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

Submodules

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

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