mirror of
https://gitee.com/mirrors_PX4/PX4-Autopilot.git
synced 2026-04-14 10:07:39 +08:00
Tools: Add scripts for ecl EKF log file analysis
This commit is contained in:
Paul Riseborough
Lorenz Meier
parent
ed5c8913ad
commit
bf06066010
27
Tools/ecl_ekf/batch_process_logdata_ekf.py
Normal file
27
Tools/ecl_ekf/batch_process_logdata_ekf.py
Normal file
@ -0,0 +1,27 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
import argparse
|
||||
import os
|
||||
|
||||
"""
|
||||
Runs process_logdata_ekf.py on all the files in the suplied directory with a .ulg extension
|
||||
"""
|
||||
|
||||
parser = argparse.ArgumentParser(description='Analyse the estimator_status and ekf2_innovation message data for all .ulg files in the specified directory')
|
||||
parser.add_argument("directory_path")
|
||||
|
||||
def is_valid_directory(parser, arg):
|
||||
if os.path.isdir(arg):
|
||||
# Directory exists so return the directory
|
||||
return arg
|
||||
else:
|
||||
parser.error('The directory {} does not exist'.format(arg))
|
||||
|
||||
args = parser.parse_args()
|
||||
ulog_directory = args.directory_path
|
||||
print("\n"+"analysing all .ulog files in "+ulog_directory)
|
||||
# Run the analysis script on all the log files found in the specified directory
|
||||
for file in os.listdir(ulog_directory):
|
||||
if file.endswith(".ulg"):
|
||||
print("\n"+"loading "+file+" for analysis")
|
||||
os.system("python process_logdata_ekf.py "+ulog_directory+"/"+file)
|
||||
577
Tools/ecl_ekf/batch_process_metadata_ekf.py
Normal file
577
Tools/ecl_ekf/batch_process_metadata_ekf.py
Normal file
@ -0,0 +1,577 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
import argparse
|
||||
import os
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
"""
|
||||
Performs a composite analysis of ekf log analysis meta data for all .ulg.csv files in the specified directory
|
||||
Generates and saves histogram plots for the meta data in population_data.pdf
|
||||
Generates and saves population summary data in population_data.csv
|
||||
"""
|
||||
|
||||
parser = argparse.ArgumentParser(description='Perform a composite analysis of ekf log analysis meta data for all .ulg.csv files in the specified directory')
|
||||
parser.add_argument("directory_path")
|
||||
|
||||
def is_valid_directory(parser, arg):
|
||||
if os.path.isdir(arg):
|
||||
# Directory exists so return the directory
|
||||
return arg
|
||||
else:
|
||||
parser.error('The directory {} does not exist'.format(arg))
|
||||
|
||||
args = parser.parse_args()
|
||||
metadata_directory = args.directory_path
|
||||
|
||||
# Run the metadata analsyis tool to generate population statistics
|
||||
# Loop through the csv files in the directory and load the metadata into a nested dictionary
|
||||
print("\n"+"analysing all .ulog.csv files in "+metadata_directory)
|
||||
population_data = {}
|
||||
for filename in os.listdir(metadata_directory):
|
||||
if filename.endswith(".mdat.csv"):
|
||||
print("loading "+filename)
|
||||
# get the dictionary of fail and warning test thresholds from a csv file
|
||||
file = open(metadata_directory+"/"+filename)
|
||||
single_log_data = { } # meta data dictionary for a single log
|
||||
for line in file:
|
||||
x = line.split(",")
|
||||
a=x[0]
|
||||
b=x[1]
|
||||
c=x[2]
|
||||
try:
|
||||
single_log_data[a]=float(b)
|
||||
except:
|
||||
single_log_data[a]=b
|
||||
file.close()
|
||||
population_data[filename]=single_log_data
|
||||
|
||||
# # print out the check levels
|
||||
# print('\n'+'The following metadata loaded from '+filename+' were used'+'\n')
|
||||
# val = population_data.get(filename, {}).get('imu_hfdang_mean')
|
||||
# print(val)
|
||||
|
||||
# Open pdf file for plotting
|
||||
from matplotlib.backends.backend_pdf import PdfPages
|
||||
output_plot_filename = "population_data.pdf"
|
||||
pp = PdfPages(metadata_directory+"/"+output_plot_filename)
|
||||
|
||||
# get statistics for the population
|
||||
population_results = {
|
||||
'master_warning_pct':[float('NaN'),'Percentage of logs with warnings'],
|
||||
'master_fail_pct':[float('NaN'),'Percentage of logs with fails'],
|
||||
'mag_warning_pct':[float('NaN'),'Percentage of logs with magnetometer sensor warnings'],
|
||||
'mag_fail_pct':[float('NaN'),'Percentage of logs with magnetometer sensor fails'],
|
||||
'yaw_warning_pct':[float('NaN'),'Percentage of logs with yaw sensor warnings'],
|
||||
'yaw_fail_pct':[float('NaN'),'Percentage of logs with yaw sensor fails'],
|
||||
'vel_warning_pct':[float('NaN'),'Percentage of logs with velocity sensor warnings'],
