def save_vis_events_per_time_graph(log: Union[EventLog, pd.DataFrame],
file_path: str):
"""
Saves the events per time 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.attributes.pandas import get as attributes_get
graph = attributes_get.get_kde_date_attribute(
log, parameters=get_properties(log))
else:
from pm4py.statistics.attributes.log import get as attributes_get
graph = attributes_get.get_kde_date_attribute(
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.DATES,
parameters={"format": format})
graphs_visualizer.save(graph_vis, file_path)
def view_events_per_time_graph(log: Union[EventLog, pd.DataFrame],
format: str = "png"):
"""
Visualizes the events per time 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.attributes.pandas import get as attributes_get
graph = attributes_get.get_kde_date_attribute(
log, parameters=get_properties(log))
else:
from pm4py.statistics.attributes.log import get as attributes_get
graph = attributes_get.get_kde_date_attribute(
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.DATES,
parameters={"format": format})
graphs_visualizer.view(graph_vis)
def test_dfDateAttribute(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 = csv_import_adapter.import_dataframe_from_path(
os.path.join("input_data", "receipt.csv"))
x, y = pd_attributes_filter.get_kde_date_attribute(df)
json = pd_attributes_filter.get_kde_date_attribute_json(df)
del json
def test_dfDateAttribute(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",
"roadtraffic100traces.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
x, y = pd_attributes_filter.get_kde_date_attribute(df)
json = pd_attributes_filter.get_kde_date_attribute_json(df)
del json
def view_events_per_time_graph(log: Union[EventLog, pd.DataFrame], format: str = "png"):
"""
Visualizes the events per time graph
Parameters
-----------------
log
Log object
format
Format of the visualization (png, svg, ...)
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.attributes.pandas import get as attributes_get
graph = attributes_get.get_kde_date_attribute(log)
else:
from pm4py.statistics.attributes.log import get as attributes_get
graph = attributes_get.get_kde_date_attribute(log)
from pm4py.visualization.graphs import visualizer as graphs_visualizer
graph_vis = graphs_visualizer.apply(graph[0], graph[1], variant=graphs_visualizer.Variants.DATES,
parameters={"format": format})
graphs_visualizer.view(graph_vis)
def save_vis_events_per_time_graph(log: Union[EventLog, pd.DataFrame], file_path: str):
"""
Saves the events per time graph in the specified path
Parameters
----------------
log
Log object
file_path
Destination path
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.attributes.pandas import get as attributes_get
graph = attributes_get.get_kde_date_attribute(log)
else:
from pm4py.statistics.attributes.log import get as attributes_get
graph = attributes_get.get_kde_date_attribute(log)
format = file_path[file_path.index(".") + 1:].lower()
from pm4py.visualization.graphs import visualizer as graphs_visualizer
graph_vis = graphs_visualizer.apply(graph[0], graph[1], variant=graphs_visualizer.Variants.DATES,
parameters={"format": format})
graphs_visualizer.save(graph_vis, file_path)
if True:
# ignore this part in true PowerBI executions
from pm4py.objects.log.adapters.pandas import csv_import_adapter
dataset = csv_import_adapter.import_dataframe_from_path(
"C:/running-example.csv")
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"])
from pm4py.statistics.attributes.pandas import get as attributes_filter
from pm4py.visualization.graphs import visualizer as graphs_visualizer
# visualize events over time graph
x_dates, y_dates = attributes_filter.get_kde_date_attribute(dataset)
graph_dates = graphs_visualizer.apply(
x_dates,
y_dates,
variant=graphs_visualizer.Variants.DATES,
parameters={
graphs_visualizer.Variants.DATES.value.Parameters.FORMAT: "png"
})
graphs_visualizer.matplotlib_view(graph_dates)
def test_get_attributes(self):
from pm4py.statistics.attributes.pandas import get
df = self.get_dataframe()
get.get_attribute_values(df, "concept:name")
get.get_kde_date_attribute(df, "time:timestamp")
get.get_kde_numeric_attribute(df, "amount")
if ENABLE_TESTS:
# TEST 7: discover variants from dataframe
t0 = time.time()
pd_case_statistics.get_variants_df(roadtraffic_df)
t1 = time.time()
T7[0] = (t1 - t0)
T7[2] = math.ceil(T7[1] / (T7[0] + 0.00000001) * 1000.0)
print(
"TEST 7 - Discover variants from Pandas dataframe - %.5f s (test score: %d)"
% (T7[0], T7[2]))
if ENABLE_TESTS:
# TEST 8: discover timeframe KDE rom dataframe
t0 = time.time()
pd_attributes_get.get_kde_date_attribute(roadtraffic_df,
"time:timestamp")
t1 = time.time()
T8[0] = (t1 - t0)
T8[2] = math.ceil(T8[1] / (T8[0] + 0.00000001) * 1000.0)
print(
"TEST 8 - Discover timeframe KDE from dataframe - %.5f s (test score: %d)"
% (T8[0], T8[2]))
if ENABLE_TESTS:
# TEST 9: discover performance spectrum from dataframe
t0 = time.time()
pspectrum.apply(roadtraffic_df, ["Create Fine", "Send Fine"])
t1 = time.time()
T9[0] = (t1 - t0)
T9[2] = math.ceil(T9[1] / (T9[0] + 0.00000001) * 1000.0)
print(