def get_map_exponential_from_aggstatistics(agg_statistics, parameters=None):
"""
Get transition stochastic distribution map (exponential variable forced) given the log and the Petri net
Parameters
-----------
agg_statistics
Aggregated statistics calculated on the DFG graph and the Petri net
parameters
Parameters of the algorithm
Returns
-----------
stochastic_map
Map that to each transition associates a random variable
"""
stochastic_map = {}
if parameters is None:
parameters = {}
del parameters
for el in agg_statistics:
if type(el) is PetriNet.Transition:
rand = RandomVariable()
rand.random_variable = Exponential()
rand.random_variable.scale = agg_statistics[el]["performance"]
stochastic_map[el] = rand
return stochastic_map
def test_exponential_variable(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"
loc = 0
scale = 5
tol = 0.2
exp = Exponential(loc=loc, scale=scale)
values = exp.get_values(no_values=400)
rand = RandomVariable()
rand.calculate_parameters(values)
if not rand.get_distribution_type() == "EXPONENTIAL":
raise Exception("Expected an exponential!")
loc_r = rand.random_variable.loc
scale_r = rand.random_variable.scale
diff_value_loc = abs(loc - loc_r) / (max(abs(loc), abs(loc_r)) + 0.000001)
diff_value_scale = abs(scale - scale_r) / (max(abs(scale), abs(scale_r)) + 0.000001)
if diff_value_loc > tol or diff_value_scale > tol:
raise Exception("parameters found outside tolerance")
def test_normal_variable(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"
mu = 53
sigma = 4
tol = 0.15
norm = Normal(mu=mu, sigma=sigma)
values = norm.get_values(no_values=400)
rand = RandomVariable()
rand.calculate_parameters(values)
if not rand.get_distribution_type() == "NORMAL":
raise Exception("Excepted a normal!")
mu_r = rand.random_variable.mu
sigma_r = rand.random_variable.sigma
diff_value_mu = abs(mu - mu_r) / (max(abs(mu), abs(mu_r)))
diff_value_sigma = abs(sigma - sigma_r) / (max(abs(sigma), abs(sigma_r)))
if diff_value_mu > tol or diff_value_sigma > tol:
raise Exception("parameters found outside tolerance")
def test_uniform_variable(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"
loc = 53
scale = 32
tol = 0.15
unif = Uniform(loc=loc, scale=scale)
values = unif.get_values(no_values=400)
rand = RandomVariable()
rand.calculate_parameters(values)
if not rand.get_distribution_type() == "UNIFORM":
raise Exception("Expected an uniform!")
loc_r = rand.random_variable.loc
scale_r = rand.random_variable.scale
diff_value_loc = abs(loc - loc_r) / (max(abs(loc), abs(loc_r)))
diff_value_scale = abs(scale - scale_r) / (max(abs(scale), abs(scale_r)))
if diff_value_loc > tol or diff_value_scale > tol:
raise Exception("parameters found outside tolerance")
def test_constant0_variable_2(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"
const = Constant0()
values = [0.0000001, 0.0000001, 0.0000002]
loglikeli = const.calculate_loglikelihood(values)
if abs(loglikeli) < 10000000:
raise Exception("problem in managing constant variables")
rand = RandomVariable()
rand.calculate_parameters(values)
if not rand.get_distribution_type() == "IMMEDIATE":
raise Exception("Expected a constant!")
