def get_dfg_visualization(self):
if self.selected_model_type == "model1":
model = mdfg_disc_factory.apply(
self.exploded_dataframe,
model_type_variant=self.selected_model_type,
node_freq_variant="type1",
edge_freq_variant="type11")
elif self.selected_model_type == "model2":
model = mdfg_disc_factory.apply(
self.exploded_dataframe,
model_type_variant=self.selected_model_type,
node_freq_variant="type21",
edge_freq_variant="type211")
elif self.selected_model_type == "model3":
model = mdfg_disc_factory.apply(
self.exploded_dataframe,
model_type_variant=self.selected_model_type,
node_freq_variant="type31",
edge_freq_variant="type11")
gviz = mdfg_vis_factory.apply(model,
parameters={
"min_node_freq":
self.selected_min_acti_count,
"min_edge_freq":
self.selected_min_edge_freq_count,
"format": "svg"
})
tfilepath = tempfile.NamedTemporaryFile(suffix='.svg')
tfilepath.close()
mdfg_vis_factory.save(gviz, tfilepath.name)
self.model_view = base64.b64encode(open(tfilepath.name,
"rb").read()).decode('utf-8')
def get_multigraph_visualization(self):
self.epsilon = float(self.epsilon)
self.noise_threshold = float(self.noise_threshold)
model = mdfg_disc_factory3.apply(self.succint_dataframe,
parameters={
"min_act_freq":
self.selected_min_acti_count,
"min_edge_freq":
self.selected_min_edge_freq_count,
"epsilon":
self.epsilon,
"noise_obj_number":
self.noise_threshold
})
gviz = mdfg_vis_factory3.apply(
model,
measure=self.selected_decoration_measure,
freq=self.selected_aggregation_measure,
projection=self.selected_projection,
parameters={
"format": "svg",
"min_act_freq": self.selected_min_acti_count,
"min_edge_freq": self.selected_min_edge_freq_count
})
tfilepath = tempfile.NamedTemporaryFile(suffix='.svg')
tfilepath.close()
mdfg_vis_factory.save(gviz, tfilepath.name)
self.model_view = base64.b64encode(open(tfilepath.name,
"rb").read()).decode('utf-8')
def get_new_visualization(self):
classifier_function = None
if self.selected_classifier == "activity":
classifier_function = lambda x: x["event_activity"]
elif self.selected_classifier == "combined":
classifier_function = lambda x: x["event_activity"] + "+" + x[
"event_objtype"]
model = mdfg_disc_factory2.apply(
self.exploded_dataframe,
classifier_function=classifier_function,
variant=self.selected_model_type,
parameters={
"min_acti_freq": self.selected_min_acti_count,
"min_edge_freq": self.selected_min_edge_freq_count
})
gviz = mdfg_vis_factory2.apply(
model,
measure=self.selected_decoration_measure,
freq=self.selected_aggregation_measure,
classifier=self.selected_classifier,
projection=self.selected_projection,
parameters={
"format": "svg",
"min_acti_freq": self.selected_min_acti_count,
"min_edge_freq": self.selected_min_edge_freq_count
})
tfilepath = tempfile.NamedTemporaryFile(suffix='.svg')
tfilepath.close()
mdfg_vis_factory.save(gviz, tfilepath.name)
self.model_view = base64.b64encode(open(tfilepath.name,
"rb").read()).decode('utf-8')
def get_petri_visualization(self):
model = petri_disc_factory.apply(self.exploded_dataframe, parameters={"min_node_freq": self.selected_min_acti_count,
"min_edge_freq": self.selected_min_edge_freq_count})
gviz = pn_vis_factory.apply(model, parameters={"format": "svg"})
tfilepath = tempfile.NamedTemporaryFile(suffix='.svg')
tfilepath.close()
mdfg_vis_factory.save(gviz, tfilepath.name)
self.model_view = base64.b64encode(open(tfilepath.name, "rb").read()).decode('utf-8')
