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(