677 lines
24 KiB
Python
677 lines
24 KiB
Python
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#!/usr/bin/env python3
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"""
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GWÖ-Wahlprüfsteine Aggregator
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Erstellt Partei-Aggregationen und Berichte.
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"""
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import sqlite3
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import json
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from pathlib import Path
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from dataclasses import dataclass
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from datetime import datetime
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@dataclass
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class ParteiStatistik:
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partei: str
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anzahl_kandidaten: int
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anzahl_antworten: int
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avg_substanz: float
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avg_gwoe: float
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avg_wortanzahl: float
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ja_quote: float # Anteil Ja-Antworten
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top_felder: list[str] # Häufigste Matrix-Felder
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beste_zitate: list[dict] # Top 3 Antworten
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schwachpunkte: list[str]
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def get_partei_statistiken(conn: sqlite3.Connection) -> list[ParteiStatistik]:
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"""Holt aggregierte Statistiken pro Partei."""
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cursor = conn.cursor()
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# Basisstatistiken
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cursor.execute("""
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SELECT
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k.partei_normalisiert AS partei,
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COUNT(DISTINCT k.id) AS anzahl_kandidaten,
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COUNT(b.id) AS anzahl_antworten,
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ROUND(AVG(b.substanz_score), 2) AS avg_substanz,
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ROUND(AVG(b.gwoe_score), 2) AS avg_gwoe,
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ROUND(AVG(b.wortanzahl), 0) AS avg_wortanzahl
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FROM kandidaten k
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LEFT JOIN antworten_raw ar ON k.id = ar.kandidat_id
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LEFT JOIN bewertungen b ON ar.id = b.antwort_id
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WHERE b.id IS NOT NULL
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GROUP BY k.partei_normalisiert
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ORDER BY avg_gwoe DESC
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""")
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basis = {row['partei']: dict(row) for row in cursor.fetchall()}
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# Ja-Quoten pro Partei
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cursor.execute("""
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SELECT
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k.partei_normalisiert AS partei,
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SUM(CASE WHEN ar.antwort_kurz = 'Ja' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS ja_quote
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FROM kandidaten k
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JOIN antworten_raw ar ON k.id = ar.kandidat_id
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GROUP BY k.partei_normalisiert
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""")
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ja_quoten = {row['partei']: row['ja_quote'] for row in cursor.fetchall()}
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# Matrix-Felder pro Partei (häufigste)
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matrix_felder = {}
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for partei in basis.keys():
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cursor.execute("""
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SELECT b.matrix_felder
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FROM bewertungen b
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JOIN antworten_raw ar ON b.antwort_id = ar.id
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JOIN kandidaten k ON ar.kandidat_id = k.id
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WHERE k.partei_normalisiert = ? AND b.matrix_felder IS NOT NULL
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""", (partei,))
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feld_counts = {}
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for row in cursor.fetchall():
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try:
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felder = json.loads(row['matrix_felder'])
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for f in felder:
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feld_counts[f] = feld_counts.get(f, 0) + 1
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except:
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pass
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# Top 5 Felder
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sorted_felder = sorted(feld_counts.items(), key=lambda x: -x[1])
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matrix_felder[partei] = [f[0] for f in sorted_felder[:5]]
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# Beste Zitate pro Partei (Top 3 nach GWÖ-Score)
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beste_zitate = {}
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for partei in basis.keys():
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cursor.execute("""
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SELECT
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k.vorname || ' ' || k.nachname AS name,
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k.kommune,
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f.kurztext AS frage,
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ar.antwort_erlaeuterung AS zitat,
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b.gwoe_score,
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b.gwoe_begruendung
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FROM bewertungen b
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JOIN antworten_raw ar ON b.antwort_id = ar.id
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JOIN kandidaten k ON ar.kandidat_id = k.id
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JOIN fragen f ON ar.frage_id = f.id
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WHERE k.partei_normalisiert = ?
