def save_vis_case_duration_graph(log: Union[EventLog, pd.DataFrame],
file_path: str):
"""
Saves the case duration graph in the specified path
Parameters
----------------
log
Log object
file_path
Destination path
"""
if type(log) not in [pd.DataFrame, EventLog, EventStream]:
raise Exception(
"the method can be applied only to a traditional event log!")
if check_is_pandas_dataframe(log):
check_pandas_dataframe_columns(log)
from pm4py.statistics.traces.generic.pandas import case_statistics
graph = case_statistics.get_kde_caseduration(
log, parameters=get_properties(log))
else:
from pm4py.statistics.traces.generic.log import case_statistics
graph = case_statistics.get_kde_caseduration(
log, parameters=get_properties(log))
format = os.path.splitext(file_path)[1][1:]
from pm4py.visualization.graphs import visualizer as graphs_visualizer
graph_vis = graphs_visualizer.apply(
graph[0],
graph[1],
variant=graphs_visualizer.Variants.CASES,
parameters={"format": format})
graphs_visualizer.save(graph_vis, file_path)
def view_case_duration_graph(log: Union[EventLog, pd.DataFrame],
format: str = "png"):
"""
Visualizes the case duration graph
Parameters
-----------------
log
Log object
format
Format of the visualization (png, svg, ...)
"""
if type(log) not in [pd.DataFrame, EventLog, EventStream]:
raise Exception(
"the method can be applied only to a traditional event log!")
if check_is_pandas_dataframe(log):
check_pandas_dataframe_columns(log)
from pm4py.statistics.traces.generic.pandas import case_statistics
graph = case_statistics.get_kde_caseduration(
log, parameters=get_properties(log))
else:
from pm4py.statistics.traces.generic.log import case_statistics
graph = case_statistics.get_kde_caseduration(
log, parameters=get_properties(log))
from pm4py.visualization.graphs import visualizer as graphs_visualizer
graph_vis = graphs_visualizer.apply(
graph[0],
graph[1],
variant=graphs_visualizer.Variants.CASES,
parameters={"format": format})
graphs_visualizer.view(graph_vis)
def test_case_statistics(self):
from pm4py.statistics.traces.generic.pandas import case_statistics
df = self.get_dataframe()
case_statistics.get_cases_description(df)
case_statistics.get_variants_df(df)
case_statistics.get_variant_statistics(df)
#case_statistics.get_variant_statistics_with_case_duration(df)
case_statistics.get_events(df, "N77802")
case_statistics.get_variants_df_with_case_duration(df)
case_statistics.get_variants_df_and_list(df)
case_statistics.get_kde_caseduration(df)
def test_dfCasedurationPlotSemilogx(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
df = pd.read_csv(os.path.join("input_data", "receipt.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
x, y = pd_case_statistics.get_kde_caseduration(df)
json = pd_case_statistics.get_kde_caseduration_json(df)
del json
if True:
# ignore this part in true PowerBI executions
import os
import pandas as pd
from pm4py.objects.log.util import dataframe_utils
log_path = os.path.join("..", "..", "tests", "input_data", "running-example.csv")
dataset = pd.read_csv(log_path)
dataset = dataframe_utils.convert_timestamp_columns_in_df(dataset)
import pandas as pd
# this part is required because the dataframe provided by PowerBI has strings
dataset["time:timestamp"] = pd.to_datetime(dataset["time:timestamp"])
dataset = dataset.sort_values("time:timestamp")
from pm4py.statistics.traces.generic.pandas import case_statistics
from pm4py.visualization.graphs import visualizer as graphs_visualizer
x_cases, y_cases = case_statistics.get_kde_caseduration(dataset)
graph_cases = graphs_visualizer.apply(x_cases, y_cases, variant=graphs_visualizer.Variants.CASES,
parameters={graphs_visualizer.Variants.CASES.value.Parameters.FORMAT: "png"})
graphs_visualizer.matplotlib_view(graph_cases)