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"""Side-channel event/drive audit. Frozen run function and CUDA math unchanged."""
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from pathlib import Path
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import argparse,json,time
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import numpy as np
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import pandas as pd
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import torch
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import yaml
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import phase4p,phase4p_finalize,phase4s,phase4v
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from phase4p_revalidate import DeterministicAPLSim,door_inputs,make_drive
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from phase4r import save,digest,arrhash
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from phase4h_audit import verify_checksum
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CATS=['PN_olfactory','KC','APL','MBON','DAN','other']
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def checks(cfg):
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vc=yaml.safe_load((Path(cfg['prior_model'])/'phase4v.yaml').read_text())
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out=phase4v.checks(vc)
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for key in ('prior_root','prior_model'):
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p=Path(cfg[key]);out['V_'+key]=verify_checksum(p,p/'checksums.sha256')
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assert all(x['passed'] for x in out.values());return out
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class Observer:
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def __init__(self,rt,watch):
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self.rt=rt;self.ext=rt.ext;self.fanout=rt.canonical_fanout
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self.watch=np.asarray(watch,np.int32);self.wg=torch.as_tensor(watch,dtype=torch.int64,device=rt.device)
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self.states=[];self.steps=[];self.nodes=[];self.forced=[]
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def __getattr__(self,name):return getattr(self.ext,name)
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def state_step(self,*a):
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w=self.wg
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pre=torch.stack((a[0][w],a[1][w],a[2][w].float(),a[3][w]))
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self.ext.state_step(*a)
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post=torch.stack((a[0][w],a[1][w],a[4][w].float()))
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self.states.append(torch.cat((pre,post)).cpu().numpy())
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def force_step(self,*a):
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tg=a[4];req=a[5].bool()
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accepted=req & ~a[2][tg].bool() & (a[1][tg]==0)
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self.ext.force_step(*a)
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x=tg[accepted].cpu().numpy().astype(np.int32)
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if len(x):self.forced.extend((self.rt.step_index,int(i)) for i in x)
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def record_fanout(self,src,dst):
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x=src.cpu().numpy().astype(np.int32)
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self.nodes.append(x);self.steps.append(np.full(len(x),self.rt.step_index,np.int32))
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return self.fanout(src,dst)
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def attach(self):self.rt.ext=self;self.rt.canonical_fanout=self.record_fanout
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def detach(self):self.rt.ext=self.ext;self.rt.canonical_fanout=self.fanout
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def events(self):
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return pd.DataFrame({'step':np.concatenate(self.steps),'neuron_idx':np.concatenate(self.nodes)})
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def classify(meta):
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cls=meta.annotation_class.fillna('').astype(str);typ=meta.annotation_type.fillna('').astype(str)
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nt=meta.neurotransmitter.fillna('').astype(str).str.strip().str.lower()
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code=np.full(len(meta),5,np.int8)
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code[cls.isin(['ALPN','olfactory'])]=0;code[cls.eq('Kenyon_Cell')]=1
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code[typ.eq('APL')]=2;code[cls.eq('MBON')]=3;code[nt.eq('dopamine')]=4
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return code
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def replay(arrival,overlay,rt):
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# Explicit rounding matches CUDA __fadd_rn/__fmul_rn, not fused operations.
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f=np.float32;v=f(rt.rest);g=f(0);ref=0;out=[];vs=[];gs=[]
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for step,a in enumerate(arrival):
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g=f(f(g*rt.gd)+a);ref=max(ref-1,0)
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v=f(v+f(rt.alpha*f(g-f(v-rt.rest)))) if ref==0 else f(rt.resetv)
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fire=ref==0 and v>=rt.threshold
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if fire:v=f(rt.resetv);ref=rt.ref_steps;out.append(step)
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vs.append(v);gs.append(g)
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# Measured applied APL difference, fixed exogenous signal for isolated replay.
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g=f(np.float64(g)+overlay[step])
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return np.asarray(out,np.int32),np.asarray(vs,np.float32),np.asarray(gs,np.float32),g
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def drive_audit(cfg,exp,name,sim,observer,result,edges,code):
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rt=sim.rt;st=np.asarray(observer.states);T=len(st);events=observer.events()
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assert np.array_equal(np.bincount(events.neuron_idx,minlength=sim.n),result['counts'])
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events.to_parquet(exp/'events'/f'{name}-whole-brain.parquet',index=False)
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pd.DataFrame(observer.forced,columns=['step','neuron_idx']).to_parquet(exp/'events'/f'{name}-accepted-input.parquet',index=False)
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np.savez_compressed(exp/'states'/f'{name}-watched.npz',state=st,
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fields=np.asarray(['pre_v','pre_g','pre_ref','arrival','post_v','post_g','natural_spike']))
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# Only real events on actual incoming edges. Delay is exact, including inhibitory edges.
