def calc_estimator_status_metrics(self) -> Dict[str, list]:
"""
calculates the estimator status metrics
:return:
"""
estimator_status_metrics = dict()
estimator_status_data = self.ulog.get_dataset('estimator_status').data
# add windowed metrics
estimator_status_metrics['{:s}_percentage_red_windowed'.format(self._check_id)] = \
calculate_windowed_mean_per_airphase(
estimator_status_data, 'estimator_status', self._test_ratio_name,
self._in_air_detector, threshold=params.ecl_red_thresh(),
window_len_s=params.ecl_window_len_s())
estimator_status_metrics['{:s}_percentage_amber_windowed'.format(self._check_id)] = \
calculate_windowed_mean_per_airphase(
estimator_status_data, 'estimator_status', self._test_ratio_name,
self._in_air_detector, threshold=params.ecl_amb_thresh(),
window_len_s=params.ecl_window_len_s())
estimator_status_metrics['{:s}_test_windowed_mean'.format(self._check_id)] = \
calculate_windowed_mean_per_airphase(
estimator_status_data, 'estimator_status', self._test_ratio_name,
self._in_air_detector, window_len_s=params.ecl_window_len_s())
return estimator_status_metrics
def calculate_metrics(self) -> Dict[str, list]:
"""
calculates the estimator status metrics
:return:
"""
estimator_status_data = self.ulog.get_dataset("estimator_status").data
imu_metrics = dict()
# calculates peak and mean for IMU vibration checks
for signal, result in [
("vibe[0]", "imu_coning"),
("vibe[1]", "imu_hfdang"),
("vibe[2]", "imu_hfdvel"),
]:
imu_metrics["{:s}_windowed_mean".format(
result)] = calculate_windowed_mean_per_airphase(
estimator_status_data,
"estimator_status",
signal,
self._in_air_detector_no_ground_effects,
window_len_s=params.ecl_window_len_s(),
)
return imu_metrics
def calculate_metrics(self) -> Dict[str, list]:
"""
calculates the estimator status metrics
:return:
"""
estimator_states_data = self.ulog.get_dataset(
self._estimator_states_msg).data
imu_metrics = dict()
for signal in [
"states[10]",
"states[11]",
"states[12]",
"states[13]",
"states[14]",
"states[15]",
]:
imu_metrics["{:s}_windowed_mean".format(
signal)] = calculate_windowed_mean_per_airphase(
estimator_states_data,
self._estimator_states_msg,
signal,
self._in_air_detector_no_ground_effects,
window_len_s=params.ecl_window_len_s(),
)
return imu_metrics
def calculate_metrics(self) -> Dict[str, list]:
"""
calculates the estimator status metrics
:return:
"""
output_tracking_error_msg = get_output_tracking_error_message(
self.ulog)
output_tracking_error_data = self.ulog.get_dataset(
output_tracking_error_msg).data
imu_metrics = dict()
# calculates the median of the output tracking error ekf innovations
for signal, result in [
("output_tracking_error[0]", "output_obs_ang_err_median"),
("output_tracking_error[1]", "output_obs_vel_err_median"),
("output_tracking_error[2]", "output_obs_pos_err_median"),
]:
# calculate a windowed version of the stat:
# TODO: currently takes the mean instead of median
imu_metrics["{:s}_windowed_mean".format(
result)] = calculate_windowed_mean_per_airphase(
output_tracking_error_data,
output_tracking_error_msg,
signal,
self._in_air_detector_no_ground_effects,
window_len_s=params.ecl_window_len_s(),
)
return imu_metrics
def calc_innovation_metrics(self) -> Dict[str, list]:
"""
calculates the innovation metrics
:return:
"""
innovation_metrics = dict()
for innov_fail_name in self._innov_fail_names:
innovation_metrics['{:s}_fail_short_window_mean'.format(innov_fail_name)] = \
calculate_windowed_mean_per_airphase(
self._innov_flags, 'estimator_status', innov_fail_name,
self._in_air_detector_no_ground_effects, threshold=0.5,
window_len_s=params.ecl_short_rolling_window_len_s())
innovation_metrics['{:s}_fail_long_window_mean'.format(innov_fail_name)] = \
calculate_windowed_mean_per_airphase(
self._innov_flags, 'estimator_status', innov_fail_name,
self._in_air_detector_no_ground_effects, threshold=0.5,
window_len_s=params.ecl_long_rolling_window_len_s())
return innovation_metrics
def calculate_metrics(self) -> Dict[str, list]:
"""
calculates the estimator status metrics
:return:
"""
estimator_status_data = self.ulog.get_dataset('estimator_status').data
imu_metrics = dict()
for signal in [
'states[10]', 'states[11]', 'states[12]', 'states[13]',
'states[14]', 'states[15]'
]:
imu_metrics['{:s}_windowed_mean'.format(signal)] = \
calculate_windowed_mean_per_airphase(
estimator_status_data, 'estimator_status', signal,
self._in_air_detector_no_ground_effects, window_len_s=params.ecl_window_len_s())
return imu_metrics
def calculate_metrics(self) -> Dict[str, list]:
"""
calculates the estimator status metrics
:return:
"""
estimator_status_data = self.ulog.get_dataset('estimator_status').data
imu_metrics = dict()
# calculates peak and mean for IMU vibration checks
for signal, result in [('vibe[0]', 'imu_coning'),
('vibe[1]', 'imu_hfdang'),
('vibe[2]', 'imu_hfdvel')]:
imu_metrics['{:s}_windowed_mean'.format(result)] = \
calculate_windowed_mean_per_airphase(
estimator_status_data, 'estimator_status', signal,
self._in_air_detector_no_ground_effects, window_len_s=params.ecl_window_len_s())
return imu_metrics
def calculate_metrics(self) -> Dict[str, list]:
"""
calculates the estimator status metrics
:return:
"""
ekf2_innovation_data = self.ulog.get_dataset('ekf2_innovations').data
imu_metrics = dict()
# calculates the median of the output tracking error ekf innovations
for signal, result in [
('output_tracking_error[0]', 'output_obs_ang_err_median'),
('output_tracking_error[1]', 'output_obs_vel_err_median'),
('output_tracking_error[2]', 'output_obs_pos_err_median')
]:
# calculate a windowed version of the stat:
# TODO: currently takes the mean instead of median
imu_metrics['{:s}_windowed_mean'.format(result)] = \
calculate_windowed_mean_per_airphase(
ekf2_innovation_data, 'ekf2_innovations', signal,
self._in_air_detector_no_ground_effects, window_len_s=params.ecl_window_len_s())
return imu_metrics