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AND ar.antwort_erlaeuterung IS NOT NULL
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AND LENGTH(ar.antwort_erlaeuterung) > 50
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ORDER BY b.gwoe_score DESC
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LIMIT 3
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""", (partei,))
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beste_zitate[partei] = [dict(row) for row in cursor.fetchall()]
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# Zusammenbauen
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statistiken = []
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for partei, stats in basis.items():
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statistiken.append(ParteiStatistik(
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partei=partei,
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anzahl_kandidaten=stats['anzahl_kandidaten'],
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anzahl_antworten=stats['anzahl_antworten'],
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avg_substanz=stats['avg_substanz'] or 0,
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avg_gwoe=stats['avg_gwoe'] or 0,
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avg_wortanzahl=stats['avg_wortanzahl'] or 0,
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ja_quote=ja_quoten.get(partei, 0),
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top_felder=matrix_felder.get(partei, []),
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beste_zitate=beste_zitate.get(partei, []),
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schwachpunkte=[] # Wird später gefüllt
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))
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return sorted(statistiken, key=lambda x: -x.avg_gwoe)
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def get_fragen_statistiken(conn: sqlite3.Connection) -> list[dict]:
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"""Statistiken pro Frage."""
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cursor = conn.cursor()
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cursor.execute("""
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SELECT
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f.nummer,
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f.kurztext,
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f.volltext,
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COUNT(b.id) AS anzahl,
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ROUND(AVG(b.substanz_score), 2) AS avg_substanz,
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ROUND(AVG(b.gwoe_score), 2) AS avg_gwoe,
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SUM(CASE WHEN ar.antwort_kurz = 'Ja' THEN 1 ELSE 0 END) AS ja_count,
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SUM(CASE WHEN ar.antwort_kurz = 'Nein' THEN 1 ELSE 0 END) AS nein_count
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FROM fragen f
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LEFT JOIN antworten_raw ar ON f.id = ar.frage_id
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LEFT JOIN bewertungen b ON ar.id = b.antwort_id
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GROUP BY f.id
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ORDER BY f.nummer
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""")
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return [dict(row) for row in cursor.fetchall()]
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def generate_partei_report(statistiken: list[ParteiStatistik], fragen_stats: list[dict], conn: sqlite3.Connection) -> str:
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"""Generiert den Markdown-Report für Parteien."""
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# Kandidaten für Bandbreiten-Berechnung holen
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cursor = conn.cursor()
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cursor.execute("""
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SELECT
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k.partei_normalisiert AS partei,
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k.vorname || ' ' || k.nachname AS name,
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k.kommune,
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ROUND(AVG(b.gwoe_score), 2) AS avg_gwoe
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FROM kandidaten k
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JOIN antworten_raw ar ON k.id = ar.kandidat_id
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JOIN bewertungen b ON ar.id = b.antwort_id
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GROUP BY k.id
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""")
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kandidaten_scores = {}
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for row in cursor.fetchall():
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if row['partei'] not in kandidaten_scores:
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kandidaten_scores[row['partei']] = []
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kandidaten_scores[row['partei']].append({
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'name': row['name'],
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'kommune': row['kommune'],
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'avg_gwoe': row['avg_gwoe']
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})
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lines = [
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"# GWÖ-Wahlprüfsteine Bayern 2026 — Partei-Auswertung",
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"",
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f"*Stand: {datetime.now().strftime('%d.%m.%Y %H:%M')}*",
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"",
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"---",
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"",
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"## Zusammenfassung",
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"",
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"| Partei | Kandidat:innen | Ø GWÖ | Bandbreite | Ø Substanz | Ja-Quote |",
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"|--------|---------------|-------|------------|------------|----------|",
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]
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for s in statistiken:
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# Bandbreite berechnen
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if s.partei in kandidaten_scores and len(kandidaten_scores[s.partei]) > 1:
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scores = [k['avg_gwoe'] for k in kandidaten_scores[s.partei]]
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bandbreite = f"{min(scores):.1f}–{max(scores):.1f}"
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elif s.partei in kandidaten_scores:
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bandbreite = f"{kandidaten_scores[s.partei][0]['avg_gwoe']:.1f}"
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else:
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bandbreite = "—"
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lines.append(f"| **{s.partei}** | {s.anzahl_kandidaten} | {s.avg_gwoe:.1f} | {bandbreite} | {s.avg_substanz:.1f}/3 | {s.ja_quote*100:.0f}% |")
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lines.extend([
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"",
