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- Konzept/, didaktik_geografie/, didaktik_simulation/, v2-modules/, v2-platform/ - 12 code-workspace-Files - STATUS-*.md - viele M/D/R-Änderungen an bereits getrackten Files - .gitignore verstärkt: **/.humaninput/, **/secret_keys.txt Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
#!/usr/bin/env python3
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"""
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Busfahrt — Zwischenfragen-Generator.
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Liest cities.json ein, ergänzt pro Stadt ein `question`-Objekt der Form:
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"question": {
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"prompt": { "de": "In welchem Land liegt Paris?",
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"easy": "In welchem Land liegt Paris?" },
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"choices": ["Frankreich", "Spanien", "Italien"],
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"correctIndex": 0
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}
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Distraktor-Länder werden deterministisch (mulberry32 auf city.id) aus dem
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Pool aller anderen Länder gewählt — damit Re-Runs dieselben Choices liefern,
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und damit das Daten-File reproduzierbar bleibt.
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Verwendung:
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python generate-city-questions.py # in-place überschreiben
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python generate-city-questions.py --dry-run # nur ausgeben, was geändert würde
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Sensationsstädte können später eine zweite Frage als
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"questionExtra": { ... } # hand-kuratiert
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bekommen — dieser Generator überschreibt das Feld nicht.
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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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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent / "assets" / "data"
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CITIES_PATH = ROOT / "cities.json"
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def mulberry32(seed: int):
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"""Deterministischer RNG (gleicher wie in der Engine), produziert [0, 1)."""
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state = [seed & 0xFFFFFFFF]
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def next_float():
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state[0] = (state[0] + 0x6D2B79F5) & 0xFFFFFFFF
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t = state[0]
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r = ((t ^ (t >> 15)) * (1 | t)) & 0xFFFFFFFF
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r = ((r + ((r ^ (r >> 7)) * (61 | r))) & 0xFFFFFFFF) ^ r
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return ((r ^ (r >> 14)) & 0xFFFFFFFF) / 4294967296.0
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return next_float
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def slug_hash(s: str) -> int:
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"""Einfacher String→u32-Hash für deterministisches Seeding."""
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h = 2166136261
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for ch in s:
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h = (h ^ ord(ch)) & 0xFFFFFFFF
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h = (h * 16777619) & 0xFFFFFFFF
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return h
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def pick_distractors(city, all_countries):
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"""Wählt 2 plausible Distraktor-Länder, die nicht das eigene Land sind."""
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pool = [c for c in all_countries if c != city["country"]]
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rng = mulberry32(slug_hash(city["id"]))
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# Fisher-Yates-ähnlich, deterministisch
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pool_shuffled = pool.copy()
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for i in range(len(pool_shuffled) - 1, 0, -1):
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j = int(rng() * (i + 1))
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pool_shuffled[i], pool_shuffled[j] = pool_shuffled[j], pool_shuffled[i]
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return pool_shuffled[:2]
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def build_question(city, all_countries):
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distractors = pick_distractors(city, all_countries)
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# Position der richtigen Antwort auch deterministisch wählen (0/1/2)
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rng = mulberry32(slug_hash(city["id"] + "_pos"))
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correct_pos = int(rng() * 3)
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choices = distractors[:]
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choices.insert(correct_pos, city["country"])
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title = city["title"]
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return {
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"prompt": {
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"de": f"In welchem Land liegt {title}?",
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"easy": f"In welchem Land liegt {title}?",
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},
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"choices": choices,
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"correctIndex": correct_pos,
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}
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def main():
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dry_run = "--dry-run" in sys.argv
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if not CITIES_PATH.exists():
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print(f"ERR: {CITIES_PATH} nicht gefunden", file=sys.stderr)
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return 1
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with CITIES_PATH.open("r", encoding="utf-8") as f:
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cities = json.load(f)
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all_countries = sorted({c["country"] for c in cities})
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print(f"Total cities: {len(cities)}")
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print(f"Unique countries: {len(all_countries)}")
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added = 0
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kept = 0
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for city in cities:
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if "question" in city:
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kept += 1
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continue
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city["question"] = build_question(city, all_countries)
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added += 1
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print(f"Neu generiert: {added}")
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print(f"Bestehend (nicht überschrieben): {kept}")
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if dry_run:
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# Sample-Output: erste 3 neuen Fragen
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first = [c for c in cities if "question" in c][:3]
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for c in first:
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print(f"\n{c['id']}:")
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print(json.dumps(c["question"], ensure_ascii=False, indent=2))
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return 0
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with CITIES_PATH.open("w", encoding="utf-8") as f:
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json.dump(cities, f, ensure_ascii=False, indent=2)
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f.write("\n")
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print(f"OK — {CITIES_PATH} geschrieben.")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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