|
||||
'vel_fail_pct':[float('NaN'),'Percentage of logs with velocity sensor fails'],
|
||||
'pos_warning_pct':[float('NaN'),'Percentage of logs with position sensor warnings'],
|
||||
'pos_fail_pct':[float('NaN'),'Percentage of logs with position sensor fails'],
|
||||
'hgt_warning_pct':[float('NaN'),'Percentage of logs with height sensor warnings'],
|
||||
'hgt_fail_pct':[float('NaN'),'Percentage of logs with height sensor fails'],
|
||||
'hagl_warning_pct':[float('NaN'),'Percentage of logs with height above ground sensor warnings'],
|
||||
'hagl_fail_pct':[float('NaN'),'Percentage of logs with height above ground sensor fails'],
|
||||
'tas_warning_pct':[float('NaN'),'Percentage of logs with airspeed sensor warnings'],
|
||||
'tas_fail_pct':[float('NaN'),'Percentage of logs with airspeed ground sensor fails'],
|
||||
'mag_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the magnetic field sensor innovation consistency test ratio'],
|
||||
'mag_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the magnetic field sensor innovation consistency test ratio'],
|
||||
'vel_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the velocity sensor innovation consistency test ratio'],
|
||||
'vel_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the velocity sensor innovation consistency test ratio'],
|
||||
'pos_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the position sensor innovation consistency test ratio'],
|
||||
'pos_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the position sensor innovation consistency test ratio'],
|
||||
'hgt_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the height sensor innovation consistency test ratio'],
|
||||
'hgt_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the height sensor innovation consistency test ratio'],
|
||||
'tas_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the airspeed sensor innovation consistency test ratio'],
|
||||
'tas_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the airspeed sensor innovation consistency test ratio'],
|
||||
'hagl_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the height above ground sensor innovation consistency test ratio'],
|
||||
'hagl_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the height above ground sensor innovation consistency test ratio'],
|
||||
'ofx_fail_pct_avg':[float('NaN'),'The mean percentage of innovation test fails for the X axis optical flow sensor'],
|
||||
'ofy_fail_pct_avg':[float('NaN'),'The mean percentage of innovation test fails for the Y axis optical flow sensor'],
|
||||
'imu_coning_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the IMU delta angle coning vibration level (mrad)'],
|
||||
'imu_coning_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the IMU delta angle coning vibration level (mrad)'],
|
||||
'imu_hfdang_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the IMU high frequency delta angle vibration level (mrad)'],
|
||||
'imu_hfdang_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the IMU delta hihg frequency delta angle vibration level (mrad)'],
|
||||
'imu_hfdvel_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the IMU high frequency delta velocity vibration level (m/s)'],
|
||||
'imu_hfdvel_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the IMU delta high frequency delta velocity vibration level (m/s)'],
|
||||
'obs_ang_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the output observer angular tracking error magnitude (mrad)'],
|
||||
'obs_ang_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the output observer angular tracking error magnitude (mrad)'],
|
||||
'obs_vel_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the output observer velocity tracking error magnitude (m/s)'],
|
||||
'obs_vel_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the output observer velocity tracking error magnitude (m/s)'],
|
||||
'obs_pos_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the output observer position tracking error magnitude (m)'],
|
||||
'obs_pos_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the output observer position tracking error magnitude (m)'],
|
||||
}
|
||||
|
||||
# get population summary statistics
|
||||
found_keys = population_data.keys()
|
||||
|
||||
# master status
|
||||
result = [population_data[k].get('master_status') for k in found_keys]
|
||||
population_results['master_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
|
||||
population_results['master_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
|
||||
|
||||