loglikeli = rand.calculate_loglikelihood(values)
if abs(loglikeli) < 10000000:
raise Exception("problem in managing constant variables (2)")
def import_net(input_file_path,
return_stochastic_information=False,
parameters=None):
"""
Import a Petri net from a PNML file
Parameters
----------
input_file_path
Input file path
return_stochastic_information
Enables return of stochastic information if found in the PNML
parameters
Other parameters of the algorithm
"""
if parameters is None:
parameters = {}
tree = etree.parse(input_file_path)
root = tree.getroot()
net = petri.petrinet.PetriNet('imported_' + str(time.time()))
marking = petri.petrinet.Marking()
fmarking = petri.petrinet.Marking()
nett = None
page = None
finalmarkings = None
stochastic_information = {}
for child in root:
nett = child
places_dict = {}
trans_dict = {}
if nett is not None:
for child in nett:
if "page" in child.tag:
page = child
if "finalmarkings" in child.tag:
finalmarkings = child
if page is None:
page = nett
if page is not None:
for child in page:
if "place" in child.tag:
place_id = child.get("id")
place_name = place_id
number = 0
for child2 in child:
if "name" in child2.tag:
for child3 in child2:
if child3.text:
place_name = child3.text
if "initialMarking" in child2.tag:
for child3 in child2:
if child3.tag == "text":
number = int(child3.text)
places_dict[place_id] = petri.petrinet.PetriNet.Place(place_id)
net.places.add(places_dict[place_id])
if number > 0:
marking[places_dict[place_id]] = number
del place_name
if page is not None:
for child in page:
if "transition" in child.tag:
trans_name = child.get("id")
trans_label = trans_name
trans_visible = True
random_variable = None
for child2 in child:
if child2.tag == "name":
for child3 in child2:
if child3.text:
if trans_label == trans_name:
trans_label = child3.text
if "toolspecific" in child2.tag:
tool = child2.get("tool")
if "ProM" in tool:
activity = child2.get("activity")
if "invisible" in activity:
trans_visible = False
elif "StochasticPetriNet" in tool:
distribution_type = None
distribution_parameters = None
priority = None
weight = None
for child3 in child2:
key = child3.get("key")
value = child3.text
if key == "distributionType":
distribution_type = value
elif key == "distributionParameters":
distribution_parameters = value
elif key == "priority":
priority = int(value)
elif key == "weight":
weight = float(value)
if return_stochastic_information:
random_variable = RandomVariable()
random_variable.read_from_string(
distribution_type, distribution_parameters)
random_variable.set_priority(priority)
random_variable.set_weight(weight)
if not trans_visible:
trans_label = None
#if "INVISIBLE" in trans_label:
# trans_label = None
trans_dict[trans_name] = petri.petrinet.PetriNet.Transition(
trans_name, trans_label)
net.transitions.add(trans_dict[trans_name])
if random_variable is not None:
stochastic_information[
trans_dict[trans_name]] = random_variable
if page is not None:
for child in page:
if "arc" in child.tag:
arc_source = child.get("source")
arc_target = child.get("target")
arc_weight = 1
for arc_child in child:
if "inscription" in arc_child.tag:
for text_arcweight in arc_child:
if "text" in text_arcweight.tag:
arc_weight = int(text_arcweight.text)
if arc_source in places_dict and arc_target in trans_dict:
petri.utils.add_arc_from_to(places_dict[arc_source],
trans_dict[arc_target],
net,
weight=arc_weight)
elif arc_target in places_dict and arc_source in trans_dict:
petri.utils.add_arc_from_to(trans_dict[arc_source],
places_dict[arc_target],
net,
weight=arc_weight)
if finalmarkings is not None:
for child in finalmarkings:
for child2 in child:
place_id = child2.get("idref")
for child3 in child2:
if "text" in child3.tag:
number = int(child3.text)
if number > 0:
fmarking[places_dict[place_id]] = number
# generate the final marking in the case has not been found
if len(fmarking) == 0:
fmarking = final_marking.discover_final_marking(net)
if return_stochastic_information and len(
list(stochastic_information.keys())) > 0:
return net, marking, fmarking, stochastic_information
return net, marking, fmarking
def get_map_from_log_and_net(log,
net,
initial_marking,
final_marking,
force_distribution=None,
parameters=None):
"""
