def construct_trace_net(trace,
trace_name_key=xes_util.DEFAULT_NAME_KEY,
activity_key=xes_util.DEFAULT_NAME_KEY):
"""
Creates a trace net, i.e. a trace in Petri net form.
Parameters
----------
trace: :class:`list` input trace, assumed to be a list of events
trace_name_key: :class:`str` key of the attribute that defines the name of the trace
activity_key: :class:`str` key of the attribute of the events that defines the activity name
Returns
-------
tuple: :class:`tuple` of the net, initial marking and the final marking
"""
net = PetriNet('trace net of %s' %
trace.attributes[trace_name_key] if trace_name_key in
trace.attributes else ' ')
place_map = {0: PetriNet.Place('p_0')}
net.places.add(place_map[0])
for i in range(0, len(trace)):
t = PetriNet.Transition('t_' + trace[i][activity_key] + '_' + str(i),
trace[i][activity_key])
# 16/02/2021: set the trace index as property of the transition of the trace net
t.properties[properties.TRACE_NET_TRANS_INDEX] = i
net.transitions.add(t)
place_map[i + 1] = PetriNet.Place('p_' + str(i + 1))
# 16/02/2021: set the place index as property of the place of the trace net
place_map[i + 1].properties[properties.TRACE_NET_PLACE_INDEX] = i + 1
net.places.add(place_map[i + 1])
add_arc_from_to(place_map[i], t, net)
add_arc_from_to(t, place_map[i + 1], net)
return net, Marking({place_map[0]: 1}), Marking({place_map[len(trace)]: 1})
def __copy_into(source_net, target_net, upper, skip):
t_map = {}
p_map = {}
for t in source_net.transitions:
name = (t.name, skip) if upper else (skip, t.name)
label = (t.label, skip) if upper else (skip, t.label)
t_map[t] = PetriNet.Transition(name, label)
if properties.TRACE_NET_TRANS_INDEX in t.properties:
# 16/02/2021: copy the index property from the transition of the trace net
t_map[t].properties[
properties.TRACE_NET_TRANS_INDEX] = t.properties[
properties.TRACE_NET_TRANS_INDEX]
target_net.transitions.add(t_map[t])
for p in source_net.places:
name = (p.name, skip) if upper else (skip, p.name)
p_map[p] = PetriNet.Place(name)
if properties.TRACE_NET_PLACE_INDEX in p.properties:
# 16/02/2021: copy the index property from the place of the trace net
p_map[p].properties[
properties.TRACE_NET_PLACE_INDEX] = p.properties[
properties.TRACE_NET_PLACE_INDEX]
target_net.places.add(p_map[p])
for t in source_net.transitions:
for a in t.in_arcs:
add_arc_from_to(p_map[a.source], t_map[t], target_net)
for a in t.out_arcs:
add_arc_from_to(t_map[t], p_map[a.target], target_net)
return t_map, p_map
def construct(pn1, im1, fm1, pn2, im2, fm2, skip):
"""
Constructs the synchronous product net of two given Petri nets.
:param pn1: Petri net 1
:param im1: Initial marking of Petri net 1
:param fm1: Final marking of Petri net 1
:param pn2: Petri net 2
:param im2: Initial marking of Petri net 2
:param fm2: Final marking of Petri net 2
:param skip: Symbol to be used as skip
Returns
-------
:return: Synchronous product net and associated marking labels are of the form (a,>>)
"""
sync_net = PetriNet('synchronous_product_net of %s and %s' %
(pn1.name, pn2.name))
t1_map, p1_map = __copy_into(pn1, sync_net, True, skip)
t2_map, p2_map = __copy_into(pn2, sync_net, False, skip)
for t1 in pn1.transitions:
for t2 in pn2.transitions:
if t1.label == t2.label:
sync = PetriNet.Transition((t1.name, t2.name),
(t1.label, t2.label))
sync_net.transitions.add(sync)
# copy the properties of the transitions inside the transition of the sync net
for p1 in t1.properties:
sync.properties[p1] = t1.properties[p1]
for p2 in t2.properties:
sync.properties[p2] = t2.properties[p2]
for a in t1.in_arcs:
add_arc_from_to(p1_map[a.source], sync, sync_net)
for a in t2.in_arcs:
add_arc_from_to(p2_map[a.source], sync, sync_net)
for a in t1.out_arcs:
add_arc_from_to(sync, p1_map[a.target], sync_net)
for a in t2.out_arcs:
add_arc_from_to(sync, p2_map[a.target], sync_net)
sync_im = Marking()
sync_fm = Marking()
for p in im1:
sync_im[p1_map[p]] = im1[p]
for p in im2:
sync_im[p2_map[p]] = im2[p]
for p in fm1:
sync_fm[p1_map[p]] = fm1[p]
for p in fm2:
sync_fm[p2_map[p]] = fm2[p]
# update 06/02/2021: to distinguish the sync nets that are output of this method, put a property in the sync net
sync_net.properties[properties.IS_SYNC_NET] = True
return sync_net, sync_im, sync_fm
def project_net_on_place(place):
"""
Project a Petri net on a place
Parameters
-------------
place
Place
Returns
-------------
net
(Place) net
im
Empty initial marking
fm
Empty final marking
"""
place_net = PetriNet()
place_net_im = Marking()
place_net_fm = Marking()
input_trans = [arc.source for arc in place.in_arcs]
output_trans = [arc.target for arc in place.out_arcs]
if len(input_trans) == 0 or len(output_trans) == 0:
raise Exception("place projection not available on source/sink places")
input_trans_visible = [trans for trans in input_trans if trans.label]
output_trans_visible = [trans for trans in output_trans if trans.label]
if not len(input_trans) == len(input_trans_visible) or not len(
output_trans) == len(output_trans_visible):
raise Exception(
"place projection not available on places that have invisible transitions as preset/postset"
)
new_place = PetriNet.Place(place.name)
place_net.places.add(new_place)
for trans in input_trans:
new_trans = PetriNet.Transition(trans.name, trans.label)
place_net.transitions.add(new_trans)
add_arc_from_to(new_trans, new_place, place_net)
for trans in output_trans:
new_trans = PetriNet.Transition(trans.name, trans.label)
place_net.transitions.add(new_trans)
add_arc_from_to(new_place, new_trans, place_net)
return place_net, place_net_im, place_net_fm
def postprocessing(net: PetriNet, initial_marking: Marking,
final_marking: Marking, A, B, pairs,
loop_one_list) -> Tuple[PetriNet, Marking, Marking]:
"""
Adding the filtered transitions to the Petri net
Parameters
------------
loop_list
List of looped activities
classical_alpha_result
Result after applying the classic alpha algorithm to the filtered log
A
See Paper for definition
B
See Paper for definition
Returns
------------
net
Petri net
im
Initial marking
fm
Final marking
"""
label_transition_dict = {}
for label in loop_one_list:
label_transition_dict[label] = PetriNet.Transition(label, label)
net.transitions.add(label_transition_dict[label])
# F L1L
# Key is specific loop element
for key, value in A.items():
if key in B:
A_without_B = value - B[key]
B_without_A = B[key] - value
pair = (A_without_B, B_without_A)
for pair_try in pairs:
in_part = pair_try[0]
out_part = pair_try[1]
if pair[0].issubset(in_part) and pair[1].issubset(out_part):
pair_try_place = PetriNet.Place(str(pair_try))
net.places.add(pair_try_place)
add_arc_from_to(label_transition_dict[key], pair_try_place,
net)
add_arc_from_to(pair_try_place, label_transition_dict[key],
net)
return net, initial_marking, final_marking
def add_arc_from_to(fr,
to,
net: PetriNet,
weight=1,
type=None) -> PetriNet.Arc:
"""
Adds an arc from a specific element to another element in some net. Assumes from and to are in the net!