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arrivals=events.merge(edges,left_on='neuron_idx',right_on='source_neuron_idx',how='inner')
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arrivals=arrivals.rename(columns={'step':'emission_step'}).drop(columns='neuron_idx')
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arrivals['arrival_step']=arrivals.emission_step+rt.delay_steps
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arrivals=arrivals[arrivals.arrival_step<T].sort_values(['target_neuron_idx','arrival_step','edge_id'],kind='stable')
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arrivals.to_parquet(exp/'drive'/f'{name}-actual-edge-arrivals.parquet',index=False)
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replayrows=[];drives=[];checks_out=[]
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for wi,node in enumerate(cfg['watch_neuron_idx']):
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a=arrivals[arrivals.target_neuron_idx==node]
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seqs={int(t):g for t,g in a.groupby('arrival_step',sort=False)}
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exact_arr=np.zeros(T,np.float32);bycat=np.zeros((T,len(CATS)),np.float64)
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for t,g in seqs.items():
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weights=g.effective_weight.to_numpy(np.float32)
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exact_arr[t]=np.cumsum(weights,dtype=np.float32)[-1]
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for c,q in g.groupby('source_category'):
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bycat[t,CATS.index(c)]=q.effective_weight.to_numpy(np.float64).sum()
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queue_exact=bool(np.array_equal(exact_arr,st[:,3,wi]));assert queue_exact,'Arrival reconstruction mismatch'
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after_overlay=np.r_[st[1:,1,wi],result['final_g'][node]]
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overlay=after_overlay.astype(np.float64)-st[:,5,wi].astype(np.float64)
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base=replay(exact_arr,overlay,rt)
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expected=np.flatnonzero(st[:,6,wi]).astype(np.int32)
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eq={'queue_exact':queue_exact,'events_exact':np.array_equal(base[0],expected),
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'V_exact':np.array_equal(base[1],st[:,4,wi]),'g_exact':np.array_equal(base[2],st[:,5,wi]),
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'final_g_exact':base[3]==result['final_g'][node]}
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checks_out.append({'neuron_idx':node,**{k:bool(v) for k,v in eq.items()}});assert all(eq.values()),'CPU replay divergence'
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shadow=np.zeros(len(CATS)+1,np.float64);shadowrows=[]
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for t in range(T):
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shadow*=float(rt.gd);shadow[:len(CATS)]+=bycat[t]
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row={'step':t,'neuron_idx':node,'actual_g_before_APL':float(st[t,5,wi]),
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'shadow_residual':float(st[t,5,wi]-shadow.sum())}
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row.update({f'g_{c}':float(shadow[j]) for j,c in enumerate(CATS+['APL_overlay'])})
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shadowrows.append(row);shadow[-1]+=overlay[t]
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pd.DataFrame(shadowrows).to_parquet(exp/'drive'/f'{name}-{node}-shadow.parquet',index=False)
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for mode in cfg['isolated_replay']['modes']:
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ar=exact_arr.copy();ov=overlay.copy()
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if mode=='omit_APL_overlay':ov[:]=0
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elif mode.startswith('omit_'):
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cat=mode[5:];ar[:]=0
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for t,g in seqs.items():
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w=g.loc[g.source_category!=cat,'effective_weight'].to_numpy(np.float32)
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if len(w):ar[t]=np.cumsum(w,dtype=np.float32)[-1]
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rr=replay(ar,ov,rt);late=rr[0]>=round(cfg['late_start_ms']/rt.dt)
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replayrows.append({'trial':name,'neuron_idx':node,'mode':mode,'spikes':len(rr[0]),'late_spikes':int(late.sum()),
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'first_ms':float(rr[0][0]*rt.dt) if len(rr[0]) else None,'last_ms':float(rr[0][-1]*rt.dt) if len(rr[0]) else None,
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'max_abs_V_difference':float(np.abs(rr[1].astype(float)-base[1]).max())})
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np.savez_compressed(exp/'replay'/f'{name}-{node}-{mode}.npz',event_steps=rr[0],v=rr[1],g=rr[2])
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for period,minstep in [('whole',0),('late',round(cfg['late_start_ms']/rt.dt))]:
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aa=a[a.arrival_step>=minstep]
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for cat,g in aa.groupby('source_category'):
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w=g.effective_weight.to_numpy(float)
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drives.append({'trial':name,'neuron_idx':node,'period':period,'source_category':cat,