Get transition stochastic distribution map given the log and the Petri net
Parameters
-----------
log
Event log
net
Petri net
initial_marking
Initial marking of the Petri net
final_marking
Final marking of the Petri net
force_distribution
If provided, distribution to force usage (e.g. EXPONENTIAL)
parameters
Parameters of the algorithm, including:
PARAM_ACTIVITY_KEY -> activity name
PARAM_TIMESTAMP_KEY -> timestamp key
Returns
-----------
stochastic_map
Map that to each transition associates a random variable
"""
stochastic_map = {}
if parameters is None:
parameters = {}
activity_key = parameters[
PARAM_ACTIVITY_KEY] if PARAM_ACTIVITY_KEY in parameters else log_lib.util.xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
PARAM_TIMESTAMP_KEY] if PARAM_TIMESTAMP_KEY in parameters else "time:timestamp"
parameters_variants = {PARAM_ACTIVITY_KEY: activity_key}
variants_idx = variants_module.get_variants_from_log_trace_idx(
log, parameters=parameters_variants)
variants = variants_module.convert_variants_trace_idx_to_trace_obj(
log, variants_idx)
parameters_tr = {PARAM_ACTIVITY_KEY: activity_key, "variants": variants}
# do the replay
aligned_traces = token_replay.apply(log,
net,
initial_marking,
final_marking,
parameters=parameters_tr)
element_statistics = performance_map.single_element_statistics(
log,
net,
initial_marking,
aligned_traces,
variants_idx,
activity_key=activity_key,
timestamp_key=timestamp_key)
for el in element_statistics:
if type(
el
) is PetriNet.Transition and "performance" in element_statistics[el]:
values = element_statistics[el]["performance"]
rand = RandomVariable()
rand.calculate_parameters(values,
force_distribution=force_distribution)
no_of_times_enabled = element_statistics[el]['no_of_times_enabled']
no_of_times_activated = element_statistics[el][
'no_of_times_activated']
if no_of_times_enabled > 0:
rand.set_weight(
float(no_of_times_activated) / float(no_of_times_enabled))
else:
rand.set_weight(0.0)
stochastic_map[el] = rand
return stochastic_map
def import_net(input_file_path, parameters=None):
"""
Import a Petri net from a PNML file
Parameters
----------
input_file_path
Input file path
parameters
Other parameters of the algorithm
"""
if parameters is None:
parameters = {}
parser = etree.XMLParser(remove_comments=True)
tree = objectify.parse(input_file_path, parser=parser)
root = tree.getroot()
net = PetriNet('imported_' + str(time.time()))
marking = Marking()
fmarking = Marking()
nett = None
page = None
finalmarkings = None
stochastic_information = {}
for child in root:
nett = child
places_dict = {}
trans_dict = {}
if nett is not None:
for child in nett:
if "page" in child.tag:
page = child
if "finalmarkings" in child.tag:
finalmarkings = child
if page is None:
page = nett
if page is not None:
for child in page:
if "place" in child.tag:
position_X = None
position_Y = None
dimension_X = None
dimension_Y = None
place_id = child.get("id")
place_name = place_id
number = 0
for child2 in child:
if child2.tag.endswith('name'):
for child3 in child2:
if child3.text:
place_name = child3.text
if child2.tag.endswith('initialMarking'):
for child3 in child2:
if child3.tag.endswith("text"):
number = int(child3.text)
if child2.tag.endswith('graphics'):
for child3 in child2:
if child3.tag.endswith('position'):
position_X = float(child3.get("x"))
position_Y = float(child3.get("y"))
elif child3.tag.endswith("dimension"):
dimension_X = float(child3.get("x"))
dimension_Y = float(child3.get("y"))
places_dict[place_id] = PetriNet.Place(place_id)
places_dict[place_id].properties[
constants.PLACE_NAME_TAG] = place_name
net.places.add(places_dict[place_id])
if position_X is not None and position_Y is not None and dimension_X is not None and dimension_Y is not None:
places_dict[place_id].properties[
constants.LAYOUT_INFORMATION_PETRI] = ((position_X,
position_Y),
(dimension_X,
dimension_Y))
if number > 0:
marking[places_dict[place_id]] = number
del place_name
if page is not None:
for child in page:
if child.tag.endswith("transition"):
position_X = None
position_Y = None
dimension_X = None
dimension_Y = None
trans_name = child.get("id")
trans_label = trans_name
trans_visible = True
random_variable = None
for child2 in child:
if child2.tag.endswith("name"):
for child3 in child2:
if child3.text:
if trans_label == trans_name:
trans_label = child3.text
if child2.tag.endswith("graphics"):
for child3 in child2:
if child3.tag.endswith("position"):
position_X = float(child3.get("x"))
position_Y = float(child3.get("y"))
elif child3.tag.endswith("dimension"):
dimension_X = float(child3.get("x"))
dimension_Y = float(child3.get("y"))
if child2.tag.endswith("toolspecific"):
tool = child2.get("tool")
if "ProM" in tool:
activity = child2.get("activity")
if "invisible" in activity:
trans_visible = False
elif "StochasticPetriNet" in tool:
distribution_type = None
distribution_parameters = None
priority = None
weight = None
for child3 in child2:
key = child3.get("key")
value = child3.text
if key == "distributionType":
distribution_type = value
elif key == "distributionParameters":
distribution_parameters = value
elif key == "priority":
priority = int(value)
elif key == "weight":
weight = float(value)
random_variable = RandomVariable()
random_variable.read_from_string(
distribution_type, distribution_parameters)
random_variable.set_priority(priority)
random_variable.set_weight(weight)
if not trans_visible:
trans_label = None
#if "INVISIBLE" in trans_label:
# trans_label = None
trans_dict[trans_name] = PetriNet.Transition(
trans_name, trans_label)
net.transitions.add(trans_dict[trans_name])
if random_variable is not None:
trans_dict[trans_name].properties[
constants.STOCHASTIC_DISTRIBUTION] = random_variable
if position_X is not None and position_Y is not None and dimension_X is not None and dimension_Y is not None:
trans_dict[trans_name].properties[
constants.LAYOUT_INFORMATION_PETRI] = ((position_X,
position_Y),
(dimension_X,
dimension_Y))
if page is not None:
for child in page:
if child.tag.endswith("arc"):
arc_source = child.get("source")
arc_target = child.get("target")
arc_weight = 1
for arc_child in child:
if arc_child.tag.endswith("inscription"):
for text_arcweight in arc_child:
if text_arcweight.tag.endswith("text"):
arc_weight = int(text_arcweight.text)
if arc_source in places_dict and arc_target in trans_dict:
add_arc_from_to(places_dict[arc_source],
trans_dict[arc_target],
net,
weight=arc_weight)
elif arc_target in places_dict and arc_source in trans_dict:
add_arc_from_to(trans_dict[arc_source],
places_dict[arc_target],
net,
weight=arc_weight)
if finalmarkings is not None:
for child in finalmarkings:
for child2 in child:
place_id = child2.get("idref")
for child3 in child2:
if child3.tag.endswith("text"):
number = int(child3.text)
if number > 0:
fmarking[places_dict[place_id]] = number
# generate the final marking in the case has not been found
if len(fmarking) == 0:
fmarking = final_marking.discover_final_marking(net)
return net, marking, fmarking
def get_map_from_log_and_net(log,
net,
initial_marking,
final_marking,
force_distribution=None,
parameters=None):
"""
Get transition stochastic distribution map given the log and the Petri net
Parameters
-----------
log
Event log
net
Petri net
initial_marking
Initial marking of the Petri net
final_marking
Final marking of the Petri net
force_distribution
If provided, distribution to force usage (e.g. EXPONENTIAL)
parameters
Parameters of the algorithm, including:
Parameters.ACTIVITY_KEY -> activity name
Parameters.TIMESTAMP_KEY -> timestamp key
Returns
-----------
stochastic_map
Map that to each transition associates a random variable
"""
stochastic_map = {}
if parameters is None:
parameters = {}
token_replay_variant = exec_utils.get_param_value(
Parameters.TOKEN_REPLAY_VARIANT, parameters,
executor.Variants.TOKEN_REPLAY)
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY,
parameters,
xes_constants.DEFAULT_NAME_KEY)
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY, parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY)
parameters_variants = {
constants.PARAMETER_CONSTANT_ACTIVITY_KEY: activity_key
}
variants_idx = variants_module.get_variants_from_log_trace_idx(
log, parameters=parameters_variants)
variants = variants_module.convert_variants_trace_idx_to_trace_obj(
log, variants_idx)
parameters_tr = {
token_replay.Parameters.ACTIVITY_KEY: activity_key,
token_replay.Parameters.VARIANTS: variants
}
# do the replay
aligned_traces = executor.apply(log,
net,
initial_marking,
