Digital Drosophila Archive
76 lines
4.4 KiB
Python
76 lines
4.4 KiB
Python
"""Exactly two registered evaluation-input batches; no brain, US or learning."""
|
|
from __future__ import annotations
|
|
|
|
import hashlib
|
|
import json
|
|
from pathlib import Path
|
|
import sys
|
|
import time
|
|
|
|
import numpy as np
|
|
|
|
import boundary_plan as PLAN
|
|
import execution_authority as A
|
|
|
|
MODEL=Path(__file__).resolve().parent
|
|
LAB=MODEL.parent/'olfactory-conditioning-lab-v1'
|
|
PINS={
|
|
'conditioning_inputs.py':'19382e066e450140bb653a3661f980ffe7cc62f412020ef57df0b6e6b3cbb8ba',
|
|
'conditioning_preflight_inputs.py':'846d2141b472f18b1bb728eed4aa33876896d4b33dece364787c81204f62995f',
|
|
'evaluation_plan.py':'991c813ff3322b230947f302b12b04ec382c3b632817f94b698101e4b17efa7d',
|
|
'trial_plan.py':'6fed3f494d075339a7e5b001d43b2598640b189e038952fbd4d41200bacac587',
|
|
'seed_registry.py':'0da033b132095ec3087b5d9f64e53c2e391fa50ab0b7653b31296eeb84799b7e',
|
|
}
|
|
|
|
|
|
def save(path,value):
|
|
with Path(path).open('x',encoding='utf-8') as f:json.dump(value,f,sort_keys=True,indent=2,allow_nan=False)
|
|
|
|
|
|
def run(authority_file,authority_sha,out_dir):
|
|
A.require(sys.dont_write_bytecode and A.digest(authority_file)==authority_sha,'Use -B with external registration pin')
|
|
reg=A.read(authority_file)
|
|
A.require(reg['schema']=='conditioning-execution-registration-v1','Wrong input generation authority')
|
|
PLAN.validate_plan(reg['plan'])
|
|
for path,sha in {**reg['source_sha256'],**reg['input_sha256'],**reg['prepared_file_sha256']}.items():
|
|
A.require(A.pin(path)['sha256']==sha,'Sampler source/input drift')
|
|
for name,sha in PINS.items():
|
|
A.require(A.digest(LAB/name)==sha and reg['input_sha256'].get(str(LAB/name))==sha,'Unpinned frozen input dependency: '+name)
|
|
A.require(not any(name in sys.modules for name in ('conditioning_inputs','conditioning_preflight_inputs','evaluation_plan')),
|
|
'Sampler needs fresh scientific imports')
|
|
sys.path.insert(0,str(LAB))
|
|
import conditioning_preflight_inputs as I
|
|
import conditioning_inputs as CI
|
|
import evaluation_plan as EP
|
|
rates=I.load_rate_tables('/data/digital-drosophila')
|
|
full=reg['original_full_plan'];A.require(A.digest(full['path'])==full['sha256'],'Full seed/trial plan changed')
|
|
streams=EP._streams(A.read(full['path']))
|
|
exp=Path(authority_file).parent.parent;out=Path(out_dir)
|
|
A.require(not out.exists() and out.parent.is_dir() and out.is_relative_to(exp),'Exclusive new sampler output required')
|
|
out.mkdir();rows=[]
|
|
for cue in ('A','B'):
|
|
declared=reg['plan']['evaluation_streams'][cue];actual=streams[(PLAN.BLOCK,cue)]
|
|
for field in ('seed','seed_bits','stream_id','derivation_sha256'):
|
|
A.require(declared[field]==actual[field],'Evaluation semantic stream differs')
|
|
A.require(declared['key']==actual['semantic_key'] and actual['split']=='evaluation','Evaluation stream provenance differs')
|
|
table=rates['tables'][cue]
|
|
with np.load(exp/f'inputs/static/{cue}-rates.npz',allow_pickle=False) as saved:
|
|
A.require(all(saved[k].dtype==table[k].dtype and saved[k].shape==table[k].shape and
|
|
saved[k].tobytes()==table[k].tobytes() for k in ('targets','rates_hz')),'Registered original PN mapping changed')
|
|
started=time.perf_counter()
|
|
result=CI.generate_cs_requests(table['targets'],table['rates_hz'],seed=declared['seed'],steps=1200,
|
|
cs_start_tick=0,cs_stop_tick=1000,device='cuda:0',max_uniform_draws=1200*len(table['targets']))
|
|
with (out/f'{cue}-requests.npz').open('xb') as f:np.savez_compressed(f,**result['requests'])
|
|
save(out/f'{cue}-sampler.json',result['audit'])
|
|
rows.append(dict(cue=cue,stream=actual,requests=len(result['requests']['tick']),US_requests=0,
|
|
uniform_draws=1200*len(table['targets']),wall_seconds=time.perf_counter()-started,
|
|
saved_requests_sha256=A.digest(out/f'{cue}-requests.npz')))
|
|
pins={p.name:A.digest(p) for p in out.iterdir() if p.is_file()}
|
|
summary=dict(schema='conditioning-boundary-sampler-v1',complete=True,registration_sha256=authority_sha,
|
|
uniform_batch_calls=2,uniform_draws=sum(r['uniform_draws'] for r in rows),rows=rows,files_sha256=pins,
|
|
brain_executed=False,learning_executed=False,US_added=False,original_rate_mapping_preserved=True,
|
|
inputs_sampled_once=True,planned_retention_reuse_checkpoints_seconds=[0,1,10],
|
|
reuse_executed_here=False,seed_search=False,learning_success_claimed=False)
|
|
save(out/'sampler-receipt.json',summary)
|
|
return summary
|