Scruff AI
34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
import pandas as pd, json
|
|
from collections import Counter
|
|
|
|
df = pd.read_parquet('/mnt/d/Resonance_Engine/traj/replay_3month_20260608_144055/replay_events.parquet')
|
|
unk = df[df['query_reason'] == 'event:?'].copy()
|
|
print('event:? rows:', len(unk))
|
|
print(unk[['minute','mid_price','regime_product','asymmetry']].head(10).to_string())
|
|
print()
|
|
kinds = []
|
|
for ev_str in unk['events_fired']:
|
|
try:
|
|
evs = json.loads(ev_str)
|
|
for e in evs:
|
|
kinds.append(e.get('kind','unknown'))
|
|
except:
|
|
kinds.append('parse_error')
|
|
print('Event kinds:', Counter(kinds))
|
|
print()
|
|
chron = []
|
|
with open('/mnt/d/Resonance_Engine/traj/replay_3month_20260608_144055/fractonaut_replay_chronicle.jsonl') as f:
|
|
for line in f:
|
|
line = line.strip()
|
|
if line:
|
|
try: chron.append(json.loads(line))
|
|
except: pass
|
|
unk_mins = set(unk['minute'].tolist())
|
|
unk_chron = [c for c in chron if c.get('minute') in unk_mins]
|
|
print('Chronicle entries for event:? minutes:', len(unk_chron))
|
|
for c in unk_chron[:3]:
|
|
resp = c.get('response','')
|
|
print('---')
|
|
print('minute=' + str(c.get('minute')) + ' rp=' + str(round(c.get('regime_product',0),4)))
|
|
print(resp[:500])
|