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'positive_arrival_sum':float(w[w>0].sum()),'negative_arrival_sum':float(w[w<0].sum()),
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'edge_event_count':len(g),'unique_sources':int(g.source_neuron_idx.nunique())})
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return checks_out,replayrows,drives,events
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def ancestry(cfg,sim,events,forced,csc,weights,code,name):
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rt=sim.rt;order,ptr=csc;by=events.sort_values(['neuron_idx','step'],kind='stable')
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times=by.step.to_numpy(np.int32);ep=np.r_[0,np.cumsum(np.bincount(by.neuron_idx,minlength=sim.n))]
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roots=set(forced);rows=[];summaries=[];gd=float(rt.gd)
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def node_info(node,prefix):
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m=sim.meta.iloc[node]
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return {prefix+'body_id':int(m.body_id),prefix+'type':None if pd.isna(m.annotation_type) else str(m.annotation_type),
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prefix+'class':None if pd.isna(m.annotation_class) else str(m.annotation_class),prefix+'category':CATS[code[node]]}
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for target in cfg['watch_neuron_idx']:
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tt=times[ep[target]:ep[target+1]]
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selected=[('first',int(tt[0]))] if len(tt) else []
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late=tt[tt>=round(cfg['late_start_ms']/rt.dt)]
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if len(late):selected.append(('first_late',int(late[0])))
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for kind,t in selected:
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node=target;path=[];status='hop_limit'
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for hop in range(cfg['ancestry']['max_hops']):
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if (t,node) in roots:status='accepted_input_root';break
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ee=order[ptr[node]:ptr[node+1]];ee=ee[weights[ee]>0]
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best=None
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for e in ee:
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src=int(sim.sources[e]);ts=times[ep[src]:ep[src+1]];j=np.searchsorted(ts,t-rt.delay_steps,side='right')-1
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if j<0:continue
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emit=int(ts[j]);arrival=emit+rt.delay_steps;score=float(weights[e])*gd**(t-arrival)
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if best is None or score>best[0]:best=(score,int(e),src,emit,arrival)
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if best is None:status='unexplained_no_positive_event_predecessor';break
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score,e,src,emit,arrival=best
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row={'trial':name,'watched_neuron_idx':target,'endpoint':kind,'backward_hop':hop,
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'source_neuron_idx':src,'target_neuron_idx':node,'source_step':emit,'arrival_step':arrival,'target_step':t,
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'edge_id':e,'raw_synapse_count':int(rt.raw_np[e]),'effective_weight':float(weights[e]),'synaptic_sign':1,
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'ranking_score':score,'arrival_age_ms':(t-arrival)*rt.dt,**node_info(src,'source_'),**node_info(node,'target_')}
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assert emit<t and arrival<=t
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path.append(row);node=src;t=emit
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rows.extend(path)
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summaries.append({'trial':name,'neuron_idx':target,'endpoint':kind,'hops':len(path),'status':status,
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'root_node':node,'root_step':t,'categories':sorted(set(x['source_category'] for x in path)),
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'claim':'ranked time-supported ancestry only; not unique cause or whole-brain necessity'})
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return rows,summaries
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def run(cfg,exp,model):
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for d in ('protocol','audit','events','states','drive','replay','ancestry','regression','performance'):(exp/d).mkdir(parents=True,exist_ok=True)
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assert not (exp/'protocol/preregistered.json').exists(),'Refusing overwrite'
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save(exp/'audit/preflight.json',checks(cfg))
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vc=yaml.safe_load((Path(cfg['prior_model'])/'phase4v.yaml').read_text())
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save(exp/'protocol/preregistered.json',{'config':cfg,'sources':{p.name:digest(p) for p in model.iterdir() if p.is_file()},
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'before_simulation':True,'selection':'all six old odors; first fixed Phase4V seed; late common KC IDs from sealed Phase4V; diagnostic not blind validation'})
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pc=yaml.safe_load((Path(cfg['runtime_root'])/'phase4p.yaml').read_text());rt=phase4p.CanonicalRuntime(pc,Path(cfg['runtime_root']),exp)