final_marking,
variant=token_replay_variant,
parameters=parameters_tr)
element_statistics = performance_map.single_element_statistics(
log,
net,
initial_marking,
aligned_traces,
variants_idx,
activity_key=activity_key,
timestamp_key=timestamp_key,
parameters={"business_hours": True})
for el in element_statistics:
if type(
el
) is PetriNet.Transition and "performance" in element_statistics[el]:
values = element_statistics[el]["performance"]
rand = RandomVariable()
rand.calculate_parameters(values,
force_distribution=force_distribution)
no_of_times_enabled = element_statistics[el]['no_of_times_enabled']
no_of_times_activated = element_statistics[el][
'no_of_times_activated']
if no_of_times_enabled > 0:
rand.set_weight(
float(no_of_times_activated) / float(no_of_times_enabled))
else:
rand.set_weight(0.0)
stochastic_map[el] = rand
return stochastic_map
#there are 6 possible traces
simulated_log = simulator.apply(net, im, variant=simulator.Variants.EXTENSIVE, parameters={simulator.Variants.EXTENSIVE.value.Parameters.MAX_TRACE_LENGTH: 5})
#saving possible traces in a dictionary
possible_traces = {}
possible_traces[0] = ['a', 'b', 'e']
possible_traces[1] = ['a', 'b', 'd', 'e']
possible_traces[2] = ['a', 'd', 'b', 'e']
possible_traces[3] = ['a', 'c', 'e']
possible_traces[4] = ['a', 'c', 'd', 'e']
possible_traces[5] = ['a', 'd', 'c', 'e']
#creating stochastic map assigning probabilities to transitions
smap = {}
for t in transitions:
rand = RandomVariable()
#we do not use the uniform distribution, but we need some kind of distribution to initialize
#the random_variable of the RandomVariable() object, so that we can call set_weight and get_weight later
rand.random_variable = Uniform()
smap[t] = rand
if t.label == "b":
rand.set_weight(0.9)
elif t.label == "c":
rand.set_weight(0.1)
elif t.label == "d":
rand.set_weight(0.2)
elif t.label == None:
rand.set_weight(0.8)
else:
rand.set_weight(1)
def import_net_from_xml_object(root, parameters=None):
"""
Import a Petri net from an etree XML object
Parameters
----------
root
Root object of the XML
parameters
Other parameters of the algorithm
"""
if parameters is None:
parameters = {}
net = PetriNet('imported_' + str(time.time()))
marking = Marking()
fmarking = Marking()
nett = None
page = None
finalmarkings = None
variables = None
stochastic_information = {}
for child in root:
nett = child
places_dict = {}
trans_dict = {}
if nett is not None:
for child in nett:
if "page" in child.tag:
page = child
if "finalmarkings" in child.tag:
finalmarkings = child
if "variables" in child.tag:
variables = child
if page is None:
page = nett
if page is not None:
for child in page:
if "place" in child.tag:
position_X = None
position_Y = None
dimension_X = None
dimension_Y = None
place_id = child.get("id")
place_name = place_id
number = 0
for child2 in child:
if child2.tag.endswith('name'):
for child3 in child2:
if child3.text:
place_name = child3.text
if child2.tag.endswith('initialMarking'):
for child3 in child2:
if child3.tag.endswith("text"):
number = int(child3.text)
if child2.tag.endswith('graphics'):
for child3 in child2:
if child3.tag.endswith('position'):
position_X = float(child3.get("x"))
position_Y = float(child3.get("y"))
elif child3.tag.endswith("dimension"):
dimension_X = float(child3.get("x"))
dimension_Y = float(child3.get("y"))
places_dict[place_id] = PetriNet.Place(place_id)
places_dict[place_id].properties[
constants.PLACE_NAME_TAG] = place_name
net.places.add(places_dict[place_id])
if position_X is not None and position_Y is not None and dimension_X is not None and dimension_Y is not None:
places_dict[place_id].properties[
constants.LAYOUT_INFORMATION_PETRI] = ((position_X,
position_Y),
(dimension_X,
dimension_Y))
if number > 0:
marking[places_dict[place_id]] = number
del place_name
if page is not None:
for child in page:
if child.tag.endswith("transition"):
position_X = None
position_Y = None
dimension_X = None
dimension_Y = None
trans_id = child.get("id")
trans_name = trans_id
trans_visible = True
trans_properties = {}
trans_guard = child.get("guard")
if trans_guard is not None:
trans_properties[
petri_properties.TRANS_GUARD] = trans_guard
random_variable = None
for child2 in child:
if child2.tag.endswith("name"):