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"---",
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"",
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"## Übergreifende Beobachtungen",
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"",
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"### Das Substanz-Problem",
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"",
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"Die Analyse zeigt ein strukturelles Muster: **Hohe Zustimmungsquoten, aber wenig konkrete Maßnahmen.**",
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"",
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"- Die meisten Parteien haben Ja-Quoten von 75–100%",
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"- Der durchschnittliche Substanz-Score liegt jedoch nur bei 0.3–1.8 von 3",
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"- Typisch: *\"Ja\"* ohne Erläuterung oder mit Floskeln wie *\"Gespräche führen\"*",
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"",
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"### Bandbreite innerhalb der Parteien",
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"",
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"Die Unterschiede *innerhalb* einer Partei sind oft größer als *zwischen* Parteien:",
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"",
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])
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for s in statistiken:
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if s.partei in kandidaten_scores and len(kandidaten_scores[s.partei]) > 1:
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scores = [k['avg_gwoe'] for k in kandidaten_scores[s.partei]]
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spanne = max(scores) - min(scores)
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if spanne >= 2:
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best = max(kandidaten_scores[s.partei], key=lambda x: x['avg_gwoe'])
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worst = min(kandidaten_scores[s.partei], key=lambda x: x['avg_gwoe'])
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lines.append(f"- **{s.partei}:** {worst['avg_gwoe']:.1f} ({worst['name']}) bis {best['avg_gwoe']:.1f} ({best['name']}) — Δ {spanne:.1f}")
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lines.extend([
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"",
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"**→ Fazit:** Das Parteilabel allein sagt wenig über die GWÖ-Affinität der einzelnen Kandidat:innen aus.",
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"",
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"---",
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"",
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"## Detailauswertung nach Parteien",
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""
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])
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for s in statistiken:
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lines.extend([
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f"### {s.partei}",
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"",
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f"**Kandidat:innen:** {s.anzahl_kandidaten} | **Antworten:** {s.anzahl_antworten} | **Ø Wortanzahl:** {s.avg_wortanzahl:.0f}",
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"",
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f"**GWÖ-Score:** {s.avg_gwoe:.1f}/10 | **Substanz:** {s.avg_substanz:.1f}/3 | **Ja-Quote:** {s.ja_quote*100:.0f}%",
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"",
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])
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if s.top_felder:
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lines.append(f"**Häufigste Matrix-Felder:** {', '.join(s.top_felder)}")
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lines.append("")
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if s.beste_zitate:
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lines.append("**Beste Antworten:**")
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lines.append("")
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for z in s.beste_zitate:
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zitat = z['zitat'][:300] + "..." if len(z['zitat']) > 300 else z['zitat']
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lines.extend([
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f"> *\"{zitat}\"*",
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f"> — {z['name']} ({z['kommune']}), zu Frage \"{z['frage']}\" (GWÖ: {z['gwoe_score']:.1f})",
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""
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])
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lines.append("---")
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lines.append("")
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# Fragen-Statistik
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lines.extend([
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"## Auswertung nach Fragen",
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"",
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"| Nr | Frage | Ja | Nein | Ø GWÖ | Ø Substanz |",
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"|----|-------|-----|------|-------|------------|",
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])
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for f in fragen_stats:
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lines.append(f"| {f['nummer']} | {f['kurztext']} | {f['ja_count']} | {f['nein_count']} | {f['avg_gwoe']:.1f} | {f['avg_substanz']:.1f} |")
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return "\n".join(lines)
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def generate_landscape_report(statistiken: list[ParteiStatistik]) -> str:
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"""Generiert die komprimierte Parteienlandschafts-Analyse."""
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# Kategorisieren
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vorreiter = [s for s in statistiken if s.avg_gwoe >= 6]
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mittelfeld = [s for s in statistiken if 3 <= s.avg_gwoe < 6]
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nachzuegler = [s for s in statistiken if s.avg_gwoe < 3]
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lines = [
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"# GWÖ-Parteienlandschaft Bayern 2026",
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"",
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"*Kommunalwahlen — komprimierte Analyse*",
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"",
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"---",
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"",
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]
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if vorreiter:
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lines.extend([
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"## 🟢 Vorreiter (GWÖ ≥ 6)",
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"",
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])
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for s in vorreiter:
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lines.append(f"**{s.partei}** (Ø {s.avg_gwoe:.1f}): Hohe GWÖ-Affinität, {s.ja_quote*100:.0f}% Zustimmung zu allen Fragen.")