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sim=DeterministicAPLSim(rt);code=classify(sim.meta);sim.install(None);weights=rt.weights.cpu().numpy().copy()
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assert len(sim.identity)==14232 and all(sim.meta.iloc[cfg['watch_neuron_idx']].annotation_class=='Kenyon_Cell')
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fields=['neuron_idx','body_id','annotation_class','annotation_type','annotation_subclass','annotation_superclass','neurotransmitter']
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sim.meta.iloc[cfg['watch_neuron_idx']][fields].to_parquet(exp/'audit/watched-metadata.parquet',index=False)
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ids=np.flatnonzero(np.isin(rt.indices_np,cfg['watch_neuron_idx']))
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edges=pd.DataFrame({'edge_id':ids,'source_neuron_idx':sim.sources[ids],'target_neuron_idx':rt.indices_np[ids],
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'raw_synapse_count':rt.raw_np[ids],'effective_weight':weights[ids],
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'source_category':np.asarray(CATS)[code[sim.sources[ids]]]})
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edges.to_parquet(exp/'audit/real-incoming-edges.parquet',index=False)
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sm=sim.meta.iloc[np.unique(edges.source_neuron_idx)][fields].copy();sm['audit_category']=np.asarray(CATS)[code[sm.neuron_idx]]
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sm.to_parquet(exp/'audit/incoming-source-metadata.parquet',index=False)
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save(exp/'audit/classification.json',{'PN_olfactory':'annotation_class ALPN or olfactory','KC':'annotation_class Kenyon_Cell',
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'APL':'annotation_type APL','MBON':'annotation_class MBON','DAN':'neurotransmitter dopamine (not inferred PAM/PPL1 name)',
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'other':'all remaining metadata classes; not an assertion of biological unknown',
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'APL_spiking_outgoing_weights':'frozen graded overlay zeros these; applied graded effect recorded separately'})
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csc=(np.argsort(rt.indices_np,kind='stable'),np.r_[0,np.cumsum(np.bincount(rt.indices_np,minlength=sim.n))])
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oc,panel,vectors,selection,mapping=door_inputs();panel=panel[panel.split=='calibration'].reset_index(drop=True)
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target=np.unique(sim.identity.loc[sim.identity.compartment.eq(vc['target_compartment']),'source_neuron_idx'].to_numpy(np.int32))
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reg=[];replays=[];drives=[];paths=[];pathsummary=[];start=time.perf_counter();torch.cuda.reset_peak_memory_stats()
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for oi in cfg['odor_indices']:
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seed=cfg['seed'];name=f'{oi}-{seed}-offset';odor=panel.iloc[oi]
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targets,rates=make_drive(oc,panel,vectors,selection,mapping,odor.odor_id,'physiological')
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assert not set(targets)&set(cfg['watch_neuron_idx'])
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actual=vc['seed_namespace']+oi*vc['odor_seed_offset']+seed
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forced=phase4v.forcing(sim,targets,rates,actual,vc,'offset')
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observer=Observer(rt,cfg['watch_neuron_idx']);observer.attach()
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try:r=phase4v.observed(sim,targets,forced,target,vc)
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finally:observer.detach()
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old=np.load(Path(cfg['prior_root'])/'states'/f'{name}.npz')
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oldev=pd.read_parquet(Path(cfg['prior_root'])/'events'/f'{name}.parquet')
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eq={k:bool(np.array_equal(r[k],old[k])) for k in ('counts','final_v','final_g','trace')}
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eq['KC_events']=arrhash(r['kc_event_step'],r['kc_event_node'])==arrhash(oldev.step.to_numpy(),oldev.neuron_idx.to_numpy())
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eq['finite']=r['finite'];reg.append({'trial':name,'prior_V_exact':eq});save(exp/'regression/comparisons.json',reg);assert all(eq.values())
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cs,rs,ds,events=drive_audit(cfg,exp,name,sim,observer,r,edges,code)
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reg[-1]['CPU_isolated_exact']=cs;replays.extend(rs);drives.extend(ds)
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pr,ps=ancestry(cfg,sim,events,observer.forced,csc,weights,code,name);paths.extend(pr);pathsummary.extend(ps)
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if oi==cfg['repeat_odor_index']:
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again=Observer(rt,cfg['watch_neuron_idx']);again.attach()
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try:rr=phase4v.observed(sim,targets,forced,target,vc)
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finally:again.detach()
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reg[-1]['repeat_full_events_exact']=bool(events.equals(again.events()))
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reg[-1]['repeat_watched_state_exact']=bool(np.array_equal(observer.states,again.states))
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assert reg[-1]['repeat_full_events_exact'] and reg[-1]['repeat_watched_state_exact']
