hit-location cylinder: raw-bytes audit of the shipped tables + chassis->table map + wedge orientation
Audit of Cyd's hit-model artifact against THE BINARY (user mandate: not vs the port). res29_scan.py parses the type-29 streams straight from BTL4.RES bytes: 18 streams / 8 distinct confirmed; the 7-wedge cells (BLH band 6, VUL band 4) and the all-fixed BLH/OWN twist patterns are authored fact; every artifact spot-check cell matched the raw bytes verbatim. Chassis->table resolved by live-dump matching (MadCat and Avatar ride DIFFERENT same-zone-set tables). Authored slice names fix the wedge orientation: FRONT = the +Z arc, W1|W2 seam = dead ahead -- corrects the #92 comment's W6-frontal claim. Collision type-0 divert re-grounded at 0x4a0361/0x4a036d: rams never touch the cylinder. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
This commit is contained in:
Joe DiPrima
co-authored by
Claude Opus 5
Claude Opus 5
parent
07072fc9f0
commit
47d9a61402
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"""Parse every type-29 DamageLookupTable stream STRAIGHT OUT of BTL4.RES bytes.
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Audit tool for Cyd's hit-location artifact: zero port code involved -- the
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stream grammar is the one byte-verified from the binary's ctors
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(@0x49ea48 table / PieSlice / @0x49e5e4 leaf ReadEntries):
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Table { i32 layerCount; Layer[layerCount] }
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Layer { i32 rotateWithTorso; i32 sliceCount; Slice[sliceCount] }
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Slice { i32 nameLen; char name[nameLen]; u8 NUL; i32 entryCount;
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Entry { f32 cumulative; i32 zoneIndex } [entryCount] }
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The scanner walks the whole file trying that grammar at every offset whose
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u32 is a plausible layer count, with strict validation (printable names,
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ascending cumulatives ending ~1.0, sane counts). False positives cannot
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survive the full-table parse.
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"""
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import struct, hashlib, json, sys
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RES = r"C:\git\bt411\content\BTL4.RES"
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d = open(RES, "rb").read()
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N = len(d)
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def u32(o): return struct.unpack_from("<i", d, o)[0]
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def f32(o): return struct.unpack_from("<f", d, o)[0]
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def parse_table(o0):
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o = o0
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layers = u32(o); o += 4
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if not (1 <= layers <= 12): return None
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out = []
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for _L in range(layers):
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if o + 8 > N: return None
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rot = u32(o); scount = u32(o + 4); o += 8
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if rot not in (0, 1) or not (1 <= scount <= 16): return None
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slices = []
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for _S in range(scount):
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if o + 4 > N: return None
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nl = u32(o); o += 4
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if not (1 <= nl <= 24) or o + nl + 1 > N: return None
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name = d[o:o + nl]
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if not all(32 <= b < 127 for b in name): return None
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if d[o + nl] != 0: return None
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o += nl + 1
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if o + 4 > N: return None
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ec = u32(o); o += 4
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if not (1 <= ec <= 16): return None
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ents = []
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prev = -1e-6
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for _E in range(ec):
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if o + 8 > N: return None
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cum = f32(o); zi = u32(o + 4); o += 8
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if not (0.0 < cum <= 1.0001) or cum < prev - 1e-5: return None
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if not (0 <= zi <= 63): return None
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ents.append((round(cum, 5), zi))
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prev = cum
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if abs(ents[-1][0] - 1.0) > 0.02: return None
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slices.append((name.decode(), ents))
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out.append((rot, slices))
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return out, o - o0
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tables = []
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o = 0
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while o < N - 8:
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v = u32(o)
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if 1 <= v <= 12:
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r = parse_table(o)
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if r:
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tables.append((o, r[0]))
