Archive MaleCNS research sources, provenance, bibliography and immutable data inventory

2026-09-29 03:38:32 +08:00
commit 3981628ff8
682 changed files with 161475 additions and 0 deletions
@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+"""Targeted GPU replays of the two Phase 4N/4M eligibility discrepancies."""
+from __future__ import annotations
+
+import hashlib
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import torch
+
+from phase4n import load_config
+from phase4i import Validator
+from phase4j import apply_v2
+from phase4l import OdorSimulator,pn_drive
+from phase4m import load_odor_inputs
+from phase4o_a import EXP,ROOT,save
+
+
+def main():
+    ncfg,mcfg,cfg,cfgpath=load_config(ROOT/'models/male-cns-lif/credit-assignment-v1/phase4n.yaml')
+    history=pd.read_parquet(Path(mcfg['experiment_root'])/'efficacy-overlay/update-history.parquet')
+    by={(int(s),int(p)):g for (s,p),g in history.groupby(['train_seed','pairing'])}
+    v=Validator(cfg,cfgpath,EXP);sim=OdorSimulator(v,cfg,EXP)
+    panel,vectors,selection,mapping=load_odor_inputs(cfg)
+    oid=selection['ODOR_A']['odor_id']
+    pn=pn_drive(oid,vectors[oid],mapping,selection['glomeruli_order'],str(mcfg['protocol']['input_mode']),float(np.median(panel.mapped_response_sum)),cfg)[:2]
+    target=str(mcfg['protocol']['target_compartment_verbatim'])
+    dan=v.dan_targets(str(mcfg['protocol']['matched_dan_type']),target)
+    lookup=np.full(v.n,-1,np.int32);lookup[v.kc]=np.arange(len(v.kc),dtype=np.int32)
+    loc=lookup[v.cand.source_neuron_idx.to_numpy(np.int32)]
+    eid_to_local=pd.Series(np.arange(len(v.edge_ids),dtype=np.int32),index=v.edge_ids.astype(np.int64))
+    rows=[]
+    for seed,pairing in [(51202,40),(51203,39)]:
+        delta=np.zeros(len(v.edge_ids),np.float32)
+        for p in range(1,pairing):
+            rec=by[(seed,p)]
+            local=eid_to_local.loc[rec.edge_id.to_numpy(np.int64)].to_numpy(np.int32)
+            frozen_trace=np.zeros(len(v.kc),np.float32)
+            frozen_trace[loc[local]]=rec.eligibility.to_numpy(np.float32)
+            delta,_=apply_v2(v,frozen_trace,target,1.,delta,cfg)
+        rec=by[(seed,pairing)]
+        local=eid_to_local.loc[rec.edge_id.to_numpy(np.int64)].to_numpy(np.int32)
+        old_trace=rec.eligibility.to_numpy(np.float32)
+        for repeat in range(15):
+            run=sim.run_rates(*pn,seed+pairing,float(mcfg['protocol']['duration_ms']),delta=delta,dan_targets=dan,
+                              dan_pulses=[float(mcfg['protocol']['dan_time_ms'])],collect_kc_spikes=True)
+            observed=run['kc_trace_at_dan'][loc[local]]
+            diff=np.abs(observed-old_trace)
+            j=int(np.argmax(diff)) if len(diff) else None
+            events=run['kc_spikes'][['step','neuron_idx']].to_numpy(np.int32)
+            row={'seed':seed,'pairing':pairing,'repeat':repeat,'event_count':len(events),
+                 'event_sha256':hashlib.sha256(events.tobytes()).hexdigest(),
+                 'trace_sha256':hashlib.sha256(observed.tobytes()).hexdigest(),
+                 'max_abs_error_vs_frozen':float(diff.max()) if len(diff) else 0.,
+                 'exact_to_frozen':bool(len(diff)==0 or float(diff.max())<=2e-5),
+                 'first_largest_error_edge_id':int(rec.edge_id.iloc[j]) if j is not None else None,
+                 'first_largest_error_KC_neuron_idx':int(v.cand.source_neuron_idx.iloc[local[j]]) if j is not None else None,
+                 'observed_eligibility':float(observed[j]) if j is not None else None,
+                 'frozen_eligibility':float(old_trace[j]) if j is not None else None,
+                 'wall_seconds':run['wall_seconds']}
+            rows.append(row)
+            print(json.dumps({'seed':seed,'pairing':pairing,'repeat':repeat,'exact':row['exact_to_frozen'],
+                              'event_sha256':row['event_sha256'][:12]}),flush=True)
+            save(EXP/'eligibility-replay/targeted-gpu-repeats.json',rows)
+    save(EXP/'eligibility-replay/targeted-summary.json',{'rows':len(rows),
+         'exact_count':sum(r['exact_to_frozen'] for r in rows),
+         'distinct_event_hashes_by_trial':{f'{s}/{p}':len({r['event_sha256'] for r in rows if r['seed']==s and r['pairing']==p}) for s,p in [(51202,40),(51203,39)]},
+         'same_seed_config_and_sealed_additive_prefix':True,
+         'note':'This is an isolated 15-repeat targeted diagnostic of the two formerly discrepant trials, not a 5x500 complete brain rerun.'})
+
+
+if __name__=='__main__':main()