Parameters
----------
fr: transition/place from
to: transition/place to
net: net to use
weight: weight associated to the arc
Returns
-------
None
"""
a = PetriNet.Arc(fr, to, weight)
if type is not None:
a.properties[properties.ARCTYPE] = type
net.arcs.add(a)
fr.out_arcs.add(a)
to.in_arcs.add(a)
return a
def merge(trgt: Optional[PetriNet] = None, nets=None) -> PetriNet:
trgt = trgt if trgt is not None else PetriNet()
nets = nets if nets is not None else list()
for net in nets:
trgt.transitions.update(net.transitions)
trgt.places.update(net.places)
trgt.arcs.update(net.arcs)
return trgt
def add_source(net, start_activities, label_transition_dict):
"""
Adding source pe
"""
source = PetriNet.Place('start')
net.places.add(source)
for s in start_activities:
add_arc_from_to(source, label_transition_dict[s], net)
return source
def add_sink(net, end_activities, label_transition_dict):
"""
Adding sink pe
"""
end = PetriNet.Place('end')
net.places.add(end)
for e in end_activities:
add_arc_from_to(label_transition_dict[e], end, net)
return end
def add_transition(net: PetriNet,
name=None,
label=None) -> PetriNet.Transition:
name = name if name is not None else 't_' + str(len(
net.transitions)) + '_' + str(time.time()) + str(
random.randint(0, 10000))
t = PetriNet.Transition(name=name, label=label)
net.transitions.add(t)
return t
def get_strongly_connected_subnets(net):
"""
Get the strongly connected components subnets in the Petri net
Parameters
-------------
net
Petri net
Returns
-------------
strongly_connected_transitions
List of strongly connected transitions of the Petri net
"""
import networkx as nx
graph, inv_dictionary = create_networkx_directed_graph(net)
sccg = list(nx.strongly_connected_components(graph))
strongly_connected_subnets = []
for sg in list(sccg):
if len(sg) > 1:
subnet = PetriNet()
imarking = Marking()
fmarking = Marking()
corr = {}
for node in sg:
if node in inv_dictionary:
if type(inv_dictionary[node]) is PetriNet.Transition:
prev_trans = inv_dictionary[node]
new_trans = PetriNet.Transition(
prev_trans.name, prev_trans.label)
corr[node] = new_trans
subnet.transitions.add(new_trans)
if type(inv_dictionary[node]) is PetriNet.Place:
prev_place = inv_dictionary[node]
new_place = PetriNet.Place(prev_place.name)
corr[node] = new_place
subnet.places.add(new_place)
for edge in graph.edges:
if edge[0] in sg and edge[1] in sg:
add_arc_from_to(corr[edge[0]], corr[edge[1]], subnet)
strongly_connected_subnets.append([subnet, imarking, fmarking])
return strongly_connected_subnets
def short_circuit_petri_net(net, print_diagnostics=False):
"""
Fist, sink and source place are identified. Then, a transition from source to sink is added to short-circuited
the given petri net. If there is no unique source and sink place, an error gets returned
:param net: Petri net that is going to be short circuited
:return:
"""
s_c_net = copy.deepcopy(net)
no_source_places = 0
no_sink_places = 0
sink = None
source = None
for place in s_c_net.places:
if len(place.in_arcs) == 0:
source = place
no_source_places += 1
if len(place.out_arcs) == 0:
sink = place
no_sink_places += 1
if (sink is not None) and (
source
is not None) and no_source_places == 1 and no_sink_places == 1:
# If there is one unique source and sink place, short circuit Petri Net is constructed
t_1 = PetriNet.Transition("short_circuited_transition",
"short_circuited_transition")
s_c_net.transitions.add(t_1)
# add arcs in short-circuited net
petri_utils.add_arc_from_to(sink, t_1, s_c_net)
petri_utils.add_arc_from_to(t_1, source, s_c_net)
return s_c_net
else:
if sink is None:
if print_diagnostics:
print("There is no sink place.")
return None
elif source is None:
if print_diagnostics:
print("There is no source place.")
return None
elif no_source_places > 1:
if print_diagnostics:
print("There is more than one source place.")
return None
elif no_sink_places > 1:
if print_diagnostics:
print("There is more than one sink place.")