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lines.append("")
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if mittelfeld:
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lines.extend([
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"## 🟡 Mittelfeld (GWÖ 3-6)",
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"",
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])
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for s in mittelfeld:
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lines.append(f"**{s.partei}** (Ø {s.avg_gwoe:.1f}): Partielle Übereinstimmung, erkennbare Offenheit für GWÖ-Themen.")
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lines.append("")
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if nachzuegler:
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lines.extend([
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|
|
"## 🔴 Zurückhaltend (GWÖ < 3)",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
for s in nachzuegler:
|
|||
|
|
lines.append(f"**{s.partei}** (Ø {s.avg_gwoe:.1f}): Geringe GWÖ-Resonanz, {(1-s.ja_quote)*100:.0f}% Ablehnung.")
|
|||
|
|
lines.append("")
|
|||
|
|
|
|||
|
|
# Kernaussagen
|
|||
|
|
lines.extend([
|
|||
|
|
"---",
|
|||
|
|
"",
|
|||
|
|
"## Kernaussagen",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
|
|||
|
|
if statistiken:
|
|||
|
|
best = statistiken[0]
|
|||
|
|
worst = statistiken[-1]
|
|||
|
|
|
|||
|
|
lines.extend([
|
|||
|
|
f"- **Höchste GWÖ-Affinität:** {best.partei} mit Ø {best.avg_gwoe:.1f}",
|
|||
|
|
f"- **Niedrigste GWÖ-Affinität:** {worst.partei} mit Ø {worst.avg_gwoe:.1f}",
|
|||
|
|
f"- **Spannweite:** {best.avg_gwoe - worst.avg_gwoe:.1f} Punkte zwischen Spitze und Ende",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
|
|||
|
|
return "\n".join(lines)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_kandidaten_ranking(conn: sqlite3.Connection) -> list[dict]:
|
|||
|
|
"""Holt Einzelranking aller Kandidat:innen."""
|
|||
|
|
cursor = conn.cursor()
|
|||
|
|
|
|||
|
|
cursor.execute("""
|
|||
|
|
SELECT
|
|||
|
|
k.vorname || ' ' || k.nachname AS name,
|
|||
|
|
k.kommune,
|
|||
|
|
k.partei_normalisiert AS partei,
|
|||
|
|
k.partei_raw,
|
|||
|
|
ROUND(AVG(b.gwoe_score), 2) AS avg_gwoe,
|
|||
|
|
ROUND(AVG(b.substanz_score), 2) AS avg_substanz,
|
|||
|
|
MAX(b.gwoe_score) AS max_gwoe,
|
|||
|
|
MIN(b.gwoe_score) AS min_gwoe,
|
|||
|
|
COUNT(b.id) AS anzahl_antworten
|
|||
|
|
FROM kandidaten k
|
|||
|
|
JOIN antworten_raw ar ON k.id = ar.kandidat_id
|
|||
|
|
JOIN bewertungen b ON ar.id = b.antwort_id
|
|||
|
|
GROUP BY k.id
|
|||
|
|
ORDER BY avg_gwoe DESC, avg_substanz DESC
|
|||
|
|
""")
|
|||
|
|
|
|||
|
|
return [dict(row) for row in cursor.fetchall()]
|
|||
|
|
|
|||
|
|
|
|||
|
|
def generate_kandidaten_report(conn: sqlite3.Connection, statistiken: list[ParteiStatistik]) -> str:
|
|||
|
|
"""Generiert den Kandidat:innen-Report."""
