Reads the live world out of guest RAM and drives the pad from it. Working: loop-rate memory reads, whole-RAM float scanning with numpy (1270 orthonormal 3x3 blocks in 6.2 s), entity enumeration by unit type (116 live instances in Stage 02), pad control written straight into the vgamepad FIFO (the CLI spawns a process per command and its tap/hold sleep inside the server, so neither is usable in a control loop), unattended mission entry, and the Hangar loadout -- the "Recommended" control is AUTO SELECT, which at 5 % progress is a no-op because only two weapons are developed and both are already mounted. Not working, and the reason the craft is not yet flown: the class 0x820af030 is NOT the live entity. It has one object per spawned thing and carries the unit-ID string, which is why it looked like the entity list, but every one of its 384 words is constant across a 29 s in-flight capture. No transform lives in it or one pointer hop from it. Input correlation (hard left yaw vs hard right, looking for a turn axis that reverses) does find self-like objects at cos = -0.99, but they cluster in what looks like a camera volume rather than the craft, and with no definition pointer near them the trick of learning one entity's layout and applying it to the rest has nothing to anchor on -- so the 33418 moving triples in a firefight cannot be split into enemies, friendlies and bullets, and there is nothing to aim at. Two dead ends are recorded so they are not repeated: RT is not the throttle (the two-state speed scan therefore found nothing), and comparing orientation matrices 2 s apart is outside the small-angle regime, which is what produced "angular velocities" of 30000. Also corrects the claim in unit-struct-runtime.md that 0x820af030 holds live state. The definition class 0x820af844 and every value derived from it are unaffected. autopilot2.py (a PD controller using body angular velocity from consecutive rotation matrices) is committed but has never had a valid config to run against, and is marked as untested.
121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
#!/usr/bin/env python3
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"""Find the player craft's live position in guest RAM, from motion alone.
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The spawn records at vtable 0x820af030 turned out to hold no live state (every
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word is constant across a 29 s capture), so the flying entity is some other
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object. Rather than guess which class, this finds it by what it must *do*:
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a position triple moves in a straight line at constant speed while coasting.
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Method: sample all of RAM K times ~dt apart, keep word indices whose float
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value changes every time, then require three consecutive such words to satisfy
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* equal step length each interval (constant speed), and
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* a constant direction (straight flight),
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* with speed in a sane range for a craft.
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Only the wholly-mechanical properties of motion are used; no offset, class or
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encoding is assumed.
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Usage: findplayer.py [samples] [interval_s]
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"""
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import math
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import os
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import struct
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import sys
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import time
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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import gmem # noqa: E402
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def snapshot(fd, size):
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"""[(start_off, np.ndarray big-endian f32)] over allocated extents."""
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out = []
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for a, b in gmem.extents(fd, size):
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n = (b - a) // 4 * 4
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if n < 64:
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continue
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buf = os.pread(fd, n, a)
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out.append((a, np.frombuffer(buf, dtype=">f4")))
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return out
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def main():
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K = int(sys.argv[1]) if len(sys.argv) > 1 else 5
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dt = float(sys.argv[2]) if len(sys.argv) > 2 else 0.35
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path = gmem.mem_path()
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fd = os.open(path, os.O_RDONLY)
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size = os.path.getsize(path)
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print(f"# sampling {K}x every {dt}s", flush=True)
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snaps, times = [], []
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for k in range(K):
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t = time.time()
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snaps.append(snapshot(fd, size))
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times.append(t)
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print(f"# sample {k} ({len(snaps[-1])} extents, "
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f"{sum(len(a) for _, a in snaps[-1])*4/1e6:.0f} MB) "
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f"in {time.time()-t:.2f}s", flush=True)
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time.sleep(max(0, dt - (time.time() - t)))
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# extents must line up across samples for a word-wise comparison
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shapes = [tuple((o, len(a)) for o, a in s) for s in snaps]
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if len(set(shapes)) != 1:
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common = set(shapes[0])
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for sh in shapes[1:]:
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common &= set(sh)
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print(f"# extents shifted between samples; using {len(common)} common ones")
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keep = lambda s: [(o, a) for o, a in s if (o, len(a)) in common]
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snaps = [keep(s) for s in snaps]
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hits = []
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for e in range(len(snaps[0])):
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base = snaps[0][e][0]
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arrs = [np.nan_to_num(s[e][1].astype(np.float64), nan=0, posinf=0, neginf=0)
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for s in snaps]
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# per-interval deltas
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d = [arrs[k + 1] - arrs[k] for k in range(K - 1)]
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moving = np.ones(len(arrs[0]), dtype=bool)
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for dk in d:
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moving &= np.abs(dk) > 1e-3
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idx = np.flatnonzero(moving)
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if len(idx) == 0:
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continue
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# candidate triples: i, i+1, i+2 all moving
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cand = idx[np.isin(idx + 1, idx) & np.isin(idx + 2, idx)]
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for i in cand:
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steps = []
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dirs = []
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ok = True
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for k in range(K - 1):
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v = np.array([d[k][i], d[k][i + 1], d[k][i + 2]])
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n = float(np.linalg.norm(v))
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ddt = times[k + 1] - times[k]
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sp = n / ddt
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if not (20.0 < sp < 3000.0):
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ok = False
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break
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steps.append(sp)
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dirs.append(v / n)
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if not ok or len(steps) < 3:
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continue
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if max(steps) - min(steps) > 0.25 * (sum(steps) / len(steps)):
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continue
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if min(float(np.dot(dirs[0], dd)) for dd in dirs) < 0.985:
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continue
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va = gmem.primary_va(base + int(i) * 4)
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pos = tuple(float(arrs[0][i + j]) for j in range(3))
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hits.append((va, base + int(i) * 4, sum(steps) / len(steps), pos))
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print(f"\n# {len(hits)} position-like triples (straight, constant speed)")
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hits.sort(key=lambda h: -h[2])
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for va, off, sp, pos in hits[:40]:
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print(f" va {va:#010x} speed {sp:8.1f}/s pos "
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f"({pos[0]:+11.1f},{pos[1]:+11.1f},{pos[2]:+11.1f})")
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if __name__ == "__main__":
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main()
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