pm4py.objects.petri_net.utils package#

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

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Submodules#

pm4py.objects.petri_net.utils.align_utils module#

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pm4py.objects.petri_net.utils.align_utils.search_path_among_sol(sync_net: PetriNet, ini: Marking, fin: Marking, activated_transitions: List[Transition], skip='>>') → Tuple[List[Transition], bool, int][source]#

(Efficient method) Searches a firing sequence among the X vector that is the solution of the (extended) marking equation

Parameters#

sync_net: Synchronous product net
ini: Initial marking of the net
fin: Final marking of the net
activated_transitions: Transitions that have non-zero occurrences in the X vector
skip: Skip transition

Returns#

firing_sequence: Firing sequence
reach_fm: Boolean value that tells if the final marking is reached by the firing sequence
explained_events: Number of explained events

pm4py.objects.petri_net.utils.align_utils.construct_standard_cost_function(synchronous_product_net, skip)[source]#: Returns the standard cost function, which is: * event moves: cost 1000 * model moves: cost 1000 * tau moves: cost 1 * sync moves: cost 0 :param synchronous_product_net: :param skip: :return:

pm4py.objects.petri_net.utils.align_utils.pretty_print_alignments(alignments)[source]#

Takes an alignment and prints it to the console, e.g.:: A | B | C | D |

Returns:: Nothing

pm4py.objects.petri_net.utils.align_utils.add_markings(curr, add)[source]#

class pm4py.objects.petri_net.utils.align_utils.SearchTuple(f, g, h, m, p, t, x, trust)[source]#: Bases: object

class pm4py.objects.petri_net.utils.align_utils.DijkstraSearchTuple(g, m, p, t, l)[source]#: Bases: object

class pm4py.objects.petri_net.utils.align_utils.DijkstraSearchTupleForAntiAndMulti(g, m, r)[source]#: Bases: object

class pm4py.objects.petri_net.utils.align_utils.TweakedSearchTuple(f, g, h, m, p, t, x, trust, virgin)[source]#: Bases: object

pm4py.objects.petri_net.utils.align_utils.get_visible_transitions_eventually_enabled_by_marking(net, marking)[source]#

Get visible transitions eventually enabled by marking (passing possibly through hidden transitions) Parameters ———- net

marking: Current marking

pm4py.objects.petri_net.utils.align_utils.discountedEditDistance(s1, s2, exponent=2, modeled=True)[source]#: Fast implementation of the discounted distance Inspired from the faster version of the edit distance

pm4py.objects.petri_net.utils.align_utils.levenshtein(seq1, seq2)[source]#: Edit distance without substitution

pm4py.objects.petri_net.utils.check_soundness module#

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pm4py.objects.petri_net.utils.check_soundness.check_source_and_sink_reachability(net, unique_source, unique_sink)[source]#

Checks reachability of the source and the sink place from all simulation nodes (places/transitions) of the Petri net

Parameters#

net: Petri net
unique_source: Unique source place of the Petri net
unique_sink: Unique sink place of the Petri net

Returns#

boolean: Boolean value that is true if each node is in a path from the source place to the sink place

pm4py.objects.petri_net.utils.check_soundness.check_source_place_presence(net)[source]#

Check if there is a unique source place with empty connections

Parameters#

net: Petri net

Returns#

place: Unique source place (or None otherwise)

pm4py.objects.petri_net.utils.check_soundness.check_sink_place_presence(net)[source]#

Check if there is a unique sink place with empty connections

Parameters#

net: Petri net

Returns#

place: Unique source place (or None otherwise)

pm4py.objects.petri_net.utils.check_soundness.check_wfnet(net)[source]#

Check if the Petri net is a workflow net

Parameters#

net: Petri net

Returns#

boolean: Boolean value that is true when the Petri net is a workflow net

pm4py.objects.petri_net.utils.check_soundness.check_source_sink_place_conditions(net)[source]#

Check some conditions on the source/sink place important for a sound workflow net

Parameters#

net: Petri net

Returns#

boolean: Boolean value (True is good)

pm4py.objects.petri_net.utils.check_soundness.check_easy_soundness_net_in_fin_marking(net, ini, fin)[source]#

Checks the easy soundness of a Petri net having the initial and the final marking

Parameters#

net: Petri net
ini: Initial marking
fin: Final marking

Returns#

boolean: Boolean value

pm4py.objects.petri_net.utils.check_soundness.check_easy_soundness_of_wfnet(net)[source]#

