def plot_j1979(self, j1979_dictionary: dict, vehicle_number: str):
self.make_and_move_to_vehicle_directory()
plot_j1979(a_timer, j1979_dictionary, vehicle_number,
force_j1979_plotting)
self.move_back_to_parent_directory()
def plot_j1979(self, j1979_dictionary: dict, vehicle_number: str):
plot_j1979(a_timer, j1979_dictionary, vehicle_number,
force_j1979_plotting)
# Threshold parameters used during semantic analysis
subset_selection_size: float = 0.25
fuzzy_labeling: bool = True
min_correlation_threshold: float = 0.85
# A timer class to record timings throughout the pipeline.
a_timer = PipelineTimer(verbose=True)
# DATA IMPORT AND PRE-PROCESSING #
pre_processor = PreProcessor(can_data_filename, pickle_arb_id_filename,
pickle_j1979_filename)
id_dictionary, j1979_dictionary = pre_processor.generate_arb_id_dictionary(
a_timer, tang_normalize_strategy, time_conversion, freq_analysis_accuracy,
freq_synchronous_threshold, force_pre_processing)
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)