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save(exp/'regression/comparisons.json',reg)
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print(json.dumps({'completed_odor':oi,'cpu_replays':len(replays),'time_supported_paths':len(pathsummary)}),flush=True)
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pd.DataFrame(replays).to_parquet(exp/'replay/results.parquet',index=False)
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pd.DataFrame(drives).to_parquet(exp/'drive/summary.parquet',index=False)
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pd.DataFrame(paths).to_parquet(exp/'ancestry/event-supported-edges.parquet',index=False)
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save(exp/'ancestry/summary.json',pathsummary)
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ref=phase4s.evidence_regression(cfg,exp,rt,sim);assert ref
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save(exp/'audit/results.json',{'phase':'4W','status':'COMPLETE-CONDITIONAL-MECHANISM-AUDIT','trials':len(cfg['odor_indices']),
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'prior_V_exact':True,'CPU_replay_exact':True,'full_event_repeat_exact':True,'reference_pass':ref,
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'rooted_time_supported_paths':sum(x['status']=='accepted_input_root' for x in pathsummary),'paths':len(pathsummary),
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'whole_brain_causal_necessity_proven':False,'learning':False,'candidate':False})
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save(exp/'performance/summary.json',{'wall_seconds_excluding_build':time.perf_counter()-start,
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'peak_VRAM_bytes':torch.cuda.max_memory_allocated(),'GPU':torch.cuda.get_device_name(0),'instrumentation_overhead_included':True})
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def seal(cfg,exp,model):
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now=checks(cfg);history=phase4p_finalize.integrity();pre=json.loads((exp/'protocol/preregistered.json').read_text())
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ok=history['all_static_checks_passed'] and now==json.loads((exp/'audit/preflight.json').read_text()) and all(digest(model/n)==h for n,h in pre['sources'].items())
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save(exp/'audit/frozen-integrity.json',{'pass':ok,'history':history,'current':now});assert ok
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r=json.loads((exp/'audit/results.json').read_text());r['integrity_pass']=True;save(exp/'audit/results.json',r)
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df=pd.read_parquet(exp/'replay/results.parquet')
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lines=['# Phase 4W — late KC feedback audit','',json.dumps(r,ensure_ascii=False),'',
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'No learning or model changes. Inputs replay six old Phase4V offset trials. All frozen source artifacts remain read-only.',
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'CPU single-cell omissions hold all other recorded arrivals and the applied APL overlay fixed. They are conditional input tests, not whole-brain causal interventions.',
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'Ancestry uses real edges and actual event timing. Ranking the largest positive decayed latest-spike contribution is a heuristic, not a unique causal explanation. All inhibitory arrivals and graded APL are retained in the separate drive audit.','',
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'| neuron_idx | replay mode | mean late spikes (6 odors) |','|---:|---|---:|']
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for (node,mode),g in df.groupby(['neuron_idx','mode']):lines.append(f'| {node} | {mode} | {g.late_spikes.mean():.3f} |')
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lines += ['', 'The six reserved novel test odors remain untouched. No candidate, parameter selection, threshold/gain change, eligibility modification, training or game integration.',
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'Full evidence: drive/summary.parquet, replay/results.parquet, ancestry/event-supported-edges.parquet, regression/comparisons.json.']
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(exp/'REPORT.md').write_text('\n'.join(lines)+'\n',encoding='utf-8')
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for root in (model,exp):
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rows=[{'path':p.relative_to(root).as_posix(),'sha256':digest(p),'bytes':p.stat().st_size} for p in sorted(root.rglob('*')) if p.is_file() and p.name not in ('manifest.json','checksums.sha256')]
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save(root/'manifest.json',{'files':rows});rows.append({'path':'manifest.json','sha256':digest(root/'manifest.json')})
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(root/'checksums.sha256').write_text(''.join(x['sha256']+' '+x['path']+'\n' for x in rows))
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check=verify_checksum(root,root/'checksums.sha256');assert check['passed'];print(json.dumps(check),flush=True)
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if __name__=='__main__':
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p=argparse.ArgumentParser();p.add_argument('task',choices=['run','seal']);p.add_argument('--config',required=True);a=p.parse_args()
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cfg=yaml.safe_load(Path(a.config).read_text());(run if a.task=='run' else seal)(cfg,Path(cfg['experiment_root']),Path(cfg['model_root']))
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Digital Drosophila Archive