Checks the easy soundness of a workflow net

Parameters#

net: Petri net

Returns#

boolean: Boolean value

pm4py.objects.petri_net.utils.consumption_matrix module#

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class pm4py.objects.petri_net.utils.consumption_matrix.ConsumptionMatrix(net: PetriNet)[source]#

Bases: object

property places: Dict[Place, int]#

Gets the places in the order in which they have been inserted in the consumption matrix

Returns#

place_indices: Dictionary associating to each place an incremental number

property transitions: Dict[Transition, int]#

Gets the transitions in the order in which they have been inserted in the consumption matrix

Returns#

trans_indices: Dictionary associating to each transition an incremental number

property c_matrix: ndarray#

Gets the Numpy representation of the consumption matrix

Returns#

C: C matrix

pm4py.objects.petri_net.utils.consumption_matrix.construct(net: PetriNet) → ConsumptionMatrix[source]#

Construct a consumption matrix given a Petri net

Parameters#

net: Petri net

Returns#

cons_mat: Consumption matrix object

pm4py.objects.petri_net.utils.decomposition module#

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pm4py.objects.petri_net.utils.decomposition.get_graph_components(places, inv_trans, trans_dup_label, tmap)[source]#

pm4py.objects.petri_net.utils.decomposition.decompose(net, im, fm)[source]#

pm4py.objects.petri_net.utils.decomposition.merge_comp(comp1, comp2)[source]#

pm4py.objects.petri_net.utils.decomposition.merge_sublist_nets(list_nets)[source]#

pm4py.objects.petri_net.utils.embed_stochastic_map module#

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Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.petri_net.utils.embed_stochastic_map.apply(smap, parameters=None)[source]#

Embed the stochastic map into the Petri net

Parameters#

smap: Stochastic map
parameters: Possible parameters of the algorithm

Returns#

void

pm4py.objects.petri_net.utils.embed_stochastic_map.extract(net, parameters=None)[source]#

Extract the stochastic map from the Petri net

Parameters#

net: Petri net
parameters: Possible parameters of the algorithm

Returns#

void

pm4py.objects.petri_net.utils.explore_path module#

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Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.petri_net.utils.final_marking module#

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.petri_net.utils.final_marking.discover_final_marking(petri)[source]#

Discovers final marking from a Petri net

Parameters#

petri: Petri net

Returns#

final_marking: Final marking

pm4py.objects.petri_net.utils.incidence_matrix module#

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Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.objects.petri_net.utils.incidence_matrix.IncidenceMatrix(net)[source]#

Bases: object

encode_marking(marking)[source]#

property a_matrix#

property places#

property transitions#

pm4py.objects.petri_net.utils.incidence_matrix.construct(net)[source]#

pm4py.objects.petri_net.utils.initial_marking module#

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.petri_net.utils.initial_marking.discover_initial_marking(petri)[source]#

Discovers initial marking from a Petri net

Parameters#

petri: Petri net

Returns#

initial_marking: Initial marking of the Petri net

pm4py.objects.petri_net.utils.murata module#

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

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pm4py.objects.petri_net.utils.murata.apply_reduction(net: PetriNet, im: Marking, fm: Marking) → Tuple[PetriNet, Marking, Marking][source]#

Apply the Murata reduction to an accepting Petri net, removing the structurally redundant places.

The implementation follows the Berthelot algorithm as in: https://svn.win.tue.nl/repos/prom/Packages/Murata/Trunk/src/org/processmining/algorithms/BerthelotAlgorithm.java

Parameters#

net: Petri net
im: Initial marking
fm: Final marking

Returns#

net: Petri net
im: Initial marking
fm: Final marking

pm4py.objects.petri_net.utils.networkx_graph module#

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.petri_net.utils.networkx_graph.create_networkx_undirected_graph(net, unique_source, unique_sink)[source]#

Create a NetworkX undirected graph from a Petri net, returning also correspondences for the unique source and the unique sink places that were discovered

Parameters#

net: Petri net
unique_source: Unique source place
unique_sink: Unique sink place

Returns#

graph: NetworkX graph
unique_source_corr: Correspondence in the NetworkX graph of the unique source place
unique_sink_corr: Correspondence in the NetworkX graph of the unique sink place
inv_dictionary: Correspondence between NetworkX nodes and Petri net entities

pm4py.objects.petri_net.utils.networkx_graph.create_networkx_directed_graph(net, weight=None)[source]#

