def plot_arb_ids(self, id_dictionary: dict, signal_dictionary: dict, vehicle_number: str): plot_signals_by_arb_id(a_timer=a_timer, arb_id_dict=id_dictionary, signal_dict=signal_dictionary, vehicle_number=vehicle_number, force=force_arb_id_plotting)
def plot_arb_ids(self, id_dictionary: dict, signal_dictionary: dict, vehicle_number: str): self.make_and_move_to_vehicle_directory() plot_signals_by_arb_id(a_timer=a_timer, arb_id_dict=id_dictionary, signal_dict=signal_dictionary, vehicle_number=vehicle_number, force=force_arb_id_plotting) self.move_back_to_parent_directory()
if j1979_dictionary: plot_j1979(a_timer, j1979_dictionary, force_j1979_plotting) # LEXICAL ANALYSIS # print("\n\t\t\t##### BEGINNING LEXICAL ANALYSIS #####") tokenize_dictionary(a_timer, id_dictionary, force_lexical_analysis, include_padding=tokenize_padding, merge=True, max_distance=tokenization_bit_distance) signal_dictionary = generate_signals(a_timer, id_dictionary, pickle_signal_filename, signal_normalize_strategy, force_lexical_analysis) plot_signals_by_arb_id(a_timer, id_dictionary, signal_dictionary, force_arb_id_plotting) # SEMANTIC ANALYSIS # print("\n\t\t\t##### BEGINNING SEMANTIC ANALYSIS #####") subset_df = subset_selection(a_timer, signal_dictionary, pickle_subset_filename, force_semantic_analysis, subset_size=subset_selection_size) corr_matrix_subset = subset_correlation(subset_df, csv_correlation_filename, force_semantic_analysis) cluster_dict = greedy_signal_clustering( corr_matrix_subset, correlation_threshold=min_correlation_threshold, fuzzy_labeling=fuzzy_labeling) df_full, corr_matrix_full, cluster_dict = label_propagation(