|
|||
|
|
|
|||
|
|
kandidaten = get_kandidaten_ranking(conn)
|
|||
|
|
|
|||
|
|
lines = [
|
|||
|
|
"# GWÖ-Wahlprüfsteine Bayern 2026 — Kandidat:innen-Ranking",
|
|||
|
|
"",
|
|||
|
|
f"*Stand: {datetime.now().strftime('%d.%m.%Y %H:%M')}*",
|
|||
|
|
"",
|
|||
|
|
"---",
|
|||
|
|
"",
|
|||
|
|
"## Gesamtranking",
|
|||
|
|
"",
|
|||
|
|
"| Rang | Name | Kommune | Partei | Ø GWÖ | Ø Substanz | Spanne |",
|
|||
|
|
"|------|------|---------|--------|-------|------------|--------|",
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
for i, k in enumerate(kandidaten, 1):
|
|||
|
|
spanne = f"{k['min_gwoe']:.1f}–{k['max_gwoe']:.1f}"
|
|||
|
|
lines.append(f"| {i} | **{k['name']}** | {k['kommune']} | {k['partei']} | {k['avg_gwoe']:.1f} | {k['avg_substanz']:.1f}/3 | {spanne} |")
|
|||
|
|
|
|||
|
|
# Kategorisierung
|
|||
|
|
vorreiter = [k for k in kandidaten if k['avg_gwoe'] >= 5]
|
|||
|
|
solide = [k for k in kandidaten if 3 <= k['avg_gwoe'] < 5]
|
|||
|
|
schwach = [k for k in kandidaten if k['avg_gwoe'] < 3]
|
|||
|
|
|
|||
|
|
lines.extend([
|
|||
|
|
"",
|
|||
|
|
"---",
|
|||
|
|
"",
|
|||
|
|
"## Kategorisierung",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
|
|||
|
|
if vorreiter:
|
|||
|
|
lines.extend([
|
|||
|
|
"### 🟢 GWÖ-Vorreiter:innen (Ø ≥ 5.0)",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
for k in vorreiter:
|
|||
|
|
lines.append(f"- **{k['name']}** ({k['kommune']}, {k['partei']}): Ø {k['avg_gwoe']:.1f}, Spanne {k['min_gwoe']:.1f}–{k['max_gwoe']:.1f}")
|
|||
|
|
lines.append("")
|
|||
|
|
|
|||
|
|
if solide:
|
|||
|
|
lines.extend([
|
|||
|
|
"### 🟡 Solide Basis (Ø 3.0–5.0)",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
for k in solide:
|
|||
|
|
lines.append(f"- **{k['name']}** ({k['kommune']}, {k['partei']}): Ø {k['avg_gwoe']:.1f}")
|
|||
|
|
lines.append("")
|
|||
|
|
|
|||
|
|
if schwach:
|
|||
|
|
lines.extend([
|
|||
|
|
"### 🔴 Wenig GWÖ-Substanz (Ø < 3.0)",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
for k in schwach:
|
|||
|
|
lines.append(f"- {k['name']} ({k['kommune']}, {k['partei']}): Ø {k['avg_gwoe']:.1f}")
|
|||
|
|
lines.append("")
|
|||
|
|
|
|||
|
|
# Bandbreite innerhalb Parteien
|
|||
|
|
lines.extend([
|
|||
|
|
"---",
|
|||
|
|
"",
|
|||
|
|
"## Bandbreite innerhalb der Parteien",
|
|||
|
|
"",
|
|||
|
|
"Die Durchschnittswerte pro Partei verdecken teils erhebliche Unterschiede zwischen einzelnen Kandidat:innen:",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
|
|||
|
|
for s in statistiken:
|
|||
|
|
partei_kandidaten = [k for k in kandidaten if k['partei'] == s.partei]
|
|||
|
|
if len(partei_kandidaten) > 1:
|
|||
|
|
scores = [k['avg_gwoe'] for k in partei_kandidaten]
|
|||
|
|
best = max(partei_kandidaten, key=lambda x: x['avg_gwoe'])
|
|||
|
|
worst = min(partei_kandidaten, key=lambda x: x['avg_gwoe'])
|
|||
|
|
spanne = max(scores) - min(scores)
|
|||
|
|
|
|||
|
|
lines.append(f"### {s.partei}")
|
|||
|
|
lines.append(f"- **Partei-Durchschnitt:** {s.avg_gwoe:.1f}")
|
|||
|
|
lines.append(f"- **Bandbreite:** {min(scores):.1f} – {max(scores):.1f} (Δ {spanne:.1f})")
|
|||
|
|
lines.append(f"- **Beste:r:** {best['name']} ({best['kommune']}) mit Ø {best['avg_gwoe']:.1f}")
|
|||
|
|
lines.append(f"- **Schwächste:r:** {worst['name']} ({worst['kommune']}) mit Ø {worst['avg_gwoe']:.1f}")
|
|||
|
|
lines.append("")
|
|||
|
|
elif len(partei_kandidaten) == 1:
|
|||
|
|
k = partei_kandidaten[0]
|
|||
|
|
lines.append(f"### {s.partei}")
|
|||
|
|
lines.append(f"- Nur 1 Kandidat:in: {k['name']} ({k['kommune']}) mit Ø {k['avg_gwoe']:.1f}")
|
|||
|
|
lines.append("")
|
|||
|
|
|
|||
|
|
return "\n".join(lines)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def generate_recommendation(statistiken: list[ParteiStatistik], conn: sqlite3.Connection) -> str:
|
|||
|
|
"""Generiert die begründete Wahlempfehlung."""
