n3wk
124 lines
4.7 KiB
Python
124 lines
4.7 KiB
Python
"""Merge per-episode analyses into ONE master JSON:
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data/dickshow_analysis.json
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Contents:
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meta counts, generated_at, rubric reference
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episodes full per-episode analyses, sorted by ep_num
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indexes derived views for fast lookup:
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by_person, by_tag_topic, edginess_ranking, saga_map
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"""
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import json
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import os
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from datetime import datetime, timezone
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from config import DATA_DIR
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from analyze import ANALYSIS_DIR, MASTER_JSON
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from feed import load_episodes
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def load_analyses() -> list[dict]:
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out = []
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if not os.path.isdir(ANALYSIS_DIR):
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return out
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for f in sorted(os.listdir(ANALYSIS_DIR)):
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if not f.endswith(".json"):
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continue
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try:
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with open(os.path.join(ANALYSIS_DIR, f), encoding="utf-8") as fh:
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out.append(json.load(fh))
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except (json.JSONDecodeError, OSError) as e:
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print(f"warning: skipping bad analysis file {f}: {e}")
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return out
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def aggregate() -> None:
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analyses = load_analyses()
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eps_meta = {e["ep_num"]: e for e in load_episodes() if e["ep_num"]}
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# attach runtime from episode metadata when available + verification status
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for a in analyses:
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m = eps_meta.get(a.get("ep_num"))
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if m and m.get("duration_sec"):
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a["runtime_minutes"] = round(m["duration_sec"] / 60)
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v = a.get("_verification") or {}
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a["verified"] = v.get("verdict") if isinstance(v, dict) else None
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analyses.sort(key=lambda a: (a.get("ep_num") is None,
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a.get("ep_num") or 0))
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by_person: dict[str, list] = {}
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by_topic: dict[str, list] = {}
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quotes_ranked = []
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ti_scores = []
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for a in analyses:
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epn = a.get("ep_num")
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label = f"Ep {epn}" if epn else (a.get("title") or "?")[:20]
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seen_p = set()
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seen_t = set()
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for tp in a.get("talking_points", []):
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for p in tp.get("people", []):
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key = p.lower().strip()
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seen_p.add(key)
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by_person.setdefault(key, []).append(
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{"episode": epn, "topic": tp.get("topic"),
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"ts": tp.get("start_ts")})
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topic = (tp.get("topic") or "").strip().lower()
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if topic:
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seen_t.add(topic)
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for t in sorted(seen_t):
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by_topic.setdefault(t, []).append(epn)
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for q in a.get("notable_quotes", []):
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quotes_ranked.append({
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"episode": epn, "ts": q.get("ts"), "ti": q.get("ti", 0),
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"quote": q.get("quote"), "why": q.get("why"),
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})
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tg = a.get("transgression", {})
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if isinstance(tg.get("overall_ti"), int):
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ti_scores.append({"episode": epn, "overall_ti": tg["overall_ti"],
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"sincerity": tg.get("sincerity"),
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"targets": tg.get("targets", [])})
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quotes_ranked.sort(key=lambda q: -q.get("ti", 0))
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ti_scores.sort(key=lambda s: -s.get("overall_ti", 0))
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n_verified = sum(1 for a in analyses if a.get("verified") in ("pass", "fix"))
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master = {
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"meta": {
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"generated_at": datetime.now(timezone.utc).isoformat(),
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"podcast": "The Dick Show",
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"feed": "https://thedickshow.com/feed/podcast/",
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"episodes_analyzed": len(analyses),
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"episodes_factchecked": n_verified,
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"total_runtime_minutes": sum(a.get("runtime_minutes") or 0
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for a in analyses),
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"scale": "Transgression Index 0-10, see rubric.py / README",
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"note": ("Companion file for routine opencode access. "
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"Per-episode analyst files live in data/analysis/, "
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"raw transcripts in transcripts/. Each episode carries "
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"'verified': pass|fix|null from the dossier fact-check "
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"pass (verify.py). Treat verified=null as unconfirmed."),
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},
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"episodes": analyses,
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"indexes": {
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"by_person": {k: v for k, v in sorted(by_person.items())},
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"topics_by_episode": {k: sorted(set(x for x in v if x))
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for k, v in sorted(by_topic.items())},
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"edginess_ranking": {
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"top_overall_ti": ti_scores[:25],
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"top_quotes": quotes_ranked[:50],
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},
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},
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}
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tmp = MASTER_JSON + ".tmp"
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with open(tmp, "w", encoding="utf-8") as f:
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json.dump(master, f, ensure_ascii=False, indent=1)
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os.replace(tmp, MASTER_JSON)
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print(f"master -> {MASTER_JSON} "
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f"({len(analyses)} episodes, {len(quotes_ranked)} ranked quotes)")
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if __name__ == "__main__":
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aggregate()
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