pm4py.visualization.petri_net.variants package#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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.
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
Submodules#
pm4py.visualization.petri_net.variants.alignments module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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.
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.visualization.petri_net.variants.alignments.apply(net: PetriNet, initial_marking: Marking, final_marking: Marking, log=None, aggregated_statistics=None, parameters: Dict[Any, Any] | None = None) str [source]#
Apply method for Petri net visualization (it calls the graphviz_visualization method)
Parameters#
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- log
(Optional) log
- aggregated_statistics
Dictionary containing the frequency statistics
- parameters
Algorithm parameters
Returns#
- viz
Graph object
pm4py.visualization.petri_net.variants.greedy_decoration_frequency module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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.
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.visualization.petri_net.variants.greedy_decoration_frequency.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum
- FORMAT = 'format'#
- DEBUG = 'debug'#
- RANKDIR = 'set_rankdir'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- AGGREGATION_MEASURE = 'aggregationMeasure'#
- FONT_SIZE = 'font_size'#
- pm4py.visualization.petri_net.variants.greedy_decoration_frequency.get_decorated_net(net: PetriNet, initial_marking: Marking, final_marking: Marking, log: EventLog, parameters: Dict[str | Parameters, Any] | None = None, variant: str = 'frequency') Digraph [source]#
Get a decorated net according to the specified variant (decorate Petri net based on DFG)
Parameters#
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- log
Log to use to decorate the Petri net
- parameters
Algorithm parameters
- variant
Specify if the decoration should take into account the frequency or the performance
Returns#
- gviz
GraphViz object
- pm4py.visualization.petri_net.variants.greedy_decoration_frequency.apply(net: PetriNet, initial_marking: Marking, final_marking: Marking, log: EventLog = None, aggregated_statistics=None, parameters: Dict[str | Parameters, Any] | None = None) Digraph [source]#
Apply frequency decoration through greedy algorithm (decorate Petri net based on DFG)
Parameters#
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- log
Log to use to decorate the Petri net
- aggregated_statistics
Dictionary containing the frequency statistics
- parameters
Algorithm parameters
Returns#
- gviz
GraphViz object
pm4py.visualization.petri_net.variants.greedy_decoration_performance module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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.
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.visualization.petri_net.variants.greedy_decoration_performance.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum
- FORMAT = 'format'#
- DEBUG = 'debug'#
- RANKDIR = 'set_rankdir'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- AGGREGATION_MEASURE = 'aggregationMeasure'#
- FONT_SIZE = 'font_size'#
- STAT_LOCALE = 'stat_locale'#
- pm4py.visualization.petri_net.variants.greedy_decoration_performance.get_decorated_net(net, initial_marking, final_marking, log, parameters=None, variant='frequency')[source]#
Get a decorated net according to the specified variant (decorate Petri net based on DFG)
Parameters#
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- log
Log to use to decorate the Petri net
- parameters
Algorithm parameters
- variant
Specify if the decoration should take into account the frequency or the performance
Returns#
- gviz
GraphViz object
- pm4py.visualization.petri_net.variants.greedy_decoration_performance.apply(net: PetriNet, initial_marking: Marking, final_marking: Marking, log: EventLog = None, aggregated_statistics=None, parameters: Dict[str | Parameters, Any] | None = None) Digraph [source]#
Apply performance decoration through greedy algorithm (decorate Petri net based on DFG)
Parameters#
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- log
Log to use to decorate the Petri net
- aggregated_statistics
Dictionary containing the frequency statistics
- parameters
Algorithm parameters
Returns#
- gviz
GraphViz object
pm4py.visualization.petri_net.variants.token_decoration_frequency module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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.
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.visualization.petri_net.variants.token_decoration_frequency.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum
- FORMAT = 'format'#
- DEBUG = 'debug'#
- RANKDIR = 'set_rankdir'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- AGGREGATION_MEASURE = 'aggregationMeasure'#
- FONT_SIZE = 'font_size'#
- pm4py.visualization.petri_net.variants.token_decoration_frequency.get_decorations(log, net, initial_marking, final_marking, parameters=None, measure='frequency', ht_perf_method='last')[source]#
Calculate decorations in order to annotate the Petri net
Parameters#
- log
Trace log
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- parameters
Parameters associated to the algorithm
- measure
Measure to represent on the process model (frequency/performance)
- 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#
- decorations
Decorations to put on the process model
- pm4py.visualization.petri_net.variants.token_decoration_frequency.apply(net: PetriNet, initial_marking: Marking, final_marking: Marking, log: EventLog = None, aggregated_statistics=None, parameters: Dict[str | Parameters, Any] | None = None) Digraph [source]#
Apply method for Petri net visualization (it calls the graphviz_visualization method) adding frequency representation obtained by token replay
Parameters#
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- log
(Optional) log
- aggregated_statistics
Dictionary containing the frequency statistics
- parameters
Algorithm parameters (including the activity key used during the replay, and the timestamp key)
Returns#
- viz
Graph object
pm4py.visualization.petri_net.variants.token_decoration_performance module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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.
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.visualization.petri_net.variants.token_decoration_performance.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum
- FORMAT = 'format'#
- DEBUG = 'debug'#
- RANKDIR = 'set_rankdir'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- AGGREGATION_MEASURE = 'aggregationMeasure'#
- FONT_SIZE = 'font_size'#
- STAT_LOCALE = 'stat_locale'#
- pm4py.visualization.petri_net.variants.token_decoration_performance.get_decorations(log, net, initial_marking, final_marking, parameters=None, measure='frequency', ht_perf_method='last')[source]#
Calculate decorations in order to annotate the Petri net
Parameters#
- log
Trace log
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- parameters
Parameters associated to the algorithm
- measure
Measure to represent on the process model (frequency/performance)
- 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#
- decorations
Decorations to put on the process model
- pm4py.visualization.petri_net.variants.token_decoration_performance.apply(net: PetriNet, initial_marking: Marking, final_marking: Marking, log: EventLog = None, aggregated_statistics=None, parameters: Dict[str | Parameters, Any] | None = None) Digraph [source]#
Apply method for Petri net visualization (it calls the graphviz_visualization method) adding performance representation obtained by token replay
Parameters#
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- log
(Optional) log
- aggregated_statistics
Dictionary containing the frequency statistics
- parameters
Algorithm parameters (including the activity key used during the replay, and the timestamp key)
Returns#
- viz
Graph object
pm4py.visualization.petri_net.variants.wo_decoration module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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.
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.visualization.petri_net.variants.wo_decoration.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum
- FORMAT = 'format'#
- DEBUG = 'debug'#
- RANKDIR = 'set_rankdir'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- AGGREGATION_MEASURE = 'aggregationMeasure'#
- FONT_SIZE = 'font_size'#
- pm4py.visualization.petri_net.variants.wo_decoration.apply(net: PetriNet, initial_marking: Marking, final_marking: Marking, log: EventLog = None, aggregated_statistics=None, parameters: Dict[str | Parameters, Any] | None = None) Digraph [source]#
Apply method for Petri net visualization (it calls the graphviz_visualization method)
Parameters#
- net
Petri net
- initial_marking
Initial marking
- final_marking
Final marking
- log
(Optional) log
- aggregated_statistics
Dictionary containing the frequency statistics
- parameters
Algorithm parameters
Returns#
- viz
Graph object