# magnetometer sensor
|
||||
result = [population_data[k].get('mag_sensor_status') for k in found_keys]
|
||||
population_results['mag_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
|
||||
population_results['mag_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
|
||||
|
||||
# yaw sensor
|
||||
result = [population_data[k].get('yaw_sensor_status') for k in found_keys]
|
||||
population_results['yaw_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
|
||||
population_results['yaw_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
|
||||
|
||||
# velocity sensor
|
||||
result = [population_data[k].get('vel_sensor_status') for k in found_keys]
|
||||
population_results['vel_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
|
||||
population_results['vel_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
|
||||
|
||||
# position sensor
|
||||
result = [population_data[k].get('pos_sensor_status') for k in found_keys]
|
||||
population_results['pos_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
|
||||
population_results['pos_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
|
||||
|
||||
# height sensor
|
||||
result = [population_data[k].get('hgt_sensor_status') for k in found_keys]
|
||||
population_results['hgt_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
|
||||
population_results['hgt_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
|
||||
|
||||
# height above ground sensor
|
||||
result = [population_data[k].get('hagl_sensor_status') for k in found_keys]
|
||||
population_results['hagl_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
|
||||
population_results['hagl_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
|
||||
|
||||
# height above ground sensor
|
||||
result = [population_data[k].get('tas_sensor_status') for k in found_keys]
|
||||
population_results['tas_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
|
||||
population_results['tas_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
|
||||
|
||||
# Mean and max innovation test levels
|
||||
# Magnetometer
|
||||
temp = np.asarray([population_data[k].get('mag_test_max') for k in found_keys])
|
||||
result1 = temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('mag_test_mean') for k in found_keys])
|
||||
result2 = temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['mag_test_max_avg'][0] = np.mean(result1)
|
||||
population_results['mag_test_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(1,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - Magnetometer Innovation Test Ratio Maximum")
|
||||
plt.xlabel("mag_test_max")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - Magnetometer Innovation Test Ratio Mean")
|
||||
plt.xlabel("mag_test_mean")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# Velocity Sensor (GPS)
|
||||
temp = np.asarray([population_data[k].get('vel_test_max') for k in found_keys])
|
||||
result1 = temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('vel_test_mean') for k in found_keys])
|
||||
result2 = temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['vel_test_max_avg'][0] = np.mean(result1)
|
||||
population_results['vel_test_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(2,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - Velocity Innovation Test Ratio Maximum")
|
||||
plt.xlabel("vel_test_max")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - Velocity Innovation Test Ratio Mean")
|
||||
plt.xlabel("vel_test_mean")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# Position Sensor (GPS or external vision)
|
||||
temp = np.asarray([population_data[k].get('pos_test_max') for k in found_keys])
|
||||
result1 = temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('pos_test_mean') for k in found_keys])
|
||||
result2 = temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['pos_test_max_avg'][0] = np.mean(result1)
|
||||
population_results['pos_test_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(3,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - Position Innovation Test Ratio Maximum")
|
||||
plt.xlabel("pos_test_max")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - Position Innovation Test Ratio Mean")
|
||||
plt.xlabel("pos_test_mean")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# Height Sensor
|
||||
temp = np.asarray([population_data[k].get('hgt_test_max') for k in found_keys])
|
||||
result1 = temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('hgt_test_mean') for k in found_keys])
|
||||
