"""Proper normalized discrete birth-mixture density; data/candidates read only from saved files."""
import signal
signal.signal(signal.SIGALRM,lambda *_:(_ for _ in()).throw(TimeoutError('120s producer cap')));signal.alarm(120)
import argparse,math,re
import numpy as np
from scipy.special import logsumexp
from io_helpers import read_json,write_json,family
from pathlib import Path

def log_probabilities(record,rows,rho,fe_shift=0.,ratio_shift=0.):
 if not -1<rho<1:raise ValueError('Covariance must be positive definite')
 cohort=np.array(record['cohorts']);weights=cohort[:,2];weights=weights/weights.sum();means=cohort[:,5:7]+[fe_shift,ratio_shift]
 values=np.array([[row['feh'],row['mgfe']]for row in rows]);errors=np.array([[row['fehError'],row['mgfeError']]for row in rows])
 if np.any(errors<=0)or not np.all(np.isfinite(errors)):raise ValueError('Positive finite marginal errors required')
 if not np.all(np.isfinite(values)):raise ValueError('Finite measured coordinates required')
 residual=(values[:,None,:]-means[None,:,:])/errors[:,None,:]
 quadratic=(residual[:,:,0]**2-2*rho*residual[:,:,0]*residual[:,:,1]+residual[:,:,1]**2)/(1-rho*rho)
 component=-.5*quadratic-np.log(2*np.pi*errors[:,0]*errors[:,1]*math.sqrt(1-rho*rho))[:,None]
 return logsumexp(component+np.log(weights)[None,:],axis=1)
def finite_rows(data):return[row for row in data['rows']if all(row.get(key)is not None for key in ['feh','mgfe','fehError','mgfeError'])]
def main():
 parser=argparse.ArgumentParser();parser.add_argument('--records',type=Path,default=Path('results/records'));parser.add_argument('--data',type=Path,default=Path(__file__).resolve().parents[1]/'inputs/own-observations.json');parser.add_argument('--output',type=Path,default=Path('results/comparison.json'));args=parser.parse_args();data=read_json(args.data);rows=finite_rows(data)
 withheld=np.array([int(re.search(r'(\d+)$',row['id'])[1])%5==0 for row in rows]);comparisons=[]
 for p in family():
  record=read_json(args.records/(p['name']+('.npz'if(args.records/(p['name']+'.npz')).exists()else'.json.gz')));scores=[]
  for rho in [-.7,0,.7]:
   for axis in ['none','feh','mgfe']:
    for shift in ([0]if axis=='none'else [-.1,.1]):
     values=log_probabilities(record,rows,rho,shift if axis=='feh'else 0,shift if axis=='mgfe'else 0)
     scores.append({'rho':rho,'shiftAxis':axis,'shiftDex':shift,'trainingLogDensity':float(values[~withheld].sum()),'withheldLogDensity':float(values[withheld].sum()),'meanLogDensity':float(values.mean())})
  comparisons.append({'candidate':p['name'],'scores':scores})
 write_json(args.output,{'sourceKind':data['kind'],'totalRows':len(data['rows']),'finiteRows':len(rows),'trainingRows':int((~withheld).sum()),'withheldRows':int(withheld.sum()),'comparisons':comparisons,'selection':'Unitbirthmass selection; not a recoveredRGBselectionfunction','solarOffset':'Rawsourcecoords preserved; zero shift is assumed, not a demonstratedHill/Asplundharmonization','interpretation':'Conditional descriptive densities for four prescribedhistories; no optimizer, likelihoodcalibration, Bayesianmodelprobability or uniquegalaxyhistory'})
 print('PASS: normalized mixture computed from saved candidates and measurements')
if __name__=='__main__':main()