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70a64e3709
Freeze the running offline FUT backend into version control as fifa17-recon/docker/fifa17-python/ - declarative and rebuildable from a fresh checkout: * OPENFUT_BIND / OPENFUT_ADVERTISE client/server split in the responders (lsx, blaze, roster, utas, pow) + entrypoint.sh; OPENFUT_ADVERTISE is required for remote mode (compose and entrypoint fail without it) * docker-compose.yml reproducing the frozen baseline container exactly (env, ports incl. the 8085->8080 POW-content remap, /state bind, restart) * .env.example / .env for site config - the LAN IP is never hardcoded in source * tools/ + data/ staged from openfut-fut-backend:python-baseline-2026-08-10, verified byte-identical to the running container at freeze time * client_arm.sh (the 105 client-side arming counterpart) * Dockerfile bakes /app/SHA256SUMS.txt so any image is self-identifying * docs/BASELINE-python-2026-08-10.md: frozen image/container/hash record, restore instructions and rebuild-equivalence procedure Secrets (redir key/cert, .env) and runtime state (docker/state) stay gitignored. The live container is untouched pending the .105 launcher audit.
258 lines
12 KiB
Python
258 lines
12 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""Build the MIXED-CONTROL staff window: known-good coach ids interleaved with
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known-bad ones, so a correct result and an incorrect one look different in the
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same screenshot and in the same probe readback.
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THIS FILE FIRES NOTHING. It prints/writes JSON. The integrate agent wires it in;
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nobody else touches utas_server.py, fut_cards.py or fut_store.py.
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WHY THIS SHAPE
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--------------
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The four coach branches of FUN_180141660 write a LOUD miss-fill -- firstname and
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lastname "DB Error", rating 0x32, rare 1, and a TABLE-UNIQUE fallback assetid.
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That gives three separable outcomes instead of two:
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HIT real name, rating == the row's `value`, assetId == carddbid
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MISS "DB Error", rating 50, assetId == this family's fallback
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WRONG-BRANCH "DB Error", rating 50, assetId == ANOTHER family's fallback
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NO-MERGE our sentinel rating survives, name empty -> the query never ran
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so every failure mode says which one it is. That is what makes the negative
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interpretable, and it is the reason coaches are the cheapest family to prove.
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Two properties were checked against the on-disk dumps and both hold:
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* no row in any of the four tables has value == 50, so rating 50 can only be a
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miss -- it can never be a hit that happens to look like one;
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* no fitnesscoachcards row has (fieldpos, posbonus, amount) == (1, 7, 1), the
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fitness miss-fill triple at +0xdd/+0xde/+0xdf, so fitness has a second,
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fully independent oracle that does not depend on reading a name at all.
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The known-bad ids are ids INSIDE each family's own carddbid band that are absent
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from the table -- 195..207 such gaps exist per family, so a bad control is never
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an out-of-range value the client might reject for an unrelated reason.
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THE SENTINEL. Every item is sent with rating SENTINEL (=1), which no coach row
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carries and which the miss-fill never writes. If a card comes back still holding
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rating 1, the merge did not run at all; that is the NO-MERGE arm and it is a
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different bug from either a hit or a miss.
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DO NOT ROUTE THIS THROUGH FUT_ID_SWEEP. sweep_items() keeps itemType "player"
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and varies only cardsubtypeid, and club_route answers a sweep BEFORE the ?type=
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filter, so a sweep-borne staff experiment is confounded twice over.
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WHICH SCREEN FIRES IT. The client's own ?type= taxonomy (FUN_18012ec50, 29 arms
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+ default) does contain headcoach / gkcoach / physio / fitnesscoach / staff --
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atoms 0x153 / 0x13f / 0x21d / 0x129 / 0x2dc. NONE of those five has ever been
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seen on the wire. A grep of every capture and log in this repo finds exactly
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three values: type=player (x14), type=manager (x2), type=custom (x1). So the
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screen to aim this at is the STAFF tab, which sends type=manager, and the reason
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these items reach it is that club_route already filters on cardsubtypeid -- not
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on itemType and not on the type string -- keeping everything outside 0..3.
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Usage:
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python3 coach_window.py # human-readable prediction table
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python3 coach_window.py --json items.json
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python3 coach_window.py --family headcoach --json one.json
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"""
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import argparse
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import json
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import os
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import sys
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HERE = os.path.dirname(os.path.abspath(__file__))
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SEEDS = os.path.join(HERE, "..", "data", "coach_seeds.json")
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# Item ids for the experiment. Far above fut_store's ITEM_ID_BASE range so a
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# stray record in the CardsDb map can always be attributed.
