from pm4py.util import constants, pandas_utils from pm4py.algo.discovery.correlation_mining import algorithm as correlation_miner from pm4py.objects.log.util import dataframe_utils from pm4py.statistics.service_time.pandas import get as soj_time_get from pm4py.statistics.start_activities.pandas import get as sa_get from pm4py.statistics.end_activities.pandas import get as ea_get from examples import examples_conf import importlib.util def execute_script(): df = pandas_utils.read_csv("../tests/input_data/interval_event_log.csv") df = dataframe_utils.convert_timestamp_columns_in_df(df, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT) act_count = dict(df["concept:name"].value_counts()) parameters = {} parameters[constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY] = "start_timestamp" parameters[constants.PARAMETER_CONSTANT_TIMESTAMP_KEY] = "time:timestamp" parameters["format"] = examples_conf.TARGET_IMG_FORMAT start_activities = sa_get.get_start_activities(df, parameters=parameters) end_activities = ea_get.get_end_activities(df, parameters=parameters) parameters["start_activities"] = start_activities parameters["end_activities"] = end_activities soj_time = soj_time_get.apply(df, parameters=parameters) dfg, performance_dfg = correlation_miner.apply(df, variant=correlation_miner.Variants.CLASSIC, parameters=parameters) if importlib.util.find_spec("grapviz"): from pm4py.visualization.dfg import visualizer as dfg_vis gviz_freq = dfg_vis.apply(dfg, activities_count=act_count, serv_time=soj_time, variant=dfg_vis.Variants.FREQUENCY, parameters=parameters) dfg_vis.view(gviz_freq) gviz_perf = dfg_vis.apply(performance_dfg, activities_count=act_count, serv_time=soj_time, variant=dfg_vis.Variants.PERFORMANCE, parameters=parameters) dfg_vis.view(gviz_perf) if __name__ == "__main__": execute_script()