for child3 in child2:
if child3.text:
if trans_name == trans_id:
trans_name = child3.text
elif child2.tag.endswith("graphics"):
for child3 in child2:
if child3.tag.endswith("position"):
position_X = float(child3.get("x"))
position_Y = float(child3.get("y"))
elif child3.tag.endswith("dimension"):
dimension_X = float(child3.get("x"))
dimension_Y = float(child3.get("y"))
elif child2.tag.endswith("toolspecific"):
tool = child2.get("tool")
if "ProM" in tool:
activity = child2.get("activity")
if "invisible" in activity:
trans_visible = False
elif "StochasticPetriNet" in tool:
distribution_type = None
distribution_parameters = None
priority = None
weight = None
for child3 in child2:
key = child3.get("key")
value = child3.text
if key == "distributionType":
distribution_type = value
elif key == "distributionParameters":
distribution_parameters = value
elif key == "priority":
priority = int(value)
elif key == "weight":
weight = float(value)
random_variable = RandomVariable()
random_variable.read_from_string(
distribution_type, distribution_parameters)
random_variable.set_priority(priority)
random_variable.set_weight(weight)
elif child2.tag.endswith(petri_properties.WRITE_VARIABLE):
# property for data Petri nets
if petri_properties.WRITE_VARIABLE not in trans_properties:
trans_properties[
petri_properties.WRITE_VARIABLE] = []
trans_properties[
petri_properties.WRITE_VARIABLE].append(
child2.text)
elif child2.tag.endswith(petri_properties.READ_VARIABLE):
# property for data Petri nets
if petri_properties.READ_VARIABLE not in trans_properties:
trans_properties[
petri_properties.READ_VARIABLE] = []
trans_properties[
petri_properties.READ_VARIABLE].append(child2.text)
# 15/02/2021: the name associated in the PNML to invisible transitions was lost.
# at least save that as property.
if trans_visible:
trans_label = trans_name
else:
trans_label = None
trans_dict[trans_id] = PetriNet.Transition(
trans_id, trans_label)
trans_dict[trans_id].properties[
constants.TRANS_NAME_TAG] = trans_name
for prop in trans_properties:
trans_dict[trans_id].properties[prop] = trans_properties[
prop]
net.transitions.add(trans_dict[trans_id])
if random_variable is not None:
trans_dict[trans_id].properties[
constants.STOCHASTIC_DISTRIBUTION] = random_variable
if position_X is not None and position_Y is not None and dimension_X is not None and dimension_Y is not None:
trans_dict[trans_id].properties[
constants.LAYOUT_INFORMATION_PETRI] = ((position_X,
position_Y),
(dimension_X,
dimension_Y))
if page is not None:
for child in page:
if child.tag.endswith("arc"):
arc_source = child.get("source")
arc_target = child.get("target")
arc_weight = 1
arc_type = None
arc_properties = {}
for arc_child in child:
if arc_child.tag.endswith("inscription"):
for text_element in arc_child:
if text_element.tag.endswith("text"):
arc_weight = int(text_element.text)
elif arc_child.tag.endswith(petri_properties.ARCTYPE):
for text_element in arc_child:
if text_element.tag.endswith("text"):
arc_type = text_element.text
if arc_source in places_dict and arc_target in trans_dict:
a = add_arc_from_to(places_dict[arc_source],
trans_dict[arc_target],
net,
weight=arc_weight,
type=arc_type)
for prop in arc_properties:
a.properties[prop] = arc_properties[prop]
elif arc_target in places_dict and arc_source in trans_dict:
a = add_arc_from_to(trans_dict[arc_source],
places_dict[arc_target],
net,
weight=arc_weight,
type=arc_type)
for prop in arc_properties:
a.properties[prop] = arc_properties[prop]
if finalmarkings is not None:
for child in finalmarkings:
for child2 in child:
place_id = child2.get("idref")
for child3 in child2:
if child3.tag.endswith("text"):
number = int(child3.text)
if number > 0:
fmarking[places_dict[place_id]] = number
if variables is not None:
net.properties[petri_properties.VARIABLES] = []
for child in variables:
variable_type = child.get("type")
variable_name = ""
for child2 in child:
if child2.tag.endswith("name"):
variable_name = child2.text
net.properties[petri_properties.VARIABLES].append({
"type":
variable_type,
"name":
variable_name
})
# generate the final marking in the case has not been found
if len(fmarking) == 0:
fmarking = final_marking.discover_final_marking(net)
return net, marking, fmarking
