import sys
import glob
import h5py
import numpy as np
from pycbc.inference.io import loadfile
from pycbc.workflow import WorkflowConfigParser
from pycbc.inference import models

def glob_to_name(i):
	return i.split('_')[1]+'_'+i.split('_')[2][:6]

for i in glob.glob('./*.hdf'):
	print(i)
	fp = loadfile(i,'r')
	cp = fp.read_config_file()
	psds = fp.read_psds()
	data = fp.read_data()
	samples = fp.read_samples(list(fp['samples'].keys()))
	print(samples)
	tt=samples['loglikelihood']
	argumnt=tt.argmax()
	##reading the maxL params
	model = models.read_from_config(cp, data=data, psds=psds)
	maxl_params = {'final_mass': samples['final_mass'][argumnt], 'final_spin': samples['final_spin'][argumnt],
              'inclination' : samples['inclination'][argumnt], 'amp220':10**samples['logamp220'][argumnt],
              'phi220':samples['phi220'][argumnt], 'amp221':samples['amp221'][argumnt],'phi221':samples['phi221'][argumnt]}
	model.update(**maxl_params)
	print(model.loglr)