Create a NetworkX directed graph from a Petri net

Parameters#

net: Petri net

Returns#

graph: NetworkX digraph
inv_dictionary: Correspondence between NetworkX nodes and Petri net entities

pm4py.objects.petri_net.utils.networkx_graph.create_networkx_directed_graph_ret_dict_both_ways(net, weight=None)[source]#

Create a NetworkX directed graph from a Petri net

Parameters#

net: Petri net

Returns#

graph: NetworkX digraph
dictionary: dict mapping Petri net nodes to NetworkX nodes
inv_dictionary: dict mapping NetworkX nodes to Petri net nodes

pm4py.objects.petri_net.utils.obj_marking module#

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class pm4py.objects.petri_net.utils.obj_marking.ObjMarking(*args, **kwargs)[source]#

Bases: dict

update([E, ]**F) → None. Update D from dict/iterable E and F.[source]#: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

class pm4py.objects.petri_net.utils.obj_marking.ObjMarkingAccessor(obj_marking, key)[source]#: Bases: object

pm4py.objects.petri_net.utils.performance_map module#

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pm4py.objects.petri_net.utils.performance_map.calculate_annotation_for_trace(trace, net, initial_marking, act_trans, activity_key, ht_perf_method='last')[source]#

Calculate annotation for a trace in the variant, in order to retrieve information useful for calculate frequency/performance for all the traces belonging to the variant

Parameters#

trace: Trace
net: Petri net
initial_marking: Initial marking
act_trans: Activated transitions during token replay of the given trace
activity_key: Attribute that identifies the activity (must be specified if different from concept:name)
ht_perf_method: Method to use in order to annotate hidden transitions (performance value could be put on the last possible point (last) or in the first possible point (first)

Returns#

annotation: Statistics annotation for the given trace

pm4py.objects.petri_net.utils.performance_map.single_element_statistics(log, net, initial_marking, aligned_traces, variants_idx, activity_key='concept:name', timestamp_key='time:timestamp', ht_perf_method='last', parameters=None)[source]#

Get single Petrinet element statistics

Parameters#

log: Log
net: Petri net
initial_marking: Initial marking
aligned_traces: Result of the token-based replay
variants_idx: Variants along with indexes of belonging traces
activity_key: Activity key (must be specified if different from concept:name)
timestamp_key: Timestamp key (must be specified if different from time:timestamp)
ht_perf_method: Method to use in order to annotate hidden transitions (performance value could be put on the last possible point (last) or in the first possible point (first)
parameters: Possible parameters of the algorithm

Returns#

statistics: Petri net element statistics (frequency, unaggregated performance)

pm4py.objects.petri_net.utils.performance_map.find_min_max_trans_frequency(statistics)[source]#

Find minimum and maximum transition frequency

Parameters#

statistics: Element statistics

Returns#

min_frequency: Minimum transition frequency (in the replay)
max_frequency: Maximum transition frequency (in the replay)

pm4py.objects.petri_net.utils.performance_map.find_min_max_arc_frequency(statistics)[source]#

Find minimum and maximum arc frequency

Parameters#

statistics: Element statistics

Returns#

min_frequency: Minimum arc frequency
max_frequency: Maximum arc frequency

pm4py.objects.petri_net.utils.performance_map.aggregate_stats(statistics, elem, aggregation_measure)[source]#

Aggregate the statistics

Parameters#

statistics: Element statistics
elem: Current element
aggregation_measure: Aggregation measure (e.g. mean, min) to use

Returns#

aggr_stat: Aggregated statistics

pm4py.objects.petri_net.utils.performance_map.find_min_max_arc_performance(statistics, aggregation_measure)[source]#

Find minimum and maximum arc performance

Parameters#

statistics: Element statistics
aggregation_measure: Aggregation measure (e.g. mean, min) to use

Returns#

min_performance: Minimum performance
max_performance: Maximum performance

pm4py.objects.petri_net.utils.performance_map.aggregate_statistics(statistics, measure='frequency', aggregation_measure=None, stat_locale: dict = {})[source]#

Gets aggregated statistics

Parameters#

statistics: Individual element statistics (including unaggregated performances)
measure: Desidered view on data (frequency or performance)
aggregation_measure: Aggregation measure (e.g. mean, min) to use
stat_locale: Dict to locale the stat strings

Returns#

aggregated_statistics: Aggregated statistics for arcs, transitions, places

pm4py.objects.petri_net.utils.performance_map.get_transition_performance_with_token_replay(log, net, im, fm)[source]#

