ensemble_hic/analysis_functions.py
def load_samples_from_cfg(config_file, burnin=35000):
    """Loads results of a simulation using a config file

    This returns posterior samples from a simulation given a config file.

    :param config_file: path to config file
    :type config_file: str

    :param burnin: number of MCMC samples to be discarded as burnin
    :type burnin: int

    :returns: posterior samples
    :rtype: :class:`numpy.ndarray`
    """

    from .setup_functions import parse_config_file

    cfg = parse_config_file(config_file)
    output_folder = cfg['general']['output_folder']
    n_beads = int(cfg['general']['n_beads'])
    n_structures = int(cfg['general']['n_structures'])
    n_samples = int(cfg['replica']['n_samples'])
    samples = load_sr_samples(output_folder + 'samples/',
                              int(cfg['replica']['n_replicas']),
                              n_samples,
                              int(cfg['replica']['samples_dump_interval']),
                              burnin)

    return samples
Loads results of a simulation using a config file

    This returns posterior samples from a simulation given a config file.

    :param config_file: path to config file
    :type config_file: str

    :param burnin: number of MCMC samples to be discarded as burnin
    :type burnin: int

    :returns: posterior samples
    :rtype: :class:`numpy.ndarray`
    
Loads results of a simulation using a config file
This returns posterior samples from a simulation given a config file.
Loads results of a simulation using a config file This returns posterior samples from a simulation given a config file .
def load_samples_from_cfg(config_file, burnin=35000):
    from .setup_functions import parse_config_file
    cfg = parse_config_file(config_file)
    output_folder = cfg['general']['output_folder']
    n_beads = int(cfg['general']['n_beads'])
    n_structures = int(cfg['general']['n_structures'])
    n_samples = int(cfg['replica']['n_samples'])
    samples = load_sr_samples(output_folder + 'samples/',
                              int(cfg['replica']['n_replicas']),
                              n_samples,
                              int(cfg['replica']['samples_dump_interval']),
                              burnin)
    return samples
def load_samples_from_cfg ( config_file , burnin = 35000 ) : from . setup_functions import parse_config_file cfg = parse_config_file ( config_file ) output_folder = cfg [ 'general' ] [ 'output_folder' ] n_beads = int ( cfg [ 'general' ] [ 'n_beads' ] ) n_structures = int ( cfg [ 'general' ] [ 'n_structures' ] ) n_samples = int ( cfg [ 'replica' ] [ 'n_samples' ] ) samples = load_sr_samples ( output_folder + 'samples/' , int ( cfg [ 'replica' ] [ 'n_replicas' ] ) , n_samples , int ( cfg [ 'replica' ] [ 'samples_dump_interval' ] ) , burnin ) return samples
Loads results of a simulation using a config file
This returns posterior samples from a simulation given a config file.
Loads results of a simulation using a config file This returns posterior samples from a simulation given a config file .
"""Loads results of a simulation using a config file

    This returns posterior samples from a simulation given a config file.

    :param config_file: path to config file
    :type config_file: str

    :param burnin: number of MCMC samples to be discarded as burnin
    :type burnin: int

    :returns: posterior samples
    :rtype: :class:`numpy.ndarray`
    """
None
:class:`numpy.ndarray`
config_file
path to config file
path to config file
burnin
number of MCMC samples to be discarded as burnin
number of MCMC samples to be discarded as burnin