def add_erroneous_drift_towards_a_value_to_sensor(sensor, probability_of_erroneous_reading, number_of_erroneous_points):
"""
This function adds erroneous drift to a single sensor time series.
This function modifies the sensor's original time series.
"""
time_series = sensor.get_time_series()
erroneous_reading = add_erroneous_drift_towards_a_value(
time_series, probability_of_erroneous_reading, number_of_erroneous_points
)
sensor.set_time_series(erroneous_reading)
def gather_time_series_from_sensors(lattice_of_sensors):
"""
This function collects the time series from the sensors in the lattice.
"""
collection_of_time_series = []
for group_of_sensors in lattice_of_sensors:
for sensor in group_of_sensors:
sensor_data = sensor.get_time_series()
collection_of_time_series.append(sensor_data)
return collection_of_time_series
def add_erroneous_reading_to_sensor(sensor, probability_of_erroneous_reading, erroneous_reading_standard_deviation):
"""
This function adds erroneous readings to a single sensor time series.
This function modifies the sensor's original time series.
"""
time_series = sensor.get_time_series()
erroneous_reading = add_erroneous_reading_to_time_series(
time_series, probability_of_erroneous_reading, erroneous_reading_standard_deviation
)
sensor.set_time_series(erroneous_reading)
def add_continous_erroneous_reading_to_sensor(
sensor, probability_of_erroneous_reading, number_of_continous_erroneous_readings
):
"""
This function adds a sequence erroneous readings to a single sensor time series.
This function modifies the sensor's original time series.
"""
time_series = sensor.get_time_series()
erroneous_reading = add_erroneous_continuous_sequence_to_time_series(
time_series, probability_of_erroneous_reading, number_of_continous_erroneous_readings
)
sensor.set_time_series(erroneous_reading)
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 add_erroneous_drift_towards_a_value_to_sensor(sensor, probability_of_erroneous_reading, number_of_erroneous_points, random_generator): """ This function adds erroneous drift to a single sensor time series. This function modifies the sensor's original time series. """ time_series = sensor.get_time_series() erroneous_reading = \ error_generator.add_erroneous_drift_towards_a_value(time_series, probability_of_erroneous_reading, number_of_erroneous_points, random_generator) #normalize erroneous_reading = tstools.normalize_to_range(erroneous_reading, 1.0) sensor.set_time_series(erroneous_reading) return erroneous_reading
def add_erroneous_reading_to_sensor(sensor, probability_of_erroneous_reading, erroneous_reading_standard_deviation, warmup_time, random_generator): """ This function adds erroneous readings to a single sensor time series. This function modifies the sensor's original time series. """ time_series = sensor.get_time_series() erroneous_reading, number_of_errors_inserted, \ list_of_errors_inserted = \ error_generator.add_erroneous_reading_to_time_series(time_series, probability_of_erroneous_reading, erroneous_reading_standard_deviation, warmup_time, random_generator) #normalize to be between 0 and 1 erroneous_reading = tstools.normalize_to_range(erroneous_reading, 1.0) sensor.set_time_series(erroneous_reading) return erroneous_reading, number_of_errors_inserted, list_of_errors_inserted
def add_continous_erroneous_reading_to_sensor(sensor, probability_of_erroneous_reading, number_of_continous_erroneous_readings, warmup_time, random_generator): """ This function adds a sequence erroneous readings to a single sensor time series. This function modifies the sensor's original time series. """ time_series = sensor.get_time_series() erroneous_reading = \ error_generator.add_erroneous_continuous_sequence_to_time_series( time_series, probability_of_erroneous_reading, number_of_continous_erroneous_readings, warmup_time, random_generator) erroneous_reading = tstools.normalize_to_range(erroneous_reading, 1.0) sensor.set_time_series(erroneous_reading) return erroneous_reading
def change_sensor_time_series_to_rare_series(sensor, loudness_of_the_area): sensor_time_series = sensor.get_time_series() new_series = merge_series([rare_event_song, sensor_time_series], [loudness_of_the_area, 1]) new_series = normalize_to_range(new_series, 1.0) #normalize to 1 sensor.set_time_series(new_series)