Gets the transition performance through the usage of token-based replay

Parameters#

log: Event log
net: Petri net
im: Initial marking
fm: Final marking

Returns#

transition_performance: Dictionary where each transition label is associated to performance measures

pm4py.objects.petri_net.utils.performance_map.get_idx_exceeding_specified_acti_performance(log, transition_performance, activity, lower_bound)[source]#

Get indexes of the cases exceeding the specified activity performance threshold

Parameters#

log: Event log
transition_performance: Dictionary where each transition label is associated to performance measures
activity: Target activity (of the filter)
lower_bound: Lower bound (filter cases which have a duration of the activity exceeding)

Returns#

idx: A list of indexes in the log

pm4py.objects.petri_net.utils.performance_map.filter_cases_exceeding_specified_acti_performance(log, transition_performance, activity, lower_bound)[source]#

Filter cases exceeding the specified activity performance threshold

Parameters#

log: Event log
transition_performance: Dictionary where each transition label is associated to performance measures
activity: Target activity (of the filter)
lower_bound: Lower bound (filter cases which have a duration of the activity exceeding)

Returns#

filtered_log: Filtered log

pm4py.objects.petri_net.utils.petri_utils module#

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.petri_net.utils.petri_utils.is_sub_marking(sub_marking: Marking, marking: Marking) → bool[source]#

pm4py.objects.petri_net.utils.petri_utils.place_set_as_marking(places) → Marking[source]#

pm4py.objects.petri_net.utils.petri_utils.get_arc_type(elem)[source]#

pm4py.objects.petri_net.utils.petri_utils.pre_set(elem, arc_type=None) → Set[source]#

pm4py.objects.petri_net.utils.petri_utils.post_set(elem, arc_type=None) → Set[source]#

pm4py.objects.petri_net.utils.petri_utils.remove_transition(net: PetriNet, trans: Transition) → PetriNet[source]#

Remove a transition from a Petri net

Parameters#

net: Petri net
trans: Transition to remove

Returns#

net: Petri net

pm4py.objects.petri_net.utils.petri_utils.add_place(net: PetriNet, name=None) → Place[source]#

pm4py.objects.petri_net.utils.petri_utils.add_transition(net: PetriNet, name=None, label=None) → Transition[source]#

pm4py.objects.petri_net.utils.petri_utils.merge(trgt: PetriNet | None = None, nets=None) → PetriNet[source]#

pm4py.objects.petri_net.utils.petri_utils.remove_place(net: PetriNet, place: Place) → PetriNet[source]#

Remove a place from a Petri net

Parameters#

net: Petri net
place: Place to remove

Returns#

net: Petri net

pm4py.objects.petri_net.utils.petri_utils.add_arc_from_to(fr, to, net: PetriNet, weight=1, type=None) → Arc[source]#: 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

pm4py.objects.petri_net.utils.petri_utils.construct_trace_net(trace, trace_name_key='concept:name', activity_key='concept:name')[source]#: Creates a trace net, i.e. a trace in Petri net form.

Parameters#

trace: list input trace, assumed to be a list of events trace_name_key: str key of the attribute that defines the name of the trace activity_key: str key of the attribute of the events that defines the activity name

Returns#

tuple: tuple of the net, initial marking and the final marking

pm4py.objects.petri_net.utils.petri_utils.construct_trace_net_cost_aware(trace, costs, trace_name_key='concept:name', activity_key='concept:name')[source]#: Creates a trace net, i.e. a trace in Petri net form mapping specific costs to transitions.

Parameters#

trace: list input trace, assumed to be a list of events costs: list list of costs, length should be equal to the length of the input trace trace_name_key: str key of the attribute that defines the name of the trace activity_key: str key of the attribute of the events that defines the activity name

Returns#

tuple: tuple of the net, initial marking, final marking and map of costs

pm4py.objects.petri_net.utils.petri_utils.acyclic_net_variants(net, initial_marking, final_marking, activity_key='concept:name')[source]#

Given an acyclic accepting Petri net, initial and final marking extracts a set of variants (in form of traces) replayable on the net. Warning: this function is based on a marking exploration. If the accepting Petri net contains loops, the method will not work properly as it stops the search if a specific marking has already been encountered.