|
|||
|
|
|
|||
|
|
kandidaten = get_kandidaten_ranking(conn)
|
|||
|
|
vorreiter = [k for k in kandidaten if k['avg_gwoe'] >= 5]
|
|||
|
|
|
|||
|
|
# Berechne Gesamtstatistik
|
|||
|
|
alle_gwoe = [k['avg_gwoe'] for k in kandidaten]
|
|||
|
|
alle_substanz = [k['avg_substanz'] for k in kandidaten]
|
|||
|
|
|
|||
|
|
lines = [
|
|||
|
|
"# GWÖ-Wahlempfehlung Bayern 2026",
|
|||
|
|
"",
|
|||
|
|
"*Basierend auf der Analyse der Wahlprüfstein-Antworten*",
|
|||
|
|
"",
|
|||
|
|
"---",
|
|||
|
|
"",
|
|||
|
|
"## Methodik",
|
|||
|
|
"",
|
|||
|
|
"Diese Empfehlung basiert auf:",
|
|||
|
|
"- GWÖ-Score (0-10) nach Matrix 2.0 für Gemeinden",
|
|||
|
|
"- Substanz der Antworten (konkrete Maßnahmen vs. Floskeln)",
|
|||
|
|
"- Zustimmungsquote zu den 6 GWÖ-Kernfragen",
|
|||
|
|
"",
|
|||
|
|
"---",
|
|||
|
|
"",
|
|||
|
|
"## Übergreifende Beobachtungen",
|
|||
|
|
"",
|
|||
|
|
"### Das Substanz-Problem",
|
|||
|
|
"",
|
|||
|
|
f"Von {len(kandidaten)} Kandidat:innen erreichen nur **{len(vorreiter)}** einen GWÖ-Durchschnitt ≥ 5.0.",
|
|||
|
|
"",
|
|||
|
|
"Die Analyse zeigt ein strukturelles Problem: **Viele Ja-Antworten ohne konkrete Maßnahmen.**",
|
|||
|
|
"",
|
|||
|
|
f"- **Ø GWÖ-Score aller Kandidat:innen:** {sum(alle_gwoe)/len(alle_gwoe):.1f}/10",
|
|||
|
|
f"- **Ø Substanz-Score:** {sum(alle_substanz)/len(alle_substanz):.1f}/3",
|
|||
|
|
f"- **Ja-Quote:** hoch (85-100% bei den meisten Parteien)",
|
|||
|
|
f"- **Aber:** Konkrete Umsetzungsideen fehlen häufig",
|
|||
|
|
"",
|
|||
|
|
"Typisches Muster: *\"Ja\"* ohne Erläuterung oder mit Floskeln wie *\"Gespräche führen\"*, *\"Unterstützung anbieten\"*.",
|
|||
|
|
"",
|
|||
|
|
"### Parteilabel ≠ Kandidat:innen-Qualität",
|
|||
|
|
"",
|
|||
|
|
"Die Bandbreite *innerhalb* der Parteien ist oft größer als *zwischen* den Parteien:",
|
|||
|
|
"",
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
# Beispiele für Bandbreite
|
|||
|
|
for s in statistiken:
|
|||
|
|
partei_kandidaten = [k for k in kandidaten if k['partei'] == s.partei]
|
|||
|
|
if len(partei_kandidaten) > 1:
|
|||
|
|
scores = [k['avg_gwoe'] for k in partei_kandidaten]
|
|||
|
|
spanne = max(scores) - min(scores)
|
|||
|
|
if spanne >= 3:
|
|||
|
|
best = max(partei_kandidaten, key=lambda x: x['avg_gwoe'])
|
|||
|
|
worst = min(partei_kandidaten, key=lambda x: x['avg_gwoe'])
|
|||
|
|
lines.append(f"- **{s.partei}:** {worst['name']} ({worst['avg_gwoe']:.1f}) bis {best['name']} ({best['avg_gwoe']:.1f}) — Δ {spanne:.1f} Punkte!")