return None
def _short_circuit_petri_net(net):
"""
Creates a short circuited Petri net,
whether an unique source place and sink place are there,
by connecting the sink with the source
Parameters
---------------
net
Petri net
Returns
---------------
boolean
Boolean value
"""
s_c_net = copy.deepcopy(net)
no_source_places = 0
no_sink_places = 0
sink = None
source = None
for place in s_c_net.places:
if len(place.in_arcs) == 0:
source = place
no_source_places += 1
if len(place.out_arcs) == 0:
sink = place
no_sink_places += 1
if (sink is not None) and (
source
is not None) and no_source_places == 1 and no_sink_places == 1:
# If there is one unique source and sink place, short circuit Petri Net is constructed
t_1 = PetriNet.Transition("short_circuited_transition",
"short_circuited_transition")
s_c_net.transitions.add(t_1)
# add arcs in short-circuited net
pn_utils.add_arc_from_to(sink, t_1, s_c_net)
pn_utils.add_arc_from_to(t_1, source, s_c_net)
return s_c_net
else:
return None
def add_arc_from_to(fr, to, net, weight=1):
"""
Adds an arc from a specific element to another element in some net. Assumes from and to are in the net!
Parameters
----------
fr: transition/place from
to: transition/place to
net: net to use
weight: weight associated to the arc
Returns
-------
None
"""
a = PetriNet.Arc(fr, to, weight)
net.arcs.add(a)
fr.out_arcs.add(a)
to.in_arcs.add(a)
return a
def apply(heu_net, parameters=None):
"""
Converts an Heuristics Net to a Petri net
Parameters
--------------
heu_net
Heuristics net
parameters
Possible parameters of the algorithm
Returns
--------------
net
Petri net
im
Initial marking
fm
Final marking
"""
if parameters is None:
parameters = {}
net = PetriNet("")
im = Marking()
fm = Marking()
source_places = []
sink_places = []
hid_trans_count = 0
for index, sa_list in enumerate(heu_net.start_activities):
source = PetriNet.Place("source" + str(index))
source_places.append(source)
net.places.add(source)
im[source] = 1
for index, ea_list in enumerate(heu_net.end_activities):
sink = PetriNet.Place("sink" + str(index))
sink_places.append(sink)
net.places.add(sink)
fm[sink] = 1
act_trans = {}
who_is_entering = {}
who_is_exiting = {}
for act1_name in heu_net.nodes:
act1 = heu_net.nodes[act1_name]
if act1_name not in act_trans:
act_trans[act1_name] = PetriNet.Transition(act1_name, act1_name)
net.transitions.add(act_trans[act1_name])
who_is_entering[act1_name] = set()
who_is_exiting[act1_name] = set()
for index, sa_list in enumerate(heu_net.start_activities):
if act1_name in sa_list:
who_is_entering[act1_name].add((None, index))
for index, ea_list in enumerate(heu_net.end_activities):
if act1_name in ea_list:
who_is_exiting[act1_name].add((None, index))
for act2 in act1.output_connections:
act2_name = act2.node_name
if act2_name not in act_trans:
act_trans[act2_name] = PetriNet.Transition(
act2_name, act2_name)
net.transitions.add(act_trans[act2_name])
who_is_entering[act2_name] = set()
who_is_exiting[act2_name] = set()
for index, sa_list in enumerate(heu_net.start_activities):
if act2_name in sa_list:
who_is_entering[act2_name].add((None, index))
for index, ea_list in enumerate(heu_net.end_activities):
if act2_name in ea_list:
who_is_exiting[act2_name].add((None, index))
who_is_entering[act2_name].add((act1_name, None))
who_is_exiting[act1_name].add((act2_name, None))
places_entering = {}
for act1 in who_is_entering:
cliques = find_bindings(heu_net.nodes[act1].and_measures_in)
places_entering[act1] = {}
entering_activities = list(who_is_entering[act1])
entering_activities_wo_source = sorted(
[x for x in entering_activities if x[0] is not None],
key=lambda x: x[0])
entering_activities_only_source = [
x for x in entering_activities if x[0] is None
]
if entering_activities_wo_source:
master_place = PetriNet.Place("pre_" + act1)
net.places.add(master_place)
add_arc_from_to(master_place, act_trans[act1], net)
if len(entering_activities) == 1:
places_entering[act1][entering_activities[0]] = master_place
else:
for index, act in enumerate(entering_activities_wo_source):
if act[0] in heu_net.nodes[act1].and_measures_in:
z = 0
while z < len(cliques):
if act[0] in cliques[z]:
hid_trans_count = hid_trans_count + 1
hid_trans = PetriNet.Transition(
"hid_" + str(hid_trans_count), None)
net.transitions.add(hid_trans)
add_arc_from_to(hid_trans, master_place, net)
for act2 in cliques[z]:
if (act2,
None) not in places_entering[act1]:
s_place = PetriNet.Place("splace_in_" +
act1 + "_" +
act2 + "_" +
str(index))
net.places.add(s_place)
places_entering[act1][(act2,
None)] = s_place
add_arc_from_to(
places_entering[act1][(act2, None)],
hid_trans, net)
del cliques[z]
continue
z = z + 1
pass
elif act not in places_entering[act1]:
hid_trans_count = hid_trans_count + 1
hid_trans = PetriNet.Transition(
"hid_" + str(hid_trans_count), None)
net.transitions.add(hid_trans)
add_arc_from_to(hid_trans, master_place, net)
if act not in places_entering[act1]:
s_place = PetriNet.Place("splace_in_" + act1 +
"_" + str(index))
net.places.add(s_place)
places_entering[act1][act] = s_place
add_arc_from_to(places_entering[act1][act], hid_trans,
net)
for el in entering_activities_only_source:
if len(entering_activities) == 1:
add_arc_from_to(source_places[el[1]], act_trans[act1], net)
else:
hid_trans_count = hid_trans_count + 1
hid_trans = PetriNet.Transition("hid_" + str(hid_trans_count),
None)
net.transitions.add(hid_trans)
add_arc_from_to(source_places[el[1]], hid_trans, net)
add_arc_from_to(hid_trans, master_place, net)
for act1 in who_is_exiting:
cliques = find_bindings(heu_net.nodes[act1].and_measures_out)
exiting_activities = list(who_is_exiting[act1])
exiting_activities_wo_sink = sorted(
[x for x in exiting_activities if x[0] is not None],
key=lambda x: x[0])
exiting_activities_only_sink = [
x for x in exiting_activities if x[0] is None
]
if exiting_activities_wo_sink:
if len(exiting_activities) == 1 and len(
exiting_activities_wo_sink) == 1:
ex_act = exiting_activities_wo_sink[0]
if (act1, None) in places_entering[ex_act[0]]:
add_arc_from_to(act_trans[act1],
places_entering[ex_act[0]][(act1, None)],
net)
else:
int_place = PetriNet.Place("intplace_" + str(act1))
net.places.add(int_place)
add_arc_from_to(act_trans[act1], int_place, net)
for ex_act in exiting_activities_wo_sink:
if (act1, None) in places_entering[ex_act[0]]:
if ex_act[0] in heu_net.nodes[act1].and_measures_out:
z = 0
while z < len(cliques):
if ex_act[0] in cliques[z]:
hid_trans_count = hid_trans_count + 1
hid_trans = PetriNet.Transition(
"hid_" + str(hid_trans_count), None)
net.transitions.add(hid_trans)
add_arc_from_to(int_place, hid_trans, net)
for act in cliques[z]:
add_arc_from_to(
hid_trans,
places_entering[act][(act1, None)],
net)
del cliques[z]
continue
z = z + 1
else:
hid_trans_count = hid_trans_count + 1
hid_trans = PetriNet.Transition(
"hid_" + str(hid_trans_count), None)
net.transitions.add(hid_trans)
add_arc_from_to(int_place, hid_trans, net)
add_arc_from_to(
hid_trans,
places_entering[ex_act[0]][(act1, None)], net)
for el in exiting_activities_only_sink:
if len(exiting_activities) == 1:
add_arc_from_to(act_trans[act1], sink_places[el[1]], net)
else:
hid_trans_count = hid_trans_count + 1
hid_trans = PetriNet.Transition("hid_" + str(hid_trans_count),
None)
net.transitions.add(hid_trans)
add_arc_from_to(int_place, hid_trans, net)
add_arc_from_to(hid_trans, sink_places[el[1]], net)
net = remove_rendundant_invisible_transitions(net)
from pm4py.objects.petri_net.utils import reduction
reduction.apply_simple_reduction(net)
return net, im, fm
def decompose(net, im, fm):
places = {x.name: x for x in net.places}
inv_trans = {x.name: x for x in net.transitions if x.label is None}
tmap = {}
for t in net.transitions:
if t.label is not None:
if t.label not in tmap:
tmap[t.label] = []
tmap[t.label].append(t)
trans_dup_label = {
x.label: x
for x in net.transitions
if x.label is not None and len(tmap[x.label]) > 1
}
trans_labels = {x.name: x.label for x in net.transitions}
conn_comp = get_graph_components(places, inv_trans, trans_dup_label, tmap)
list_nets = []
for cmp in conn_comp:
net_new = PetriNet("")
im_new = Marking()
fm_new = Marking()
lmap = {}
for el in cmp:
if el in places:
lmap[el] = PetriNet.Place(el)
net_new.places.add(lmap[el])
elif el in inv_trans:
lmap[el] = PetriNet.Transition(el, None)
net_new.transitions.add(lmap[el])
elif el in trans_labels:
lmap[el] = PetriNet.Transition(el, trans_labels[el])
net_new.transitions.add(lmap[el])
for el in cmp:
if el in places:
old_place = places[el]
for arc in old_place.in_arcs:
st = arc.source
if st.name not in lmap:
lmap[st.name] = PetriNet.Transition(
st.name, trans_labels[st.name])
net_new.transitions.add(lmap[st.name])
add_arc_from_to(lmap[st.name], lmap[el], net_new)
for arc in old_place.out_arcs:
st = arc.target
if st.name not in lmap:
lmap[st.name] = PetriNet.Transition(
st.name, trans_labels[st.name])
net_new.transitions.add(lmap[st.name])
add_arc_from_to(lmap[el], lmap[st.name], net_new)
if old_place in im:
im_new[lmap[el]] = im[old_place]
if old_place in fm:
fm_new[lmap[el]] = fm[old_place]
lvis_labels = sorted(
[t.label for t in net_new.transitions if t.label is not None])
t_tuple = tuple(
sorted(
list(
int(
hashlib.md5(t.name.encode(
constants.DEFAULT_ENCODING)).hexdigest(), 16)
for t in net_new.transitions)))
net_new.lvis_labels = lvis_labels
net_new.t_tuple = t_tuple
if len(net_new.places) > 0 or len(net_new.transitions) > 0:
list_nets.append((net_new, im_new, fm_new))
return list_nets
def apply(dfg, parameters=None):
"""
Applies the DFG mining on a given object (if it is a Pandas dataframe or a log, the DFG is calculated)
Parameters
-------------
dfg
Object (DFG) (if it is a Pandas dataframe or a log, the DFG is calculated)
parameters
Parameters
"""
if parameters is None:
parameters = {}
dfg = dfg
start_activities = exec_utils.get_param_value(
Parameters.START_ACTIVITIES, parameters,
dfg_utils.infer_start_activities(dfg))
end_activities = exec_utils.get_param_value(
Parameters.END_ACTIVITIES, parameters,
dfg_utils.infer_end_activities(dfg))
activities = dfg_utils.get_activities_from_dfg(dfg)
net = PetriNet("")
im = Marking()
fm = Marking()
source = PetriNet.Place("source")
net.places.add(source)
im[source] = 1
sink = PetriNet.Place("sink")
net.places.add(sink)
fm[sink] = 1
places_corr = {}
index = 0
for act in activities:
places_corr[act] = PetriNet.Place(act)
net.places.add(places_corr[act])
for act in start_activities:
if act in places_corr:
index = index + 1
trans = PetriNet.Transition(act + "_" + str(index), act)
net.transitions.add(trans)
pn_util.add_arc_from_to(source, trans, net)
pn_util.add_arc_from_to(trans, places_corr[act], net)
for act in end_activities:
if act in places_corr:
index = index + 1
inv_trans = PetriNet.Transition(act + "_" + str(index), None)
net.transitions.add(inv_trans)
pn_util.add_arc_from_to(places_corr[act], inv_trans, net)
pn_util.add_arc_from_to(inv_trans, sink, net)
for el in dfg.keys():
act1 = el[0]
act2 = el[1]
index = index + 1
trans = PetriNet.Transition(act2 + "_" + str(index), act2)
net.transitions.add(trans)
pn_util.add_arc_from_to(places_corr[act1], trans, net)
pn_util.add_arc_from_to(trans, places_corr[act2], net)
return net, im, fm
def apply_dfg_sa_ea(
dfg: Dict[str, int],
start_activities: Union[None, Dict[str, int]],
end_activities: Union[None, Dict[str, int]],
parameters: Optional[Dict[Union[str, Parameters], Any]] = None
) -> Tuple[PetriNet, Marking, Marking]:
"""
Applying Alpha Miner starting from the knowledge of the Directly Follows graph,
and of the start activities and end activities in the log (possibly inferred from the DFG)
Parameters
------------
dfg
Directly-Follows graph
start_activities
Start activities
end_activities
End activities
parameters
Parameters of the algorithm including:
activity key -> name of the attribute that contains the activity
Returns
-------
net : :class:`pm4py.entities.petri.petrinet.PetriNet`
A Petri net describing the event log that is provided as an input
initial marking : :class:`pm4py.models.net.Marking`
marking object representing the initial marking
final marking : :class:`pm4py.models.net.Marking`
marking object representing the final marking, not guaranteed that it is actually reachable!
