pm4py.algo.discovery.inductive.variants.im_d.data_structures package

Submodules

pm4py.algo.discovery.inductive.variants.im_d.data_structures.subtree module

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

class pm4py.algo.discovery.inductive.variants.im_d.data_structures.subtree.SubtreeDFGBased(dfg, master_dfg, initial_dfg, activities, counts, rec_depth, noise_threshold=0, initial_start_activities=None, initial_end_activities=None)[source]

Bases: object

check_sa_ea_for_each_branch(conn_components)[source]

Checks if each branch of the parallel cut has a start and an end node of the subgraph

Parameters:conn_components – Parallel cut
Returns:True if each branch of the parallel cut has a start and an end node
Return type:boolean
detect_cut(second_iteration=False)[source]

Detect generally a cut in the graph (applying all the algorithms)

detect_loop_cut(conn_components, this_nx_graph, strongly_connected_components)[source]

Detect loop cut

Parameters:
  • conn_components – Connected components of the graph
  • this_nx_graph – NX graph calculated on the DFG
  • strongly_connected_components – Strongly connected components
detect_parallel_cut(orig_conn_components, this_nx_graph, strongly_connected_components)[source]

Detects parallel cut

Parameters:
  • orig_conn_components – Connected components of the graph
  • this_nx_graph – NX graph calculated on the DFG
  • strongly_connected_components – Strongly connected components
detect_sequential_cut(conn_components, this_nx_graph, strongly_connected_components)[source]

Detect sequential cut in DFG graph

Parameters:
  • conn_components – Connected components of the graph
  • this_nx_graph – NX graph calculated on the DFG
  • strongly_connected_components – Strongly connected components
detect_xor_cut(conn_components, this_nx_graph, strongly_connected_components)[source]

Detects XOR cut

Parameters:
  • conn_components – Connected components
  • this_nx_graph – NX graph calculated on the DFG
  • strongly_connected_components – Strongly connected components
initialize_tree(dfg, initial_dfg, activities, second_iteration=False)[source]

Initialize the tree

Parameters:
  • dfg – Directly follows graph of this subtree
  • initial_dfg – Referral directly follows graph that should be taken in account adding hidden/loop transitions
  • activities – Activities of this subtree
  • second_iteration – Boolean that indicates if we are executing this method for the second time
put_skips_in_seq_cut()[source]

Puts the skips in sequential cut

Module contents

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.