|
|||
|
|
|
|||
|
|
lines.extend([
|
|||
|
|
"",
|
|||
|
|
"---",
|
|||
|
|
"",
|
|||
|
|
"## Wahlempfehlung",
|
|||
|
|
"",
|
|||
|
|
"### Keine pauschale Parteiempfehlung möglich",
|
|||
|
|
"",
|
|||
|
|
"**Für Bayern können wir keine übergreifende Wahlempfehlung auf Parteiebene geben.**",
|
|||
|
|
"",
|
|||
|
|
"Die Unterschiede zwischen einzelnen Kandidat:innen derselben Partei sind zu groß. ",
|
|||
|
|
"Ein Grünen-Kandidat kann GWÖ-Vorreiter sein, während ein anderer kaum Substanz liefert. ",
|
|||
|
|
"Das gleiche gilt für ÖDP, Freie Wähler und andere.",
|
|||
|
|
"",
|
|||
|
|
"**→ Empfehlung: Prüfen Sie die konkreten Kandidat:innen in Ihrer Kommune!**",
|
|||
|
|
"",
|
|||
|
|
"Siehe dazu: [Kandidat:innen-Ranking](kandidaten-ranking.md)",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
|
|||
|
|
if vorreiter:
|
|||
|
|
lines.extend([
|
|||
|
|
"### 🟢 GWÖ-Vorreiter:innen (individuell empfehlenswert)",
|
|||
|
|
"",
|
|||
|
|
"Diese Kandidat:innen zeigen überdurchschnittliches GWÖ-Engagement:",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
for k in vorreiter:
|
|||
|
|
lines.append(f"- **{k['name']}** ({k['kommune']}, {k['partei']}): Ø {k['avg_gwoe']:.1f}")
|
|||
|
|
lines.append("")
|
|||
|
|
|
|||
|
|
# Eingeschränkt
|
|||
|
|
eingeschraenkt = [s for s in statistiken if 3 <= s.avg_gwoe < 5]
|
|||
|
|
if eingeschraenkt:
|
|||
|
|
lines.extend([
|
|||
|
|
"### ⚠️ Parteien mit partieller Übereinstimmung",
|
|||
|
|
"",
|
|||
|
|
"Partei-Durchschnitt im Mittelfeld — individuelle Prüfung empfohlen:",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
for s in eingeschraenkt:
|
|||
|
|
lines.append(f"- **{s.partei}** (Ø {s.avg_gwoe:.1f})")
|
|||
|
|
lines.append("")
|
|||
|
|
|
|||
|
|
# Nicht empfohlen
|
|||
|
|
nicht_empfohlen = [s for s in statistiken if s.avg_gwoe < 3]
|
|||
|
|
if nicht_empfohlen:
|
|||
|
|
lines.extend([
|
|||
|
|
"### ❌ Geringe GWÖ-Resonanz",
|
|||
|
|
"",
|
|||
|
|
"Diese Parteien zeigen im Durchschnitt wenig GWÖ-Affinität:",
|
|||
|
|
"",
|
|||
|
|
])
|
|||
|
|
for s in nicht_empfohlen:
|
|||
|
|
lines.append(f"- **{s.partei}** (Ø {s.avg_gwoe:.1f})")
|
|||
|
|
lines.append("")
|
|||
|
|
|
|||
|
|
lines.extend([
|
|||
|
|
"---",
|
|||
|
|
"",
|
|||
|
|
"## Fazit",
|
|||
|
|
"",
|
|||
|
|
"Die bayerischen Kommunalwahlen 2026 zeigen: **GWÖ-Unterstützung ist Sache einzelner Personen, nicht ganzer Parteien.**",
|
|||
|
|
"",
|
|||
|
|
"Wer GWÖ-affine Bürgermeister:innen wählen möchte, sollte:",
|
|||
|
|
"1. Das Kandidat:innen-Ranking konsultieren",
|
|||
|
|
"2. Die konkreten Antworten der lokalen Kandidat:innen lesen",
|
|||
|
|
"3. Bei Interesse nachfragen: *\"Welche konkreten Maßnahmen planen Sie?\"*",
|
|||
|
|
"",
|
|||
|
|
"---",
|
|||
|
|
"",
|
|||
|
|
"## Disclaimer",
|
|||
|
|
"",
|
|||
|
|