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters,
pm_util.xes_constants.DEFAULT_NAME_KEY)
if start_activities is None:
start_activities = dfg_utils.infer_start_activities(dfg)
if end_activities is None:
end_activities = dfg_utils.infer_end_activities(dfg)
labels = set()
for el in dfg:
labels.add(el[0])
labels.add(el[1])
for a in start_activities:
labels.add(a)
for a in end_activities:
labels.add(a)
labels = list(labels)
alpha_abstraction = alpha_classic_abstraction.ClassicAlphaAbstraction(
start_activities, end_activities, dfg, activity_key=activity_key)
pairs = list(
map(
lambda p: ({p[0]}, {p[1]}),
filter(
lambda p: __initial_filter(alpha_abstraction.parallel_relation,
p),
alpha_abstraction.causal_relation)))
for i in range(0, len(pairs)):
t1 = pairs[i]
for j in range(i, len(pairs)):
t2 = pairs[j]
if t1 != t2:
if t1[0].issubset(t2[0]) or t1[1].issubset(t2[1]):
if not (__check_is_unrelated(
alpha_abstraction.parallel_relation,
alpha_abstraction.causal_relation, t1[0], t2[0])
or __check_is_unrelated(
alpha_abstraction.parallel_relation,
alpha_abstraction.causal_relation, t1[1],
t2[1])):
new_alpha_pair = (t1[0] | t2[0], t1[1] | t2[1])
if new_alpha_pair not in pairs:
pairs.append((t1[0] | t2[0], t1[1] | t2[1]))
internal_places = filter(lambda p: __pair_maximizer(pairs, p), pairs)
net = PetriNet('alpha_classic_net_' + str(time.time()))
label_transition_dict = {}
for i in range(0, len(labels)):
label_transition_dict[labels[i]] = PetriNet.Transition(
labels[i], labels[i])
net.transitions.add(label_transition_dict[labels[i]])
src = __add_source(net, alpha_abstraction.start_activities,
label_transition_dict)
sink = __add_sink(net, alpha_abstraction.end_activities,
label_transition_dict)
for pair in internal_places:
place = PetriNet.Place(str(pair))
net.places.add(place)
for in_arc in pair[0]:
add_arc_from_to(label_transition_dict[in_arc], place, net)
for out_arc in pair[1]:
add_arc_from_to(place, label_transition_dict[out_arc], net)
return net, Marking({src: 1}), Marking({sink: 1})
def merge_comp(comp1, comp2):
net = PetriNet("")
im = Marking()
fm = Marking()
places = {}
trans = {}
for pl in comp1[0].places:
places[pl.name] = PetriNet.Place(pl.name)
net.places.add(places[pl.name])
if pl in comp1[1]:
im[places[pl.name]] = comp1[1][pl]
if pl in comp1[2]:
fm[places[pl.name]] = comp1[2][pl]
for pl in comp2[0].places:
places[pl.name] = PetriNet.Place(pl.name)
net.places.add(places[pl.name])
if pl in comp2[1]:
im[places[pl.name]] = comp2[1][pl]
if pl in comp2[2]:
fm[places[pl.name]] = comp2[2][pl]
for tr in comp1[0].transitions:
trans[tr.name] = PetriNet.Transition(tr.name, tr.label)
net.transitions.add(trans[tr.name])
for tr in comp2[0].transitions:
if not tr.name in trans:
trans[tr.name] = PetriNet.Transition(tr.name, tr.label)
net.transitions.add(trans[tr.name])
for arc in comp1[0].arcs:
if type(arc.source) is PetriNet.Place:
add_arc_from_to(places[arc.source.name], trans[arc.target.name],
net)
else:
add_arc_from_to(trans[arc.source.name], places[arc.target.name],
net)
for arc in comp2[0].arcs:
if type(arc.source) is PetriNet.Place:
add_arc_from_to(places[arc.source.name], trans[arc.target.name],
net)
else:
add_arc_from_to(trans[arc.source.name], places[arc.target.name],
net)
lvis_labels = sorted(
[t.label for t in net.transitions if t.label is not None])
t_tuple = tuple(
sorted(
list(
int(
hashlib.md5(t.name.encode(
constants.DEFAULT_ENCODING)).hexdigest(), 16)
for t in net.transitions)))
net.lvis_labels = lvis_labels
net.t_tuple = t_tuple
return (net, im, fm)
def apply(dfg: Dict[Tuple[str, str], int],
parameters: Optional[Dict[Any, Any]] = None):
"""
Applies the DFG mining on a given object (if it is a Pandas dataframe or a log, the DFG is calculated)
Parameters
-------------
dfg
Object (DFG) (if it is a Pandas dataframe or a log, the DFG is calculated)
parameters
Parameters:
- Parameters.START_ACTIVITIES: the start activities of the DFG
- Parameters.END_ACTIVITIES: the end activities of the DFG
Returns
-------------
net
Petri net
im
Initial marking
fm
Final marking
"""
if parameters is None:
parameters = {}
start_activities = exec_utils.get_param_value(
Parameters.START_ACTIVITIES, parameters,
{x: 1
for x in dfg_utils.infer_start_activities(dfg)})
end_activities = exec_utils.get_param_value(
Parameters.END_ACTIVITIES, parameters,
{x: 1
for x in dfg_utils.infer_end_activities(dfg)})
artificial_start_activity = exec_utils.get_param_value(
Parameters.PARAM_ARTIFICIAL_START_ACTIVITY, parameters,
constants.DEFAULT_ARTIFICIAL_START_ACTIVITY)
artificial_end_activity = exec_utils.get_param_value(
Parameters.PARAM_ARTIFICIAL_END_ACTIVITY, parameters,
