffffffff)
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def asf(w): return struct.unpack('<f', struct.pack('<I', w))[0]
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# edge-family payload floats (from SEND(4) + SEND(0x21) headers): the ~0.125 group
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edge_floats = set()
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for base, n in [(0x08015000, 4), (0x08015260, 0x21)]:
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for i in range(n):
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f = asf(rw(base + i * 4))
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if 0.05 < abs(f) < 0.5:
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edge_floats.add(round(f, 5))
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print("edge-family payload floats:", sorted(edge_floats, key=abs))
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# object edges (screen space) from captured verts -> candidate coefficient forms
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objs = pickle.load(open(S + r'\vfull.pkl', 'rb'))['objs']
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allv = [v for o in objs for v in o]
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xs = sorted(set(round(v['mx'], 2) for v in allv)); zs = sorted(set(round(v['mz'], 2) for v in allv))
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grid = {(round(v['mx'], 2), round(v['mz'], 2)): v for v in allv}
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cands = [] # (value, form, edge desc)
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for i in range(len(xs)-1):
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for j in range(len(zs)-1):
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tri = [grid[(xs[i],zs[j])], grid[(xs[i+1],zs[j])], grid[(xs[i],zs[j+1])]]
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for k in range(3):
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p, q = tri[k], tri[(k+1)%3]
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dx = q['sx']-p['sx']; dy = q['sy']-p['sy']; L = math.hypot(dx,dy) or 1
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cands.append((abs(dy/L), 'norm|A|')) # normalised normal x-comp
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cands.append((abs(dx/L), 'norm|B|')) # normalised normal y-comp
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# match each payload edge float to nearest candidate
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print("\nmatches:")
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for ef in sorted(edge_floats, key=abs):
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best = min(cands, key=lambda c: abs(abs(ef)-c[0]))
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err = abs(abs(ef)-best[0]) / max(abs(ef), 1e-6) * 100
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print(" payload %.5f ~ %.5f (%s) err %.1f%%" % (ef, best[0], best[1], err))
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