''' 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 . ''' from pm4py.algo.conformance.tokenreplay import algorithm as tr_algorithm from pm4py.util.colors import get_string_from_int_below_255 from collections import Counter from copy import copy import matplotlib as mpl import matplotlib.cm as cm import math from typing import Optional, Dict, Any, Union from pm4py.objects.log.obj import EventLog from pm4py.objects.petri_net.obj import PetriNet, Marking from pm4py.objects.conversion.log import converter as log_converter import pandas as pd def give_color_to_direction_dynamic(dir): """ Assigns a color to the direction (dynamic-defined colors) Parameters -------------- dir Direction Returns -------------- col Color """ dir = 0.5 + 0.5 * dir norm = mpl.colors.Normalize(vmin=0, vmax=1) nodes = [0.0, 0.01, 0.25, 0.4, 0.45, 0.55, 0.75, 0.99, 1.0] colors = ["deepskyblue", "skyblue", "lightcyan", "lightgray", "gray", "lightgray", "mistyrose", "salmon", "tomato"] cmap = mpl.colors.LinearSegmentedColormap.from_list("mycmap2", list(zip(nodes, colors))) #cmap = cm.plasma m = cm.ScalarMappable(norm=norm, cmap=cmap) rgba = m.to_rgba(dir) r = get_string_from_int_below_255(math.ceil(rgba[0] * 255.0)) g = get_string_from_int_below_255(math.ceil(rgba[1] * 255.0)) b = get_string_from_int_below_255(math.ceil(rgba[2] * 255.0)) return "#" + r + g + b def give_color_to_direction_static(dir): """ Assigns a color to the direction (static-defined colors) Parameters -------------- dir Direction Returns -------------- col Color """ direction_colors = [[-0.5, "#4444FF"], [-0.1, "#AAAAFF"], [0.0, "#CCCCCC"], [0.5, "#FFAAAA"], [1.0, "#FF4444"]] for col in direction_colors: if col[0] >= dir: return col[1] def compare_element_usage_two_logs(net: PetriNet, im: Marking, fm: Marking, log1: Union[EventLog, pd.DataFrame], log2: Union[EventLog, pd.DataFrame], parameters: Optional[Dict[Any, Any]] = None) -> Dict[Any, Any]: """ Returns some statistics (also visual) about the comparison of the usage of the elements in two logs given an accepting Petri net Parameters ------------- net Petri net im Initial marking fm Final marking log1 First log log2 Second log parameters Parameters of the algorithm (to be passed to the token-based replay) Returns ---------------- aggregated_statistics Statistics about the usage of places, transitions and arcs in the net """ if parameters is None: parameters = {} log1 = log_converter.apply(log1, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters) log2 = log_converter.apply(log2, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters) tr_parameters = copy(parameters) tr_parameters[tr_algorithm.Variants.TOKEN_REPLAY.value.Parameters.ENABLE_PLTR_FITNESS] = True rep_traces1, pl_fit_trace1, tr_fit_trace1, ne_act_model1 = tr_algorithm.apply(log1, net, im, fm, parameters=tr_parameters) rep_traces2, pl_fit_trace2, tr_fit_trace2, ne_act_model2 = tr_algorithm.apply(log2, net, im, fm, parameters=tr_parameters) tr_occ1 = Counter([y for x in rep_traces1 for y in x["activated_transitions"]]) tr_occ2 = Counter([y for x in rep_traces2 for y in x["activated_transitions"]]) pl_occ1 = Counter({p: pl_fit_trace1[p]["c"] + pl_fit_trace1[p]["r"] for p in pl_fit_trace1}) pl_occ2 = Counter({p: pl_fit_trace2[p]["c"] + pl_fit_trace2[p]["r"] for p in pl_fit_trace2}) all_replayed_transitions = set(tr_occ1.keys()).union(set(tr_occ2.keys())) all_replayed_places = set(pl_occ1.keys()).union(set(pl_occ2.keys())) all_transitions = all_replayed_transitions.union(set(net.transitions)) all_places = all_replayed_places.union(set(net.places)) aggregated_statistics = {} for place in all_places: aggregated_statistics[place] = {"log1_occ": pl_occ1[place], "log2_occ": pl_occ2[place], "total_occ": pl_occ1[place] + pl_occ2[place]} aggregated_statistics[place]["label"] = "(%d/%d/%d)" % ( pl_occ1[place], pl_occ2[place], pl_occ1[place] + pl_occ2[place]) dir = (pl_occ2[place] - pl_occ1[place]) / (pl_occ1[place] + pl_occ2[place]) if (pl_occ1[place] + pl_occ2[ place]) > 0 else 0 aggregated_statistics[place]["direction"] = dir aggregated_statistics[place]["color"] = give_color_to_direction_dynamic(dir) for trans in all_transitions: aggregated_statistics[trans] = {"log1_occ": tr_occ1[trans], "log2_occ": tr_occ2[trans], "total_occ": tr_occ1[trans] + tr_occ2[trans]} if trans.label is not None: aggregated_statistics[trans]["label"] = trans.label+" " else: aggregated_statistics[trans]["label"] = "" aggregated_statistics[trans]["label"] = aggregated_statistics[trans]["label"] + "(%d/%d/%d)" % ( tr_occ1[trans], tr_occ2[trans], tr_occ1[trans] + tr_occ2[trans]) dir = (tr_occ2[trans] - tr_occ1[trans]) / (tr_occ1[trans] + tr_occ2[trans]) if (tr_occ1[trans] + tr_occ2[ trans]) > 0 else 0 aggregated_statistics[trans]["direction"] = dir aggregated_statistics[trans]["color"] = give_color_to_direction_dynamic(dir) for arc in trans.in_arcs: aggregated_statistics[arc] = aggregated_statistics[trans] for arc in trans.out_arcs: aggregated_statistics[arc] = aggregated_statistics[trans] return aggregated_statistics