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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.
162 lines
6.6 KiB
Python
162 lines
6.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""Turn the raw table dumps written by db_dump.py into ONE file the FUT card
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pool can consume: data/player_facts.json.
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Input data/tables/{players,teamplayerlinks,teams,leagues,nations,
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leagueteamlinks,playerattributesmapping}.json
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Output data/player_facts.json
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{"meta": {...},
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"players": [ {"id":20801,"rating":94,"pos":27,"pos2":16,"pos3":25,"pos4":-1,
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"nation":38,"team":243,"league":53,
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"attrs":[90,93,82,91,33,80], # PAC SHO PAS DRI DEF PHY
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"gk":false}, ... ]}
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THE SIX CARD ATTRIBUTES ARE NOT COLUMNS. `players` stores the 29 base
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attributes; the six numbers a FUT card shows are a weighted sum of them. The
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weights are not guessed here -- they are read out of the game's own
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`playerattributesmapping` table, which maps each base attribute id to its
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percentage contribution to speed / shooting / passing / dribbling / defending /
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physical (and to the five gk* stats). Every column of that table sums to
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exactly 100.
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The one inference this file makes is attributeid -> players column name, and it
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is safe for this purpose: wherever two attributes could be swapped (marking vs
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standingtackle at 30 each, shotpower vs longshots at 20 each, ...) they carry
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EQUAL weight, so the computed six are identical either way. The assignments
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that actually move a number -- 45/55 acceleration/sprintspeed, 45 finishing,
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50 dribbling, 35 shortpassing, 30 ballcontrol, 50 strength, 25 stamina,
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20 aggression, 20 interceptions, 15 longpassing -- are each the unique
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attribute with that weight.
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VALIDATION (see the report): overallrating in `players` agrees with
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data/roster.json, extracted from a completely different memory structure, on
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17547 of 17547 shared players.
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"""
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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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TABLES = os.path.join(HERE, '..', 'data', 'tables')
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OUT = os.path.join(HERE, '..', 'data', 'player_facts.json')
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# FUT card slot -> [(players column, percent)], read off playerattributesmapping.
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OUTFIELD = [
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('pace', [('acceleration', 45), ('sprintspeed', 55)]),
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('shooting', [('finishing', 45), ('shotpower', 20), ('longshots', 20),
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('positioning', 5), ('volleys', 5), ('penalties', 5)]),
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('passing', [('shortpassing', 35), ('vision', 20), ('crossing', 20),
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('longpassing', 15), ('freekickaccuracy', 5), ('curve', 5)]),
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('dribbling', [('dribbling', 50), ('ballcontrol', 30), ('agility', 10),
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('balance', 5), ('reactions', 5)]),
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('defending', [('marking', 30), ('standingtackle', 30), ('interceptions', 20),
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('headingaccuracy', 10), ('slidingtackle', 10)]),
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('physical', [('strength', 50), ('stamina', 25), ('aggression', 20),
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('jumping', 5)]),
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]
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# Keeper card face. The five gk* columns are used at 100% -- they ARE the card
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# numbers, no arithmetic. The speed slot is the only weighted one.
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GK = [
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('diving', [('gkdiving', 100)]),
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('handling', [('gkhandling', 100)]),
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('kicking', [('gkkicking', 100)]),
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('reflexes', [('gkreflexes', 100)]),
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('speed', [('acceleration', 60), ('sprintspeed', 40)]),
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('positioning', [('gkpositioning', 100)]),
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]
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POSITION_NAMES = ['GK', 'SW', 'RWB', 'RB', 'RCB', 'CB', 'LCB', 'LB', 'LWB',
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'RDM', 'CDM', 'LDM', 'RM', 'RCM', 'CM', 'LCM', 'LM',
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'RAM', 'CAM', 'LAM', 'RF', 'CF', 'LF', 'RW', 'RS', 'ST',
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'LS', 'LW']
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def load(name):
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with open(os.path.join(TABLES, name + '.json')) as fh:
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return json.load(fh)
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def weighted(row, spec):
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return int(round(sum(row[c] * w for c, w in spec) / 100.0))
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def main():
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players = load('players')['rows']
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tpl = load('teamplayerlinks')['rows']
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teams = {t['teamid']: t for t in load('teams')['rows']}
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nations = {n['nationid']: n['nationname'] for n in load('nations')['rows']}
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# A player appears in teamplayerlinks once per club AND once per national
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# side. The club is the row whose team is not a national team; nations
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# and teams share an id space only through teamnationlinks, so the cheap,
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# reliable discriminator is: the club link is the one with the lowest
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# teamid that is not the player's own nation-team. Keep every link too.
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nat_team = set()
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for t in load('teamnationlinks')['rows']:
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nat_team.add(t['teamid'])
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clubs = {}
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alllinks = {}
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for l in tpl:
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alllinks.setdefault(l['playerid'], []).append(l)
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if l['teamid'] in nat_team:
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continue
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prev = clubs.get(l['playerid'])
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if prev is None or l['teamid'] < prev['teamid']:
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clubs[l['playerid']] = l
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out = []
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for p in players:
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pid = p['playerid']
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gk = p['preferredposition1'] == 0
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spec = GK if gk else OUTFIELD
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club = clubs.get(pid)
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out.append({
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'id': pid,
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'rating': p['overallrating'],
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'potential': p['potential'],
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'pos': p['preferredposition1'],
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'pos2': p['preferredposition2'],
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'pos3': p['preferredposition3'],
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'pos4': p['preferredposition4'],
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'posname': POSITION_NAMES[p['preferredposition1']]
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if 0 <= p['preferredposition1'] < len(POSITION_NAMES) else None,
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'nation': p['nationality'],
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'nationname': nations.get(p['nationality']),
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'team': club['teamid'] if club else 0,
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'teamname': teams.get(club['teamid'], {}).get('teamname') if club else None,
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'jersey': club['jerseynumber'] if club else 0,
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'foot': p['preferredfoot'],
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'skillmoves': p['skillmoves'],
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'weakfoot': p['weakfootabilitytypecode'],
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'height': p['height'],
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'weight': p['weight'],
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'gk': gk,
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'attrs': [weighted(p, s) for _, s in spec],
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})
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doc = {
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'meta': {
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'source': 'FIFA17.exe resident database, via tools/db_dump.py',
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'players': len(out),
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'attr_order_outfield': [k for k, _ in OUTFIELD],
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'attr_order_gk': [k for k, _ in GK],
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'weights_outfield': {k: dict(v) for k, v in OUTFIELD},
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'weights_gk': {k: dict(v) for k, v in GK},
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'position_enum': POSITION_NAMES,
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},
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'players': out,
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}
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with open(OUT, 'w') as fh:
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json.dump(doc, fh, ensure_ascii=False, separators=(',', ':'))
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sys.stderr.write("wrote %s: %d players (%d keepers)\n"
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% (OUT, len(out), sum(1 for x in out if x['gk'])))
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return 0
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if __name__ == '__main__':
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sys.exit(main())
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