CydandClaude Fable 5
afc3fd839e
Bring the graphics-dev collaborator's dpl3-revive into the repo as first-class
project code (they've handed it off; it's ours now). This is the proven
Division renderer that our in-process rt_draw has been trying to be.
What's here:
- parser/ B2Z/V2Z/SVT/SCN/SPL/BGF/BMF/BSL decoders (pure Python).
- spec/ reverse-engineered format + the definitive VelociRender wire
protocol (from the original DIVISION source, matches our live
VPX node/action tables exactly).
- source-ref/ read-only copies of the original DIVISION C (BIZREAD.C,
DPLTYPES.H, DPL.H) that define the formats.
- patha/ the "virtual VelociRender board": vrboard.py (24-action protocol
server), vrview.py (numpy software rasterizer, the reference),
vrview_gl.py (moderngl GPU backend, 832x512@60Hz), plus the
run/replay/regress tooling and evidence renders. Drives FLYK/BLADE/
Star Trek demos AND our btl4opt/rpl4opt game binaries.
- viewer/ WebGL archive generators (.py); prebuilt HTML/data regeneratable.
- samples/ small test models/textures.
- bt*.raw.bin real BTL4OPT arena wire captures (kept for offline renderer
testing/regression against OUR game).
.gitignore keeps the multi-hundred-MB demo capture dumps + debug logs +
regeneratable viewer bundles out of history (they stay on disk).
Phase 0 of the integration is validated: their board decodes our bt8 capture
with zero errors (3748 nodes, 507 instances, 4 mechs) and renders our arena
(terrain/dome/sky, correct Division DAC gamma). Plan + status in memory;
integration continues in emulator/RENDERER-COLLAB.md.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
#!/usr/bin/env python3
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"""
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heading_test.py -- identify the 0x1d matrix convention + shark nose axis by
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correlating every candidate basis vector against the swim velocity across the
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whole capture. The right candidate tracks velocity with a consistently high dot.
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python heading_test.py [capture]
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"""
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import sys, struct
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import numpy as np
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sys.path.insert(0, '.')
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from vrboard import Assembler
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from decode_anim import find_framed_start
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path = sys.argv[1] if len(sys.argv) > 1 else 'cap7.raw.bin'
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data = open(path, 'rb').read()
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asm = Assembler(); asm.feed(data[find_framed_start(data):])
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recs = []
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for m in asm:
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if m.iserver: continue
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if m.action == 0x1d and len(m.payload) >= 56:
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h = struct.unpack_from('<I', m.payload, 4)[0]
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if h != 1:
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f = struct.unpack_from('<12f', m.payload, 8)
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recs.append((np.array(f[:9]).reshape(3, 3), np.array(f[9:12])))
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print(f'{len(recs)} shark 0x1d records')
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cands = {}
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for name, vec in (('row0', lambda M: M[0]), ('row2', lambda M: M[2]),
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('col0', lambda M: M[:, 0]), ('col2', lambda M: M[:, 2])):
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dots = []
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for i in range(10, len(recs) - 10, 25):
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M, t = recs[i]
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_, t2 = recs[i + 5]
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v = t2 - t
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n = np.linalg.norm(v)
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if n < 0.5: # skip near-stationary spans
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continue
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v = v / n
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b = vec(M); b = b / np.linalg.norm(b)
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dots.append(float(np.dot(v, b)))
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dots = np.array(dots)
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cands[name] = dots
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print(f'{name}: mean={dots.mean():6.3f} median={np.median(dots):6.3f} '
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f' frac>0.9={np.mean(dots > 0.9):.2f} frac<-0.9={np.mean(dots < -0.9):.2f}'
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f' frac|.|<0.5={np.mean(np.abs(dots) < 0.5):.2f}')
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