<s> """<STR_LIT>""" <EOL> try : <EOL> from collections import OrderedDict as od <EOL> except ImportError : <EOL> from ordereddict import OrderedDict as od <EOL> from copy import copy <EOL> from multiprocessing import Pool <EOL> import os <EOL> import warnings <EOL> import math <EOL> import io_mp <EOL> import sampler <EOL> from data import Data <EOL> def run ( cosmo , data , command_line ) : <EOL> """<STR_LIT>""" <EOL> starting_folder = command_line . IS_starting_folder <EOL> if not starting_folder : <EOL> raise io_mp . ConfigurationError ( <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> chains = [ ] <EOL> if len ( starting_folder ) == <NUM_LIT:1> : <EOL> starting_folder = starting_folder [ <NUM_LIT:0> ] <EOL> if os . path . isdir ( starting_folder ) : <EOL> for elem in os . listdir ( starting_folder ) : <EOL> if elem . find ( "<STR_LIT>" ) != - <NUM_LIT:1> : <EOL> chains . append ( elem ) <EOL> else : <EOL> chains = starting_folder <EOL> starting_folder = os . path . sep . join ( chains [ <NUM_LIT:0> ] . split ( os . path . sep ) [ : - <NUM_LIT:1> ] ) <EOL> chains = [ elem . split ( os . path . sep ) [ - <NUM_LIT:1> ] for elem in chains ] <EOL> new_experiments = recover_new_experiments ( <EOL> data , command_line , starting_folder ) <EOL> if not new_experiments : <EOL> raise io_mp . ConfigurationError ( <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> ignore_likelihood = False <EOL> data . cosmo_arguments [ '<STR_LIT>' ] = '<STR_LIT>' <EOL> try : <EOL> del data . cosmo_arguments [ '<STR_LIT>' ] <EOL> del data . cosmo_arguments [ '<STR_LIT>' ] <EOL> except KeyError : <EOL> pass <EOL> data . initialise_likelihoods ( new_experiments ) <EOL> pool = Pool ( ) <EOL> args = [ ( data , cosmo , command_line , starting_folder , <EOL> elem , ignore_likelihood ) for elem in chains ] <EOL> print '<STR_LIT>' <EOL> pool . map ( translate_chain_star , args ) <EOL> pool . close ( ) <EOL> pool . join ( ) <EOL> def recover_new_experiments ( data , command_line , starting_folder ) : <EOL> """<STR_LIT>""" <EOL> modified_command_line = copy ( command_line ) <EOL> modified_command_line . folder = starting_folder <EOL> modified_command_line . param = os . path . join ( starting_folder , '<STR_LIT>' ) <EOL> print '<STR_LIT>' <EOL> print '<STR_LIT>' <EOL> with open ( modified_command_line . param , '<STR_LIT:r>' ) as init : <EOL> for line in init : <EOL> if line . find ( '<STR_LIT>' ) != - <NUM_LIT:1> : <EOL> _ , experiments = line . split ( '<STR_LIT:=>' ) <EOL> experiments = experiments . strip ( ) <EOL> print '<STR_LIT>' <EOL> new_experiments = [ <EOL> elem for elem in data . experiments if elem not in experiments ] <EOL> print '<STR_LIT>' , <EOL> print '<STR_LIT:U+002CU+0020>' . join ( new_experiments ) <EOL> return new_experiments <EOL> def translate_chain ( data , cosmo , command_line , <EOL> starting_folder , chain_name , ignore_likelihood = False ) : <EOL> """<STR_LIT>""" <EOL> input_path = os . path . join ( starting_folder , chain_name ) <EOL> output_path = os . path . join ( command_line . folder , chain_name ) <EOL> print '<STR_LIT>' , input_path <EOL> parameter_names = data . get_mcmc_parameters ( [ '<STR_LIT>' ] ) <EOL> with open ( input_path , '<STR_LIT:r>' ) as input_chain : <EOL> with open ( output_path , '<STR_LIT:w>' ) as output_chain : <EOL> for line in input_chain : <EOL> params = line . split ( ) <EOL> if not ignore_likelihood : <EOL> loglike = - float ( params [ <NUM_LIT:1> ] ) <EOL> else : <EOL> loglike = <NUM_LIT:0> <EOL> N = float ( params [ <NUM_LIT:0> ] ) <EOL> for index , param in enumerate ( parameter_names ) : <EOL> data . mcmc_parameters [ param ] [ '<STR_LIT>' ] = float ( params [ <NUM_LIT:2> + index ] ) <EOL> data . update_cosmo_arguments ( ) <EOL> newloglike = sampler . compute_lkl ( cosmo , data ) <EOL> weight = math . exp ( newloglike ) <EOL> newloglike += loglike <EOL> sampler . accept_step ( data ) <EOL> io_mp . print_vector ( [ output_chain ] , N * weight , newloglike , data ) <EOL> print output_path , '<STR_LIT>' <EOL> def translate_chain_star ( args ) : <EOL> """<STR_LIT>""" <EOL> return translate_chain ( * args ) </s>
