def execute_script():
log = pm4py.read_xes("../tests/input_data/running-example.xes")
dfg, sa, ea = pm4py.discover_dfg(log)
tree = pm4py.discover_process_tree_inductive(log)
heu_net = pm4py.discover_heuristics_net(log)
net, im, fm = pm4py.discover_petri_net_alpha(log)
bpmn = pm4py.convert_to_bpmn(tree)
ts = ts_discovery.apply(log)
x_cases, y_cases = case_statistics.get_kde_caseduration(log)
gviz1 = dfg_visualizer.apply(dfg)
gviz2 = tree_visualizer.apply(tree)
gviz3 = hn_visualizer.apply(heu_net)
gviz4 = pn_visualizer.apply(net, im, fm)
gviz5 = bpmn_visualizer.apply(bpmn)
gviz6 = ts_visualizer.apply(ts)
gviz7 = graphs_visualizer.apply(x_cases, y_cases, variant=graphs_visualizer.Variants.CASES,
parameters={graphs_visualizer.Variants.CASES.value.Parameters.FORMAT: "svg"})
print("1", len(dfg_visualizer.serialize_dot(gviz1)))
print("1", len(dfg_visualizer.serialize(gviz1)))
print("2", len(tree_visualizer.serialize_dot(gviz2)))
print("2", len(tree_visualizer.serialize(gviz2)))
print("3", len(hn_visualizer.serialize(gviz3)))
print("4", len(pn_visualizer.serialize_dot(gviz4)))
print("4", len(pn_visualizer.serialize(gviz4)))
print("5", len(bpmn_visualizer.serialize_dot(gviz5)))
print("5", len(bpmn_visualizer.serialize(gviz5)))
print("6", len(ts_visualizer.serialize_dot(gviz6)))
print("6", len(ts_visualizer.serialize(gviz6)))
print("7", len(graphs_visualizer.serialize(gviz7)))
def test_convert_to_bpmn_from_tree(self):
tree = pm4py.read_ptml("input_data/running-example.ptml")
bpmn = pm4py.convert_to_bpmn(tree)
self.assertTrue(isinstance(bpmn, BPMN))
"""
import pandas as pd
import pm4py
'''
def import_csv(file_path):
event_log = pandas.read_csv(file_path, sep=';')
event_log = pm4py.format_dataframe(event_log, case_id='case_id', activity_key='activity', timestamp_key='timestamp')
start_activities = pm4py.get_start_activities(event_log)
end_activities = pm4py.get_end_activities(event_log)
print("Start activities: {}\nEnd activities: {}".format(start_activities, end_activities))
'''
file_path = r'file'
log = pm4py.format_dataframe(pd.read_csv(file_path, sep=';'),
case_id='case_id',
activity_key='activity',
timestamp_key='timestamp')
#log = log[log['@@index']< 40]
process_tree = pm4py.discover_tree_inductive(log)
bpmn_model = pm4py.convert_to_bpmn(process_tree)
pm4py.view_bpmn(bpmn_model)
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
from pm4py.visualization.process_tree import visualizer as pt_visualizer
tree = inductive_miner.apply_tree(log)
gviz = pt_visualizer.apply(tree)
pt_visualizer.view(gviz)
log_csv_small = dataframe_utils.convert_timestamp_columns_in_df(log_csv_small)
print(log_csv_small.head())
# create xes log from data frame
log = log_converter.apply(log_csv_small)
# inductive miner
# create the process tree
tree = inductive_miner.apply_tree(log)
# viz tree
gviz = pt_visualizer.apply(tree)
pt_visualizer.view(gviz)
# bpmn & viz bpmn
bpmn_model = pm4py.convert_to_bpmn(tree)
gviz = bpmn_visualization.apply(bpmn_model)
bpmn_visualization.view(gviz)
# creatig the graph from log
dfg = dfg_discovery.apply(log)
# viz
gviz = dfg_visualization.apply(dfg, log=log, variant=dfg_visualization.Variants.FREQUENCY)
dfg_visualization.view(gviz)
## bigger process model
log_csv_big = pd.read_csv('./../ressource/process.csv')
# log_csv_process = dataframe_utils.convert_timestamp_columns_in_df(log_csv)
