<s> import os <EOL> import numpy as np <EOL> from montepython . likelihood_class import Likelihood <EOL> class bao ( Likelihood ) : <EOL> def __init__ ( self , path , data , command_line ) : <EOL> Likelihood . __init__ ( self , path , data , command_line ) <EOL> self . z = np . array ( [ ] , '<STR_LIT>' ) <EOL> self . data = np . array ( [ ] , '<STR_LIT>' ) <EOL> self . error = np . array ( [ ] , '<STR_LIT>' ) <EOL> self . type = np . array ( [ ] , '<STR_LIT:int>' ) <EOL> for line in open ( os . path . join ( <EOL> self . data_directory , self . file ) , '<STR_LIT:r>' ) : <EOL> if ( line . find ( '<STR_LIT:#>' ) == - <NUM_LIT:1> ) : <EOL> self . z = np . append ( self . z , float ( line . split ( ) [ <NUM_LIT:0> ] ) ) <EOL> self . data = np . append ( self . data , float ( line . split ( ) [ <NUM_LIT:1> ] ) ) <EOL> self . error = np . append ( self . error , float ( line . split ( ) [ <NUM_LIT:2> ] ) ) <EOL> self . type = np . append ( self . type , int ( line . split ( ) [ <NUM_LIT:3> ] ) ) <EOL> self . num_points = np . shape ( self . z ) [ <NUM_LIT:0> ] <EOL> def loglkl ( self , cosmo , data ) : <EOL> chi2 = <NUM_LIT:0.> <EOL> for i in range ( self . num_points ) : <EOL> da = cosmo . angular_distance ( self . z [ i ] ) <EOL> dr = self . z [ i ] / cosmo . Hubble ( self . z [ i ] ) <EOL> dv = pow ( da * da * ( <NUM_LIT:1> + self . z [ i ] ) * ( <NUM_LIT:1> + self . z [ i ] ) * dr , <NUM_LIT:1.> / <NUM_LIT> ) <EOL> if ( self . type [ i ] == <NUM_LIT:3> ) : <EOL> rs = cosmo . rs_drag ( ) * self . rs_rescale <EOL> theo = dv / rs <EOL> elif ( self . type [ i ] == <NUM_LIT:4> ) : <EOL> theo = dv <EOL> else : <EOL> raise io_mp . LikelihoodError ( <EOL> "<STR_LIT>" % self . name + <EOL> "<STR_LIT>" % self . type [ i ] + <EOL> "<STR_LIT>" % i ) <EOL> chi2 += ( ( theo - self . data [ i ] ) / self . error [ i ] ) ** <NUM_LIT:2> <EOL> lkl = - <NUM_LIT:0.5> * chi2 <EOL> return lkl </s>
