import pm4py
import pandas as pd

###### Extract metadata for petri nets on filtered logs ######

def eval_pm(data, net, initial_marking, final_marking):
    """Caculate fitness, precision, generalizability, and simplicity for petri net"""
    fitness          = pm4py.fitness_token_based_replay(data, net, initial_marking, final_marking)
    precisison       = pm4py.precision_token_based_replay(data, net, initial_marking, final_marking)
    generalizability = pm4py.algo.evaluation.generalization.algorithm.apply(data, net,
                                                                            initial_marking, final_marking)
    simplicity       = pm4py.algo.evaluation.simplicity.algorithm.apply(net)
    return [fitness['average_trace_fitness'], precisison, generalizability, simplicity]


def pn_infos(log, colname, filter):
    """Create data frame with relevant infos for petri nets on filtered logs"""
    filtered_log = pm4py.filter_event_attribute_values(log, colname, [filter])
    
    net, im, fm = pm4py.discover_petri_net_inductive(filtered_log)
    eval = eval_pm(filtered_log, net, im, fm)
    is_sound = pm4py.check_soundness(net, im, fm)
    eval.append(is_sound[0])
    eval.append(len(net.arcs))
    eval.append(len(net.transitions))
    eval.append(len(net.places))
    variants = pm4py.get_variants(filtered_log)
    eval.append(len(variants))

    sorted_variants = dict(sorted(variants.items(), key=lambda item: item[1], reverse = True))
    eval.append({k: sorted_variants[k] for k in list(sorted_variants)[:1]})

    eval = pd.DataFrame(eval).T
    eval.columns = ["fitness", "precision", "generalizability", "simplicity",
                    "sound", "narcs", "ntrans", "nplaces", "nvariants", "mostfreq"]
    eval.index = [str(filter).zfill(3)]
    return eval