result2 = temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['hgt_test_max_avg'][0] = np.mean(result1)
|
||||
population_results['hgt_test_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(4,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - Height Innovation Test Ratio Maximum")
|
||||
plt.xlabel("pos_test_max")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - Height Innovation Test Ratio Mean")
|
||||
plt.xlabel("pos_test_mean")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# Airspeed Sensor
|
||||
temp = np.asarray([population_data[k].get('tas_test_max') for k in found_keys])
|
||||
result1 = temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('tas_test_mean') for k in found_keys])
|
||||
result2 = temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['tas_test_max_avg'][0] = np.mean(result1)
|
||||
population_results['tas_test_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(5,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - Airspeed Innovation Test Ratio Maximum")
|
||||
plt.xlabel("tas_test_max")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - Airspeed Innovation Test Ratio Mean")
|
||||
plt.xlabel("tas_test_mean")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# Height Above Ground Sensor
|
||||
temp = np.asarray([population_data[k].get('hagl_test_max') for k in found_keys])
|
||||
result1 = temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('hagl_test_mean') for k in found_keys])
|
||||
result2 = temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['hagl_test_max_avg'][0] = np.mean(result1)
|
||||
population_results['hagl_test_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(6,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - HAGL Innovation Test Ratio Maximum")
|
||||
plt.xlabel("hagl_test_max")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - HAGL Innovation Test Ratio Mean")
|
||||
plt.xlabel("hagl_test_mean")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# Optical Flow Sensor
|
||||
temp = np.asarray([population_data[k].get('ofx_fail_percentage') for k in found_keys])
|
||||
result1 = temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('ofy_fail_percentage') for k in found_keys])
|
||||
result2 = temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['ofx_fail_pct_avg'][0] = np.mean(result1)
|
||||
population_results['ofy_fail_pct_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(7,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - Optical Flow X Axis Fail Percentage")
|
||||
plt.xlabel("ofx_fail_percentage")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - Optical Flow Y Axis Fail Percentage")
|
||||
plt.xlabel("ofy_fail_percentage")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
|
||||
# IMU coning vibration levels
|
||||
temp = np.asarray([population_data[k].get('imu_coning_peak') for k in found_keys])
|
||||
result1 = 1000.0 * temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('imu_coning_mean') for k in found_keys])
|
||||
result2 = 1000.0 * temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['imu_coning_max_avg'][0] = np.mean(result1)
|
||||
population_results['imu_coning_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(8,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - IMU Coning Vibration Peak")
|
||||
plt.xlabel("imu_coning_max (mrad)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - IMU Coning Vibration Mean")
|
||||
plt.xlabel("imu_coning_mean (mrad)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# IMU high frequency delta angle vibration levels
|
||||
temp = np.asarray([population_data[k].get('imu_hfdang_peak') for k in found_keys])
|
||||
result1 = 1000.0 * temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('imu_hfdang_mean') for k in found_keys])
|
||||
result2 = 1000.0 * temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['imu_hfdang_max_avg'][0] = np.mean(result1)
|
||||
population_results['imu_hfdang_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(9,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - IMU HF Delta Angle Vibration Peak")
|
||||
plt.xlabel("imu_hfdang_max (mrad)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - IMU HF Delta Angle Vibration Mean")
|
||||
plt.xlabel("imu_hfdang_mean (mrad)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# IMU high frequency delta velocity vibration levels
|
||||
temp = np.asarray([population_data[k].get('imu_hfdvel_peak') for k in found_keys])
|
||||
result1 = 1000.0 * temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('imu_hfdvel_mean') for k in found_keys])
|
||||
