pm4py.algo.simulation.montecarlo.utils.replay module#

class pm4py.algo.simulation.montecarlo.utils.replay.Parameters(*values)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
TOKEN_REPLAY_VARIANT = 'token_replay_variant'#
PARAM_NUM_SIMULATIONS = 'num_simulations'#
PARAM_FORCE_DISTRIBUTION = 'force_distribution'#
PARAM_ENABLE_DIAGNOSTICS = 'enable_diagnostics'#
PARAM_DIAGN_INTERVAL = 'diagn_interval'#
PARAM_CASE_ARRIVAL_RATIO = 'case_arrival_ratio'#
PARAM_PROVIDED_SMAP = 'provided_stochastic_map'#
PARAM_MAP_RESOURCES_PER_PLACE = 'map_resources_per_place'#
PARAM_DEFAULT_NUM_RESOURCES_PER_PLACE = 'default_num_resources_per_place'#
PARAM_SMALL_SCALE_FACTOR = 'small_scale_factor'#
PARAM_MAX_THREAD_EXECUTION_TIME = 'max_thread_exec_time'#
pm4py.algo.simulation.montecarlo.utils.replay.get_map_from_log_and_net(log, net, initial_marking, final_marking, force_distribution=None, parameters=None)[source]#

Get transition stochastic distribution map given the log and the Petri net

Parameters:

  • log – Event log

  • net – Petri net

  • initial_marking – Initial marking of the Petri net

  • final_marking – Final marking of the Petri net

  • force_distribution – If provided, distribution to force usage (e.g. EXPONENTIAL)

  • parameters –

    Parameters of the algorithm, including:

    Parameters.ACTIVITY_KEY -> activity name Parameters.TIMESTAMP_KEY -> timestamp key

Returns:

Map that to each transition associates a random variable

Return type:

stochastic_map