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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()
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