diff --git a/Tools/ecl_ekf/analyse_logdata_ekf.py b/Tools/ecl_ekf/analyse_logdata_ekf.py
index 7c00a3fd239cbe2922557b9a78f2be4e99b1e462..a886a0b1d30d6d7e8e85a40ef56a86d7330f5698 100644
--- a/Tools/ecl_ekf/analyse_logdata_ekf.py
+++ b/Tools/ecl_ekf/analyse_logdata_ekf.py
@@ -8,7 +8,7 @@ import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_levels,
- plot=False, output_plot_filename=None):
+ plot=False, output_plot_filename=None, late_start_early_ending=True):
if plot:
# create summary plots
@@ -502,6 +502,11 @@ def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_leve
using_evpos = ((2 ** 12 & estimator_status['control_mode_flags']) > 0) * 1
using_evyaw = ((2 ** 13 & estimator_status['control_mode_flags']) > 0) * 1
using_evhgt = ((2 ** 14 & estimator_status['control_mode_flags']) > 0) * 1
+
+ # define flags for starting and finishing in air
+ b_starts_in_air = False
+ b_finishes_in_air = False
+
# calculate in-air transition time
if (np.amin(airborne) < 0.5) and (np.amax(airborne) > 0.5):
in_air_transtion_time_arg = np.argmax(np.diff(airborne))
@@ -509,6 +514,7 @@ def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_leve
elif (np.amax(airborne) > 0.5):
in_air_transition_time = np.amin(status_time)
print('log starts while in-air at ' + str(round(in_air_transition_time, 1)) + ' sec')
+ b_starts_in_air = True
else:
in_air_transition_time = float('NaN')
print('always on ground')
@@ -519,6 +525,7 @@ def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_leve
elif (np.amax(airborne) > 0.5):
on_ground_transition_time = np.amax(status_time)
print('log finishes while in-air at ' + str(round(on_ground_transition_time, 1)) + ' sec')
+ b_finishes_in_air = True
else:
on_ground_transition_time = float('NaN')
print('always on ground')
@@ -1008,22 +1015,27 @@ def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_leve
plt.close("all")
# Do some automated analysis of the status data
- # find a late/early index range from 5 sec after in_air_transtion_time to 5 sec before on-ground transition time for mag and optical flow checks to avoid false positives
- # this can be used to prevent false positives for sensors adversely affected by close proximity to the ground
- late_start_index = np.amin(np.where(status_time > (in_air_transition_time + 5.0)))
- early_end_index = np.amax(np.where(status_time < (on_ground_transition_time - 5.0)))
- num_valid_values_trimmed = (early_end_index - late_start_index + 1)
# normal index range is defined by the flight duration
start_index = np.amin(np.where(status_time > in_air_transition_time))
- end_index = np.amax(np.where(status_time < on_ground_transition_time))
+ end_index = np.amax(np.where(status_time <= on_ground_transition_time))
num_valid_values = (end_index - start_index + 1)
+ # find a late/early index range from 5 sec after in_air_transtion_time to 5 sec before on-ground transition time for mag and optical flow checks to avoid false positives
+ # this can be used to prevent false positives for sensors adversely affected by close proximity to the ground
+ # don't do this if the log starts or finishes in air or if it is shut off by flag
+ late_start_index = np.amin(np.where(status_time > (in_air_transition_time + 5.0)))\
+ if (late_start_early_ending and not b_starts_in_air) else start_index
+ early_end_index = np.amax(np.where(status_time <= (on_ground_transition_time - 5.0))) \
+ if (late_start_early_ending and not b_finishes_in_air) else end_index
+ num_valid_values_trimmed = (early_end_index - late_start_index + 1)
# also find the start and finish indexes for the innovation data
- innov_late_start_index = np.amin(np.where(innov_time > (in_air_transition_time + 5.0)))
- innov_early_end_index = np.amax(np.where(innov_time < (on_ground_transition_time - 5.0)))
- innov_num_valid_values_trimmed = (innov_early_end_index - innov_late_start_index + 1)
innov_start_index = np.amin(np.where(innov_time > in_air_transition_time))
- innov_end_index = np.amax(np.where(innov_time < on_ground_transition_time))
+ innov_end_index = np.amax(np.where(innov_time <= on_ground_transition_time))
innov_num_valid_values = (innov_end_index - innov_start_index + 1)
+ innov_late_start_index = np.amin(np.where(innov_time > (in_air_transition_time + 5.0))) \
+ if (late_start_early_ending and not b_starts_in_air) else innov_start_index
+ innov_early_end_index = np.amax(np.where(innov_time <= (on_ground_transition_time - 5.0))) \
+ if (late_start_early_ending and not b_finishes_in_air) else innov_end_index
+ innov_num_valid_values_trimmed = (innov_early_end_index - innov_late_start_index + 1)
# define dictionary of test results and descriptions
test_results = {
'master_status': ['Pass',
@@ -1044,6 +1056,12 @@ def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_leve
'Airspeed sensor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
'imu_sensor_status': ['Pass',
'IMU sensor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
+ 'imu_vibration_check': ['Pass',
+ 'IMU vibration check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
+ 'imu_bias_check': ['Pass',
+ 'IMU bias check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
+ 'imu_output_predictor_check': ['Pass',
+ 'IMU output predictor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
'flow_sensor_status': ['Pass',
'Optical Flow sensor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
'filter_fault_status': ['Pass',
@@ -1291,16 +1309,19 @@ def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_leve
(test_results.get('imu_hfdvel_peak')[0] > check_levels.get('imu_hfdvel_peak_warn')) or
(test_results.get('imu_hfdvel_mean')[0] > check_levels.get('imu_hfdvel_mean_warn'))):
test_results['master_status'][0] = 'Warning'
- test_results['imu_sensor_status'][0] = 'Warning - Vibration'
+ test_results['imu_sensor_status'][0] = 'Warning'
+ test_results['imu_vibration_check'][0] = 'Warning'
if ((test_results.get('imu_dang_bias_median')[0] > check_levels.get('imu_dang_bias_median_warn')) or
(test_results.get('imu_dvel_bias_median')[0] > check_levels.get('imu_dvel_bias_median_warn'))):
test_results['master_status'][0] = 'Warning'
- test_results['imu_sensor_status'][0] = 'Warning - Bias'
+ test_results['imu_sensor_status'][0] = 'Warning'
+ test_results['imu_bias_check'][0] = 'Warning'
if ((test_results.get('output_obs_ang_err_median')[0] > check_levels.get('obs_ang_err_median_warn')) or
(test_results.get('output_obs_vel_err_median')[0] > check_levels.get('obs_vel_err_median_warn')) or
(test_results.get('output_obs_pos_err_median')[0] > check_levels.get('obs_pos_err_median_warn'))):
test_results['master_status'][0] = 'Warning'
- test_results['imu_sensor_status'][0] = 'Warning - Output Predictor'
+ test_results['imu_sensor_status'][0] = 'Warning'
+ test_results['imu_output_predictor_check'][0] = 'Warning'
# check for failures
if ((test_results.get('magx_fail_percentage')[0] > check_levels.get('mag_fail_pct')) or
(test_results.get('magy_fail_percentage')[0] > check_levels.get('mag_fail_pct')) or