Parameters#

type activity_key:: str
param net:: An acyclic workflow net
param initial_marking:: The initial marking of the net.
param final_marking:: The final marking of the net.
param activity_key:: activity key to use

Returns#

return:: variants: list Set of variants - in the form of Trace objects - obtainable executing the net

pm4py.objects.petri_net.utils.petri_utils.get_transition_by_name(net: PetriNet, transition_name) → Transition | None[source]#

Get a transition by its name

Parameters#

net: Petri net
transition_name: Transition name

Returns#

transition: Transition object

pm4py.objects.petri_net.utils.petri_utils.decorate_places_preset_trans(net: PetriNet)[source]#

Decorate places with information useful for the replay

Parameters#

net: Petri net

pm4py.objects.petri_net.utils.petri_utils.decorate_transitions_prepostset(net: PetriNet)[source]#

Decorate transitions with sub and addition markings

Parameters#

net: Petri net

pm4py.objects.petri_net.utils.petri_utils.get_places_shortest_path(net, place_to_populate, current_place, places_shortest_path, actual_list, rec_depth, max_rec_depth)[source]#

Get shortest path between places lead by hidden transitions

Parameters#

net: Petri net
place_to_populate: Place that we are populating the shortest map of
current_place: Current visited place (must explore its transitions)
places_shortest_path: Current dictionary
actual_list: Actual list of transitions to enable
rec_depth: Recursion depth
max_rec_depth: Maximum recursion depth

pm4py.objects.petri_net.utils.petri_utils.get_places_shortest_path_by_hidden(net: PetriNet, max_rec_depth)[source]#

Get shortest path between places lead by hidden transitions

Parameters#

net: Petri net
max_rec_depth: Maximum recursion depth

pm4py.objects.petri_net.utils.petri_utils.invert_spaths_dictionary(spaths)[source]#

Invert the shortest paths (between places) dictionary, from target-source to source-target

Parameters#

spaths: Shortest paths dictionary

Returns#

inv_spaths: Inverted shortest paths dictionary

pm4py.objects.petri_net.utils.petri_utils.remove_unconnected_components(net: PetriNet) → PetriNet[source]#

Remove unconnected components from a Petri net

Parameters#

net: Petri net

Returns#

net: Cleaned Petri net

pm4py.objects.petri_net.utils.petri_utils.get_s_components_from_petri(net, im, fm, rec_depth=0, curr_s_comp=None, visited_places=None, list_s_components=None, max_rec_depth=6)[source]#

Gets the S-components from a Petri net

Parameters#

net: Petri net
im: Initial marking
fm: Final marking
curr_s_comp: Current S component
visited_places: Visited places
list_s_components: List of S-components
max_rec_depth: Maximum recursion depth

Returns#

s_components: List of S-components

pm4py.objects.petri_net.utils.petri_utils.remove_arc(net: PetriNet, arc: Arc) → PetriNet[source]#

Removes an arc from a Petri net

Parameters#

net: Petri net
arc: Arc of the Petri net

Returns#

net: Petri net

pm4py.objects.petri_net.utils.projection module#

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.petri_net.utils.projection.project_net_on_place(place)[source]#

Project a Petri net on a place

Parameters#

place: Place

Returns#

net: (Place) net
im: Empty initial marking
fm: Empty final marking

pm4py.objects.petri_net.utils.projection.project_net_on_matrix(net, activities, parameters=None)[source]#

Project a Petri net with: - only visible transitions - where each place preset/postset is disjoint - with unique visible transitions on a numeric matrix

Parameters#

net: Petri net
activities: List of activities
parameters: Possible parameters of the algorithm

pm4py.objects.petri_net.utils.reachability_graph module#

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.objects.petri_net.utils.reachability_graph.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

MAX_ELAB_TIME = 'max_elab_time'#

PETRI_SEMANTICS = 'petri_semantics'#

pm4py.objects.petri_net.utils.reachability_graph.staterep(name)[source]#: Creates a string representation for a state of a transition system. Necessary because graphviz does not support symbols simulation than alphanimerics and ‘_’. TODO: find a better representation.

Parameters#

name: the name of a state

Returns#

Version of the name filtered of non-alphanumerical characters (except ‘_’).

pm4py.objects.petri_net.utils.reachability_graph.marking_flow_petri(net, im, return_eventually_enabled=False, parameters=None)[source]#

Construct the marking flow of a Petri net

Parameters#

net: Petri net
im: Initial marking
return_eventually_enabled: Return the eventually enabled (visible) transitions

pm4py.objects.petri_net.utils.reachability_graph.construct_reachability_graph_from_flow(incoming_transitions, outgoing_transitions, use_trans_name=False, parameters=None)[source]#

Construct the reachability graph from the marking flow

Parameters#

incoming_transitions: Incoming transitions
outgoing_transitions: Outgoing transitions
use_trans_name: Use the transition name

Returns#

re_gr: Transition system that represents the reachability graph of the input Petri net.

pm4py.objects.petri_net.utils.reachability_graph.construct_reachability_graph(net, initial_marking, use_trans_name=False, parameters=None) → TransitionSystem[source]#: Creates a reachability graph of a certain Petri net. DO NOT ATTEMPT WITH AN UNBOUNDED PETRI NET, EVER.