result2 = 1000.0 * temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['imu_hfdvel_max_avg'][0] = np.mean(result1)
|
||||
population_results['imu_hfdvel_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(10,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - IMU HF Delta Velocity Vibration Peak")
|
||||
plt.xlabel("imu_hfdvel_max (m/s)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - IMU HF Delta Velocity Vibration Mean")
|
||||
plt.xlabel("imu_hfdvel_mean (m/s)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# Output Observer Angular Tracking
|
||||
temp = np.asarray([population_data[k].get('output_obs_ang_err_peak') for k in found_keys])
|
||||
result1 = 1000.0 * temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('output_obs_ang_err_mean') for k in found_keys])
|
||||
result2 = 1000.0 * temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['obs_ang_max_avg'][0] = np.mean(result1)
|
||||
population_results['obs_ang_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(11,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - Output Observer Angular Tracking Error Peak")
|
||||
plt.xlabel("output_obs_ang_err_peak (mrad)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - Output Observer Angular Tracking Error Mean")
|
||||
plt.xlabel("output_obs_ang_err_mean (mrad)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# Output Observer Velocity Tracking
|
||||
temp = np.asarray([population_data[k].get('output_obs_vel_err_peak') for k in found_keys])
|
||||
result1 = temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('output_obs_vel_err_mean') for k in found_keys])
|
||||
result2 = temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['obs_vel_max_avg'][0] = np.mean(result1)
|
||||
population_results['obs_vel_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(12,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - Output Observer Velocity Tracking Error Peak")
|
||||
plt.xlabel("output_obs_ang_err_peak (m/s)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - Output Observer Velocity Tracking Error Mean")
|
||||
plt.xlabel("output_obs_ang_err_mean (m/s)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# Output Observer Position Tracking
|
||||
temp = np.asarray([population_data[k].get('output_obs_pos_err_peak') for k in found_keys])
|
||||
result1 = temp[np.isfinite(temp)]
|
||||
temp = np.asarray([population_data[k].get('output_obs_pos_err_mean') for k in found_keys])
|
||||
result2 = temp[np.isfinite(temp)]
|
||||
|
||||
if (len(result1) > 0 and len(result2) > 0):
|
||||
population_results['obs_pos_max_avg'][0] = np.mean(result1)
|
||||
population_results['obs_pos_mean_avg'][0] = np.mean(result2)
|
||||
|
||||
plt.figure(13,figsize=(20,13))
|
||||
|
||||
plt.subplot(2,1,1)
|
||||
plt.hist(result1)
|
||||
plt.title("Gaussian Histogram - Output Observer Position Tracking Error Peak")
|
||||
plt.xlabel("output_obs_ang_err_peak (m)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.hist(result2)
|
||||
plt.title("Gaussian Histogram - Output Observer Position Tracking Error Mean")
|
||||
plt.xlabel("output_obs_ang_err_mean (m)")
|
||||
plt.ylabel("Frequency")
|
||||
|
||||
pp.savefig()
|
||||
|
||||
# close the pdf file
|
||||
pp.close()
|
||||
print('Population summary plots saved in population_data.pdf')
|
||||
|
||||
# don't display to screen
|
||||
#plt.show()
|
||||
|
||||
# clase all figures
|
||||
plt.close("all")
|
||||
|
||||
# write metadata to a .csv file
|
||||
population_results_filename = metadata_directory + "/population_data.csv"
|
||||
file = open(population_results_filename,"w")
|
||||
|
||||
file.write("name,value,description\n")
|
||||
|
||||
# loop through the dictionary and write each entry on a separate row, with data comma separated
|
||||
# save data in alphabetical order
|
||||
key_list = list(population_results.keys())
|
||||
key_list.sort()
|
||||
for key in key_list:
|
||||
file.write(key+","+str(population_results[key][0])+","+population_results[key][1]+"\n")
|
||||
|
||||
file.close()
|
||||
|
||||
print('Population summary data saved in population_data.csv')
|
||||
|
||||
single_log_results = {
|
||||
'filter_faults_max':[float('NaN'),'Largest recorded value of the filter internal fault bitmask. Should always be zero.'],
|
||||
'hagl_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the height above ground sensor innovation consistency test.'],
|
||||
'hagl_percentage_amber':[float('NaN'),'The percentage of in-flight height above ground sensor innovation consistency test values > 0.5.'],
|
||||
