def main(): number_of_time_points = 20000 #number of time points to generate time_series_header = ["Time series A", "Time series B", "Time series C"] run_id = "" if len(sys.argv) >= 2: run_id = str(sys.argv[1]) #we are using a lattice of sensors that reads data from different #songs. We combine the listening from 3 different readings. time_series = generate_predictable_parabola_time_series(number_of_time_points) time_series_b = generate_predictable_time_series(number_of_time_points) #series b time_series_c = generate_predictable_parabola_time_series(number_of_time_points, 100) #series c, right shift by 100 rare_event_song = generate_predictable_sin_time_series(number_of_time_points) data_to_write = [time_series, time_series_b, time_series_c] ft.save_data_to_file(data_to_write, "the_series_" + run_id + ".csv") ft.save_data_to_file([rare_event_song], "rare_song_" + run_id + ".csv") normalized_time_series = tstools.normalize_to_range(time_series) normalized_time_series_b = tstools.normalize_to_range(time_series_b) normalized_time_series_c = tstools.normalize_to_range(time_series_c) data_to_write = [normalized_time_series, normalized_time_series_b, normalized_time_series_c] ft.save_data_to_file(data_to_write, "the_series_normalized_" + run_id +".csv") rare_event_song_normalized = tstools.normalize_to_range(rare_event_song) ft.save_data_to_file([rare_event_song_normalized], "rare_song_normalized_" + run_id + ".csv")
def save_data_to_file(list_of_data, list_of_file_names): "This function save all the data from the list into csv files" for data, name in zip(list_of_data, list_of_file_names): ft.save_data_to_file(data, name)
