Scruff AI
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
"""Survey Fractonaut output for interesting/distinctive content."""
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import json, sys, re
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from collections import Counter
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def load(p):
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out = []
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with open(p) as f:
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for line in f:
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line = line.strip()
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if not line: continue
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try: out.append(json.loads(line))
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except: pass
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return out
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def text_of(e):
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return (e.get("response") or e.get("observation") or e.get("error") or "").strip()
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paths = sys.argv[1:] or [
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"/mnt/d/Resonance_Engine/traj/regime_20260608T133638/fractonaut_observations.jsonl",
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"/mnt/d/Resonance_Engine/fractonaut_chronicle.jsonl",
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]
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for p in paths:
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print("=" * 78)
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print(f"FILE: {p}")
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print("=" * 78)
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es = load(p)
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print(f"entries: {len(es)}")
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texts = [text_of(e) for e in es]
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texts = [t for t in texts if t]
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print(f"non-empty: {len(texts)}")
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lens = sorted(len(t) for t in texts)
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if not lens:
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print("(no text)\n"); continue
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print(f"length min/med/max: {lens[0]} / {lens[len(lens)//2]} / {lens[-1]}")
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boiler = sum(1 for t in texts if re.match(r"^[Aa]symmetry", t))
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idle = sum(1 for t in texts if t.lower().startswith("idle"))
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cycle_ref = sum(1 for t in texts if "cycle 11943500" in t)
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market_words = sum(1 for t in texts if re.search(r"\b(market|trade|buy|sell|price|volatility|liquidity|order|volume|spike|absorb|bid|ask|flow)\b", t, re.I))
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pattern_words = sum(1 for t in texts if re.search(r"\b(resembl|similar|recall|earlier|history|like .*(before))\b", t, re.I))
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print(f"start with 'asymmetry': {boiler}/{len(texts)}")
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print(f"start with 'idle': {idle}/{len(texts)}")
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print(f"reference cycle 11943500: {cycle_ref}/{len(texts)}")
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print(f"mention market/trade words: {market_words}/{len(texts)}")
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print(f"mention pattern/recall: {pattern_words}/{len(texts)}")
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pairs = sorted(zip([len(t) for t in texts], texts), reverse=True)
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print("\n--- 5 longest responses ---")
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for L, t in pairs[:5]:
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print(f"[{L} chars] {t[:800]}")
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print()
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novel = [t for t in texts if not re.match(r"^[Aa]symmetry", t) and not t.lower().startswith("idle")]
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print(f"\n--- {len(novel)} non-boilerplate (showing 10) ---")
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for t in novel[:10]:
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print(f" - {t[:400]}")
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words = Counter()
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for t in texts:
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for w in re.findall(r"[a-zA-Z]+", t.lower()):
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if len(w) >= 5: words[w] += 1
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print("\n--- top 20 words (len>=5) ---")
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for w, c in words.most_common(20):
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print(f" {c:5d} {w}")
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print()
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