def execute_script():
# loads the dataframe from the CSV file
csv_path = os.path.join("..", "tests", "input_data", "running-example.csv")
df = csv_import_adapter.import_dataframe_from_path(csv_path)
# calculates the Matrix Container object
mco = sna_transformer.apply(df)
# calculates the Handover of Work matrix
hw_matrix = handover_of_work.apply(mco)
# calculates the Similar Activities matrix
sim_act_matrix = similar_activities.apply(mco)
# shows the Handover of Work graph
gviz = sna_vis_factory.apply(mco, hw_matrix, parameters={"format": "svg"})
sna_vis_factory.view(gviz)
# shows the Similar Activities graph
gviz = sna_vis_factory.apply(mco,
sim_act_matrix,
parameters={
"format": "svg",
"threshold": 0.0
})
sna_vis_factory.view(gviz)
# calculates the Real Handover of Work matrix
real_hw_matrix = real_handover_of_work.apply(mco,
parameters={"format": "svg"})
gviz = sna_vis_factory.apply(mco, real_hw_matrix)
def apply(log, variant="handover", parameters=None):
"""
Gets the Social Network according to the specified metric and arc threshold
Parameters
-------------
log
Log
variant
Variant of the algorithm to use
parameters
Possible parameters of the algorithm (arc threshold)
Returns
-------------
sna
Social Network representation
"""
if parameters is None:
parameters = {}
parameters["metric_normalization"] = True
metric = sna_factory.apply(log, variant=variant, parameters=parameters)
pyvis_repr = sna_vis_factory.apply(metric,
variant="pyvis",
parameters=parameters)
return open(pyvis_repr).read()
def execute_script():
# loads the log from XES file
log_path = os.path.join("..", "tests", "input_data", "receipt.xes")
log = xes_importer.apply(log_path)
# calculates the Matrix Container object
mco = sna_transformer.apply(log)
# calculates the Handover of Work matrix
hw_matrix = handover_of_work.apply(mco)
# calculates the Similar Activities matrix
sim_act_matrix = similar_activities.apply(mco)
# shows the Handover of Work graph
gviz = sna_vis_factory.apply(mco, hw_matrix, variant="pyvis")
sna_vis_factory.view(gviz, variant="pyvis")
# shows the Similar Activities graph
gviz = sna_vis_factory.apply(mco,
sim_act_matrix,
parameters={"threshold": 0.0},
variant="pyvis")
sna_vis_factory.view(gviz, variant="pyvis")
def execute_script():
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "running-example.xes"))
hw_values = sna_factory.apply(log, variant="handover")
wt_values = sna_factory.apply(log, variant="working_together")
sub_values = sna_factory.apply(log, variant="subcontracting")
ja_values = sna_factory.apply(log, variant="jointactivities")
gviz_sub = pn_vis_factory.apply(sub_values,
variant="networkx",
parameters={"format": "svg"})
gviz_hw = pn_vis_factory.apply(hw_values, variant="pyvis")
gviz_wt = pn_vis_factory.apply(wt_values,
variant="networkx",
parameters={"format": "svg"})
gviz_ja = pn_vis_factory.apply(ja_values, variant="pyvis")
pn_vis_factory.view(gviz_sub, variant="networkx")
pn_vis_factory.view(gviz_hw, variant="pyvis")
pn_vis_factory.view(gviz_wt, variant="networkx")
pn_vis_factory.view(gviz_ja, variant="pyvis")
def work_handover():
try:
print(
"Kindly enter the name of your formatted file\n"
"Note: please include .csv file extension and make sure that input file has no blank rows or columns"
)
filename = str(input())
dataframe = csv_import_adapter.import_dataframe_from_path(filename,
sep=",")
from pm4py.objects.conversion.log import factory as conversion_factory # lib to convert csv to xes
log = conversion_factory.apply(dataframe)
from pm4py.algo.enhancement.sna import factory as sna_factory
hw_values = sna_factory.apply(log, variant="handover")
from pm4py.visualization.sna import factory as sna_vis_factory
gviz_hw_py = sna_vis_factory.apply(hw_values, variant="pyvis")
sna_vis_factory.view(gviz_hw_py, variant="pyvis")
except FileNotFoundError:
print("Please check your file name")