Parameters#

net: Petri net initial_marking: initial marking of the Petri net.

Returns#

re_gr: Transition system that represents the reachability graph of the input Petri net.

pm4py.objects.petri_net.utils.reduction module#

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.petri_net.utils.reduction.reduce_single_entry_transitions(net)[source]#

Reduces the number of the single entry transitions in the Petri net

Parameters#

net: Petri net

pm4py.objects.petri_net.utils.reduction.reduce_single_exit_transitions(net)[source]#

Reduces the number of the single exit transitions in the Petri net

Parameters#

net: Petri net

pm4py.objects.petri_net.utils.reduction.apply_simple_reduction(net)[source]#

Apply a simple reduction to the Petri net

Parameters#

net: Petri net

pm4py.objects.petri_net.utils.reduction.apply_fst_rule(net)[source]#

Apply the Fusion of Series Transitions (FST) rule

Parameters#

net: Reset Inhibitor net

pm4py.objects.petri_net.utils.reduction.apply_fsp_rule(net, im=None, fm=None)[source]#

Apply the Fusion of Series Places (FSP) rule

Parameters#

net: Reset Inhibitor net

pm4py.objects.petri_net.utils.reduction.apply_fpt_rule(net)[source]#

Apply the Fusion of Parallel Transitions (FPT) rule

Parameters#

net: Reset Inhibitor net

pm4py.objects.petri_net.utils.reduction.apply_fpp_rule(net, im=None)[source]#

Apply the Fusion of Parallel Places (FPP) rule

Parameters#

net: Reset Inhibitor net

pm4py.objects.petri_net.utils.reduction.apply_elt_rule(net)[source]#

Apply the Elimination of Self-Loop Transitions (ELT) rule

Parameters#

net: Reset Inhibitor net

pm4py.objects.petri_net.utils.reduction.apply_elp_rule(net, im=None)[source]#

Apply the Elimination of Self-Loop Places (ELP) rule

Parameters#

net: Reset Inhibitor net

pm4py.objects.petri_net.utils.reduction.apply_a_rule(net)[source]#

Apply the Abstraction (A) rule

Parameters#

net: Reset Inhibitor net

pm4py.objects.petri_net.utils.reduction.apply_r_rule(net)[source]#

Apply the Reset Reduction (R) rule

Parameters#

net: Reset Inhibitor net

pm4py.objects.petri_net.utils.reduction.power_set(iterable, min=0)[source]#

pm4py.objects.petri_net.utils.reduction.apply_reset_inhibitor_net_reduction(net, im=None, fm=None)[source]#

Apply a thorough reduction to the Reset Inhibitor net

Parameters#

net: Reset Inhibitor net

pm4py.objects.petri_net.utils.synchronous_product module#

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.petri_net.utils.synchronous_product.construct(pn1, im1, fm1, pn2, im2, fm2, skip)[source]#

Constructs the synchronous product net of two given Petri nets.

Parameters:

pn1 – Petri net 1
im1 – Initial marking of Petri net 1
fm1 – Final marking of Petri net 1
pn2 – Petri net 2
im2 – Initial marking of Petri net 2
fm2 – Final marking of Petri net 2
skip – Symbol to be used as skip

Returns#

return:: Synchronous product net and associated marking labels are of the form (a,>>)

pm4py.objects.petri_net.utils.synchronous_product.construct_cost_aware(pn1, im1, fm1, pn2, im2, fm2, skip, pn1_costs, pn2_costs, sync_costs)[source]#

Constructs the synchronous product net of two given Petri nets.

Parameters:

pn1 – Petri net 1
im1 – Initial marking of Petri net 1
fm1 – Final marking of Petri net 1
pn2 – Petri net 2
im2 – Initial marking of Petri net 2
fm2 – Final marking of Petri net 2
skip – Symbol to be used as skip
pn1_costs – dictionary mapping transitions of pn1 to corresponding costs
pn2_costs – dictionary mapping transitions of pn2 to corresponding costs
pn1_costs – dictionary mapping pairs of transitions in pn1 and pn2 to costs
sync_costs – Costs of sync moves

Returns#

return:: Synchronous product net and associated marking labels are of the form (a,>>)