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o += r[1]
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continue
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o += 1
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print("tables parsed from raw RES bytes: %d" % len(tables))
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def canon(t):
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return json.dumps([[rot, [[n, e] for n, e in sl]] for rot, sl in t])
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groups = {}
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for off, t in tables:
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h = hashlib.md5(canon(t).encode()).hexdigest()[:8]
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groups.setdefault(h, []).append((off, t))
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print("distinct by content: %d" % len(groups))
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for h, g in groups.items():
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t = g[0][1]
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rots = "".join(str(r) for r, _ in t)
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sls = ",".join(str(len(sl)) for _, sl in t)
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print(" %s copies=%d layers=%d rot=%s slices=%s" %
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(h, len(g), len(t), rots, sls))
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# ---- spot checks against the artifact's DATA (percent deltas, entry order) ----
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def deltas(ents):
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out = []; prev = 0.0
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for cum, zi in ents:
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out.append(round(cum - prev, 4)); prev = cum
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return out
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def find(pred, what):
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hits = [h for h, g in groups.items() if pred(g[0][1])]
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print("%-58s %s" % (what, hits if hits else "NOT FOUND"))
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return hits
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print("\n--- artifact spot checks (band index 0 = first parsed = bottom) ---")
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find(lambda t: len(t) == 7 and len(t[6][1]) == 8 and
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t[6][1][0][0] == "TopRight" and deltas(t[6][1][0][1]) == [0.6, 0.4],
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"LOK band6 TopRight 60/40 (missle-first)")
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find(lambda t: len(t) == 7 and len(t[6][1]) == 8 and
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t[6][1][0][0] == "TopRight" and deltas(t[6][1][0][1]) == [0.4, 0.6],
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"THR band6 TopRight 40/60")
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find(lambda t: len(t) == 7 and
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all(len(ents) == 1 and abs(ents[0][0] - 1.0) < .001
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for _n, ents in t[6][1]),
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"OWN band6 single-entry 100% cells")
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find(lambda t: len(t) == 7 and len(t[6][1]) == 7,
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"BLH: 7-slice band 6")
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find(lambda t: len(t) == 7 and len(t[4][1]) == 7,
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"VUL/BAT: 7-slice band 4")
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find(lambda t: len(t) == 7 and len(t[5][1]) == 8 and
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t[5][1][1][0] == "FrontChest" and deltas(t[5][1][1][1]) == [0.1, 0.1, 0.1, 0.1, 0.5, 0.1],
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"AVA-A band5 FrontChest ..50% utorso")
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find(lambda t: len(t) == 7 and len(t[6][1]) == 8 and
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t[6][1][0][0] == "TopRight" and deltas(t[6][1][0][1]) == [0.5, 0.15, 0.35],
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"AVA-B/VUL/SND2 band6 TopRight 50/15/35")
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find(lambda t: len(t) == 7 and len(t[4][1]) == 8 and
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t[4][1][1][0] == "FrontWaist" and deltas(t[4][1][1][1]) == [0.3, 0.3, 0.4],
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"SND2 band4 FrontWaist 30/30/40")
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find(lambda t: t[0][1][0][0] == "RightFoot" and deltas(t[0][1][0][1]) == [0.5, 0.3, 0.2],
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"band0 RightFoot 50/30/20 (common)")
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find(lambda t: len(t) == 7 and len(t[3][1]) == 8 and
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t[3][1][0][0] == "RightHip" and deltas(t[3][1][0][1]) == [0.4, 0.3, 0.3],
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"BLH band3 RightHip 40/30/30")
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find(lambda t: len(t) == 7 and len(t[4][1]) == 7 and
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t[4][1][0][0] == "RightWaist" and deltas(t[4][1][0][1]) == [0.15, 0.1, 0.25, 0.25, 0.1, 0.15],
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"VUL band4 RightWaist 15/10/25/25/10/15")
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# twist-pattern census
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pats = {}
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for h, g in groups.items():
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rots = "".join(str(r) for r, _ in g[0][1])
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pats.setdefault(rots, []).append(h)
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print("\nrot patterns:", {k: len(v) for k, v in pats.items()})
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