"Diese Empfehlung bezieht sich ausschließlich auf die Übereinstimmung mit GWÖ-Werten ",
|
|||
|
|
"und ersetzt keine umfassende politische Bewertung. Die Analyse basiert auf den ",
|
|||
|
|
"freiwilligen Antworten der Kandidat:innen auf die ECOnGOOD-Wahlprüfsteine.",
|
|||
|
|
"",
|
|||
|
|
f"*Erstellt: {datetime.now().strftime('%d.%m.%Y')}*"
|
|||
|
|
])
|
|||
|
|
|
|||
|
|
return "\n".join(lines)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
import argparse
|
|||
|
|
|
|||
|
|
parser = argparse.ArgumentParser(description='GWÖ-Wahlprüfsteine Aggregator')
|
|||
|
|
parser.add_argument('--db', type=Path, default=Path(__file__).parent / 'wahlpruefsteine.db',
|
|||
|
|
help='Pfad zur SQLite-Datenbank')
|
|||
|
|
parser.add_argument('--output', type=Path, default=Path(__file__).parent / 'output',
|
|||
|
|
help='Ausgabeverzeichnis')
|
|||
|
|
|
|||
|
|
args = parser.parse_args()
|
|||
|
|
|
|||
|
|
# Output-Verzeichnis erstellen
|
|||
|
|
args.output.mkdir(parents=True, exist_ok=True)
|
|||
|
|
|
|||
|
|
# Datenbank öffnen
|
|||
|
|
conn = sqlite3.connect(args.db)
|
|||
|
|
conn.row_factory = sqlite3.Row
|
|||
|
|
|
|||
|
|
# Statistiken holen
|
|||
|
|
print("Lade Statistiken...")
|
|||
|
|
statistiken = get_partei_statistiken(conn)
|
|||
|
|
fragen_stats = get_fragen_statistiken(conn)
|
|||
|
|
|
|||
|
|
if not statistiken:
|
|||
|
|
print("FEHLER: Keine Bewertungen in der Datenbank. Erst analyzer.py ausführen!")
|
|||
|
|
return 1
|
|||
|
|
|
|||
|
|
print(f"Gefunden: {len(statistiken)} Parteien/Gruppen")
|
|||
|
|
|
|||
|
|
# Reports generieren
|
|||
|
|
print("Generiere Reports...")
|
|||
|
|
|
|||
|
|
# 1. Partei-Report
|
|||
|
|
partei_report = generate_partei_report(statistiken, fragen_stats, conn)
|
|||
|
|
partei_path = args.output / "partei-auswertung.md"
|
|||
|
|
partei_path.write_text(partei_report)
|
|||
|
|
print(f" → {partei_path}")
|
|||
|
|
|
|||
|
|
# 2. Parteienlandschaft
|
|||
|
|
landscape_report = generate_landscape_report(statistiken)
|
|||
|
|
landscape_path = args.output / "parteienlandschaft.md"
|
|||
|
|
landscape_path.write_text(landscape_report)
|
|||
|
|
print(f" → {landscape_path}")
|
|||
|
|
|
|||
|
|
# 3. Kandidat:innen-Ranking
|
|||
|
|
kandidaten_report = generate_kandidaten_report(conn, statistiken)
|
|||
|
|
kandidaten_path = args.output / "kandidaten-ranking.md"
|
|||
|
|
kandidaten_path.write_text(kandidaten_report)
|
|||
|
|
print(f" → {kandidaten_path}")
|
|||
|
|
|
|||
|
|
# 4. Wahlempfehlung
|
|||
|
|
recommendation = generate_recommendation(statistiken, conn)
|
|||
|
|
rec_path = args.output / "wahlempfehlung.md"
|
|||
|
|
rec_path.write_text(recommendation)
|
|||
|
|
print(f" → {rec_path}")
|
|||
|
|
|
|||
|
|
conn.close()
|
|||
|
|
print("\nFertig!")
|
|||
|
|
|
|||
|
|
return 0
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
exit(main())
|