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ID_BASE = 950000000
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SENTINEL_RATING = 1
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# A DISTINCT, unmistakable nation + league per family. This is the SECOND
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# question the same window answers, and it is free: the four coach branches of
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# FUN_180141660 never write nation (+0x148), leagueId (+0x154), teamid (+0x94) or
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# position (+0x146), and none of the four tables even HAS a nation/league/team
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# column, so these values cannot change the hit/miss outcome. Whatever a coach
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# card shows for country or league therefore came from US.
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#
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# Both the good and the bad ids of a family carry the same pair, so a "DB Error"
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# card flying a Brazilian flag is itself direct proof that the miss-fill leaves
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# our fields alone.
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#
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# nation 14 (England) is deliberately AVOIDED: 14 is what the PLAYER miss-fill
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# writes, and a value that doubles as a known failure fingerprint is not a probe.
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FACE = { # family -> (nationid, leagueid)
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"headcoach": (54, 13), # Brazil, Premier League
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"gkcoach": (45, 19), # Spain, Bundesliga
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"physio": (27, 16), # Italy, Ligue 1
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"fitnesscoach": (21, 53), # Germany, LaLiga Santander
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}
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def _item(item_id, carddbid, cardsubtypeid, nation=0, league=0):
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"""One staff item.
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EXACTLY the field set fut_store._item() already builds and that this client
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is live-proven to parse. The only changes are cardsubtypeid and the ids. No
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new atom is introduced: the wire shape of a real staff item has NEVER been
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observed, and inventing one -- a scalar where the parser wants an object --
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is the change class that busy-loops the client at 0x1801c7f1a.
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resourceId carries NO version nibble. The staff branches compare
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`carddbid == *(u32*)(record+0x18)` on the RAW dword; players are the only
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family that masks with & 0xffffff. A high byte here breaks every lookup and
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does it silently.
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itemType stays "player" for the first run. The merge dispatches on
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cardsubtypeid alone, and club_route's ?type= filter already keys on
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cardsubtypeid (anything not in 0..3 survives a non-player type), so nothing
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needs itemType to be changed in order for these to reach the STAFF tab.
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Flipping it to the taxonomy name ("headcoach"/"gkcoach"/"physio"/
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"fitnesscoach", atoms 0x153/0x13f/0x21d/0x129) is a separate, later,
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one-variable experiment.
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"""
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return {
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"id": item_id,
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"resourceId": carddbid, # == carddbid, raw, no version byte
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"assetId": carddbid,
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"cardassetid": carddbid,
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"definitionId": carddbid,
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"cardsubtypeid": cardsubtypeid,
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"itemType": "player",
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"rareflag": 1, # overwritten by the merge either way
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"rating": SENTINEL_RATING,
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"preferredPosition": "ST",
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"nation": nation,
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"teamid": 0,
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"leagueId": league,
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"playStyle": 250,
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# zeros so that, for head coach and GK coach, the ONE slot the merge
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# writes (attrs[row.attribute] = row.amount) stands out against five
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# untouched zeros -- that single write verifies two columns at once.
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"attributeList": [{"index": i, "value": 0} for i in range(6)],
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"itemState": "free",
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"owners": 1,
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"untradeable": True,
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"contract": 7,
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"fitness": 99,
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}
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def build(families=None):
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with open(SEEDS) as f:
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spec = json.load(f)
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fams = spec["families"]
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order = [f for f in ("headcoach", "gkcoach", "physio", "fitnesscoach")
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if families is None or f in families]
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items, predict, n = [], [], 0
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for fam in order:
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d = fams[fam]
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sub, fb = d["cardsubtypeid"], d["miss_fill_assetid"]
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nat, lg = FACE[fam]
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good = [r["carddbid"] for r in d["seeds"]]
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bad = d["bad_controls"]
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rows = {r["carddbid"]: r for r in d["seeds"]}
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# INTERLEAVE. The client pages the club (start=N&count=11 observed), so
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# good and bad must alternate or a page can come back all-good/all-bad
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# and prove nothing on its own screenshot.
