arcattackandClaude Fable 5
8ed6184d65
The recovered system: fire channels = LBE4ControlsManager buttonGroups (0x40/0x45/0x46/0x47); default groups = the per-mech type-6 controls-map resource in BTL4.RES, installed by the T0 CreateStreamedMappings the port already called -- it needed only the TriggerState attribute (id 0x13 PINNED to the binary value; fireImpulse@0x31C is the binary's TriggerState) and an input feed. Keyboard/harness now push press/release edges into the button groups; the gBT*Trigger bypasses, per-type keyboard split and 1,0 pulse hack are retired -- weapons sharing a button fire TOGETHER (madcat Trigger = 4 weapons). Myomers @4b9550/@4b95b8 misattribution corrected (they are MechWeapon ConfigureMappables/ChooseButton). Verified 2-node: kill through the authentic chain (12 hits vs ~36 pre-groups). Config-mode session (regrouping UI) = the remaining stage, KB-scoped. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
98 lines
4.5 KiB
Python
98 lines
4.5 KiB
Python
#!/usr/bin/env python3
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"""Discriminate the two competing punch-chunk role models from the authored eye:
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Model A (mask/hull): visible = g1 (coarse hull) MINUS g0 silhouette (g0 = dmg_set mask)
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Model B (lattice): visible = g0 alone (g1/g2 hidden as 'damage plates')
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For each mech render, per punch patch set:
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cov0 = screen coverage of g0 (if ~solid over the view, drawing it opaque blinds -> kills B)
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cov1 = coverage of g1
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covA = coverage of (g1 AND NOT g0) -> Model A visible frame
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openA = fraction of g1 footprint punched open by g0
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Also save a visual 3-panel per mech: g0 | g1 | A-composite.
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"""
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import os, re, math, sys, struct
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import numpy as np
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from PIL import Image
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import importlib.util
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spec = importlib.util.spec_from_file_location("pg", os.path.join(os.path.dirname(__file__), "punch_geom.py"))
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# punch_geom.py runs a loop on import; import its parse via exec of the functions only
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src = open(os.path.join(os.path.dirname(__file__), "punch_geom.py")).read()
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src = src.split("for path in sys.argv")[0]
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ns = {}
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exec(src, ns)
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parse = ns["parse"]
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ROOT = r"C:\git\bt411"
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MECHS = {"madcat":("MAD","MAX"), "bhk1":("BLH","BLX"), "thor":("THR","THX"),
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"owens":("OWN","OWX"), "sunder":("SND","SNX"), "loki":("LOK","LOX"),
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"vulture":("VUL","VUX"), "avatar":("AVA","AVX")}
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W, H = 320, 240
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def jointeye(skl_path):
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t = open(skl_path, encoding="latin1").read()
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m = re.search(r"\[jointeye\]", t)
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blk = t[m.start():m.start()+400]
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g = lambda k: float((re.search(rf"{k}=([-\d.e]+)", blk) or [None,"0"])[1])
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return np.array((g("tranx"), g("trany"), g("tranz")))
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def raster_mask(tris, eye, fwd):
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"""double-sided coverage rasterization -> bool mask"""
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up = np.array([0.,1.,0.])
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r = np.cross(up, fwd); r /= np.linalg.norm(r); u2 = np.cross(fwd, r)
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tanf = math.tan(math.radians(75)/2); aspect = W/H
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m = np.zeros((H,W), bool)
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for tri in tris:
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s = []
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ok = True
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for p in tri:
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d = p - eye
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cx, cy, cz = np.dot(d,r), np.dot(d,u2), np.dot(d,fwd)
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if cz <= 0.02: ok = False; break
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s.append((cx/(cz*tanf*aspect)*0.5*W + 0.5*W, -cy/(cz*tanf)*0.5*H + 0.5*H))
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if not ok: continue
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(ax,ay),(bx,by),(cx,cy) = s
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x0=max(0,int(min(ax,bx,cx))); x1=min(W-1,int(math.ceil(max(ax,bx,cx))))
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y0=max(0,int(min(ay,by,cy))); y1=min(H-1,int(math.ceil(max(ay,by,cy))))
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den=(by-cy)*(ax-cx)+(cx-bx)*(ay-cy)
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if abs(den)<1e-9 or x1<x0 or y1<y0: continue
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for py in range(y0,y1+1):
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for px in range(x0,x1+1):
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w0=((by-cy)*(px+.5-cx)+(cx-bx)*(py+.5-cy))/den
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w1=((cy-ay)*(px+.5-cx)+(ax-cx)*(py+.5-cy))/den
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w2=1-w0-w1
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if w0>=-1e-6 and w1>=-1e-6 and w2>=-1e-6: m[py,px]=True
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return m
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def tris_of(g):
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V = g["verts"]
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return [np.array([V[a][:3],V[b][:3],V[c][:3]]) for (a,b,c) in g["faces"] if max(a,b,c)<len(V)]
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rows = []
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for name,(pfx,xpfx) in MECHS.items():
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eye = jointeye(os.path.join(ROOT,"content","VIDEO",pfx+".SKL"))
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P = parse(os.path.join(ROOT,"content","VIDEO","GEO",xpfx+"_COP.BGF"))
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views = [("fwd", np.array([0.,0.,-1.]))]
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if name == "madcat": views.append(("rear", np.array([0.,0.,1.])))
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for vname, fwd in views:
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m0 = np.zeros((H,W),bool); m1 = np.zeros((H,W),bool)
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for pt in P:
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if not pt["punch"] or len(pt["geoms"])<3: continue
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m0 |= raster_mask(tris_of(pt["geoms"][0]), eye, fwd)
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m1 |= raster_mask(tris_of(pt["geoms"][1]), eye, fwd)
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A = m1 & ~m0
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tot = W*H
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cov0 = m0.sum()/tot*100; cov1 = m1.sum()/tot*100; covA = A.sum()/tot*100
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openA = (m1&m0).sum()/max(1,m1.sum())*100
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print(f"{name:8s} {vname:4s} g0(maskB-drawn?)={cov0:5.1f}% g1(hull)={cov1:5.1f}% "
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f"A=hull-minus-mask visible={covA:5.1f}% hull punched open={openA:5.1f}%")
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img = np.zeros((H, W*3, 3), np.uint8); img[:] = (150,160,190) # 'sky'
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img[:, :W][m0] = (200,60,40) # g0 footprint
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img[:, W:2*W][m1] = (60,60,70) # g1 footprint
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pane = np.zeros((H,W,3),np.uint8); pane[:] = (150,160,190); pane[A] = (35,25,45)
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img[:, 2*W:] = pane # Model A composite
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rows.append((f"{name}-{vname}", img))
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out = np.concatenate([r[1] for r in rows], 0)
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Image.fromarray(out).save(os.path.join(os.path.dirname(__file__), "PUNCH_ROLES.png"))
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print("order:", [r[0] for r in rows])
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print("panels: LEFT g0 footprint (red) | MID g1 hull footprint (grey) | RIGHT Model A visible (dark = frame)")
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