def change_sensor_time_series_to_rare_series(sensor, loudness_of_the_area, start_index, end_index): sensor_time_series = sensor.get_time_series() cropped_sensor_time_series = crop_time_series(sensor_time_series, start_index, end_index) cropped_rare_event_song = crop_time_series(rare_event_song, start_index, end_index) merged_series = tstools.merge_series([cropped_rare_event_song, cropped_sensor_time_series], [loudness_of_the_area, 1]) new_series = sensor_time_series[:] #to append previous valus to the new time series for i in range(len(merged_series)): new_series[start_index + i] = merged_series[i] new_series = tstools.normalize_to_range(new_series, 1.0) #normalize to 1 sensor.set_time_series(new_series)
def set_time_series_according_to_sensor_weight(self, list_of_time_series): """ Sets the time series of a sensor depending on the location of the grid. It normalizes the data so it falls betweem 0 and 1 for the neural network. """ lattice_of_sensors = [] for row in range(self.dimension_of_lattice): for col in range(self.dimension_of_lattice): weigth_from_b = row / (self.dimension_of_lattice * 1.0) #in vertical increase b weigth_from_a = col / (self.dimension_of_lattice * 1.0) #in horizontal increase a weight_from_c = 0 total_weight = weigth_from_a + weigth_from_b if total_weight < 1: weight_from_c = 1 - total_weight list_of_weights = [weigth_from_a, weigth_from_b, weight_from_c] time_series_for_sensor = \ tstools.merge_series(list_of_time_series, list_of_weights) time_series_for_sensor = \ tstools.normalize_to_range(time_series_for_sensor, 1) self.set_sensor_time_series(row, col, time_series_for_sensor)