constants.DEFAULT_ARTIFICIAL_END_ACTIVITY)
enriched_dfg = copy(dfg)
for act in start_activities:
enriched_dfg[(artificial_start_activity, act)] = start_activities[act]
for act in end_activities:
enriched_dfg[(act, artificial_end_activity)] = end_activities[act]
activities = set(x[1] for x in enriched_dfg).union(
set(x[0] for x in enriched_dfg))
net = PetriNet("")
im = Marking()
fm = Marking()
left_places = {}
transes = {}
right_places = {}
for act in activities:
pl1 = PetriNet.Place("source_" + act)
pl2 = PetriNet.Place("sink_" + act)
trans = PetriNet.Transition("trans_" + act, act)
if act in [artificial_start_activity, artificial_end_activity]:
trans.label = None
net.places.add(pl1)
net.places.add(pl2)
net.transitions.add(trans)
petri_utils.add_arc_from_to(pl1, trans, net)
petri_utils.add_arc_from_to(trans, pl2, net)
left_places[act] = pl1
right_places[act] = pl2
transes[act] = trans
for arc in enriched_dfg:
hidden = PetriNet.Transition(arc[0] + "_" + arc[1], None)
net.transitions.add(hidden)
petri_utils.add_arc_from_to(right_places[arc[0]], hidden, net)
petri_utils.add_arc_from_to(hidden, left_places[arc[1]], net)
im[left_places[artificial_start_activity]] = 1
fm[right_places[artificial_end_activity]] = 1
return net, im, fm
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
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_id = child.get("id")
trans_name = trans_id
trans_visible = True
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
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)
# 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
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
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 transform_net(self, net0, initial_marking0, final_marking0, s_map, avg_time_starts):
"""
Transform the source Petri net removing the initial and final marking, and connecting
to each "initial" place a hidden timed transition mimicking the case start
Parameters
-------------
net0
Initial Petri net provided to the object
initial_marking0
Initial marking of the Petri net provided to the object
final_marking0
Final marking of the Petri net provided to the object
s_map
Stochastic map of transitions (EXPONENTIAL distribution since we assume a Markovian process)
avg_time_starts
Average time interlapsed between case starts
Returns
-------------
net
Petri net that will be simulated
initial_marking
Initial marking of the Petri net that will be simulated (empty)
final_marking
Final marking of the Petri net that will be simulated (empty)
s_map
Stochastic map of transitions enriched by new hidden case-generator transitions
"""
# copy the Petri net object (assure that we do not change the original Petri net)
[net1, initial_marking1, final_marking1] = copy([net0, initial_marking0, final_marking0])
# on the copied Petri net, add a sucking transition for the final marking
for index, place in enumerate(final_marking1):
suck_transition = PetriNet.Transition("SUCK_TRANSITION" + str(index), None)
net1.transitions.add(suck_transition)
add_arc_from_to(place, suck_transition, net1)
hidden_generator_distr = Exponential()
hidden_generator_distr.scale = avg_time_starts
s_map[suck_transition] = hidden_generator_distr
# on the copied Petri net, remove both the place(s) in the initial marking and
# the immediate transitions that are connected to it.
target_places = []
for place in initial_marking1:
out_arcs = list(place.out_arcs)
for target_arc in out_arcs:
target_trans = target_arc.target
if len(target_trans.in_arcs) == 1:
out_arcs_lev2 = list(target_trans.out_arcs)
for arc in out_arcs_lev2:
target_place = arc.target
target_places.append(target_place)
net1 = remove_transition(net1, target_trans)
net1 = remove_place(net1, place)
# add hidden case-generation transitions to the model.
# all places that are left orphan by the previous operation are targeted.
for index, place in enumerate(target_places):
hidden_generator_trans = PetriNet.Transition("HIDDEN_GENERATOR_TRANS" + str(index), None)
net1.transitions.add(hidden_generator_trans)
add_arc_from_to(hidden_generator_trans, place, net1)
hidden_generator_distr = Exponential()
hidden_generator_distr.scale = avg_time_starts
s_map[hidden_generator_trans] = hidden_generator_distr
# the simulated Petri net is assumed to start from an empty initial and final marking
initial_marking = Marking()
final_marking = Marking()
return net1, initial_marking, final_marking, s_map
def construct_cost_aware(self, pn1, im1, fm1, pn2, im2, fm2, skip,
pn1_costs, pn2_costs, sync_costs):
"""
Constructs the synchronous product net of two given Petri nets.