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mixed, gi, bi = [], 0, 0
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while gi < len(good) or bi < len(bad):
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for _ in range(3):
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if gi < len(good):
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mixed.append((good[gi], True)); gi += 1
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if bi < len(bad):
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mixed.append((bad[bi], False)); bi += 1
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for cid, is_good in mixed:
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iid = ID_BASE + n; n += 1
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items.append(_item(iid, cid, sub, nat, lg))
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if is_good:
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r = rows[cid]
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v = r["value"]
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p = {"id": iid, "family": fam, "carddbid": cid, "expect": "HIT",
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"rating": v, "tier": 3 if v >= 75 else (2 if v >= 65 else 1),
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"rare": r["rare"], "assetId": cid, "name": "a real person",
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"face": "nation %d / leagueId %d must SURVIVE" % (nat, lg)}
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if fam in ("headcoach", "gkcoach"):
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p["attrs"] = "index %d == %d, other five == 0" % (
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r["attribute"], r["amount"])
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elif fam == "physio":
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p["stat_byte"] = "+%#x == %d" % (0xDD + r["attribute"],
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r["amount"])
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else:
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p["stat_bytes"] = "+0xdd/+0xde/+0xdf == %d/%d/%d" % (
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r["fieldpos"], r["posbonus"], r["amount"])
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else:
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p = {"id": iid, "family": fam, "carddbid": cid, "expect": "MISS",
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"rating": 50, "tier": 1, "rare": 1, "assetId": fb,
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"name": "DB Error DB Error",
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"face": "nation %d / leagueId %d must SURVIVE" % (nat, lg)}
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if fam in ("headcoach", "gkcoach"):
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p["attrs"] = "index 0 == 15, other five == 0"
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elif fam == "physio":
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p["stat_byte"] = "+0xdd == 15"
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else:
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p["stat_bytes"] = "+0xdd/+0xde/+0xdf == 1/7/1"
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predict.append(p)
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# ONE deliberate cross-family item per family: this family's BEST-KNOWN
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# good id sent under the NEXT family's cardsubtypeid. It must miss, and
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# its fallback assetid must name the OTHER table. That is the only item
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# in the window that can distinguish "the subtype picks the table" from
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# "the id band picks the table", and it is interpretable in both
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# directions: a real name here would refute the dispatch outright.
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other = order[(order.index(fam) + 1) % len(order)]
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if other != fam:
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osub = fams[other]["cardsubtypeid"]
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ofb = fams[other]["miss_fill_assetid"]
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iid = ID_BASE + n; n += 1
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# the cross-family item keeps the SOURCE family's face pair, so if
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# it ever renders it is visibly the head-coach flag on a gkcoach slot
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items.append(_item(iid, good[0], osub, nat, lg))
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predict.append({"id": iid, "family": "%s-id/%s-subtype" % (fam, other),
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"carddbid": good[0], "expect": "MISS (cross-family)",
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"rating": 50, "tier": 1, "rare": 1, "assetId": ofb,
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"name": "DB Error DB Error",
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"refutes": "a real name here means cardsubtypeid does "
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"NOT select the table"})
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return items, predict
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--json", metavar="PATH", help="write the item array")
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ap.add_argument("--family", action="append",
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choices=["headcoach", "gkcoach", "physio", "fitnesscoach"])
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a = ap.parse_args()
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items, predict = build(a.family)
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nfam = len({p["family"] for p in predict if "/" not in p["family"]})
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print("%d items across %d familie(s), including %d deliberate cross-family "
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"controls. Every item carries rating=%d as the NO-MERGE sentinel.\n"
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% (len(items), nfam, sum(1 for p in predict if "/" in p["family"]),
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SENTINEL_RATING))
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print("%-11s %-24s %-9s %-20s %-5s %-4s %-8s %s"
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% ("id", "family", "carddbid", "expect", "rat", "tier", "assetId", "extra"))
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for p in predict:
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extra = p.get("attrs") or p.get("stat_byte") or p.get("stat_bytes") or ""
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print("%-11d %-24s %-9d %-20s %-5d %-4d %-8d %s"
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% (p["id"], p["family"], p["carddbid"], p["expect"], p["rating"],
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p["tier"], p["assetId"], extra))
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n_hit = sum(1 for p in predict if p["expect"] == "HIT")
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print("\npredicted: HIT=%d MISS=%d (a run where all %d agree is the proof; "
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"any single disagreement names its own failure mode)"
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% (n_hit, len(predict) - n_hit, len(predict)))
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if a.json:
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with open(a.json, "w") as f:
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json.dump({"itemData": items, "predictions": predict}, f, indent=1)
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print("\nwrote %s" % a.json)
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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