'hagl_percentage_red':[float('NaN'),'The percentage of in-flight height above ground sensor innovation consistency test values > 1.0.'],
|
||||
'hagl_sensor_status':['Pass','Height above ground sensor check summary. This sensor data is normally sourced from a rangefinder sensor. 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'],
|
||||
'hagl_test_max':[float('NaN'),'The maximum in-flight value of the height above ground sensor innovation consistency test ratio.'],
|
||||
'hagl_test_mean':[float('NaN'),'The mean in-flight value of the height above ground sensor innovation consistency test ratio.'],
|
||||
'hgt_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the height sensor innovation consistency test.'],
|
||||
'hgt_percentage_amber':[float('NaN'),'The percentage of in-flight height sensor innovation consistency test values > 0.5.'],
|
||||
'hgt_percentage_red':[float('NaN'),'The percentage of in-flight height sensor innovation consistency test values > 1.0.'],
|
||||
'hgt_sensor_status':['Pass','Height sensor check summary. This sensor data can be sourced from either Baro, GPS, range fidner or external vision system. 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'],
|
||||
'hgt_test_max':[float('NaN'),'The maximum in-flight value of the height sensor innovation consistency test ratio.'],
|
||||
'hgt_test_mean':[float('NaN'),'The mean in-flight value of the height sensor innovation consistency test ratio.'],
|
||||
'imu_coning_mean':[float('NaN'),'Mean in-flight value of the IMU delta angle coning vibration metric (rad)'],
|
||||
'imu_coning_peak':[float('NaN'),'Peak in-flight value of the IMU delta angle coning vibration metric (rad)'],
|
||||
'imu_hfdang_mean':[float('NaN'),'Mean in-flight value of the IMU delta angle high frequency vibration metric (rad)'],
|
||||
'imu_hfdang_peak':[float('NaN'),'Peak in-flight value of the IMU delta angle high frequency vibration metric (rad)'],
|
||||
'imu_hfdvel_mean':[float('NaN'),'Mean in-flight value of the IMU delta velocity high frequency vibration metric (m/s)'],
|
||||
'imu_hfdvel_peak':[float('NaN'),'Peak in-flight value of the IMU delta velocity high frequency vibration metric (m/s)'],
|
||||
'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'],
|
||||
'in_air_transition_time':[float('NaN'),'The time in seconds measured from startup that the EKF transtioned into in-air mode. Set to a nan if a transition event is not detected.'],
|
||||
'mag_percentage_amber':[float('NaN'),'The percentage of in-flight consolidated magnetic field sensor innovation consistency test values > 0.5.'],
|
||||
'mag_percentage_red':[float('NaN'),'The percentage of in-flight consolidated magnetic field sensor innovation consistency test values > 1.0.'],
|
||||
'mag_sensor_status':['Pass','Magnetometer 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'],
|
||||
'mag_test_max':[float('NaN'),'The maximum in-flight value of the magnetic field sensor innovation consistency test ratio.'],
|
||||
'mag_test_mean':[float('NaN'),'The mean in-flight value of the magnetic field sensor innovation consistency test ratio.'],
|
||||
'magx_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the X-axis magnetic field sensor innovation consistency test.'],
|
||||
'magy_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the Y-axis magnetic field sensor innovation consistency test.'],
|
||||
'magz_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the Z-axis magnetic field sensor innovation consistency test.'],
|
||||
'master_status':['Pass','Master check status which can be either Pass Warning or Fail. 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'],
|
||||
'ofx_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the optical flow sensor X-axis innovation consistency test.'],
|
||||
'ofy_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the optical flow sensor Y-axis innovation consistency test.'],
|
||||
'on_ground_transition_time':[float('NaN'),'The time in seconds measured from startup that the EKF transitioned out of in-air mode. Set to a nan if a transition event is not detected.'],
|
||||
'output_obs_ang_err_mean':[float('NaN'),'Mean in-flight value of the output observer angular error (rad)'],
|
||||
'output_obs_ang_err_peak':[float('NaN'),'Peak in-flight value of the output observer angular error (rad)'],
|
||||
'output_obs_pos_err_mean':[float('NaN'),'Mean in-flight value of the output observer position error (m)'],
|
||||
'output_obs_pos_err_peak':[float('NaN'),'Peak in-flight value of the output observer position error (m)'],
|