:param pn1: Petri net 1
:param im1: Initial marking of Petri net 1
:param fm1: Final marking of Petri net 1
:param pn2: Petri net 2
:param im2: Initial marking of Petri net 2
:param fm2: Final marking of Petri net 2
:param skip: Symbol to be used as skip
:param pn1_costs: dictionary mapping transitions of pn1 to corresponding costs
:param pn2_costs: dictionary mapping transitions of pn2 to corresponding costs
:param pn1_costs: dictionary mapping pairs of transitions in pn1 and pn2 to costs
:param sync_costs: Costs of sync moves
Returns
-------
:return: Synchronous product net and associated marking labels are of the form (a,>>)
"""
sync_net = PetriNet('synchronous_product_net')
t1_map, p1_map = copy_into(pn1, sync_net, True, skip)
t2_map, p2_map = copy_into(pn2, sync_net, False, skip)
costs = dict()
lst_t_pn1 = []
lst_t_pn2 = []
for t in pn1.transitions:
lst_t_pn1.append(t)
for t in pn2.transitions:
lst_t_pn2.append(t)
lst_t_pn1.sort(key=lambda k: k.name)
lst_t_pn2.sort(key=lambda k: k.name)
for t1 in lst_t_pn1:
costs[t1_map[t1]] = pn1_costs[t1]
for t2 in lst_t_pn2:
costs[t2_map[t2]] = pn2_costs[t2]
for t1 in lst_t_pn1:
for t2 in lst_t_pn2:
if t1.label == t2.label:
sync = PetriNet.Transition((t1.name, t2.name),
(t1.label, t2.label))
sync_net.transitions.add(sync)
costs[sync] = sync_costs[(t1, t2)]
# copy the properties of the transitions inside the transition of the sync net
for p1 in t1.properties:
sync.properties[p1] = t1.properties[p1]
for p2 in t2.properties:
sync.properties[p2] = t2.properties[p2]
for a in t1.in_arcs:
add_arc_from_to(p1_map[a.source], sync, sync_net)
for a in t2.in_arcs:
add_arc_from_to(p2_map[a.source], sync, sync_net)
for a in t1.out_arcs:
add_arc_from_to(sync, p1_map[a.target], sync_net)
for a in t2.out_arcs:
add_arc_from_to(sync, p2_map[a.target], sync_net)
sync_im = SyncMarking()
sync_fm = SyncMarking()
for p in im1:
sync_im[p1_map[p]] = im1[p]
for p in im2:
sync_im[p2_map[p]] = im2[p]
for p in fm1:
sync_fm[p1_map[p]] = fm1[p]
for p in fm2:
sync_fm[p2_map[p]] = fm2[p]
# update 06/02/2021: to distinguish the sync nets that are output of this method, put a property in the sync net
sync_net.properties[properties.IS_SYNC_NET] = True
return sync_net, sync_im, sync_fm, costs
def get_transition(counts, label):
"""
Create a transitions with the specified label in the Petri net
"""
counts.inc_no_visible()
return PetriNet.Transition(str(uuid.uuid4()), label)
def get_new_hidden_trans(counts, type_trans="unknown"):
"""
Create a new hidden transition in the Petri net
"""
counts.inc_no_hidden()
return PetriNet.Transition(type_trans + '_' + str(counts.num_hidden), None)
def processing(log: EventLog, causal: Tuple[str, str], follows: Tuple[str,
str]):
"""
Applying the Alpha Miner with the new relations
Parameters
-------------
log
Filtered log
causal
Pairs that have a causal relation (->)
follows
Pairs that have a follow relation (>)
Returns
-------------
net
Petri net
im
Initial marking
fm
Final marking
"""
# create list of all events
labels = set()
start_activities = set()
end_activities = set()
for trace in log:
start_activities.add(trace.__getitem__(0))
end_activities.add(trace.__getitem__(len(trace) - 1))
for events in trace:
labels.add(events)
labels = list(labels)
pairs = []
for key, element in causal.items():
for item in element:
if get_sharp_relation(follows, key, key):
if get_sharp_relation(follows, item, item):
pairs.append(({key}, {item}))
# combining pairs
for i in range(0, len(pairs)):
t1 = pairs[i]
for j in range(i, len(pairs)):
t2 = pairs[j]
if t1 != t2:
if t1[0].issubset(t2[0]) or t1[1].issubset(t2[1]):
if get_sharp_relations_for_sets(
follows, t1[0],
t2[0]) and get_sharp_relations_for_sets(
follows, t1[1], t2[1]):
new_alpha_pair = (t1[0] | t2[0], t1[1] | t2[1])
if new_alpha_pair not in pairs:
pairs.append((t1[0] | t2[0], t1[1] | t2[1]))
# maximize pairs
cleaned_pairs = list(filter(lambda p: __pair_maximizer(pairs, p), pairs))
# create transitions
net = PetriNet('alpha_plus_net_' + str(time.time()))
label_transition_dict = {}
for label in labels:
if label != 'artificial_start' and label != 'artificial_end':
label_transition_dict[label] = PetriNet.Transition(label, label)
net.transitions.add(label_transition_dict[label])
else:
label_transition_dict[label] = PetriNet.Transition(label, None)
net.transitions.add(label_transition_dict[label])
# and source and sink
src = add_source(net, start_activities, label_transition_dict)
sink = add_sink(net, end_activities, label_transition_dict)
# create places
for pair in cleaned_pairs:
place = PetriNet.Place(str(pair))
net.places.add(place)
for in_arc in pair[0]:
add_arc_from_to(label_transition_dict[in_arc], place, net)
for out_arc in pair[1]:
add_arc_from_to(place, label_transition_dict[out_arc], net)
return net, Marking({src: 1}), Marking({sink: 1}), cleaned_pairs
def test_figure42(self):
net = PetriNet("figure_4_2")
p_1 = PetriNet.Place("p_1")
p_2 = PetriNet.Place("p_2")
p_3 = PetriNet.Place("p_3")
p_4 = PetriNet.Place("p_4")
p_5 = PetriNet.Place("p_5")
p_6 = PetriNet.Place("p_6")
p_7 = PetriNet.Place("p_7")
