schrodinger.application.desmond.replica_dE module

class schrodinger.application.desmond.replica_dE.replica_energy(rep_number, filename=None, de_array=None)

Bases: object

__init__(rep_number, filename=None, de_array=None)

getNumber()

getRMin()

getRMax()

getFMin()

getFMax()

getRMean()

getFMean()

getRHistogram(his_min, his_max, nbins)

getFHistogram(his_min, his_max, nbins)

class schrodinger.application.desmond.replica_dE.replica_container(basename, energy_output, de_array: Optional[numpy.ndarray] = None, task_type=None, n_win=12)

Bases: object

__init__(basename, energy_output, de_array: Optional[numpy.ndarray] = None, task_type=None, n_win=12)

read_dE_Replicas()

getReplica(number)

get_nrep()

printInfo()

export()

process_bennett_log(data)

export_dE_data(data, bennett_dG, bennett_per_replica)

class schrodinger.application.desmond.replica_dE.replicas_monitor(basename, cfg, task_type)

Bases: object

__init__(basename, cfg, task_type)

get_histogram(times, data)

Converts a sparse doubled-time list into a count of the time that each replica spends in each state.

Parameters

• times (numpy array of floats) – Time series

• data (numpy array of int) – State the replica was in at each time point

Returns

Counts of occurence in each state

Return type

numpy array of int

Note: the precision of the time in the logfile can be lower than the exchange frequency and thus result in small rounding errors when computing duration. This should be resolved by dividing by the interval and rounding.

get_nrep()

export()