||||
'output_obs_vel_err_mean':[float('NaN'),'Mean in-flight value of the output observer velocity error (m/s)'],
|
||||
'output_obs_vel_err_peak':[float('NaN'),'Peak in-flight value of the output observer velocity error (m/s)'],
|
||||
'pos_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the velocity sensor consolidated innovation consistency test.'],
|
||||
'pos_percentage_amber':[float('NaN'),'The percentage of in-flight position sensor consolidated innovation consistency test values > 0.5.'],
|
||||
'pos_percentage_red':[float('NaN'),'The percentage of in-flight position sensor consolidated innovation consistency test values > 1.0.'],
|
||||
'pos_sensor_status':['Pass','Position 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'],
|
||||
'pos_test_max':[float('NaN'),'The maximum in-flight value of the position sensor consolidated innovation consistency test ratio.'],
|
||||
'pos_test_mean':[float('NaN'),'The mean in-flight value of the position sensor consolidated innovation consistency test ratio.'],
|
||||
'tas_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the airspeed sensor innovation consistency test.'],
|
||||
'tas_percentage_amber':[float('NaN'),'The percentage of in-flight airspeed sensor innovation consistency test values > 0.5.'],
|
||||
'tas_percentage_red':[float('NaN'),'The percentage of in-flight airspeed sensor innovation consistency test values > 1.0.'],
|
||||
'tas_sensor_status':['Pass','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'],
|
||||
'tas_test_max':[float('NaN'),'The maximum in-flight value of the airspeed sensor innovation consistency test ratio.'],
|
||||
'tas_test_mean':[float('NaN'),'The mean in-flight value of the airspeed sensor innovation consistency test ratio.'],
|
||||
'tilt_align_time':[float('NaN'),'The time in seconds measured from startup that the EKF completed the tilt alignment. A nan value indicates that the alignment had completed before logging started or alignment did not complete.'],
|
||||
'vel_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the velocity sensor consolidated innovation consistency test.'],
|
||||
'vel_percentage_amber':[float('NaN'),'The percentage of in-flight velocity sensor consolidated innovation consistency test values > 0.5.'],
|
||||
'vel_percentage_red':[float('NaN'),'The percentage of in-flight velocity sensor consolidated innovation consistency test values > 1.0.'],
|
||||
'vel_sensor_status':['Pass','Velocity 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'],
|
||||
'vel_test_max':[float('NaN'),'The maximum in-flight value of the velocity sensor consolidated innovation consistency test ratio.'],
|
||||
'vel_test_mean':[float('NaN'),'The mean in-flight value of the velocity sensor consolidated innovation consistency test ratio.'],
|
||||
'yaw_align_time':[float('NaN'),'The time in seconds measured from startup that the EKF completed the yaw alignment.'],
|
||||
'yaw_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the yaw sensor innovation consistency test.'],
|
||||
'yaw_sensor_status':['Pass','Yaw sensor check summary. This sensor data can be sourced from the magnetometer or an external vision system. 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'],
|
||||
}
|
||||
32
Tools/ecl_ekf/check_level_dict.csv
Normal file
32
Tools/ecl_ekf/check_level_dict.csv
Normal file
@ -0,0 +1,32 @@
|
||||
mag_fail_pct,0.0
|
||||
yaw_fail_pct,0.0
|
||||
vel_fail_pct,0.0
|
||||
pos_fail_pct,0.0
|
||||
hgt_fail_pct,0.0
|
||||
tas_fail_pct,0.0
|
||||
hagl_fail_pct,1.0
|
||||
flow_fail_pct,1.0
|
||||
mag_amber_fail_pct,50.0
|
||||
vel_amber_fail_pct,50.0
|
||||
pos_amber_fail_pct,50.0
|
||||
hgt_amber_fail_pct,50.0
|
||||
hagl_amber_fail_pct,50.0
|
||||
tas_amber_fail_pct,50.0
|
||||
mag_amber_warn_pct,5.0
|
||||
vel_amber_warn_pct,5.0
|
||||
pos_amber_warn_pct,5.0
|
||||
hgt_amber_warn_pct,5.0
|
||||
hagl_amber_warn_pct,5.0
|
||||
tas_amber_warn_pct,5.0
|
||||
imu_coning_peak_warn,1.0E-5
|
||||
imu_coning_mean_warn,3.6E-6
|
||||
imu_hfdang_peak_warn,2.4E-3
|
||||
imu_hfdang_mean_warn,6.0E-4
|
||||
imu_hfdvel_peak_warn,2.5E-2
|
||||
imu_hfdvel_mean_warn,3.6E-3
|
||||
obs_ang_err_peak_warn,9.0E-2
|
||||
obs_ang_err_mean_warn,8.0E-3
|
||||
obs_vel_err_peak_warn,0.3
|
||||
obs_vel_err_mean_warn,0.05
|
||||
obs_pos_err_peak_warn,1.0
|
||||
obs_pos_err_mean_warn,0.15
|
||||
|
1314
Tools/ecl_ekf/process_logdata_ekf.py
Normal file
1314
Tools/ecl_ekf/process_logdata_ekf.py
Normal file
File diff suppressed because it is too large
Load Diff
Loading…
x
Reference in New Issue
Block a user