p_8 = PetriNet.Place("p_8")
net.places.add(p_1)
net.places.add(p_2)
net.places.add(p_3)
net.places.add(p_4)
net.places.add(p_5)
net.places.add(p_6)
net.places.add(p_7)
net.places.add(p_8)
t_1 = PetriNet.Transition("t_1", "t_1")
t_2 = PetriNet.Transition("t_2", "t_2")
t_3 = PetriNet.Transition("t_3", "t_3")
t_4 = PetriNet.Transition("t_4", "t_4")
t_5 = PetriNet.Transition("t_5", "t_5")
t_6 = PetriNet.Transition("t_6", "t_6")
t_7 = PetriNet.Transition("t_7", "t_7")
t_8 = PetriNet.Transition("t_8", "t_8")
net.transitions.add(t_1)
net.transitions.add(t_2)
net.transitions.add(t_3)
net.transitions.add(t_4)
net.transitions.add(t_5)
net.transitions.add(t_6)
net.transitions.add(t_7)
net.transitions.add(t_8)
petri_utils.add_arc_from_to(p_1, t_1, net)
petri_utils.add_arc_from_to(t_1, p_6, net)
petri_utils.add_arc_from_to(t_1, p_4, net)
petri_utils.add_arc_from_to(p_4, t_4, net)
petri_utils.add_arc_from_to(p_4, t_5, net)
petri_utils.add_arc_from_to(t_2, p_6, net)
petri_utils.add_arc_from_to(t_2, p_4, net)
petri_utils.add_arc_from_to(t_4, p_3, net)
petri_utils.add_arc_from_to(t_4, p_5, net)
petri_utils.add_arc_from_to(t_5, p_7, net)
petri_utils.add_arc_from_to(t_7, p_4, net)
petri_utils.add_arc_from_to(p_3, t_2, net)
petri_utils.add_arc_from_to(p_3, t_3, net)
petri_utils.add_arc_from_to(p_5, t_2, net)
petri_utils.add_arc_from_to(p_5, t_3, net)
petri_utils.add_arc_from_to(p_5, t_4, net)
petri_utils.add_arc_from_to(p_7, t_6, net)
petri_utils.add_arc_from_to(p_8, t_7, net)
petri_utils.add_arc_from_to(p_8, t_8, net)
petri_utils.add_arc_from_to(t_3, p_2, net)
petri_utils.add_arc_from_to(p_6, t_6, net)
petri_utils.add_arc_from_to(t_6, p_5, net)
petri_utils.add_arc_from_to(t_8, p_8, net)
initial_marking = Marking()
initial_marking[p_1] = 1
final_marking = Marking()
final_marking[p_2] = 1
self.assertFalse(woflan.apply(net, initial_marking, final_marking, parameters={"print_diagnostics": False}))
def test_mcg(self):
net = PetriNet("mcg")
p_1 = PetriNet.Place("p_1")
p_2 = PetriNet.Place("p_2")
p_3 = PetriNet.Place("p_3")
p_4 = PetriNet.Place("p_4")
p_5 = PetriNet.Place("p_5")
net.places.add(p_1)
net.places.add(p_2)
net.places.add(p_3)
net.places.add(p_4)
net.places.add(p_5)
t_1 = PetriNet.Transition("t_1", "t_1")
t_2 = PetriNet.Transition("t_2", "t_2")
t_3 = PetriNet.Transition("t_3", "t_3")
t_4 = PetriNet.Transition("t_4", "t_4")
t_5 = PetriNet.Transition("t_5", "t_5")
t_6 = PetriNet.Transition("t_6", "t_6")
net.transitions.add(t_1)
net.transitions.add(t_2)
net.transitions.add(t_3)
net.transitions.add(t_4)
net.transitions.add(t_5)
net.transitions.add(t_6)
petri_utils.add_arc_from_to(p_1, t_1, net)
petri_utils.add_arc_from_to(t_1, p_2, net)
petri_utils.add_arc_from_to(p_2, t_3, net)
petri_utils.add_arc_from_to(t_3, p_3, net, weight=2)
petri_utils.add_arc_from_to(p_3, t_4, net)
petri_utils.add_arc_from_to(t_4, p_2, net)
petri_utils.add_arc_from_to(p_1, t_2, net)
petri_utils.add_arc_from_to(t_2, p_4, net)
petri_utils.add_arc_from_to(p_4, t_5, net)
petri_utils.add_arc_from_to(t_5, p_5, net, weight=2)
petri_utils.add_arc_from_to(p_5, t_6, net)
petri_utils.add_arc_from_to(t_6, p_4, net)
initial_marking = Marking()
initial_marking[p_1] = 1
mcg = minimal_coverability_graph.apply(net, initial_marking)
def apply(tree, parameters=None):
"""
Apply from Process Tree to Petri net
Parameters
-----------
tree
Process tree
parameters
Parameters of the algorithm
Returns
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
"""
if parameters is None:
parameters = {}
del parameters
counts = Counts()
net = PetriNet('imdf_net_' + str(time.time()))
initial_marking = Marking()
final_marking = Marking()
source = get_new_place(counts)
source.name = "source"
sink = get_new_place(counts)
sink.name = "sink"
net.places.add(source)
net.places.add(sink)
initial_marking[source] = 1
final_marking[sink] = 1
initial_mandatory = check_tau_mandatory_at_initial_marking(tree)
final_mandatory = check_tau_mandatory_at_final_marking(tree)
if initial_mandatory:
initial_place = get_new_place(counts)
net.places.add(initial_place)
tau_initial = get_new_hidden_trans(counts, type_trans="tau")
net.transitions.add(tau_initial)
add_arc_from_to(source, tau_initial, net)
add_arc_from_to(tau_initial, initial_place, net)
else:
initial_place = source
if final_mandatory:
final_place = get_new_place(counts)
net.places.add(final_place)
tau_final = get_new_hidden_trans(counts, type_trans="tau")
net.transitions.add(tau_final)
add_arc_from_to(final_place, tau_final, net)
add_arc_from_to(tau_final, sink, net)
else:
final_place = sink
net, counts, last_added_place = recursively_add_tree(
tree, tree, net, initial_place, final_place, counts, 0)
reduction.apply_simple_reduction(net)
places = list(net.places)
for place in places:
if len(place.out_arcs) == 0 and not place in final_marking:
remove_place(net, place)
if len(place.in_arcs) == 0 and not place in initial_marking:
remove_place(net, place)
return net, initial_marking, final_marking
def get_new_place(counts):
"""
Create a new place in the Petri net
"""
counts.inc_places()
return PetriNet.Place('p_' + str(counts.num_places))
